3. Project 3 - Level 1 -  Data Cleaning (NewYorkCity AirBnb Bookings).ipynb

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   },
   "source": [
    "### Importing Libraries"
   ]
  },
  {
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   "source": [
    "# Importing necessary libraries for analysing the dataset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset into the pandas dataframe"
   ]
  },
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "# Importing dataset\n",
    "airbnb_data = pd.read_csv(r\"C:\\Users\\james\\OneDrive\\Documents\\OASIS INFOBYTE INTERNSHIP\\PROJECT LIST DATA ANALYTICS\\LEVEL 1\\CLEANING DATA\\AIRBNB BOOKINGS DATA ANALYSIS\\Dataset\\AB_NYC_2019.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2                NaN                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let's look at the data\n",
    "airbnb_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding the data"
   ]
  },
  {
   "cell_type": "code",
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of Airbnb Dataset is (48895, 16)\n"
     ]
    }
   ],
   "source": [
    "# Checking the shape of datset\n",
    "print(f'The shape of Airbnb Dataset is {airbnb_data.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The names of the features present in the dataset are: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'name',\n",
       " 'host_id',\n",
       " 'host_name',\n",
       " 'neighbourhood_group',\n",
       " 'neighbourhood',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'room_type',\n",
       " 'price',\n",
       " 'minimum_nights',\n",
       " 'number_of_reviews',\n",
       " 'last_review',\n",
       " 'reviews_per_month',\n",
       " 'calculated_host_listings_count',\n",
       " 'availability_365']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the feature names\n",
    "print(f' The names of the features present in the dataset are: ')\n",
    "list(airbnb_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     },
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    "outputId": "8e36687f-ec5c-41f8-86ff-c6c3c81cd9bf",
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.131355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The following are the categorical features in the dataset: ['name', 'host_name', 'neighbourhood_group', 'neighbourhood', 'room_type', 'last_review']\n"
     ]
    }
   ],
   "source": [
    "#Checking for categorical columns\n",
    "cat_cols = airbnb_data.select_dtypes(include = 'object').columns\n",
    "print(f' The following are the categorical features in the dataset: {list(cat_cols)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1647796618029,
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     },
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.188424",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The following are the non categorical features in the dataset:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'host_id',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'price',\n",
       " 'minimum_nights',\n",
       " 'number_of_reviews',\n",
       " 'reviews_per_month',\n",
       " 'calculated_host_listings_count',\n",
       " 'availability_365']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for numeric/ non categorical columns\n",
    "num_cols = airbnb_data.select_dtypes(exclude = 'object').columns\n",
    "print(' The following are the non categorical features in the dataset:') \n",
    "list(num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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     "shell.execute_reply": "2024-04-15T05:31:22.291460Z"
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    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1647796619505,
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      "displayName": "Kunal Mahadik",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2                NaN                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.345455Z",
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     "iopub.status.idle": "2024-04-15T05:31:22.367463Z",
     "shell.execute_reply": "2024-04-15T05:31:22.365776Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1647796620914,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "4zA4uKUHZk4n",
    "outputId": "e2a50920-ad4e-43a8-c004-4c8f0e2cfe1f",
    "papermill": {
     "duration": 0.050722,
     "end_time": "2024-04-15T05:31:22.370585",
     "exception": false,
     "start_time": "2024-04-15T05:31:22.319863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48890</th>\n",
       "      <td>36484665</td>\n",
       "      <td>Charming one bedroom - newly renovated rowhouse</td>\n",
       "      <td>8232441</td>\n",
       "      <td>Sabrina</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>40.67853</td>\n",
       "      <td>-73.94995</td>\n",
       "      <td>Private room</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48891</th>\n",
       "      <td>36485057</td>\n",
       "      <td>Affordable room in Bushwick/East Williamsburg</td>\n",
       "      <td>6570630</td>\n",
       "      <td>Marisol</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bushwick</td>\n",
       "      <td>40.70184</td>\n",
       "      <td>-73.93317</td>\n",
       "      <td>Private room</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48892</th>\n",
       "      <td>36485431</td>\n",
       "      <td>Sunny Studio at Historical Neighborhood</td>\n",
       "      <td>23492952</td>\n",
       "      <td>Ilgar &amp; Aysel</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.81475</td>\n",
       "      <td>-73.94867</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>115</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48893</th>\n",
       "      <td>36485609</td>\n",
       "      <td>43rd St. Time Square-cozy single bed</td>\n",
       "      <td>30985759</td>\n",
       "      <td>Taz</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.75751</td>\n",
       "      <td>-73.99112</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48894</th>\n",
       "      <td>36487245</td>\n",
       "      <td>Trendy duplex in the very heart of Hell's Kitchen</td>\n",
       "      <td>68119814</td>\n",
       "      <td>Christophe</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>40.76404</td>\n",
       "      <td>-73.98933</td>\n",
       "      <td>Private room</td>\n",
       "      <td>90</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               name   host_id  \\\n",
       "48890  36484665    Charming one bedroom - newly renovated rowhouse   8232441   \n",
       "48891  36485057      Affordable room in Bushwick/East Williamsburg   6570630   \n",
       "48892  36485431            Sunny Studio at Historical Neighborhood  23492952   \n",
       "48893  36485609               43rd St. Time Square-cozy single bed  30985759   \n",
       "48894  36487245  Trendy duplex in the very heart of Hell's Kitchen  68119814   \n",
       "\n",
       "           host_name neighbourhood_group       neighbourhood  latitude  \\\n",
       "48890        Sabrina            Brooklyn  Bedford-Stuyvesant  40.67853   \n",
       "48891        Marisol            Brooklyn            Bushwick  40.70184   \n",
       "48892  Ilgar & Aysel           Manhattan              Harlem  40.81475   \n",
       "48893            Taz           Manhattan      Hell's Kitchen  40.75751   \n",
       "48894     Christophe           Manhattan      Hell's Kitchen  40.76404   \n",
       "\n",
       "       longitude        room_type  price  minimum_nights  number_of_reviews  \\\n",
       "48890  -73.94995     Private room     70               2                  0   \n",
       "48891  -73.93317     Private room     40               4                  0   \n",
       "48892  -73.94867  Entire home/apt    115              10                  0   \n",
       "48893  -73.99112      Shared room     55               1                  0   \n",
       "48894  -73.98933     Private room     90               7                  0   \n",
       "\n",
       "      last_review  reviews_per_month  calculated_host_listings_count  \\\n",
       "48890         NaN                NaN                               2   \n",
       "48891         NaN                NaN                               2   \n",
       "48892         NaN                NaN                               1   \n",
       "48893         NaN                NaN                               6   \n",
       "48894         NaN                NaN                               1   \n",
       "\n",
       "       availability_365  \n",
       "48890                 9  \n",
       "48891                36  \n",
       "48892                27  \n",
       "48893                 2  \n",
       "48894                23  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.426748Z",
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     "shell.execute_reply": "2024-04-15T05:31:22.494207Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1647796622575,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
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    "papermill": {
     "duration": 0.104929,
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.395755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48895 entries, 0 to 48894\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              48895 non-null  int64  \n",
      " 1   name                            48879 non-null  object \n",
      " 2   host_id                         48895 non-null  int64  \n",
      " 3   host_name                       48874 non-null  object \n",
      " 4   neighbourhood_group             48895 non-null  object \n",
      " 5   neighbourhood                   48895 non-null  object \n",
      " 6   latitude                        48895 non-null  float64\n",
      " 7   longitude                       48895 non-null  float64\n",
      " 8   room_type                       48895 non-null  object \n",
      " 9   price                           48895 non-null  int64  \n",
      " 10  minimum_nights                  48895 non-null  int64  \n",
      " 11  number_of_reviews               48895 non-null  int64  \n",
      " 12  last_review                     38843 non-null  object \n",
      " 13  reviews_per_month               38843 non-null  float64\n",
      " 14  calculated_host_listings_count  48895 non-null  int64  \n",
      " 15  availability_365                48895 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking the information of the dataset\n",
    "airbnb_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.553370Z",
     "iopub.status.busy": "2024-04-15T05:31:22.552770Z",
     "iopub.status.idle": "2024-04-15T05:31:22.599036Z",
     "shell.execute_reply": "2024-04-15T05:31:22.597741Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1647796623970,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
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     "duration": 0.076687,
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.525134",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The missing values before cleaning the data are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id                                    0\n",
       "name                                 16\n",
       "host_id                               0\n",
       "host_name                            21\n",
       "neighbourhood_group                   0\n",
       "neighbourhood                         0\n",
       "latitude                              0\n",
       "longitude                             0\n",
       "room_type                             0\n",
       "price                                 0\n",
       "minimum_nights                        0\n",
       "number_of_reviews                     0\n",
       "last_review                       10052\n",
       "reviews_per_month                 10052\n",
       "calculated_host_listings_count        0\n",
       "availability_365                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the null values\n",
    "print(f'The missing values before cleaning the data are:')\n",
    "airbnb_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BFUVvBrxdpcI",
    "papermill": {
     "duration": 0.023127,
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     "start_time": "2024-04-15T05:31:22.626630",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The features like last_reviews and reviews_per_month have most null values in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.699604Z",
     "iopub.status.busy": "2024-04-15T05:31:22.699153Z",
     "iopub.status.idle": "2024-04-15T05:31:22.722857Z",
     "shell.execute_reply": "2024-04-15T05:31:22.721282Z"
    },
    "id": "I60XlK4hOku5",
    "papermill": {
     "duration": 0.05385,
     "end_time": "2024-04-15T05:31:22.726662",
     "exception": false,
     "start_time": "2024-04-15T05:31:22.672812",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Filling missing values\n",
    "airbnb_data['name'].fillna('Absent', inplace = True)\n",
    "airbnb_data['host_name'].fillna('Absent', inplace =  True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.778695Z",
     "iopub.status.busy": "2024-04-15T05:31:22.778224Z",
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     "shell.execute_reply": "2024-04-15T05:31:22.803061Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1647796627648,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "dywrTW5FdkgR",
    "outputId": "b92e28ff-4a89-4298-d9bb-3544d0ec965e",
    "papermill": {
     "duration": 0.055503,
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.751176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>2787</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>2845</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>4632</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>4869</td>\n",
       "      <td>LisaRoxanne</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>7192</td>\n",
       "      <td>Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                              name  host_id  \\\n",
       "0  2539                Clean & quiet apt home by the park     2787   \n",
       "1  2595                             Skylit Midtown Castle     2845   \n",
       "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
       "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
       "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
       "\n",
       "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews  \\\n",
       "0     Private room    149               1                  9   \n",
       "1  Entire home/apt    225               1                 45   \n",
       "2     Private room    150               3                  0   \n",
       "3  Entire home/apt     89               1                270   \n",
       "4  Entire home/apt     80              10                  9   \n",
       "\n",
       "   calculated_host_listings_count  availability_365  \n",
       "0                               6               365  \n",
       "1                               2               355  \n",
       "2                               1               365  \n",
       "3                               1               194  \n",
       "4                               1                 0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the unncessary columns.\n",
    "airbnb_data_new = airbnb_data.drop(['last_review','reviews_per_month'], axis =1)\n",
    "airbnb_data_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.856664Z",
     "iopub.status.busy": "2024-04-15T05:31:22.855629Z",
     "iopub.status.idle": "2024-04-15T05:31:22.893049Z",
     "shell.execute_reply": "2024-04-15T05:31:22.891891Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1647796628959,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "BOOoquWpeUwD",
    "outputId": "08568c65-6afc-4c52-917c-19a6263b7652",
    "papermill": {
     "duration": 0.066154,
     "end_time": "2024-04-15T05:31:22.896008",
     "exception": false,
     "start_time": "2024-04-15T05:31:22.829854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of missing values after cleaning the data are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id                                0\n",
       "name                              0\n",
       "host_id                           0\n",
       "host_name                         0\n",
       "neighbourhood_group               0\n",
       "neighbourhood                     0\n",
       "latitude                          0\n",
       "longitude                         0\n",
       "room_type                         0\n",
       "price                             0\n",
       "minimum_nights                    0\n",
       "number_of_reviews                 0\n",
       "calculated_host_listings_count    0\n",
       "availability_365                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values in the new dataset\n",
    "print(f'The number of missing values after cleaning the data are:')\n",
    "airbnb_data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:22.993622Z",
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      "displayName": "Kunal Mahadik",
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      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "CK50iT17fsP4",
    "outputId": "1595a47b-578c-4a39-e48b-5cc298929b1c",
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     "duration": 0.085957,
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     "exception": false,
     "start_time": "2024-04-15T05:31:22.967346",
     "status": "completed"
    },
    "tags": []
   },
   "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>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13221</th>\n",
       "      <td>Sonder (NYC)</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>Blueground</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>Blueground</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7279</th>\n",
       "      <td>Kara</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7482</th>\n",
       "      <td>Kazuya</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7484</th>\n",
       "      <td>Kazuya</td>\n",
       "      <td>Queens</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7483</th>\n",
       "      <td>Kazuya</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13220</th>\n",
       "      <td>Sonder</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6544</th>\n",
       "      <td>Jeremy &amp; Laura</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            host_name neighbourhood_group  calculated_host_listings_count\n",
       "13221    Sonder (NYC)           Manhattan                             327\n",
       "1838       Blueground           Manhattan                             232\n",
       "1837       Blueground            Brooklyn                             232\n",
       "7279             Kara           Manhattan                             121\n",
       "7482           Kazuya            Brooklyn                             103\n",
       "7484           Kazuya              Queens                             103\n",
       "7483           Kazuya           Manhattan                             103\n",
       "13220          Sonder           Manhattan                              96\n",
       "6544   Jeremy & Laura           Manhattan                              96"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see who has the most listings and in which neighbourhood.\n",
    "host_areas = airbnb_data_new.groupby(['host_name','neighbourhood_group'])['calculated_host_listings_count'].max().reset_index()\n",
    "top_hosts = host_areas.sort_values(by = ['calculated_host_listings_count'], ascending = False).head(9)\n",
    "top_hosts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EJOwR4kyjNzz",
    "papermill": {
     "duration": 0.023777,
     "end_time": "2024-04-15T05:31:23.166676",
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     "start_time": "2024-04-15T05:31:23.142899",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From the above result, we can conclude that the name of the host with the most listings is Sonder(NYC) who has 327 listings in Manhattan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
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     "elapsed": 713,
     "status": "ok",
     "timestamp": 1647796633288,
     "user": {
      "displayName": "Kunal Mahadik",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the hosts with most listings.\n",
    "plt.rcParams['figure.figsize'] = (8,6)\n",
    "host_name = top_hosts['host_name']\n",
    "host_lisitng = top_hosts['calculated_host_listings_count']\n",
    "plt.bar(host_name,host_lisitng)\n",
    "plt.title('Hosts with most listings in NYC',{'fontsize':18})\n",
    "plt.xlabel('Host Names',{'fontsize':18})\n",
    "plt.ylabel('Number of host listings',{'fontsize':18})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:23.705728Z",
     "iopub.status.busy": "2024-04-15T05:31:23.704632Z",
     "iopub.status.idle": "2024-04-15T05:31:23.726528Z",
     "shell.execute_reply": "2024-04-15T05:31:23.725363Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1647796633757,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "7k8N8TFifqvp",
    "outputId": "6a714d52-ecc5-43ca-bbaf-365c19eaa9d8",
    "papermill": {
     "duration": 0.053109,
     "end_time": "2024-04-15T05:31:23.729087",
     "exception": false,
     "start_time": "2024-04-15T05:31:23.675978",
     "status": "completed"
    },
    "tags": []
   },
   "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</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>21661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>20104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queens</td>\n",
       "      <td>5666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>1091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  neighbourhood_group  count\n",
       "2           Manhattan  21661\n",
       "1            Brooklyn  20104\n",
       "3              Queens   5666\n",
       "0               Bronx   1091\n",
       "4       Staten Island    373"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Airbnb listed in each neighbourhood\n",
    "data =airbnb_data_new.groupby(['neighbourhood_group'])['id'].count().reset_index().rename(columns = {'id':'count'}).sort_values(by='count', ascending = False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:23.781733Z",
     "iopub.status.busy": "2024-04-15T05:31:23.780770Z",
     "iopub.status.idle": "2024-04-15T05:31:24.090033Z",
     "shell.execute_reply": "2024-04-15T05:31:24.088674Z"
    },
    "executionInfo": {
     "elapsed": 904,
     "status": "ok",
     "timestamp": 1647796635519,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "DXZ0nP8Y2PAE",
    "outputId": "28029251-3ae2-4e64-f678-674846083f75",
    "papermill": {
     "duration": 0.3381,
     "end_time": "2024-04-15T05:31:24.092652",
     "exception": false,
     "start_time": "2024-04-15T05:31:23.754552",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the number of listings in each neighbourhood through bar plot\n",
    "plt.rcParams['figure.figsize'] = (8,6)\n",
    "ax = sns.barplot(x = data['neighbourhood_group'], y = data['count'], data = data)\n",
    "ax.set_xlabel('Neighbourhood Group')\n",
    "ax.set_ylabel('Number of listings')\n",
    "ax.set_title('Number of listings in each neighbourhood group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:24.198049Z",
     "iopub.status.busy": "2024-04-15T05:31:24.196893Z",
     "iopub.status.idle": "2024-04-15T05:31:24.217003Z",
     "shell.execute_reply": "2024-04-15T05:31:24.215650Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1647796636761,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "aBszQgZOkAxL",
    "outputId": "e871ff09-d0b5-4463-826a-31cee49d5b4a",
    "papermill": {
     "duration": 0.051235,
     "end_time": "2024-04-15T05:31:24.219959",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.168724",
     "status": "completed"
    },
    "tags": []
   },
   "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</th>\n",
       "      <th>number_of_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queens</td>\n",
       "      <td>629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Staten Island</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  neighbourhood_group  number_of_reviews\n",
       "3              Queens                629\n",
       "2           Manhattan                607\n",
       "1            Brooklyn                488\n",
       "4       Staten Island                333\n",
       "0               Bronx                321"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding which neighbourhood has got maximum number of reviews. \n",
    "areas_reviews = airbnb_data_new.groupby(['neighbourhood_group'])['number_of_reviews'].max().reset_index().sort_values(by = 'number_of_reviews', ascending = False)\n",
    "areas_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:24.273563Z",
     "iopub.status.busy": "2024-04-15T05:31:24.273119Z",
     "iopub.status.idle": "2024-04-15T05:31:24.578074Z",
     "shell.execute_reply": "2024-04-15T05:31:24.576714Z"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1647796638371,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "Kw2TZhNzmjST",
    "outputId": "c65fabc2-29a0-45d5-966b-c725a75d779f",
    "papermill": {
     "duration": 0.335551,
     "end_time": "2024-04-15T05:31:24.581134",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.245583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the number of reviews in each neighbourhood group through pie chart\n",
    "plt.rcParams['figure.figsize'] = (8,6)\n",
    "reviews = areas_reviews['number_of_reviews']\n",
    "plt.pie(reviews, labels = areas_reviews['neighbourhood_group'], autopct ='%0.2f%%', startangle =90, explode = [0.1,0.1,0.1,0.1,0.1], shadow = True )\n",
    "plt.title('Number of reviews in each neighbourhood group', {'fontsize': 14})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vKXId1QWrlYk",
    "papermill": {
     "duration": 0.02933,
     "end_time": "2024-04-15T05:31:24.640615",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.611285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The above chart shows the share percentage of reviews for each neighborhood group. It can be stated that most reviews are from Queens and Manhattan, which indicates that residents enjoy living in these areas. The majority of the company's revenue comes from these neighborhoods as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:24.695753Z",
     "iopub.status.busy": "2024-04-15T05:31:24.695334Z",
     "iopub.status.idle": "2024-04-15T05:31:24.753785Z",
     "shell.execute_reply": "2024-04-15T05:31:24.752417Z"
    },
    "executionInfo": {
     "elapsed": 639,
     "status": "ok",
     "timestamp": 1647796642495,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "6oyayRBSrG1m",
    "outputId": "63597316-a2e7-4cc1-d128-eb6b07b34b21",
    "papermill": {
     "duration": 0.089062,
     "end_time": "2024-04-15T05:31:24.756444",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.667382",
     "status": "completed"
    },
    "tags": []
   },
   "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>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.933500e+04</td>\n",
       "      <td>3.933500e+04</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "      <td>39335.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.854335e+07</td>\n",
       "      <td>6.343950e+07</td>\n",
       "      <td>40.729499</td>\n",
       "      <td>-73.952678</td>\n",
       "      <td>121.093911</td>\n",
       "      <td>6.731867</td>\n",
       "      <td>24.767739</td>\n",
       "      <td>6.139189</td>\n",
       "      <td>107.411339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.095482e+07</td>\n",
       "      <td>7.629615e+07</td>\n",
       "      <td>0.054271</td>\n",
       "      <td>0.044903</td>\n",
       "      <td>55.768666</td>\n",
       "      <td>20.338870</td>\n",
       "      <td>46.016512</td>\n",
       "      <td>30.180530</td>\n",
       "      <td>129.318011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.539000e+03</td>\n",
       "      <td>2.438000e+03</td>\n",
       "      <td>40.499790</td>\n",
       "      <td>-74.244420</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.038469e+06</td>\n",
       "      <td>7.181263e+06</td>\n",
       "      <td>40.690325</td>\n",
       "      <td>-73.982580</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.908748e+07</td>\n",
       "      <td>2.748892e+07</td>\n",
       "      <td>40.723060</td>\n",
       "      <td>-73.955590</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.842896e+07</td>\n",
       "      <td>9.634135e+07</td>\n",
       "      <td>40.764040</td>\n",
       "      <td>-73.938050</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.648724e+07</td>\n",
       "      <td>2.743213e+08</td>\n",
       "      <td>40.911690</td>\n",
       "      <td>-73.712990</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       host_id      latitude     longitude         price  \\\n",
       "count  3.933500e+04  3.933500e+04  39335.000000  39335.000000  39335.000000   \n",
       "mean   1.854335e+07  6.343950e+07     40.729499    -73.952678    121.093911   \n",
       "std    1.095482e+07  7.629615e+07      0.054271      0.044903     55.768666   \n",
       "min    2.539000e+03  2.438000e+03     40.499790    -74.244420     49.000000   \n",
       "25%    9.038469e+06  7.181263e+06     40.690325    -73.982580     75.000000   \n",
       "50%    1.908748e+07  2.748892e+07     40.723060    -73.955590    105.000000   \n",
       "75%    2.842896e+07  9.634135e+07     40.764040    -73.938050    155.000000   \n",
       "max    3.648724e+07  2.743213e+08     40.911690    -73.712990    269.000000   \n",
       "\n",
       "       minimum_nights  number_of_reviews  calculated_host_listings_count  \\\n",
       "count    39335.000000       39335.000000                    39335.000000   \n",
       "mean         6.731867          24.767739                        6.139189   \n",
       "std         20.338870          46.016512                       30.180530   \n",
       "min          1.000000           0.000000                        1.000000   \n",
       "25%          1.000000           1.000000                        1.000000   \n",
       "50%          2.000000           6.000000                        1.000000   \n",
       "75%          5.000000          26.000000                        2.000000   \n",
       "max       1250.000000         607.000000                      327.000000   \n",
       "\n",
       "       availability_365  \n",
       "count      39335.000000  \n",
       "mean         107.411339  \n",
       "std          129.318011  \n",
       "min            0.000000  \n",
       "25%            0.000000  \n",
       "50%           37.000000  \n",
       "75%          209.000000  \n",
       "max          365.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the descriptive statistics\n",
    "airbnb_data_new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hBszfFUMrJrt",
    "papermill": {
     "duration": 0.027426,
     "end_time": "2024-04-15T05:31:24.811190",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.783764",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "According to the summary statistics,  the average price is $152.72, the average minimum nights stay is 7.03 nights, and the average number of reviews is 23.27 reviews per listing. We also learn that a host has an average of 7.14 places listed and availability averages 112.78 vacant days per year.\n",
    "\n",
    "\n",
    "Above all, the minimum price is displayed as 0 and the maximum price as 10,000. There's a problem with the data, thus it's necessary to investigate and seek for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:24.867150Z",
     "iopub.status.busy": "2024-04-15T05:31:24.866768Z",
     "iopub.status.idle": "2024-04-15T05:31:24.882565Z",
     "shell.execute_reply": "2024-04-15T05:31:24.881384Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1647796644759,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "EpoITzPzrGwd",
    "outputId": "ddc51de3-01fa-438b-ec39-f8babff2fe49",
    "papermill": {
     "duration": 0.046784,
     "end_time": "2024-04-15T05:31:24.885031",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.838247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.093911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>269.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39335.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price\n",
       "mean      121.093911\n",
       "median    105.000000\n",
       "max       269.000000\n",
       "count   39335.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for outliers\n",
    "airbnb_data_new.agg({'price':['mean','median','max','count']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4VdqvlhIsTOj",
    "papermill": {
     "duration": 0.026692,
     "end_time": "2024-04-15T05:31:24.938633",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.911941",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Since there are no available rooms on Airbnb, the minimum value of zero makes no sense, while the maximum value of 10,000 appears excessively large. Observe that the mean is significantly greater than the median. This suggests that there is bias in the data.\n",
    "\n",
    "A boxplot be utilized to better comprehend pricing distribution?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:24.997346Z",
     "iopub.status.busy": "2024-04-15T05:31:24.996227Z",
     "iopub.status.idle": "2024-04-15T05:31:25.277339Z",
     "shell.execute_reply": "2024-04-15T05:31:25.276085Z"
    },
    "executionInfo": {
     "elapsed": 430,
     "status": "ok",
     "timestamp": 1647796652980,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "LsyuC53XrGr8",
    "outputId": "5a26cabb-ef09-4c1e-df3b-267480806734",
    "papermill": {
     "duration": 0.313645,
     "end_time": "2024-04-15T05:31:25.279929",
     "exception": false,
     "start_time": "2024-04-15T05:31:24.966284",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the boxplot of the price data.\n",
    "plt.figure(figsize=(10,5))\n",
    "ax = sns.boxplot(y='price', data=airbnb_data_new).set_title('Price Distribution by neighbourhood group')\n",
    "sns.set_theme(style='white')\n",
    "plt.xlabel('Neighbourhood')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohJSkJiWsiZT",
    "papermill": {
     "duration": 0.028315,
     "end_time": "2024-04-15T05:31:25.336456",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.308141",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As expected there are outliers present in the data. In order to fix this, we will be using the quantile based flooring and capping.\n",
    "First, let's print the 10th and 90th percentile of the price column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:25.395164Z",
     "iopub.status.busy": "2024-04-15T05:31:25.393971Z",
     "iopub.status.idle": "2024-04-15T05:31:25.403121Z",
     "shell.execute_reply": "2024-04-15T05:31:25.402075Z"
    },
    "executionInfo": {
     "elapsed": 569,
     "status": "ok",
     "timestamp": 1647796655545,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "t1w0kMcUrGnM",
    "outputId": "909811ba-607f-4342-ab64-283fbb8de8f4",
    "papermill": {
     "duration": 0.041083,
     "end_time": "2024-04-15T05:31:25.405680",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.364597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the low quantile\n",
    "q_low = airbnb_data_new['price'].quantile(0.10)\n",
    "q_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:25.466863Z",
     "iopub.status.busy": "2024-04-15T05:31:25.465459Z",
     "iopub.status.idle": "2024-04-15T05:31:25.474484Z",
     "shell.execute_reply": "2024-04-15T05:31:25.473556Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1647796655914,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "NPOvssgprGij",
    "outputId": "d978c729-3492-47fb-cb6e-3e3bc6996f96",
    "papermill": {
     "duration": 0.040942,
     "end_time": "2024-04-15T05:31:25.476907",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.435965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the high quantile\n",
    "q_high = airbnb_data_new['price'].quantile(0.9)\n",
    "q_high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HKC7fVQzuHGv",
    "papermill": {
     "duration": 0.028413,
     "end_time": "2024-04-15T05:31:25.535084",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.506671",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Looks like the 10th percentile is 49USD and the 90th percentile is 269USD. Since we are dealing with Airbnb listings, it is not uncommon for a few places to be much more expensive than others, especially luxurious ones.\n",
    "\n",
    "Given that we are going to perform calculations in our data and the mean can be skewed by outliers, let's go ahead and implement the above technique by removing the values that don't apply to the specified range – set between 49USD and 269USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:25.596562Z",
     "iopub.status.busy": "2024-04-15T05:31:25.595378Z",
     "iopub.status.idle": "2024-04-15T05:31:25.629069Z",
     "shell.execute_reply": "2024-04-15T05:31:25.627452Z"
    },
    "id": "zHcEOa5srGZG",
    "papermill": {
     "duration": 0.066418,
     "end_time": "2024-04-15T05:31:25.632124",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.565706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#removing the values below 10th percentile and above 90th percentile\n",
    "airbnb_data_new = airbnb_data_new.drop(airbnb_data_new[airbnb_data_new['price']<q_low].index)\n",
    "airbnb_data_new = airbnb_data_new.drop(airbnb_data_new[airbnb_data_new['price']>q_high].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:25.695630Z",
     "iopub.status.busy": "2024-04-15T05:31:25.693756Z",
     "iopub.status.idle": "2024-04-15T05:31:25.983380Z",
     "shell.execute_reply": "2024-04-15T05:31:25.981852Z"
    },
    "executionInfo": {
     "elapsed": 411,
     "status": "ok",
     "timestamp": 1647796678949,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "d9JlH9C2u5pb",
    "outputId": "f70232a1-68a8-430e-b300-2c82a5349910",
    "papermill": {
     "duration": 0.323045,
     "end_time": "2024-04-15T05:31:25.986447",
     "exception": false,
     "start_time": "2024-04-15T05:31:25.663402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets create a new boxplot and check the result.\n",
    "plt.figure(figsize=(10,5))\n",
    "ax = sns.boxplot(y='price', data=airbnb_data_new).set_title('Price Distribution')\n",
    "sns.set_theme(style='white')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:26.046759Z",
     "iopub.status.busy": "2024-04-15T05:31:26.046085Z",
     "iopub.status.idle": "2024-04-15T05:31:26.793048Z",
     "shell.execute_reply": "2024-04-15T05:31:26.791009Z"
    },
    "executionInfo": {
     "elapsed": 1276,
     "status": "ok",
     "timestamp": 1647796680625,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "snb9v4cRu5lx",
    "outputId": "075ec32d-7dfa-4f56-9728-c8e622b71497",
    "papermill": {
     "duration": 0.781224,
     "end_time": "2024-04-15T05:31:26.796856",
     "exception": false,
     "start_time": "2024-04-15T05:31:26.015632",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CyAxRqCaEEEIIIWSGKFQTQgghhBAyQxSqCSGEEEIImSEK1YQQQgghhMwQhWpCCCGEEEJmiEI1IYQQQgghM0ShmhBCCCGEkBmiUE0IIYQQQsgMUagmhBBCCCFkhihUE0IIIYQQMkMUqgkhhBBCCJkhCtWEEEIIIYTMEIVqQghxKFVVzS6BEELyBoVqQvLciRMn8KlPfQp1dXVml0IsZN++ffjjP/5jdHZ2ml0KIYTkBQrVhOS5/fv3IxgMorq62uxSiIW8/vrriMfjaGpqMrsUQgjJCxSq80h3dze+9rWv0YckIYQQQkiOUajOI/v27cO5c+ewefNms0shFqLr+oj/J4QQQkjmUajOI0Zo0jTN5EqIFVGoJoQQQrKHQjUhhBBCCCEzRKGaEIdIJBJml0AIIYTkLQrVeYgxZnYJxIJisZjZJRBCCCF5i0J1HqLeWTKc8XyIRqMmV0IIsTpN09Df3292GYTYEoVqQvJcOBwe8f+EEDKe//iP/8Cf/dmfob6+3uxSCLEdCtV5xGj7oJFqMlwoFBrx/4QQMp4jR44AALq6ukyuhBD7oVCdh6inmgxHoZoQMlU0sZmQqaNQTUieC/b3AQD1SZIx0br2ZCwUqgmZOgrVhOS5vr7BUN3Xa3IlxIpUVTW7BGIhxpVOCtWETB2F6jxEPdXEoGka+oMDAIDewXBNyHAUnsgIg58f8Xjc5EIIsR8K1XmIeqqJIRQKQdM0cJZs/6ATLjIahWoynPEOQaGakKmjUE1IHjNm8M/2SUgkVASDQZMrIlZDoZqMhTaLImTqKFTnEaM3kkYjiaGjowMAMC8gj7hNiIHCExmB2j8ImTYK1XmEPhzJaMZI9byCZKju7Ow0sxxiQRSeyHC0Aysh00ehOo9EIhEAdDmXDDFGpucW0Eg1GRudjJPhjM8Pel4QMnUUqvOIEaqN/yekubkZAbeEYreA4AwtLS1ml0QshkYkyXCxwSsXFKoJmToK1XnE+HCkD0liaGpqQrGLgTOGYreEpqYms0siFmGMSNJJODGoqpqam0OfI4RMHYXqPELbUZPRmpsaUeqRAAAlbobmpkaTKyJWEY6EAVB4IkOGPxfoeUGG27BhA770pf8fXcGYBIXqPDIwkNzkIzi42QdxtmAwiN6+fkQSGk62hlHqkdDU1Ew76BEAQyPU4XDY5EqIVQy/akFXMMhwTz/9NGprL6Gnp8fsUiyNQnUeMdYgprWICQBcvnwZANARSuBEaxgVPgmxeJxaQAgAurJFrkehmkyGFkKYGIXqPGJsR22MWBNnq6urAwAoIrnD5mxfsg3ECNvEuXRdRziUHKGmUE0Mw1s+6AoGGQstwTkxCtV5pK+vFwAwMBCkS/wEdXV1KHBJEDwZqmd5k/9thG3iXLFYLPUeERygK1skyQjSEoAIhWoyBgrVE6NQnSfi8TjCoRCYcEPXdfT395tdEjHZxepqzPYOvcQlzlDuk3CxutrEqogVDL+aRe8VxGCEah/nNFJNxkTtHxOjUJ0n+vr6AADcXQgA6O3tNbMcYrKenh5ca2zEgkJlxNcXFMi4UF1Now0OZ7xfMJegUE1SjD5qL+fUU03GRKt/TIxCdZ4wtp/mruIRt4kznTt3DgCwqFAe8fVFhTLi8TguXrxoRlnEIoyTblEgIzQQonYxAmBopNrLOMIUqskYKFRPjEJ1nmhrawMASN6yEbeJM509exay4JjjHxmqFw6OXJ89e9aMsohFGCPVwi9D13VaMYgAGJq06uMc8XicLvWT69D65ROjUJ0njBAtPLMAxihUO5iu6zh65AgWBoYmKRq8MkelX8bxY8dMqo5YQWqkOpA8yaK1Zwkwsqd6+G1CDBSqJ0ahOk+0tbWBCwVMckHIPgrVDnb16lW0tLZiZalrzO+vKFFQdeECBSkH6+rqAhiDNHjloru72+SKiBWEQiEIxuBiPHWbkOEoVE+MQnWeaGxsBFP8yRuyD9eu0XbUTnXo0CEAwMqSsUP1qlIXdF3HkSNHclkWsZDu7m5IbgncLQAMhmzieOFwGArnUFjyCheFagKMXPGDJrBOjEJ1nrhypR5MCQAAuFKIhoar0DTN5KqIGQ7s34+5BTICLjHm92f7JBS6JRw4cCDHlRGr6OrqAnNzcHdyQyAaqSZAMkQrbChUU/sHAWinzamgUJ0HgsEgenq6wV3J5fS4K4BYLIb29naTKyO5du3aNVyqrcWNs8YepQYAxhhuLFVw/PhxWnrRoTo6OgC3AJM5uCxotSACYDBUA5BppJoMM/zkikL1xChU54GrV68CAIQxUj0Yruvr602riZhj165dYAy4udw94f3WVrihqir27t2bo8qIlXR0doB7kqPUwiMlQzZxvFAoBAk6tX+QEYYHabp6MTEK1Xng0qVLAADuLgEACFfRiK8TZ9A0DW/s2oWlRQoKlLFbPwyzfTJm+2Xs2rkzR9URq4hEIggNhCAGQzXcnK5qEQBAaGAACmPU/kFGGP48oOfExChU54GamhoIxQsuewAATMgQ7kLU1NSYXBnJpTNnzqCjsxNrJxmlNqwtd+FSbS1d0XAYY1Sae8Tg/0sUqgmAoZ5qmUI1GYZCdfooVOeB6upqMKV4xNeYqxjV1Reh67pJVZFc27JlCzyywKrS9EL1zWUeCM6wdevWLFdGrGQoVEuD/y/Q19dHW9cThMNhyGCQQe0fZIjxPOBcolA9CQrVNhcMBtHc3AzuKRnxdeEuRX9/H61X7RA9PT04cOAA1pa5IAs2+Q8A8CkcN5Qq2LVzJ6096iCpUO2VUv+v6zotq0cQjkQgMwbOGGTOKVQTAEOj04rkpefEJChU21x1dTUAQHhKR3zduH3hwoWc10Ryb+fOnVBVFW+v9Ezp594+24uBUAhvvfVWliojVmO0ehjL6Rkj1jRZ0dkSiQTi8Xiqn1phnFZ6IACGhWrZQ6F6EhSqba6qqgoAg3CPDNXcXQTGJQrVDqDrOrZu3YIFhQrKBkcf07WoUEapV8bWLVuyVB2xms7OTgi3DDZ4RcMI1dRX7WxGgDb6qWXG6FI/ATAUqmXZi1CInhMToVBtcxcuXIBwF4EJGfGey4j3XAYAMMbB3SU4X1VlcoUk26qqqtDc3IK3VaTXSz0cYwy3lLtwvqoKTU1NWaiOWE1XVxe4e+it39hVkbatd7brQjVoTWKSlAzVDLLkRiRCoXoiFKptTNM0XLxYk1pKL95bh3hvXer7wlOKK5evIBaLmVUiyYEdO3ZAEXzCDV8msrbcDcaSLSQk/3V2dQKuobd+JnMwwain2uFSo5GDoVoCrfRAksLhMCRJhuAyotEoLYAwAQrVNtbS0oJwOJQK1aNxdwk0TcWVK1dyWxjJmUgkgn179+LGWQpcYnov54BLYGmRgp07dkBV1QxXSKwmOVI9tI45YwzCLdNW5Q6XGqkevC0DiFCoJkg+NwSXIXhyUjNNbB8fhWobMzZ3EZ6xQ7VwF4+4H8k/x44dQyQaxdryqU1QHG1tuQedXV3Ug5/ndF1HX18fmGtU772LU6h2OCMoScZINWPU/kEADIZqIUFwOXWbjI1CtY3V1tYme6ddgTG/z2QfuOSiUJ3Hjh07BrcksKBQnvzOE1hWooCz5PFI/gqHw9BUDVwZ+dbPZIb+YL9JVRErMIIShWoyWjgcBmcSOE+ejNPzYnwUqm2ssbER3BUAY2NvSc0YA1MCNAEtT+m6jiOHD2NpkQTB0lubejweiWN+QMHRI0cyVB2xooGBAQDJPurhmCIQDAbNKIlYhDFSbWz8IoHRZX4CIPncYExADIZqel6Mj0K1jbW0tAKSd8L7MNmXvB/JO3V1dejp7cXykulNUBxtebGCy1euoLOzMyPHI9aTCtXXjVTz1PeIMxk7aorUSDVol00CIBmikyPVInWbjC2rofqnP/0p7r33Xtx777147LHHAABf//rXcc899+CBBx7AAw88gO3btwMA9u/fj/vvvx/33HMPfvSjH2WzrLzR1tYGLvsmvA+Xfejq6kQikchRVSRXLl68CABYVKhk5HjGcYzjkvxjrObARk1qZRJd6nc6Y5UoafCil2CMQjUBAESjMXAuwFlypJqeF+Ob2k4RU7B//368+eabeOWVV8AYw1//9V9j+/btOHv2LJ599lmUl5en7huJRPCNb3wDv/nNb1BZWYnPfe5z2LNnD+66665slWd7AwMDCIdDcBVMHqp1XUdnZycqKipyVB3JhaamJsiCI+DKzLlxqTc5CtHc3JyR4xHrMVZ3YXxkuxBjDJqqmVESsYjUSPVg+4cAQ0JVoWkaOKeL2k4Wi0XBmQuMRqonlbVXSllZGb72ta9BURTIsoylS5eiqakJTU1N+OY3v4n7778f//Ef/wFN03D69GksXLgQ8+fPhyRJuP/++7GFdnibUOpJzSc5L6IeqLzV2NiIEo8An2E/tcEjcfgVCY2NjRk5HrGe1JKJo9/5OYOu69A0CtZOZYxUi9RI9civE+dKxBODI9XJNw668j2+rI1UL1++PPXfV65cwebNm/H888/j8OHD+Pa3vw2v14vPfe5zWLduHbxeL8rKylL3Ly8vR2sr9QFPJPXhOGmgYiPvT/JG47UGlGRolNpQ4uZobLyW0WMS6xj3fYMNfZ9GJZ3JCEp88MlgPAvos4MkVBUSGNhgqKbnxPiy/u5ZU1ODz3zmM/inf/onLFmyBD/72c9QWloKj8eDhx56CHv27Blzdx6WodG3fJW6jDvJn5BeBPkrHI7AI2f2JeyWaBc1QpzI+Iww3lE4DciQQaqaAGc8lctopHp8WQ3Vx44dw1/8xV/gy1/+Mj72sY+huroaW7duTX1f13VIkoSKigp0dHSkvt7W1jai55rMHG0rmn/44CX7TNJ1pC7xkfwjy4PrmWujnjeaDsYYJClrFy+JxRlByRjO4sOuXhBnU1UNYDRSnY6sfXo2NzfjC1/4Ah5//HHce++9AJLB7vvf/z56e3sRj8fxwgsv4O6778batWtx+fJl1NfXQ1VVbNq0CXfeeWe2SssLgUBywxddnbhXWlOTM/oLCwuzXhPJLcY4Mn2qpANgdPk/bxmhWldHPnN0NTnAQVcInUvTNHDGUs8BY6SaRiWJrmtgYDBOuWiQbnxZG5Z4+umnEY1G8eijj6a+9slPfhJ/8zd/g0996lNIJBK45557cN999wEAHn30UXzxi19ENBrFXXfdhQ9+8IPZKi0veDweyLIMLTHxMlj64PeLiopyUBXJJSE4Mr1gg6YDMoXqvDVeqIam0yi1w2mahuGnVMZ/U4AiSXTCnY6svYs+8sgjeOSRR8b83oMPPnjd126//Xa89tpr2Son7zDGUFhYhJ7YxP2veiICj8cLRcnMWsbEOkpKS9HX1JPRY/bFdawoLc3oMYl1eDye5H+MOhvTExo8HrcJFRGr0HV9cDQyiUI1MYx+CtBzYnw0JGVjZWWzoCdCE95Hj4dQUlqSo4pILlVWzkF3NHNvbpquozucQGVlZcaOSazF50uua6/FRoZqLabB7/ebURKxCE3TRiwKQ6GakKmjUG1j8+fPB+L9E95Hj/dj4YIFOaqI5FJlZSX6IgnER1/Kn6a+qAZV0ylU5zEjOOvxUSPVcQ0F/gIzSiIWMtYFflq7nHDOoUNLDVnTspvjo9+Mjc2fPx9qPDJuX7WuqVCj/cnwTfLOnDlzAACd4cxMJOoYPA6F6vwlhIDL7YIeHzl7nyV0GqkmY058psmrRAgO6Dr0wWeIEMLkiqyLQrWNGWFZi/aN+X0t1g9Ap1Cdp5YsWQIAaApmJlQ39SdGHJfkp4KCguvaP/SYllpRiBBgKGBTqCbJkWo91QpEI9Xjo9+MjS0YbOvQor1jft/4+gJq/8hLc+bMgcfjRlMwnpHjNQXjqJxdQSOWea4wUAg9OjRSres61GiCQrXDMcaum5BGCAAIIUHXNQDJk3FaKWh8FKptbNasWfB4vdCiPWN+X4v2gHOBefPm5bYwkhOccyxbtjxjI9XNAyqWr1iZkWMR6yosLARiw9KTqkNXdQrVDmeMRhr0YV8nziZJyVBt9NdTqB4fvVpsjDGGxYsXQ4uNPVKtRnsxb97coV3USN5ZsWIFWgcSSIzeIW+KgjEVvZEEli9fnqHKiFUVFhZCH9b+oQ2OWlOodrZkqB5CoZoYZFmCpmvQ9eR7BYXq8dGrxeYWL1oEPdo79rJHsV4sXrw490WRnFm6dClUTUdbaGaj1cZo97JlyzJRFrGwZE/1sPaPwYBNodrZOOfQ9OEj1dQ/S5KEkKBrKjSdRqonQ68Wm5s7dy40NX7dduW6loAaG8DcuXNNqozkghGCm/tn1lfdHIyDMUaTFB0gEAhAi6upXRWNgE2h2tmM8GwM0Bj5mkI1cSnK4Eh1MlTT1e/x0avF5ozlz5IrfQzR4gMjvk/y0+zZs+HzemfcV93Un8Ccytnwer0ZqoxYlRGe9cEwbUxaLCigdaqdzFgmzWgMMv6fQjWRFRm6rkIbbP+gUD0+erXYnLFWsR4Ljvi6cdv4PslPjDEsWrwY7SF18jtPoCOiYdFiGqV2AiM8G8vqaYMbwVCodrbUSPXgbW3wv+hSP5FleXCiIoXqyVCotrny8nIwxqDFR4ZqY6S6oqLCjLJIDs2ZMwfd0enveqZqye3J6QTMGYyrEXoi+ZwxdlekqxTOZoRnI0wb7yi00QdRFAU61NRERQrV46NQbXOSJMHt8UBXYyO+rqtRMMZo9MkBKisr0R9NIKpOL1j3RlVouk6h2iFSoXowTOsJDbIs0welw6XaPwaHqo2eagrVRJZlaLqWav9QFMXkiqyLQnUe8PsLxgjVMXg8XuqHcwCjb747Mr0WkK7Bn5s9e3bGaiLWNdZItdvjNrMkYgGje6pV2pKaDEq2f6jU/pEGSlx5IFAwdqimnfGcobi4GAAQik1vpHpgcMSypKQkYzUR63K7kwE6FaoTeuprxLlGt3/oAATntE05SY5Ua7T6RzooVOcBr9cDaKNWf9AS8Hg85hREcsr4O0fV6W0AE03oI45D8lvqAzG1zIMOWabLuU43uv1D0wFOo9QERvsHrf6RDgrVeUDXdWCMwYQxN4Qhece4nD/dUB1TKVQ7ifGBqA/24OuqDkWhD0mnu35JPZ1aPwiAwfYPTYWuaeBcUFvpBOg3kweS4XlUqmYstSMWyW+pkerENEeqVR2MMbhcrkyWRSwqNclo2JAkjTwRI0Drw1b/4NT6QZAM1aqWHKmWZVpicSIUqvPAeCPSukah2gmMv/90P//oY9NZUuFpcEhS13XIEoVqpxs9Uq2D1qgmSal1qnUVQtBzYiIUqvNAJBIF+KgnOhOIxqJj/wDJK4lEsp9eTDMdC54MVpo2/bWuiX1cN/FMH+NrxHGGtilP3tZ0nS7zEwDDTrg0lU60JkGvmDzQH+wH4yNHmphQEBoYMKkikkupUM2nF4zEYKAyjkPyWypAD7vCRaGaDI1UD63+QaGaAMNWhtESkCTqs58IvWLyQGhgAEyMnL3PuIxQKESjjw4Qj8cBDIXjqRJ85HFIfqORajKW67cpTy6pR8jQSHWC2j8mQa8Ym9M0DeFweMyRal3XEQ6HTaqM5MrA4BUJtzS9YOSWkm8DwWBwknuSfHDdHAzK0wRDJ1bDnx2MQjXBsJFqXaUVYSZBrxibCwaD0DQNTBq5cgMTydu9vb1mlEVyyAjD0w7Vg83YFKqdIRWqh41O0xUtMvpqxRhrShGHMq5iJEM1xcaJ0G/H5ozQzMTIHdGY5B7xfZK/jDDskab3cjZ+boB68B1hKFQj9f+0pj0Za6R62ksKkbwyNIk1uU41GR+Fapvr6ekBMBSiDcZt4/skfw2NVE/v5eyWaaTaSUaPSjPGoKqqSdUQq0gtzWlyHcR6UiPVmkaTVydBvx2bS4VqMXb7B4Xq/Dc0Uj29j0MP9VQ7ihGgGbV/kGFGX61gY3yNONPQJFaN2j8mQb8dm+vr6wMwxkj1YKg2vk/yVzAYhCL4tJfUM3qxKVQ7QypAD2v/UDUaqXa6sUaqKVQTYOQynIyuZUyIQrXNpUL1dUvqCXAhU6h2gGAwCI88/Zeywhk4YxSqHeL6UM1opJqMOVJNzwsCDOu313WwaQ7eOAWFapvr6+sDlxQwdv2fkksuCtUOEAqFoEx3O0Uk3zDdMqeJig4xFKoHnzMMUFUKT06X2kRq8GyLA9RrTwAMn8Sq05r2k6BQbXPBYBB81Ci1QecKjT46QDQaxQwGqgEAMmeIxWKZKYhYmhGqU5maMWjU/uF4Rqg2BiIFY7TLKhmB4vTkKFTbXCQSAdh4OxyJ5PdJXovFYpBn+G5Hodo5rlunmpbUIxh7pDpBI9UEw98fGL1XTIJCtc1FIhHobJx1I7mEcJhCdb6LRqOQZtD+AQASTx6H5L+xJypS+4fTxeNxAEOhQIAhMfg1QgyUqSdGodrmkiPVY4dqxiWEI7RNeb7TNBUznTvCGU1Kcorr/s6MQaOeasczrlRJg1cwBGNIqCr1VZOhlWEYo1Q9CQrVNhePJ8YN1WAciTj1xBFChow1UVHXKVQ7ndEqaIRqo6WMrmCRoVDNodF7xYQoVNvcZP1NOuiskhAyZOgDMnmbNvkgwLBQPXhbGuwPolBNhiY3M2gavVdMhEK1zem6PvTpOBpjoEztEPR3JmmidarJWJKrCPHUkmnGiDVNdidDoZrTe8UkKFTb3GQj0XSpJv9xxmecqTV9aCtakt9SPbK0TjUZZmBgAMqw9wDX4POD1q8nxnsGZxJUWmZxQvQpanMTbhmq6+BjbApD8gvnHDO9IqeDQrVTpEK18efmDKpKH5ROFwqF4Br2eaIMhupQKGRWScQiUmuYc4mWWZwEfYranBB8gtm4OgUlBxCSNOMJ2boOCDHOhFeSV4wPSOMyP2O0cx5JbiQmD7utDA7I0Eg1Md4fBBe0IdAkKHHZHOcCEzXUUqjOf4wxaDNsAKGWbOcYGqlmqf9XExSqnW4gGMTwvXmN9g/alZek1jDnMhIJWrt8IpS4bI5zhnEjka6nRqNI/kokEhAz/DszWqfaMYzVHNjghkFMMMTjcfr7O1xvby88wwZh3IMnXf39/WaVRCwiGaoZBJeTy/iScVGotrnkBKPxV/+giYr5T00kMMMNFSEY6LKeQxirOaRCtZT8GKBt6p2tv78f7mEn5zIYBGPo6+szsSpiBdFoFEIIcC4Qj9P7xEQoVNtc8lLueImKUa+kAyQScYgZbqlIodo5UusOD4ZpI1zT0mnOFYvFEIlG4R42sZ0xBo8Q6O3tNbEyYgXxeBycS+CDPdV0VWt8FKptTtM0sPFW+KD1Zx0hFApBnmGoVjhDiCYkOYKxmoMxQm38fzgcNq0mYi4jOHtGzcFxg6Gnp8eEioiVRCIRCC5B8OTWQLQh0PgoVNtcJBoBxgnVjAnEonSpJp+pqorOzi4Uumf2Ug64ONrb2zJUFbEy43I+VwZDtSJGfJ04T3d3NwDAO2puhgdAz+D3iHOFw2EILoFzOXWbjI1Ctc319faBSe4xv8eEC8FgP7WA5LGenh6omoZC18yWwyt0CXT39KZmeZP81dPTAy6L1Ag1d4vU14kzpUL1qJFqL+fo6uoyoyRiIZFIBJzJEBSqJ0Wh2sbi8ThCoQEw4Rrz+0xyQ9d1WhIpj7W1JUeXi2YYqotcArquo7OzMxNlEQvr7e0FH/Z8YS4K1U43Uaju7e2lNkKHC4VC4MPaP2hDoPFRqLYx43LtuCPVg1+nD8v8denSJQDALK80o+PM8ooRxyP5q6urC1CG3vq5iw99nTiS8bf3jGol9DEOVdOoNcjh+vuDEEKBEMmVzClUj49CtY01NjYCALjsG/P7xteN+5H8c/r0aRR7JBS7ZzZSPadAhkviOH36dIYqI1bV1NwE7hs6CWOCQ/LKaGlpMbEqYqbOzk54hXTdeve+wZFruoLlbAMDQUhCgTQYqunq9/goVNtYbW0tAIC7i8f8PncVAoyl7kfyi6qqOHP6NBYFZjZKDQCCMSwISDh58kQGKiNWFY/H0dXZBeEb9ZzxCjQ3N5tTFDFdR0cHfGNsIEWhmgDJreqToTrZakqhenwUqm2srq4OQvGBj9f+wSUIVyFqa+tyXBnJhUuXLmEgFMKSImXyO6dhSZGC5uYWtLa2ZuR4xHpaWlqg6zqEXx7xdeGT0dTUZFJVxGydFKrJOKLRKOLxOCThgiQlP2tol83xUai2serqakApnPA+zFWEizUXoevjbGVObGvnzp2QBMPS4rEnqk7VypLkcXbt2pWR4xHruXbtGgCA+2VE6/sRrU9+OAq/jN7eXhqBcqiOjo5UgB7OyzgYgPb29twXRSzB6KeXhAucJTeAoVA9PgrVNtXc3Izm5mZIvooJ7yd5K9Df10cT0PJMKBTCG7t2YXWpC145My/jEo+EpcUubNnye1qGMU9duXIFACAFFETq+xExQnVhcgSqvr7erNKIScLhMAZCIfjHCNWcMfglCR0dHSZURqzACNWy5AZjDIrspomrE6BQbVOHDh0CAEj+uRPeT/jnAGCp+5P8sGfPHkSiUdxW6cnocW+b7UZXVzeOHDmS0eMSa7h8+TKkAiW1RrXBCNWXL182oyxiIiMw+8fZRMwHhg4aqXas1Ei1lLySKQkXbV0/AQrVNnXw4EEIdxG44p/wflxyQXjLcODgwRxVRrJN0zS8vmkTKvwy5hXIk//AFKwodSHgkrBp48aMHpdYQ21dLXjg+ucMdwsIl0Sh2oGM1g4/H3sFIT9jqfXwifMYAVoWyblbgrvQ20OhejwUqm2ou7sbVVVVEL45ad1f8s/B1fp6moiUJw4ePIj6q1fx7rkesDEmF82EYAx/MMeNU6dP4/z58xk9NjFXMBhEW2tbalR6OMYYeKGMmpoaEyojZkqNVI/R/pH8ukBXVxdtAONQqVA9uCCCLHnQTXtfjItCtQ3t3r0bmqZBKlyY1v2lwAIADDt37sxuYSTrNE3Db59/DqVeGWvKxl71ZaZuq/TCpwj89vnns3J8Yg5jaU1pnImtosiF+qv1iMViuSyLmMwYqR5roiKQDNvxRIIu+TtUb28vGOOpjV9kyY2+PnoujIdCtc3ouo7t23dAeEohXBOv/GHgshfCNxs7duykCWg2d/DgQVypv4q75nvAMzxKbVAEw7vnenDy1Ckarc4jFy9eBDB+qJaKXdBUjVpAHKajowO+MTZ+MRgj2DRZ0Zl6enqgyO7UVVFZciMajSISiZhcmTVRqLaZS5cuoaHhKqTCxVP6ObloMbq6OmnHPBvLxSi1wRitfp5Gq/NGTU0NJL8CrozdO2uEbWoBcZb29vYx16g2GL3WFKqdqbe3N9X6AQy1gdAKIGOjUG0zu3btAuMCcmDBlH5O8s8FlxRqAbGxXIxSG4zR6lM0Wp03ai7VgBeOP7GVewSEW6IdWB2mY5JQ7aORakfr6emF4ENXt6TBUE3tQGOjUG0j8Xgcb+zeDeGfCyamtose4wLCvwD79x9AKBTKUoUkW3I5Sm2g3ur80d/fj472jnFbP4BhkxUv0Ui1k3R2do47SREAPIxBMEa7KjpUT08PZGlo6VaZQvWEKFTbyNGjRzEQDEIuXDStn5cLFyEej2H//v2ZLYxkXS5HqQ3UW50/6urqAGDMlT+GkwpdaGhooMmKDhEKhRCJRsedpAgkT7Z8QlCodqj+/n5IYuhkXB78b2r/GBuFahvZtWsXhOyG8M2e1s9zTymEqwA7d9I21Hbz6quvosQj5WyU2nBbpRdeWeC1117L6eOSzEqt/FE08Zb2okiBpmq4evVqLsoiJjNaOiYK1cDgBjDU/uE4qqoiHA6lNn4BkArYtFX52ChU20Q8HsfxEyfAffPAxtn5ajKMMQj/fJw/fw4DAwMZrpBkS2NjI86fP49bK9w5G6U2KILh5jIXDh48SJf7bKyxsRHCLYG7xp6kaBCB5Ej2tWvXclEWMVl3dzcAwM8mfl54GUMXhWrHCQaDAABpWLupsbQeheqxZTVU//SnP8W9996Le++9F4899hgAYP/+/bj//vtxzz334Ec/+lHqvlVVVfjEJz6BD3zgA3j44YeRSCSyWZrtVFdXIxaNQvinN0ptEL7Z0DQNZ86cyVBlJNu2b98OzoC1FbkdpTa8bbYHqqpi9+7dpjw+mblrjY3gPmnS+wlfciIjbRTlDF1dXQAA7yQj1V7O0UUbfjiOEZyHt38wxiDLbgrV48haqN6/fz/efPNNvPLKK9iwYQPOnTuHTZs24Rvf+AaefPJJbN68GWfPnsWePXsAAF/5ylfwzW9+E1u3boWu63jxxRezVZotnTx5EgCD5C2f0XGEpxSMSzh16lRG6iLZpaoqdu7YgeXFLhSMsxRatlX4JMwtkLFt61ZTHp/MXOO1a+D+ybe0Z4JB8itobGzMQVXEbMZItZdPfAXMyzkikQitTewwxqIGkhj53iG4jHA4bEZJlpe1UF1WVoavfe1rUBQFsixj6dKluHLlChYuXIj58+dDkiTcf//92LJlCxobGxGJRHDLLbcAAD7+8Y9jy5Yt2SrNlk6dOg3hKZnyqh+jMS4gvGU4ceJkZgojWXX16lX09PbixrKJe2GzbfUsF642NFALiA1FIhH09vamNVINAMwraKTaIbq7uyExDhmThOrBlkMjhBNnMIIz56NDtUSriI0ja6F6+fLlqZB85coVbN68GYwxlJWVpe5TXl6O1tZWtLW1jfh6WVkZWltbs1WaLTU1NYGnuYPiZJhSiJbWFui6npHjkewxVm2Yk8YoYzZVDj4+rWFsP8aJEHend6WDuQR6enuyWBGxir6+PniFSO2WNx7PYHsIrfjgLEZwFqNCNWcSjVSPI+sTFWtqavCZz3wG//RP/4QFC67fsIQxNma4m+xF7iTxeBx9fb1gkjcjx+OyF2oiQW+QNlBXVwdZcJR6zGn9MMweHOU0Qj6xD+N1Pt5OiqNxF6d+SYfo7e1FOjM1PIOfx3Slylmi0SgAQIiRV7k4lxEJUyvQWLIaqo8dO4a/+Iu/wJe//GV87GMfQ0VFxYhledra2lBeXn7d19vb21FePrPe4XxiXHJjsmeSe6aHDS7kTuuOWt/ly5dR4RU5X/VjNI/MUeSRcPnyZVPrIFNnhGo2ycofBqYIRCNRWqvaAfp6e5FOY5mb0Ui1ExkLRvBRq8NwxhFPxM0oyfKyFqqbm5vxhS98AY8//jjuvfdeAMDatWtx+fJl1NfXQ1VVbNq0CXfeeSfmzp0Ll8uFY8eOAQA2bNiAO++8M1ul2U7P4KxrLjKz+gMb3BGph2ZzW14kHIZbssZVG7dgqZELYh/GJVwmp/d2zwfvR8tu5r/e3l64J1n5AwDcgxMZKVQ7SzyeDM6jl/FlXKS+R0ZKb+bKNDz99NOIRqN49NFHU1/75Cc/iUcffRRf/OIXEY1Gcdddd+GDH/wgAODxxx/HI488goGBAdx444349Kc/na3SbEdRkpMTdV3LzAF1dcRxiXVJsoyIRVrfNR0Qwtw2FDJ1qpp8vad9sWPwjjTnIv8NDAygPI0nhgwGBjrRcppxQzXjiMeo/WMsWQvVjzzyCB555JExvzfW7myrVq3CunXrslWOrXm9g73UWmbODHU1MfK4xLIkSYKqWSPcqHqyHmJT6abqwbtRqM5vuq4jHIlAUSZvAGGMwSUErfjgMMZ7wHWhGgwavT+MiXZUtAEj/OoZCtVGOKdQbX2yLCNhkfeuhKZDls1dhYRMnaZN7woXher8FolEoGkalDRPthTGaKSakElQqLYBj8cDzgX0RGYut2hq8jgFBQUZOR7Jnjlz5qAzrJo+KhBNaOiNJFBZWWlqHWTquNEzm+5zSB/1cyQvGaPOaYdqMBqpJmQS9K5pA0IIzJ49G1o0M5NEtGgfAoFC+Hy+jByPZM+iRYsQTWjoiaim1tEaSrYMLV682NQ6yNR5PMnVfvQ0L3noieTIttudmYnRxJqMSceTbfxiEMN+hjgDo/kVU0ah2iYWLlwAPRHMyLH0WD8WLJifkWOR7DJCbOtAwtQ6jMdftGiRqXWQqTPCsa6m1waiq8kPUJfL3F08SXYZAVlKc6RaAhClbcodZWhi+sj3Dl3XIEk0aX0sFKptYt68edCi/TNeAUTXdejxfsyfT6HaDhYsWADGGJqD5obq5mACPq93xM6nxB6McDyVkWpZlmmllzw35VDNGCIUqh3FeA/Q9LFCNc2vGQuFaptYsGABdF2DFpvZTmd6IgItER1zd0tiPW63G0uXLEF9n7lrgl7tS+DG1atpp1MbSk10jqc5Uh3X4PZQ60e+Mzb3SffUSQK1fziNsdrT6PYPGqkeH4VqmzAuu2uRnhkdR4v2jDgesb41N92Exv44EiYtrReMqegIxbFmzRpTHp/MjN/vBzC1UG38DMlfxm55Is0TZc4Y1IS5V8xIbqVCtTZyTo+ma5BppHpMFKptYu7cueBcpELxdFGotp/Vq1cjoelo7DdntLq+N56qg9iPEZC1WHqTXfW4RisDOYCxKVC6IYADUKe5PCOxJ2ODOE0fHapVuNw052IsFKptQpZlzJ07F2q0d0bHUaO9KC4uoZEoG7nxxhsBAA0mtYA09MWhyDKWLl1qyuOTmVEUBbIspz1SjbgGv4/eH/KdMVLNRq3+UR2NoDp6fe80H/YzxBnGC9W6rtKeBeOgUG0j8+bNBUvMbPF9PRbEvHlzM1QRyYVAIICK8nLTRqqbggksWbqEdlO0MY/Xk36oToBOuh3A2BSIj+r+uBCL4MIYW1AzsGlvJETsyQjOmnZ9qDYCNxmJQrWNlJeXQ40NzGzNSDWE8vLyzBVFcmLFypVoHsj9WtWqrqN5IIEVK1bm/LFJ5ni93tT605PR4hrttkquQ3OUncdYOWis9g8K1WOjUG0j5eXl0LUEdHV6M7B1TYUaC6GioiLDlZFsW758OXoiCQRjuR0pag8lEFc1LF++PKePSzLL5/OlPVKtxVUK1YSQYSPVI9t+NI1C9XgoVNuIsUawHp/eVrF6IgwAmDVrVsZqIrmxcOFCAEBXOLc9jV3h5AgFLcFobz6vL611qnVNh67SSDUhZKin+vrVPyhUj4dCtY2kthvWp9cGYPyccRxiH6WlpQCA3mhuR6p7o8nnDJ2I2ZuiKGDpPHUGd1OkSUhkNNqp2nnGbf/QEhSqx0Gh2kZSH3TTDNUYPNukD0z7Ma5S9KW5LFqm9EU1KLJMS6zZnCzLQBrrnOsahWqn4Dz58T+VsMypsdpRUu0f14VqWv1jPBSqbST1JNZmNlJNLwb78Xq98Ljd6IvmOlSrKCkpoZ0UbS4Zqie/nxGqaRQq/xmv6XQztQYdjFNkcBIjK+jDVn3RdQ26rlOOGAe9QmwkNbKQ9tvgaPqI4xB7cbtdSHdVtEyJazr11+YBSZJSgXlCg/cRgrYgzndT/TzRQZ8dTmMsozp8pFrTkx9CFKrHRq8QGwmHkxMNGZ/ek9n4OeM4xF445zNbTnEadJ0+SAnJR8aJU7rn6RSqnSc1Uq2PHKkGQPsWjINeITYy81AtjTgOsRfGeDptsRmlAXTJNw/k+mSMWF8qVKf51NB0HRJdwXCUob77oSeJEarpBGts9FuxkVAouZSeEY6nbDCMG8ch9sI5m3bjz7TptOlD3kjn70h/a8cYGqlO711FAyBodNJRGGNgjI1qEaIWsYlQqLaRvr6+5H+I6U0iYoM/19/fn6mSSA5Fo1HIo/cUzjJJMMSi09tsiFhH+iPVyeeXquZ+906SW8bl+3SfGTrokr8TJdsOh7d/0NysidBvxUa6urrAGAcTrmn9PGMcQvags7Mzw5WRXAiHI3CJ3IZql2AIhahdyO7i8ThYGidkbPATgUJ1/ptqT7UGnUYnHWi8lZ9oRaixUai2ke7ubnDZM6MnMxNudHd3Z7AqkguqqiIWj0OZYqjWdR39URUdoQSONIem3FurCEY9+HlAVdX03u0Hg3cikdudO0nuDfVUp9n+odNItRON95lB8zTGRq8QG+ns7ASEe0bH0IUb7R0dGaqI5IoRbF3S1EL10eYwuiLJsajXL/UDOvCOOekvkecSDJFIZEqPSawnkUik1RxvnLBTqM5/Ux+ppp5qp2I02SJtNFJtIx2dnWDSzLYY55IbXZ1dGaqI5IoRqqc6Ul3dFZ3w9mQUwRBPJBCPx6f0c8RaEokE9HSeOjRS7RjTCdU0Uu08uq6PmsDMhr5OrkOh2ka6u7rBpJmNVDPJg76+XuqZtBljxRaXmNpLNj5qvazRtydjPB61gNhbLBZLs/0j+X90EpX/ptz+AZqc5jSapkHTNDA29Hc3/ptOvMdGrxCbiMViCIUGZjxSzSQPdF0fWkmE2EKq/cOEiYrDH5/YU9oTFRkD44xCtQMM7aiYHtr8xXmMwbfhoZpTqJ4QvUJsoqenB0ByouFMGCPdNFnRXqKDy9rlekk9eTBUU1+1vcXiMSDNEzImOIVqB6BQTSZjvA9wGqlOG71CbCIYDALAtJfTMxhrVRvHI/ZgjBjkOFOnHk/T0u28JFaUSCTSGqkGAMYZtYc5gNH+MZVQTUvqOYsRqhkb+rsboToWi5lSk9VRqLaJgYEBAAAT09ui3GCEauN4xB6MUJvrpUGNh6NQbW/J1T/Suy/jjEahHMBY6UVPM1broLWJnca4QimG7eLMGIMQcurqKRmJQrVNDI1UT283RQMb3KqcQrW9pHaxyvGHmvF4NNPb3qYyUg1GoZoQMhSqOR85mCe4RC2B46BQbRPGRDHGZ7akkTHSbawmQezB6GVUcxxuNdqSNi+oqpr+ZQ5OOyoSQobm8ohRuYNC9fjok9ImUiNHbKZ/ssFwRh+atuJ2JyeYxtXchurY4OMZj0/sKe0dFZG8vEvvD4QQ4wq5GHWFXAiFrnaPg0K1TQx9yM3wT0Yzd23JCLWxXIdqjUJ1PtA0Lf2RakY99E5gtHSlu1seA7WBOY0RqqVRoZpzGf39/WaUZHkUqm0iFapn2lM7+PM0EmUvpoVqGqnOC5qqpf/WQe0fjpBaUSjN+3PQ88JpxgvVknBRqB4HhWqbGFr9YaYT1diI4xF78Pl8AIBIIrehOpLQwRijUG1zqjaFnmpGV7KcILWxR5r3Z6BQ7TTGJnHSqKV8JeFCXx+F6rFQqHYYWhLJngoKCgAAoURuT4ZCcQ0Ffj9NVLS5qfRU64zRSbcDGCdO6Z9r6RSqHaa7uxuy7AbnI9cnVyQPgsF+OvkeA31SEmIDQgj4vB6E47kNO+GElgr0xL6S7R80Uk2GGH9jKc2xagFGG344THd3NxTJc93XFdkDXdfR29trQlXWRqHaoWjCif0UFBQglOP2j3BcR0EgkNPHJJmlaVry9Z72OtV0md8JjIAs0jzZkhgQow0/HKWzswuSuL71Tx4M2p2dnbkuyfIoVNvE0MhRBv5kdHnXlrw+H6Jqbv9uUQ3w+/05fUySWamthtNdUo8zxOI0IpnvjFCd7s4HEhiFaodpa2uFIvuu+7pLSX4mtLe357oky6NQbROZW6caYEykPmiJffh8PkRzPFIdVXV4vd6cPibJrNQmDVJ67x1MYoiEaWOHfJfa2CPNkWrBGG1N7SCRSAQ9PT1wK9e3/xmhuqWlJddlWR6FaptIheBMhGrOqWfShrxeH2I5vsAQVXV4PNf31BH7MEI1E2m+d0gc4Ug4ixURKzB26VXSDNUKYwjTLnqO0draCgBwu64P1ZJQoMhuCtVjoFBtE7FYDIyLjKzewZigCSc25HK5kON5ioipGlwu1+R3JJaVCtVSmpt8CI5ohEYk810oFAKQfqiWB0M1zcdxhubmZgBDo9KjueQCNDU157IkW6BQbRM9PT3gUmbWCmaSGz09PRk5FskdVVXTnmuWKYL6723P2MCByem93XOZYyA0QH/3PGeMVMvpjlQjuX09tQ46Q319PQDA6yoa8/tuVyGuXLmSu4JsgkK1TXR2dgJjzMKdDp270dHRkZFjkdxJJBJp9z9misQZfYjaXFdXFwCAu9ObksY8Apqq0Y5peW5gYAAAIKe5pJ4xom38HMlv9fX18LgKIIQ85ve97iL09fXSAN0oFKptoqOzE2yM9SKng0tudHTQUjh2E4/HIVhuL70KCtW2193dDQDgbjHJPZO4K3k/I4yT/BQMBuEWAjzNE3XX4AZQxpUPkt/q6i7DPWyUur27Fu3dtanbXncxgKERbZJEodoGVFVFe1sbmJSZVRiY7ENfX2/q8h+xh/6+PrhEbkeqFU4fonbX1dUFxhmYkmb7x+CIthHGSX7q6+uDewoT392D4dvYuprkr0gkgqamxlRwBoC2rhq0ddWkbvs8JQCA2tra637eyShU28DVq1cRi8UgBp/EMyU8JdB1HZcuXcrI8Uj26bqOpqZGlHjSG23MlBI3R+O1azl9TJJZLS0tED457UnO3JcM1cZEJZKf+vv7oUzh/kYAp7ag/FdTUwNd11HgLRv3PrLkhscVQHV1dQ4rsz4K1TZgPGmFpzQjxxPu0hHHJdbX19eHgVAYpTkO1aUegZbWVtphz8au1NeD+dPd4iPZJsJlgYaGhixWRczW29ODqczSoZFq5zCygd87a8L7+TylqKq6kIuSbINCtQ1UV1dDSG4wOTM72zHJBeEqwIUL9GKwi6amJgBAqSf9cJQJpR4Jqqqira0tp49LMiORSKC5uQlSQfpjkowxiAIZV69ezWJlxGw9PT3w8vQjgGfwvjQxLf9duHABHnch5ElWHPN7y9Dd3UU7Kw5DodriVFXFkSNHwTyzMrJGtYF7ynDy5CnaIcsmzp8/DwCY7cttqK4cHOGsqqrK6eOSzGhqaoKmahCBsWfwj4cXyLhSf4XWJM5Tmqaht68vFZTTITEGFxcUqvOcpmk4e+Ys/J6JR6kBIOArBwCcPXs222XZBoVqizt37hx6e3sgBRZk9LhSYAGi0QiOHTuW0eOS7Dh27Cgq/DICrty2f1T4JPhdEj1PbMq4jCsVT20DH6nYhf6+/tSuaiS/BINBqKoK7xR36PVwThNY89zly5cxEBpAob9y0vt63SWQJRdOnz6dg8rsgUK1xe3duxdcyJD8czJ6XOEth5Dd2Lt3b0aPSzIvFArh/PkqLCua2mhjJnDGsLRQwonjx6mv2oaqqqogXBK4f2rPHanElfp5kn+MYDyV9g8A8ICWWsx3Z86cAQAE/LMnvS9jDAXeCpw6dSrbZdkGhWoLi8ViePPNt8B9c8B4Zi/7M8bB/fNx6PBhWszf4k6dOgVVVbFsiqONmbKsWEF/MIiLFy+a8vhk+s6dPwderEy5dUwUKuCyoFCdpzo7k/sU+KYYqv2co5P6Z/PayZMn4XUXwiX70rp/wD8b7e3ttFrQIArVFrZnzx4MDAQhFy3JyvHloiVIxOPYtm1bVo5PMmPXrp3wuwQWTLEvNlOWl7ggC4433njDlMcn09Pb24umxibIpVPfiZUxBlGs4MzZM1mojJjN2FHXN8X2Dx/j6Orupl77PBWLxXD69BkEfJO3fhiKCuYCAI4fP56tsmwl7VfU6dOn8bvf/Q6xWAwnTpzIZk0EyXWJX3llA4S7GMJbnpXHEO5iSL5yvPrqa3Rp36K6u7tx5PAR3FzmguC53fjF4JY4bihVsHv3G4hEIqbUQKbu5MmTAAC5bOqhGgDkcg+uNVyjy/15aLoj1T7OEU8kaFm9PHX+/HnE47FUUE6HWymA21VAoXpQWq+o9evX4+tf/zqeeuop9Pf34+/+7u/w4osvZrs2Rzt58iQaGq5CLl6R0VU/RpOLV6KzswP79+/P2mOQ6XvjjTegahreVpGZLeqn620VHoTDERw4cMDUOkj6Tpw4AeGSIKbZNiSXJ59zRjgn+aOzsxNeISCm+NniHwzhxkg3yS/Hjh0D5wIBf0XaP8MYQ6F/Dk6dOoV4PJ7F6uwhrVD9m9/8Bi+88AL8fj9KS0uxfv16/M///E+2a3O09evXQ8iejK/6MZrwz4FwFeDll9fTJT2L0XUd27ZtxfyAgjJvbpfSG21RoYwSj4RtW7eaWgdJj67rOHb8GMQs17RPykWhAuGW6MpkHmpra4N/iq0fAIXqfHfs2DEUeMsh+NRaDYv8cxCNRmkOBtIM1Zxz+P1DG49UVlZCiNwu7eUkly5dwsmTJyGKVoDx7P6eGWOQileitvYSLYtjMadPn0ZjYxPePnt6l+8ziTGGWyvcOHvuHOrr680uh0zi6tWr6OnugTyDKxyMMYgyN44dPwZN0zJYHTFbW2sr/NM42fIPfh7RZlD5p729HQ0NDVNq/TAU+ivBGaelV5FmqC4qKkJVVVVqxOO1115DYWFhVgtzsnXr1oELBUrx0pw8nly4GEL24KWXXsrJ45H0bN78OjyywOpp9sRm2ttmeyBxht///vdml0ImYfQ3yuXeGR1HqfCgv68fdXV1mSiLWICu62jv6EiNOk+FhzEIxihU5yHjPWM6oVoIGQW+Chw9SqE6rVfVN77xDXzlK19BbW0t3vOe9+CJJ57AI488ku3aHKmxsRFv7d8PqWgpmEh/a+GZYFxAKl6BU6dOoaamJiePSSbW2dmJgwcP4W0VLsgmTVAczSdz3DjLhV07dyIUCpldDpnAsePHIAVcEDNsGzL6qqkFJH/09fUhFoulRp2ngjGGAiHRttR56NixY3Arfnhc0xswLfTPwdWr9Y5vDUorVC9duhSvvPIKXnnlFfzyl7/E1q1bsXLlymzX5kibNm0CYwxySW5/v3LRMnCh4LXXXsvp45Kx7dq1C5qm4bbZ5k5QHO0dlR6EIxG89dZbZpdCxhGNRnHu7DlI5TNf15y7JchFLrqsm0eMXTID0xipBoACAK0tLRmsiJhNVVWcOnUKAV/ltOdgFBUkN6hz+kYwab2qDh48iE984hNYunQpGGO46667aOQiC6LRKHbt2gXhnwcu5faSPxMyRGAB3nzrLQSDwZw+Nrne7jfewPyAghKPuRMUR5tXkJywuGfPHrNLIeOorq5GIpGYceuHQZS5caH6AmKxWEaOR8xltG4UTHO+TgEXtH19nqmrq0MoFEJhGrsojsfrLoYiuylUp3Onxx57DD/4wQ8AAMuXL8cvfvGL1G2SOfv370coFIJclJte6tHkoqVIxOO0yYfJ6uvrcbWhAWvKzNlBcSKMMaye5cLp06dTWx0Tazl//jyAoa3GZ0oudUNNqNQalidSoVpMc6RacPQHgwiHw5ksi5jIWDYz4E9/05fRkluWz8bJkycdvZJYWq+qeDyO1atXp26vXr2aRi2yYOvWrRCugqxt9jIZ4S6G5CnBFlo2zVR79uwBY8DqWdYL1QBwU5kbuq7jzTffNLsUMobz589DKnSBK5lZOUga3JGRlsvKDy0tLXALAdc0ltQDhka4abJi/jhz5gx8nmIo8szaDQv9leju7nb0luVpvao8Hg/27t2bun3gwAF4veldWgwGg7jvvvtw7do1AMDXv/513HPPPXjggQfwwAMPYPv27QCSo7T3338/7rnnHvzoRz+a6r/D9sLhMM6fPw/hn5/VzV4mIwoW4Gp9Pe2iZqKTJ05gQUCBP0OhKNPKfRJKvTK1gFmQqqqoqqqCyNAoNQBwl4AUcKVGwIm9tbS0oGCagRoY6sVuob7qvKBpGi5cqIbfUzbjYxX4kse4cOHCjI9lV2k1bD788MP4whe+AElK3p1zjp/85CeT/typU6fwyCOP4MqVK6mvnT17Fs8++yzKy4dGYyORCL7xjW/gN7/5DSorK/G5z30Oe/bswV133TXFf459Xbp0CbquQ3hnmVqH8CQfv7q6GrfffruptThRIpHAlStX8I6K3Kz8Ml1z/QK1l6gdwGra29sRiUTgK/ZPfucp4IUy6i7Tsnr5oLW5GQUzGLgJDI5UU191frh27RrC4RDmlM48VHtcRZAkBRcuXMD73ve+DFRnP2mdrq5duxa7d+/Gk08+iZ///OfYsmXLiHaQ8bz44ov453/+51SADoVCaGpqwje/+U3cf//9+I//+A9omobTp09j4cKFmD9/PiRJwv33348tW7bM7F9mM9XV1QAA7i41tQ7uLgZjPFUPya2GhgbEEwlU+q01QXG0Sr+Eru4e6qu2GOOKoPBn9qRM+GV0dnQiEolk9LgktzRNQ1t7eyoYT4ebMSic00h1njA+6wu8Mw/VjDH43KWoOu/cVrEJP7lfffVVPPDAA3jmmWdGfH3//v0AgL/8y7+c8ODf+973Rtzu7OzEH/zBH+Db3/42vF4vPve5z2HdunXwer0oKxv6g5aXlzvuLLimpgbCVQAumdtHy7gAdxfh4sWLptbhVLW1tQCASv/UtonNNaO+2tpa3HbbbSZXQwypUF2Q2eePcbympiYsWbIko8cmudPV1YWEqiLgmn77B2OMVgDJI3V1dRBChtsVyMjxfJ5SXG2oQiKRSHU3OMmE/2JjO+JMBaz58+fjZz/7Wer2Qw89hA0bNuCDH/zgdfc1s6/YDKFQCOAWmZjGXRgYoM09zGCM/Ba6rNlPbTDq6+npMbcQMkJTUxOEIoFn+PljhOrGxkYK1TZmjC7PZKQaAAoYQ3NTUyZKIia7evUqPK7CjGUur7sImqaiqakJCxYsyMgx7WTCUP33f//3AIBZs2bhy1/+8owfrLq6GleuXMEHPvABAMntUiVJQkVFxYhdeNra2kb0XDtB8gltnWVouEV28XMaYykiq59TGuU5eekkK+rp6QH3ZP6EjLuTHxW9vb0ZPzbJnUyF6kLOUdXaCl3XHTcAlm/q66/C48pc26nHVQQg2croxFCd1jWg3bt3Z+TBdF3H97//ffT29iIej+OFF17A3XffjbVr1+Ly5cuor6+HqqrYtGkT7rzzzow8pl1Y641JB5vB7HAyfbYJ1YP1aZpmbiFkhIGBAehS5p88TOap4xP7amlpAQPgn+ZuioYAF4jF4zSnwuaCwSB6e3tSQTgTPO7kNucNDQ0ZO6adpNXwMm/ePHzmM5/BrbfeCp/Pl/r6ZD3Vo61atQp/8zd/g0996lNIJBK45557cN999wEAHn30UXzxi19ENBrFXXfdNWZLSD4TQoDpFgkougoxzY0ByMzYLaTSSLW19Pf3pwJwJjHOwCVOu63aXEtLCwokCWKGZ+0FQqSOV1JSkonSiAmMDgGXkrnVggSXoMieEd0HTpJWqC4qKgIAnDt3DkIIFBQUTOlBdu3alfrvBx98EA8++OB197n99tvx2muvTem4+WTu3Lk4euw4dF0zdZRY13Ug1o/5828xrQYnMz6g+qIait3W7avui6oAQB+oFhMcGACbwSS0iXBZ0Ei1zTU1NaEAM7+SUTjYPtLc3Iwbb7xxxscj5ujs7AQAKHJ6+46kS5G9qWM7TVrvvn/913+NixcvYs+ePdi5cycaGhrwxS9+Mdu1OcrSpUuhayq0aN+0fl7XdWjxMLRoH2Ldl6Y9gqgnwlATESxdas5W6U43b948AEBHKGFyJRPrCCVDtVEvsQZd15CBzDQ2Rlcm7K65qSkViGeigHMwwNE75+UDY5O3me6kOJosPOjooFA9rm984xv4kz/5E5w6dQqnTp3CBz7wATz88MPZrs1RjBCrRabXoxbvuQQ9HoSuRhFtOYp4z6VpHcd4fJrhb4758+cDADrCFg/V4QQkSaCiosLsUsgwjLGszne21twPMhUDAwMIDgwgkIHWPsEYCiSJ1qq2OWP1JlnKcKiWPI7tt0/r1RUOh/Gnf/qnkGUZiqLgoYcecmy/TLbMmTMHbrcbanh6v9dEf9OEt9OlhjvAucDixYun9fNkZgKBAAIFBWgdsHaobh1IYO6cuRDCui0qzpTF0EuD1LZmjCpnYqQaAArA0ETL6tlaKBQCYxycZXgJTiEjEgln9Jh2kVaonj9/Po4fP566ffHiRbrsm2FCCKxduxZaqHV6l1j1xMS306SGWrBq1Sq43e5p/TyZuRtuvBEN/arZZYxL03U09CdwA/VSWg7nDMhiiwaf4aoRxDyZDtWFXNBa1TYXDochSUrGr0AJLiMajdpu4n0mpDVRsbW1FQ899BBWrlwJSZJw/vx5lJWV4f777wcAbNy4MatFOsWtt96KQ4cOQY/1g2Vod6Op0BIRqOFuvP3t9+X8scmQNWvW4NChQ+iLqghYcBOYloEEogkNa9asMbsUMooiK4CanVCtqzpk2do7fZLxGaPKgQxdXSoUHMGBAfT390958QJiDeFwGIJn/jUthJw6/vAV45wgrVD91a9+Ndt1ECRDNQAkBlqgmBCq1YHmEXUQc9x0000AgCu9Mdxcntlet0y40hMDAArVFiQrMvRgtkK1RqHaxpqbm+ETEuQMjUoOXwGEQrU9RaNR8AxduRjOaCeJxWIUqsfyzne+M9t1EACzZ89GeUUFugZagZIVOX/8xEAbfH4/TVI02aJFi+D1eFDfG7dkqK7vjaNydgVKSzO3CxfJDJfiArJwxVXXdWiaBkVRMn9wkhON166hMIOX+YsGR7ybmpqwYkXuP6/IzKmqCpaFeRjGssCqat02xmyhBjmLufmmm6BFOkxZukqPdOCmNWuob9JkQgjcuHo16vutN1lR03Vc7U9gzU03m10KGYMkSdC1LL136KCJqTbW2NiIogyOSga4ABs8LrGnRCKRlX0xjGMmEtb7DMs2Sk8Ws3r1amiJKLTY9Narni4tHoIa7cfq1atz+rhkbDfddBM6BuIIxqw10aM9lEA4rlLrh0Vlbck7PcvHJ1nV39+P/mAQhRncKVcwhoAk0QogNpacSEgj1ZlEodpijFCrhtpz+rjGUn60O5Y1GM+Dq30xkysZqb43DoD6qZ2KQrU9GaPJmRypBoAAGK41NGT0mCR3VFXN0kh18n3Ciat/UKi2mNmzZ0OSJGix/pw+rvF4CxcuzOnjkrEtXboUQgg09sfNLmWEpmAcRYWFKC8vN7sUMgYKvWQs165dAwAUi7SmUaWtWAhcu3bNkeEpH2Tr72b0aTvxeUGh2mIYYyguKYWeyO3C6Xo8DK/XB5fLldPHJWOTJAlzKmejI2yty2cdIRUL6MTLsuLxeHb2fxk8phN7JPNBQ0NDchfEDM+XKRYSYvE42ttze2WVZEZyomIWYuDgyT21fxBLKJtlQqhOhGk1B4uZv2AhOsPWOdPXdR3tYTW1lTqxnoHQAJicncu5XBYIhUIZPzbJvoaGBhQJCTzDVzKKByeuNlALiC0lEmpWrm7RREViKcXFxWBaNKePqasRlJQU5/QxycTmzZuHrnAciWyt5jBFwbiGaEKj3VQtLBQKgUnZeVvnMkc47Myth+3uan09irIQnoo5hWo7i8ViGd+iHBhapzoet1b7Yi5QqLagaDQKnWW2921STEIkEsntY5IJlZSUQNOBSMIao9UDgyuRlJSUmFwJGU9oIJSVkWoAYBJHMBjMyrFJ9oTDYbS2taE0w/3UAODmHD4h4cqVKxk/Nsm+eDwGlpXNX2ikmlhIb28fkIWtQyfChILevtwu40cmZvS3x62RqREfHDGnDUCsKRKJYGBgANydpbWkXRwdnR3ZOTbJmqtXrwIASrK0xngJY7hy+XJWjk2yKxaLZ2Wk2gjqsZi1Vq/KBQrVFtTX1wcmcjthkAkX+vtzu+IImVgqVKvWaP8wwj1NZrWmtrY2AIDwZeeEnPsktLS0ZOXYJHuMUeRsjFQDQImQ0NDQ4MhJaXYXDochRObfL8TgoKAT28UoVFtMIpFAV1cXmJTb7amZ5EZoYIAmIlmILCffmKzSU52gkWpLMwIv92UnPAmfhP6+fkd+UNrZlStXIHOe8ZU/DKVCIJ5I0M6KNqPrOkKhECSe+fdzSSSPOTAwkPFjWx2Faoupq6tDPB6D8OR2JQ7hnQVd13HhwoWcPi4Zn9HjrghrrD2s8GQd1HtvTUaoFt7shGo+OAJOo9X2cqmmBrO4yNoa5rOk5POtrq4uK8cn2RGJRKBpKoTIfKg2junEORgUqi3m3LlzAADhLcvp4wpPKcBY6vGJ+foGe9y9WZp4NlVGHX3Ue29Jly9fhnBLYK7s9M6KgJJ6HGIPqqqi7vJlzMpSPzWQXAFEYgyXLl3K2mOQzDPaPaVshGougXPhyM8Ka3xak5Rz585BuArAc93+wWUIdwnOnqVQbRV9fX1gDHBL1hip9srJOpz4RmkHNZdqwAvlrI1IigIZTHDU1tZm5fgk8xobGxGLxVCWpX5qAOCMoVRIqKVQbStdXV0AAEX2ZuX4LtmbegwnoVBtIdFoFCdOngT3mLMFtPCW48KFCxSaLKKnpwdeOfMbNkyXR+ZgALq7u80uhYwSi8XQ0NAAqSh7k0gZY5AKFVyqpfBkFzU1NQCAMim7S7TOEgKXLl2iyYo20tnZCSB7oVqWvKnHcBIK1RZy9OhRxKJRSAFzdqyTCuZD01QcOHDAlMcnIzU1NaHYbY1ADQCCMRR5ZDQ3N5tdChnl8uXL0FQNoii7k0h5kYLa2loKTzZRXV0NhfPUJi3ZUiEkRKJR2gTGRrIfqj3o6HDeEpwUqi3kzTffhJDdEF5zRqq5uxjCVYB9+9405fHJSI3XrqF0hmsORxMa3G43PvKRj8DtdiM6w41kStwMjY3XZnQMknlVVVUAALnEndXHkUtciEaiqK+vz+rjkMy4UFWF8ixOUjTMlpKTWKurq7P6OCRzWltbIYQEKUvL97oUPzo6Ohx3Ak6h2iIikQgOHz4M7psHxsz5szDGIPzzcebMafT09JhSA0kKh8Po6u5GqWdml20jCR333HMPPvvZz+Luu+9GJDGz5flKPQJNjU3QdWss80eSqqqqIPkU8Bk+XyYjlbpTj0esLRKJ4Ep9PSqy3PoBAAHO4RGCVo+ykaamJriVQNZOuNyuAFRVRXt7e1aOb1UUqi3i0KFDiMVikAILTK1DCiyApml46623TK3D6Yw1X0s9MxupdksM27Ztwy9+8Qts3759xpMeZ3kkhCMRR05AsSpd13Hu/DnwkuyvH869EoRHplBtA9XV1dB1HRVS9nfnZYyhnAucp9WjbOPatUa4lIKsHd8zeOympqasPYYVUai2iN27d0Mo3pwvpTeacBdBuIvwxhu7Ta3D6a5dS7ZYlM1wzWGXxBGJRLBx40ZEIhG4pJm95GcN1mPUR8zX2tqK3p5eyCXZ3+mSMQZRotDSmzZw5swZMACVORipBoA5koSm5mY64baBRCKBtrY2eJRA1h7D7SoEAMdtCkSh2gJ6e3tx/PgJcP+CrPe+pUMULEB19QXa5MFEDQ0N4AwomeFIdaaVeZP10IQk6zAuuRutGdkmlbrR0dHhyJn9dnLmzBmUSTKUHLUTzhkcET979mxOHo9MX2NjIzRNhcddlLXHkCU3ZMnluPkXFKot4ODBg9A0FXLhQrNLAQDIgy0o1AJinoaGBpR4ZEjc/JOs4fwyh1sWFKotpKqqClwWEIW52T5eGhwRp/5Z64rFYrhYXY3KLK5PPdosIUHhnEK1DVy5cgUA4HUXZ+0xGGPwuIpw+fKVrD2GFVGotoDjx49DKF5wV5HZpQAAuOKHcBfh2LHjZpfiWM1NjSix0HJ6BsYYSt3ccX1yVnbu/DmIYiVnV7mkIheY4Dh//nxOHo9M3fnz55FQVcyVs99PbeCMoVIInDxxImePSabnypUrYIzD48pe+weQDO319VegaTNbdcpOKFSbTFVVnDhxEtwz2xKtHwbunY3z588jEomYXYojtbe3oyhL203PVKGLo72tzewyCJIrPFytvwqpOPv91AbGGaQiBRdrLubsMcnUnDhxApyxVEtGrsyTFDS3tFDroMXV1dXB6y4Cz/L65V53MaLRqKOeDxSqTVZTU4NwOAThn212KSNIvtlQ1QRdyjNBKBTCQCiMgEVDdcAl0NHRQcvqWUB9fT10Xc9pqAYAUaSgru6y49agtYvjx46hUkiQczxQM39wZPzkyZM5fVySPl3XUVNzCV53SdYfy+8tBQBcctAW9hSqTWZcQhXeCpMrGUl4y8AYXeI1g7GuZ6FFQ3WhiyMai9F29hZQW1sLABBZ3J58LKLIhVg0Sm1AFtTd3Y0r9fWYl8PWD0MRFygQEo4fp9ZBq2pvb0d/fx/8ntKsP5bHlRwNr6mpyfpjWQWFapM1NTVByG5wKbcfipNhXEAofvrQNEF/fz8AwCtbpx1oOJ+cfNsIBoMmV0Jqa2shXBJ4jleJkQZDvBHqiXUcPXoUALBAys3E1eEYY5gnBE6cOIF4PJ7zxyeTM0aNfd7sh2rOBXzuYtTU0Eg1yZHm5mZA8pldxph0yUeh2gRGH7sirBmq5cEVSajf3nz19fXgASnn8zFEgQzGGa5evZrTxyWTO3z4MAqEhFJhzpWuRbKCSCRCrYMWdfHiRTDG4ctB+wcAeD2luHSpxjGtYhSqTdbY1AQm+80uY0xc8aO5uYV6Z3NsKFRb8+VphH0K1ebSdR0N1xrA/bm/zM84g/AptAmQxcRiMZw4fhwLpNyfaBnmygokxnD48GFTHp9MrKqqCn5PadYnKRoKvOWIRqOOOQG35qe2g/T394NJudm0YaqYcCESCTvmDNMqotEoAEC26KuTQrU19PT0IDQQgijI/WV+AGB+4ZgPSrs4ffo0orEYFsrmPCcAQGYMcyUJhw4epAEZi0kkEqipuQSfd1bOHrNg8LGcsq69RT+2ncNKy+iNxw41ktwxPibpeWEuY5RYTGOkWtd1aOEE1P4YInV90wo/okBGc0sznXRbyP79+6Fwjrk5XkpvtMWyC+0dHdRzbzGXL19GPB5DgbcsZ4/pUgqgyB4K1SQ3OOOAZc/mk3VxTk+TXJKk5C5omkWfFsY6/kadxBzG2q/TCdXRy/3QBhLQoxoGTnYgerl/yscQPhmaqqVWqyHmSiQSOLB/PxZKMiSTT3gXywoYkiGfWMe5c+cAAAFf7lYbY4zB7ynDmTNncvaYZqK0ZLLkaJ9F09MgGpHMLSOsqhY92TLqEiZNhCJJLS0tAGPg3qmf3MSaBya8nQ7hS4b51tbWKf8sybyzZ88iODCApSa2fhjcnGOuLGPf3r3UAmIhZ86cgccdgCJ7c/q4Af9stLe3o80Bm4ZRqDaZz+eDrsbMLmNMuhqDx5vbFx8ZCtUJi+7sqg5+RsomrINLhrS0tEDyJVfhmCpd1Se8nQ7ul1J1EPO9+eabkDnHfAuEagBYKrvQ0tqKy5cvm10KQXL35rNnz6LAk/s9MYyRcSesCEOh2mSzZs2CngibXcaY9EQYpSXZX8uSjOR2Jyeuxi3a/xEbDGBGncQczc3NYDlen3o47pHAOEsuC0pMFY/H8dabb2KRBVo/DItlBZwx7N271+xSCJL91KFQCAF/7kO1110MWXLh1KlTOX/sXKNQbbLS0hIw1ZqrKOiJMGbNolCda0ZYjU1j9DAXKFSbT9d1XGu8Bl5g3tUCxhiEX0FjY6NpNZCkEydOIDgwgOWKdTYR83COeZKMPbt3Q9MsetnNQYxdLgv9c3L+2IwxBHyVOHbseN63A1GoNllpaSnUeMiaTzQ1jNJSCtW5RqGaTKanpwfhUBjCxFANANwvcLWBltUz2549e+AWAvNMXvVjtOWygo7OTlRVVZldiuMdO3YMfm8pFNljyuMXFcxBb28Prly5Ysrj5wqFapOVlJRA1xKAZq0tXXVdgxqnUG0Glys52mTV9g+jLkWxRu+mE81kOb1MEn4FrS2tSCQSptbhZKFQCAcPHsQSIUNYpPXDsFhxQeYcu3fvNrsURxsYGMCFC9Uo9OV+lNpQVDAXQDLc5zMK1SYzQqtmsb5qPREFdB0lJbnZypQMMZYwtOLFC2CoLlr9wzzGaI9ZG78YRECGqqq0s6KJ9u/fj1gshpWumbd+6LqOAU1Dt6riXDQ84yuoMmNYJMnYu3cvYjFrTsh3giNHjkDTVBQF5plWgyJ74feW4sCBg6bVkAsUqk1mhFarTVY06qGR6tyz/hKGyQ9a69eZv6qqqiB5ZYhpLKeXSVKJO1UPMceOHTtQJEmoEDN/LpyLRdCraQjrOvaGBnAuNvP5PisVF0KhEA4dOjTjY5Hp2b9/P1yKN6ebvoyluGABLl6sRkdHh6l1ZBOFapMVFRUBGBwZthBdTdZTWFhociXOY4RVS/bZg3ZUtIJz586Bl5jffsN9EoRbolBtkpaWFpw7dw4rJCUjr8f6UaPJo29Px1xJhl8I7Nq5c8bHIlMXiURw9OgxFBXMN/09u7RwIQDgwIEDptaRTRSqyYTMfhE6kXGZVExj/eFckAbrikatdSLoFG1tbejq6oJUav5EUcYYRImCc+fPmV2KI73xxhsAgBUZWvUjMWojstG3p4MzhuWyguMnTqCrq2vGxyNTc+TIEcTjsVSgNZPHXQivpwj79u0zu5SsoVBtstRSQ5YLr8l6VFU1uQ7nMT54ChRrvjz9crKu7u5ukytxpqNHjwIA5FnmzOIfTSrzoK21DQ0NDWaX4iiapmHHtu2YJ8sosPj8hlWKG5qmYdeuXWaX4jg7d+yES/HldGvyicwqXIKqqio0NTWZXUpWWPNT20GGLvFbLFRbvAUhnxlh1a9Y84PSr1CoNtOevXsgBVwQAWssn+aa4wOAvB59sqIzZ86graMdqxTzr1hMpkgIVEoytm/bRp8pOdTZ2YnjJ45jVtESMGaNuDereAkYWN6eYFnjt+xgxqgkE+b3Rw5n1EOX63LP6iPVBYNhn54budfR0YGq81WQ53ot05rFPRLkMg92795NgSmHtm3bBhfnWGyRbckns0pxoam5GefPnze7FMd44403oOs6yoqXmV1Kikv2obBgDnbs2JGXV8Kt+antIDU1NQAA4S42uZKRuKsQjItUfSR36urq4FMEPJI1QtNoxW4BwRnq6urMLsVx9u7dC13X4ZrvN7uUEZR5PjQ3N+PSpUtml+II/f39OLB/P5bLimW2JZ/M0sE1q7dv3252KY6gqip+//stCPgq4HEFzC5nhPLiZejs7MzLNaspVJvs4sWLEK6A9UaqGQd3FeHixYtml+I458+dxfwCyTIjkaPJgqHSL+H8OZqclkuJRAKvbXwNcqnb9E1fRlPm+sBlgVdffdXsUhxh9+7diCcSuMEGrR8GmTEsk2Ts27cPAwMDZpeT944dO4a2tlZUlK40u5TrFBcugEvxYuPGjWaXknEUqk2kaRqqqy+Cuaw1Sm3g7hJcqq1FPG6t3R7zWXd3N1pa27DAIv2y41lQIKPmUg1t6JBDe/bsQWdHJ9wri8wu5TpcEVAW+bF37160tLSYXU5e03UdW7dsQbkkY5Zk7jrlU3Wjy41YLIa9e/eaXUre27hxI1yKFyUWWPVjNM44yotX4OTJk3m3cRSFahMdP34cvb09kAa377QayT8HsWgUb731ltmlOMbZs2cBAPOtHqoLZSQSKqqrq80uxRFUVcWLL74IqcgFucIaq36M5llWCDDg5ZdfNruUvFZTU4P6q1exSrHW1c10lAkJpZKMrVu2mF1KXmtoaMDJkydRXrwC3CITFEcrL1kBzgU2bdpkdikZZc3ftkNs3LgRQvZCKjBv69CJCN9sCFcAr72Wf5dorGrfvn3wuwTmFFg7VC8uVCALjjfffNPsUhxh//79aGpqgntFoWXbgrhHgrLQj+07tqOtrc3scvLW1q1bIXOO5RlamzqXGGO4QVZQW1eH2tpas8vJWxs2bADnAhWlK8wuZVyK7EFp4SJs27Ydvb29ZpeTMRSqTdLY2Ijjx49DFFpnqZvRGGOQipahpuYi9VbnQDAYxJEjh7G61AVh0eBkcEkcK4pl7Nu3F4lEwuxy8lo0GsUvn/klpEIXlLk+s8uZkGdlMTRdx69+9SuzS8lLoVAIe3bvxlJJhmLRz43JrFBckBjD1q1bzS4lL3V1dWHXrl0oK14GWbLmVS3DnLLViMdj2Lx5s9mlZIw9X5V5YP369WCMQ7bQUjdjkQsXgwsZ69atM7uUvLd//34kEipuLrfH5KObyz3o7w/ixIkTZpeS11599VV0tHfAe3OJZUepDcIrwb08gH379tHW5Vmwb98+RGMx3OCyx3vEWFycY4msYPcbbyASiZhdTt7ZuHEjEokEKmfdaHYpk/K6i1EcmIeNGzfmzXOBQrUJLl26hG3bt0MqXg4uWfvNkQkZUvFKHDhwAKdPnza7nLy2c+dOlHplzPHbY/LR0mIFXllg586dZpeStzo7O/HCiy9CmeODXGbtUSeDZ0URhFfGz3/+86EdY0lGbN2yBSWShAphj/eI8dyguBGORGi+ToYNDAzg9dc3o6RwoeWW0RvPnLI16O/vx44dO8wuJSMoVOeYruv4+c9/DiG54Jq12uxy0qKUroJQ/Piv//p5Xi7WbgX19fU4f/48bq1wWX400iBxhrXlLhw8cIA2gsmS//mf/0E8HoN3TYnZpaSNSRyeG4tQW1tLJ1wZdPnyZdRcuoRVsn3eI8ZTKUkokiSasJhhmzdvRjgcwtyym8wuJW0F3nIEfOV4ed3LedFKSKE6x/bs2YMLFy5AmnWT5damHg/jEuSytWhouIot9CaYFZs3b4bEGd5m0ZUdxnNbpQeqpmHbtm1ml5J3zp49izfeeAPu5YWWW5d6Msp8P+RSN375zC/R399vdjl5Ydu2bRCMYaUNJyiOxhjDKllB1YULaGhoMLucvBCJRPDKKxtQVDAHfm+p2eWkjTGGOWU3oaOzA3v27DG7nBmjUJ1DoVAITz/9SwhPCeTCJWaXMyVSwTxIvgr8z69/je7ubrPLySuhUAi7du7E6lkueGV7vSRLPRKWFiv4/e8301WMDEokEvjZkz+D5JPhseC61JNhjMG7thTBYBC//vWvzS7H9qLRKN7YtQuLJRlubq/3iPGsVNzgjNEJeYZs374d/f19mFt+s9mlTFlRwVz4PCV48cUXbf85kh+vTpt49tln0dPTDVfFbba7fMcYg1LxdkTCETzzzDNml5NX9u3bh0g0itsq7TVKbbhttgddXd15ueWsWTZu3IhrDdfguakETLLn27RU5IJ7SQBbtm6l1YNm6MCBAxgIhWw9QXE0L+dYKMnYuWMHbTA2Q/F4HC+//DICvnIEfBVmlzNlxmh1U1MTDh48aHY5M2LPd2sbunTpEjZt2gS5eBmExz79kcMJVwByySq88cYbNGkxg3Zs344yn4x5Fl+bejwrSlzwKSJvJpqYraenB88//zzk2V7IlV6zy5kRz40lEG6Bn//859B13exybGv7tm0ICAlzJXu+R4znBpcb/cEgDh8+bHYptrZnzx50dnZiTpZ6qXVdRyweQjjSi5bO6qy8lksLF8DjDuDFF1+09XsFheoc0DQNP/3Zz8AlN1xl9rs0M5wy60YIlx8/+9nPaHQhA65du4YL1dW4pdy+k48EZ7i5zIXDhw/n1SL+Znn++ecRiUbhu8n6S+hNhssc7huKcPHiRVrpYZpaWlpw+swZrJIV2z8fRpsvyfALge3bt5tdim2pqoqXXnoJPk8JirK0O3NrVzUisX7E1QguNx5Ea1fmd9JljKNy1hrU1dXZepnWrIbqYDCI++67L7W3+/79+3H//ffjnnvuwY9+9KPU/aqqqvCJT3wCH/jAB/Dwww/nxQzQ4d566y3UXroEuexm20xOHA/jEpSyt6GpqYlGJjNg165d4IzZZm3q8dxS4Yaqqnkx0cRMDQ0N2Lp1K1yLCyAK7P1eYXAtLIBU6MIzv3qGTsSnwXifXemy/wTF0ThjWCErOH78ODo6Oswux5YOHjyIpqYmzCm7KWsnXd191ya8nSllRUvgUnx48cUXs3L8XMhaqD516hQ+9alP4cqVKwCSM1O/8Y1v4Mknn8TmzZtx9uzZ1AfwV77yFXzzm9/E1q1boeu6rX+ho6mqiueeex7CXQgpsNDscjJC+OdA8s7C7373AmKxmNnl2NrBgwewqFBGgSLMLmVGKnwyyn0yDh86ZHYptvbMM88AgsF7Q7HZpWQMYwyeNcVoa23D66+/bnY5tqKqKnZs3475sgw/t/d7xHhWKW7ouk7LL06DrutYt24dPO4ASgsXZO1xNC0x4e1M4VxgdukNOHfuHC5cuJCVx8i2rIXqF198Ef/8z/+M8vJyAMDp06excOFCzJ8/H5Ik4f7778eWLVvQ2NiISCSCW265BQDw8Y9/PK+Wbdu7dy8aG69BLl1j2e3Ip4oxBnnWGnR1ddJWszPQ3d2NhoZrWFKUHyOSSwplnK+qotHIaaqrq8ORI0fgWh4Ad+VXgFIqvJDLPXhp3Ut0Ij4Fp06dQmdXF1Yp9r6SNZFCITBHlrFj+3Zb99Ka4fTp07h06RIqS2/Mm3xRUbICsuTCy+teNruUacnaX+F73/sebrvtttTttrY2lJWVpW6Xl5ejtbX1uq+XlZWhtbU1W2XllK7reP7530K4iyAVzDO7nIwS3goIbzleeOFFClHTdPbsWQDAojwJ1YuKZMTjcduOMJhtw4YN4BKHe4k9dkKbKveKQvT19mHv3r1ml2IbO3bsgFsILJbz4z1iPKtkF1paW3H+/HmzS7GVdevWQZE9KCteZnYpGSOEjPKSlTh0+JAt1zDP2anNWGegjLFxv54Pampq0NLSDLl4Rdb/Tboah9vtxkc+8hG43W7oanaDLmMMcskK9Pb24MyZM1l9rHx1+vRpuCSOSptsSz6ZhYUKGAM9H6ahs7MTe/bsgbLQD27zVqDxyGUeSIUurH9lPY1IpiEYDOLggQNYJskQefKZOJ4ligsy5zRPZwrq6upw8uRJzC69ATzPWoMqZ60CYxzr1683u5Qpy1morqioGDERoa2tDeXl5dd9vb29PdUyYncHDhwAGIOUpRm5w+laHPfccw8++9nP4u6774auZX/0WPLNBhdS8t9Jpqy5uQllHpE3H5geiaPILaO5udnsUmzn9ddfh6ZrcC8rNLuUrGGMwbUsgIarDbae3Z8r+/btQzyRyOvWD4PMGJZKMvbt3YtwOGx2Obawfv16CCGjonSl2aVknCwlR993v7EbnZ2dZpczJTkL1WvXrsXly5dRX18PVVWxadMm3HnnnZg7dy5cLldq44gNGzbgzjvvzFVZWfXW/v0Q3nIwkf1Z24zL2LZtG37xi19g+/btYDz765kyLoF7K3HgwEFompb1x8s33V3d8Cv50Qdn8EmgHTenSNd17HpjF+QKL4Qvv9YhHs013w+hCFolJg07tm9HqSRjlsivUcjxrHK5EY3FaJAmDa2trdi3bx/Ki5dDsvmKYuOZM2s1VFXFxo0bzS5lSnL2ie5yufDoo4/ii1/8Ij784Q9jyZIl+OAHPwgAePzxx/GDH/wAH/rQhxAOh/HpT386V2VlTXNzM5qbmiD5sz9KDQBMyIhEIti4cSMikQiYyM2Hs1QwF729Paitrc3J4+WTnp4e+Gy2LflkfApHT3eX2WXYSm1tLTo7OqHM9ZldStYxziDN9uDgoYN5t3RqJjU0NOBiTQ1WyHLetENOZraQUChJ2EFrVk9qw4YNgA5UzrrR7FKyxu0qQEnhQmzevBkDAwNml5O2rDdz7tq1K/Xft99+O1577bXr7rNq1SqsW7cu26XkVGNjIwBAuO25e2K6uDu59FdTUxOWL19ucjX2oWka+oNB+IrsvWPeaD6Zo6WHNoCZCqNNTJmdX8+F8ShzfOi/2opz585h7dq1ZpdjSbt27QIDsMIBrR8GxhhWSAqOnD2bag8l1+vp6cHWrdtQWrQYLiW/T8TnlK3BmUtXsHnzZvzxH/+x2eWkJb+GySykra0NAMDk/H7S88F/X76s2JIrjDEIwaHm2YQtVdMhy/ndwpBp+/fvhzzLnXfL6I1HrvCAS5wu849DVVXs2rkTC2QFXu6sj+gVSrJVcvhgHBnptddeQzwew9wsbUluJX5vKYoK5uKVVzYgEomYXU5anPWKzaHW1lYwLsCk/B5pYFyCkD2pkwiSHsYYfF4fIon8CtURVYff7ze7DNvo6+vDtWvXIJd7zC4lZ5jgECUunBlcUpKMdPr0aXR1d2Olkn87KE4mMLhm9c4dO2iFmDEEg0Fs2rQJpYUL4XHn76Tm4eaW34T+/j7bbGVPoTpLurq6IGSPM/rhJI/tZuhagd/vQySRXxM8I3EN/oICs8uwjYsXLwIApBJnBSipxIWGq1cRCoXMLsVydu7cCRfnWJjna1OPZ+XgmtW03v31Nm3ahHA4jLnl+T9KbQj4KhDwVWDdupdtsXEUheosSSQSQJ7scDQ5RpOOpsFfEMBAPL9GY0IqUEChOm0XLlxILrtZ7LBQXZrcmvrSpUtml2IpoVAIB/bvx1JJgeSEAZkxLFEUyJxTC8gowWAQ69e/guLAfPg8pWaXk1PzKtaiq6vTFrttOyX15VxyiTmnvCkyWlJvGubOnYuuSP783lRdR1c4gTlz5phdim1UV1dDLlTAJGe9FRsnEdXV1SZXYi379+9HLB7HSpezTrKGUxjHYknG3j17bTEymSvr169HOBzC/Iq3mV1KzhX6K1Hor8QLL7xg+d5qZ72T55CjQjWjUD0dCxYsQF80gXCetIB0h1Womo4FCxaYXYpt1F2uAy903sROrghIPgVXrlwxuxRL2bVzJ4okCRUiP3ZZna6ViguhcAhHjhwxuxRL6O7uxquvvorSosXweYrNLscU8ytuQV9fn+XXraZQnSW6rgOOuXxHoXo65s+fDwDoCOVH60z74L/D+HeRiXV3d6Ovtw+i0JmjkiwgobauzuwyLKOtrQ1nzp7FcklxxlycCcyRZPiEwK6dO80uxRKeffZZxOMJzK+4xexSTFPgK0dxYB5eeuklS28wRqGaEJMYI7qtA/kRqlsHEmCMYd68eWaXYgv19fUAACngzAlpUkBBc1MTXeIftHv3bgBDy8o5GWcMyyUFx44dQ09Pj9nlmKq2thbbt2/H7NJV8LgCZpdjqoWzb0MkEsWzzz5rdinjolBNiEkqKirg83rRHMyPUN08kMCcykp4PM5ZHm4mLl++DAAQhc4M1aJQgaZpuHr1qtmlmE7XdezcuROVkoyAQ7Yln8xKlwuqpmHv3r1ml2IaXdfx85//HLLkxrwK2ijJ4y7E7NJV2L59u2UnOVOozhJaY5NMhjGGpcuWoiWLI9UyZxPezqSWARXLaFfNtF26dAmST3HMpi+jGZMVrfrhmEs1NTVoamqiUephSoSEMkl2dAvInj17UFVVhXnlt0ASzjz5Hm1exVrIkhv/9V//Zcm2UwrVWRIOh6Ezh0w24RJCobDZVdjS0qXL0DqQgKpl5yRs5aj1j0ffzpSBmIbeSAJLly7NyvHz0YXqavBCh7xHjIF7JQhFoKamxuxSTPfGG29AMIalDl2bejzLZQW1dXVoaGgwu5Sc6+3txS9+8d/we2ehvGSZ2eVYhiQUzK+4FdXV1fj9739vdjnXoVCdJT29vWDcGW+QTLjQ29drdhm2tGTJEiQ0HR1hNSvHv63SgxI3h09muHdZAW6rzE5rRstAHACwePHirBw/3wSDQbS1tjpuferhGGPgRQou1lw0uxRTJRIJ7Nm9G4skGS6HbUs+meWKCwzJkw6neeqppxAMBrFk7h1gjtnzIj1lxUtRVDAHzzzzK8vt5kx/qSzp6+0DE874wGTChWB/v9ll2NLChQsBAG1ZagFhjKHAJTDLK+Edld6srSrQNrjyh/HvIRM7f/48AEAqdptcibmkYheu1l9FMBg0uxTTHD9+HP3BILV+jMHLOebJyRYQK17qz5ajR49i9+7dmFt2k2OX0JsIYwyL596ORELFkz970lLtthSqs0BVVQSD/WCSM94kmeRCPB6nLYenYd68eeCcp0KpXbUNJBAoKEBxMX0ApOPEiRPggkMqdcZ7xHjkCg80TcPp06fNLsU0u3fvhlsIzKfWjzGtkF3o7OpKnYjmu2AwiJ/85KfweooctR35VLkVP+ZXvA3Hjh/DTgv13VOozoKmpiaoqgquOGP5GzH47zSWCCPpk2UZcyor0TrYPmFXbSEVCxctMrsM2zh67CjELDeYcPZbsFTiBpcFjh8/bnYppgiFQjh48CCWSjKEw9emHs9ixQWZ89SSg/nuySefRHd3N5bOfTc4d+Yk5nTNLl2JgL8C//VfP0dLS4vZ5QCgUJ0VtbW1AADudsaonfHvrKONHKZl/oIF6I5a5/LVdHRFNFqfOk0tLS1oaW6BXEFLDzLOIGa5cOz4MUtdws2VAwcOIB6PU+vHBGTGsEiS8ea+fYjH7T34MJndu3dj3759mFe+Fn7vLLPLsTzGOJbNew9UVcP/9//9O1Q1O3OTpoJCdRbU1taCcQHukIXameSBkNypkwkyNRUVFegOJ2wbKsIJDeG4itmzZ5tdii28+eabAABlttfkSqxBqfSio73DkauA7N69GwHalnxSyxUXBkIhHD161OxSsqatrQ1PPvmfKPCVY275GrPLsQ2X4seiynfhwoUqrFu3zuxyKFRnQ1XVBXBXkWNm7DLGAFcxzp+vMrsUW6qoqEBC0xGM23MiTk8kOTpQUVFhciXWp+s6duzcAbnUDeGXzasjrsHtduMjH/kI3G43dBOfe8pcP5jgluqLzIXe3l6cPn0aSyXZMtuSx3R9xPMiZpET/XmSDLcQ2Ldvn9mlZIWqqnj88ccRi8WxbN57HJMdMmVW0WKUFi7C888/jwsXLphaC/3lMqy3txcXL1ZD+Jw1aif5Z6Ox8Rqam5vNLsV2jBHe7oj5l66mg0J1+mpqatB4rRHKAr+pdehxDffccw8++9nP4u677zY1VHOZQ670YPee3Xl/eX+4/fv3Q9M0LJOt0/oR1fURz4uoRUK1YAxLhIzDhw4hEomYXU7Gvfjii6iqqsLiOe+C21Vgdjm2wxjDknm3Q5G9+Ld/+zcMDAyYVguF6gw7evQodF2H5J9rdik5JfnnAAAOHz5sciX2U1paCgAIxuw5Ut0/WLfx7yDj27lzJ5jgUOb6TK2DyRzbtm3DL37xC2zfvh1MNvejwLWgAKGBEA4dOmRqHbm0d88eFEsSSi20LbmLsRHPC5dFRtABYJmiIBqL4ciRI2aXklHnzp3Db3/7W8wqWoKyYto8a7okoWDZvPeivb0d//nkf5rWTkmhOsMOHz4MIXsdM0nRwJUCCHchDh2iUD1VRUVFAOwbqoMxDYwxBALOmEMwXf39/dixYweUuV5wxdwgxWSOSCSCjRs3IhKJmB6q5QoPJJ+CDa9uMLWOXOnu7sa58+ct1foBAApjI54XioVqq5Rk+IRIzUnIB8FgEI//2+NwKX4snvsus8uxvQJfOeaVr8WevXtM2zCIQnUGhUIhHDlyFNw3x1JvlLkifHNx7txZdHd3m12KrQQCATDG7Buq4xoKAwEIC424WdHmzZsRi8XgXl5kdimWwxiDsqwA1ReqUVWV/3MzDh06BF3XscRCrR9WxxnDQiHj2NGjiEajZpeTEf/1n/+Fzq5OLJv/XkiC1inPhLnlNyHgq8B//ud/mrLMHoXqDHrrrbcQj8cgFy0yuxRTSIWLoGmaY9YTzRQhBAoDAdtOVAzGVBQVF5ldhqXFYjG8tvG15IhsIX14jsW9sADCJeHll182u5SsO3DgAAolCSV0IjoliwdbQE6dOmV2KTO2e/du7Nm7B/PK16LAW2Z2OXmDMY5l89+DRNycZfYoVGfQzp07IVwF4G5n9pYKVwDCU4odDpvFnwmFhQGEbBqqQ3EdRUXOaneaqp07d6Kvtw8eGqUeF5M4lEV+HDp8GA0NDWaXkzUDAwM4deoUFglrtX7YwVxJhotzHDhwwOxSZmTk8nm0a2KmuRQ/Fs0xZ5k9CtUZ0tLSgnPnzkEEFjn6jVIKLMLV+npas3qKAoVFtg3VYRXUTz2BWCyG3/3ud5BL3JDK3GaXY2nuZYXgEsdzzz1ndilZc/LkSaiqikW0LfmUCcYwX8g4cvgwNM2e75e6ruOJJ55ILp833x7L5yXU2IilFhNqzOySJlVWvASlRYvx/PO/zenGdNb/a9rEjh07AABy4SJzCzGZXLgAjIvU74OkJxAIIGTPFfUwENdQWFhodhmWtWXLFnR1dcFzY7GjT7jTwV0CriUFeOutt/J2h9bjx49D4RwVEm34Mh3zZRm9fX24fPmy2aVMy7Zt23D69GksnP12uBV7LJ+nqvERSy2qqj2Wvlw8512QhIInnngiZ20gFKozQFVVbNu2HZK/Elw2d6ksszHhgvDPw85du/JmMkkuFBYWYsCGExUTmo5IXKWR6nFEIhG88MILkMs8kMtpW/J0uJcXgisCv3n2N2aXknG6ruP4sWOYKyQIOsGalvmDI/wnTpwwuZKp6+zsxNNPP42AvwLlJSvMLidtQsgjlloUwryNq6ZCllxYNOddqKurw/r163PymBSqM+DEiRPo7u6CVLjE7FIsQS5agnAoZPu+t1wqKipCOK4ioVljs4V0DQy2rBQXU0/1WF599VX09fXBcyP9ftLFFQHXsgCOHjmKc+fOmV1ORjU2NqKjsxPzZXuEEivycY5SScbxY8fMLmXKnnzyScSicSyZe4etrlpJQhmx1KKdViopLVyIksKFeP7553Ht2rWsPx6F6gzYunUrhOyGVOCsDV/GI7zlEK4CbN261exSbMMIpQM266s2lgE01tomQ7q7u/HSupegVPogl1Iv9VR4lhVCeCT88pe/NG0Th2w4e/YsAGCuZJ9QYkVzhcCFC9W22oHz2LFjOHz4MOaWr4XHRVf2cmnxnHcBOsdTTz2V9ceiUD1Dvb29OHz4CHjBQltMOMgFxhhEYBHOnj1ryjqRdmSEarutVW3USyPV1/vtb3+LaDQG7xr63UwVkzjcNxTh4sWLeOutt8wuJ2Oqq6vhEQKFnD4rZmK2JCOeiOPKlStml5IWVVXx1FNPweMKoHLWDWaX4ziK7MHc8ptw7NgxHD9+PKuPRa/sGdqzZw80TYVcuNjsUizFmLBp1q5GdlNSUgIA6Ivaa7ZifyxZr1E/SWpoaMDWrVvhWlwAUUCjktPhWlgAqdCFZ371jK1GJCdyoaoK5Zzb6tK/FRmTPKurq02uJD1btmzBtWvXsGD228E5rU1uhtmlN8DjCuCpp57K6qRFCtUztGPHDghPCYS7yOxSLIXLPghfBbZv32HbpY9yqby8HADQG7XX76onokIIQSPVo/zqV78CBIN3Ff1eposxBs/qYrS1tmHz5s1mlzNjwWAQ1xobUWGTSV5W5mMcPiHhwoULZpcyqXA4jGeffRaF/tkoDsw3uxzH4lxg/uxb0dDQkNXVyShUz8DVq1dx+fJlSIGFZpdiSXLhIrS3t9nijc9sgUAALkVBT8ReI9U9URWzSktpi/Jhzpw5g8OHD8O9IgDupt/LTMgVyVVTnv/t8+jv7ze7nBmpr68HAMyipfRmjDGGWZzhig2W1du2bRuCwSDmV9xKVyhMVhJYAL93FtatW5e10WoK1TNg9PpJBQtMrsSaJP88MMbzqicyWxhjKC8vQ4/N2j96oxrKKyrMLsMyNE3Dfz/1FCSvDPcyWrt7phhj8N5UgtBACC+88ILZ5cxIc3MzAKCILv9nRCEXaG5utvSV0Hg8jvXrX0HAX4ECH21FbjbGGOaUrUFLS0vWViejUD0Db775JoS3DFym9WfHwoQM7puNN998y9JvfFYxZ+48dEbs83vSdR0dYQ1z59KqN4bdu3fjcl0d3DcWgQl6e80EqdAF18ICbNq0CU1NTWaXM21NTU3gjKGAJilmRKEQiMXj6OrqMruUce3duxddXZ2YM2uN2aWQQSWB+fC4A1j30rqsrCxEr+5punbtGq5evQqpYJ7ZpViaXDAfXV2duHjxotmlWN6CBQvQGUpAtcla1QNxDeG4ivnzqU8QSG5H/j+//jXkYjeU+X6zy8kr3huLoTMdv/7Nr80uZdqampoQEBI4tQBkhDHib+UTrU2bNsHnKUYRLbdrGYxxVJauRm1dbVYmulKonqYjR44AAIXqSUgFcwHGUr8vMr758+dD03V02aSvun1wX/UFC6j9CUh+gHZ1dsKzhrYjzzTukeBaFsBbb75l2xP0np4eeGGPE2Y78A6O+Pf29ppcydiamppw6dIlzCpaSu8HFlNatAicC+zZsyfjx6ZQPU3Hjx+HcBc6flvyyTChQHhmZX1tyHxgjPi2DSRMriQ9baFknTRSDfT39ye3I6/wQi6jdrBs8CwvgnBLeOaZZ2y5IUxoYAAy7WWQMcpgUA2FQiZXMjYjsJUWLTK3EHIdSSgoKpiHvXv3ZnzCIr3CpyEWi+HsuXPgXpqglQ7hrUBtbS36+vrMLsXSFixYAME5moP2WJO3ORhHoKCA1qgGsG7dOoRCIdroJYuYzOFaWYizZ8/imA23qA4NDKSCIJk5q4fqN97YjUL/bLho4M2SZhUtRl9fH06dOpXR41Konobz588jEY9D8s02u5QhTJr4tokk/2zouo7Tp0+bXYqlKYqChYsWoiloj5HqpqCKFStXOP7SZnd3NzZu2ghlgR9SocvscvKae3EAkk/Gs88+a7vR6nAkAhnOfq1kkvG7DIfDJldyvfb2djQ3N6E4QK1xVlVcMA+cCwrVVlBVVQUAEB7rLJEjFcyZ8LaZuLsEjEup3xsZ3/LlK9A0oFo+MMRUHe2hOJYtW252KaZbv3494vE4bfSSA4wzuFYWora2FkePHjW7nCnhjEGnnuqMMX6T3IKrqRh7MxR4y02uhIyHcwGfpxTnz2c2l1jv2WgDNTU1EO5CMAvtjCUXLQOT/WDCBdfs2yAXLTO7pBTGOLi7CBcv1phdiuUtX74ckbiKzrC1Jys2BePQ9WS9Ttbd3Y3XN78O13w/hN867wf5zLWgAJJPxnPPPWf5k8/hPB4P4jaq1+qM36XHY705DFVVVRBCgs9DJ9pW5veU4dKlGsTjmWu5pFA9Rbqu4+LFGjCXtV4sjDFw2QPuCkApXma5S/LcVYK6utqs7WKUL2688UYAQH1fzORKJlbfGwNjDDfccIPZpZhqw4YNiMfj8NAodc4MH622U2+1x+tFnEaqMyYG64bq6uqL8LlLwWhiqqUV+MqQSCRwOYM7c9JffIq6urrQ29sD4abJWVMhPCWIxWK4du2a2aVY2rx581AYCKC+19qTFet741i4YD4KCgrMLsU0kUgEW7ZugTLHR6PUOeZaUADhkbFx00azS0mb1+dDlEaqMyZm4ZHqzo4OuBRaq97qXHLyb5TJDYQoVE9RS0sLAIArzg0T02H8vlpbW02uxNoYY1i9Zg2u9ll3sqKq6WjoT2DNTTebXYqp3nzzTYQGQnAvCZhdiuMwzqAs9OH48ROp7b+trry8HP2UqTOmb/CqZ0WFtVbh0nUdff19kITb7FLIJGQpObE8k2udU6ieovb2dgAAk70mV2IvTEr+vozfHxnfzTffjJ5IAp1hawbra/1xxFUNa9Y4d+tdXdexcdNGSAEXpFn04WkG1+Lkyczvf/97kytJz4IFCzCgJhDVNLNLyQtdg6F63jxrbcAWiUQQj8chS/S+YHXS4N8ok8v9UqieIiMUcgrVU8IkNxjjFKrTcOuttwIALnVbs6/6UncMnHOsXbvW7FJMc+XKFdTV1kFZ7Lfc/AWnEB4JSqUX23dst8VcDWPn0S7NurVKo5b8G33bSro1FbNKS+H1WuuzOBZLvm/zwW3UiXXxwZ73aDSauWNm7EgO0d3dDS5kME49lFPBGINQvBntXcpXlZWVqJxdgUvdmXuhZ1JtTwyrVq6E3+/cnsETJ04AAFxzaGMHMylzfQj2BzM60ShbFi5cCADoTFjzChQALFSUCW9bSaemYuGiRWaXcR2/P3miHU9EzC6FTCKhJj9ji4qKMnZMCtVTpOs6QCNTJMvefts7cKU3gbhmrSbMYExDU38ct7797WaXYqqTJ09CCrjAPdbZZMmJ5LLk5duTJ0+aW0gaysrKUDZrFq4lrDsJebXiRiHn8DCGO70+rFas2cIQ1FR0JRK46aabzC7lOkIIBAKFiCestykNGSkWT/6Nioszt3oTheop4pyDVkWaPrpUnp53vOMdiKsa6izWAlLdlRx9ecc73mFyJeaJx+M4e+4spDLaPdFs3C1BKnTZIlQzxnDbO96Ba2oCqkVXAWGMwcc5ioXAapfHsu/XVwfXFb7ttttMrmRsxcXFqcBGrMs48aGRatNZ8w3R6uy0UYPZbrrpJng9HlR1WusSYlVHFBXl5Vi8eLHZpZjm6tWriMfikEqtOYrnNKLEheqL1WaXkZbbbrsNcU1Ds4VHq+3gajyG0pKSVJ+61SxdugQD4XZoOk1KtbK+gVZwzlOtWZlAoXqKvF4vNDUBXbNuX5wV6boOXY1ZblKJVcmyjHe+612o7opDtUgLSDih4XJvHHe8+92WHcHKBWNSC5Pp7dMKuMwRj9kjpN58882QZRl1cWtdgbKTuK6jUU3gne96l2Xfh971rnchnoiif4CWkLWy7v5rWL16dUbnB9GnwhQtWrQIgA4tmrklWJxAjw9AU+ODvz+SjjvuuAPhuIorvdb4AL7YGYWq6bjjjjvMLsVUxpa2TFjzA91xBIOqqtBssFSd2+3Ge97zHtTEY7Rl+TRdjEUQ0zS8733vM7uUcd1yyy2QJAndfQ1ml0LGEYn2IRTuxrve9a6MHpdC9RQZl721aI+5hdiMGukBAEe3DUzVrbfeCq/Hg9Nt1mgBOdUWQdmsWVixYoXZpZjKCNXgFKqtgA3+HRIWXlVjuA9/+MOIaRouxqzxurYTXddxLhbDksWLsXLlSrPLGZfH48Ett9yCrr56qHRV25LaumsBAO985zszelwK1VM0e/ZsKIoCNdJtdim2okW7wRizbA+cFblcLrz3zjtR1RlDVDV3FK4vquJyTwzve//7k5N1HczYFlmPWHe94cmMHmW386i7FklAkiTbPC9XrlyJJYsX41wsRvNMpqhFTaAzEceH773Xsq0fhj/6oz9CNBZCc/t5s0uZMc6lCW/bTSweQkvnebz73e9GZWVlRo9tj3chCzE2vVCDDdAtvIi/lei6DrX/KlatWgW3myZ3TcX73vc+xFQNVR3mrll9ui0CfbAep1u5ciU8Xg9izSGzS5k2pdI34W270HUdidZI6nK7HTDG8OF770VnIo4GmrA4JScjYXg9Htx1111mlzKp1atX413veheaOs7afiWQ4sC8CW/bTUPrSQA6/vzP/zzjx6ZQPQ333XcftHgEiX7ql0qHOtAMNdqP+++/3+xSbOeGG25A5ewKnGw171Kxrus42RbBqpUrMWfOHNPqsApJknDb229DojVs25FG1+ICcJ8E5uLw3TILrsUFZpc0LWp/HIlgLOOXcLPtfe97H8rL/v/t3Xd4VNXW+PHvZEhIIyShBwJCCAkSEpBeApcmPUDoVRADSFG4FJEmVaVJC3r16uu9It6rLwQpIqAU6VWkSCe0kEIKKdPb+f3Bj7xG6SmTZNbnefJAZs7ss2bO5MyafdbeuxzHDEX3PVTQEixmbppNRPbuXWQ6Z4YPH46iWIlL+s3eoeRKBd8gXF1K4ax2pXrlplTwLbylN0+j1aeRnHaNrl275nkvNUhS/ULq1atHpUp+mO9ftXcoRYIp7Sre3j40a9bM3qEUOSqVio6dOnMzw0SS1j69WrHpJlJ0Fjp36WKX/RdGjRs3xmqwYEkrnKtePo1KpcLJrQTqUi641vAq9JfSH8cUrwXyvi4yvzk7OzN02DBSLGauyUwgT6UoCsf0OrxLl6ZHjx72DueZValShW7dupGUdoWU9MK/6ufjqFQqXJzdcXMtTcUyQUX2fGGxGLl65xc8S5Wif//++bIPSapfgJOTE927d8OqT8WiSbR3OIWaVZ+KVZtAly6di8zl2cKmQ4cOODs7czzePpcQj8fr8SpVipYtW9pl/4VRo0aN8Czlif5MKkohmfLQ0Vi1ZoxXMwkLC6NMmTL2Due5tWrVipeqVeOEUV9oF4MpLG6ZTSRYzAwaPLjI9FI/NHz4cGrXrs31uENodKn2DsdhKYqNK3f2YzJrmTlzBl5eXvmyH0mqX1CHDh3w86uMKfE4NkvR7K3Kb4rVjDHhKGXKlJXSj1zw8vKidevWnE02ojcX7IDF+wYrV9KMdOzUCRcXlwLdd2Hm4eHBWxPewpxuRH9BBi0XNMWmoD2ZTElnF9566y17h/NCnJycGD5iBBkWC78ZinbNbX6yKAqHjXoqVaxIhw4d7B3Oc3N2dmbGjBn4+vhw5fbeIl9fXVTdSjhFRlY8b775JnXq1Mm3/UhS/YJcXV2ZNm0q2EwYE49LXdwjGJJ+xWbSMHXqlDydXN0Rde/eHbPVxsnEgj0hH7urQ+XkROfOnQt0v0VBs2bN6NChA/or6ZhT5IOyIOmvpGNONTD2zbGUL1/e3uG8sAYNGtCiRQtOGfXct8rUa49yQq8jw2Jh/IQJRfZqp7e3N7PnzEbBzOVbuzFbZDrFghSf/DsJKRfo3r07HTt2zNd9SVKdCwEBAQwf/hqWrLuY06/ZO5xCxZxxC0vGDfr375+v3wodRY0aNahfvz5H4/WYrAXzBU5jsnEqycDf/vY3ypUrVyD7LGqioqKoUKEC2pMpWO1U8+5oTIk6DJfSCQ8PLxKzQDzN6NGjcXVz4xedVjpn/uSexcwZo55XX32V0NBQe4eTKzVq1GD69OkYTBlciN2J0aS1d0jFnqIo3E78lVsJJ2nWrDkjR47M931KUp1LERER1K9fH1PSacyyehIAFk0CxsQT1AoKYsCAAfYOp9jo378/WpOVXwuot/roXS0Wm0Lfvn0LZH9FkZubG9OnT8fVyYWsXxKw3JdSsPxkuJVF1pEkqlWrxtixY4vsgKk/8vHxYdTo0SRYzJw3Sg/mQ1ZFYZ9eh7e3NyNGjLB3OHmiUaNGzJ8/HxsGLsTuQG/IsHdIxZai2Lhx9yh3753j1Vdf5Z13pqFWq/N9v5JU55KTkxPvvPMOtWrVwhB/GHPGTXuHZFeWrLsY4g5Q1b8Kc2bPLpA3saOoU6cOISEhHLqrx5zPg+P0ZhsnEg20bNmSKlWK9pyk+a1mzZosXbIUb8/SZB1MxJRUdOevLqwURUF/+T7aU8mE1q3L4g8XF6uSsjZt2vBK/focNehJkzIQAI7rdaRazIwbP75YHeu6devy4Ycf4uLqxO83dqDRpdg7pGLHZrNy5fZ+ktKu0LdvX8aPH19guYgk1XnAw8ODBQvmE1KnDob4o5jTY+0dkl2YM+9guHuIGgE1+OCD9yldurS9Qyp2BgwYQJbRwqmE/O2tPhSnxWS10a9fv3zdT3Hh7+/P8mXLqVKpMpojSRhvZ9k7pGJDURS0Z1LR/X6fVq1bMXfuXNzd3e0dVp5SqVRMnDQJj1Ke/KTTYHbwMpBbZhO/GfV07tyZJk2a2DucPBcQEMDSpUspXboUv8fuICntqpT+5BGDKYvfY38kLeMWr7/+OsOGDSvQK1p2SaqHDRtG165d6dGjBz169ODMmTNs3bqVLl260KFDB9avX2+PsHLFzc2NuXPnUr9+fQwJxzGlXXaoPxJzeiyGu4cJCqrF+4sWUapU0VxMorALCwsjNDSU/XE6DJb8mQkk02jlaIKeVq1a89JLL+XLPoqjMmXKsHjxYuq8XAfNyWS0Z1JQ8ukYOQqrzkLWwUSMsZn07NmTyX+fjLOzs73Dyhc+Pj5MmTqV+xYLB3Uae4djNxqblT16LdWqVeONN96wdzj5xs/PjxUrPqJu3RBi4w5zPe4QVpuMy8iNtIzbnL/2Azb0zJw5k169ehV4DAWeVCuKQmxsLJs3b87+qVixIitWrOCbb75h8+bNfPvtt1y7VvQG/pUsWZLZs2fTtGlTjEmnMcQfRSnmfySKzYoh4SSGhOOEhtZl/vz5xa4XqbAZPnw4OpOVw3H5U2aw77YWBSeGDBmSL+0XZ56ensyfP59u3bphuJ5J5t54zKlSJ/u8FEXBcDOTzN13UWXaGD9+PCNHjsTJqXhfXK1Xrx59+/XjksnIFQesr7YpCrt1GhS1munTpxf7aTy9vb2ZN28egwYNIiU9lvPXt6MzpNs7rCLHpti4GX+Sy7f2UrVqFVatWkXTpk3tEkuBn6FiY2NRqVRERUURERHB119/zeHDh2natCne3t64u7vTsWNHduzYUdCh5QlnZ2feffddhgwZgjXrNvpbP2M1Fs/BCDaTBv3t3ZjTr9G7d2/mz5+Pm5ubvcMq9gIDA2nZsgVH4/Vkmax52nayzsJvSQa6dOlCxYoV87RtR+Hs7Mzo0aNZuHAh3q5eZO6PR3suFcUqvdbPwqa3kHUkCe2vKdSuFcza6Oh8nwarMBk0aBB1Xn6ZX/Q6ki2OVV99WK8l3vygjtpRxnKo1WoGDhzIvHnzUJewcf76du6lXXOoK925YTBmcSF2Jwkpv9OlSxeWLltq18+uAk+qMzMzadasGWvXruVf//oX//3vf4mPj88xZVf58uVJSkoq6NDyjJOTE/3792fBggV4lAT9zZ+K3QBGc1Yc+pu7KKkyMnv2bIYPHy6DEgvQ0KHDsCqw91beTsv00w0NJUuWlFrqPBAWFsba6LW82uFVDFczyNybgCXN8Xofn5WiKBhvZ5G5+y6kmYmKiuL99993uC93arWad6ZPp7SPNzt1GnQ2x/gydtFo4JzRQEREBG3atLF3OAWufv36rFmzmqCgWlyPO8TlW3tkoZgnUBSFxNTLnL22FYsti6lTp/Lmm2/avTyswJPq+vXrs2TJEtzd3fH19aVPnz6sXr36L9sVh6mSwsLCWLN6NbWDgzDEH8UQfwzFWrTLQRSbBUPirxjiDlK9elVWr15F48aN7R2Ww/Hz86Nrt26cTtKTmEfzI8feN3IlzUi//v3x9vbOkzYdnbu7OxMmTGDu3LmUKuFOxr54NKeTsRnz9gpDUWfJMJF1MBHNyWRqVq/JmtVriIiIKPblHo/j4+PDrNmzMTqp2KXNKvbLmCdazOzXa6kXFsbrr79u73DspkyZMrz//iJGjhyJRp/E2aubSUm/Ib3Wf2I0abh44ydu3D1KSEgdoqOjadWqlb3DAuyQVJ88eZIjR45k/64oCpUrVyYl5f+mlbl3716RXiXrj3x9fXn//UX07dsXS+ZN9Dd3YNHmTy+8c+kaOJeukS9tA1j1qehv7sJ8/wrdu3dn6ZIlVKhQId/2J55s4MCBeLh7sDNW88STbv0KbtSv8OSyHJuisPOmlvLlyhEREZHXoTq8Bg0a8PHaj4mIiMB8S0vGT3Hor2eg5PPUiE/iWq0UrtXsO6DYZrKi/S2FjD1xuOidGDNmDIsXL6Zy5cp2jaswqFmzJhMnTSLBYuaA7sl/43kp2MWVYBfXAtkXPBiYuFOnoXz58kx75x2Hv+KpVqvp2bMnq1at4qXq1bh6ez9Xbv9i91UYy/sGUt430K4xKIrCvbSrnL26FYM5jbFjx7JgwfxClS8WeFKdlZXFkiVLMBqNaDQaNm3axNKlSzly5AhpaWno9Xp27dpVaL515AW1Ws2wYcNYsmQJ5Xy90N/eiyHxFIotb+vlnL2r4+xdPU/bhAeDEY33zqC7+TPeni4sXLiQUaNG2f0yi6Pz9PRk0ODB3Eg3cSXN9Njt6lVwo95TkurTSXqSNGZGvP56sR8cZC+enp5ERUWxZs0a6tYOQXcmlcw98Zjv2ecSb8lqpShpp6RaURQMsZlk/nQX440sunTuwmeffkbXrl0dPqn6o/DwcPr27ctFk5HfjAXzPgkq6UpQyYJJqk2Kje1aDYqzM7PnzJFZo/7A39+fpUuXMGzYMDI1dzlzdTMp92Pt1mtdzieAcj4Bdtk3POidvnRzN9fjDhMUHEh0dDSdO3cudFUNJQp6h23atOHMmTP07NkTm83GoEGDaNCgAZMmTWLYsGGYzWb69OlT5JckfZTg4GDWrFnNV199xdatW7HpkihZsTFq97L2Du2xrIb7GBOOYTWk06FDB9544w2Z3aMQ6dy5Mz9s28ZPN5Op6eOC2un5TzBGq429t3UEBwXRokWLfIhS/FHVqlVZsGABR48e5Z+f/5Pkgwm4+HngXtcXtUfx/6JqTtGjP5uGOd1ISEgIo0aNonr1vO8MKC6GDBlCQnw8Bw8dwstJTYBLSXuHlCdsisJPWg33bVbmzphN1apV7R1SoaNWq+nbty+NGzdm1apVXL16gJT0G1Sv3ISSLsVnQZwnURQbiamXuZN0mhIlnBg1ahRdu3YttKVhKqWIF+vExcXRrl07du/eXaRGC587d46PVqwgJTkFlzJBuJSti8qp8PTQKIoNU8oFzKkXKF26NG+9NYFGjRrZOyzxCMeOHWPhwoV0CShFY7/n/8Kz56aG/Xe0LFu2jKCgoHyIUDyOyWTi+++/59tvv8VsMVMywAu3YB+cnAvnB0ZuWDVmdOfTMMVrKVO2DFFvRNG8efNC19NUGBmNRmbOnMn1K1eI8PSiQomi/eVLURT267RcMBkYP368Q83u8qKsVivbtm3jq6++wmpV8K9Qn4plgov134/OcJ/YuCNk6ZJ55ZVXGDduXKEo9XhS3ln8ztxFRN26dVkbHc2rr3bAlHoJ/a1dWPVp9g4LAKsxA8OtnzGlnCc8vCUff7xWEupCrHHjxoTUqcO+O8+/IEyG0cqReD3h4S0lobYDFxcX+vXrx2effUbbNm0xXMskc1ccBjvXW+clm8mK9mwqGT/HoUqzMHToUD79x6e0aNGiWCcEeenhGghlypVjh05DprVoD3Q9Y9RzwWSgd+/eklA/I7VaTY8ePVi7di0hIXW4GX+c32N3FMt5rW02K3cSf+Pc1W0oTnomT57M3LlzC0VC/TTSU10InDx5klWrVpORkYFzmZdxKfsyKlXBf99RFBvmtCuYUs7h7u7GhPHjpRygiLh27RqTJk0i3N+Ddi89+2XBzVcyOJdq5pNP/uFwU5cVRteuXeOLL77g/PnzlPAqiVuID84V3Ipk8qnYHtRNGy9nYDNZ6dChA0OGDMHHx8feoRVZcXFxTJk8GReTiZ4eXrgW0kvgT3LNZOQnbRYtWrRg2rRphfYyfmGmKAp79+7ls8/+iV6vp3K5UPzKh+Bkh7whr2XpkomNO4zOkE7r1q2JioqidOnS9g4rB+mpLuQaNmzIxx+vJTw8HFPKeQx2WDDGZtJguL0X473faNSwAZ98/LEk1EVIzZo1CQ9vybF4PVrzs/VWp+otnLlnpEuXrpJQFxI1a9bk/fffZ+bMmZT18CHrcCJZhxKxZD5+IGphZErUkbn7LrqzqYQE12HVqlVMmDBBEupcqlKlCrPnzEED7NBmYSlifWLxZjN7dBpqBwfz97//XRLqF6RSqWjbti2ffPIxzZo15U7Sac5f+wGtPtXeob0wq83CzfgT/H7tR0q6OTFnzhymTJlS6BLqp5Ge6kLm0KFDREdHo9MZcKnYEOfSL+X7Ps1ZcZgSjuPirObNN8fQpk2bItkz5uju3LnDuHHjaF7ZjQ7Vnz6KftPlDC7et/L5F19IslMImc1mfvzxR9avX4/eoKdkTS/cg31QlSi8iYhVa0Z3NhVTgg4/Pz+ioqJo0KCBnE/y2IEDB1iyZAkBzi508ChVJF7f+1YL32uy8K1QgaXLluLl5WXvkIqNw4cP8/HHn5CZmYFfuRCqlA/DqRCN0XqaDE0iN+4eQW/MpFOnTgwfPhwPDw97h/VYT8o7C3z2D/FkLVq0oHbt2ixevIQLF45i1aVQskL9fBnEqCg2TMlnMaVeIiCgJu++O13mnS7C/P39ad26NYcO7KdZZXc8XR7/nknRWTibbKBnz16SUBdSzs7ORERE0Lp1a/71r3/x888/Y47T4Rbig0tlj0KVSClWG/qrGRguZ+Bcwpnhw4cTEREh027mk/DwcJKTk/nyyy/x1Oto7l54ExAAne3B1HklPT2YN3+eJNR5rHnz5oSGhvL555+ze/du7mfeJqBKSzwL8cxi8KB3+lbCSZJSL1O+fAVmvf1OkZ/5rfB2eTiwhwvGREZGYk6/hv72HmzmvF2O2mbRo7+9D1PqJTp16sTSpbKQS3EwcOBALDaFo/G6J253KE6Li7MLkZGRBRSZeFGlS5fm7bffZunSpfhXqIzm+D2yDiVizSocJSEPSj3i0V+4T/OmzfjHJ5/Qu3dvSajzWa9evejSpQtnjHp+L6A5rF+ERVHYoc3C4KRiznvvSalZPvH09GTixInMmzcPV/cSnL/+I3H3zqIohXOZe40ulfPXtpGUepmIiAjWro0u8gk1SFJdaKnVakaMGMGMGTNwVnTob+7Cok3Mk7atuhQMN3fhZE5n0qRJjBs3Tj4Aiwk/Pz+aNG7Mr0lGzI+ZPUJrtnEu2Ujbdu1kOfIiJDg4mJUrVjJmzBicNSoydt9Fd/G+3WYJsRmtZB1LIutwIuW9yrJgwQKmT59OuXLl7BKPo1GpVIwaNYoGDRpwQKfjtrlwfMn6I0VR2K3N4p7FzJSpU6lVq5a9Qyr2XnnlFdaujaZF8+bcSTzNhdhdGEwae4eVTVFs3L13jvPXt1PSTc3ChQuJiorC1bXgVvHMT5JUF3LNmjVj1aqVVK5UAUPcfixZ8blqz6K9hyFuH2V9vfjoo+W0bds2jyIVhUW37t3RmaycT370sra/Juqx2BS6du1awJGJ3FKr1XTt2pVPP/2U1q1ao794n6z9CVg15gKNw5SkI3NPPNYkA0OHDiU6Opp69eoVaAziwfth2rRpvPRSNX7SaUi15O0qvbl1TK8j1mzi9ZEjadasmb3DcRienp5Me2cakyZNwmTN4NzVrSTfj7V3WBhNGi7c2MXtxF9p3rwZa9dGExYWZu+w8pQk1UWAn58fixd/SPWXqmO4exBL1t0XaseiTcIQt59KFSuyZMkSXnrppbwNVBQKoaGh+FepwvEE/V+WtLUqCicTDdStG0K1atXsFKHILW9vbyZPnsy0adNwNjiRuTcew62sfF/CWLHa0J5NJetQIpXKVGDFRyvo16+fXOmyI3d3d96bOxfP0qXZrtOgsxWOy/0XjQZOG/V06dKFHj162Dsch/NwhpA1a9ZQMzCAa3cOcD3uMDabfeY4T8+K59y1bRjNGUycOJF33nmnWC5LL0l1EVGqVCkWLVpIjYAaGO4ewpwV91yPt2gTMcQdoHLlSnzwwfv4+vrmU6TC3lQqFV26diUhy8w9Xc4T6K10ExkGC127drNTdCIvhYeHEx0dzctBtdGeSkZz/B42U/58aFoyTGTuS8BwLYNu3bqxcuVKWV68kChTpgxz3nsPk5OKnbosrHae1CvBYma/Xku9evUYNWpUoRpU62gqVqzIhx9+QN++fbmXdpULN3ZhMhdcDb6iKMQn/86lGz9Tya8ia9aspl27dsX2PSFJdRHi6enJooULCQysifHuYSyaZysFseiSMcQdwL+KHx+8/77M9uAAHi7/fDElZwnIhVQjJV1caNCggZ0iE3mtXLlyLFq0iBEjRmBNNJC5Jz7P57U23tWQuS8ed0ry3nvvMXr0aEqWLJmn+xC5ExAQwMRJk0g0m9mv0+T7VYvH0dis7NJpqFChAu+88w5qddGZ2q24UqvVDBs2jGnTpmE0p3P++g9odCn5vl+rzcK1Owe5lXCSps2asWzZUipVqpTv+7UnSaqLGA8PDxYsWIB/VX+MCcexWR5dN/uQYjVhSjhK+XJlef/992VgmoPw9fUlOCiIS2n/l1zZFIVLaSYaNGxYbAaFiAecnJyIjIxk2bJleLq4ozmQiDntyeeGZ2WIzURz7B5BgbVYG72Whg0b5km7Iu+Fh4fTr18/LpmMnDfmzfF/HmZFYYdWg83Zmdlz5uDp+eyru4r8Fx4eztKlSynl5c7vsTvytc7aZNZxIXYnKemxDB48mOnT38HNzS3f9ldYSFJdBLm7uzNt6lScsGBMOP7EHglD0q8oFn2RXJlI5E7zFi1I1JhJ0z8YvBSXZUZjtNK8eXM7RybyS82aNVm2dBllvH3RHEzElPTkqRWfRFEUdJfuo/0thYYNG7JgwQL5Ul4EDB48mMaNG3NIryXeXHADWBVFYb9OQ4rFzLRp0/D39y+wfYtnV6NGDVauXEFwcDDX7hwgKfVKnu/DaNZy4cZOzNYsZs6cyYABAxxm9UzHeJbFULVq1RgxYgQWTTzm9OuP3MaceRtLxk369+9PcHBwAUco7K1Ro0YA3Mh40Ft9I/3Bv1L6UbxVrFiRZUuX4V/FH82RJIx3nn86LUVR0J1NRX/hPm3atGHmzJlydaOIcHJyYvLkyVSoUIHdei36Ahq4eMlk5IrJyMBBg7LPPaJwKl26NAsWzOeVV14h9u4RklIv51nbRpOWi7G7UDCxYMECmjZtmmdtFwWSVBdh3bp1IywsDNO937BZcg48UKxmTEmnCAysRb9+/ewUobAnPz8/Snl6EJf5oLcqLtOMf5XKcknWAfj4+LD4w8XUDq6N5sS95+6x1l+4j+F6Jj179mTixImUKCGL7xYl7u7uTH/3XYwq2FMA9dWpVgsH9VpCQ0Pl86aIcHFxYebMmTRo0IDYu0dJTL2U6zaNJg0Xb+wEJzMLFiygdu3aeRBp0SJJdRHm5OTE2LFjUWwWzPdz9labM25gsxgZM2a0fCA6KJVKRVBwbeI0VhRFIU5jJbj2y/YOSxQQDw8P5s2bR9WqVdGdTMGqe7Y5jE0JWvSX0+nQoQOvv/66w1y2LW4CAgKIGjWK22YTp/NxxUWzovCTToOnlxdTpkyRgYlFyMPEulGjRty4e4x7aVdfuC2zxcCFG7vAycrChQsd9uq4nC2LOD8/Pxo0aIAl4zqK8mAqLUVRsKRfpVZQkKxg5eCCg4NJ1pqJ11jQm60EBQXZOyRRgFxdXR+syqoqgfb4vaeuvmjVmtGeSqF69eqMGTOm2E575Sg6depEeMuWHNfruGfJn/rqQzoN6VYrU6dNk5mliiBnZ2feffddwsLqcSP+2AvNCqIoNq7e3o/VZmDhwgUOnXdIUl0MREREYDPrMaVcxKJNxHz/ClZjFj0iIuwdmrCzhwv8XEo1Asi8wg6ocuXKTJw4EXOaAd3Z1Mdup1htaI7fo2QJF2bMmIGLi0sBRinyg0qlYtz48fj4+LBXr83z+avvmE1cNBmJjIwkNDQ0T9sWBcfZ2Zlp06bi6+vL1du/YH7KrGJ/djvxNBmaBMaOHevQCTVIUl0s1KtXj8qVq2BKOY/+9j6MSafx9S0jszyI7DlBr9835vhdOJYWLVoQERGBITYTy/9/L/zZw/v+PunvVKxYsYAjFPnFw8ODCW+9RZrFwknDi88G82cmxcYveh2V/fwYNGhQnrUr7MPLy4sZM97FYjNw7c4BFOXZBrimZtwiPvk8nTp1on379vkcZeEnxbbFgJOTEx9++AF37/7f8uUVK1aUWmpBxYoVUalUxGsseHi4F8tlYcWzGTx4MD/v/hn9pfuUapYzaVYsNoxXMwkLC3O40fqOoGHDhrRt25a9e/ZQw7kk5fLgs+GITofGamXOpElyVaOYCAwM5M0332TNmjUkplyiUrknj8ExWwzE3j1CzZo1GTVqVAFFWbhJT3Ux4e3tTZ06dbJ/ypQpY++QRCHg4uJCubIP3guV/SrbORphT+7u7vTq2QtTgg5Les7easPNLKwGCwMHDrRTdCK/RUVF4e3tzS96ba5nA0m0mLlgMtCjZw+HHZBWXHXo0IF69eoRl3z2qWUgd5J+w2YzM3HiRJydnQsowsJNkmohirl3Z8zk7bffZuKkSfYORdhZt27dcHN3Q/NrMtozKdk/xisZhNQNoU6dOvYOUeQTT09PXh85kmSLmcumR5cAPQtFUTik1+Hj7S1lH8WQSqXijTfewGo1EZd05rHb6Qzp3Eu7QqdOnahWrVoBRli4SVItRDFXs2ZN2rdvLyucCTw9PRkyeAglTSVQxZuzfzxc3Hlt2Gv2Dk/ks1atWlErMJDjRj3mF+ytvmoycs9i5rXhwx1i2WlHVK1aNTp27EhS2mV0hvRHbnMr4SSurm7yxepPpOhWCCEcSEREBBEyM5BDcnJyImrUKKZOncppg47Gbh7P9XizonDMaCCgRg3atGmTT1GKwmDIkCHs3r2HxJSL1KjSLMd9OsN90rPuMmzYMEqXLm2nCAsn6akWQgghHERwcDDh4eGcMRqfewnz3416NFYLb0RFyaJAxVzp0qVp2bIFqZk3sdpyznF+L+0aarWaV1991U7RFV7yVyGEEEI4kIEDB2JRbJx7jpUWrYrCWZOR0Lp1CQkJycfoRGHx6quvYrGYSE2/lX2bzWYlJSOWJk2aSC/1I0j5hxBCCOFA/P39adqkCb+dOEk9VzdcVE/vX7tsMqK1WunTt28BRCgKgzp16lCpUiVi7x7hVuIJ4MFAVavVTMeOHe0cXeEkSbUQQgjhYPr07cvRY8e4YDRQz9X9idvaFIUzpge11PXq1SuYAIXdqVQq3n77bQ4fPpzjdm9vb3kfPIYk1UIIIYSDCQoKIqROHX6/dJmwkm6oVKrHbnvHYibdYmFUnz5P3E4UPw/XvhDPRmqqhRBCCAfUsVMnMq0W4i3mJ2532WiglKenrLYpxFNIUi2EEEI4oKZNm+Lm6vrExWAMNhs3LWbatG0rq+YJ8RSSVAshhBAOyNXVlVatWxNrMWNSHj293jWTEaui0K5duwKOToiiR5JqIYQQwkG1adMGs83GbfOjS0Cum01U9fenRo0aBRyZEEWPJNVCCCGEgwoODsbTw4NbZtNf7jPabCRYzDSRWmohnokk1UIIIYSDUqvVNGjYkDtWC4qi5LjvjsWMAjRs2NA+wQlRxEhSLYQQQjiwhg0bordauWe15Lj9ltmEh7s7QUFBdopMiKJFkmohhBDCgdWvXx/gL1PrJVithNWrh1qttkdYQhQ5klQLIYQQDqx06dJUrFCBJMv/9VRrbTayrBZq165tx8iEKFokqRZCCCEcXHDt2tyzWbPrqu/9/17r4OBge4YlRJEiSbUQQgjh4IKDg9FarWzTZLItK4Mjeh1qtVqm0hPiOZSwdwBCCCGEsK/GjRtz8OBBTMYHqyu6Ae3q1sXFxcW+gQlRhEhSLYQQQji4cuXK8cEHH9g7DCGKNCn/EEIIIYQQIpckqRZCCCGEECKXJKkWQgghhBAilySpFkIIIYQQIpckqRZCCCGEECKXJKkWQgghhBAilySpFkIIIYQQIpckqRZCCCGEECKXJKkWQgghhBAilySpFkIIIYQQIpckqRZCCCGEECKXJKkWQgghhBAilySpFkIIIYQQIpckqRZCCCGEECKXJKkWQgghhBAilySpFkIIIYQQIpdK2DuA3LJarQAkJibaORIhhBBCCFGcPcw3H+aff1Tkk+rk5GQABg8ebOdIhBBCCCGEI0hOTqZatWo5blMpiqLYKZ48YTAYOH/+POXKlUOtVts7HCGEEEIIUUxZrVaSk5MJCQnB1dU1x31FPqkWQgghhBDC3mSgohBCCCGEELkkSbUQQgghhBC5JEm1EEIIIYQQuSRJtRBCCCGEELkkSbUQQgghhBC5JEm1EEIIIYQQuSRJtRBCCCGEELkkSbUQQgghhBC5JEm1HR07doz69evTo0cPIiIi6Ny5M5988kmetN22bVvi4uJy3BYTE8P06dPzpH2Rd+Li4ggKCmLOnDk5br948SJBQUHExMQ8d5tDhw7l2LFjz7z92bNnWbp0KQB37txhxowZz71PUXB0Oh0ffPABHTt2JCIigsGDB3P06FF7hyXy0I4dO4iMjCQiIoLu3bvz+eefZ9+3evVqTp48+cTH79mzhy+//DJPYgkKCnri/Y/6vHkRa9asYc2aNbluRzxZfuYejq6EvQNwdCEhIaxbtw4ArVZLly5d6NChAzVr1rRzZKIgeXt7c+DAAaxWK2q1GoDt27fj6+tbIPu/du0aqampAMTHx3Pnzp0C2a94foqiMG7cOGrUqMG2bdtwdnbmwoULjB49mhUrVtCwYUN7hyhyKSkpicWLFxMTE4OPjw9arZahQ4dSvXp12rVrx4kTJ2jSpMkT2/j9998LKFpRFEnukT8kqS5EDAYDarWaUqVK0bZtW0JDQ7l48SLffPMN+/bt48svv0SlUlGnTh1mz56Nh4cHe/fuZeXKldhsNvz9/Zk/fz5ly5bNbvPGjRuMHj2aJUuWZN925MgRVq1axX//+18ANm3axG+//UZYWBgHDhwgIyODO3fu0KJFC+bOnVvQL4ND8vDwIDg4mBMnTtC0aVMADh06RPPmzQH4+uuv2bx5M3q9HpVKxcqVKwkICKBt27ZERERw8OBB9Ho9ixcvJiQkBID//d//ZfHixWRkZDBz5kzatm3LlStXWLBgATqdjrS0NEaMGEHPnj1ZvXo1Op2OTz75hO3btxMXF8e8efOYOXMmc+fO5erVq6SkpFC9enWio6NJSUlh/PjxBAYGcvHiRcqUKcOqVavw9va210voME6dOsWNGzf47LPPcHZ2BuDll19mzJgxrF27FovFwvjx42nSpAlxcXEMGzaMPXv2kJKSwpw5c0hMTESlUjF58mSaN2+OVqtl/vz5XL16FavVSlRUFN26dSMmJuaR54PExESmTJmCTqfDycmJWbNmUa9ePfu+KMXM/fv3MZvNGAwG4MH54cMPP6RkyZJ8//33nD9/nlmzZhEdHU1GRgYrVqzAYDCQkZHB1KlTCQwMzD6/+/n50alTp+c6xo/ztGOv0WiYMWMGSUlJ3Lt3j4YNG7JkyRKOHz/Op59+iqurK9evXycoKIhly5bh4uLC559/znfffYePjw9eXl6Ehobm50srHuFFco+WLVvSsWNHTp06hVqtZuXKlZQoUYLIyEi+/vpr/P396d27N5MnT+Zvf/ubvZ9igZHyDzs7f/48PXr0oHv37rRt25bGjRtTvnx5AFq1asXOnTtJSUnhH//4B+vWrWPr1q24ubkRHR1Namoqc+bMYe3atWzdupVXXnmF+fPnZ7edkJDA+PHj+eCDD3Kc+Jo2bUpycjK3b98GHiTVkZGRAJw+fZrVq1ezZcsW9u7dy+XLlwvuxXBwnTt3ZufOncCDcoygoCCcnZ3RaDT8/PPPrFu3jm3bttG+fXu++eab7Md5e3uzYcMGBgwYwKeffpp9u5eXFzExMcyaNYu1a9cCDxLtsWPHsnHjRr766itWrFiBl5cXb731Fm3btuXNN99k1qxZhISE8N5773H69GmcnZ359ttv+emnnzAajfzyyy8AXLp0iREjRrBt2za8vLzYunVrAb5ajuvcuXPUrl07O6F+qHHjxpw5c+axj1u0aBG9e/cmJiaGTz75hDlz5qDRaPjkk0+oU6cOMTExrF+/nn/84x/ZVyoedT7YsGEDf/vb34iJiWHq1KmcOnUqX5+vIwoODqZdu3a0b9+ePn36sHTpUmw2G9WqVaNnz56EhISwcOFCgoKC+Prrr1m4cCGbNm1i0aJFfPzxx9SsWZMBAwYwYMAAevfu/dzH+HGeduz37dtH7dq1+fbbb9m5cye//fZbdo/56dOnmTNnDj/++CPx8fEcPHiQc+fOsXHjRjZt2sSXX35JYmJi/r2oIofc5B4AycnJNGvWjO+//55GjRqxfv16KlWqxJQpU5g7dy5r166lfv36DpVQg/RU292fL8GMGTOGzz77DICwsDAATpw4QZs2bfDx8QGgf//+vPvuuzRu3JjQ0FCqVKmSffvDxwJMnDiRunXr0qBBgxz7VKlU9OrViy1bthAZGUlqaiphYWFcv36d+vXr4+npCYC/vz8ZGRn5+wKIbG3atMm+6vDjjz/SuXNntm/fjqenJ8uXL+eHH37g5s2bHDhwgNq1a2c/Ljw8HIDAwEB27dqVfXv79u0BqFmzJvfv3wdg+vTpHDhwgE8//ZTLly+j0+meGFOjRo3w9vZm/fr1xMbGcvPmzezHlClThpdffjl73/JesS+DwYDNZnvs/YcPHyY2NpbVq1cDYLFYuHPnDocPH8ZgMLBx40bgQb321atXAR55PmjWrBkTJkzg4sWLtG7dmiFDhuTzM3NM8+bNY+zYsRw8eJCDBw/Sr18/li1bxquvvppju6VLl7J371527NjBmTNn0Gq1f2nreY/x4zzt2Hfr1o2zZ8/yr3/9i9jYWNLT07PPF4GBgVSsWBGAgIAAMjIyuHHjBq1bt8bDwwOATp06PfE9LPJObnKPh/742fOwxr937978+OOPbN26lW3bthXY8ykspKe6EPHw8KB9+/b8+uuvAJQsWRLgLycZRVGwWCyPvf2hmTNncvv27eyexT/q1asXP/zwA9u2baNHjx7Ztz/cJzxIvhVFyf0TE8/E09OT4OBgTp06xdGjR7NLPxISEujfvz9ZWVm0atWKXr165TguD4+ZSqXK0d7D2uw/3j5x4kR++uknAgICmDRp0lNj2r17N1OmTMHV1ZXIyEgaNWqUvW95r9hH3bp1uXjxImazGYC0tDQUReHMmTPZpT8Pj8Ufzwc2m41///vfbN68mc2bN/Ptt99Sq1YtbDYbS5cuzb79u+++y/6wfNQxbtCgAT/88AMtW7Zk+/btjBkzpqCeusPYt28f27dvp0KFCvTu3ZsVK1Ywa9YsNmzY8JdtBw0axNmzZwkJCXnssXjeY/w4Tzv269atY8mSJfj6+jJkyBACAgKeeL5QqVQ5PsdKlJB+Pnt43tzjoT9+9jw8zkajkcTERKxWq0NeeZCkuhCxWq0cP348u/fvocaNG7Nnzx7S09MB+O6772jSpAlhYWGcOXMme9T1t99+m2PwSmhoKHPnzmXevHl/6ZGsXLkyFStW5L///W+OpFrYV+fOnVm+fDkhISHZHzDu7u5Uq1aN4cOHExYWxv79+7FarS/U/qFDh3jrrbdo3749J06cAMgeHPnwZPnH/x85coTOnTvTu3dvypYty4kTJ1543yJvNGjQgBo1arB48WLMZjObNm1i4MCBfPzxx4wbNw4fHx+uXbsGwM8//5z9uKZNm2aXDV27do2IiAj0ej1NmzblP//5DwD37t0jIiKChISEx+5/yZIlbN68mV69ejFnzhwuXLiQj8/WMbm6urJ8+fLsc7uiKFy7di37CpVarcZqtZKens7Nmzd5++23ad26NYcOHcr++/zj3/HzHuPHedqxP3ToEP379yciIgKVSsWlS5ee2PPcrFkz9u3bR1ZWFkajkZ9++um5YxK597y5x5OsXLmSpk2b8u677zJjxgyHu/IgXwvt7GFdE4Ber6du3bpERUWxefPm7G2Cg4MZPXo0Q4cOxWw2U6dOHebNm4enpyfz589n/PjxmM1m/Pz8WLRoUY72GzduTJMmTVi5ciXBwcE57uvSpQu7du2iQoUK+f9ExTNp06YNM2fO5O23386+zdnZGZvNRpcuXXBxcSE0NDT70u3zmjBhAoMGDcLLy4vq1atTuXJl4uLiCA0NJTo6mmXLljFy5EiysrKYOnUqb7zxBlOmTGHHjh24uLhQr169PJk6S7w4lUrF2rVrWb58OV27dsXZ2RkvLy+qVq3K/v37GTlyJO+++y4bN26kXbt22Y+bNWsWc+bMoXv37sCDBMnT05Px48czd+5cunXrhtVqZerUqVStWvWxU7YNHTqUyZMns2nTJtRqNe+9916BPG9H0rRpU8aPH8+YMWOyr0iEh4czbty47P+/9957LF68mL59+9K1a1c8PT2pV68eBoMBnU5Ho0aNeOeddyhbtuxzH+PHedqxf+2115g7dy7/8z//g4eHB/Xr1ycuLo6qVas+sr3atWvz2muv0adPH7y8vPDz83uBV0u8iNzkHo9z+vRpdu7cyZYtW/D09GTTpk188cUXREVF5fvzKSxUilyzdUgWi4Vp06bRqVOnv9ToCSGKHpvNxi+//EKbNm3sHYoQQjgkKf9wQIqiEB4ejkqlyh7MJoQo2pycnCShFkIIO5KeaiGEEEIIIXJJeqqFEEIIIYTIJUmqhRBCCCGEyCVJqoUQQgghhMglSaqFECIPREVFZc8P/TjTp0/niy++eOR9QUFBpKWl5Udo+d7+uXPnaNu2bb60LYQQRYXMUy2EEHngn//8p71DEEIIYUeSVAshxJ8cO3aMFStW4O/vz9WrVzGZTMyZM4dXXnmFZcuWZa8s+fLLLzNr1iw8PT1p27Ytq1atom7dunz22Wds2LABDw8PGjZsyO7du9mzZw/wYIGEAQMGkJKSQmBgIMuXL8fd3R14sBrZuXPnsNlsTJw4MXuKvLVr1/LDDz+gVqupXr06s2fPply5cgwdOpTBgwfTqVMngBy/h4SE0K5dOy5dusSyZcsAWLNmDWfOnCE9PZ2RI0cyePDgJ7afmJjI3LlzuXv3Loqi0LNnT9544w0AvvnmG/7973/j6elJrVq1nul1PXv2LHPnzsVsNlO1alXi4+OZPn06AIsWLcLd3R2dTseGDRvYtGkT69atw8nJibJlyzJ79myqV6/O9OnTCQwMZOTIkQA5fm/bti3t27fn5MmTZGVlMWLECAYNGpQXbwkhhHgqKf8QQohHOHv2LK+//jrff/89ffr0ITo6ms8++wy1Wk1MTAxbtmyhfPny2QnrQwcOHCAmJoYNGzYQExODVqvNcX9SUhJffvklO3fuJCkpiV27dmXfV6VKFTZt2sTSpUuZPn06aWlpbNy4kQMHDrBhwwa2bt1KYGBgdiL6JGazmTZt2rBz507q1q0LgL+/PzExMURHR/Phhx9iNpuf2P6UKVNo0qQJW7du5T//+Q9btmzhhx9+4OLFi0RHR/P111+zceNGnJ2dnxqPxWJhwoQJvP3222zdupWhQ4dy8eLF7PuvXr3K8uXL2bJlC6dOneLzzz/nq6++YsuWLXTr1o1x48bxLDPAGgwGNm7cyLp161i9ejWXL19+6mOEECIvSFIthBCP4OfnR+3atQF4+eWXycjIYN++fezZs4eePXvSo0cPfv75Z65fv57jcb/88gudOnXCy8sLlUqV3Rv8UPv27XFzc0OtVhMYGJijznngwIEA1KpVi4CAAE6fPs3+/fuJjIzM7s0eNmwYR48exWQyPfU5NGzYMMfv3bp1Ax4sD20ymdBoNI9tPysri19//TU7/lKlShEZGcn+/fs5cuQILVq0oFy5cgD079//qbFcuXIFgNatWwMPluIODAzMvr9SpUpUrlwZePDFpEuXLvj6+gIQGRlJUlIScXFxT93PoEGDUKlUVKxYkfDwcA4dOvTUxwghRF6Q8g8hhHgEV1fX7P+rVCoURcFmszFjxozsxFCr1WI0GnM8rkSJEjl6VNVq9V/u/3O7Dzk5/V8/h6Iof2kLHixHbrFYcmz3kNlszrHtw0T5z/tWqVTZj31S+4+7789x//k5Poparf5Le3983B9jfVSPtKIoj9z3n5/zH19fm82W4zUVQoj8JGcbIYR4Ri1btmT9+vWYTCZsNhuzZ8/mo48+yrFN69at2bVrF1lZWQBs2LDhmdvftGkTAL///ju3bt0iLCyMli1bEhMTg06nA2DdunU0atQIFxcXfH19OX/+PAC3b99+oVKHx7VfqlQpwsLCWL9+PQBZWVl8//33NG/enObNm3Po0CESExNzxP0kAQEBuLi4sH//fuBBec2VK1eyE/w/x7R9+/bsXvyNGzfi7e1NtWrV8PHxyX7OaWlpnDx5Msdjv//+ewDi4+M5dOgQrVq1eu7XRAghXoT0VAshxDMaO3YsixcvplevXlitVmrXrv2X+uZmzZrRr18/+vfvj6urK4GBgbi5uT1T+3fu3KFnz56oVCo++ugjvL296dOnDwkJCfTt2xebzUa1atWy67jffPNNpk+fzi+//EKNGjX+Uu7xLJ7U/rJly5g/fz4xMTGYTCa6d+9OZGQkKpWKqVOn8tprr+Hh4UFoaOhT91OiRAnWrFnDe++9x0cffcRLL71E2bJlcXV1Ra/X59i2RYsWDB8+nNdeew2bzYavry+ffvopTk5ODB06lClTptCxY0eqVKlC48aNczw2Li6OyMhIDAYDs2bNokaNGs/9mgghxItQKc8y8kMIIcQzOXfuHKdPn2bYsGEAfPnll5w5c4aVK1faN7BCYPHixYwcOZKyZcuSkJCQXZfu5eWVJ+3/cQYWIYQoaNJTLYQQeah69er885//5LvvvkOlUlGpUiUWLFhg77AKxJYtWx67uE337t2pXLkyw4cPz64VX7hwYZ4l1EIIYW/SUy2EEEIIIUQuyUBFIYQQQgghckmSaiGEEEIIIXJJkmohhBBCCCFySZJqIYQQQgghckmSaiGEEEIIIXLp/wHpT36iN56hewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now lets see the price distribution by neighbourhood gropus\n",
    "plt.figure(figsize=(12,8))\n",
    "ax = sns.violinplot(x=\"neighbourhood_group\", y=\"price\", data=airbnb_data_new).set_title('Price Distribution by neighbourhood groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ya5LO8x5CNel",
    "papermill": {
     "duration": 0.03281,
     "end_time": "2024-04-15T05:31:26.863553",
     "exception": false,
     "start_time": "2024-04-15T05:31:26.830743",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "By looking at the violin plot, we can see that Manhattan has a higher price range and is the most expensive one. Brooklyn has the second-highest rental prices, while the Bronx appears as the most affordable one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sHSuYDlVEQ6I",
    "papermill": {
     "duration": 0.034338,
     "end_time": "2024-04-15T05:31:26.931433",
     "exception": false,
     "start_time": "2024-04-15T05:31:26.897095",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Identifying the Busiest Host\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:27.000276Z",
     "iopub.status.busy": "2024-04-15T05:31:26.999236Z",
     "iopub.status.idle": "2024-04-15T05:31:27.073644Z",
     "shell.execute_reply": "2024-04-15T05:31:27.072617Z"
    },
    "executionInfo": {
     "elapsed": 366,
     "status": "ok",
     "timestamp": 1647796682536,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "BLYR_n-fDTAH",
    "outputId": "f3969d43-0339-4e35-8e45-a533d206b269",
    "papermill": {
     "duration": 0.11124,
     "end_time": "2024-04-15T05:31:27.076630",
     "exception": false,
     "start_time": "2024-04-15T05:31:26.965390",
     "status": "completed"
    },
    "tags": []
   },
   "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>host_name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>room_type</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>number_of_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14729</th>\n",
       "      <td>Jj</td>\n",
       "      <td>4734398</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5194</th>\n",
       "      <td>Carol</td>\n",
       "      <td>2369681</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3280</th>\n",
       "      <td>Asa</td>\n",
       "      <td>12949460</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31453</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>792159</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18686</th>\n",
       "      <td>Linda</td>\n",
       "      <td>2680820</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Queens</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7176</th>\n",
       "      <td>Dani</td>\n",
       "      <td>42273</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>Angela</td>\n",
       "      <td>23591164</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Queens</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Agnes</td>\n",
       "      <td>277379</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>John</td>\n",
       "      <td>2267153</td>\n",
       "      <td>Private room</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8210</th>\n",
       "      <td>Dennis &amp; Naoko</td>\n",
       "      <td>307962</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>Queens</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            host_name   host_id        room_type neighbourhood_group  \\\n",
       "14729              Jj   4734398     Private room           Manhattan   \n",
       "5194            Carol   2369681     Private room           Manhattan   \n",
       "3280              Asa  12949460  Entire home/apt            Brooklyn   \n",
       "31453           Wanda    792159     Private room            Brooklyn   \n",
       "18686           Linda   2680820     Private room              Queens   \n",
       "7176             Dani     42273  Entire home/apt            Brooklyn   \n",
       "2434           Angela  23591164     Private room              Queens   \n",
       "465             Agnes    277379     Private room           Manhattan   \n",
       "14973            John   2267153     Private room           Manhattan   \n",
       "8210   Dennis & Naoko    307962  Entire home/apt              Queens   \n",
       "\n",
       "       number_of_reviews  \n",
       "14729                607  \n",
       "5194                 540  \n",
       "3280                 488  \n",
       "31453                480  \n",
       "18686                474  \n",
       "7176                 467  \n",
       "2434                 466  \n",
       "465                  458  \n",
       "14973                451  \n",
       "8210                 441  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Busiest hosts would be those who have maximum number of reviews as people are booking frequently at those hosts.\n",
    "\n",
    "busiest_hosts = airbnb_data_new.groupby(['host_name','host_id','room_type','neighbourhood_group'])['number_of_reviews'].max().reset_index()\n",
    "busiest_hosts = busiest_hosts.sort_values(by='number_of_reviews', ascending=False).head(10)\n",
    "busiest_hosts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:27.146382Z",
     "iopub.status.busy": "2024-04-15T05:31:27.145642Z",
     "iopub.status.idle": "2024-04-15T05:31:27.653369Z",
     "shell.execute_reply": "2024-04-15T05:31:27.652002Z"
    },
    "executionInfo": {
     "elapsed": 928,
     "status": "ok",
     "timestamp": 1647796683824,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "FKq5aEocEg42",
    "outputId": "716f6266-03de-4df5-d914-584d07e49ed3",
    "papermill": {
     "duration": 0.546996,
     "end_time": "2024-04-15T05:31:27.656781",
     "exception": false,
     "start_time": "2024-04-15T05:31:27.109785",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KKLiJiIiIWAkFNxEREREroeAmIiIiYiUU3ERERESshIKbiIiIiJVQcJNspaalW7oEs1hLnSIiInnBztIFyOPJwd4W//fDLV3GX1o3q52lSxARESkw6nETERERsRIKbiIiIiJWQsFNRERExEoouImIiIhYCQU3ERERESuh4CYiIiJiJRTcRERERKyEgpuIiIiIlVBwExEREbESCm4iIiIiVkLBTURERMRKKLiJiIiIWAmL3mQ+MDCQy5cvY2d3v4wJEybw66+/8vnnn5OWlkavXr3o3r07AFFRUUyZMoWUlBRatWrFsGHDLFm6iIiISIGzWHAzGo2cPn2anTt3moJbYmIiw4YNIzQ0FAcHB7p06UK9evV45plnGDVqFMuWLaNMmTL069ePXbt24evra6nyRURERAqcxYLb6dOnMRgM9O3bl8uXL/PGG29QtGhR6tevj7OzMwAtWrQgMjKSV155hfLly1O2bFkA/P39iYyMVHATERGRJ4rFznG7fv06Xl5e/Pvf/+arr75ixYoV/Pbbb7i6uprWcXNzIzExkaSkpGzbRURERJ4kFgtutWrVYvr06RQpUoSSJUvSsWNH5s2bl2U9g8GA0WjMtl1ERETkSWKx4BYbG0t0dLTpsdFoxMPDg+TkZFNbUlISbm5uuLu7Z9suIiIi8iSxWHC7ceMG06dPJyUlhZs3b7J27VpmzJhBdHQ0V65c4c6dO2zZsgUfHx9q1KhBfHw8Z8+eJT09nfXr1+Pj42Op0kVEREQs4pEuTkhLS2Pfvn3Y2NjQoEED09Wh5mjcuDE//fQT7du3JyMjg27dulG7dm2GDRtGYGAgaWlpdOzYkZdeegmAqVOnMmjQIFJSUvD19aVly5aPUrqIiIiI1TE7aaWmpjJp0iTOnz/Pl19+SWpqKp07d+b48eMAVKxYkSVLllCqVCmzdz506FCGDh2aqc3f3x9/f/8s63p5eREREWH2tkX+KDUtHQd7W0uX8VDWUKOIiFiW2cFt/vz5rFq1ig4dOgAQFhbGsWPHCAwMpEqVKkydOpW5c+cyYcKEfCtW5O9ysLfF//1wS5fxUOtmtbN0CSIi8pgzO7ht2rSJjh07MmnSJAA2b97MU089xYgRI7Czs+PcuXOsXr063woVERERedKZfXHCxYsXqVmzJgB37tzh+++/x8vLy3ReW5kyZbh+/Xq+FCkiIiIiuQhuLi4upik59uzZQ2pqKq+++qpp+YkTJzRFh0gBSU1Lt3QJf8kaahQRsTZmD5XWq1ePJUuW4OjoSEhICIULF6ZZs2Zcv36db7/9llWrVtGlS5f8rFVE/kvn7ImIPJnM7nEbNWoUnp6eTJs2jStXrjBp0iScnJw4deoU06ZNo0aNGgwcODA/axWRfyhr6J2zhhpF5J/P7B43JycnFi9ezJUrVyhWrBgODg4AVKlShZUrV1KjRo18K1JE/tnUgygiYh6ze9z69evHN998Q0pKiim0ARQpUkShTURERKQA5Oqq0gkTJtCkSRP8/f2ZMWMGsbGxZGRk5Gd9IiJWxVqGVK2lThHJzOyh0vDwcJKTk9m7dy979uwhNDSURYsW4eTkhLe3N76+vvj4+FCyZMn8rFdE5LFmDcO+YP7Qr7Xc0cNa6hR5VLm6V6mLiwvt27enffv2GI1Gjhw5wt69e/n222+JjIzExsaGI0eO5FetIiJSwP5pQVTE2v2tm8z/8ssvxMbGmv4lJCRgMBioUKFCXtcnIiIiIv9ldnD76quviIuLIy4ujqtXrwLwwgsv0LRpU+rVq0edOnUoUaJEvhUqIiLyqKxhSNUaahTLMTu4TZ06FYPBgLu7OyNGjCAgIIDixYvnZ20iIiJ5yhqGfjXsKw9j9lWlQUFBNG/enLt37zJ9+nRat27NsGHDWL58OT///HN+1igiIiIi5KLHrUePHvTo0QOA48ePExMTw4EDB5g7dy7Xr1/H2dmZunXrMm/evHwrVkRERP7HGoZVraFGa/K3Lk7w9PTE09OT1q1bs2/fPkJCQjh06BBbt27N6/pEREQkBxr6ffLkKrj9/vvv7N+/n5iYGGJiYoiPjwfuB7l+/frRqFGjfClSRERERHIR3F5//XVOnDhBRkYGTz31FA0aNODtt9+mUaNGuLq65meNIiIiIkIue9z69u1Lo0aNqFWrFra2Gq8WERGRvGEt58JZuk6zg9vatWtNPyclJZGQkECFChVwdHTEzs4OGxuzL1AVERERycQaztcDy5+zl6u0FRcXR0BAAL6+vnTp0oXDhw9z4MABXn31VTZu3JhfNYqIiIgIuQhuBw8epHfv3ty6dYs333zT1F68eHHs7Oz44IMP2LVrV74UKSIiIiK5CG5z587lmWeeITw8nHfeeQej0QhA9erViYiIoGLFiixYsCDfChURERF50pkd3H744QcCAgIoVKgQBoMh07JixYrxxhtvcOrUqTwvUERERETuy9U5bg4ODjkuS0lJISMj45ELEhEREZHsmR3catSowfr167Nddvv2bVavXk316tXzrDARERERyczs4DZ48GCOHj1Kjx49CAsLw2AwcPDgQZYuXUq7du04f/48/fv3z89aRURERJ5oZs/jVqtWLRYsWMDYsWOZNm0aAHPmzAHA1dWV2bNnU79+/fypUkRERERyd+cEb29vtm7dypEjRzh37hwZGRl4eHhQrVo17Oz+1v3qRURERMRMuU5bBoOBatWqUa1atfyoR0RERERykGNwCwwM5F//+hdeXl6mx3/FYDCwZMmSvKtORERERExyDG7nz5/n9u3bmR6LiIiIiOXkGNy2b9/+0Md5Zdq0aVy9epWpU6dy7NgxgoKCuHnzJnXq1GH8+PHY2dnx22+/MXz4cC5fvsxzzz3HzJkzKVq0aL7UIyIiIvK4Mns6kM8++4xz587l6c6jo6NZu3at6fHw4cMZM2YMmzdvxmg0smrVKgDGjx9Pt27diIyMpFq1anz22Wd5WoeIiIiINTA7uM2bNw8/Pz+6dOlCSEgIV65ceaQdX7t2jTlz5pjmfrtw4QJ3796lZs2aAAQEBBAZGUlaWhrff/89LVq0yNQuIiIi8qQxO7ht27aNYcOGkZqaysSJE/Hx8aFv376sW7eOO3fu5HrHH3/8McOGDcPJyQmApKQkXF1dTctdXV1JTEzk6tWrFCtWzDTdyIN2ERERkSeN2cHNw8ODd955h9DQUCIjI/nXv/7FxYsXGT58OA0aNOD9999n165dZm1r9erVlClTxnTFKoDRaMyynsFgyLFdRERE5Enzt2bNffbZZxkwYAADBgzg1KlTzJo1iw0bNrBx40aOHTv2l8/fuHEjly5dol27dvz+++/cvn0bg8FAcnKyaZ1Lly7h5uZGyZIluXnzJunp6dja2praRURERJ40fyu4paenEx0dzaZNm9ixYwdXrlzBw8ODNm3amPX8xYsXm34ODQ3lwIEDTJkyhTZt2hAXF0ft2rUJCwvDx8cHe3t76tSpw8aNG/H39ze1i4iIiDxpzA5uGRkZREVFERkZyXfffce1a9dwdnamVatW+Pv78/LLLz9yMTNnziQoKIhbt25RtWpV06S/Y8eOZeTIkXz++eeUKVOG2bNnP/K+RERERKyN2cHNy8uL69evU6hQIZo0aULbtm3x9vZ+5HuUBgQEEBAQAICnpydr1qzJso6HhwfLli17pP2IiIiIWDuzU9dLL72Ev78/zZo1o0iRIvlZk4iIiIhkw+zg9sUXX5h+zsjI4MqVKzg5OeHg4JAvhYmIiIhIZmZPBwJw9uxZBg0aRO3atfHx8SEuLo7o6Gg6depEbGxsftUoIiIiIuQiuJ05c4ZOnTpx4MABGjVqZJpfzdbWltOnT/PWW2/x448/5ledIiIiIk88s4Pb7NmzKVSoEBs3bmTcuHGm4PbKK6+wceNGXFxcmD9/fr4VKiIiIvKkMzu4xcTE0LVrV0qVKpXlzgXu7u5069aNw4cP53mBIiIiInKf2cEtNTXVdF/R7Njb25OSkpInRYmIiIhIVmYHN09PT7Zv357tsnv37hEREUHlypXzrDARERERyczs4NavXz+ioqL44IMPiImJAeDChQts27aNwMBAjh49Su/evfOtUBEREZEnndnzuDVu3JhPPvmEyZMns2HDBgDGjBmD0WjE0dGRDz/8kBYtWuRboSIiIiJPulzdqzQgIAA/Pz+ioqL49ddfycjIwMPDgwYNGlCiRIn8rFNERETkiWd2cGvXrh0dO3bkzTffxM/PLz9rEhEREZFs5GoC3sKFC+dnLSIiIiLyEGYHt4YNG7JlyxZSU1Pzsx4RERERyYHZQ6Wenp4sWbKERo0aUb16dUqVKoWNTebcZzAYmDx5cp4XKSIiIiK5CG6ff/656ee9e/dmu46Cm4iIiEj+MTu4HT9+PD/rEBEREZG/YPY5biIiIiJiWQpuIiIiIlZCwU1ERETESii4iYiIiFiJHIPbzp07SU5OLshaREREROQhcgxuH3zwATt37jQ9DgwMJDo6uiBqEhEREZFs5BjcjEYjcXFx3LlzB4ADBw5w+fLlAitMRERERDLLcR43Pz8/1q5dS1hYmKlt+PDhDB8+PMeNGQwGjh49mqcFioiIiMh9OQa38ePH8+KLL3Ly5ElSU1MJDw+ndu3alC1btiDrExEREZH/yjG4OTg40KNHD9PjsLAwOnfujL+/f4EUJiIiIiKZ/a1bXiUnJ/Pbb79hb2+Pu7s7JUuWzJfiREREROR/zA5uAIcPH2bChAkcOnQoU3uNGjUYPXo01atXz9PiREREROR/zA5uJ06coGfPngC88cYbVKxYkYyMDE6fPs26desIDAxk1apVVKpUKd+KFREREXmSmR3cPv30U4oWLcrKlSvx8PDItOzdd9+lY8eOzJ8/n7lz5+Z5kSIiIiKSi1texcbG0q1btyyhDaB06dJ07dqV/fv352lxIiIiIvI/Zge31NRUihYtmuPyYsWKcffu3TwpSkRERESyMju4ValShfXr13Pv3r0sy9LS0li3bh0vvPBCnhYnIiIiIv9jdnB7++23OXToED169GDz5s2cOHGCEydOsGnTJnr06MGRI0d46623crXzuXPn8tprr9G6dWsWL14MQFRUFP7+/vj5+TFnzhzTuseOHaNDhw60aNGC0aNHZxsgRURERP7JzL44oVmzZowZM4aZM2cydOhQU7vRaMTR0ZEPP/yQli1bmr3jAwcOEBMTQ0REBPfu3eO1117Dy8uLUaNGsWzZMsqUKUO/fv3YtWsXvr6+DB8+nEmTJlGzZk1GjRrFqlWr6NatW64OVkRERMSa5Woet+7du9O6dWuio6M5f/48RqORZ555hgYNGuDs7JyrHb/yyissXboUOzs7EhMTSU9P5/r165QvX950Wy1/f38iIyN5/vnnuXv3LjVr1gQgICCAefPmKbiJiIjIEyVXwQ3A2dmZVq1a5cnO7e3tmTdvHl9++SUtW7YkKSkJV1dX03I3NzcSExOztLu6upKYmJgnNYiIiIhYC7PPccsvgwcPJjo6moSEBM6cOZNlucFgwGg0ZtsuIiIi8iSxWHD75ZdfOHbsGACFCxfGz8+P/fv3k5ycbFonKSkJNzc33N3dM7VfunQJNze3Aq9ZRERExJIsFtzOnz9PUFAQqamppKamsm3bNrp06UJ8fDxnz54lPT2d9evX4+Pjg4eHB46OjsTFxQEQFhaGj4+PpUoXERERsQizz3HLyMjAxibvcp6vry8//fQT7du3x9bWFj8/P1q3bk3JkiUZNGgQKSkp+Pr6mq5UnTlzJkFBQdy6dYuqVasSGBiYZ7WIiIiIWAOzg1u7du3o2LEjb775Zp7tfPDgwQwePDhTm5eXFxEREVnW9fT0ZM2aNXm2bxERERFrY3YX2pkzZyhcuHB+1iIiIiIiD2F2cGvYsCFbtmwhNTU1P+sRERERkRyYPVTq6enJkiVLaNSoEdWrV6dUqVJZznkzGAxMnjw5z4sUERERkVwEt88//9z08969e7NdR8FNREREJP+YHdyOHz+en3WIiIiIyF/4W/N7ZGRkkJycrPPdRERERApQroLb2bNnGTRoELVr16ZRo0bExcURHR1Np06diI2Nza8aRURERIRcTgfSqVMnDhw4QKNGjUzttra2nD59mrfeeosff/wxP2oUEREREXIR3GbPnk2hQoXYuHEj48aNM934/ZVXXmHjxo24uLgwf/78fCtURERE5ElndnCLiYmha9eulCpVCoPBkGmZu7s73bp14/Dhw3leoIiIiIjcZ3ZwS01NxcnJKcfl9vb2pKSk5ElRIiIiIpKV2cHN09OT7du3Z7vs3r17REREULly5TwrTEREREQyMzu49evXj6ioKD744ANiYmIAuHDhAtu2bSMwMJCjR4/Su3fvfCtURERE5Eln9gS8jRs35pNPPmHy5Mls2LABgDFjxmA0GnF0dOTDDz+kRYsW+VaoiIiIyJPO7OAGEBAQgJ+fH/v27ePcuXNkZGTg4eFBgwYNKFGiRH7VKCIiIiLkMrgBFCtWDD8/P65cuYKNjY0Cm4iIiEgByVVw++WXX5g7dy579+7lzp07ADz11FM0bdqUIUOGULp06XwpUkRERERyEdwOHTpEYGAgaWlp+Pj4UK5cOYxGI/Hx8URERLB7926++eYbypUrl5/1ioiIiDyxzA5uM2fOpFixYoSEhGQJZydPniQwMJBp06bx73//O8+LFBEREZFcTAfy008/ERgYmG2P2gsvvEBgYCDR0dF5WpyIiIiI/I/Zwc3JyYn09PQclxctWpRChQrlSVEiIiIikpXZwa179+589dVX/Pzzz1mWJSYmsmzZMt544408LU5ERERE/ifHc9w++uijLG0pKSm0b9+eRo0a8dxzz2EwGLhw4QK7d+/G0dExXwsVERERedLlGNzWrl2b45N27NjBjh07MrXdvn2bBQsWMHTo0DwrTkRERET+J8fgdvz48YKsQ0RERET+gtnnuImIiIiIZeXqzglhYWHs27ePS5cukZGRkWW5wWBgyZIleVaciIiIiPyP2cFtzpw5LFiwAHt7e0qVKoWNjTrrRERERAqS2cFt7dq1NGzYkODgYAoXLpyfNYmIiIhINszuNrt58yYtWrRQaBMRERGxELODW6NGjYiJicnPWkRERETkIcweKh0zZgy9e/fm/fffp1mzZpQqVQqDwZBlvbp16+ZpgSIiIiJyn9nB7bfffuPGjRts2LCBjRs3ZlluNBoxGAwcO3YsTwsUERERkfvMDm4TJkzg+vXr9OnTh2effRY7u1zNJCIiIiIij8js9HXq1CkGDhxI375982zn8+fPZ9OmTQD4+voyYsQIoqKimDJlCikpKbRq1Yphw4YBcOzYMYKCgrh58yZ16tRh/PjxCo8iIiLyRDH74oTSpUvn6dxtUVFR7N27l7Vr1xIWFsaRI0dYv349o0aN4rPPPmPjxo0cPnyYXbt2ATB8+HDGjBnD5s2bMRqNrFq1Ks9qEREREbEGZiext99+myVLlvDzzz/nyY5dXV0ZOXIkDg4O2NvbU7FiRc6cOUP58uUpW7YsdnZ2+Pv7ExkZyYULF7h79y41a9YEICAggMjIyDypQ0RERMRamD3WePz4cQwGA23btqVs2bK4uLhga2ubaZ3c3PKqUqVKpp/PnDnDxo0b6dmzJ66urqZ2Nzc3EhMTSUpKytTu6upKYmKiuaWLiIiI/COYHdx27NiBra0tpUuXJi0tjYSEhDwp4NSpU/Tr148PP/wQOzs74uPjMy03GAwYjcYsz8tuKhIRERGRfzKzg9v27dvzfOdxcXEMHjyYUaNG0bp1aw4cOEBycrJpeVJSEm5ubri7u2dqv3TpEm5ubnlej4iIiMjjzGJ3ik9ISGDAgAHMnDmT1q1bA1CjRg3i4+M5e/Ys6enprF+/Hh8fHzw8PHB0dCQuLg6AsLAwfHx8LFW6iIiIiEWY3eMWGBho1npLly41a71FixaRkpLC1KlTTW1dunRh6tSpDBo0iJSUFHx9fWnZsiUAM2fOJCgoiFu3blG1alWz6xERERH5pzA7uJ0/fz5LW0ZGBlevXiUlJQUPD49MFxz8laCgIIKCgrJdFhERkaXN09OTNWvWmL19ERERkX+aRz7HLT09nW3bthEUFESfPn3yrDARERERyeyRz3GztbXFz8+PTp06MXPmzLyoSURERESykWcXJzz77LMcP348rzYnIiIiIn+SJ8EtNTWViIgISpUqlRebExEREZFsPPJVpampqcTHx3P9+nUGDRqUZ4WJiIiISGaPdFUp3D/HrUKFCrRp04Zu3brlWWEiIiIikplF75wgIiIiIuaz2J0TRERERCR3cuxxmz9//t/a4MCBA/92MSIiIiKSs0cObgaDIdNjBTcRERGR/JFjcNu2bdtfPvnmzZvMmTOHnTt3Ymdnp/uHioiIiOSjHIObh4fHQ5+4ceNGpk6dSlJSEi+//DLjxo3jhRdeyPMCRUREROQ+s68qfeDcuXOMHz+effv2Ubx4cSZNmkTHjh3zozYRERER+QOzg1taWhoLFy7kiy++ICUlhddff53hw4dTokSJ/KxPRERERP7LrOAWExPD+PHjiY+Pp1KlSowdO5Y6derkd20iIiIi8gcPDW5Xrlxh8uTJbNiwgUKFCvH+++/Tu3dv7OxyPcIqIiIiIo8oxwT2zTff8Omnn3L9+nWaNGlCUFAQZcqUKcjaREREROQPcgxu48ePN/28fft2s255ZTAYOHr0aN5UJiIiIiKZ5Bjc2rdvn2VyXRERERGxnByD29SpUwuyDhERERH5C7rJvIiIiIiVUHATERERsRIKbiIiIiJWQsFNRERExEoouImIiIhYCQU3ERERESuh4CYiIiJiJRTcRERERKyEgpuIiIiIlVBwExEREbESCm4iIiIiVkLBTURERMRKKLiJiIiIWAmLB7ebN2/Spk0bzp8/D0BUVBT+/v74+fkxZ84c03rHjh2jQ4cOtGjRgtGjR3Pv3j1LlSwiIiJiERYNbj/99BNdu3blzJkzANy9e5dRo0bx2WefsXHjRg4fPsyuXbsAGD58OGPGjGHz5s0YjUZWrVplwcpFRERECp5Fg9uqVasYO3Ysbm5uABw8eJDy5ctTtmxZ7Ozs8Pf3JzIykgsXLnD37l1q1qwJQEBAAJGRkRasXERERKTg2Vly55988kmmx0lJSbi6upoeu7m5kZiYmKXd1dWVxMTEAqtTRERE5HFg8XPc/shoNGZpMxgMObaLiIiIPEkeq+Dm7u5OcnKy6XFSUhJubm5Z2i9dumQaXhURERF5UjxWwa1GjRrEx8dz9uxZ0tPTWb9+PT4+Pnh4eODo6EhcXBwAYWFh+Pj4WLhaERERkYJl0XPc/szR0ZGpU6cyaNAgUlJS8PX1pWXLlgDMnDmToKAgbt26RdWqVQkMDLRwtSIiIiIF67EIbtu3bzf97OXlRURERJZ1PD09WbNmTUGWJSIiIvJYeayGSkVEREQkZwpuIiIiIlZCwU1ERETESii4iYiIiFgJBTcRERERK6HgJiIiImIlFNxERERErISCm4iIiIiVUHATERERsRIKbiIiIiJWQsFNRERExEoouImIiIhYCQU3ERERESuh4CYiIiJiJRTcRERERKyEgpuIiIiIlVBwExEREbESCm4iIiIiVkLBTURERMRKKLiJiIiIWAkFNxEREREroeAmIiIiYiUU3ERERESshIKbiIiIiJVQcBMRERGxEgpuIiIiIlZCwU1ERETESii4iYiIiFgJBTcRERERK6HgJiIiImIlFNxERERErISCm4iIiIiVUHATERERsRIKbiIiIiJWwqqC27p163jttddo3rw5ISEhli5HREREpEDZWboAcyUmJjJnzhxCQ0NxcHCgS5cu1KtXj+eff97SpYmIiIgUCKsJblFRUdSvXx9nZ2cAWrRoQWRkJAMHDnzo89LT0wG4ePFifpcIQNrtKwWyn7/r/PnzZq/7uB8L/LOOJzfHAjqegvZP+qyBjudxpu/O4y23709uPcgrD/LLnxmMRqMxXyvIIwsWLOD27dsMGzYMgNWrV3Pw4EEmTpz40OfFxsbSvXv3gihRREREJE+EhIRQp06dLO1W0+OWXb40GAx/+bxq1aoREhKCq6srtra2+VGaiIiISJ5IT0/n0qVLVKtWLdvlVhPc3N3diY2NNT1OSkrCzc3tL59XqFChbBOriIiIyOOofPnyOS6zmqtKGzRoQHR0NFeuXOHOnTts2bIFHx8fS5clIiIiUmCsqsdt2LBhBAYGkpaWRseOHXnppZcsXZaIiIhIgbGaixNEREREnnRWM1QqIiIi8qRTcBMRERGxEgpuIiIiIlZCwU1ERETESii4iYiIiFgJBbfHWGhoKCNHjmT06NEcOnTI0uVkcfPmTcaPH0+bNm1o164dPXv25MiRI4+83f3799OzZ888qPDRnTx5ksqVK7N582ZLl5Kj3r17s3XrVtPjadOmUatWLVJTU01tDRs25Ny5c397Hw8+i5aS3Wfi0KFDjB49OlfbqVy5cl6W9cjOnz9PtWrVaNeuHe3ataNFixYMHjyY5OTkv7W9du3a5XGFD5ef34/g4GCCg4PzfLt/xRq+84/ir77LI0eOJDQ01Ozt5fVn+GH69u1LYmKi2etPnDiRNm3a0LVrV65cyf4eqMHBwfj5+XH37l1T26P8DSqI35UKblbgk08+oXr16pYuI5OMjAz69u1L8eLFCQsLIzw8nAEDBtC3b1+uXr1q6fLyTGhoKC1atGDFihWWLiVHXl5e/L//9/9Mj6OioqhRowZxcXEAnD17liJFilC2bFlLlZgvqlevzieffGLpMh6Zm5sb4eHhhIeHExkZSfny5Rk8ePDf2lZ4eHgeV/dw1vD9yK1/4jHlt7z8DD/MF198gbu7u1nrHj9+nJiYGNatW8cLL7xAREREjuv+9ttvzJ49O6/KzHdWMwHvk6xnz54MHDiQevXqWboUk/3795OUlMTgwYOxsbmf/+vXr8+UKVPIyMggKCiIU6dOkZyczHPPPcf8+fNJTk7m7bffpkSJEjg6OvLll18yefJkoqOjMRgMtG3blnfeecfCR/Y/9+7dIyIigpCQELp06cKvv/5KuXLlmDZtGvv27cPW1pamTZsycOBAEhMTGTVqFDdu3ODSpUu0bt2aDz74oEDqrF+/PpMnTwYgMTERBwcHWrZsyd69e/Hy8iI2NpYGDRrk+J4MHDiQSpUqcezYMUqVKsXcuXNxdnYmLCyMzz//nGLFiuHh4UGRIkUA2LRpE4sXL+bu3bukpKQwadIk6tatWyDH+kf79+9n/vz5LFu2jJ49e1K9enXi4uK4cuUKQUFB+Pr6cv78eYYPH87t27epUaOG6bmWfL8exmAwMGjQILy9vTl+/Dhff/11rt6zypUrc+LEiQKpNafvR5MmTWjbti179+7lzp07TJs2jWrVqnHy5ElGjhxJeno6derUYffu3WzdupXk5GQ+/vhjLl68iMFg4P3336dBgwaZ9vX1118THh7OnTt3MBgMfPrpp1SsWLHAjmn//v1MmjQJW1tbatasyS+//PLQz11OxxQdHc2MGTMAKF68OLNmzaJkyZJ5fhzmiI+P5+OPP+batWsUKVKE0aNHmya137lzJ8uXL+fy5cv079+fzp07ExwcTGJiImfPnuXChQt06tSJf/3rX1m2++fPsKenJwsXLmTTpk2kp6fTsGFDhg8fzoULF3L8HDds2JAWLVoQFxeHra0tn376KWXLlqVJkyYsXbqUmzdv8vHHH3Pv3j0cHR2ZMmUKzz77bKY6ypQpw7Vr1zhy5AhHjx59aG90ly5d2LhxI35+fllukXny5EkmTpzI7du3uXLlCr179yYwMJA7d+4QFBTEiRMnMBgM9OnTh/bt22d67uTJk0lOTmbGjBkcOnSITz75hJSUFEqUKMGECRMeelurh1GPm/wtR48epXr16qbQ9oCvry+nT5/G3t6elStXsnXrVlJSUti1axdw/5fFjBkz+Oqrr/jmm29ISEggIiKC1atXs2XLFnbu3GmBo8nezp07efrpp3nuuedo1qwZK1as4MKFC+zevZuIiAhWrFjBmTNnSElJYf369bRp04ZVq1YRERHB8uXLc+yaz2svvvgiv/76KykpKezduxdvb2+8vb3Zu3cvALGxsbi4uOT4nhw/fpzevXuzfv16nJycWLduHYmJicycOZOQkBBWrlzJrVu3gPs9rStWrOA///kPERER9O3bl0WLFhXIcf6VtLQ0Vq5cyUcffcTcuXOB+0MlAQEBhIeH8/LLL5vWteT79VccHBwoX7483333Xa7es4KW3ffjAWdnZ9asWUOXLl1YsGABcH8IbsiQIYSHh1O2bFnS09OB+yMKHTp0IDQ0lM8//5yPP/6YmzdvmrZ18+ZNvvvuO5YtW8b69etp1qwZy5cvL7BjSktLY8SIEcyYMYOwsDDs7DL3d2T3ucvpmD777DPGjRtHaGgojRs35ujRo/lyHOYYPnw4PXv2ZN26dXz00UcMGTLEdHpFamoqq1evZsGCBcyZM8f0nBMnTrBo0SJWr17NwoULuX79erbbfvAZPn36NLt37+bw4cOsWbOGsLAwEhMTTb1fOX2OL126hJeXF2FhYdStW5eQkJBM21+yZAm9e/cmNDSUnj178uOPP2apoVChQtSoUYMOHTrw1ltvZfr+/1nx4sUZN24co0ePzjRkCrB69Wreffddvv32W5YuXWp6PYKDgylRogTr169nyZIlBAcHc/z4cdPzgoODuXjxItOnTyc9PZ333nuPMWPGEBERQZcuXXjvvfdyrOevqMdN/hYbGxtyuulG3bp1cXZ2JiQkhNOnT3PmzBlu374NQKlSpXjmmWeA+z0mr7/+Ora2thQuXBh/f3+io6Np0qRJgR3Hw4SGhtKmTRsAXnvtNT744AOGDh2Ko6MjXbp0oXHjxqbHffr0ISYmhkWLFnHq1CnS0tK4c+dOgdRpa2tLjRo1OHToEHv37qV79+6ULVuWu3fv8vvvv/PDDz8watQoLl68mON7UrVqVQAqVapkek6tWrVwcXEBwN/fn5iYGGxsbPj3v//N9u3biY+P58CBA1nCu6U0atQIuH8M165dA+DAgQPMmjULgLZt2xIUFARg0ffLHAaDgapVq1K2bFmz37OCltP3AzK/F1u2bOHatWtcuHABX19fADp06MDSpUuB+0P7p0+fZt68ecD9Xq8/no9ZrFgxZs2axYYNGzhz5gx79uyhSpUqBXZMLVu2pFSpUnh6egLQsWPHTEP02X3ucjqmBz30zZo1o2nTpnh7e+fLcfyV27dv8+uvv+Ln5wdAzZo1KV68OKdPnwagadOmGAwGKlWqlOnUl3r16uHg4ECpUqVwdnbmxo0bOe7DYDBQqFAhoqOjOXjwIAEBAQDcvXuXp59+mtq1az/0c/zH1zU2NjbTtn19fZkwYQJ79uyhcePGtGjRItPyjIwM3n77bWrVqsWzzz7LN998g7OzM0ePHqVPnz7Z1tusWTM2bdrE7Nmzadq0qal95MiR7NmzhwULFnDixAnTdzAmJsY00lGyZEmaNm3KgQMHKFasGLt37+bKlSusWbMGOzs7Tp48iZOTk6lHs1WrVnz88cfcuHGDp556KsfXMCcKbo+Z2NhYypYti7u7O0ajEVtbW0uXlK1q1aqxfPlyjEYjBoPB1D579mxeeuklgoODCQwMJCAggKtXr5pCXqFChUzrZmRkZNqm0Wg0/V+4pV2+fNn0f4pLly7FaDRy/fp1tmzZwurVqzlw4AC7d++mS5cuLFu2jJUrV3Lu3DnatGlDs2bNiIqKyjHY5ocH57kdPHjQNBTj5eXFtm3bcHZ25sCBA8ybNy/b98TR0dG0HYPBYHpP//j+POhluHXrFh06dKBdu3bUrVuXypUrZ/m/YUt5cBx//DwCpuM0GAymZVOnTrXo+/UwqampxMfHc+7cOebOnWv2e1aQHvb9+GN9D15vW1vbHGvMyMhgyZIlODs7A/eHsV1cXPjuu+8ASEhIoGfPnvTo0QMfHx9cXFw4duxYgR3T7t27s/yu+qPsPnc5HVOVKlVo3LgxO3bsYMaMGRw8eDDb4ca89ue/K0WLFs3yfvzx9++Dvzt//i6Z+7l78Bl+/vnniYmJ4c0336R3794AXL9+HVtbW65evfrQ7f3xdf3zflq2bEmtWrXYsWMHS5YsYdeuXUyaNMm0/Pjx41y7do333nuPjIwM3n33XQYNGvSX592NGTOGNm3amN43gKFDh+Lk5ETjxo157bXX2LBhg+n1+qM/vn4eHh4MGzaMCRMmsGLFimw/P4/y9+7x+F9lMfn2229Nv7BOnDjx2J5QXqdOHUqVKsX8+fNNH749e/YQGhrKnj17aNWqFR06dMDFxYXvv/8+2w9o/fr1CQsLIz09nTt37rBu3brH5jy+iIgI6tevz+7du9m+fTs7duygf//+jB07lh49elC3bl0+/PBDKlasSHx8PPv27aNPnz60atWKhIQEEhMTH/rLPq/Vr1+f8PBwXnjhBVPI8vb2ZvHixXh7exMdHW3We/JA7dq1+emnn0zHsXHjRgDOnDmDjY0N/fv3N70+j0vYzk6DBg1MwzJbtmwxDQVZ+v3KSUZGBsHBwdSoUYNz587l6j0rSDl9P1auXJnt+k899RTlypUzDfX+cWi3fv36pqHPn3/+mbZt22bq/Tx06BDly5enV69e1KhRI98+czkd0969e7l+/brp3EFzhqVzOqZOnTpx69YtevXqRa9evQpsqPTPf1fKly9P2bJlTUH7xx9/JDk5mUqVKj3yvv74GS5Xrpzpd9OtW7e4d+8eAwYMeOQrdocOHcrBgwfp0qULQ4YMyfI6uru7k5yczIULF7CxsaF+/frcunXrL3umnZ2dGTduHJ999pmpbd++fQwePJhmzZrx/fffA5Cenk79+vVZs2YNAFeuXGHbtm288sorAFSsWJFOnTpRuHBhQkJCqFChAteuXePgwYMAbNy4kaeffjpTQMwN9bg9Zt555x1GjBjB119/TenSpfn000/Zt2+fpcvKwmAw8NlnnzFlyhTatGmDnZ0dJUqUYOHChdja2vLBBx8QGRmJg4MDNWvW5Pz581m20blzZ86cOUO7du1IS0ujbdu2NG/enP3791vgiDILDQ1l2LBhmdq6devG//3f/+Hq6kqbNm0oXLgwVapUwcfHh9u3bzNixAicnJwoVaoU1apV4/z585QrV65A6n3hhRe4du0a3bp1M7XVr1+foUOH4u3tTdGiRc16Tx5wcXEhKCiIXr16UbhwYZ5//nkAPD09qVKlCq1ataJQoULUrVuX3377Ld+PD+73GtSqVcv02N3dHVdX14c+5+OPP2b48OGsWLGC6tWrU7RoUQD69etn0ffrj5KSkkwnTmdkZFClShVmzZpFYmJirt6zgvSw70exYsWyfc60adMYNWoUn376KZUrVzb1vgcFBfHxxx/j7+8PwPTp0zNtw9vbm2+++YbXXnsNBwcHXnrpJU6dOlWgx7Ro0SI+/PBDbGxseO655zKNHGQnp2N67733GDlyJHZ2djg6OjJ+/Pg8P47sZPd3pXnz5owbN47g4GDs7e0JDg7GwcHhb20/p88wQJMmTTh+/DhvvPEG6enpNGrUiNdff50LFy787ePp378/o0eP5rPPPsPW1jbL9BulSpVi4sSJvPvuu9y7d4+KFSsSHh7ORx99REBAAB4eHjluu1mzZrRo0YKkpCQABg0aRLdu3XBycuK5557Dw8OD8+fPM2DAAMaNG4e/vz/p6en079+fF198MdPFQePGjaNr1640b96cOXPmMHHiRO7cuUPx4sUznTuYWwbj4zI+IDlq3749EyZMMI2Pi4hYm/nz5/PGG2/g5ubGli1bWLdunUXmacutjIwMZs6cycCBAylSpAiLFy8mMTHRovMaypNNPW6PuY4dO1KoUCHTibEiItbo6aef5q233sLOzg4nJyermYPPxsYGZ2dnOnbsiL29PR4eHlZTu/wzqcdNRERExEro4gQRERERK6HgJiIiImIlFNxERP7r2rVrmWbtFxF53Ci4iYhVqVy5MidPnszSXq9evUeeSqZFixY5Tm/Ss2dP2rZta5oL7oGRI0cybdq0R9qviIi5FNxERP7rwS2LcnLixAnTbYxERCxBwU1E/nHOnj1Lv379qFu3Lk2bNuWLL74w3aImKioKf39/6tSpg7+/P+Hh4QCmeyl26tTJNMv8nwUEBLBkyZIs90584O7du4wbN47mzZtTs2ZN/Pz8TNt6cG/e6dOnU7duXXx8fNi+fTuffPIJderUoUmTJkRHR5u2tWXLFtq0aUOdOnV48803iY+PNy2bMWMG3t7eeHl50adPn0z39hSRfzYFNxGxOl26dKFOnTqZ/j24nU1qaiq9e/emYsWK7Nu3j4ULF7Jy5UpWrFgBwEcffcTAgQOJjY1l1KhRjBs3jps3bxIaGgrA6tWradasWbb7ffHFF+nXrx8jR47k1q1bWZYvWrSIX375hdDQUOLi4ggICGDixImm5UePHqVUqVKmEDdw4EA8PDyIjo6mdevWzJw5E4CDBw8yatQoxo8fT3R0NI0bN6Zfv36kpaURHR3Npk2bWL9+PXv27KF06dJWMZGtiOQNBTcRsTorVqwgNjY207/ixYsDEBcXx40bN3jvvfdwcHCgYsWKvP3226xduxa4f/Pq9evXEx0dTe3atYmLi8vxNk3Z6d+/P87OzkydOjXLsu7duzNv3jyKFClCQkICRYsWJTEx0bTc3t6eN99803T/RBsbGwIDA7G3t6dBgwam8+vWrFlD+/btqV27Nvb29vTq1Yt79+6xf/9+7O3tuXz5MqtXr+bXX39l4sSJTJ8+/VFeThGxIgpuIvKPcvnyZdzd3bGz+9+NYZ5++mkuXrwI3O8Vc3R05L333qNevXpMnz6dtLQ0s7dvZ2fH9OnTWbduHTt37sy07MaNGwwfPhwvLy+GDBlCXFwcf5zjvGjRoqa6bGxsKFq0KDY2NqbHD250n5CQwKpVqzL1KF6+fJmEhATq1KnDlClT2L17N23btqVVq1ZZ6hCRfy7d8kpE/lHKlClDYmIi9+7dM4Wk8+fP4+LiQmpqKr/++iszZ87EaDTyww8/MHDgQKpXr07r1q3N3keFChV4//33CQoK4sUXX6REiRIAjB07looVK/Kf//wHOzs7vv/+ezZt2pTrY3B1daVPnz4MGTLE1HbmzBnc3d1JSEigQoUKfP3119y6dYuQkBCGDh1KXFwctra2ud6XiFgX9biJyD/KSy+9hIuLC7NnzyY1NZVffvmFRYsW4e/vD8B7773H6tWrAXB3d8dgMODs7AzcH8o0dx63Hj16UKlSpUy9XTdv3qRQoULY2tqSkJDA3LlzAXLVowfQvn17Vq9ezZEjRzAajWzdupU2bdqQkJDATz/9RL9+/Th37hxFixbFyckJJycnhTaRJ4SCm4j8o9jb2/Of//yHU6dO4e3tTa9evejYsSNvvvkmDg4OzJs3j+XLl/Pyyy/TuXNnevbsibe3N3D/qtHevXubzod7GIPBwJQpU3BycjK1ffTRR+zcuZOXX36ZHj164OvrS5EiRfjll19ydQyvvPIKI0eOZMSIEbz88svMnTuXTz/9lAoVKtCyZUsCAgLo2rUrtWrVYtWqVZqiROQJopvMi4iIiFgJ9biJiIiIWAkFNxEREREroeAmIiIiYiUU3ERERESshIKbiIiIiJVQcBMRERGxEgpuIiIiIlZCwU1ERETESvx/RayrGR+KuskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the busiest hosts data through bar plot.\n",
    "host_name = busiest_hosts['host_name']\n",
    "reviews = busiest_hosts['number_of_reviews']\n",
    "plt.title('Busiest Hosts', {'fontsize':18})\n",
    "plt.xlabel('Host Names',{'fontsize':13})\n",
    "plt.ylabel('Number of reviews',{'fontsize':18})\n",
    "plt.bar(host_name, reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:27.732891Z",
     "iopub.status.busy": "2024-04-15T05:31:27.732436Z",
     "iopub.status.idle": "2024-04-15T05:31:28.154495Z",
     "shell.execute_reply": "2024-04-15T05:31:28.153188Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1647796684285,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "ZBzqlwFKFCIW",
    "outputId": "2e7ad81c-962a-4372-f836-1e0172fb53fe",
    "papermill": {
     "duration": 0.46608,
     "end_time": "2024-04-15T05:31:28.157298",
     "exception": false,
     "start_time": "2024-04-15T05:31:27.691218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histogram for room types to look for which are most preferred room types.\n",
    "plt.rcParams['figure.figsize'] = (10,5)\n",
    "hp = sns.histplot(airbnb_data_new['room_type'], color= 'red')\n",
    "hp.set_xlabel('Room type') \n",
    "hp.set_ylabel('Number of listings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:28.224447Z",
     "iopub.status.busy": "2024-04-15T05:31:28.224028Z",
     "iopub.status.idle": "2024-04-15T05:31:28.227993Z",
     "shell.execute_reply": "2024-04-15T05:31:28.227148Z"
    },
    "id": "mqGcaW__tOCy",
    "papermill": {
     "duration": 0.040904,
     "end_time": "2024-04-15T05:31:28.230166",
     "exception": false,
     "start_time": "2024-04-15T05:31:28.189262",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### Looking at the result above, we can clonclude that the top 5 Busiest Hosts are:\n",
    "Ji, Carol, Asa, Wanda,Linda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5icd0cjsSRqg",
    "papermill": {
     "duration": 0.03281,
     "end_time": "2024-04-15T05:31:28.294617",
     "exception": false,
     "start_time": "2024-04-15T05:31:28.261807",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The above mentioned hosts are busiest because they host Entire home/apt and private room which are the most preferred room types according to above histogram plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2jD0XpxrX6gu",
    "papermill": {
     "duration": 0.032416,
     "end_time": "2024-04-15T05:31:28.360783",
     "exception": false,
     "start_time": "2024-04-15T05:31:28.328367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Most Popular Rooms in the Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:28.430541Z",
     "iopub.status.busy": "2024-04-15T05:31:28.429892Z",
     "iopub.status.idle": "2024-04-15T05:31:28.445896Z",
     "shell.execute_reply": "2024-04-15T05:31:28.444625Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1647796686584,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "57ZD095E8Jp9",
    "outputId": "ea951155-edbf-4f7d-a09d-a16e75ec3e56",
    "papermill": {
     "duration": 0.055711,
     "end_time": "2024-04-15T05:31:28.448947",
     "exception": false,
     "start_time": "2024-04-15T05:31:28.393236",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood\n",
       "Williamsburg          3415\n",
       "Bedford-Stuyvesant    2963\n",
       "Harlem                2311\n",
       "Bushwick              1846\n",
       "Upper West Side       1619\n",
       "East Village          1590\n",
       "Hell's Kitchen        1576\n",
       "Upper East Side       1561\n",
       "Crown Heights         1257\n",
       "Midtown               1002\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find the top 10 neighbourhoods with most listings\n",
    "df1 = airbnb_data_new.groupby(['neighbourhood'])['id'].count().nlargest(10)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:28.519261Z",
     "iopub.status.busy": "2024-04-15T05:31:28.518523Z",
     "iopub.status.idle": "2024-04-15T05:31:28.964632Z",
     "shell.execute_reply": "2024-04-15T05:31:28.963733Z"
    },
    "executionInfo": {
     "elapsed": 946,
     "status": "ok",
     "timestamp": 1647796687889,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "08i6lBl481Vz",
    "outputId": "f491ef25-0d26-4a2f-d58b-2e3732b3acd3",
    "papermill": {
     "duration": 0.483861,
     "end_time": "2024-04-15T05:31:28.966963",
     "exception": false,
     "start_time": "2024-04-15T05:31:28.483102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the top 10 neighbourhood listings through bar plot\n",
    "plt.figure(figsize=(12,6))\n",
    "x = list(df1.index)\n",
    "y = list(df1.values)\n",
    "x.reverse()\n",
    "y.reverse()\n",
    "\n",
    "plt.title(\"Top 10 Neighbourhoods with the Most Listings\", {'fontsize':18})\n",
    "plt.ylabel(\"Neighbourhood\", {'fontsize':18})\n",
    "plt.xlabel(\"Total Listings\", {'fontsize':18})\n",
    "\n",
    "plt.barh(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "80Y-d5xg_Q0k",
    "papermill": {
     "duration": 0.035241,
     "end_time": "2024-04-15T05:31:29.036930",
     "exception": false,
     "start_time": "2024-04-15T05:31:29.001689",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The neighborhoods with the most listings are in Manhattan and Brooklyn – which is understandable – given that tourists are more likely to stay in those areas. Williamsburg, in Brooklyn, appears first with 3,415 listings. In Manhattan, Bedford-Stuyvesant has the most listings, totaling 2,311 offers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-15T05:31:29.106688Z",
     "iopub.status.busy": "2024-04-15T05:31:29.105818Z",
     "iopub.status.idle": "2024-04-15T05:31:29.137959Z",
     "shell.execute_reply": "2024-04-15T05:31:29.136758Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1647796689186,
     "user": {
      "displayName": "Kunal Mahadik",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "16288081313373243624"
     },
     "user_tz": -330
    },
    "id": "VuymF_Smakrf",
    "outputId": "cdadeffc-f10b-49c6-8a45-c91928e157f3",
    "papermill": {
     "duration": 0.070121,
     "end_time": "2024-04-15T05:31:29.140612",
     "exception": false,
     "start_time": "2024-04-15T05:31:29.070491",
     "status": "completed"
    },
    "tags": []
   },
   "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</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>room_type</th>\n",
       "      <th>number_of_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>Private room</td>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Lower East Side</td>\n",
       "      <td>Private room</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Park Slope</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Bushwick</td>\n",
       "      <td>Private room</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Queens</td>\n",
       "      <td>Flushing</td>\n",
       "      <td>Private room</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>South Slope</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Queens</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>Private room</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Village</td>\n",
       "      <td>Private room</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Queens</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Queens</td>\n",
       "      <td>Springfield Gardens</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    neighbourhood_group        neighbourhood        room_type  \\\n",
       "255           Manhattan               Harlem     Private room   \n",
       "270           Manhattan      Lower East Side     Private room   \n",
       "196            Brooklyn           Park Slope  Entire home/apt   \n",
       "132            Brooklyn             Bushwick     Private room   \n",
       "358              Queens             Flushing     Private room   \n",
       "212            Brooklyn          South Slope  Entire home/apt   \n",
       "348              Queens        East Elmhurst     Private room   \n",
       "241           Manhattan         East Village     Private room   \n",
       "318              Queens              Astoria  Entire home/apt   \n",
       "419              Queens  Springfield Gardens  Entire home/apt   \n",
       "\n",
       "     number_of_reviews  \n",
       "255                607  \n",
       "270                540  \n",
       "196                488  \n",
       "132                480  \n",
       "358                474  \n",
       "212                467  \n",
       "348                466  \n",
       "241                451  \n",
       "318                441  \n",
       "419                434  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb_data_new.groupby(['neighbourhood_group','neighbourhood','room_type'])['number_of_reviews'].max().reset_index().sort_values(by = 'number_of_reviews', ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgqWRyYxdKod",
    "papermill": {
     "duration": 0.035487,
     "end_time": "2024-04-15T05:31:29.209899",
     "exception": false,
     "start_time": "2024-04-15T05:31:29.174412",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The most popular neighbourhood is Harlem which is situated in Manhattan and the room type prefered here is Private Room."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Id74WlSBkS-v",
    "papermill": {
     "duration": 0.033103,
     "end_time": "2024-04-15T05:31:29.278729",
     "exception": false,
     "start_time": "2024-04-15T05:31:29.245626",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Key Findings: \n",
    "1. Manhattan has most number of listings, followed by Brooklyn and Queens. Staten Island has least number of listings.\n",
    "2. Manhattan and Brooklyn make up for 87% of listings available in NYC.\n",
    "3. Brooklyn and Manhattan are most liked neighbourhood groups by people.\n",
    "4. Queens has significantly less host listings than Manhattan. So, we should take enough steps to encourage host listings in Queens.\n",
    "5. The maximum demand is for private rooms and enitre home/apartment. People are more interested in cheaper rentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e_cw_FkK3STp",
    "papermill": {
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    "### Conclusion:\n",
    "\n",
    "We can conclude from the analysis that Manhattan is the top neighbourhood group when it comes to the number of listings and highest rental prices. Given that Manhattan is a world-famous for its museums, stores, parks and theatres - and its substantial number of tourists throughout the year, hence the prices are much higher in this borough.\n",
    "\n",
    "The number 2 neighbourhood group is Brooklyn having significant number of listings and more affordable prices if compared to Manhattan. \n",
    "\n",
    "For other neighbourhood groups namely Queens, Bronx and Staten island there aren't as many listing options available, especially on Staten Island. Considering that those are residential areas, it is possible that many guests choose these locations to save up money or perhaps to visit family and friends who live in this area. \n",
    "\n",
    "For our data exploration purpose, it would have been nice to have couple of additional features like positive and negative reviews. Depending upon that we could have gauged the popularity of each neighbourhood."
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