Airbnb correlation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f19a9a7",
   "metadata": {},
   "source": [
    "## Checking for correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bd7cfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "608dd159",
   "metadata": {},
   "outputs": [],
   "source": [
    "nycab=pd.read_excel(r'Desktop\\Updated Neighbourhoods.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af0cf0b7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Listings</th>\n",
       "      <th>minp</th>\n",
       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
       "      <th>minscores</th>\n",
       "      <th>maxscores</th>\n",
       "      <th>avgscores</th>\n",
       "      <th>rev</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>164.893491</td>\n",
       "      <td>40.603550</td>\n",
       "      <td>1276.952663</td>\n",
       "      <td>130.527929</td>\n",
       "      <td>2.691183</td>\n",
       "      <td>4.992189</td>\n",
       "      <td>4.694911</td>\n",
       "      <td>5171.485207</td>\n",
       "      <td>472.491124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>325.346080</td>\n",
       "      <td>29.109649</td>\n",
       "      <td>2304.694187</td>\n",
       "      <td>60.605082</td>\n",
       "      <td>1.521094</td>\n",
       "      <td>0.035514</td>\n",
       "      <td>0.176663</td>\n",
       "      <td>10448.897669</td>\n",
       "      <td>920.690703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>379.000000</td>\n",
       "      <td>111.240000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>1437.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>166.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>157.270000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>5443.000000</td>\n",
       "      <td>458.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2127.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>413.780000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>85558.000000</td>\n",
       "      <td>6289.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Listings        minp          maxp        avgp   minscores  \\\n",
       "count   169.000000  169.000000    169.000000  169.000000  169.000000   \n",
       "mean    164.893491   40.603550   1276.952663  130.527929    2.691183   \n",
       "std     325.346080   29.109649   2304.694187   60.605082    1.521094   \n",
       "min       1.000000   10.000000     73.000000   55.410000    1.000000   \n",
       "25%      13.000000   25.000000    225.000000   92.000000    1.000000   \n",
       "50%      37.000000   35.000000    379.000000  111.240000    3.000000   \n",
       "75%     166.000000   43.000000   1000.000000  157.270000    4.000000   \n",
       "max    2127.000000  295.000000  10000.000000  413.780000    5.000000   \n",
       "\n",
       "        maxscores   avgscores           rev          acc  \n",
       "count  169.000000  169.000000    169.000000   169.000000  \n",
       "mean     4.992189    4.694911   5171.485207   472.491124  \n",
       "std      0.035514    0.176663  10448.897669   920.690703  \n",
       "min      4.720000    3.500000      8.000000     3.000000  \n",
       "25%      5.000000    4.650000    378.000000    39.000000  \n",
       "50%      5.000000    4.710000   1437.000000   110.000000  \n",
       "75%      5.000000    4.790000   5443.000000   458.000000  \n",
       "max      5.000000    5.000000  85558.000000  6289.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycab.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77fd4727",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>NeighbourhoodArea</th>\n",
       "      <th>Listings</th>\n",
       "      <th>minp</th>\n",
       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>285</td>\n",
       "      <td>106.17</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1547</td>\n",
       "      <td>87</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Belmont</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>95.38</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.75</td>\n",
       "      <td>315</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx Park</td>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "      <td>58.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.47</td>\n",
       "      <td>507</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Castle Hill-Unionport</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>157</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Concourse-Concourse Village</td>\n",
       "      <td>70</td>\n",
       "      <td>19</td>\n",
       "      <td>271</td>\n",
       "      <td>84.81</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3140</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  neighbourhood_group_cleansed            NeighbourhoodArea  Listings  minp  \\\n",
       "0                        Bronx                     Allerton        24    29   \n",
       "1                        Bronx                      Belmont        13    20   \n",
       "2                        Bronx                   Bronx Park        15    40   \n",
       "3                        Bronx        Castle Hill-Unionport         1    73   \n",
       "4                        Bronx  Concourse-Concourse Village        70    19   \n",
       "\n",
       "   maxp    avgp  minscores  maxscores  avgscores   rev  acc  \n",
       "0   285  106.17       3.00       5.00       4.65  1547   87  \n",
       "1   200   95.38       3.67       5.00       4.75   315   38  \n",
       "2    85   58.53       1.00       5.00       4.47   507   33  \n",
       "3    73   73.00       4.89       4.89       4.89   157    4  \n",
       "4   271   84.81       3.00       5.00       4.68  3140  196  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycab.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1088dc3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
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       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>285</td>\n",
       "      <td>106.17</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1547</td>\n",
       "      <td>87</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>95.38</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.75</td>\n",
       "      <td>315</td>\n",
       "      <td>38</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "      <td>58.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.47</td>\n",
       "      <td>507</td>\n",
       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>157</td>\n",
       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>271</td>\n",
       "      <td>84.81</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3140</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   minp  maxp    avgp  minscores  maxscores  avgscores   rev  acc\n",
       "0    29   285  106.17       3.00       5.00       4.65  1547   87\n",
       "1    20   200   95.38       3.67       5.00       4.75   315   38\n",
       "2    40    85   58.53       1.00       5.00       4.47   507   33\n",
       "3    73    73   73.00       4.89       4.89       4.89   157    4\n",
       "4    19   271   84.81       3.00       5.00       4.68  3140  196"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=nycab.drop(['neighbourhood_group_cleansed','NeighbourhoodArea','Listings'], axis=1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67d77997",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>minp</th>\n",
       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
       "      <th>minscores</th>\n",
       "      <th>maxscores</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.603550</td>\n",
       "      <td>1276.952663</td>\n",
       "      <td>130.527929</td>\n",
       "      <td>2.691183</td>\n",
       "      <td>4.992189</td>\n",
       "      <td>4.694911</td>\n",
       "      <td>5171.485207</td>\n",
       "      <td>472.491124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.109649</td>\n",
       "      <td>2304.694187</td>\n",
       "      <td>60.605082</td>\n",
       "      <td>1.521094</td>\n",
       "      <td>0.035514</td>\n",
       "      <td>0.176663</td>\n",
       "      <td>10448.897669</td>\n",
       "      <td>920.690703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>379.000000</td>\n",
       "      <td>111.240000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>1437.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>157.270000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>5443.000000</td>\n",
       "      <td>458.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>295.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>413.780000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>85558.000000</td>\n",
       "      <td>6289.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             minp          maxp        avgp   minscores   maxscores  \\\n",
       "count  169.000000    169.000000  169.000000  169.000000  169.000000   \n",
       "mean    40.603550   1276.952663  130.527929    2.691183    4.992189   \n",
       "std     29.109649   2304.694187   60.605082    1.521094    0.035514   \n",
       "min     10.000000     73.000000   55.410000    1.000000    4.720000   \n",
       "25%     25.000000    225.000000   92.000000    1.000000    5.000000   \n",
       "50%     35.000000    379.000000  111.240000    3.000000    5.000000   \n",
       "75%     43.000000   1000.000000  157.270000    4.000000    5.000000   \n",
       "max    295.000000  10000.000000  413.780000    5.000000    5.000000   \n",
       "\n",
       "        avgscores           rev          acc  \n",
       "count  169.000000    169.000000   169.000000  \n",
       "mean     4.694911   5171.485207   472.491124  \n",
       "std      0.176663  10448.897669   920.690703  \n",
       "min      3.500000      8.000000     3.000000  \n",
       "25%      4.650000    378.000000    39.000000  \n",
       "50%      4.710000   1437.000000   110.000000  \n",
       "75%      4.790000   5443.000000   458.000000  \n",
       "max      5.000000  85558.000000  6289.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90cd8cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b2f6255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation,cbar = True, square = True, cmap= 'Blues', annot = True, fmt = '.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d040ff",
   "metadata": {},
   "source": [
    "##### No strong correlation found overall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101b73c6",
   "metadata": {},
   "source": [
    "### Checking for each neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f2e34f9",
   "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>neighbourhood_group_cleansed</th>\n",
       "      <th>NeighbourhoodArea</th>\n",
       "      <th>Listings</th>\n",
       "      <th>minp</th>\n",
       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
       "      <th>minscores</th>\n",
       "      <th>maxscores</th>\n",
       "      <th>avgscores</th>\n",
       "      <th>rev</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>University Heights (South)-Morris Heights</td>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>378</td>\n",
       "      <td>99.96</td>\n",
       "      <td>3.60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.69</td>\n",
       "      <td>906</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Wakefield-Woodlawn</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>643</td>\n",
       "      <td>107.37</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.73</td>\n",
       "      <td>2543</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>West Farms</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>165</td>\n",
       "      <td>101.67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Westchester Square</td>\n",
       "      <td>5</td>\n",
       "      <td>65</td>\n",
       "      <td>185</td>\n",
       "      <td>98.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.94</td>\n",
       "      <td>223</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Williamsbridge-Olinville</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>275</td>\n",
       "      <td>94.31</td>\n",
       "      <td>3.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.70</td>\n",
       "      <td>1390</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   neighbourhood_group_cleansed                          NeighbourhoodArea  \\\n",
       "31                        Bronx  University Heights (South)-Morris Heights   \n",
       "32                        Bronx                         Wakefield-Woodlawn   \n",
       "33                        Bronx                                 West Farms   \n",
       "34                        Bronx                         Westchester Square   \n",
       "35                        Bronx                   Williamsbridge-Olinville   \n",
       "\n",
       "    Listings  minp  maxp    avgp  minscores  maxscores  avgscores   rev  acc  \n",
       "31        27    36   378   99.96       3.60        5.0       4.69   906   63  \n",
       "32        60    22   643  107.37       1.00        5.0       4.73  2543  176  \n",
       "33         3    55   165  101.67       1.00        5.0       3.50     8    8  \n",
       "34         5    65   185   98.00       4.69        5.0       4.94   223   11  \n",
       "35        42    40   275   94.31       3.33        5.0       4.70  1390  110  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfbronx=nycab.loc[(nycab['neighbourhood_group_cleansed']=='Bronx')]\n",
    "dfbronx.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "974bf92e",
   "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>minp</th>\n",
       "      <th>maxp</th>\n",
       "      <th>avgp</th>\n",
       "      <th>minscores</th>\n",
       "      <th>maxscores</th>\n",
       "      <th>avgscores</th>\n",
       "      <th>rev</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>285</td>\n",
       "      <td>106.17</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1547</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>95.38</td>\n",
       "      <td>3.67</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.75</td>\n",
       "      <td>315</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>85</td>\n",
       "      <td>58.53</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.47</td>\n",
       "      <td>507</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>73.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>4.89</td>\n",
       "      <td>157</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>271</td>\n",
       "      <td>84.81</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3140</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   minp  maxp    avgp  minscores  maxscores  avgscores   rev  acc\n",
       "0    29   285  106.17       3.00       5.00       4.65  1547   87\n",
       "1    20   200   95.38       3.67       5.00       4.75   315   38\n",
       "2    40    85   58.53       1.00       5.00       4.47   507   33\n",
       "3    73    73   73.00       4.89       4.89       4.89   157    4\n",
       "4    19   271   84.81       3.00       5.00       4.68  3140  196"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=dfbronx.drop(['neighbourhood_group_cleansed','NeighbourhoodArea','Listings'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "beebe771",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_bronx = df2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a8ac84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MmcmNl59LvZhY/vr3kcXrRt99I1ffci9NklP479OjSWnajHtu/BMAxw44mWGXXRWq2IcUFR1N72HXMO35+3yHbfc7nUbND2flDN8RIkcMGMKSye+ya3seCyY8D4BFRXPGbU+FMLV3RQ4mLErjr/0PwzBmr80hLX83/dskAjBzTQ5ndk6hQb1oLjyymf8+jn99uyZ0oSupT78BLJgzg2v/MJSYmFhuuOP+4nWj7ryB6/5+H4lNkhgzeiQFBdtxztG2fUeuvvmu0IWuhI69+rH8xzn8+29/oF69GM6/9o7ida+PvpNzr/47CU2SGXTpCMY/PYqvx79M8zYd6HPKkBCm9m7AwBOZ8d10zhl8BrFxsdw/av+pL66/dgT3PTCK1NSm3HPHbWRnZ+EcdOrUmXtG3h+60B6dMPBEZkyfxlmDTyc2No4H/7l/26675ipGPvhPUlObctftfyc7OxvnHJ06d+be+yo+0itSqEIDdrC5JWZ2tnPuUzO7nPI7NK8f6gFCUaEJptkTyz9ktbb4YFnaoRtFsOyCap9tO2xde2zrQzeKYEu2hv8wcnX8rltkHc5fWVG1/A04to6X6bM1J/6icUF7r80ff3lYPnkHrdA45z71X10K3A20KXEfBxyyQyMiIiKBpQqN90nBbwK34fuG7Wp/34KIiIhITfLaoUl3zh14pj8REREJA6rQeO/QjDSzl/Cd0a/4S2ecc+Ueti0iIiISTF47NFfi++KouuwfcqrwPDQiIiISRCrQeO7QHOmc6xHQJCIiIiJV5LVDM9vMujrnlgY0jYiIiFSa5tB479AMAC43s9X45tAY4JxzPQOWTERERMQjrx2aMwOaQkRERKpMFRrv3+W0NtBBRERERKoq/L/NUEREROQQvA45iYiISJjSkJMqNCIiIlILqEIjIiIS4VShUYVGREREagFVaERERCKdCjSq0IiIiEjkU4VGREQkwmkOjSo0IiIiUguoQiMiIhLhVKFRhUZERERqAVVoREREIpwqNKrQiIiISC2gCo2IiEikU4Em8B2a2RNHB/ohQqrf0LtCHSGg6vfsH+oIAfXZfYNDHSFgnp29NtQRAqpzalyoIwTUlW8vDHWEgFq9MTfUEQJq7t0nhTrC/zuq0IiIiEQ4zaHRHBoRERGpBVShERERiXCq0KhCIyIiIrWAOjQiIiIS8TTkJCIiEuE05KQKjYiIiNQCqtCIiIhEOFVoVKERERGRWkAVGhERkUinAo0qNCIiIhL5VKERERGJcJpDowqNiIiI1AKq0IiIiEQ4VWgq0aExs2bAMYAD5jnn0gKWSkRERKQSPA05mdlfgLnA+cAwYLaZ/SmQwURERMQbMwvaJVx5rdDcBvRyzmUCmFkSMAt4JVDBRERERLzy2qHZAOSXWM4H1td8HBEREam08C2cBI3XDs1GYI6ZTcQ3h2YoMNfMbgFwzv07QPlEREREDslrh2aV/7LPRP//8TUbR0RERCornOe2BIvXDs2jzrmdJW8ws2TnXEYAMomIiIhUitcT6801s377FszsAnyTgkVERERCzmuF5lLgFTP7FmgBJAGnBCqUF845Xn3+cX6cO5OYmFj+etv9tOvQuUy7MaP/warlS6lTpw7tO3VjxE33UKdOeJ9P8IWRlzJ4YHfSs/LpO/zhcts8cfswBvXvRsHO3YwY+QYLf9kQ5JRVd0qP5oy+tDdRUcab01bx9GfLSq2/fnBnhh3XBoA60UbHFgl0vP4jcrbvDkHaynPO8daL/2bRvFnUi4nlqlvupc0RZffNFx67j9UrlhFdpw7tOnblihvuCvt9E6Br0wYM79kMM2PWmmy+XJ5Zav3RhyVwRsdkAHbtLeKdhZvZmLsrFFGrZO3i+cx45z8UuSK6nnAmfYZcVGp99ub1THnlCdLXraLfeZfT68xhIUpaNUe2iOeKY1oRZcY3KzKZ+POWUuv7HtaIC49qjsNRWATj5m3g163bQ5S2cvq1a8Ktpx9BlBkTF23m9e/XlVrfu3Uijw/rzqZc34DD1F/TeXnG2lBErXEacvJYoXHOLQYeAq4BTgaud86F9B30x7kzSdu4njGvfcSIm+7hpTGjy2034JQzeeqVD3h87Hh279rFN59/HNygVfDGp7MZet1zFa4fNKAr7Vun0H3oA1z/z3cYc/fFQUxXPVFmPPbHPlz4xLccf9ckzu93OJ1aJJRq8+znv3DSfZM56b7JjJqwiFm/pEdMZwbgp/mzSNu4nsdeep8rb7yTcc8+Vm67404exCNj3+Oh599m9+5dTPtiYrntwokBFx3ZnGdnrmPUVyvp26oRzeLrlWqTuX0P/56+hoem/MakXzK4pFeL0IStgqKiQqa/9Rxn3fxPLhk1lhVzviVrU+k3vJgG8ZxwybX0GnRBiFJWnRn8qd9hjP56FbdMXEb/to1p2Si2VJvFm/O5/dNfuOPTX3lh1lquPr51iNJWTpTB7YM68LfxP3HR2LkM6ppK2+T6ZdotXJ/LH16ezx9enl9rOjPi4/XEei8DNwE9gSuBT83sugDmOqT5309j4GlDMDM6du3B9m35ZGeWndLT+9gBxScDOqJzNzLTt5Tz08LLzB9WkZVbUOH6s07sydv/mwvA3MVraBQfR7PkhArbh5Pe7Zqwess21qZvZ09hER/NWcfg3q0qbH9+v8P5YHZkvej8MHs6/U8d7N/nelCwPZ+crLL75pFH9y/eN9t17EZ2xtYQpK2cNk3iSN++m8yCPRQ6WLAhlyOblz424LesHezYUwTA6qwCGseFf9Vpn62//Uqj1OY0SmlOdJ26dDjmRFb/+H2pNvUTEmnathNR0dEhSll1RyTXZ0veLrZu201hkWPW6myOPqxRqTa79hYVX4+pE+U7rjUCdGuRwIbsHWzK2cneIseXS7cysENyqGMFjU6s530Ozc/Ayc651c65L4B+QO/AxTq0rIx0klObFS8nJTcl6yBvCHv37uW7rydx1NHHByNeQLVITWRDWnbx8sYtObRITQxdoEpo3rg+G7P2d9Y2ZRXQvHFcuW3j6kVzao/mfDo/sk55lJ2RTlJK0+LlJsmpZGekV9h+7969zPrmc3r06Vdhm3CRGFuH7B17ipezd+ylUVzdCtv3b9OYJVu2BSNajdiWk0nDJinFyw0bJ7M9J/Mg94gsTerXI7NEtTOzYDeNG5R9/o5u3Yh/n9uFO09tz39mRcYHipT4GLbk7R/a3Jq/i5T4mDLterRM4K0/9+Wpi3rQrpwKjkQuTx+dnHNPHrCcC/w5IIk8cq7sx4aD9RxfGvMIXXr0pkuPXoGMFRTlbWZ5v49wVH728tsOOqolc1ZkRNRwk085G3SQDzWvP/cYnbofRafuEbBvVuLDWcfk+hx/eCJPTF8TsDg1rtydMXw/kVZWuVtSzibPW5fLvHW5dGnagIuOasE/v1oZ6GjVVv6zVHrjfk3L55znZrNjTyHHt2/CY8O6M+yFucGIF3DhXDkJFk8dGjPrAIwGugLFA67OuXYVtB8BjAD4x+inGXbJldVPCkye+B5TJn0MQPtOXcnYuv/7MTMzttA4KaXc+014Yyx5udmMuOnuGskRahu35NCqWePi5ZZNE9mcnhvCRN5tyiqgZZP9n4paNKlPWs6Octue3681H0bIcNPXn04ongPTtkPXUkObWRlbK9w3P3rrJfJzs7nihkeDkrO6cnbspXGJikzjuDrklqjY7NMyIYZLe7fguVnr2L67MJgRq6Vh42S2Ze2vpm3LzqBBYpMQJqpZmQW7SWqwf85TUv16ZBeUff72WbZlO03j6xEfE03+rvB+Hrfm76Jpwv6KTGp8DOn5pT8MldwXZ63K4vZBUTSKq1vuPiyRx+uQ06vAf4C9+CYFvw68UVFj59xY51xf51zfmurMAJw59EL+9eLb/OvFtzmm/0lM/3oSzjmWL11M/QYNaZxUdrx0yqSPWTR/Njfd/RBRUV43N7x9Nm0xl5x1DADH9GhD3rYdpGXkhTiVNz+uzqJd03haJzegbnQU5x3bms9/LDu/PD6uLsd3SuXzHyLj6K3Tzh7OqGffZNSzb9L7uIHMnPI5zjlW/rKYuAYNSWxSdt/8dvJEfv5hNtfeMSpi9s212TtIbViPpPp1iTbo06oRP20uPaTUOK4OV/U7jHHzN7J1W2RV11LbdiJ3yyby0tMo3LuHFXOn0eao8B8K9GpVRgHNEmJIaViP6Cjj+LaNmb+h9IehpiUmebdtEkedaAv7zgzA0k35HNY4jhaNYqkTZZzRNZXvVpSeu1ayM9e1eTxRRu3pzFgQL2HK62y9OOfcFDMz59xa4H4z+w4YGcBsB9XrmP78MGcmN15+LvViYvnr3/dHGX33jVx9y700SU7hv0+PJqVpM+650ffl4McOOJlhl10VqtiejBt9BSf06UByYkNWTh7FqBcmUbeObwLiS+/PYPKMJQwa0I0ln4ykYOcerr7/zRAn9q6wyHHHG/OZcNtJREcZb0//jV835nHFyUcA8NpUX2n7rD6tmPpzGgUR9Ol+nyOP7s9P82Zx258vICYmlr/cfG/xuifuu4k//e0eGielMO7ZR0lKbcaoW/8CQJ/jT+LcS/4SqtieFDkYvzCN6/u3JsqM79fmsDl/Fye09VUMv1udzZAuKTSsF81FRzX338fx6NTVoYztWVR0NCdc+lc+efIeXFERXQacQVLLNvz87WcAdD/pd2zPzWLCqBvZvaMAM2PR1x9zyagXqRfXIMTpD63IwStzNnD3ae2JijK+XZHJhpydnNYxCYCvl2dy7OGJDGzfhMIix+69jqemrQltaI8KneNfX65gzMU9iYoyPl20md8yCjjff5Tdhz9u4pTOKVzQuwWFRY6de4u45+OlIU4tNcm8zL0ws5nACcD7wDf4vtvpEedcp0Pdd9G6/MiY3FFF/YbeFeoIAVW/Z/9QRwioz+4bHOoIAfP6ok2hjhBQnVPLn0xeW8xclRPqCAG1emNkDJNX1dy7TwpqLaPdLZOC9l7727+HhGWdxmud+yagPnAj0Af4A/DHAGUSERERqRSvQ04O35yZw4F9MwL/i++8NCIiIhJCOsrJe4fmLeA2YDFQdIi2IiIi8v+UmZ0JPA1EAy855x4pp81JwFP4iiQZzrkTq/u4Xjs06c65T6r7YCIiIlLzwqVAY2bRwHPA6cAGYJ6ZfeKcW1qiTSLwPHCmc26dmaXWxGN77dCMNLOXgClA8akYnXMf1kQIERERqRWOAVY6534DMLN3gaFAyUPKLgE+dM6tA3DO1cj3vnjt0FwJdMZXGto35OQAdWhERERCLIzm0LQESn5fzQbg2APadATqmtm3QDzwtHPu9eo+sNcOzZHOuR7VfTARERGJbCW/DcBvrHNu7L7V5dzlwEPK6+A7YvpUIA743sxmO+eWVyeX1w7NbDPrWnIMTERERP7/8XdexlawegNwWInlVsCBJ8XagG8i8HZgu5lNB44EqtWh8XoemgHAQjP71cx+MrPFZvZTdR5YREREaoZZ8C6HMA/oYGZtzawecDFw4EFFE4ETzKyOmdXHNyS1rLq/A68VmjOr+0AiIiJSuznn9prZ9cAX+A7bfsU5t8TMrvGvf8E5t8zMJgM/4ZuX+5Jz7ufqPranDo3/+5tEREQkDIXRpGCcc5OASQfc9sIBy/8C/lWTjxsZX/ErIiIichBeh5xEREQkTIVRgSZkVKERERGRiKcKjYiISISLilKJRhUaERERiXiq0IiIiEQ4zaFRhUZERERqAVVoREREIlw4nYcmVFShERERkYinCo2IiEiEU4FGFRoRERGpBVShERERiXCaQxOEDs0Hy9IC/RAhVb9n/1BHCKiCn2aGOkJArcs/MdQRAqa2v8Al168b6ggBNbhrUqgjBNTGVvGhjiC1jIacREREJOJpyElERCTC1faKrBeq0IiIiEjEU4VGREQkwqlAowqNiIiI1AKq0IiIiEQ4zaFRhUZERERqAVVoREREIpwKNKrQiIiISC2gCo2IiEiE0xwaVWhERESkFlCFRkREJMKpQKMKjYiIiNQCqtCIiIhEOM2hUYVGREREagFVaERERCKcCjSq0IiIiEgtUKkOjZklmFl8oMKIiIiIVIWnIScz6wu8CsT7Fi0H+JNzbkEAs4mIiIgHmhTsfQ7NK8BfnXPfAZjZAHwdnJ6BCiYiIiLildcOTf6+zgyAc26GmeUHKJOIiIhUggo03js0c83sReAdwAEXAd+aWW8A59wPAconIiIickheOzRH+f8fecDtx+Pr4JxSU4G82rx0AT9+OBZXVES7486gy+nDS61fM28qv0z5AIA69WLpc9FfadyyXbBjVtkpPZoz+tLeREUZb05bxdOfLSu1/vrBnRl2XBsA6kQbHVsk0PH6j8jZvjsEaSvnhZGXMnhgd9Kz8uk7/OFy2zxx+zAG9e9Gwc7djBj5Bgt/2RDklFXnnGPSa8+w/Mc51I2J5fxr76BFu45l2mVv3cx7Tz9IwbZ8WrTtwAXX302dOnVDkLhyuqY2YFjPpkSZMXNtDl8tzyy1/uhWCZzeMQmAXXuLeHdhGhvzdoUiapWsXDiXya8/R1FREb1PHsKAob8vtd45x+Rxz7Fi4Rzq1ovh3Gtvp3nbss9vuFq1aB5fv/E8RUVFHHXSYI475+JS651zfPXG86xaOJe6MTGcNeI2mrXtEKK0lbNxyXzmTRiLc0UccfwZ9Bh0Yan1uWnrmfnGU2StX0mvs/9It9MvCFHSmqc5NB6PcnLOnXyQS9A7M0VFhSyY8B8GXvMAZ979PGsXTCN387pSbRomNeOUGx/hzDufpduZFzP/3WeDHbPKosx47I99uPCJbzn+rkmc3+9wOrVIKNXm2c9/4aT7JnPSfZMZNWERs35Jj4jODMAbn85m6HXPVbh+0ICutG+dQvehD3D9P99hzN0XV9g2HK1YOIfMtI3c9PSbDL3qVj59+cly233x1oscN2Q4Nz/9JnEN4vnhm0lBTlp5Blx4ZDOem7WeUV+vom+rBJrF1yvVJqNgD09+t5aHv1nN579mcEmv5qEJWwVFRYVMenUMl94xmusef4WfZ31D+oY1pdqsXDiXrLQN3PDk65x91S189vLToQlbBUVFhXw57hkuvP1hRjz2EktnTyVj49pSbVYtmkt22kaueeI1Bv/5Jia/NiZEaSunqKiQOeP/w6nXP8A59/6HNfOnk3PA+0K9BvEcM/xqup16fohSSiB56tCY2S3lXP5sZkcFOF+5stYuJz6lOQ2TmxFdpy6tew9k4+LZpdokt+tCvfoNAUhq05kdORmhiFolvds1YfWWbaxN386ewiI+mrOOwb1bVdj+/H6H88HstRWuDzczf1hFVm5BhevPOrEnb/9vLgBzF6+hUXwczZITKmwfbpbNm8lRA8/AzDisY1d2bN9OfnbpKoZzjtVLfqRbvxMBOOrEQSybNyMUcSulTZM40rfvJrNgD4UOFmzIo2fz0mdyWJ21gx17ioqvJ8ZFzvk7N678hSbNWtK4aQui69Sl23En88v8WaXa/LJgJj1P8D2/rTp0ZWfBtjLPb7jatOpXGjdtQePU5kTXqUuXfiexfEHp7Vux4Hu6DzgNM6PlEV3ZtX0b2yJg+zLXLCc+pQXxyb5ta9NnIOsXlX5fiItPJLlNRyw6cvZJr8yCdwlXXs9D0xe4Bmjpv4wATgL+a2a3ByZaxXbkZBKXmFK8XD8xmR25Ff/B/fb9lzTr0jcY0WpE88b12Zi1/w1/U1YBzRvHlds2rl40p/Zozqfz1wcrXsC1SE1kQ1p28fLGLTm0SE0MXaBKysvOoFFSavFyo6Rk8rJKd6gL8vOIrd+Q6OhoX5smKWXahKPE2Dpk79hbvJyzYw+JsRW/ORx/eCJLtmwPRrQakZ+dQULS/teWhKQU8rNLPy/5WRk0KtmmSQr5EfDcAWzLziChyf7s8U2Sy25fdgYJJfbf8tqEo4KcTBo0Ti5ert84mYKDvC9I7eO1Q5ME9HbO3eqcuxVfBycFGAhcEaBslVNBt3HL8p/4bfaXHDn0iuDmqYbyNsW58tsOOqolc1ZkRMxwkxflb38Fv4BwVF7WMttUXpsw/uhzEBU9Mx2S63N8m0QmLtka1DzVUf5udsDzUl6bCHnuyvs7sjLbF5n7pqtwT/z/wcyCdglXXuturYGS75h7gMOdczvMrMxsPzMbga+Kw+9ufJDeQ2p2DkRcYhI7ctKLlwtyMohLaFKmXc7G1cx7ZwwnXvsAMQ0iZ8hiU1YBLZvUL15u0aQ+aTk7ym17fr/WfBhBw01ebNySQ6tmjYuXWzZNZHN6bggTHdqcLz5i/pTPAGjZvjO5mfvfxHMzM0go8ckRoH58I3YWbKOwsJDo6Ghys9JJaJwU1MxVkbNzL41LDCElxtUld+feMu1aJMRwaa/mPP/9erbvLgxmxGpJaJJMXub+15a8zHTiD3he4pOSyS3ZJqtsm3AV3ySFvKz92fOzMmh44PY1SSGvxP6bn5VBfGL4b1+DxGS2l6gkFWRnUL9R+OeWmuO1QvM2MNvMRprZSGAm8I6ZNQCWHtjYOTfWOdfXOde3pjszAE1adyQ/fRPbMtMo3LuHdT9Mp2WPY0u12Z61lZkvP0y/y24lPrVljWcIpB9XZ9GuaTytkxtQNzqK845tzec/lj3KJz6uLsd3SuXzHyLnCCAvPpu2mEvOOgaAY3q0IW/bDtIy8kKc6uCOHXQe1z32Etc99hJdju7Pwulf4pxj/fKlxNZvUOYNz8xo27UXS2ZPA2DhtC/o3Ld/KKJXytrsHaQ2rEdS/bpEG/RplcDizaVPSdU4rg4jjm3FuAWb2LotsiqHLdt3JjNtI9lbN1O4dw9Lvp9Kpz7Hl2rTqffx/PSd7/ndsGIpMeU8v+GqRbtOZKdtJMe/fctmf0uH3seVatOh93H8PONrnHNsXOnbvgM7PeEo6fCO5G/dSH6G731hzYLpHNbz2EPfsZbQHBqPFRrn3CgzmwQMwFd/vcY5N9+/+tJAhatIVHQ0vYddw7Tn7/Mdtt3vdBo1P5yVM3xHiRwxYAhLJr/Lru15LJjwPAAWFc0Ztz0V7KhVUljkuOON+Uy47SSio4y3p//GrxvzuOLkIwB4bepKAM7q04qpP6dREEGfgAHGjb6CE/p0IDmxISsnj2LUC5OoW8c3l+Sl92cwecYSBg3oxpJPRlKwcw9X3/9miBNXTsde/Vj+4xye/NsfqFsvhvOvvaN43euj7+Tcq/9OQpNkzrh0BO89PYop41+meZsO9DllSAhTe1Pk4L1FaVzX/zCiML5fm8Pm/N0MaJMIwIw1OQzunEKDetFcfGQzAAqd47Fv14QudCVERUcz5IobeHP0HTj/Yc2ph7Vh/lefAtD39LPp0OtYViycwzM3XUbdmFiGXn1biFN7FxUdzemXX8+7j92FKyqi54mDSGnVhh+m+Lav96ln0/6oY1i1aA4v3Ho5devF8LsRfw9xam+ioqM55qJr+frZe3FFRRxx3OkktjicX6f73hc6DRzCjtwsPnv0JvbsLACLYtnUiZxz7wvUi6t/iJ8ukcC8zE0ws6eB8c65WYdsfID7vlhRqwc2n3t7/qEbRbCCn2aGOkJAjXv17lBHCJhpq8N7mK66+h9eu78nd3dhUagjBNTGvMiq3lXWPaceEdRaxglPzAjae+13tw4IyzqN1yGnH4B/mNlKM/uX/8sqRURERMKC1yGnccA4M2sCXAA8amatnXORcfpIERGRWiycjz4KFq8Vmn2OADoDbYBfajyNiIiISBV4PVPwo2a2AngQ+Bno45w7O6DJRERERDzyeh6a1fi+iLIdEAP0NDOcc9MDlkxEREQ80YiT9w5NIfAN0ApYCPQDvicE37ItIiIiciCvc2huBI4G1jrnTgZ6AekHv4uIiIgEg776wHuHZqdzbieAmcU4534BOgUuloiIiIh3XoecNphZIvAx8JWZZQObAhVKREREvAvjwknQeD0PzXn+q/eb2VSgETA5YKlEREREKsFrhaaYc25aIIKIiIhI1YTz3JZgqeyJ9URERETCTqUrNCIiIhJeVKBRhUZERERqAVVoREREIlyUSjSq0IiIiEjkU4VGREQkwqlAowqNiIiI1ALq0IiIiES4cPouJzM708x+NbOVZnbnQdodbWaFZjasJn4H6tCIiIhIjTCzaOA5YDDQFfi9mXWtoN2jwBc19djq0IiIiEhNOQZY6Zz7zTm3G3gXGFpOuxuAD4CtNfXAmhQsIiIS4aLCZ1JwS2B9ieUNwLElG5hZS+A84BTg6Jp6YFVoRERExDMzG2Fm80tcRpRcXc5d3AHLTwF3OOcKazKXKjQiIiIRLphfTumcGwuMrWD1BuCwEsutgE0HtOkLvOvPnAwMMbO9zrmPq5Mr4B2a7IK9gX6IkPrsvsGhjhBQ6/JPDHWEgLr8yodDHSFgZn8yOtQRAurcJ6aHOkJATb/vtFBHCKiEuLqhjiCBMQ/oYGZtgY3AxcAlJRs459ruu25mrwH/q25nBlShERERiXjhcmI959xeM7se39FL0cArzrklZnaNf/0LgXpsdWhERESkxjjnJgGTDrit3I6Mc+6KmnpcdWhEREQinJU7F/f/Fx3lJCIiIhHPU4XGzBoAO5xzRWbWEegMfO6c2xPQdCIiInJIYXQempDxWqGZDsT6T4YzBbgSeC1QoUREREQqw+scGnPOFZjZn4FnnHOPmdmPgQwmIiIi3gTzPDThymuFxszsOOBS4DP/bZpQLCIiImHBa6fkJuAu4CP/8eTtgKkBSyUiIiKeqUDjsUPjnJsGTPNPDsY59xtwYyCDiYiIiHjlacjJzI4zs6XAMv/ykWb2fECTiYiIiCdRZkG7hCuvc2ieAgYBmQDOuUXAwABlEhEREakUzyfWc86tP+CmGv3abxEREZGq8jopeL2ZHQ84M6uHb/7MssDFEhEREa/CeCQoaLxWaK4BrgNaAhuAo/zLIiIiIiF3yAqNmUUDTznnLg1CHhEREakknVjPQ4XGOVcIpPiHmkRERETCjtc5NGuAmWb2CbB9343OuX8HIpSIiIh4pwKN9w7NJv8lCogPXBwRERGRyvN6puAHAMws3rfotgU0lYiIiHgWzie8CxavZwru7v927Z+BJWa2wMy6BTaaiIiIiDdeh5zGArc456YCmNlJwH+B4wMTS0RERLxSfcZ7h6bBvs4MgHPu231fVBkqXVIbcEHPpkSZ8f3aHL5anllqfd9WCZzWMQmAXXuLeG9hGhvzdoUiapU453jrxX+zaN4s6sXEctUt99LmiM5l2r3w2H2sXrGM6Dp1aNexK1fccBd16nh9WkPDOcek155h+Y9zqBsTy/nX3kGLdh3LtMveupn3nn6Qgm35tGjbgQuuv5s6deqGIHHlvDDyUgYP7E56Vj59hz9cbpsnbh/GoP7dKNi5mxEj32DhLxuCnLLqnHO8+tzj/Dh3JjExsfz19vtp16Hsvjnm4X+wavlS6tSpQ/tO3Rhx8z1hv28CDOycwsjzuxJlxvjZ63lhyqoybY49ogn3ndeVOlFRZG/fzcXPzg5B0qpxzvHck48yd9Z3xMTGcvu9o+jQqWuF7Z95YjRffPYx//tmThBTVo1zjicee5hZM6YTGxvLfQ8+TOcuZQcTHrj3Ln5YMI+GDX1TQkc++DAdO3cJdlypYV5PrPebmd1rZm38l38AqwMZ7GAMGH5kM/4zaz0Pfb2KPq0SaBZf+qjyzII9PP3dWh75ZjVf/JrBxb2ahyZsFf00fxZpG9fz2Evvc+WNdzLu2cfKbXfcyYN4ZOx7PPT82+zevYtpX0wMctLKW7FwDplpG7np6TcZetWtfPryk+W2++KtFzluyHBufvpN4hrE88M3k4KctGre+HQ2Q697rsL1gwZ0pX3rFLoPfYDr//kOY+6+OIjpqu/HuTNJ27ieMeM+YsTN9/DS06PLbTfg1DN56tUPePy/49m9exffTPo4uEGrIMrgwWHduOLFuZzxyDTO6d2CI5o2LNUmPq4Oo4Z156r/zmfQo9O57rUfQpS2auZ+P4ON69cybsL/uPnO+3j6sX9W2PbXZUvYvi0/iOmqZ9aM6axft5YPPpnMXfc+wKMPPVhh2xtvvo233vuIt977qFZ0ZswsaJdw5bVD8ycgBfjQf0kGrgxUqEM5vEkcGdt3k1mwh0IHCzbk0aN56YOvVmftYMeeouLriXHh/8mwpB9mT6f/qYMxM47o3IOC7fnkZGWUaXfk0f2Ld7J2HbuRnbE1BGkrZ9m8mRw18AzMjMM6dmXH9u3kZ5eusDnnWL3kR7r1OxGAo04cxLJ5M0IRt9Jm/rCKrNyCCtefdWJP3v7fXADmLl5Do/g4miUnBCtetc2fNY2Bpw/BzOjYtQfbt+WTnVl23+x97IDiffOITt3IzNgSgrSVc+ThiazNKGB95g72FDo+/XETp/doWqrN0N4t+eKnNDbl7AQgc9vuUEStslnTp3L64LMxM7p2P5Jt2/LJzEgv066wsJCxz/6bq667OQQpq2b6t98w5KyhmBk9eh5Ffn4eGenh/5ooNcNTh8Y5l+2cu9E519t/uck5lx3ocBVJjK1D9o69xcs5O/aQGFtxh+W4wxNZumV7hevDUXZGOkkp+19ImySnkl3Oi84+e/fuZdY3n9OjT79gxKuWvOwMGiWlFi83Skom74DOWkF+HrH1GxIdHe1r0ySlTJtI1SI1kQ1p+/98Nm7JoUVqYugCVVJWRjrJKc2Kl5NSmpJ1kI703r17+e7rSRx1dPhPuWvWKJbN2TuKl9NydtKsUWypNm1TG9Aori7vXN+PT24dwPlHtwx2zGrJSN9KStP9z19KStNy3/Qnvv8Oxw04iaTklGDGq5atW7fQtNn+bUtt2oytW8vfN//z7FNcMnwo//7XaHbvjqxOaXmiLHiXcOX1KKevzCyxxHJjM/siYKmqwFVwe4fk+hzXJpGJSyKtl17OFh1kR3r9ucfo1P0oOnXvFbhINcV52bby2oTxX1IllLcZrrzfSZgqL+vBytAvPf0IXXr2pkuP8N83y9uKAze3TpTR/bBG/GnsPC5/YQ7Xn9GBtikhnVJYKa6cv60Dn7+M9K1M++Yrzhv++2DFqhke983rbryZCR9P4rW3JpCXm8vrr/43GOkkwLyOwyQ753L2LTjnss0staLGZjYCGAFw0jX30/2MC6sV8kA5O/fSuMQQUmJcXXJ37i3TrkVCDL/v1Zz/fL+egt2FNZohEL7+dELxHJi2HbqSmb6/RJ+VsZXGSeV/UvrorZfIz83mihseDUrOqpjzxUfMn/IZAC3bdyY3c38HMzczg4TGyaXa149vxM6CbRQWFhIdHU1uVjoJjZOCmjlQNm7JoVWzxsXLLZsmsjk9N4SJDm3yxPeY4p8D075jVzLS04rXZaZvqXDfnPD6WPJysxlx893BiFltm3N30rxxXPFys8RYtuTtLN0mZydZ23ezY3chO3YXMndVFl1axLM6PXyrwBPff5dJn3wAQMcu3Ujfsv/5S0/fUqYKs3L5L2zasI4/Dj8LgF07d/LHYb/j9fc/C15ojya8+xYff/g+AF27dWdL2v5t27oljZSUsvtmcorv7atevXqcPfR83nz9leCEDaBwntsSLF47NEVm1to5tw7AzA6n4qIIzrmx+A715oaPltX4R8912TtIaViPpPp1ydmxhz6tEnht3sZSbRrH1eEvx7bijQWbSI+QMe7Tzh7OaWcPB2Dh3Bl8/en79DvxDFb9+jNxDRqS2CS5zH2+nTyRn3+YzR0PP0tUlNcpUcF37KDzOHbQeQD8+sP3zPniY3ocfwobViwjtn4D4g/orJgZbbv2YsnsafTsfwoLp31B5779QxG9xn02bTHXXDyQ9yYv4JgebcjbtoO0jLxQxzqoM4deyJlDfR9Mfpg9g8kT36P/yYNYsexn6jdoSOOksvvmlEkfs2j+bO771/NhvW+W9NO6XNokN6BVkzi25O7k7F4t+NsbP5Zq89XPW3jggm5ERxl1o42jDk/klW9/C1Fib4YOu5ihw3yTz2fPnM7E99/h5NMHs2zJTzRoEF+mQ9Ov/0AmfFZ8YCtnnXJsWHZmAIZffCnDL/Z9d/KM6d8yYfzbnHHmEH5evIiGDeOLOy8lZaRvJTklFecc06Z+TfsjOgQ7tgSA1w7NPcAMM5vmXx6IvwITCkUOJixK46/9D8MwZq/NIS1/N/3bJAIwc00OZ3ZOoUG9aC48spn/Po5/fbsmVJEr7cij+/PTvFnc9ucLiImJ5S8331u87on7buJPf7uHxkkpjHv2UZJSmzHq1r8A0Of4kzj3kr+EKrYnHXv1Y/mPc3jyb3+gbr0Yzr/2juJ1r4++k3Ov/jsJTZI549IRvPf0KKaMf5nmbTrQ55QhIUzt3bjRV3BCnw4kJzZk5eRRjHphEnXr+OYCvfT+DCbPWMKgAd1Y8slICnbu4er73wxx4srpdWx/fpg7kxv/eC71YmL5620ji9eNvvtGrr7lXpokp/Dfp0aT0rQZ99z4JwCOHXAywy67KlSxPSkscoz84Gdev+YYoqKMCXM2sCJtG5cc3xqAt2etY9WWbUxfls7nt59AkYPxs9exPC1yTp5+7PEnMHfWd/xx+O+IiYnltn+MKl539y1/5Za77i+3ExAJ+p9wIrNmTOf8swcRGxvLvQ/sP23CTdeN4J6R/yQlNZV7776dnOwsnHN07NSFO/8x8iA/VSKFeR27N7NkoB++YebvnXOeZmgGokITTi7tGVmHg1fWuvyKj9apDS6/svzzxNQGsz8p/3Dq2uLcJ6aHOkJATb/vtFBHCKiEuPA/p1R1NIoL7vTZy95aFLT32jcuPTIsx7e8TgruD+xwzv0PaATc7R92EhEREQk5rwPb/wEKzOxI4DZgLfB6wFKJiIiIZzqxnvcOzV7nG5saCoxxzj0NxB/iPiIiIiJB4XVScL6Z3QX8ARhoZtFA7R4AFRERiRDhfMK7YPFaobkI2AX82TmXBrQE/hWwVCIiIiKV4KlC4+/E/LvE8jo0h0ZERCQshPPclmDxepTT+Wa2wsxyzSzPzPLNLLzPBCYiIiL/b3idQ/MYcLZzblkgw4iIiEjlqT7jfQ7NFnVmREREJFx5rdDMN7PxwMf4JgcD4Jz7MBChRERExLsozaHx3KFJAAqAM0rc5gB1aERERCTkvB7ldGWgg4iIiEjVqEBziA6Nmd3unHvMzJ7BV5EpxTl3Y8CSiYiIiHh0qArNvonA8ymnQyMiIiKhp/PQHKJD45z71H91KXA30KbEfRw6uZ6IiIiEAa+Tgt/E9y3bi4GiwMURERERqTyvHZp059wnAU0iIiIiVaIRJ+8dmpFm9hIwBZ2HRkRERMKM1w7NlUBnoC77h5x0HhoREZEwoBPree/QHOmc6xHQJCIiIiJV5LVDM9vMujrnlgY0jYiIiFSaCjTeOzQDgMvNbDW+OTQGOOdcz4AlExEREfHIa4fmzICmEBERkSrTifW8f5fT2kAHEREREakqrxWaKrv22NaBfoiQenZ27e7r1fZe/+xPRoc6QsD0O+euUEcIqM/ffTDUEQLq5fnrQh0hoDbl7g51hIAaO7xbUB8vKqiPFp70OxAREZGIF/AKjYiIiARWba+me1HpCo2ZRZlZQiDCiIiIiFSFpw6Nmb1tZglm1gDfN2//ama3BTaaiIiIeBFlwbuEK68Vmq7OuTzgXGAS0Bq4LFChRERERCrD6xyaumZWF1+H5lnn3B4zc4GLJSIiIl6Fc+UkWLxWaF4E1gANgOlmdjiQF6hQIiIiIpXh9cR6Y4AxJW5aa2YnByaSiIiIVIaOcvI+Kbipmb1sZp/7l7sClwc0mYiIiEQcMzvTzH41s5Vmdmc56y81s5/8l1lmdmRNPK7XIafXgC+AFv7l5cBNNRFAREREagcziwaeAwYDXYHf+4sgJa0GTvR/wfUoYGxNPLbXDk2yc+49oAjAObcXKKyJACIiIlI9YXTY9jHASufcb8653cC7wNCSDZxzs5xz2f7F2UCrGvkdeGy33cySAAdgZv2A3JoIICIiIrVGS2B9ieUN/tsq8mfg85p4YK+Hbd8CfAK0N7OZQAowrCYCiIiISPUEc06wmY0ARpS4aaxzbt+wUXlJyj3Ni//goj8DA2oi1yE7NP7xsBP9l074wv7qnNtTEwFEREQkcvg7LxXNe9kAHFZiuRWw6cBGZtYTeAkY7JzLrIlch+zQOOcKzWyoc+5JYElNPKiIiIjUnKjwOWx7HtDBzNoCG4GLgUtKNjCz1sCHwGXOueU19cBeh5xmmtmzwHhg+74bnXM/1FQQERERiWzOub1mdj2+I6OjgVecc0vM7Br/+heA+4Ak4Hn/+XP2Ouf6VvexvXZojvf//2DJ3MAp1Q0gIiIi1eP1CJ9gcM5Nwve9jyVve6HE9b8Af6npx/V6pmCdFVhERETClqcOjZk1AkYCA/03TQMedM7p0G0REZEQC58pNKHjtUr1CpAPXOi/5AGvBiqUiIiISGV4nUPT3jl3QYnlB8xsYQDyiIiISCWF0VFOIeO1QrPDzIpPfGNm/YEdgYkkIiIiUjleKzTXAuP8c2kAsoErApLII+ccLz/zLxbMmUFMbCw33PEA7Tt2KdPu2cceYNWvS3E4WrQ6nBvufIC4uPohSFw5XZs2YHjPZpgZs9Zk8+Xy0ucdOvqwBM7omAzArr1FvLNwMxtzd4UiaqV1TW3AsJ5NiTJj5tocvjpw21olcHrHJMC3be8uTGNjXmRsG/j2zVefe5wf584kJiaWv95+P+06dC7TbszD/2DV8qXUqVOH9p26MeLme6hTx+ufZGi8MPJSBg/sTnpWPn2HP1xumyduH8ag/t0o2LmbESPfYOEvG4Kcsnqcc4z/75P8PH8W9WJiueKme2ndvlOZdlP/N4Epn4wnPW0jT7z5OQ0TEoMftgo2LV3ADx+MxRUV0f64M+h6xvBS69fMm8rSrz8AoG5MLH0v/CuNW7ULRdRK69a0IRf1akaUwYzfcpj8a0ap9Ue2iGdot1QcjsIieG9hGiszC0KUtmapQOOxQuOcW+icOxLoCfR0zvVyzi0KbLSD+2HOTDZtXMfzb07k2lv/wYtPji633Z+uu5UnXx7PUy+/R3JqMyZ9ND7ISSvPgIuObM6zM9cx6quV9G3ViGbx9Uq1ydy+h39PX8NDU35j0i8ZXNKrRfk/LMwYcOGRzXhu1npGfb2Kvq0SymxbRsEenvxuLQ9/s5rPf83gkl7NQxO2in6cO5O0jesZM+4jRtx8Dy89Xf6+OeDUM3nq1Q94/L/j2b17F99M+ji4QavgjU9nM/S65ypcP2hAV9q3TqH70Ae4/p/vMObui4OYrmb8vOB7tm5az6gXJ/CH6+7krf88Vm679l16ctOoZ0hKbRbkhFVXVFTIggn/4aRrH2DIPc+zdsE0cjevK9WmQVIzTvvbIwy561m6DbqYee8+G6K0lWPAJb2bM+a7tYycvIqjWzeieXxMqTa/bNnOg1+tYtRXvzFu/kb+2DcyXjfFG08dGjN72MwSnXN5zrk8M2tsZv8MdLiDmTvzW04+4yzMjE5de7J9ez5Zmell2tVv0BDwferavXsXFgHd2DZN4kjfvpvMgj0UOliwIZcjm8eXavNb1g527CkCYHVWAY3jwvuT/T5lty2Pngds2+pS27aDxAjZtn3mz5rGwNOHYGZ07NqD7dvyyc7MKNOu97EDMDPMjCM6dSMzY0sI0lbOzB9WkZVb8Sfas07sydv/mwvA3MVraBQfR7PkhGDFqxGL5kyn38mDMTPade7Oju3byM0q+/y1bt+J5KaR1dnOWruchsnNaZjcjOg6dWndZyAbFs8u1SalXRfq1fe9bia37UxBTtltD0dtm8SxddtuMrbvodA55q3P5ciWpV9bdhUWFV+PiY4q/wuGIlQYfdt2yHidQzPYOZezb8H/td9DApLIo8yMrSSlNi1eTkpOJSujbIcG4JlHR3LlBaezcd0afnfeRcGKWGWJsXXI3rH/q7Kyd+ylUVzdCtv3b9OYJVu2BSNatfm2bW/xcs6OPSTGVtxhOf7wRJZs2V7h+nCUlZFOcsr+T+1JKU3JythaYfu9e/fy3deTOOro4ytsEylapCayIS27eHnjlhxapCaGLlAV5GSm0yRl/2tLYlIK2eV8WIpEBTmZ1G+cUrxcPzGZHTkVf43Oqu+/pHnXap/ANSgS4+qSVbD/dTOnYE+5H/SOahHPg4OO4IYTWjNu3sZgRpQA89qhiTaz4tqdmcUBMQdpH3jldK0r6jjecMcDvDzhC1q1bsuMqV8GNFaNqEQPuGNyfY4/PJGPf674DTPcVfQpqUNyfY5vk8jEJZG1bc6V3aKDVQZfevoRuvTsTZcevQIZKyjK28zyfh/hrLy0kVDZrbIKtm3L8p/47fsvOWroFcHNU0Xl73tlb1u4KZ/7vljJ8zPXM7R7auCDSdB4reW/CUwxs1fx/b3/CRhXUeOSXy0+8tExXPiHP1U3JwCTPhrPV599BMARnbuRuXV/iT4zYyuNk1MquivR0dH0P/kMPh4/jlMHD62RPIGSs2MvjUtUZBrH1SF3R9kvN2+ZEMOlvVvw3Kx1bN9dGMyIVZazc2+pT02JcXXJ3bm3TLsWCTFc2qs5z3+/PiK2bfLE95jinwPTvmNXMtLTitdlpm+hcVL5++aE18eSl5vNiJvvDkbMgNu4JYdWzRoXL7dsmsjm9PA//+bUz95nxpefANCmQxey0ve/tuRkppPYJDlU0WpU/cQkCrL3V5sKcjKIa9SkTLvsjauZ+84YTrz2AWIaRMaQYXbBHprU3/+6mVi/LjnlvLbssyKjgJSG9WhYL5ptEfAacyg6bNv7Vx88ZmY/Aafhqx+Mcs59cZD2xV8tvnTT9hr7eDbkvIsY4h8ymv/9d0z6eDwDThnE8mWLqd+gIU0OeNNwzpG2aT3NW7bGOcf876fTqnXbmooTMGuzd5DasB5J9euSs2MPfVo14tUDSqON4+pwVb/DGDd/I1u37Q5R0soru20JvFbOto04thXjFmyKmG07c+iFnDn0QgB+mD2DyRPfo//Jg1ix7GfqN2hI46Syb4hTJn3Movmzue9fzxMVFU7fxFJ1n01bzDUXD+S9yQs4pkcb8rbtIC0jL9SxDunk3w3j5N8NA2DxvJlM/ex9jh54Oqt/XUJc/QY0qiUdmiatO5KfvoltGWnEJSaxbsF0jr/itlJttmdtZcZLD9PvsltJSG0ZoqSVt6bUa8tejj6sES/NKX2EXUqDeqRv972mtE6MJTrKakVnRny8fvVBA+BL59xkM+sEdDKzus65smWDIOnTbwAL5szg2j8MJSYmlhvuuL943ag7b+C6v99HYpMkxoweSUHBdpxztG3fkatvvitUkT0rcjB+YRrX929NlBnfr81hc/4uTmjr++T73epshnRJoWG9aC46qrn/Po5Hp64OZWxPihy8tyiN6/ofRhT7tm03A9okAjBjTQ6DO6fQoF40Fx/pm4dS6ByPfbsmdKErqdex/flh7kxu/OO51IuJ5a+3jSxeN/ruG7n6lntpkpzCf58aTUrTZtxzo6+CeeyAkxl22VWhiu3JuNFXcEKfDiQnNmTl5FGMemESdetEA/DS+zOYPGMJgwZ0Y8knIynYuYer738zxIkrr3vf41m8YBb/uHo49WJiuPzGfxSve+aBW7js+rtITErhm0/f44sP3yQvO4sHb7yM7n2O4483hHelLSo6mr7Dr+Hb5+/DuSLa9TudRs0PZ8UM3/cIdhgwhJ8nv8uu7XnMf+95332iohl0+1MhTO1NkYN3ftzMTQMP950SYnU2m/N2MbCd73Vz+m/Z9G6VwHGHN6LQOXYXOv77fWSdUuBgVKAB8zK+bWYLgBOAxsBsYD5Q4Jy79FD3rckKTTh6dvbaUEcIqFo9dwAY0bdVqCMETL9zwr/zXh2fv/tgqCME1LdrK56sWxtsyo2M6mtVjR3eLagvnqO+Xhm099p7TzsiLN8YvM6hMedcgZn9GXjGPwT1YyCDiYiIiDfhfDh1sHgduDczOw64FPjMf1tknRxEREREai2vnZKbgLuAj5xzS8ysHTA1YKlERETEM6vM+T5qKa8dmjnOuXP2LTjnfjOz2j2ALSIiIhHD65DTPDPrt2/BzC4AZgUmkoiIiFSGvvrAe4XmEuAVM/sWaAEkAacEKpSIiIhIZXg9sd5iM3sIeAPIBwY652rPAfwiIiIRLJwrJ8Hi9cR6LwPtgZ5AR+BTM3vWOfdcIMOJiIiIeOF1yOln4C/Odxa+1f75NP8OXCwRERHxqrafBNULr0NOTx6wnAv8OSCJRERERCrJ65BTB2A00BWI3Xe7c65dgHKJiIiIR5pD4/2w7VeB/wB7gZOB1/FNEBYREREJOa8dmjjn3BR83+m01jl3PzpsW0RERMKE10nBO80sClhhZtcDG4HUwMUSERERrzQn2HuF5iagPnAj0Af4A/DHAGUSERERqRSvFRqHb87M4UBd/23/xXdeGhEREQmhKJVoPHdo3gJuAxYDRYGLIyIiIlJ5Xjs06c65TwKaRERERKpEh21779CMNLOXgCnArn03Ouc+DEgqERERkUrw2qG5EuiMb/7MviEnB6hDIyIiEmKaQuO9Q3Okc65HQJOIiIiIVJHXDs1sM+vqnFsa0DQiIiJSaVGoROO1QzMAuNzMVuObQ2OAc87psG0REREJOa8dmjMDmkJERESqTHNoPHZonHNrq/oAS7bmVvWuEaFzalyoIwRUcv26h24Uwc59YnqoIwTM5+8+GOoIATX44vtCHSGgnht7e6gjBNQRSbX7tVOCz2uFRkRERMKUzkPj/bucRERERMKWKjQiIiIRTt/lpAqNiIiI1AKeOjRm9jczSzCfl83sBzM7I9DhRERERLzwWqH5k3MuDzgDSMH3VQiPBCyViIiIeGYWvEu48tqh2bcJQ4BXnXOLStwmIiIiElJeJwUvMLMvgbbAXWYWz/4vqRQREZEQ0qRg7x2aPwNHAb855wrMLAnfsJOIiIhIyHkdcnJAV+BG/3IDIDYgiURERKRSNIfGe4fmeeA44Pf+5XzguYAkEhEREakkr0NOxzrnepvZjwDOuWwzqxfAXCIiIuKRTirn/Xewx8yi8Q09YWYpaFKwiIiIhAmvFZoxwEdAqpk9BAwD/hGwVCIiIuKZhfPkliA5ZIfGzKKA1cDtwKn4zj9zrnNuWYCziYiIiHhyyA6Nc67IzJ5wzh0H/BKETCIiIlIJqs94n0PzpZldYKppiYiISBjyOofmFnznnik0s53+25xzLiEwsURERMQrnSnYY4fGORcf6CAiIiIiVeW1QoOZnQMM9C9+65z7X2AiiYiISGWoPuNxDo2ZPQL8DVjqv/zNf5uIiIhIyHmt0AwBjnLOFQGY2TjgR+DOQAUTERER8crzkBOQCGT5rzeq+SiV45zjs9eeYfmPc6gbE8sF195Bi3Ydy7TL2rqZ955+kB3b8mnetgPDrr+bOnXqhiBx5axdPJ8Z7/yHIldE1xPOpM+Qi0qtz968nimvPEH6ulX0O+9yep05LERJK2/lwrlMfv05ioqK6H3yEAYM/X2p9c45Jo97jhUL51C3XgznXns7zduWfW7D1cDOKYw8vytRZoyfvZ4Xpqwq0+bYI5pw33ldqRMVRfb23Vz87OwQJK0a5xzj//skP8+fRb2YWK646V5at+9Upt3U/01gyifjSU/byBNvfk7DhMTgh62kF0ZeyuCB3UnPyqfv8IfLbfPE7cMY1L8bBTt3M2LkGyz8ZUOQU1bPb4vmMeWN5ykqKuLIkwbT75yLS63P3LSOSWMfZ8ualZww/EqO/d3wECWtvJWL5vLF68/hiorodfIQ+p9T9rXli9efY6X/teWcayLrteVgNCfY+2Hbo4Efzew1f3VmAVD+X3uQLF84h8y0jdz89Juce9WtfPLyk+W2+/KtFzl+yHBufvpN4hrEs+CbSUFOWnlFRYVMf+s5zrr5n1wyaiwr5nxL1qa1pdrENIjnhEuupdegC0KUsmqKigqZ9OoYLr1jNNc9/go/z/qG9A1rSrVZuXAuWWkbuOHJ1zn7qlv47OWnQxO2CqIMHhzWjStenMsZj0zjnN4tOKJpw1Jt4uPqMGpYd67673wGPTqd6177IURpq+bnBd+zddN6Rr04gT9cdydv/eexctu179KTm0Y9Q1JqsyAnrLo3Pp3N0Osq/t7dQQO60r51Ct2HPsD1/3yHMXdfXGHbcFRUVMhX455h+O0P85fHXmLp7KlkbCz92hLbIJ7TLruOY4ZEzock8G3b5FfHcMnto7n2Xwd/bbnu36/zu7/cwqRXIue1RQ7NU4fGOfcO0A/40H85zjn3biCDHcqyeTM5auAZmBmHdezKzu3byc/OLNXGOcdvS36kW78TAeh14iCWzZsRiriVsvW3X2mU2pxGKc2JrlOXDsecyOofvy/Vpn5CIk3bdiIqOjpEKatm48pfaNKsJY2btiC6Tl26HXcyv8yfVarNLwtm0vME33PbqkNXdhZsK/PchqsjD09kbUYB6zN3sKfQ8emPmzi9R9NSbYb2bskXP6WxKcd3BoTMbbtDEbXKFs2ZTr+TB2NmtOvcnR3bt5GblVGmXev2nUhu2jwECatu5g+ryMotqHD9WSf25O3/zQVg7uI1NIqPo1ly5Jy9YvOqX0ls2oLEVN9rS5d+J7FiQem/vwaNGtO8fSeioitTwA+9TSt/oXHT0q8tvx6wbcsj+LXlUMwsaBcPWc40s1/NbKWZlZmaYj5j/Ot/MrPeNfE78Dop+DygwDn3iXNuIrDTzM6tiQBVlZ+dQaOk1OLlhKRk8g54US3IzyO2fkOi/W/6CU1SyrQJR9tyMmnYJKV4uWHjZLbn1I4/uvzsDBKS9m9bQlIK+dmln5P8rAwalWzTJIX8CHjeAJo1imVz9o7i5bScnTRrFFuqTdvUBjSKq8s71/fjk1sHcP7RLYMds1pyMtNpkrK/k5aYlEJ2ZnoIEwVPi9RENqRlFy9v3JJDi9TE0AWqpPzsDBJKvLbEN0lmW3Zk/G0dSt6Bry3lvG4cuP0JTcq+/kj1+L/I+jlgMNAV+L2ZdT2g2WCgg/8yAvhPTTy21yGnkc653H0LzrkcYGRNBKgq51zZG8t0HMu2iYiTHZe3bbXkoLxyN+3AbSt38yNj+8tLeeA214kyuh/WiD+NncflL8zh+jM60DalQVDy1YTyn57IeH6qq7zNLPe1KFzV4teW8nbMA/fL8t82asf2RwXxcgjHACudc78553YD7wJDD2gzFHjd+cwGEs2s2uVcrzXF8rahwvua2Qh8vS5G/ONRTrvgD1WIVtbsLz5i/pTPAGjZvjO5mVuL1+VlZpDQOLlU+/rxjdhZsI3CwkKio6PJy0onvnFSjWQJpIaNk9mWtf8T77bsDBokNglhopqT0CSZvBKf5vMyyz4n8UnJ5JZsEyHPG8Dm3J00bxxXvNwsMZYteTtLt8nZSdb23ezYXciO3YXMXZVFlxbxrE7fHuy4nk397H1mfPkJAG06dCErfUvxupzMdBKbJFd011pl45YcWjVrXLzcsmkim9NzD3KP8BLfJIW8Eq8t+VkZNIyQv61DKfPakpVeZtsSmiSX2v7y2ki1tQTWl1jeABzroU1LYHN1HthrhWa+mf3bzNqbWTszexLfxOByOefGOuf6Ouf61lRnBqDfoPO4/rGXuP6xl+h6dH8WTv8S5xzrly8lpn6DMm96Zkbbrr1YMnsaAD9O+4IuffvXWJ5ASW3bidwtm8hLT6Nw7x5WzJ1Gm6P6hTpWjWjZvjOZaRvJ3rqZwr17WPL9VDr1Ob5Um069j+en73zP7YYV5T+34eqndbm0SW5AqyZx1I02zu7Vgq9/3lKqzVc/b+Hodk2IjjJi60Zx1OGJrNyyLUSJvTn5d8O49+nXuffp1znq2IHMnvq5b47aLz8TV78Bjf6fdGg+m7aYS846BoBjerQhb9sO0jLyQpzKu+btOpGdtpEc/9/fstnfckTv40Idq0a0aN+ZrANeWzoe8NrSsU/p15bYuMh5bTmUYM6hMbMRZja/xGVEySjlxDuwNualTaV5rdDcANwLjPcH+RK4rroPXh0de/Vj+Y9z+Pff/kC9ejGcf+0dxeteH30n5179dxKaJDPo0hGMf3oUX49/meZtOtDnlCEhTO1NVHQ0J1z6Vz558h5cURFdBpxBUss2/PytrzrV/aTfsT03iwmjbmT3jgLMjEVff8wlo16kXlx4D11ERUcz5IobeHP0HbiiIo46aTCph7Vh/lefAtD39LPp0OtYViycwzM3XUbdmFiGXn1biFN7V1jkGPnBz7x+zTFERRkT5mxgRdo2Ljm+NQBvz1rHqi3bmL4snc9vP4EiB+Nnr2N5Wnh3aErq3vd4Fi+YxT+uHk69mBguv/EfxeueeeAWLrv+LhKTUvjm0/f44sM3ycvO4sEbL6N7n+P44w13hzD5oY0bfQUn9OlAcmJDVk4exagXJlG3jm8O3kvvz2DyjCUMGtCNJZ+MpGDnHq6+/80QJ66cqOhoTr/8et577C5cURE9ThxESqs2/DjF9/fX69Sz2ZaTxbh7r/O9tkQZ8yd/yF8efYmY+uH/2nLmFTfw9iO+15YjTxpMaqs2LPjat219TjubI446lpUL5/DczZdRJyaWcyLotSWcOOfGAmMrWL0BOKzEcitgUxXaVJpVdvzXP+GngXPO08eSCQs3RdAAc+Vt3rYr1BECKrl++J+zpzrueX1RqCMEzKvX1o5P3hUZfPF9oY4QUM+NvT3UEQKqXpTXAYLI9Ic+rYI6OSeY77XDj2pR4baZWR1gOXAqsBGYB1zinFtSos3vgOvxnbT3WGCMc+6Y6ubyepTT22aWYGYNgCXAr2amrq2IiIgUc87txddZ+QJYBrznnFtiZteY2TX+ZpOA34CVwH+Bv9bEY3sdcurqnMszs0v9Qe7AN4fmXzURQkRERKounI40dM5NwtdXKHnbCyWuOwIwbcVrza+umdUFzgUmOuf2UAMTeERERERqgtcKzYvAGmARMN3MDgciZ2q/iIhILVa7ZyR546lD45wbA4wpcdNaMzs5MJFEREREKsdTh8bMbinn5lwzW+CcW1izkURERKQywmkOTah4rVL1Ba7Bdya/lvjOAnwS8F8zq93HFoqIiEjY8zqHJgno7ZzbBmBmI4H3gYH4jnZ6LDDxRERERA7Na4emNbC7xPIe4HDn3A4zq91nlhMREQlzGnDy3qF5G5htZhP9y2cD7/hPtLc0IMlEREREPPJ6lNMoM5sEDMDXEbzGOTffv/rSQIUTERGRQ9OcYO9HOT0NjHfOPR3gPCIiIiKV5nXI6QfgH2bWEfgIX+dm/iHuIyIiIkEQpVk03g7bds6Nc84NAY7B9y2aj5rZioAmExEREfHIa4VmnyOAzkAbNBlYREQkLGgOjccKjZntq8g8CPwM9HHOnR3QZCIiIiIeea3QrAaOB9oBMUBPM8M5Nz1gyURERMQT0xwazx2aQuAboBWwEOgHfA+cEphYIiIiIt55/S6nG4GjgbXOuZOBXkB6wFKJiIiIZ2bBu4Qrrx2anc65nQBmFuOc+wXoFLhYIiIiIt55HXLaYGaJwMfAV2aWDWwKVCgRERHxTueh8f7VB+f5r95vZlOBRsDkgKUSERERqYTKnocG59y0QAQRERGRqgnnuS3B4nUOjYiIiEjYMudcQB+gYE+AHyDErnx7YagjBNTgrkmhjhBQp7ZvGuoIAfPy/HWhjhBQrRvHhDpCQF034rFQRwis+ORQJwioHdPvD2rN5Iul6UF7rx3UNSUs60GVHnISERGR8KIhJw05iYiISC2gCo2IiEiE01cfqEIjIiIitYAqNCIiIhEuSgUaVWhEREQk8qlCIyIiEuE0h0YVGhEREakFVKERERGJcDoPjSo0IiIiUguoQiMiIhLhNIdGFRoRERGpBVShERERiXA6D40qNCIiIlILqEIjIiIS4TSHRhUaERERqQXUoREREZGIpyEnERGRCKcT66lCIyIiIrWAKjQiIiIRTgUajxUaM7vZzFoFOoyIiIhIVXit0CQAX5hZFvAu8L5zbkvgYomIiIhXUZpE461D45x7AHjAzHoCFwHTzGyDc+60gKY7eCYeG/0QM7+bTmxsLA88NJouXbuVaXffPXeyYP48GjaMB+DBh0bTqXOXYMettCNbxHPFMa2IMuObFZlM/Ll0/7HvYY248KjmOByFRTBu3gZ+3bo9RGkrZ9WieXz9xvMUFRVx1EmDOe6ci0utd87x1RvPs2rhXOrGxHDWiNto1rZDiNJWnnOO5558lLmzviMmNpbb7x1Fh05dK2z/zBOj+eKzj/nfN3OCmLLqNi1dwA8fjMUVFdH+uDPoesbwUuvXzJvK0q8/AKBuTCx9L/wrjVu1C0XUKvlt0Tym+PfPI08aTL8D9s/MTeuYNPZxtqxZyQnDr+TY3w2v4CeFnxdGXsrggd1Jz8qn7/CHy23zxO3DGNS/GwU7dzNi5Bss/GVDkFNW3enHHMHjN55JdFQUr332A4+/NaPU+sSGsbx451DatmzCrt17ufqRiSxdvTVEaaWmVXZS8FYgDcgEUms+jnczvpvOunVrmTjpC/5x/4M8POqBCtvedOttjP/gY8Z/8HFEdGbM4E/9DmP016u4ZeIy+rdtTMtGsaXaLN6cz+2f/sIdn/7KC7PWcvXxrUOUtnKKigr5ctwzXHj7w4x47CWWzp5Kxsa1pdqsWjSX7LSNXPPEawz+801Mfm1MiNJWzdzvZ7Bx/VrGTfgfN995H08/9s8K2/66bAnbt+UHMV31FBUVsmDCfzjp2gcYcs/zrF0wjdzN60q1aZDUjNP+9ghD7nqWboMuZt67z4YobeUVFRXy1bhnGH77w/ylgv0ztkE8p112HccMGRailFX3xqezGXrdcxWuHzSgK+1bp9B96ANc/893GHP3xRW2DTdRUcZTNw9h6G1v0euPzzH81O50PjylVJvbLzuBRSvTOObK//Dnhz7i8RvPDFHammdBvIQrr3NorjWzb4EpQDJwlXOuZyCDHcq0qVM465yhmBk9jzyK/Pw80tNrR0/7iOT6bMnbxdZtuykscsxanc3RhzUq1WbX3qLi6zF1osAFO2XVbFr1K42btqBxanOi69SlS7+TWL5gVqk2KxZ8T/cBp2FmtDyiK7u2b2NbdmaIElferOlTOX3w2ZgZXbsfybZt+WRmpJdpV1hYyNhn/81V190cgpRVk7V2OQ2Tm9MwuRnRderSus9ANiyeXapNSrsu1KvfEIDktp0pyMkIRdQq2bzqVxKbtiCxxP654oD9s0GjxjRv34mo6Mg7pmLmD6vIyi2ocP1ZJ/bk7f/NBWDu4jU0io+jWXJCsOJVy9FdWrJqYxZrNmezZ28hE6b8zFkDOpVq07lNCt8uWA3A8nUZHN4skdTGDUIRVwLAa4XmcOAm51w359xI59zSQIbyYuuWLTRr1rx4uWnTZmzdUv60nufGPMWF553D44+OZvfu3cGKWGVN6tcjc/v+nJkFu2ncoG6Zdke3bsS/z+3Cnae25z+z1pZZH462ZWeQ0GT/p6b4JsnkZ5d+w8vPziAhKfWgbcJZRvpWUpo2K15OSWlKRjmd7Ynvv8NxA04iKTmlzLpwVZCTSf3G+/PWT0xmR07Fnc1V339J8659gxGtRuSXs39ui6B9r7papCayIS27eHnjlhxapCaGLlAltEhOYMPWvOLljel5tEwp3RlbvHILQwf6qvR9u7SkddPEMm0ilko03jo0zrk7gYZmdiWAmaWYWduAJjtkprK3WTmTom646RY++vRz3hz/Prm5Obz68n+DkK56yt1fytneeetyueXjZTw+9TcuOqpFoGPVCFfOE1fmO0jKf3IDlKjmuXKerAP3zYz0rUz75ivOG/77YMUKnAqemy3Lf+K377/kqKFXBDdPdZS374XzK3gNK++pLO9vNhx5yf74WzNIjI9l9svXcO35x7BoxWb2FhaVvaNEJE81UzMbCfQFOgGvAnWBN4H+FbQfAYwAeOb5F/jTX0bUSNjx77zFh+9PAKBb9x6kpW0uXrdlSxopqWWn9aSk+G6rV68eQ889n9dfe6VGsgRSZsFukhrUK15Oql+P7II9FbZftmU7TePrER8TTf6uwmBErLL4JinkZe0ffsnPyqBh46SybTK3lmoTn1i6TbiZ+P67TPrENxG2Y5dupG9JK16Xnr6lTBVm5fJf2LRhHX8cfhYAu3bu5I/Dfsfr738WvNBVUD8xiYLs/c9fQU4GcY2alGmXvXE1c98Zw4nXPkBMg8j5BOxl/6zNNm7JoVWzxsXLLZsmsjk9N4SJvNuYnker1P37WsuUBDZllJ6fll+wi6sfmVi8/Mv4m1izOSdYEQNKX07pfcjpPOAcYDuAc24TEF9RY+fcWOdcX+dc35rqzABc9PtLiyf3nnzKqfzvk4k45/hp0UIaNowv7ryUtG9ejXOOqd9MoX2HjjWWJ1BWZRTQLCGGlIb1iI4yjm/bmPkbSr+oNI3f3+Fp2ySOOtEW9p0ZgBbtOpGdtpGcrZsp3LuHZbO/pUPv40q16dD7OH6e8TXOOTauXEpM/QZh/6YydNjFvPj6BF58fQL9B57CV59/inOOpT8vokGD+DIdmn79BzLhs6m89dFk3vpoMjGxsWHfmQFo0roj+emb2JaRRuHePaxbMJ1WPY4t1WZ71lZmvPQw/S67lYTUliFKWjXNy9k/jzhg/6zNPpu2mEvOOgaAY3q0IW/bDtIy8g5xr/Aw/5dNHNEqicObJ1K3TjTDT+3OZzN/LdWmUcNY6taJBuDKs3ozY9Fa8gt2hSKuBIDXWW27nXPOzByAmYV8FtWAgScy47vpnDP4DGLjYrl/1P5DEK+/dgT3PTCK1NSm3HPHbWRnZ+EcdOrUmXtG3h+60B4VOXhlzgbuPq09UVHGtysy2ZCzk9M6+t7Uv16eybGHJzKwfRMKixy79zqemrYmtKE9ioqO5vTLr+fdx+7CFRXR88RBpLRqww9TPgWg96ln0/6oY1i1aA4v3Ho5devF8LsRfw9x6so59vgTmDvrO/44/HfExMRy2z9GFa+7+5a/cstd95NcTuc7EkRFR9N3+DV8+/x9OFdEu36n06j54ayYMQmADgOG8PPkd9m1PY/57z3vu09UNINufyqEqb3bt3++598/e/j3zx/9+2evU89mW04W4+69jt07CrAoY/7kD/nLoy8RUz/kL4uHNG70FZzQpwPJiQ1ZOXkUo16YVPwG/9L7M5g8YwmDBnRjyScjKdi5h6vvfzPEib0rLCzi5qcm8enjlxEdZYyb9CPL1qTzl3N8c7he+mQ+nQ9P5qV7zqOw0PHL2nSuKVGtiXQRNCofMHao8VHzDf7fC7QETgdGA38C3nbOPXOoByjYEyEDsFV05dsLQx0hoAZ3De/KSHWd2r5pqCMEzMvz1x26UQRr3Tgm1BEC6roRj4U6QmDFJ4c6QUDtmH5/ULsYc3/LDdp77THtGoVl9+mQFRp/ZeZc4A4gD988mvucc18FOJuIiIh4EJY9jCDzOuT0PZDjnLstkGFEREREqsJrh+Zk4GozW4t/YjBAqE+uJyIiIgLeOzSDA5pCREREqk5jTp6/nDIyTkMrIiIi/y9F3peRiIiISCk6sV7lv21bREREJOyoQiMiIhLhdGI9VWhEREQkSMysiZl9ZWYr/P83LqfNYWY21cyWmdkSM/ubl5+tDo2IiEiEsyBequlOYIpzrgMwxb98oL3Arc65LkA/4Doz63qoH6wOjYiIiATLUGCc//o44NwDGzjnNjvnfvBfzweW4fv6pYPSHBoREZFIFzlzaJo65zaDr+NiZgf9pl4zawP0AuYc6gerQyMiIiKemdkIYESJm8Y658aWWP810Kycu95TycdpCHwA3OScyztUe3VoREREIlwwz0Pj77yMPcj60ypaZ2ZbzKy5vzrTHNhaQbu6+DozbznnPvSSS3NoREREJFg+AS73X78cmHhgAzMz4GVgmXPu315/sDo0IiIiEc4seJdqegQ43cxWAKf7lzGzFmY2yd+mP3AZcIqZLfRfhhzqB2vISURERILCOZcJnFrO7ZuAIf7rM6jCNGd1aERERCJc5BzkFDgachIREZGIpw6NiIiIRDwNOYmIiEQ6jTmpQiMiIiKRTxUaERGRCBfME+uFq4B3aKJq4KD1cLZ6Y26oIwTUxlbxoY4QUAlxdUMdIWA25e4OdYSAOiIpLtQRAis+OdQJAis/I9QJpJZRhUZERCTC1fLagSeaQyMiIiIRTxUaERGRCKcCjSo0IiIiUguoQiMiIhLpVKJRhUZEREQinyo0IiIiEU7noVGFRkRERGoBVWhEREQinM5DowqNiIiI1AKq0IiIiEQ4FWhUoREREZFaQBUaERGRSKcSjSo0IiIiEvnUoREREZGIpyEnERGRCKcT66lCIyIiIrWAKjQiIiIRTifWU4VGREREagFVaERERCKcCjSq0IiIiEgt4KlDY2bnmVmjEsuJZnZuwFKJiIiIdxbES5jyOuQ00jn30b4F51yOmY0EPg5IKg+cczw6+iFmTJ9GbFwsox56hC5du5Vpd+/ddzJ//lziG8YD8OBDj9C5S5dgx620fu2acOvpRxBlxsRFm3n9+3Wl1vduncjjw7qzKXcnAFN/TeflGWtDEbXSNi6Zz7wJY3GuiCOOP4Megy4stT43bT0z33iKrPUr6XX2H+l2+gUhSlo1zjmeeOxhZs2YTmxsLPc9+DCdu5TdNx+49y5+WDCPhv59c+SDD9Oxc/jvm92aNuSiXs2IMpjxWw6Tf80otf7IFvEM7ZaKw1FYBO8tTGNlZkGI0lbeykVz+eL153BFRfQ6eQj9z/l9qfXOOb54/TlWLpxD3XoxnHPN7TRv2zFEaSvv9GOO4PEbzyQ6KorXPvuBx9+aUWp9YsNYXrxzKG1bNmHX7r1c/chElq7eGqK0lfPCyEsZPLA76Vn59B3+cLltnrh9GIP6d6Ng525GjHyDhb9sCHJKCRSvHZryKjkhnX8z47vprFu7hk8//5LFPy3inw/ez1vvTii37S233s7pg84MbsBqiDK4fVAHrn9nEVvzdjHuyj58tyKD1Rml3xQWrs/llgmLQ5SyaoqKCpkz/j+cfuM/qZ+YzKRHb+awnv1IbN66uE29BvEcM/xq1i/6PoRJq27WjOmsX7eWDz6ZzM+LF/HoQw/y6pvjy2174823cerpg4KcsOoMuKR3c56cvobsgr3cfVo7Fm3KZ3P+ruI2v2zZzqJNqwBo2SiGq/sdxn1frAxR4sopKipk8qtjuPSux0hISuGlf/yVjr2PI6VVm+I2KxfOJSttA9f9+3U2rlzGpFee5s+jngtd6EqIijKeunkIv7vlDTam5zFj7FX8b8av/LI2vbjN7ZedwKKVaVz0j/F0bJ3MUzcPYcjNr4cwtXdvfDqbF8ZP46VRfyx3/aABXWnfOoXuQx/gmB5tGHP3xQz84+NBThkYOg+N9zk0883s32bW3szamdmTwIJABjuUqd9M4exzzsXM6HnkUeTn55GeHhmfIg6lW4sENmTvYFPOTvYWOb5cupWBHZJDHatGZK5ZTnxKC+KTmxNdpy5t+gxk/aLZpdrExSeS3KYjFh2Zc9anf/sNQ84aipnRo6dv38yoJftm2yZxbN22m4zteyh0jnnrczmyZXypNrsKi4qvx0RH4YIdsho2rfyFxk1b0rhpC6Lr1KXbcSfz64JZpdosXzCTniecgZnRqkNXdhZsIz87M0SJK+foLi1ZtTGLNZuz2bO3kAlTfuasAZ1KtencJoVvF6wGYPm6DA5vlkhq4wahiFtpM39YRVZuxdXAs07sydv/mwvA3MVraBQfR7PkhGDFkwDz2qG5AdgNjAfeA3YA1wUqlBdbt26habNmxctNmzZj65Yt5bZ9ZsyTDDvvbP71yMPs3r07WBGrLCU+hi15+z/xbs3fRUp8TJl2PVom8Naf+/LURT1ol1w/mBGrrCAnkwaN93fO6jdOpiA3Mt4MvDpw30xt2oytW8vv0Pzn2ae4ZPhQ/v2v0RGxbybG1SWrYE/xck7BHhrHle14HtUingcHHcENJ7Rm3LyNwYxYLXnZGSQkpRQvJzRJIT+r9JBafnYGCU0OaJNduk24apGcwIatecXLG9PzaJlS+g198cotDB3oG/rs26UlrZsmlmkTqVqkJrIhLbt4eeOWHFqkJoYuUA0yC94lXHnq0Djntjvn7nTO9fVf7nbObQ90uEOEKnOTlfObvvHmW5j4v8m8Pf4DcnNzeeWlscFIVy3l7y+lt/fXtHzOeW42l748n/fmb+SxYd2DEa3aXER9Xq8ij/vmdTfezISPJ/HaWxPIy83l9Vf/G4x01VLei1k5m8vCTfnc98VKnp+5nqHdUwMfrKaUsy0HPnflbW+klPvLf/5Kb9Djb80gMT6W2S9fw7XnH8OiFZvZW6LqFsm8bL9ELq9HOX1lZokllhub2RcHaT/CzOab2fyX/1tzHYh3336LC88fyoXnDyUlJZUtaWnF67ZsSSMltewLZ0pKKmZGvXr1GHre+fz8c/jPOdmav4umCfsrMqnxMaTnl/70vn13ITv2FAIwa1UWdaKiaBRXN6g5q6JBYjLbS3yaLcjOoH6jpBAmqhkT3n2LSy88j0svPI/kA/bNrVvSSElJKXOf5BL75tlDz2dJBOyb2QV7aFJ//36WWL8uOTv3Vth+RUYBKQ3r0bBedDDiVVtCk2TyMvfPJ8nLSqdh46SybbIO3iZcbUzPo1Xq/mpLy5QENmXkl2qTX7CLqx+ZSL8/v8CfH/qI5MQGrNmcE+SkgbFxSw6tmjUuXm7ZNJHN6bkhTFRzdJCT9yGnZOdczr4F51w2UOHHLufc2H3VnD9fNaKaEfe7+JJLee/Dibz34UROPvU0Pv3kY5xz/LRoIQ0bxpOSUjbSvnk1zjmmTvmaI47oUGN5AmXppnwOaxxHi0ax1IkyzuiayncrSpe0kxrUK77etXk8UQa5O/Yc+KPCTtLhHcnfupH8jDQK9+5hzYLpHNbz2FDHqrbhF1/KW+99xFvvfcSJJ5/KpP9NxDnH4p98+2ZyOftmRol9c9rUr2kfAfvmmuwdpDasR1L9ukSbcfRhjVi0qfQbYkqJfbN1YizRUca23YXBjlolLdp3JittI9lbN1O4dw9Lvp9Kxz7Hl2rTsc/x/PTdlzjn2LBiKbFxDYiPkA7N/F82cUSrJA5vnkjdOtEMP7U7n838tVSbRg1jqVvH1wG98qzezFi0lvyCXeX9uIjz2bTFXHLWMQAc06MNedt2kJaRd4h7SaTwOuuyyMxaO+fWAZhZG8otzgbPCQNPZMb0aZw1+HRiY+N48J/7D9G77pqrGPngP0lNbcpdt/+d7OxsnHN06tyZe+97IISpvSl0jn99uYIxF/ckKsr4dNFmfsso4PxeLQD48MdNnNI5hQt6t6CwyLFzbxH3fLw0xKm9iYqO5piLruXrZ+/FFRVxxHGnk9jicH6dPgmATgOHsCM3i88evYk9OwvAolg2dSLn3PsC9eIiY55Q/xNOZNaM6Zx/9iBiY2O594H9++ZN143gnpH/JCU1lXvvvp2c7Cycc3Ts1IU7/zEyhKm9KXLwzo+buWng4USZMXN1NpvzdjGwne9T7/TfsundKoHjDm9EoXPsLnT89/vIOSw2KjqaM6+4gbcfuQNXVMSRJw0mtVUbFnz9KQB9TjubI446lpUL5/DczZdRJyaWc66+LcSpvSssLOLmpybx6eOXER1ljJv0I8vWpPOXc/oC8NIn8+l8eDIv3XMehYWOX9amc80jE0Oc2rtxo6/ghD4dSE5syMrJoxj1wqTiztlL789g8owlDBrQjSWfjKRg5x6uvv/NECeuQeFcOgkS8zJ+aGZnAmOBaf6bBgIjnHMVDjvts3Nv7Z40MfCxb0MdIaCGHtsq1BEC6vrj24U6QsDc9r9loY4QUAPbNTp0owh21c0vhTpCYOVHxkTqqtrx47NB7WKsSt8RtPfa9ilxYdl98lShcc5NNrO+wAhgITAR35FOIiIiIiHnqUNjZn8B/ga0wteh6Qd8D5wSsGQiIiLiSaQcaRdIXicF/w04GljrnDsZ6AWkH/wuIiIiIsHhdVLwTufcTjPDzGKcc7+YWadD301EREQCLZxPeBcsXjs0G/znofkY+MrMsoFNgQolIiIiUhleJwWf5796v5lNBRoBkwOWSkRERDxTgaYK35jtnJt26FYiIiIiwROZX2csIiIi+6lE4/koJxEREZGwpQqNiIhIhNN5aFShERERkVpAFRoREZEIp/PQqEIjIiIitYAqNCIiIhFOBRpVaERERKQWUIVGREQkwmkOjSo0IiIiUguoQyMiIiIRT0NOIiIiEU9jTqrQiIiISMRThUZERCTCaVKwKjQiIiJSC6hCIyIiEuFUoFGFRkRERGoBc86FOkONMrMRzrmxoc4RKNq+yFabt682bxto+yJdbd++zbm7g/Zm3rxRvSoXhMysCTAeaAOsAS50zmVX0DYamA9sdM6ddaifXRsrNCNCHSDAtH2RrTZvX23eNtD2Rbravn2R4k5ginOuAzDFv1yRvwHLvP7g2tihERER+X/FgvivmoYC4/zXxwHnlrs9Zq2A3wEvef3B6tCIiIhIsDR1zm0G8P+fWkG7p4DbgSKvP7g2HuVUa8dI/bR9ka02b19t3jbQ9kW62r19QTzMycxGUHoIb2zJ+Ulm9jXQrJy73uPx558FbHXOLTCzkzznqm2TgkVERP6/ScvbE7Q382YJdaszKfhX4CTn3GYzaw5865zrdECb0cBlwF4gFkgAPnTO/eFgP1tDTiIiIhHOgnippk+Ay/3XLwcmHtjAOXeXc66Vc64NcDHwzaE6M1BLOjRmdo6ZHWymtEiN0L4mIlItjwCnm9kK4HT/MmbWwswmVecHa8hJJMyZWR3n3N5Q5wik2ryNZhbtnCsMdY4DmZnhew/wPOlSwtfW/OANOaXGV33IKZDCvkJjZm3M7Bcze8nMfjazt8zsNDObaWYrzOwYM7vCzJ71t3/NzMaY2Swz+83MhvlvP8nMppvZR2a21MxeMLOQbr/HbTvGvy0/+v/v5L/vLWb2iv96D//965vZ/Wb2hpl94/8ZV4VyG/35PjazBWa2xMxGmNm1ZvZYifVXmNkz/uv3+n8nX5nZO2b2d//t35rZU/7fwc9mdkwActbUvtbcv68t9P+cE/y3n2lmP5jZIjOb4r+tif/385OZzTaznv7b7zezsWb2JfC6maWY2QdmNs9/6e9vd6L/cRb695H4Km5XZfezch/XzG43s8X+bdz3yeso/7b95P/7a1ziOX3YzKYBfzOzPmY2zb+vfGG+8XXM7Ebz/c3+ZGbvHuI5rIl9rczjmVlDM3vVv20/mdkF/tt/77/tZzN7tMTjbDOzB81sDnCcmf3BzOb6f18vmlm0//Ka/76LzezmSuyuVeLfF5aZ2fPAD8C9/v3pJzN7wN/mUTP7a4n73G9mtwY6W0048Pn331be3125z6dEOOdcWF/wnU1wL9ADXwdsAfAKvqG8ocDHwBXAs/72rwET/G27Aiv9t58E7ATaAdHAV8CwCNi2BKCOv/1pwAf+61HAdOA8fGdS7O+//X5gERAHJAPrgRYh3s4m/v/jgJ+BpvueF//tnwMDgL7AQn+7eGAF8Hd/m2+B//qvDwR+DuN97VbgHv/1aP+2pPifi7YH/E6eAUb6r58CLCzxPC4A4vzLbwMD/NdbA8v81z8t8dw33LevBGE/K/O4wGBgFlD/gG38CTjRf/1B4KkSz+nz/ut1/fdN8S9fBLziv74JiPFfTwzCvlbm8YBH9+X2LzcGWgDr/M9tHeAb4Fz/eofvDKgAXfy/r7r+5eeBPwJ9gK9K/MyDblsN7uNFQD/gDHxH/pj/ef4fvr+tXsC0EvdZCrQO9utGFbevvOe/vL+7Ms9nqLNX97Ilb7cL1iXU21rRJVIO217tnFsMYGZL8J1l0JnZYnx/oAf62PnKqEvNrGmJ2+c6537z/5x38L2wvR/Y6Id0qG1rBIwzsw74XiTrAjjniszsCnxvFi8652aW+JkTnXM7gB1mNhU4Bt+bVqjcaGbn+a8fBrQFfjOzfvjeSDoBM/GdFXJfdszs0wN+zjsAzrnpZpZgZonOuZwazloT+9o84BUzq+tfv9B8hx5Od86t9m9Dlr/tAOAC/23fmFmSmTXyr/tk3+8CXyejq1lxpTfBXxWZCfzbzN7CdxTAhipuV2X3szKPa2anAa865wr2baN/WxKdc9P89xuHrxO4z3j//52A7sBX/m2MBjb71/0EvGVmH3Po/bgm9rXyHu80fJMT8W9btpkNxHeERrr/Z7yFr0PwMVAIfOBvfiq+zss8/7bFAVvxdXLa+StGnwFfHmLbaspa59xsM3scX6fmR//tDYEOzrmXzSzVzFrg66xlO+fWBSlbdR34/I+g/L+7Ms9nUFMGQA2c8C7iRUqHZleJ60UllosofxtKti/5LB84xhgOE4gOtW2jgKnOufPMrA2+T7X7dAC24fukWFLYbKf/jfw04DjnXIGZfYvvMLzxwIXAL8BH/jfXQ/1FBmO7qr2v+TtcA/Gd5fINM/sXkEP5ecvb5n3ttpe4LQrf73DHAW0fMbPPgCHAbDM7zTn3yyFyVns/c86VeVz/tlT2Odm3jQYscc4dV06b3+HrKJyDb4ikmytnvk0N7mtlHq+CbTvYz9jp9s+bMWCcc+6ucjIfCQwCrvNn/NNBfmZNKfk7H+2ce7GcNu8Dw/CdS+Sgw3zhooLnfxG+TmyZ5oTH67/UoLCfQ1PDjjGztuabO3MRMCPUgTxoBGz0X79i343+T75P43vhTTL//A2/oWYWa2ZJ+Iba5gUnarka4fuEV2BmnfGVugE+xHfK69+z/1P6DOBsf/aG+N5YSroIwMwGALnOudxAh68KMzsc30mh/gu8DPQGvgdONLO2/jZN/M2nA5f6bzsJyHDO5ZXzY78Eri/xGEf5/2/vnFvsnHsU35BQ5yrGrtR+VsHjfgn8yczq79tG/3OUbf55RPjOLbGvWlPSr0CKmR3nv29dM+vm/1s9zDk3Fd9ZQxPxVRIq2oZq7WsHebwDf/+NgTn4ntNk832J3u8r2LYpwDAzS933ezGzw80sGYhyzn0A3ItvPwmmL/A9Xw39uVruy4ivE3Mxvk5NqKvYXpX3/MdQ/t9dec9nZIug47YDJVIqNDXle3yHiPXA90byUWjjePIYvqGAW/CN0e/zJL75B8vN7M/AVDOb7l83F18JuzUwyjm3KaiJS5sMXGNmP+F705oNxSX7pUBX59xc/23zzOwTfJ+q1uJ7oyzZack2s1n45nsE45NsVZ0E3GZme/BVNv7onEs33yTFD/1vmlvxHbJ4P/Cq//dTwP7zMxzoRuA5f7s6+Pbfa4CbzOxkfEMcS/HNEamKyu5nZR7XObfL39Gab2a7gUnA3f5tesHf0fkNuPLAB3fO7fZ3lsb4O1F18J36fDnwpv82A548yDBjTexr0eU9npn9E9/v/2f/Nj/gnPvQzO4CpvrbTnLOlXdOjaVm9g/gS/9zvwdfRWYHvud+3wfLMhWcQHLOfWlmXYDv/QWrbcAf8HXGl5hvSHOj85+mPgKU9/yn4xt2OvDvrszzia/jKxHs/81h2/5Pv393Hr6CPJKZ2f3ANufc46HOUhVm1tA5t83/5jcdGOGc+8FfPv67c25+aBNKbVHRvhbqXCJVkbFtb9DezJMb1gnLOs3/twqNhL+xZtYV39yHcXqDkQDSviZSi/y/qdCIiIjUVpnbg1ehSWoQnhWa/2+TgkVERKQW0pCTiIhIhNN5aFShERERkVpAFRoREZEId8jTkv4/oAqNiIiIRDx1aERERCTiqUMjIiIiEU9zaERERCKc5tCoQiMiIiK1gDo0IiIiEvE05CQiIhLhdGI9VWhERESkFlCFRkREJMJpUrAqNCIiIlILqEIjIiIS4VSgUYVGREREagFVaERERCKdSjSq0IiIiEjkU4VGREQkwuk8NKrQiIiISC2gCo2IiEiE03loVKERERGRWkAVGhERkQinAo0qNCIiIlILqEIjIiIS6VSiUYVGREREIp86NCIiIhLxNOQkIiIS4XRiPVVoREREpBZQhUZERCTC6cR6qtCIiIhILWDOuVBnEBEREakWVWhEREQk4qlDIyIiIhFPHRoRERGJeOrQiIiISMRTh0ZEREQinjo0IiIiEvH+D2wTabnTK92pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation_bronx,cbar = True, square = True, cmap= 'Blues', annot = True, fmt = '.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a26d55e",
   "metadata": {},
   "source": [
    "### Creating a function to display for all neighbourhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "218fa1ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correl(name):\n",
    "    dfnbh=nycab.loc[(nycab['neighbourhood_group_cleansed']==name)]\n",
    "    #dfnbh.tail()\n",
    "    df2=dfnbh.drop(['neighbourhood_group_cleansed','NeighbourhoodArea','Listings'], axis=1)\n",
    "    correlation_nbh = df2.corr()\n",
    "    plt.figure(figsize=(10,10))\n",
    "    sns.heatmap(correlation_nbh,cbar = True, square = True, cmap= 'Blues', annot = True, fmt = '.1f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5d11b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correl('Bronx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a375c041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(nycab.neighbourhood_group_cleansed.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7b76930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correl('Staten Island')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a757981d",
   "metadata": {},
   "source": [
    "##### No other insightful correlation found in each neighbourhood as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f8bb20c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}