{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Get the Data**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kagglehub\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "# Download Kaggle dataset\n",
    "path = kagglehub.dataset_download('dylanjcastillo/7k-books-with-metadata')\n",
    "\n",
    "# Move dataset to data directory\n",
    "os.makedirs('./data', exist_ok=True)\n",
    "\n",
    "for file in os.listdir(path):\n",
    "    source      = os.path.join(path, file)\n",
    "    destination = os.path.join('./data', file)\n",
    "    shutil.move(source, destination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn13</th>\n",
       "      <th>isbn10</th>\n",
       "      <th>title</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>authors</th>\n",
       "      <th>categories</th>\n",
       "      <th>thumbnail</th>\n",
       "      <th>description</th>\n",
       "      <th>published_year</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9780002005883</td>\n",
       "      <td>0002005883</td>\n",
       "      <td>Gilead</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marilynne Robinson</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>http://books.google.com/books/content?id=KQZCP...</td>\n",
       "      <td>A NOVEL THAT READERS and critics have been eag...</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>247.0</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9780002261982</td>\n",
       "      <td>0002261987</td>\n",
       "      <td>Spider's Web</td>\n",
       "      <td>A Novel</td>\n",
       "      <td>Charles Osborne;Agatha Christie</td>\n",
       "      <td>Detective and mystery stories</td>\n",
       "      <td>http://books.google.com/books/content?id=gA5GP...</td>\n",
       "      <td>A new 'Christie for Christmas' -- a full-lengt...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.83</td>\n",
       "      <td>241.0</td>\n",
       "      <td>5164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9780006163831</td>\n",
       "      <td>0006163831</td>\n",
       "      <td>The One Tree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stephen R. Donaldson</td>\n",
       "      <td>American fiction</td>\n",
       "      <td>http://books.google.com/books/content?id=OmQaw...</td>\n",
       "      <td>Volume Two of Stephen Donaldson's acclaimed se...</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>479.0</td>\n",
       "      <td>172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9780006178736</td>\n",
       "      <td>0006178731</td>\n",
       "      <td>Rage of angels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sidney Sheldon</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>http://books.google.com/books/content?id=FKo2T...</td>\n",
       "      <td>A memorable, mesmerizing heroine Jennifer -- b...</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>3.93</td>\n",
       "      <td>512.0</td>\n",
       "      <td>29532.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9780006280897</td>\n",
       "      <td>0006280897</td>\n",
       "      <td>The Four Loves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clive Staples Lewis</td>\n",
       "      <td>Christian life</td>\n",
       "      <td>http://books.google.com/books/content?id=XhQ5X...</td>\n",
       "      <td>Lewis' work on the nature of love divides love...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>170.0</td>\n",
       "      <td>33684.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          isbn13      isbn10           title subtitle  \\\n",
       "0  9780002005883  0002005883          Gilead      NaN   \n",
       "1  9780002261982  0002261987    Spider's Web  A Novel   \n",
       "2  9780006163831  0006163831    The One Tree      NaN   \n",
       "3  9780006178736  0006178731  Rage of angels      NaN   \n",
       "4  9780006280897  0006280897  The Four Loves      NaN   \n",
       "\n",
       "                           authors                     categories  \\\n",
       "0               Marilynne Robinson                        Fiction   \n",
       "1  Charles Osborne;Agatha Christie  Detective and mystery stories   \n",
       "2             Stephen R. Donaldson               American fiction   \n",
       "3                   Sidney Sheldon                        Fiction   \n",
       "4              Clive Staples Lewis                 Christian life   \n",
       "\n",
       "                                           thumbnail  \\\n",
       "0  http://books.google.com/books/content?id=KQZCP...   \n",
       "1  http://books.google.com/books/content?id=gA5GP...   \n",
       "2  http://books.google.com/books/content?id=OmQaw...   \n",
       "3  http://books.google.com/books/content?id=FKo2T...   \n",
       "4  http://books.google.com/books/content?id=XhQ5X...   \n",
       "\n",
       "                                         description  published_year  \\\n",
       "0  A NOVEL THAT READERS and critics have been eag...          2004.0   \n",
       "1  A new 'Christie for Christmas' -- a full-lengt...          2000.0   \n",
       "2  Volume Two of Stephen Donaldson's acclaimed se...          1982.0   \n",
       "3  A memorable, mesmerizing heroine Jennifer -- b...          1993.0   \n",
       "4  Lewis' work on the nature of love divides love...          2002.0   \n",
       "\n",
       "   average_rating  num_pages  ratings_count  \n",
       "0            3.85      247.0          361.0  \n",
       "1            3.83      241.0         5164.0  \n",
       "2            3.97      479.0          172.0  \n",
       "3            3.93      512.0        29532.0  \n",
       "4            4.15      170.0        33684.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./data/books.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. Explore the Data**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6810 entries, 0 to 6809\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   isbn13          6810 non-null   int64  \n",
      " 1   isbn10          6810 non-null   object \n",
      " 2   title           6810 non-null   object \n",
      " 3   subtitle        2381 non-null   object \n",
      " 4   authors         6738 non-null   object \n",
      " 5   categories      6711 non-null   object \n",
      " 6   thumbnail       6481 non-null   object \n",
      " 7   description     6548 non-null   object \n",
      " 8   published_year  6804 non-null   float64\n",
      " 9   average_rating  6767 non-null   float64\n",
      " 10  num_pages       6767 non-null   float64\n",
      " 11  ratings_count   6767 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 638.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>isbn13</th>\n",
       "      <td>6810.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780676621541.416016</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>isbn10</th>\n",
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       "      <td>6810</td>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <td>The Lord of the Rings</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subtitle</th>\n",
       "      <td>2381</td>\n",
       "      <td>2009</td>\n",
       "      <td>A Novel</td>\n",
       "      <td>226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>authors</th>\n",
       "      <td>6738</td>\n",
       "      <td>3780</td>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>categories</th>\n",
       "      <td>6711</td>\n",
       "      <td>567</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>2588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thumbnail</th>\n",
       "      <td>6481</td>\n",
       "      <td>6481</td>\n",
       "      <td>http://books.google.com/books/content?id=2lVyR...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>6548</td>\n",
       "      <td>6474</td>\n",
       "      <td>No Marketing Blurb</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>published_year</th>\n",
       "      <td>6804.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1998.630364</td>\n",
       "      <td>10.484257</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_rating</th>\n",
       "      <td>6767.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.933284</td>\n",
       "      <td>0.331352</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.96</td>\n",
       "      <td>4.13</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_pages</th>\n",
       "      <td>6767.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348.181026</td>\n",
       "      <td>242.376783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>3342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratings_count</th>\n",
       "      <td>6767.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21069.099897</td>\n",
       "      <td>137620.678647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>5992.5</td>\n",
       "      <td>5629932.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique  \\\n",
       "isbn13          6810.0    NaN   \n",
       "isbn10            6810   6810   \n",
       "title             6810   6398   \n",
       "subtitle          2381   2009   \n",
       "authors           6738   3780   \n",
       "categories        6711    567   \n",
       "thumbnail         6481   6481   \n",
       "description       6548   6474   \n",
       "published_year  6804.0    NaN   \n",
       "average_rating  6767.0    NaN   \n",
       "num_pages       6767.0    NaN   \n",
       "ratings_count   6767.0    NaN   \n",
       "\n",
       "                                                              top  freq  \\\n",
       "isbn13                                                        NaN   NaN   \n",
       "isbn10                                                 9042003405     1   \n",
       "title                                       The Lord of the Rings    11   \n",
       "subtitle                                                  A Novel   226   \n",
       "authors                                           Agatha Christie    37   \n",
       "categories                                                Fiction  2588   \n",
       "thumbnail       http://books.google.com/books/content?id=2lVyR...     1   \n",
       "description                                    No Marketing Blurb     6   \n",
       "published_year                                                NaN   NaN   \n",
       "average_rating                                                NaN   NaN   \n",
       "num_pages                                                     NaN   NaN   \n",
       "ratings_count                                                 NaN   NaN   \n",
       "\n",
       "                                mean               std              min  \\\n",
       "isbn13          9780676621541.416016  606891056.586608  9780002005883.0   \n",
       "isbn10                           NaN               NaN              NaN   \n",
       "title                            NaN               NaN              NaN   \n",
       "subtitle                         NaN               NaN              NaN   \n",
       "authors                          NaN               NaN              NaN   \n",
       "categories                       NaN               NaN              NaN   \n",
       "thumbnail                        NaN               NaN              NaN   \n",
       "description                      NaN               NaN              NaN   \n",
       "published_year           1998.630364         10.484257           1853.0   \n",
       "average_rating              3.933284          0.331352              0.0   \n",
       "num_pages                 348.181026        242.376783              0.0   \n",
       "ratings_count           21069.099897     137620.678647              0.0   \n",
       "\n",
       "                            25%              50%              75%  \\\n",
       "isbn13          9780330341506.0  9780553108095.5  9780809982553.5   \n",
       "isbn10                      NaN              NaN              NaN   \n",
       "title                       NaN              NaN              NaN   \n",
       "subtitle                    NaN              NaN              NaN   \n",
       "authors                     NaN              NaN              NaN   \n",
       "categories                  NaN              NaN              NaN   \n",
       "thumbnail                   NaN              NaN              NaN   \n",
       "description                 NaN              NaN              NaN   \n",
       "published_year           1996.0           2002.0           2005.0   \n",
       "average_rating             3.77             3.96             4.13   \n",
       "num_pages                 208.0            304.0            420.0   \n",
       "ratings_count             159.0           1018.0           5992.5   \n",
       "\n",
       "                            max  \n",
       "isbn13          9789042003408.0  \n",
       "isbn10                      NaN  \n",
       "title                       NaN  \n",
       "subtitle                    NaN  \n",
       "authors                     NaN  \n",
       "categories                  NaN  \n",
       "thumbnail                   NaN  \n",
       "description                 NaN  \n",
       "published_year           2019.0  \n",
       "average_rating              5.0  \n",
       "num_pages                3342.0  \n",
       "ratings_count         5629932.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "category_counts = df['categories'].value_counts().head(20).sort_values(ascending=True)\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "category_counts.plot(kind='barh', color='skyblue')\n",
    "plt.title('Top 20 Book Categories')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Category')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective: Utilize the most important features: `isbn13`, `categories`, `description`.\n",
    "\n",
    "Observations:\n",
    "\n",
    "- `isbn13` is completely unique and can be used as a stable identifier for books → `isbn10` is redundant and can be safely removed.\n",
    "\n",
    "- `subtitle` has ~65% missing values → It can either be dropped or concatenated with `title` to form a richer `full_title` feature for textual modeling.\n",
    "\n",
    "- `categories` contains 567 unique values, but many of them are sparse (appearing only once) →  Requires cleaning and filtering first - keeping only frequent categories or clustering similar ones.\n",
    "\n",
    "- `description` is the most critical feature for semantic recommendation. With only ~3.85% missing values, coverage is good → However, it's important to analyze whether the missing entries are biased toward specific categories or metadata (e.g., low popularity books).\n",
    "\n",
    "- Other features such as `authors`, `published_year`, `average_rating`, `ratings_count`, and `num_pages` can be considered later as auxiliary features, especially for ranking or filtering. `thumbnail` feature will be used for visualizing book cover if needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Check for NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iRYqkeVtMRsLCwvTxxx/r4MGD2rp1qzp27CgvL68cax8AAAAAAPcyh5Ieixcv1gcffKCOHTtaH/gp3Xhg56FDh3KscblpyJAhcnV1VcWKFRUUFJSp5160aNFCo0aN0quvvqpatWrp8uXL6ty5c461afLkyRowYIBq1Kih06dPa+XKlTZJpjv58MMPdf78eVWvXl2dOnVS//79VbRo0RxrHwAAAAAA9zKHnulx8uRJlS9fPs381NRUu8+0yA/Cw8P1yy+/2Mzr2rWrzeeqVaumeYbHuHHj0jzY81aRkZFp5sXExKSZd2u5jRs3tn5+/PHH7ZbbtWvXNO1r27atTTnVqlXT9u3bbdZp165duvWGhobyJhYAAAAAgNNw6E6PihUr6qeffkoz//PPP1e1atWy3SgAAAAAAIDscuhOj9GjR6tLly46efKkUlNTtWLFCh0+fFiLFy/WqlWrcrqNAAAAAAAAWZalOz2OHz8uY4yeeOIJrVy5Uj/88IN8fHw0evRoHTx4UCtXrlTz5s3vVlsBAAAAAAAyLUt3eoSFhSkuLk5FixZVgwYNVKhQIe3du1fBwcF3q30AAAAAAAAOydKdHrc/5PLbb7/VlStXcrRBAAAAAAAAOcGhB5nexJs+AAAAAADAvSpLSQ+LxSKLxZJmHgAAAAAAwL0mS8/0MMaoa9eu8vDwkCRdu3ZNffr0kY+Pj816K1asyLkWAgAAAAAAOCBLSY8uXbrYfH7hhRdytDEAAAAAAAA5JUtJjwULFtytdgAAAAAAAOSobD3IFMgOr+IN7smyctrVUz/lWFn38nbmlPS20ZFtz4/7K6ttvrl+Tm9rdsu7Nf7Wf988Hm4v39Htdlb38vbdrbbdy9ss3d323evbfidZ+Z2zt+6d4vPT/klvW3LyXCCv3a3fnX+avNp/XsUb3LFuR76v9srMj9+RrG771VM/WWNy4zjPj33M7X2GI/vY3r+zgqQHAAAAAABwSiQ9AAAAAACAUyLpAQAAAAAAnBJJDwAAAAAA4JRIegAAAAAAAKdE0gMAAAAAADglkh4AAAAAAMApkfQAAAAAAABOiaQHAAAAAABwSiQ9JDVu3FgDBw7Ms/pDQ0M1c+bMbJfTtWtXtW3bNtvlAAAAAADgDNzyugGQtm/fLh8fn0yvHxMTo7Jly2r37t2qWrWqdf6sWbNkjLkLLQQAAAAAIP8h6ZGHkpKS5O7urqCgoBwpLyAgIEfKAQAAAADAGfzjhrdcuXJFnTt3lq+vr4oVK6bp06fbLE9MTNSQIUNUokQJ+fj4qE6dOoqMjLQu/+2339SmTRsVLFhQPj4+euCBB7R69Wrr8v379+vxxx+Xv7+//Pz81KBBAx07dkzS/w8/mTBhgooXL64KFSpISju8xWKxaO7cuWrVqpW8vLxUrlw5ff7559blZcuWlSRVq1ZNFotFjRs3tin/1m3p37+/ihYtKk9PTz388MPavn27dXlkZKQsFovWrVunmjVrytvbW/Xq1dPhw4eztY8BAAAAALgX/OOSHkOHDtXGjRv11Vdf6fvvv1dkZKR27dplXd6vXz/98ssvWr58ufbs2aP27durZcuWOnr0qCTp5ZdfVmJion788Uft3btXU6ZMka+vryTp5MmTatiwoTw8PLR+/Xrt3LlT3bt31/Xr163lr1u3TocPH9batWu1atWqdNs5atQoPf3004qOjlbHjh317LPP6uDBg5Kkbdu2SZJ++OEHxcXFacWKFXbLePXVV/W///1PixYt0q5du1S+fHm1aNFC586ds1lv5MiRmj59unbs2CE3Nzd1797dgT0LAAAAAMC95R81vCU+Pl4ffvihPvnkEz3yyCOSpEWLFqlkyZKSpNjYWC1YsECxsbEqXry4JGnIkCFas2aNFixYoIkTJyo2NlZPP/20KlWqJEkqV66ctfx3331XAQEBWr58uQoUKCBJCg8Pt2mDj4+P5s+fL3d39wzb2r59e/Xs2VOS9Prrr2vt2rWaPXu25syZYx0OU7hwYYWEhNiNv3LliubOnauFCxeqVatWkqR58+Zp7dq1+vDDDzV06FDruhMmTFCjRo0kScOHD1fr1q117do1eXp6pik3MTFRiYmJNvOMMbJYLBluDwAAAAAAue0fdafHsWPHlJSUpDp16ljnFSpUyDrMZO/evUpJSVF4eLh8fX2t08aNG61DVPr376833nhD9evX15gxY7Rnzx5rWVFRUWrQoIE14WFPpUqV7pjwkKS6deum+XzzTo/MbmtycrLq169vnVegQAHVrl07TTmVK1e2/rtYsWKSpDNnztgtd9KkSQoICLCZTOrlTLcLAAAAAIDc8o9KetxJfHy8XF1dtXPnTkVFRVmngwcPatasWZKknj176vjx4+rUqZP27t2rmjVravbs2ZIkLy+vO9aRlbe05JZbkzQ379hITU21u+6IESN08eJFm8ni4pcr7QQAAAAAICv+UUmP++67TwUKFNDWrVut886fP68jR45IuvFg0JSUFJ05c0bly5e3mW4dRlKqVCn16dNHK1as0ODBgzVv3jxJN+6Y+Omnn5ScnJzttm7ZsiXN54iICEmy3imSkpKS4ba6u7tr8+bN1nnJycnavn27Klas6HC7PDw85O/vbzMxtAUAAAAAcC/6Rz3Tw9fXVz169NDQoUNVuHBhFS1aVCNHjpSLy43cT3h4uDp27KjOnTtr+vTpqlatmv766y+tW7dOlStXVuvWrTVw4EC1atVK4eHhOn/+vDZs2GBNRvTr10+zZ8/Ws88+qxEjRiggIEBbtmxR7dq1rUNoMuuzzz5TzZo19fDDD2vJkiXatm2bPvzwQ0lS0aJF5eXlpTVr1qhkyZLy9PRM87paHx8fvfTSSxo6dKgKFSqk0qVLa+rUqUpISFCPHj1yYG8CAAAAAHBv+0fd6SFJ06ZNU4MGDdSmTRs1a9ZMDz/8sGrUqGFdvmDBAnXu3FmDBw9WhQoV1LZtW23fvl2lS5eWdOPuipdfflkRERFq2bKlwsPDNWfOHEk3Hiy6fv16xcfHq1GjRqpRo4bmzZuX4TM+0jNu3DgtX75clStX1uLFi7Vs2TLrHRpubm56++239f7776t48eJ64okn7JYxefJkPf300+rUqZOqV6+uX3/9Vd99950KFiyY5fYAAAAAAJDf/KPu9JBu3O3x8ccf6+OPP7bOu/VNJgUKFNC4ceM0btw4u/E3n9+RnsqVK+u7776zu2zhwoV258fExKSZV7x4cX3//ffp1tOzZ0/r213SK9/T01Nvv/223n77bbtlNG7cWMYYm3lVq1ZNMw8AAAAAgPzoH3enBwAAAAAA+Gcg6QEAAAAAAJzSP254S37A8BIAAAAAALKPOz0AAAAAAIBTIukBAAAAAACcEkkPAAAAAADglEh6AAAAAAAAp0TSAwAAAAAAOCWSHgAAAAAAwCmR9AAAAAAAAE7JYowxed0I5G9u7iXyugkAAAAAgH+Q60knM7Ued3oAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAATomkBwAAAAAAcEokPQAAAAAAgFMi6QEAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnlK+THjExMbJYLIqKikp3ncjISFksFl24cEGStHDhQgUGBmaq/KysmxWZaTcAAAAAAMiefJ30cMQzzzyjI0eO5HUzAAAAAADAXeaW1w3IbV5eXvLy8srrZtzzkpKS5O7untfNAAAAAADAYXl6p0fjxo3Vr18/9evXTwEBASpSpIhGjRolY4wkyWKx6Msvv7SJCQwM1MKFC23mHTp0SPXq1ZOnp6cefPBBbdy4Md06bx+yEh0drSZNmsjPz0/+/v6qUaOGduzYYRPz3XffKSIiQr6+vmrZsqXi4uJsls+fP18RERHy9PTU/fffrzlz5tgs37Ztm6pVqyZPT0/VrFlTu3fvztT+McaofPnyevPNN23mR0VFyWKx6Ndff5UkXbhwQT179lRQUJD8/f3VtGlTRUdHW9c/duyYnnjiCQUHB8vX11e1atXSDz/8YFNmaGioXn/9dXXu3Fn+/v7q3bt3ptoIAAAAAMC9Ks+HtyxatEhubm7atm2bZs2apbfeekvz58/PUhlDhw7V4MGDtXv3btWtW1dt2rTR2bNnMxXbsWNHlSxZUtu3b9fOnTs1fPhwFShQwLo8ISFBb775pj7++GP9+OOPio2N1ZAhQ6zLlyxZotGjR2vChAk6ePCgJk6cqFGjRmnRokWSpPj4eD3++OOqWLGidu7cqbFjx9rEZ8Risah79+5asGCBzfwFCxaoYcOGKl++vCSpffv2OnPmjL799lvt3LlT1atX1yOPPKJz585Z2/DYY49p3bp12r17t1q2bKk2bdooNjbWptw333xTVapU0e7duzVq1KhMtREAAAAAgHtVng9vKVWqlGbMmCGLxaIKFSpo7969mjFjhnr16pXpMvr166enn35akjR37lytWbNGH374oV599dU7xsbGxmro0KG6//77JUlhYWE2y5OTk/Xee+/pvvvus9Y1fvx46/IxY8Zo+vTpeuqppyRJZcuW1YEDB/T++++rS5cuWrp0qVJTU/Xhhx/K09NTDzzwgP744w+99NJLmdq2rl27avTo0dq2bZtq166t5ORkLV261Hr3x6ZNm7Rt2zadOXNGHh4ekm4kL7788kt9/vnn6t27t6pUqaIqVapYy3z99df1xRdf6Ouvv1a/fv2s85s2barBgwdn2J7ExEQlJibazDPGyGKxZGp7AAAAAADILXl+p8dDDz1kc8Fct25dHT16VCkpKZkuo27dutZ/u7m5qWbNmjp48GCmYgcNGqSePXuqWbNmmjx5so4dO2az3Nvb25rwkKRixYrpzJkzkqQrV67o2LFj6tGjh3x9fa3TG2+8YS3n4MGDqly5sjw9Pe22906KFy+u1q1b66OPPpIkrVy5UomJiWrfvr2kG8Nz4uPjVbhwYZs2nDhxwtqG+Ph4DRkyRBEREQoMDJSvr68OHjyY5k6PmjVr3rE9kyZNUkBAgM1kUi9nensAAAAAAMgteX6nR0YsFov1+R43JScn52gdY8eO1fPPP69vvvlG3377rcaMGaPly5frySeflCSboS63tyk+Pl6SNG/ePNWpU8dmPVdX1xxrY8+ePdWpUyfNmDFDCxYs0DPPPCNvb29rG4oVK6bIyMg0cTefXTJkyBCtXbtWb775psqXLy8vLy+1a9dOSUlJNuv7+PjcsS0jRozQoEGDbOYVLHy/YxsGAAAAAMBdlOdJj61bt9p83rJli8LCwuTq6qqgoCCbh4YePXpUCQkJacrYsmWLGjZsKEm6fv26du7caTNs407Cw8MVHh6uV155Rc8995wWLFhgTXpkJDg4WMWLF9fx48fVsWNHu+tERETo448/1rVr16x3e2zZsiXTbZOkxx57TD4+PtahOz/++KN1WfXq1XX69Gm5ubkpNDTUbvzmzZvVtWtX6zbFx8crJiYmS224ycPDwzqM5iaGtgAAAAAA7kV5PrwlNjZWgwYN0uHDh7Vs2TLNnj1bAwYMkHTjGRPvvPOOdu/erR07dqhPnz5p7ryQpHfffVdffPGFDh06pJdfflnnz59X9+7d71j31atX1a9fP0VGRuq3337T5s2btX37dkVERGS6/ePGjdOkSZP09ttv68iRI9q7d68WLFigt956S5L0/PPPy2KxqFevXjpw4IBWr16d5m0sd+Lq6qquXbtqxIgRCgsLsxke06xZM9WtW1dt27bV999/r5iYGP38888aOXKk9S00YWFhWrFihaKiohQdHa3nn39eqampWWoDAAAAAAD5TZ4nPTp37qyrV6+qdu3aevnllzVgwADr61KnT5+uUqVKqUGDBnr++ec1ZMgQ67COW02ePFmTJ09WlSpVtGnTJn399dcqUqTIHet2dXXV2bNn1blzZ4WHh6tDhw5q1aqVxo0bl+n29+zZU/Pnz9eCBQtUqVIlNWrUSAsXLlTZsmUlSb6+vlq5cqX27t2ratWqaeTIkZoyZUqmy7+pR48eSkpKUrdu3WzmWywWrV69Wg0bNlS3bt0UHh6uZ599Vr/99puCg4MlSW+99ZYKFiyoevXqqU2bNmrRooWqV6+e5TYAAAAAAJCfWMztD83IRY0bN1bVqlU1c+bMvGpCvvHTTz/pkUce0e+//25NZtwr3NxL5HUTAAAAAAD/INeTTmZqvTx/pgcylpiYqL/++ktjx45V+/bt77mEBwAAAAAA96o8H97yT9enTx+bV83eOvXp00fLli1TmTJldOHCBU2dOjWvmwsAAAAAQL6Rp8NbIJ05c0aXLl2yu8zf319FixbN5RZlHcNbAAAAAAC5KbPDW0h6INtIegAAAAAAclNmkx4MbwEAAAAAAE6JpAcAAAAAAHBKJD0AAAAAAIBTIukBAAAAAACcEkkPAAAAAADglEh6AAAAAAAAp0TSA3DQ1VM/5XUTAACAE+HcAvndvf4dvtfbh7uDpAcAAAAAAHBKJD0AAAAAAIBTIukBAAAAAACcEkkPAAAAAADglEh6AAAAAAAAp0TSAwAAAAAAOCWSHgAAAAAAwCmR9AAAAAAAAE6JpAcAAAAAAHBKJD2cSOPGjTVw4MC8bgYAAAAAAPcEt7xuALIuMjJSTZo00fnz5xUYGGidv2LFChUoUCDvGgYAAAAAwD3kH5f0SElJkcVikYvLvXeTS1JSktzd3R2OL1SoUA62BgAAAACA/C3Pr/zXrFmjhx9+WIGBgSpcuLAef/xxHTt2TJJUr149DRs2zGb9v/76SwUKFNCPP/4oSUpMTNSQIUNUokQJ+fj4qE6dOoqMjLSuv3DhQgUGBurrr79WxYoV5eHhodjYWG3fvl3NmzdXkSJFFBAQoEaNGmnXrl02dR06dEgPP/ywPD09VbFiRf3www+yWCz68ssvrev8/vvv6tChgwIDA1WoUCE98cQTiomJydS2d+3aVW3bttWECRNUvHhxVahQQZL08ccfq2bNmvLz81NISIief/55nTlzRpIUExOjJk2aSJIKFiwoi8Wirl27Sko7vCU0NFQTJ05U9+7d5efnp9KlS+uDDz6wacPPP/+sqlWrytPTUzVr1tSXX34pi8WiqKioTG0DAAAAAAD3qjxPely5ckWDBg3Sjh07tG7dOrm4uOjJJ59UamqqOnbsqOXLl8sYY13/008/VfHixdWgQQNJUr9+/fTLL79o+fLl2rNnj9q3b6+WLVvq6NGj1piEhARNmTJF8+fP1/79+1W0aFFdvnxZXbp00aZNm7RlyxaFhYXpscce0+XLlyXduCOkbdu28vb21tatW/XBBx9o5MiRNm1PTk5WixYt5Ofnp59++kmbN2+Wr6+vWrZsqaSkpExt/7p163T48GGtXbtWq1atspb7+uuvKzo6Wl9++aViYmKsiY1SpUrpf//7nyTp8OHDiouL06xZs9Itf/r06apZs6Z2796tvn376qWXXtLhw4clSZcuXVKbNm1UqVIl7dq1S6+//nqaJBMAAAAAAPlVng9vefrpp20+f/TRRwoKCtKBAwfUoUMHDRw4UJs2bbImOZYuXarnnntOFotFsbGxWrBggWJjY1W8eHFJ0pAhQ7RmzRotWLBAEydOlHQjiTBnzhxVqVLFWk/Tpk1t6v3ggw8UGBiojRs36vHHH9fatWt17NgxRUZGKiQkRJI0YcIENW/e3Brz6aefKjU1VfPnz5fFYpEkLViwQIGBgYqMjNSjjz56x+338fHR/PnzbYa1dO/e3frvcuXK6e2331atWrUUHx8vX19f6zCWokWL2jzTw57HHntMffv2lSQNGzZMM2bM0IYNG1ShQgUtXbpUFotF8+bNs97NcvLkSfXq1Svd8hITE5WYmGgzzxhj3X4AAAAAAO4VeX6nx9GjR/Xcc8+pXLly8vf3V2hoqCQpNjZWQUFBevTRR7VkyRJJ0okTJ/TLL7+oY8eOkqS9e/cqJSVF4eHh8vX1tU4bN260DpGRJHd3d1WuXNmm3j///FO9evVSWFiYAgIC5O/vr/j4eMXGxkq6cRdFqVKlrAkPSapdu7ZNGdHR0fr111/l5+dnrbtQoUK6du2aTf0ZqVSpUprneOzcuVNt2rRR6dKl5efnp0aNGln3SVbdut0Wi0UhISHWoTKHDx9W5cqV5enpme423m7SpEkKCAiwmUzq5Sy3CwAAAACAuy3P7/Ro06aNypQpo3nz5ql48eJKTU3Vgw8+aB0e0rFjR/Xv31+zZ8/W0qVLValSJVWqVEmSFB8fL1dXV+3cuVOurq425fr6+lr/7eXlleZOhC5duujs2bOaNWuWypQpIw8PD9WtWzfTw1Ju1l+jRg1rUuZWQUFBmSrDx8fH5vOVK1fUokULtWjRQkuWLFFQUJBiY2PVokWLLLXtptvf5mKxWJSamprlcm4aMWKEBg0aZDOvYOH7HS4PAAAAAIC7JU+THmfPntXhw4c1b9486/CVTZs22azzxBNPqHfv3lqzZo2WLl2qzp07W5dVq1ZNKSkpOnPmjDU+szZv3qw5c+bosccek3TjgaR///23dXmFChX0+++/688//1RwcLAkafv27TZlVK9eXZ9++qmKFi0qf3//LNWfnkOHDuns2bOaPHmySpUqJUnasWOHzTo37wxJSUnJVl0VKlTQJ598osTERHl4eEhKu4238/DwsK57E0NbAAAAAAD3ojwd3lKwYEEVLlxYH3zwgX799VetX78+zV0EPj4+atu2rUaNGqWDBw/queeesy4LDw9Xx44d1blzZ61YsUInTpzQtm3bNGnSJH3zzTcZ1h0WFqaPP/5YBw8e1NatW9WxY0d5eXlZlzdv3lz33XefunTpoj179mjz5s167bXXJP3/RX7Hjh1VpEgRPfHEE/rpp5904sQJRUZGqn///vrjjz8c2ielS5eWu7u7Zs+erePHj+vrr7/W66+/brNOmTJlZLFYtGrVKv3111+Kj493qK7nn39eqamp6t27tw4ePKjvvvtOb775ps02AgAAAACQX+Vp0sPFxUXLly/Xzp079eCDD+qVV17RtGnT0qzXsWNHRUdHq0GDBipdurTNsgULFqhz584aPHiwKlSooLZt22r79u1p1rvdhx9+qPPnz6t69erq1KmT+vfvr6JFi1qXu7q66ssvv1R8fLxq1aqlnj17Wt/ecvMZGN7e3vrxxx9VunRpPfXUU4qIiFCPHj107do1h+/8CAoK0sKFC/XZZ5+pYsWKmjx5sjURcVOJEiU0btw4DR8+XMHBwerXr59Ddfn7+2vlypWKiopS1apVNXLkSI0ePdpmGwEAAAAAyK8s5tb3wSJDmzdv1sMPP6xff/1V9913X143565YsmSJunXrposXL9rc+ZIRN/cSd7lV96arp36SV/GsDasCAABID+cWyO/u9e/wvd4+ZM31pJOZWi/PH2R6L/viiy/k6+ursLAw/frrrxowYIDq16/vVAmPxYsXq1y5cipRooSio6M1bNgwdejQIdMJDwAAAAAA7lUkPTJw+fJlDRs2TLGxsSpSpIiaNWum6dOnZzr+1jfI3O7bb7/N8sNX74bTp09r9OjROn36tIoVK6b27dtrwoQJed0sAAAAAACyjeEtd9Gvv/6a7rISJUo4zd0UDG8BAADIPs4tkN/d69/he719yBqGt9wDypcvn9dNAAAAAADgHytP394CAAAAAABwt5D0AAAAAAAATomkBwAAAAAAcEokPQAAAAAAgFMi6QEAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAATomkBwAAAAAAcEokPQAAAAAAgFMi6QEAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAATomkBwAAAAAAcEpOn/Ro3Lix+vfvr1dffVWFChVSSEiIxo4dK0mKiYmRxWJRVFSUdf0LFy7IYrEoMjJSkhQZGSmLxaLvvvtO1apVk5eXl5o2baozZ87o22+/VUREhPz9/fX8888rISEh023q16+f+vXrp4CAABUpUkSjRo2SMca6zscff6yaNWvKz89PISEhev7553XmzBmbcr7++muFhYXJ09NTTZo00aJFi2SxWHThwgXrOps2bVKDBg3k5eWlUqVKqX///rpy5Yp1+Zw5c6xlBAcHq127dlnbwQAAAAAA3KOcPukhSYsWLZKPj4+2bt2qqVOnavz48Vq7dm2Wyhg7dqzeeecd/fzzz/r999/VoUMHzZw5U0uXLtU333yj77//XrNnz85Sm9zc3LRt2zbNmjVLb731lubPn29dnpycrNdff13R0dH68ssvFRMTo65du1qXnzhxQu3atVPbtm0VHR2tF198USNHjrSp49ixY2rZsqWefvpp7dmzR59++qk2bdqkfv36SZJ27Nih/v37a/z48Tp8+LDWrFmjhg0bZmm/AAAAAABwr3LL6wbkhsqVK2vMmDGSpLCwML3zzjtat26dwsLCMl3GG2+8ofr160uSevTooREjRujYsWMqV66cJKldu3basGGDhg0blqnySpUqpRkzZshisahChQrau3evZsyYoV69ekmSunfvbl23XLlyevvtt1WrVi3Fx8fL19dX77//vipUqKBp06ZJkipUqKB9+/ZpwoQJ1rhJkyapY8eOGjhwoHXb3377bTVq1Ehz585VbGysfHx89Pjjj8vPz09lypRRtWrVMmx3YmKiEhMTbeYZY2SxWDK13QAAAAAA5JZ/xJ0elStXtvlcrFixNENFslJGcHCwvL29rQmPm/OyUuZDDz1kkyioW7eujh49qpSUFEnSzp071aZNG5UuXVp+fn5q1KiRJCk2NlaSdPjwYdWqVcumzNq1a9t8jo6O1sKFC+Xr62udWrRoodTUVJ04cULNmzdXmTJlVK5cOXXq1ElLliy54xCdSZMmKSAgwGYyqZczvd0AAAAAAOSWf0TSo0CBAjafLRaLUlNT5eJyY/NvfZZGcnLyHcuwWCzplpkTrly5ohYtWsjf319LlizR9u3b9cUXX0iSkpKSMl1OfHy8XnzxRUVFRVmn6OhoHT16VPfdd5/8/Py0a9cuLVu2TMWKFdPo0aNVpUoVm2eC3G7EiBG6ePGizWRx8cvuJgMAAAAAkOP+EcNb0hMUFCRJiouLsw7ruPWhpnfT1q1bbT5v2bJFYWFhcnV11aFDh3T27FlNnjxZpUqVknTj+Ru3qlChglavXm0zb/v27Tafq1evrgMHDqh8+fLptsPNzU3NmjVTs2bNNGbMGAUGBmr9+vV66qmn7K7v4eEhDw8Pm3kMbQEAAAAA3Iv+EXd6pMfLy0sPPfSQJk+erIMHD2rjxo167bXXcqXu2NhYDRo0SIcPH9ayZcs0e/ZsDRgwQJJUunRpubu7a/bs2Tp+/Li+/vprvf766zbxL774og4dOqRhw4bpyJEj+u9//6uFCxdK+v8kxLBhw/Tzzz+rX79+ioqK0tGjR/XVV19ZH2S6atUqvf3224qKitJvv/2mxYsXKzU1VRUqVMiVfQAAAAAAwN30j056SNJHH32k69evq0aNGho4cKDeeOONXKm3c+fOunr1qmrXrq2XX35ZAwYMUO/evSXduANl4cKF+uyzz1SxYkVNnjxZb775pk182bJl9fnnn2vFihWqXLmy5s6da317y807MSpXrqyNGzfqyJEjatCggapVq6bRo0erePHikqTAwECtWLFCTZs2VUREhN577z0tW7ZMDzzwQK7sAwAAAAAA7iaLufWBFsgVjRs3VtWqVTVz5swcLXfChAl677339Pvvv+douXfi5l4iV+u7V1w99ZO8ijfI62YAAAAnwbkF8rt7/Tt8r7cPWXM96WSm1vtHP9Mjv5szZ45q1aqlwoULa/PmzZo2bZp16AoAAAAAAP90JD1yWGxsrCpWrJju8gMHDuRYXUePHtUbb7yhc+fOqXTp0ho8eLBGjBiRY+UDAAAAAJCfMbwlh12/fl0xMTHpLg8NDZWbm3PlmhjeAgAAkH2cWyC/u9e/w/d6+5A1DG/JI25ubhm+IhYAAAAAAOSOf/zbWwAAAAAAgHMi6QEAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAATomkBwAAAAAAcEokPQAAAAAAgFMi6QEAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAATomkBwAAAAAAcEr/yKRHaGioZs6cmdfNAAAAAAAAd5FTJz0WLlyowMDANPO3b9+u3r17536D7mFdu3ZV27Zt87oZAAAAAADkGLe8boCjkpKS5O7u7lBsUFBQDrcGAAAAAADca/LNnR6NGzdWv379NHDgQBUpUkQtWrTQW2+9pUqVKsnHx0elSpVS3759FR8fL0mKjIxUt27ddPHiRVksFlksFo0dO1ZS2uEtFotF8+fP15NPPilvb2+FhYXp66+/tqn/66+/VlhYmDw9PdWkSRMtWrRIFotFFy5ckCT99ttvatOmjQoWLCgfHx898MADWr16daa2bf/+/Xr88cfl7+8vPz8/NWjQQMeOHZMkpaamavz48SpZsqQ8PDxUtWpVrVmzxhobGRlp0w5JioqKksViUUxMjKT/v+Plu+++U0REhHx9fdWyZUvFxcVJksaOHatFixbpq6++su6ryMjITP7PAAAAAABwb8o3SQ9JWrRokdzd3bV582a99957cnFx0dtvv639+/dr0aJFWr9+vV599VVJUr169TRz5kz5+/srLi5OcXFxGjJkSLpljxs3Th06dNCePXv02GOPqWPHjjp37pwk6cSJE2rXrp3atm2r6Ohovfjiixo5cqRN/Msvv6zExET9+OOP2rt3r6ZMmSJfX987btPJkyfVsGFDeXh4aP369dq5c6e6d++u69evS5JmzZql6dOn680339SePXvUokUL/etf/9LRo0eztO8SEhL05ptv6uOPP9aPP/6o2NhY6/4YMmSIOnToYE2ExMXFqV69elkqHwAAAACAe02+Gt4SFhamqVOnWj9XqFDB+u/Q0FC98cYb6tOnj+bMmSN3d3cFBATIYrEoJCTkjmV37dpVzz33nCRp4sSJevvtt7Vt2za1bNlS77//vipUqKBp06ZZ6923b58mTJhgjY+NjdXTTz+tSpUqSZLKlSuXqW169913FRAQoOXLl6tAgQKSpPDwcOvyN998U8OGDdOzzz4rSZoyZYo2bNigmTNn6t13381UHZKUnJys9957T/fdd58kqV+/fho/frwkydfXV15eXkpMTLzjvkpMTFRiYqLNPGOMLBZLptsCAAAAAEBuyFd3etSoUcPm8w8//KBHHnlEJUqUkJ+fnzp16qSzZ88qISEhy2VXrlzZ+m8fHx/5+/vrzJkzkqTDhw+rVq1aNuvXrl3b5nP//v31xhtvqH79+hozZoz27NmTqXqjoqLUoEEDa8LjVpcuXdKpU6dUv359m/n169fXwYMHM1X+Td7e3taEhyQVK1bMun1ZMWnSJAUEBNhMJvVylssBAAAAAOBuy1dJDx8fH+u/Y2Ji9Pjjj6ty5cr63//+p507d1rvfEhKSspy2bcnHSwWi1JTUzMd37NnTx0/flydOnXS3r17VbNmTc2ePfuOcV5eXllu661cXG78FxpjrPOSk5PTrGdv+26NyawRI0bo4sWLNpPFxS/L5QAAAAAAcLflq6THrXbu3KnU1FRNnz5dDz30kMLDw3Xq1Cmbddzd3ZWSkpLtuipUqKAdO3bYzNu+fXua9UqVKqU+ffpoxYoVGjx4sObNm3fHsitXrqyffvrJbqLC399fxYsX1+bNm23mb968WRUrVpT0/2+iuflQUunG3SNZldl95eHhIX9/f5uJoS0AAAAAgHtRvk16lC9fXsnJyZo9e7aOHz+ujz/+WO+9957NOqGhoYqPj9e6dev0999/OzTsRZJefPFFHTp0SMOGDdORI0f03//+VwsXLpQk6wX/wIED9d133+nEiRPatWuXNmzYoIiIiDuW3a9fP126dEnPPvusduzYoaNHj+rjjz/W4cOHJUlDhw7VlClT9Omnn+rw4cMaPny4oqKiNGDAAOt+KFWqlMaOHaujR4/qm2++0fTp07O8jaGhodqzZ48OHz6sv//+224SBgAAAACA/CTfJj2qVKmit956S1OmTNGDDz6oJUuWaNKkSTbr1KtXT3369NEzzzyjoKAgm4egZkXZsmX1+eefa8WKFapcubLmzp1rfXuLh4eHJCklJUUvv/yyIiIi1LJlS4WHh2vOnDl3LLtw4cJav3694uPj1ahRI9WoUUPz5s2zDkfp37+/Bg0apMGDB6tSpUpas2aN9fW50o1hK8uWLdOhQ4dUuXJlTZkyRW+88UaWt7FXr16qUKGCatasqaCgoDR3lwAAAAAAkN9YjCMPdoAmTJig9957T7///nteNyXPubmXyOsm5Imrp36SV/EGed0MAADgJDi3QH53r3+H7/X2IWuuJ53M1Hr56pW1eWnOnDmqVauWChcurM2bN2vatGnq169fXjcLAAAAAACkI98Ob8ltR48e1RNPPKGKFSvq9ddf1+DBgzV27Ng7xvXp00e+vr52pz59+tz9hgMAAAAA8A/F8Ja77MyZM7p06ZLdZf7+/ipatGgutyjnMbwFAAAg+zi3QH53r3+H7/X2IWsY3nKPKFq0qFMkNgAAAAAAyG8Y3gIAAAAAAJwSSQ8AAAAAAOCUSHoAAAAAAACnRNIDAAAAAAA4JZIeAAAAAADAKZH0AAAAAAAAzskA2XDt2jUzZswYc+3atXs6Li/qzC9xeVFnfonLizrzS1xe1Jlf4vKizvwSlxd15pe4vKgzv8TlRZ35JS4v6swvcXlRJ9t478TlRZ35JS4v6syLbbwVSQ9ky8WLF40kc/HixXs6Li/qzC9xeVFnfonLizrzS1xe1Jlf4vKizvwSlxd15pe4vKgzv8TlRZ35JS4v6swvcXlRJ9t478TlRZ35JS4v6syLbbwVw1sAAAAAAIBTIukBAAAAAACcEkkPAAAAAADglEh6IFs8PDw0ZswYeXh43NNxeVFnfonLizrzS1xe1Jlf4vKizvwSlxd15pe4vKgzv8TlRZ35JS4v6swvcXlRZ36Jy4s62cZ7Jy4v6swvcXlRZ15s460sxhiTrRIAAAAAAADuQdzpAQAAAAAAnBJJDwAAAAAA4JRIegAAAAAAAKdE0gMAAAAAADglkh4AAAAAAMApueV1A5C//P333/roo4/0yy+/6PTp05KkkJAQ1atXT127dlVQUFAetxAAAAAAgBt4ZS0ybfv27WrRooW8vb3VrFkzBQcHS5L+/PNPrVu3TgkJCfruu+9Us2bNHKszJSVFv/32m0JDQ+Xi4qLExER99dVXSk1NVZMmTaxtuBsuXLigzz77TLGxsSpTpozat2+vgICAu1bfrbp166YJEyaoePHiuVKfI/Jy/+Cf5eLFizZJVke+Z+PGjdPLL7+sIkWKZLheSkqKXF1drZ+3bdum1NRUVatWLdvviM+Mo0ePWo+p8uXL33H9nNg3kZGRqlOnjry8vDJcL6/3DeCIxMRE/fHHHypZsuQdv6fXr1/X/v37bY6pihUrqkCBAlmqc+HChXryySfveDzm9TF1r+6bvDz3Axx1/fp1bdiwwfob3qRJE5vj256c+A3PbH8j5V2fk9V9k1P9jQ0DZFKdOnVM7969TWpqapplqamppnfv3uahhx7KUpldu3Y1J0+etLssOjraFCtWzLi4uJgHH3zQxMbGmgcffND4+PgYX19fU7BgQbNt2zaHtsWeJ5980nz22WfGGGP27dtnihQpYoKCgkydOnVMcHCwCQkJMQcOHMiwjOTkZBMVFWXWrFlj1qxZY6KiokxSUlK660dHR9udChQoYL744gvr5/Rcv37dHDt2zKSkpBhjjLl27Zr59NNPzbJly8zp06ftxiQlJZmhQ4ea++67z9SqVct8+OGHNstPnz5tXFxc7sr+McaYCxcumEOHDplDhw6ZCxcu3HF9e8aOHWv++uuvO653/fp1m89bt241v/zyi7l27ZpD9WbFkSNHzA8//GCOHj2aqfWz+t1JT0bH1E2OfG9ySnJysvn+++/N/Pnzzdq1a9P8H91q3rx5JiIiwri4uNhMERERZv78+XZjLl68mGa6cOGCKVCggNm6dat13u1iYmJMjRo1jKurq2nZsqW5ePGiadasmbFYLMZisZhy5cqZw4cPp9vWb775xvTo0cMMHTrUHDx40GbZuXPnTJMmTdLETJw40fzwww/WdR555BFrfS4uLqZly5bm/PnzObZv0lOgQIEMj93s7pt3333XPPLII6Z9+/bW7b3pr7/+MmXLls1UO7N6TOVEX7NhwwaTkJDgUOzp06dNXFycQ7FZde3aNfPrr79mum/L7X2TW33xggULzM8//2yMMebq1aume/fuxtXV1bi4uBg3Nzfz4osv2q0zJSXFjBw50gQGBlq/1zenwMBA89prr1n7y8y4m8dUVFSUef311827776b5nfw4sWLplu3bnbj8su+yc65X071NcZkvb8x5sZxmJ3vdH7ob7LyG36r7O6bBQsWZLqvcqS/ceQ3vF+/fmblypXGGGN+//13c//99xtXV1cTHBxsXF1dTaVKlcwff/xht77c/A03Jnt9Tm7um5zub25F0gOZ5unpmebLfquDBw8aT09Pu8scubhv0aKFadeundm7d68ZMGCAiYiIMO3btzdJSUkmOTnZvPDCC6ZZs2Z263Pkwr5gwYLW7WvVqpV5/vnnTWJiorW8Hj16mEcffdRufY4epDcvbm6PuXW+vQTEzX3qyInBmDFjTHBwsJk2bZoZOXKkCQgIML1797bZNxaLJU1cdvaPMfnnAja3L14d/e44mjDL7RNKR3/4pk6dary9vc3w4cPNhg0bzIEDB8yBAwfMhg0bzIgRI4yPj4+ZNm1amrjbv183pzsdU08//bRp1KiRWblypenQoYOpX7++ady4sfnjjz/MqVOnTIsWLUzbtm3t7pclS5YYV1dX07p1a/Pwww8bT09P88knn1iXp5dILFmypNm1a5cxxpiePXuaatWqmV27dpmrV6+aqKgo89BDD5kePXrk2L6pVq2a3clisZiIiAjr55zcN7NmzTLe3t7m5ZdfNi+88IJxd3c3EydOvOO+yS8JIWOMOXv2rHn66adNqVKlTJ8+fcz169dNjx49rG2tW7euOXXqlN1YRy5gHb14Ncb5T7TLli1rtmzZYowxZsiQISY0NNSsWLHCHDx40Hz55ZcmPDzcDB06NE3c0KFDTVBQkHnvvffMiRMnTEJCgklISDAnTpww77//vilatKh59dVX08QVLFjQ7mSxWExAQID18+0cPaa+++474+7ubh544AFTunRpU7hwYbN+/Xrr8vSOp/y0bxw993O0rzEme/3N999/b1q1amUCAwOtx1NgYKBp1aqVWbt2rd2Y9Nzt/ia3L+xze9842t84+hseHBxs9u7da4wxpkOHDqZZs2bWfvzs2bPm8ccfN+3atUsT5+hvuKPHlDGO9zm5vW8c7W8yg6QHMi00NNQsWrQo3eWLFi0yZcqUsbvMkYv7ggULWju4hIQE4+rqarZu3Wpdvm/fPlO4cGG79TlyYe/l5WV+/fVXY4wxxYoVs16M3HT48GETEBBgtz5HD9IqVaqY1q1bm4MHD5qYmBgTExNjTpw4Ydzc3MzatWut8+xx9MSgfPny1h8wY4w5evSoKV++vOnatatJTU1NtwPLzv7JLxewuX3xaozj3x1HE2a5fULp6A9f6dKlzaeffmp3nxljzPLly02pUqXSzC9RooRp3bq1Wb9+vYmMjDSRkZFmw4YNxtXV1SxYsMA673ZBQUFm9+7dxpgbfwW3WCzmp59+si7fuXOnCQ4OttuWqlWrmlmzZlk/f/rpp8bHx8d6EZnevvHw8LAe36GhoWbjxo02y3fs2GGKFSuWJs7RfePm5mZatmxpxo4da53GjBljXFxcTN++fa3zbpedfVOxYkWzZMkS6+fNmzeboKAgM2rUKGNM/k8IGWNM9+7dzYMPPmhmz55tGjVqZJ544glTuXJls2nTJvPzzz+bWrVqmc6dO6eJc/QC1tGL17zYN7ndF3t4eJjffvvNGGNMeHi4+fbbb22Wb9y40ZQuXTpNXHBwsFmzZo3dbTDGmDVr1piiRYumme/r62tat25tFi5caJ0WLFhgXF1dzYQJE6zzbufoMVW3bl3zn//8xxhz4w7bKVOmGF9fX+t2ZnRhn1/2jaPnfo72NcY43t8sXLjQuLm5mWeffdYsWLDArF692qxevdosWLDAPPfcc6ZAgQJm8eLFaeLyor/J7YtXR/dNXlzYO/ob7unpaY4fP26MufEduvV7aowxe/fuNUWKFEkT5+hvuKPHlDGO9zm5vW8c7W8yg6QHMu2dd94xHh4epn///uarr74yW7ZsMVu2bDFfffWV6d+/v/Hy8jLvvvuu3VhHLu4DAwPNkSNHjDE37iRwdXU1O3futC4/ePBguh2fIxf2derUMR988IEx5sYP0hdffGGz/PvvvzchISF263P0IE1MTDQDBgwwFStWtEkiuLm5mf3796dbnjGOnxh4eXmZEydO2Mz7448/THh4uOnYsaM5efKk3Q4sO/snv1zA5vbFqzGOf3ccTZjl9gmloz98np6eGf5VZ//+/cbLyyvN/LNnz5q2bduaJk2a2Pz16U7HlJ+fn7WdKSkpxs3NzURFRVmXHz161Pj5+dmN9fHxscbetH79euPr62vmzp2b7r4JDw83q1atMsbcuJDdvHmzzfLdu3cbf3//NHGO7ptNmzaZ++67z4wePdrm7qG7uW/s9Td79+41wcHBZvjw4fk+IWTMjSTwzf+7mwn177//3rp806ZNpkSJEmniHL2AdfTiNS/2TW73xWXKlLEmjkqUKGG2b99us/zAgQPGx8cnTZy3t7fZs2eP3W0w5sYdcvbijh49ar3IvHz5snX+3Tqm/P39rX98uGnJkiXGx8fHrFy5MsML+/yybxw993O0rzHG8f4mLCzMvPPOO+luy7vvvmvKly+fZn5e9De5ffHq6L7Jiwt7R3/DK1eubJYvX26MMSYiIiLN3Ss///yzKVSoUJo4R3/DHT2mjHG8z8ntfeNof5MZJD2QJcuXLzd16tQxbm5u1r8qu7m5mTp16mR4MuXIxf0jjzxievToYf744w8zbtw4U758eZtbffv27WsaNGhgN9aRC/tVq1aZQoUKmQULFpgFCxaY0NBQM3/+fLN582bz0UcfmVKlStn965kx2T9IV69ebUqWLGkmTpxo7Yzu1IE5emJQtmzZNEMTjDHm5MmTJjw83DRv3txuB5ad/ZNfLmBz++LVGMe/O44mzHL7hNLRH74GDRqYzp07m+Tk5DTLrl+/bjp37mwaNmyY7nbOmTPHFC9e3CxdutQYc+f98tBDD5nXXnvNGGPMRx99ZN2um8aPH29q1KhhN7ZYsWLml19+STM/MjLS+Pr6mpEjR9rdN9OmTTMRERHm6NGjZvr06aZu3brWC5rjx4+bxo0b2/0LWnb2zYULF8yzzz5r6tSpY63rbu6bUqVKmR9//DHN/P3795vg4GDTuXPnfJ0QMubGMXxrgrFAgQLWv4wac+P/0t4x7OgFrKMXr3mxb3K7L/7Pf/5j6tata86fP2+GDx9u2rRpY704uHLliunQoYPdYZiPPfaYefTRR+0+K+qvv/4yLVu2NK1bt7a7jcnJyebVV1819913n9m0aZMx5u4dU0FBQWbHjh1p5i9btsx4e3ubuXPnpnthn1/2jaPnfo72NcY43t94eHiYQ4cOpbsthw4dsjvsOy/6m9y+eHV03+TFhb2jv+ELFiwwJUuWNBs2bDCLFy82ERER5ocffjAnT54069evN5UqVTI9e/ZME5ed33BHjiljHO9zcnvfZKe/uROSHnBIUlKSOXXqlDl16lSWHraYlYv7bdu2mcKFCxsXFxcTFBRk9u3bZ+rUqWNCQkJM8eLFjZeXl92Ld2Mcv7D//PPPTcmSJdMMG/D09DQDBw5M94FNOXGQnj592rRq1co0aNAgUx2YoycGPXr0MN27d7db5h9//GHKly+f7omBo/snv1zA5vbFqzHZ/+5kNWGW2yeUjv7wRUdHm5CQEFO4cGHz5JNPmj59+pg+ffqYJ5980hQuXNgUK1bM5kTPnv3795sqVaqY55577o77Zc2aNcbT09O4u7sbT09Ps3HjRhMeHm5q165tHnroIePq6ppuYveJJ54wo0ePtrtsw4YNxsfHJ91j6t///rcpUKCAuf/++42np6dxcXEx7u7uxsXFxdSsWdPug+lyYt989NFHJiQkxLz//vumQIECd23fPPfcc2bgwIF2l+3bt88EBQXl64SQMTfuurr5F83Vq1cbPz8/M336dOvyuXPnmgcffDBNnKMXsI5evObFvsntvjgxMdH861//MgULFjTNmzc3np6extvb24SFhRkfHx9TunRpu2P6bz7byM3NzVSrVs20bNnStGzZ0lSrVs24ubmZypUrm9jY2Ay3dd26daZ06dJmxIgRd+2Yat68ud0hSMYYs3TpUlOgQIF0+5r8sm8cPfdztK8xxvH+pnr16un+sccYY1599VVTvXp1u8tyu7/J7YvX7Oyb3L6wz85v+PTp0423t7fx8vKy/nbfnNq2bWuTuLkpJ37Ds3JMGeN4n5Pb+yYn+pv0kPRArsvKxX18fLzZsWOH9cC4evWqmT9/vpk9e3aGGeTsXNhfv37dbNu2zSxfvtwsXbrUbNiwwVy6dCnDbcrJg3TWrFmmbdu25vfff89wPUdPDGJiYjIcTnHy5Ml0bx00xrH9k18uYHP74tWYnPnuZOWYyosTSkd++Iwx5tKlS2bOnDmmc+fO5tFHHzWPPvqo6dy5s5k7d67dB9jak5iYaF555RVTtWrVNH/lut2JEyfM559/br2j5fTp02bUqFFm8ODBNs9auF1kZKTN801ut379etO1a9d0lx84cMBMnTrV9OnTx/Tu3duMGTPGfP/993bflHVTTuybI0eOmFq1ahmLxXLHEyZH9010dLT56KOP0l2+d+/edG/jzg8JIWOM+eSTT4yrq6spX7688fDwMJ999pkpXry46dChg3n22WeNu7u73du8Hb2AdfTiNS/2TV70xcYY8+2335q+ffuali1bmkcffdR06dLFfPDBByY+Pj7dmJSUFLN69WozevRo07t3b9O7d28zevRo8+2332b6bQF///23efLJJ01gYGCG5yjGOHZMrVixIt1+2Jgbdwo1btw4w3rzw75x5NwvO32NMY71Nze/i5UqVTKvvPKKmTx5spk8ebJ55ZVXTOXKlY2vr2+aoTK3y63+JrcvXnNi3+TWhX12f8PPnz9v/vvf/5rJkyebiRMnmgULFljvqE1PTvyGZ+WYMsZ+n/Paa69l2Ofkxb7Jif7GHosxxjj+wlvAcW+//bY2bNig2bNnq2TJkjla9m+//aZDhw6pRYsWdpefOnVKa9euVZcuXXKsztTUVH333XfasmWLzXul69atq0cffVQuLi45VtdNV65c0aFDh1ShQgX5+vrq2rVrWrJkia5evarmzZurQoUKOV6noy5fvqxPPvnE7v55/vnn5e/vf8cykpKSNHz4cG3YsEErVqxQ2bJl0103JiZGO3fuVI0aNRQaGqo///xT77zzjq5evarWrVurSZMmaWI2btyon3/+WSNGjLBb5oYNG7R48WItWLDA7vKDBw9q1apVOn78uFJTU1WsWDHVr19fzZo1k8ViSbetOfXdyewx5cj3Zs+ePdq5c6e6detmt8x9+/bpf//7n8aMGWN3+YULF7R27do0+yYsLCxT24a7IzU1VZcvX5a/v3+G39G84sgxlRN9zdGjR9WxY0ft2LFD+/btU8WKFTNcf/PmzdqyZYvq1q2revXq6cCBA5o8ebISEhLUpk0bu781X3zxhX788UfNmDHDbplLly7VvHnztGHDBrvL16xZo5UrV6bZN88//7x8fHzsxuTFvsmLvhhwhCP9TUxMjObOnWv3mOrTp49CQ0PvWG9u9DfZPaYc+Q3PiX1z9uxZ9erVSxs2bNCWLVvueF5rr7959913lZCQkG5/g38Gkh64523btk2//PKLTYdZr1491apVK1fbcf78ea1cuVKdO3fO8bL/+OMPBQYGytfX12Z+cnKyfvnlFzVs2DBH6zt79qz27NmjKlWqqFChQvr777/14YcfKjExUe3bt1dERESOxgGOSElJ0W+//abQ0FC5uLgoMTFRX331lVJTU9WkSRMFBwfbjTtz5oz27dunGjVqKCAgQH/++acWLVqk1NRUtW7dWpUqVcrR+m66cuWKdu7cqbi4OLm4uKhcuXKqXr36HZMJjsadPn1aW7dutfaNxYoVU+3atRUSEpJh3PXr17V//36bPrVixYoqUKBAluoLCQlRnTp17lhfRm5ue073cTnhXk8I5aX8tm+Sk5Pv+P2+1YULF/TZZ58pNjZWZcqUUfv27RUQEHDHuOvXr2vDhg2KjY1VaGioGjduLFdX1xyL+/vvv1WkSJFMb0dOxeZ2n+pofZJ0/Phxbdq0yaY/bd68eaYSenkpvx1TziglJcXmuNu6dasSExNVt27ddPsPY4xiYmJUqlQpubm5KSkpSV988YUSExP12GOPZemY69atmyZMmKDixYtnqd2Oxjkam5ycrJiYGBUtWjRT/eJNJ06c0K+//qpixYrpwQcfzFSMo32xXQ7fIwJkUWJiovn000/NwIEDzbPPPmueffZZM3DgQPPf//7XJCYmpln/zz//NA8//LCxWCymTJkypnbt2qZ27dqmTJkyxmKxmIcfftj8+eefDrXl3LlzGb5+156oqKgMb6c1xqR721VKSor1afu3OnXqlKlVq5ZxcXExrq6uplOnTja3CWb0tHFH69y6dasJCAgwFovFFCxY0OzYscOULVvWhIWFmfvuu894eXnZPNgyu3EZ6dq1qzl58mSWYowxpkmTJum+yjen60tKSjJHjhwxFy5cyJW48+fPmw8++MC89tprZt68eenGX7t2zeZ5Or/++qv5z3/+Y1544QUzcuTIOw7lsFff/Pnz063v888/N1euXMnStmQnLjo62hQrVsy4uLiYBx980DoMyMfHx/j6+pqCBQuabdu2pYm7eUutxWIxISEhJioqypQsWdKEhYWZChUqGA8PD/Pdd9/lWH3G3BjyNXToUOPt7W3zWuWbfdfXX39tNy4lJcUMHTrUeHl5ZSkuPj7edOzY0bi6uho3NzdTtGhRU7RoUePm5mZcXV3NCy+8YHefp6SkmJEjR5rAwMA0rzkODAw0r732mt3+xNH6MiMz/ao9SUlJdvs3Z3D69GmHts3ROEf74bFjx9p9FtHdqjMzfeqnn35qcz4xe/ZsU7p0aePi4mIKFy5sxo0bZzfuySefNJ999pkx5sZwvSJFipigoCBTp04dExwcbEJCQuw+BLZfv37WN8X9/vvv5v777zeurq4mODjYuLq6mkqVKtk8hDu7cS4uLqZJkyZmyZIl5tq1axnsrbRcXFxM06ZNsxyb232qo/XFx8ebdu3a2by6PSQkxLi6uhpfX98M3yRyaxkbN240y5cvN//973/Njh07MhxmeFNycrKJiooya9asMWvWrDHR0dGZet7d7XFRUVGZfk5edmLTK8+R/uPmPsvPTp06ZerVq2dcXV1Nw4YNzblz50zr1q2t36Xw8HBz6tSpNHGHDh0yZcqUMS4uLqZ8+fLm+PHjpkaNGsbHx8d4e3ubIkWK2B3KER0dbXcqUKCA+eKLL6yfcyouO7FTpkwxCQkJxpgb5zqDBw+2DnFyc3Mz3bp1s/u9e+mll6zXMwkJCebpp5+2OTabNGlid1iUo31xZpD0QK44evSoKVeunPH09DSNGjUyHTp0MB06dDCNGjUynp6epnz58ubo0aM2MU8//bSpW7eu3XFqhw4dMvXq1Uv34ZB3Yu9E++LFixlOP/30U7on5xcvXjTt27c3np6epmjRombUqFE2D/VML3nRuXNnU6dOHbN9+3azdu1aU6NGDVOzZk1z7tw5a5zFYsnROps1a2Z69uxpLl26ZKZNm2ZKlixp8xCqbt262X2PuaNxxjje2X711Vd2J1dXV/POO+9YP+dUfY527o7GGeN4B9+oUSNr3KZNm4yHh4epXLmyeeaZZ0y1atWMt7e3+fnnn3OsPovFYvz9/U2vXr3Mli1b7G6LPY7GtWjRwrRr187s3bvXDBgwwERERJj27dubpKQkk5ycbF544QXTrFmzNHEPP/ywefnll83ly5fNtGnTTIkSJczLL79sXT5kyBBTr169HKvPGGOGDRtmIiIizMqVK83atWtNw4YNzZQpU8zBgwfNqFGj0j1JdzSuR48eJiwszKxZs8bmmL9+/br57rvvTHh4uN0Hyw0dOtQEBQWZ9957z5w4ccIkJCSYhIQEc+LECfP++++bokWLmldffTXH6ssMR5Me6cUlJSWZoUOHmvvuu8/UqlXLfPjhhzbLM0okv/vuu+aRRx4x7du3T/Ncm7/++suULVs23fY4Envp0iXTsWNHU7p0adO5c2eTmJho+vbtaz0pbNiwod2x3Y7GOdov2vs9vHDhgilQoIDZunWrdZ49ud0Xu7i4WP8Y8tFHHxlPT08zevRo880335g33njD+Pj4mHnz5qWJK1iwoDl48KAxxphWrVqZ559/3po8SUpKMj169LD7cNjg4GDrc1A6dOhgmjVrZk0EnT171jz++ON2z1McjbNYLKZly5bG3d3dFCxY0PTr18/6is47cTQ2t/tUR+vr3bu3qV+/vtm7d685evSoadeunXn11VfNlStXzIcffmi8vb1tXrt+K0cT0I4mkh2Ny25sRnK6LzbGsX4xO324o7GdOnUy9erVM19//bV55plnTL169UyDBg3MH3/8YX777TdTv359m+/gTU888YT517/+Zfbs2WMGDhxoIiIizBNPPGGSkpLMtWvXTJs2bcwLL7yQJu5mX337/9+t8+2109G47MTe2qdOmzbNFCxY0Hz00Udm//795pNPPjFFixY1U6ZMyTBuxIgRpmTJkmb9+vXmypUr1rcX3fqQ2Zsc7Yszg6QHckWzZs3ME088YffE6OLFi+aJJ55I8yX29fW1eRXn7Xbs2GF8fX3tLnMkgXHzgE9vyqgz6d+/vwkPDzefffaZmTdvnilTpoxp3bq19UBNL3lRvHhxm3ee3+wkq1atas6ePZth5+5onQULFrRe1CYlJRkXFxebNuzcudPuO94djTPm7nTwt8bnVH050blnJe7mfnWkg/f397f+BaFRo0bmlVdesVn+2muvmfr16+dYfRaLxYwfP95Uq1bNWCwW88ADD5gZM2aYv//+2+52ZTfu1u9bQkKCcXV1tfm+7du3zxQuXDhN3K2vAU1OTjZubm42J/ZHjhwxAQEBOVafMTeein/rm23++OMP4+vra/1r6vjx403dunVzLC4wMDDNKxVvtWnTJhMYGJhmfnBwcIYPMV6zZo0pWrRojtVnzI39mtHk7++foyfaY8aMMcHBwWbatGlm5MiRJiAgwPTu3du6PL1+cdasWcbb29u8/PLL5oUXXjDu7u42D2/LqC92NLZfv37m/vvvN2+//bZp3LixeeKJJ8yDDz5oNm3aZDZu3GgqVqxo/vOf/+RYXHb6xYx+E++lE22LxWKNq127tpk6darN8jlz5phq1aqlifPy8rL2G8WKFUtz7nH48GG7/Yanp6f1rrqSJUva9BnG3Hh4ZpEiRXIs7ub2/fXXX+bNN980FStWNC4uLqZ69epmzpw5GT4A0dHY3O5THa2vSJEiNm9DOnfunPH09LTehfbOO++YqlWr2t1GRxPQjiaSHY3LbmxGcjrp4Wi/6Ggfnp3YW99sc/bsWWOxWGySNOvWrTPlypVLExcUFGT9bsbHxxuLxWJ++ukn6/LNmzeb0qVLp4mrUqWKad26tTl48KCJiYkxMTEx5sSJE8bNzc2sXbvWOi+n4rITe2ufWq1aNfP+++/bLP/kk0/MAw88kGHcgw8+aH374k1fffWVCQ8PTxPnaF+cGSQ9kCu8vLwyfCr8nj17jJeXl828woULm8jIyHRjNmzYkO5FiCMJDH9/fzNlyhQTGRlpd5o3b166PwilS5c2GzZssH7+66+/TO3atc2jjz5qrl27lm7n7uPjk+bWt+TkZNO2bVtTuXJls2fPnrtS580nNxtzI7l07Ngx6+fffvvN7rvTHY0zxvHO9ubrWm8fxnSnN5TkZeeelThjHO/gfXx8rMmL4OBgm3fRG3NjuIu9pKCj9d26jTt27DAvvfSSCQwMNB4eHqZ9+/bm+++/t7t9jsYFBgZaj42kpCTj6upqM3zq4MGDpmDBgmniihQpYvbt22eMufH6ThcXF5vX9EVHR9u9mHC0PmOM8fPzszkWbr46+ObT/vfv32+8vb1zLM7f399s377dbluMufGGHn9//zTzvb29zZ49e9KNi46ONj4+PjlW3806Bw8ebBYuXGh3GjdunN1+qlq1ahlO999/v9248uXLW4cNGHPjLsPy5cubrl27mtTU1HT7xYoVK9r8FXjz5s0mKCjIjBo1yhiTcdLD0dhSpUpZn5h/8uRJY7FYbNq+atUqU6FChRyLc7RfLFGihGndurVZv3699fdww4YNxtXV1SxYsMA6z5686IvPnDljjLnRF9jrF/38/NLE1alTx3zwwQfW+r744gub5d9//70JCQlJE1e5cmWzfPlyY4wxERERZu3atTbLf/75Z1OoUKEci7t1v9y6bvfu3Y2fn5/x9vY2nTp1ShOXndjc7lNzor6bdbq5uVm/D0eOHEn3HMXRBLSjiWRH47IT62if6mji2tF+0dE+PDuxnp6eNm/J8/Hxsbn7/LfffktzjWLMjfOpW4cE+fr6Ws+vjLnxdj4PD480cYmJiWbAgAGmYsWKNudgdzq3dTQuO7G39qmFCxdOcy13/Phxu+cpt/fFN4/pm2JiYuzuU0f74swg6YFcUaxYMZuO6HZff/21KVasmM28vn37mjJlypgVK1bY/AXi4sWLZsWKFSY0NNT069fPbnmOJDAaN26c7l/jjbmRzU4vu+zl5ZXmGQqXLl0ydevWNU2bNjXHjx+329FWqlTJfP7552nm30x83ByLnJN13n///WbdunXWz6tWrbLeRmyMMVu2bDElS5bMsThjstdRv/XWW6ZUqVI235+79cOQE517VuKMcbyDb9q0qfWvmPXq1UvzjJrPP//c7l8YHK3P3gnz1atXzeLFi03jxo2Ni4uLCQ0NzbG4Rx55xPTo0cP88ccfZty4caZ8+fKmW7du1uV9+/Y1DRo0SBP3xBNPmMcff9xs2rTJ9O7d29SsWdO0bt3axMfHmytXrph27dqZli1b5lh9xtzY/2+88Yb187Jly2zufNi7d6/dk3tH455//nlTrVo1u3fC7dq1y9SoUcN07NgxzbLHHnvMPProo3afw/DXX39Zk4w5Vd/NbZw5c6bdZcak/1dCDw8P06VLFzN27Fi704svvmg3zsvLyyY5a8yNC5jw8HDTsWNHc/LkyUzH7d271wQHB5vhw4dneKLtaKyHh4fNiba3t7fNq2ZjYmLs9huOxjnaL549e9a0bdvWNGnSxOY5E/fqifbixYvNV199ZUqWLJlmiN++ffvsJuhWrVplChUqZBYsWGAWLFhgQkNDzfz5883mzZvNRx99ZEqVKmWGDh2aJm7BggWmZMmSZsOGDWbx4sUmIiLC/PDDD+bkyZNm/fr1plKlSnaHfjkad+sdMLeLj4838+fPtzv0Izuxud2nOlpf8+bNbYYgTJs2zea8cteuXXaTJcY4noB2NJHsaFx2Yh3tUx1NXDvaLzrah2cntnTp0jZ3IQ0bNsycPXvW+jkqKsrud+e+++6zubNjzpw55tKlS9bPO3fuzPACffXq1aZkyZJm4sSJ1u/cnfrU7MQ5EmuxWMyECRPMrFmzTLFixdI8vyU6OtrueYrFYjEvvviieeWVV0zRokXT/IFr586ddvepo31xZpD0QK4YNWqUKViwoHnrrbdMdHS0OX36tDl9+rSJjo42b731lilUqJAZM2aMTcy1a9dMnz59rON4PT09bd6d/tJLL6X7MC5HEhgffPCBmTVrVroxp0+fTvcd7xUqVDDffPNNmvmXL182devWNVWqVLHb0b766qvpjk1LTk42//rXv9Lt3B2tc+zYsWbZsmV2yzTGmP/85z/mqaeeyrG4WznaUe/evdtUrFjR9O7d21y5cuWu/TBkp3N3JM4Yxzv4n3/+2QQEBJgxY8aY2bNnmyJFipjXXnvNLFmyxIwePdoEBgbaPQYcrS+jE2ZjbvxFxd4t9Y7Gbdu2zRQuXNi4uLiYoKAgs2/fPlOnTh0TEhJiihcvbry8vNKMETbmxl/zwsLCjMViMREREeaPP/4w//rXv4ybm5txc3MzQUFBdh+462h9xhjzww8/GA8PD1O7dm3TsGFD4+bmZmbMmGFdPm3aNNO0adMcizt37pxp2bKlsVgsplChQub+++83999/vylUqJBxcXExrVq1MufPn08Td/NBgm5ubqZatWqmZcuWpmXLlqZatWrGzc3NVK5c2eZiOrv1GWPMhAkT0u03b7apa9euaebXqFHDzJkzJ9243bt32+3fypYta/f/6eTJkyY8PNw0b9483bsubv1L70379+83wcHBpnPnzun2xY7GFi9e3Oa7+Nxzz9kcK/v27bPbbzgad5Oj/fCcOXNM8eLFrbcp36sn2rdOtyYVjTFm/vz5doe3GHMjUVyyZMk0w3E8PT3NwIEDbZ5nc6vp06cbb29v4+XlZT1fuTm1bdvW7sP6HI2zl0TOLEdjc7tPdbS+nTt3mkKFCpmQkBBTunRp4+7ubnPO8s4775jOnTvb3UZHE9COJpIdjctOrKN9qqOJa0f7RUf78OzE/utf/8pwG9955x27v8Uvvvii3WcE3TRp0iTz2GOPpbvcmBvXFq1atTINGjTIUp/qaFxWY8uUKWNCQ0Ot063nKMYYM3PmTPPQQw+liWvUqJFp3Lixdbp9P73++uumUaNGdut0tC++E5IeyDWTJ082xYoVsxl6YrFYTLFixTJMUFy8eNGsX7/eLF261CxdutSsX78+w3GrxtxIYGTUgWWUwLhdZp7c/e9//zvdh6peunTJ1KlTx25Hm5ycnOG2JCcnpzs+r1+/fg7VeSdXrlzJ8lPhsxLnaEedkJBgXnzxRRMWFmZcXV3vyg+Do527o3E3OdrB//zzz+ahhx5Kc6JfokSJDL//jtTn6Alzdk7S4+PjzY4dO6wn/1evXjXz5883s2fPtvuA41vd/syQH374waxcuTLDZ4lkp76oqCjzn//8xwwePDjdITs5GWfMjdvDP/roIzNx4kQzceJE89FHH1mHPKUnJSXFrF692owePdr07t3b9O7d24wePdp8++23d3z43YEDB7Jcn6P69+9vBgwYkO7yX3/91TRu3DjN/B49epju3bvbjfnjjz9M+fLl7faLzz33nBk4cKDduH379pmgoKB0+1NHY1u2bGnee+89u3HG3LgbwN5f3h2Nu5Wj/fD+/ftNlSpVzHPPPXdPnmjfycqVKzMcGnD9+nWzbds2s3z5crN06VKzYcMGm7/cpuf8+fPmv//9r5k8ebKZOHGiWbBggd23NmQ3buHChQ79Pmc31pjc71Mdqe/UqVPmgw8+MLNnz87Sd9PRBLSjiWRH47IT62if6mji2tF+0dE+PLuxGdm6dWuGQ/TTc/z4cbtvfbFn1qxZpm3btub333/PUh2OxmU39qZffvklw+cvpufYsWMZ1utoX5wRizHGOPayW8AxJ06c0OnTpyVJISEhKlu2bB63KGPu7u6Kjo5WREREuuucP39ep06d0gMPPGB3+eXLl7Vr1y41atQox9qVF3XGxcVp7ty52rRpk+Li4uTi4qJy5cqpbdu26tq1q837ze/k7bff1oYNGzR79myVLFky03Fff/21NmzYoBEjRqho0aJ3vb5bbdmyRR4eHqpWrVqOx6WkpGjXrl06fvy4UlNT/6+9e4+P6Vr/B/7smSQmMoncSUgmQVxSQZpGJJwk0kSExrU0iGspSkn1RlFFi2qrHFoOx6VVQkpQNByEIoomhGpRSVziHncJSdR8vn/Mb/Yvk9kzSYZOOvG8X695vTKz59l77T2TZ++1Zu21yMPDg4KCgsje3r7C9RcUFOjE+fj4VBhT1e1duHCBvLy8SCaTVbju8nHe3t4kCEKV4hgzxYULF+j06dMUGxsrufzKlSu0c+dOGjRokM7rJ06coKysLBoyZIhk3MmTJ2nDhg00depUvWWmxt6+fZtkMhk5OjpKxqWlpZGtrS1FRkY+kzgppuTF0tJSmjBhAu3Zs4dSU1OrfA6vzlzMmCHHjx+nlJQUKikpodjYWIqJialUnFqtph07dtChQ4d0rm1DQ0OpY8eOBs+ZpsY9bay5mJoXTc3hTxvLaj5u9GD/CPn5+TR16lRavny5zuuPHj2irKwscnZ2Jn9/f51lxcXFlJKSQgMHDpRc56lTp+jQoUMUGhpKzZo1o9OnT9P8+fOppKSEEhMTKSoqSuf948ePl1zP/PnzKTExkVxcXIiIaO7cuRXuT1FREaWkpFBOTg55eHhQ3759xfiyjh49Sk5OTuJF46pVq2jx4sV08eJFUqlUNGbMGEpISJDcxltvvUV9+vShf/3rXxWWp7yFCxfSkSNHqHPnzpSQkECrVq2iWbNmkVqtpp49e9L06dPJyspKJyYzM5Oio6OpcePGZGtrS7/88gv169ePSktLaceOHeTv70/bt2+vVCWdsco6cuQI/fLLLzoXdmFhYRQcHGzS+u7cuUNbtmwxmDfUarXkBaNaraZLly6Rt7e3ZBwAOn/+PHl5eZGVlRWVlpbSxo0bqaSkhDp37kyurq6VLmNUVBStWLGCVCqV5PKSkhKSyWRkbW1NRES5ubm0fPlyMW+8/vrrkhXRDRs2UFxcHNWuXbvSZdE6fvw4ZWVlUWRkJDVs2JB+//13+vrrr0mtVlOPHj0MXmRqpaen6zWWdu3alfz8/KpcFsYqIpU3QkNDqU2bNiat7+/IG+bMGYacO3dOvE5p0aKF5HssLd9wrmGmKC0tpU2bNkleb3Tr1o1sbGyqvM7r16/Tf/7zH/roo48kl1+6dIkcHR1JqVTqvP748WP65ZdfKDw8XC/m1q1bdOLECWrVqhU5OzvTzZs3admyZVRSUkK9e/c2+gOtlIYNG9KOHTsq/f8BgPbu3SvmjdjYWDE3lN83hUIh5rH9+/fr1G9Gjx5NoaGhenFffvklvfrqq1XOZZUtPGPVTuqewDNnzkClUom3w4SHh+Py5cvicmMDy6WlpcHGxgbOzs5QKBRIS0uDm5sboqOjERUVBblcrjMoJ6Dpht+6dWude9AiIyMhCAKCg4MRGRmJDh06SG6vefPm4qBHFy9ehI+PD+rUqYPg4GA4OzvD3d1db9BRQDOCu3bU9qVLl8LW1hZjx47FokWLkJSUBKVSqTfPeNnyymQy+Pn5Yfbs2eKAWxWZMWMG7O3t0atXL9SrVw+zZ8+Gi4sLPvnkE8ycORNubm746KOP9OLatWun08Vx1apVCAkJAaC5579169YYO3aswe2WlJRg3bp1SEpKQkJCAhISEpCUlISUlBRxytSqunbtGqZNm2ZweX5+vuR90aWlpXr3iGvdvHkT6enp4udZUFCA2bNnY9q0aeL0e5Xl6+tbqS7OZanVaqSnp2PJkiXYsmULSktLJd+Xn5+vc0/vvn370K9fP7Rv3x79+/fXG8BP64svvjB4y1RFtmzZgilTpuDAgQMANNO4xcXFITY2Vm92hbIePnyIZcuWYciQIejUqRM6d+6MMWPGGBwjAwCuX7+O9u3bQxAEqFQqtGnTBm3atBFzQvv27U26bcbQ/cf37t1D7969oVAo4O7ujilTpujc5mMs35w+fVosV+PGjZGXl4egoCDY2dmhdu3acHV1lfwebN68WfIhl8uxcOFC8Xl5ERER+OGHHwBopoutVasWWrZsiddeew2BgYGoXbu25OcvCAIcHBwwfPhwHDp0qNLHbMOGDZDL5XBxcYFSqcTOnTvh6OiI6OhoxMbGQi6X64zSX9b169fRpk0byGQyWFlZQSaTISgoCPXq1YNcLq9wQLLDhw9j3rx5mDBhAiZMmIB58+bpTe9ZFbdv39Yb9LcsQ7f4PHnyRGeU/vLUajXy8vLw+PFjAJp8t3btWnz77beS994b06FDhyr/j+bl5eF///uf0S7YxcXFOvkkJycHH374IRITEzFp0iTJcxSguR1OO+2nKbKzs7Fs2TJxoMiTJ09i1KhRGDFihNFbTQBNjpk2bRpGjhyJN998E1988YXRnGopeUObM2QymVlyBgCMGjVKPB8+fPgQvXr10pnNrkOHDpLnS0vJN0+bawDT840peeNZ5gygZuWNp8kZQNXzxtmzZ9GwYUMoFApERESgT58+6NOnDyIiIqBQKNC4cWOd2Vyqsh9S//9XrlxBcHAwZDIZ5HI5BgwYoPO/ZyhvHD58GHXq1IEgCHByckJmZiZ8fX3h5+eHRo0awdbWVnK8G0BzK4vUQy6XY+LEieLz8uLi4nD37l0AmkGtQ0JCIAiCeHtSs2bNxEGny2rTpo04AcGmTZsgk8nQtWtXfPDBB+jRowesra0lJ7gQBAFyuRzR0dFYu3atyfUDKdzowczC0Ila+/jqq6/0/sG7d++OLl26oKCgAGfPnkWXLl3g6+srnkCMVUJCQ0MxadIkAJoBqZycnHQGS5wwYQJiYmJ0YmbNmgVfX1+9xpDK3LdcdtyC/v37IywsTEwSDx48QHR0NPr27asXZ2trK56kAgMDxVk1tFavXg1/f3+D29y1axfGjRsHV1dXWFtbo2vXrtiyZYvRe/MbNWqEDRs2ANAkZLlcju+//15cnpqaisaNG0uWtfzo5tbW1rh27RoAzawfnp6ektus6ScUU08mgOWcUBYvXgwrKysEBQXBwcEBq1atgr29PYYNG4YRI0bA1tZWchyRs2fPQqVSwd3dHV5eXhAEAV26dEFISAjkcjl69+4tXvSV1atXL4SGhkre93369GmEhYVJjmlz7949o4/9+/dLfvZjx45FkyZN8MMPP2Dp0qVQqVTo0qWLeHyuXbtmcPambt26oWvXrjhx4gSSkpLQvHlzdOvWDaWlpSguLkZ8fDwSExP14rSVjfJjspR9SJXVwcFBvICLiIjA22+/rbN88uTJaNeuneT2pk+fjsDAQAiCgBdeeAFfffWV0XvkAeDFF18UB/nTDvA3ffp0cfkXX3yB1q1bS8a+9tpr6N69O+7du4fi4mKMGTNGHExw9+7dcHFxkfzeWErlFdB8H7UzbZmjAmsplVfA/BVYS8kb5s4ZgO6g0hMnTkSDBg2Qnp6OoqIiHDhwAI0aNcKECRP04iwl35iaawDN961du3ZVzjfmbvQCan7eqI5G9ujoaHTr1k1yjL179+6hW7dukpMOHD9+3Ohj3bp1kp//wIEDERISgl9//RU7d+5EUFAQXnrpJdy+fRuA4bwRHR2NYcOG4f79+/j888/RoEEDnVmehgwZgu7du0vuoyAIaNCggc5YST4+PuI4cD4+PvD19ZWM0373R40aBX9/f7GhKz8/H0FBQRg5cqRenJ2dnfi+kJAQzJ49W2f5ggULJAeVFgQBK1asQLdu3WBtbQ0XFxeMGzfOpDFVyuNGD2YWppyo3d3ddablUqvVGDlyJLy9vZGbm2v0ItTBwUGsRGtHii870I522qzyjhw5giZNmuCdd94RW7ar2ujRsGFDvQEJMzIy4OXlpRfn4uKCzMxMcX+zs7N1lufk5EjOY11+m6WlpVi3bp14QvD09MSHH34o2ZBQfl5xa2trnfmzDU13qFKpxF/4AU3DgiAI4rS1586dg0KhkCxrTT+hmHoy0cZawgnF399fbJRLT0+HQqHA119/LS5fsWIFmjdvrhcXFxeHESNGiAMCz549G3FxcQA0o/T7+PjozdwEaOa7NzY4VmZmJpRKpeT+lZ0FofzDUKXA29sbe/bsEZ8XFBSgTZs26NixI4qLi43mGzc3Nxw7dgyAZuA+QRB0prHLyMiQnD5YO8p++YvpinKOnZ2dOIBo3bp1JfOGoWOj3VZmZiZGjRoFR0dH1KpVC7179zY4kKqdnZ04DaBarYa1tbVObs7NzZXcHqDJxWXzS2FhIaytrcVcsGrVKjRt2lQvzlIqr4D5K7CWUnkFzF+BtZS8Ye6cod1HbVyLFi3EWXi0Nm/ejCZNmujFWUq+MTXXAKbnG3M3egE1P29URyO7ra2t0WugEydOSF6LG/ssjOUNT09PnR5E2s+8devWuHXrlsG84eTkJPY4Li0thUwm01lPVlYW6tevL7kPI0aMQOvWrfV6LFdmJi3t96Zp06Z6DWq7du2SvL6tU6cOjh8/DkBTv9H+rZWTk2Nw2nHt9q5fv47PPvsMzZo1g0wmQ3BwMJYsWWLygKbc6MHMwtPTE5s2bTK4XGqqLHt7e8nbCUaPHo0GDRpg3759Rhs9cnJyxOdKpVKnl8L58+cNVtAfPHiAgQMHomXLlvjtt99gbW1dqYsJ7a/xnp6eesnT0PYSExPx+uuvAwB69+6NyZMn6yyfOXMmAgICDG5T6peHCxcuYOrUqeKvCOX5+voiLS0NgKbSKZPJkJKSIi7ftm0bfHx89OLGjRuHFi1aIC0tDenp6ejQoYPOSN/bt29Ho0aNJMta008opp5MtPtoCScUqcaysp/puXPnJLdXu3ZtnV+sSkpKYG1tLV74bNq0SfL75uLigr179+q9rrVnzx64uLjove7g4IDPPvsMe/fulXwsXbpU8rO3tbXV66Z7//59hIaGIioqCnl5eQbzTfljo1QqdfLPxYsXUatWLcnYuXPnwsvLS6dXTkXfm6ioKMyZMweAZjrB8rdrrF+/XrLCJJUzHj16hO+++w6RkZGQyWSSn0W9evXExtnbt29DEASdit6RI0dQr149ybK6ubnp7MvDhw8hk8nE28dyc3Mlj42lVF61+2jOCqylVF612zRnBdZS8oa5cwage53i6uqqc3wBzXWK1HnYUvKNqbkGMD3fmLvRC6j5eaM6Gtk9PDwke8Zq/fjjj/Dw8NB73cXFBcuWLcP58+clH9u2bZP8/O3s7PR68jx+/Bjdu3dHy5YtceLECYNx2mMD6NdtLly4YLBuA2h6cnt5eWHBggXia5X53mjzhru7u2TekPq/6tq1q9iIFhsbq9fbeenSpfDz85PcnlTdZt++fRg0aBDs7OxgZ2dnsLzGcKMHM4v4+HhMmTLF4PLs7Gy91vDg4GB89913ku8fPXo0HB0dDV6EtmzZUqzYA5qeHWW70O/bt8/gL+9aycnJqFu3LmQyWaUuJgICAhAYGAilUon169frLP/5558lK8uXL1+Gj48PwsPDMX78eNja2qJ9+/YYPnw4wsPDYWNjg23bthncprHu3Wq1WvKkMnnyZLi5uWHYsGHw9fXFhAkT4O3tjUWLFmHx4sXw8vLSa8kHNI1Bffr0gZWVFQRBQFhYmM7F3o4dO3QaT8p6Hk4oppxMAMs5oWgbGgHN91YQBJ3v5t69e9GgQQO9OE9PT51bgu7cuQNBEMSGlby8PMn9e/PNN6FSqZCamqrTQ+jevXtITU2Fj48PxowZoxcXGRlpdApsqVwDaBqcpP7XHjx4gNDQULRq1cpgvmnUqJHOBes333yj03CUlZVlsFEA0DT6+vv744033kBRUVGF35uDBw+iTp06mDp1KhYsWABXV1dMnjwZq1evxkcffQRHR0fJY1D2lz4pZ8+e1bkNUCsxMREhISH4/vvvER8fj9jYWLRt2xanTp3C6dOnERERYXD67B49eqBXr14oLCxEaWkpkpKSdG6fO3TokOSxsZTKqzbWnBVYS6m8AuavwFpK3jB3zgA0n+OIESPw9ttvw93dXe/6ICsrC66urnpxlpJvTM01gOn5pjoavYCanTeqo5F9ypQpcHJywty5c3H8+HFcu3YN165dw/HjxzF37lw4OztL9kjt2LEjZsyYIVkWwHDeCAgI0KsnAP//OlV7u2R5zZo107kFf+vWrWJva0DzHZe6Divr0qVLiIqKQqdOnXD16tVKfW86d+6MHj16wMnJSe9a/tChQ5I95//44w+4uLhg4MCBmDFjBpRKJRITE/Hpp59i4MCBqFWrFlasWKEXV1HeuHfvnt5QAJXFjR7MLPbt26fTCFFeYWGh3gln5syZYjd4KaNGjTLY3XjRokXYunWrwdiJEyeKPSyMyc/Px6ZNm1BYWGj0fR9//LHOo/xAS++++y4SEhIkY+/cuYMPPvgA/v7+UCgUsLGxgUqlQr9+/fDrr78a3KaPj0+luheX9+TJE3z66ad45ZVXMHPmTKjVaiQnJ8PLywsuLi4YPHiw0f199OiR5H2fxjwvJ5SqnkwAyzmhjB49Gn5+fvjkk0/Qpk0bDBo0CM2aNUNaWhq2b9+OgIAADB06VC9u0KBBiIiIwKlTp5CXlyfeB6y1d+9eyVu/iouLMXLkSNjY2EAmk0GhUEChUEAmk8HGxgajRo1CcXGxXtySJUsMjp8CaLoblx2QV+utt94yWHG/f/8+QkJCDFZ6R4wYgaVLlxrc5qxZs9C5c2eDywHNxdmIESPg5+cHuVxe4ffm4MGDaNu2rV4PqPr16xu8b72ihlJDrl27hpiYGCiVSsTGxuLu3bsYM2aM2GvCz89P54K9rNzcXDRq1AhWVlawtraGo6OjOHgzoLktSqpLtaVUXgHzV2AtpfIKmL8CayhvCILwj8ob1ZEzIiIidAZpL7/9GTNmICIiQjLWEvKNqbkGMD3fVFejF1Bz80Z1NLIDmltvPTw8dHoLCoIADw8Pg+eU1NRUrFq1yuA+3r59GytXrtR7/f3335e8pRvQXKd27dpV8nvz8ccfIzk52eD2PvzwQ/Ts2dPgci21Wo2ZM2eKY50Y+94MHjxY57Fu3Tqd5e+99x5iY2MlY3NycpCQkAB7e3sxZ1hbWyMsLAwbN26UjDE1b1QGN3owxszin3ZCkar4PIsTSlVOJoDlnFAKCwsxfPhwtGjRAm+88QZKSkrw+eefw8bGBoIgIDIyUnK9169fFy+WZTIZVCqVTjfiH374Af/+978NbvfevXtIT0/HmjVrsGbNGqSnp0uODfO0bt++rffLV1n37983+kugMXl5ebhy5Uql3rt582YkJSVV+jO6ceMGDh06hIMHD+r0UpJy/vx5cWyVZyE3N1evF52UoqIi7NixA1u2bKn0rARP0+hlqBIG1IxGL0upvALGK7CCIPwtFVhAkzd2794t5o3du3eblDcq+n8xlDe0cVXNG9q4quaMsWPHmvwZabeZm5uL/Px8o+8tm28Mzdqhdf78eclB1U3NQZXJN1K5pjLbMzXfVGejF1Az88azbmQv27hTUd4ANP97Bw8erNR33FSPHz82mo8eP35s0ix7RUVFkt9TQzIzMzFv3jxxvDtTFBYW4tGjR0bfo1arce3aNVy5csXgjITmIADAs58IlzHGpJ07d05nDnRfX99nvo2//vqLHj58SA4ODgaXX758ucrzgD98+JDkcjnVqlWrwvdmZWXRgQMHaODAgeTk5FSl7ZRVVFREcrmcFAqFwfcAoBs3bpBarSZXV1fJOdP/LsXFxfT48WOyt7c3+r6zZ89SSUkJNWvWjKysrMxUOmbp7t+/T1lZWTo5IygoyOD/tqnu3LlDV65coRdeeEFy+YMHD+jo0aMUERFR5XWfO3eOFAoFeXh4VPjeH3/8kfbs2UMTJ04kd3f3Km+LiCgvL49sbGyoQYMGBt9TUFBAeXl5pFarycPDg3x8fAy+98KFC+Tt7U2CIJhUHkNlfPjwodF88PDhQ8rIyKCSkhJq27Ytubq6mrw9GxsbOn78ODVv3pzjqnmb/+S4quabvytvVCVnEBFt2bKF0tPTa3TeqEzOIHq2eYPVLHzlyRgzK19fX72Gjvz8fJo6dSotX768SusyFGdlZWW0UnT16lWaNm1albd369atSpczKCiIgoKCjJazMm7fvl1hrCAIVLduXZ3XnvUxNUShUJBCoagwzs/Pr8rbe/ToEWVlZZGzszP5+/vrLCsuLqaUlBQaOHBgtcdZUlkt6dicOnWKDh06RKGhodShQwc6ffo0zZ8/n1atWkWJiYkUFRUlub2ycc2aNRPjSkpKDMY5OTnRtWvXaMWKFVWKe5ptSsU1adKEfvrpJ5owYUKl4sLCwqhp06ZV3l5YWBiFhITQ6dOn6bPPPjMap1KpTN6/pynrhQsX6NKlSxQaGkqurq6Vihs/frzkup48eUKzZ88mFxcXIiKaO3fucxVnSWWtjmOj5eDgQB06dKCioiJKSUmhXbt20R9//EEJCQlifFlOTk46P2po43JycsjDw4P69u1bqQYPqTip7RmKvXnzJtWuXZtSUlIqHWvKNt3c3MTtbN26lTw9PQ0em/I/Kpm6j2XjPD09ycPDw2hc7dq1KSYmRoxdsWJFhds8evQoOTk5idemq1atosWLF9PFixdJpVLRmDFjKCEhwWLjLKmsT7OPFaq2PiaMMfb/ZGdnG+w6/jzHVcc2/ylxZ86cgUqlEru0hoeH4/Lly+JyQ6PiS8WV7Sb+rOOqY5uWEvc0sWlpabCxsYGzszMUCgXS0tLg5uaG6OhoREVFQS6X64y/U11xllTW5+HYCIKA1q1b63Tjj4yMhCAICA4ORmRkJDp06PDcxVlSWavj2DRv3lwc7PLixYvw8fFBnTp1EBwcDGdnZ7i7u0ve6mDuOKlYlUpl0jZNjTP3salsOZ8mtmXLluLtc0uXLoWtrS3Gjh2LRYsWISkpCUqlEsuWLbPYOEsq69PsY0W40YMx9rfbvHmz0cdXX30lWfGp6XGWVFZzx3Xv3h1dunRBQUEBzp49iy5dusDX11cc8d5QZdnccZZUVks6NqGhoZg0aRIAzUxaTk5OOoPeTZgwATExMdUeZ0llfR6OzaxZs+Dr66vXIFLRII81Pc6Sylodx6bs+BP9+/dHWFgY7t69C0AzKGl0dDT69u1b7XGWVFZLOja2trbiGBqBgYF6g7mvXr0a/v7+FhtnSWV9mn2sCDd6MMb+dtpfecsPglX2IVXxqelxllRWc8e5u7vjxIkT4nO1Wo2RI0fC29sbubm5BivL5o6zpLJa0rFxcHDA2bNnAWhmnLKystIZAPe3336TnNXI3HGWVNbn4dgAmiktmzRpgnfeeUccNK8yFd+aHmdJZTV3XNnKcsOGDfVmNsnIyJCcZczccZZUVks6Ni4uLuI0ue7u7sjOztZZnpOTIzmdr6XEWVJZn2YfKyIz7aYYxhirPA8PD0pNTSW1Wi35OHr06HMZZ0llNXfco0ePdAYrEwSBFi1aRPHx8RQREUF//vnnPyLOkspqScdG+14iIplMRgqFgurUqSMus7e3p3v37v0j4iyprM/DsQkODqasrCwqKCigl156iU6ePFmpwRRrepwllbU6jo32fcXFxXoDiNavX58KCgr+EXGWVFZLOTZxcXG0aNEiIiKKiIig9evX6yxPSUmhxo0bW2ycJZX1afaxQiY1lTDGWBXEx8djypQpBpdnZ2dDEPSnkK3pcZZUVnPHBQcH47vvvpOMGT16NBwdHSV7CJg7zpLKaknHpmXLlkhLSxOfl5+qct++ffD19a32OEsq6/NwbMpLTk5G3bp1IZPJKtUT4nmJs6SymiNOEAQEBAQgMDAQSqUS69ev11n+888/o379+tUeZ0lltaRjc/nyZfj4+CA8PBzjx4+Hra0t2rdvj+HDhyM8PBw2NjbYtm2bxcZZUlmfZh8rwrO3MMb+du+99x4VFRUZXN64cWPas2fPcxdnSWU1d1yPHj0oOTmZBgwYoLds4cKFpFarafHixdUeZ0lltaRjM2rUKHry5In4vEWLFjrL09LSJGfvMHecJZX1eTg25SUkJFD79u0pKyurSlOU1/Q4SyqrOeKmTp2q81ypVOo837JlC/3rX/+q9jhLKqslHRtPT086duwYzZ49m7Zs2UIA6MiRI5Sfn0/t2rWjjIwMeumllyw2zpLK+jT7WBEBAEyKZIwxxhhjjDHGGPsH4zE9GGOMMcYYY4wxViNxowdjjDHGGGOMMcZqJG70YIwxxhhjjDHGWI3EjR6MMcYYY4wxxhirkbjRgzHGGGM1XmRkJCUlJT31egYPHkzdu3d/6vWYg4+PD82bN6+6i8EYY4xVK270YIwxxpjFGTx4MAmCQCNHjtRbNnr0aBIEgQYPHiy+lpqaSjNmzHjq7c6fP59Wrlz51OthjDHGmHlwowdjjDHGLJKXlxetXbuWHj16JL5WXFxMa9asIW9vb533Ojs7k729/VNvs06dOuTo6PjU62GMMcaYeXCjB2OMMcYs0osvvkheXl6Umpoqvpaamkre3t4UGBio897yt7d888035OfnRwqFgurWrUuvvvqquGz9+vUUEBBAtra25OLiQtHR0VRUVERE+re3REZG0tixY+n9998nZ2dnqlevHn388cc62z59+jS1b9+eFAoF+fv7065du0gQBNq0aZPkfi1ZsoQ8PT1JrVbrvN6tWzcaOnQoERHl5uZSt27dqG7duqRUKik4OJh27dpl8FidP3+eBEGg7Oxs8bW7d++SIAi0d+9e8bWTJ09SXFwcKZVKqlu3Lg0YMIBu3rxZqWPDGGOM/RNxowdjjDHGLNbQoUNpxYoV4vPly5fTkCFDjMZkZmbS2LFjafr06XTmzBnavn07hYeHExHR1atXqW/fvjR06FA6deoU7d27l3r27EkADK7v22+/JTs7Ozp8+DDNmTOHpk+fTjt37iQioidPnlD37t2pdu3adPjwYVqyZAlNmjTJaPl69+5Nt27doj179oiv3b59m7Zv3079+/cnIqLCwkLq3Lkz7d69m44dO0adOnWi+Ph4unjxovEDZsTdu3cpKiqKAgMDKTMzk7Zv307Xr1+nPn36mHxsGGOMsepmVd0FYIwxxhgzVWJiIk2cOJEuXLhAREQZGRm0du1and4L5V28eJHs7OzolVdeIXt7e1KpVGLPkKtXr9Jff/1FPXv2JJVKRUREAQEBRsvQsmVLmjp1KhER+fn50cKFC2n37t0UExNDO3fupNzcXNq7dy/Vq1ePiIg+/fRTiomJMbg+JycniouLozVr1tDLL79MRJoeFq6urtShQwciImrVqhW1atVKjJkxYwZt3LiRfvzxRxozZozR8hqycOFCCgwMpJkzZ4qvLV++nLy8vOjPP/+kwsLCKh8bxhhjrLpxTw/GGGOMWSw3Nzfq0qULrVy5klasWEFdunQhV1dXozExMTGkUqmoYcOGNGDAAFq9ejU9fPiQiDSNCS+//DIFBARQ7969aenSpXTnzh2j62vZsqXOcw8PD7px4wYREZ05c4a8vLzEBg8iojZt2lS4X/3796cNGzZQSUkJERGtXr2aEhISSCbTXLoVFhbSu+++S82bNydHR0dSKpV06tSpp+rpcfz4cdqzZw8plUrx0axZMyLS3E5jyrFhjDHGqhs3ejDGGGPMog0dOpRWrlxJ3377rTjmhTH29vZ09OhRSk5OJg8PD/roo4+oVatWdPfuXZLL5bRz505KS0sjf39/WrBgATVt2pTOnTtncH3W1tY6zwVB0BuPo6ri4+MJAG3bto3y8/Np//794q0tRETvvvsubdy4kWbOnEn79++n7OxsCggIoNLSUsn1aRtLyt6K8vjxY533FBYWUnx8PGVnZ+s8zp49S+Hh4SYdG8YYY6y6caMHY4wxxixap06dqLS0lB4/fkyxsbGVirGysqLo6GiaM2cOnThxgs6fP0/p6elEpGm0aNeuHU2bNo2OHTtGNjY2tHHjRpPK1rRpU8rPz6fr16+Lr/36668VxikUCurZsyetXr2akpOTqWnTpvTiiy+KyzMyMmjw4MHUo0cPCggIoHr16tH58+cNrs/NzY2INLfvaJUd1JRIMzDs77//Tj4+PtS4cWOdh52dHRE922PDGGOMmQOP6cEYY4wxiyaXy+nUqVPi3xXZunUr5eXlUXh4ODk5OdFPP/1EarWamjZtSocPH6bdu3dTx44dyd3dnQ4fPkwFBQXUvHlzk8oWExNDjRo1okGDBtGcOXPowYMHNHnyZCLSNCAY079/f3rllVfo999/p8TERJ1lfn5+lJqaSvHx8SQIAk2ZMsVo7xJbW1tq27YtzZ49m3x9fenGjRtiObRGjx5NS5cupb59+4qz0eTk5NDatWvpv//9L2VmZj7TY8MYY4yZA/f0YIwxxpjFc3BwIAcHh0q919HRkVJTUykqKoqaN29OixcvpuTkZHrhhRfIwcGB9u3bR507d6YmTZrQ5MmT6csvv6S4uDiTyiWXy2nTpk1UWFhIwcHBNGzYMHH2FoVCYTQ2KiqKnJ2d6cyZM9SvXz+dZXPnziUnJycKCwuj+Ph4io2N1ekJImX58uX0119/UVBQECUlJdEnn3yis9zT05MyMjLoyZMn1LFjRwoICKCkpCRydHQkmUz2zI8NY4wxZg4CeJ4xxhhjjDGzycjIoPbt21NOTg41atSouovDGGOM1Wjc6MEYY4wx9jfauHEjKZVK8vPzo5ycHBo3bhw5OTnRgQMHqrtojDHGWI3HY3owxhhjjP2NHjx4QB988AFdvHiRXF1dKTo6mr788svqLhZjjDH2XOCeHowxxhhjjDHGGKuReCBTxhhjjDHGGGOM1Ujc6MEYY4wxxhhjjLEaiRs9GGOMMcYYY4wxViNxowdjjDHGGGOMMcZqJG70YIwxxhhjjDHGWI3EjR6MMcYYY4wxxhirkbjRgzHGGGOMMcYYYzUSN3owxhhjjDHGGGOsRvo/nuCF8zlQnaoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "sns.heatmap(df.T.isna(), cbar=False)\n",
    "plt.xlabel(\"Missing values\")\n",
    "plt.ylabel(\"Features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "\n",
    "- `average_rating`, `num_pages` and `ratings_count` exhibit a pattern where if one is missing, the others tend to be missing as well → Check whether this has any effect on the most important feature, `description`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Check for books where `description` is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "df['missing_desc'] = np.where(df['description'].isna(), 1, 0)\n",
    "df['age_of_book']  = 2025 - df['published_year']\n",
    "\n",
    "cols_to_consider     = ['age_of_book', 'num_pages', 'average_rating', 'ratings_count', 'description']\n",
    "new_cols_to_consider = ['age_of_book', 'num_pages', 'average_rating', 'ratings_count', 'missing_desc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>cnt of NaN</th>\n",
       "      <th>pct of NaN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age_of_book</td>\n",
       "      <td>6</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>num_pages</td>\n",
       "      <td>43</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>average_rating</td>\n",
       "      <td>43</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ratings_count</td>\n",
       "      <td>43</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>description</td>\n",
       "      <td>262</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rows with any NaN in considered features</td>\n",
       "      <td>303</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    feature  cnt of NaN  pct of NaN\n",
       "0                               age_of_book           6        0.09\n",
       "1                                 num_pages          43        0.63\n",
       "2                            average_rating          43        0.63\n",
       "3                             ratings_count          43        0.63\n",
       "4                               description         262        3.85\n",
       "5  Rows with any NaN in considered features         303        4.45"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_nan_counts = (\n",
    "    df[cols_to_consider].isna().sum()\n",
    "    .reset_index()\n",
    "    .rename(columns={'index': 'feature', 0: 'cnt of NaN'})\n",
    ")\n",
    "row_nan_total = df[cols_to_consider].isna().any(axis=1).sum()\n",
    "\n",
    "stats = pd.concat([\n",
    "    col_nan_counts,\n",
    "    pd.DataFrame([{\n",
    "        'feature': 'Rows with any NaN in considered features',\n",
    "        'cnt of NaN': row_nan_total,\n",
    "    }])\n",
    "], ignore_index=True)\n",
    "\n",
    "stats['pct of NaN'] = round(stats['cnt of NaN'] / len(df) * 100, 2)\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The correlation between considered columns\n",
    "# Spearman is less sensitive to outliers and suitable for non continuous data than Peason\n",
    "correlation_matrix = df[new_cols_to_consider].corr(method='spearman')\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='Blues', center=0)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=10)\n",
    "plt.title('Spearman Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the heatmap shows low correlation between `missing_desc` and other features, and the percentage of missing values is low in these columns (< 5%), one might conclude that it is acceptable to drop all rows with NaN in any of the following columns: `published_year`, `average_rating`, `ratings_count`, `num_pages`, and `description`.\n",
    "\n",
    "However:\n",
    "\n",
    "- Some books that have a `description` but no `ratings_count` may be newly published and still valuable for recommendation, albeit possibly in a lower priority tier.\n",
    "- Some books that lack a `description` but have other metadata (e.g., `ratings_count`, `num_pages`, etc.) could still be used by generating a synthetic `description` using LLMs from `title` and `category`.\n",
    "\n",
    "Therefore:\n",
    "\n",
    "- Only remove rows that have missing values in `published_year`, `average_rating`, `num_pages`, or `ratings_count`, as these are harder to impute or approximate meaningfully.\n",
    "- For rows missing `description`, experiment with two approaches:\n",
    "  1. Remove them entirely along with the above features -> Main approach.\n",
    "  2. Generate synthetic descriptions using metadata and include them in the recommender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6507"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Approach 1\n",
    "books = df[df[cols_to_consider].notna().all(axis=1)]\n",
    "len(books)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Remove short value (meaningless description) in `description`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "books['words_in_desc'] = books['description'].str.split().str.len()\n",
    "\n",
    "sns.histplot(books['words_in_desc'], bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Range 1 - 4:\n",
      "\n",
      "6                                 Donation.\n",
      "11                           Fantasy-roman.\n",
      "748    Includes bibliographical references.\n",
      "770                   Bound For Oregon (PB)\n",
      "775                      No Marketing Blurb\n",
      "Name: description, dtype: object\n",
      "\n",
      "Range 5 - 14:\n",
      "\n",
      "2     Volume Two of Stephen Donaldson's acclaimed se...\n",
      "38    Koko is Peter Straub's foray into the psycholo...\n",
      "70    A matter of life and death - and the Throne of...\n",
      "77                       Nonsense rhyming story. 4 yrs+\n",
      "84    A study guide for the novel There's a Boy in t...\n",
      "Name: description, dtype: object\n",
      "\n",
      "Range 15 - 24:\n",
      "\n",
      "10    One of Sidney Sheldon's most popular and bests...\n",
      "32    Tells the story of three generations of Chines...\n",
      "36    A magical and fantastic collection of early st...\n",
      "57    A young drifter finds more than he bargained f...\n",
      "68    A new 'The Doors of Perception' for the 21st c...\n",
      "Name: description, dtype: object\n",
      "\n",
      "Range 25 - 34:\n",
      "\n",
      "9     Kate Blackwell is an enigma and one of the mos...\n",
      "14    Available in the U.S. for the first time, this...\n",
      "20    It is 1937, and Charles Ashworth, a Canon to t...\n",
      "21    Reissue of the author's most famous and well-l...\n",
      "46    No one believes a little girl when she insists...\n",
      "Name: description, dtype: object\n"
     ]
    }
   ],
   "source": [
    "ranges = [(1, 4), (5, 14), (15, 24), (25, 34)]\n",
    "\n",
    "for lower, upper in ranges:\n",
    "    print(f\"\\nRange {lower} - {upper}:\\n\")\n",
    "    print(books[books['words_in_desc'].between(lower, upper)]['description'].head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "books_final = books[books['words_in_desc'] >= 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3. Final Cleaning**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn13</th>\n",
       "      <th>isbn10</th>\n",
       "      <th>title</th>\n",
       "      <th>subtitle</th>\n",
       "      <th>authors</th>\n",
       "      <th>categories</th>\n",
       "      <th>thumbnail</th>\n",
       "      <th>description</th>\n",
       "      <th>published_year</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>missing_desc</th>\n",
       "      <th>age_of_book</th>\n",
       "      <th>words_in_desc</th>\n",
       "      <th>full_title</th>\n",
       "      <th>full_desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9780002005883</td>\n",
       "      <td>0002005883</td>\n",
       "      <td>Gilead</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marilynne Robinson</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>http://books.google.com/books/content?id=KQZCP...</td>\n",
       "      <td>A NOVEL THAT READERS and critics have been eag...</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>247.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>199</td>\n",
       "      <td>Gilead</td>\n",
       "      <td>9780002005883 A NOVEL THAT READERS and critics...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9780002261982</td>\n",
       "      <td>0002261987</td>\n",
       "      <td>Spider's Web</td>\n",
       "      <td>A Novel</td>\n",
       "      <td>Charles Osborne;Agatha Christie</td>\n",
       "      <td>Detective and mystery stories</td>\n",
       "      <td>http://books.google.com/books/content?id=gA5GP...</td>\n",
       "      <td>A new 'Christie for Christmas' -- a full-lengt...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.83</td>\n",
       "      <td>241.0</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>205</td>\n",
       "      <td>Spider's Web: A Novel</td>\n",
       "      <td>9780002261982 A new 'Christie for Christmas' -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9780006178736</td>\n",
       "      <td>0006178731</td>\n",
       "      <td>Rage of angels</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sidney Sheldon</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>http://books.google.com/books/content?id=FKo2T...</td>\n",
       "      <td>A memorable, mesmerizing heroine Jennifer -- b...</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>3.93</td>\n",
       "      <td>512.0</td>\n",
       "      <td>29532.0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>57</td>\n",
       "      <td>Rage of angels</td>\n",
       "      <td>9780006178736 A memorable, mesmerizing heroine...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9780006280897</td>\n",
       "      <td>0006280897</td>\n",
       "      <td>The Four Loves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clive Staples Lewis</td>\n",
       "      <td>Christian life</td>\n",
       "      <td>http://books.google.com/books/content?id=XhQ5X...</td>\n",
       "      <td>Lewis' work on the nature of love divides love...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>170.0</td>\n",
       "      <td>33684.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45</td>\n",
       "      <td>The Four Loves</td>\n",
       "      <td>9780006280897 Lewis' work on the nature of lov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9780006280934</td>\n",
       "      <td>0006280935</td>\n",
       "      <td>The Problem of Pain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clive Staples Lewis</td>\n",
       "      <td>Christian life</td>\n",
       "      <td>http://books.google.com/books/content?id=Kk-uV...</td>\n",
       "      <td>\"In The Problem of Pain, C.S. Lewis, one of th...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>176.0</td>\n",
       "      <td>37569.0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>75</td>\n",
       "      <td>The Problem of Pain</td>\n",
       "      <td>9780006280934 \"In The Problem of Pain, C.S. Le...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          isbn13      isbn10                title subtitle  \\\n",
       "0  9780002005883  0002005883               Gilead      NaN   \n",
       "1  9780002261982  0002261987         Spider's Web  A Novel   \n",
       "3  9780006178736  0006178731       Rage of angels      NaN   \n",
       "4  9780006280897  0006280897       The Four Loves      NaN   \n",
       "5  9780006280934  0006280935  The Problem of Pain      NaN   \n",
       "\n",
       "                           authors                     categories  \\\n",
       "0               Marilynne Robinson                        Fiction   \n",
       "1  Charles Osborne;Agatha Christie  Detective and mystery stories   \n",
       "3                   Sidney Sheldon                        Fiction   \n",
       "4              Clive Staples Lewis                 Christian life   \n",
       "5              Clive Staples Lewis                 Christian life   \n",
       "\n",
       "                                           thumbnail  \\\n",
       "0  http://books.google.com/books/content?id=KQZCP...   \n",
       "1  http://books.google.com/books/content?id=gA5GP...   \n",
       "3  http://books.google.com/books/content?id=FKo2T...   \n",
       "4  http://books.google.com/books/content?id=XhQ5X...   \n",
       "5  http://books.google.com/books/content?id=Kk-uV...   \n",
       "\n",
       "                                         description  published_year  \\\n",
       "0  A NOVEL THAT READERS and critics have been eag...          2004.0   \n",
       "1  A new 'Christie for Christmas' -- a full-lengt...          2000.0   \n",
       "3  A memorable, mesmerizing heroine Jennifer -- b...          1993.0   \n",
       "4  Lewis' work on the nature of love divides love...          2002.0   \n",
       "5  \"In The Problem of Pain, C.S. Lewis, one of th...          2002.0   \n",
       "\n",
       "   average_rating  num_pages  ratings_count  missing_desc  age_of_book  \\\n",
       "0            3.85      247.0          361.0             0         21.0   \n",
       "1            3.83      241.0         5164.0             0         25.0   \n",
       "3            3.93      512.0        29532.0             0         32.0   \n",
       "4            4.15      170.0        33684.0             0         23.0   \n",
       "5            4.09      176.0        37569.0             0         23.0   \n",
       "\n",
       "   words_in_desc             full_title  \\\n",
       "0            199                 Gilead   \n",
       "1            205  Spider's Web: A Novel   \n",
       "3             57         Rage of angels   \n",
       "4             45         The Four Loves   \n",
       "5             75    The Problem of Pain   \n",
       "\n",
       "                                           full_desc  \n",
       "0  9780002005883 A NOVEL THAT READERS and critics...  \n",
       "1  9780002261982 A new 'Christie for Christmas' -...  \n",
       "3  9780006178736 A memorable, mesmerizing heroine...  \n",
       "4  9780006280897 Lewis' work on the nature of lov...  \n",
       "5  9780006280934 \"In The Problem of Pain, C.S. Le...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_final['full_title'] = np.where(\n",
    "    books_final['subtitle'].isna(),\n",
    "    books_final['title'],\n",
    "    books_final[['title', 'subtitle']].astype(str).agg(': '.join, axis=1)\n",
    ")\n",
    "\n",
    "books_final['full_desc'] = books_final[['isbn13', 'description']].astype(str).agg(' '.join, axis=1)\n",
    "books_final.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn13</th>\n",
       "      <th>full_title</th>\n",
       "      <th>authors</th>\n",
       "      <th>categories</th>\n",
       "      <th>description</th>\n",
       "      <th>full_desc</th>\n",
       "      <th>published_year</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>thumbnail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9780002005883</td>\n",
       "      <td>Gilead</td>\n",
       "      <td>Marilynne Robinson</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>A NOVEL THAT READERS and critics have been eag...</td>\n",
       "      <td>9780002005883 A NOVEL THAT READERS and critics...</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>361.0</td>\n",
       "      <td>http://books.google.com/books/content?id=KQZCP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9780002261982</td>\n",
       "      <td>Spider's Web: A Novel</td>\n",
       "      <td>Charles Osborne;Agatha Christie</td>\n",
       "      <td>Detective and mystery stories</td>\n",
       "      <td>A new 'Christie for Christmas' -- a full-lengt...</td>\n",
       "      <td>9780002261982 A new 'Christie for Christmas' -...</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>3.83</td>\n",
       "      <td>5164.0</td>\n",
       "      <td>http://books.google.com/books/content?id=gA5GP...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9780006178736</td>\n",
       "      <td>Rage of angels</td>\n",
       "      <td>Sidney Sheldon</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>A memorable, mesmerizing heroine Jennifer -- b...</td>\n",
       "      <td>9780006178736 A memorable, mesmerizing heroine...</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>3.93</td>\n",
       "      <td>29532.0</td>\n",
       "      <td>http://books.google.com/books/content?id=FKo2T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9780006280897</td>\n",
       "      <td>The Four Loves</td>\n",
       "      <td>Clive Staples Lewis</td>\n",
       "      <td>Christian life</td>\n",
       "      <td>Lewis' work on the nature of love divides love...</td>\n",
       "      <td>9780006280897 Lewis' work on the nature of lov...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>33684.0</td>\n",
       "      <td>http://books.google.com/books/content?id=XhQ5X...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9780006280934</td>\n",
       "      <td>The Problem of Pain</td>\n",
       "      <td>Clive Staples Lewis</td>\n",
       "      <td>Christian life</td>\n",
       "      <td>\"In The Problem of Pain, C.S. Lewis, one of th...</td>\n",
       "      <td>9780006280934 \"In The Problem of Pain, C.S. Le...</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>37569.0</td>\n",
       "      <td>http://books.google.com/books/content?id=Kk-uV...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          isbn13             full_title                          authors  \\\n",
       "0  9780002005883                 Gilead               Marilynne Robinson   \n",
       "1  9780002261982  Spider's Web: A Novel  Charles Osborne;Agatha Christie   \n",
       "3  9780006178736         Rage of angels                   Sidney Sheldon   \n",
       "4  9780006280897         The Four Loves              Clive Staples Lewis   \n",
       "5  9780006280934    The Problem of Pain              Clive Staples Lewis   \n",
       "\n",
       "                      categories  \\\n",
       "0                        Fiction   \n",
       "1  Detective and mystery stories   \n",
       "3                        Fiction   \n",
       "4                 Christian life   \n",
       "5                 Christian life   \n",
       "\n",
       "                                         description  \\\n",
       "0  A NOVEL THAT READERS and critics have been eag...   \n",
       "1  A new 'Christie for Christmas' -- a full-lengt...   \n",
       "3  A memorable, mesmerizing heroine Jennifer -- b...   \n",
       "4  Lewis' work on the nature of love divides love...   \n",
       "5  \"In The Problem of Pain, C.S. Lewis, one of th...   \n",
       "\n",
       "                                           full_desc  published_year  \\\n",
       "0  9780002005883 A NOVEL THAT READERS and critics...          2004.0   \n",
       "1  9780002261982 A new 'Christie for Christmas' -...          2000.0   \n",
       "3  9780006178736 A memorable, mesmerizing heroine...          1993.0   \n",
       "4  9780006280897 Lewis' work on the nature of lov...          2002.0   \n",
       "5  9780006280934 \"In The Problem of Pain, C.S. Le...          2002.0   \n",
       "\n",
       "   num_pages  average_rating  ratings_count  \\\n",
       "0      247.0            3.85          361.0   \n",
       "1      241.0            3.83         5164.0   \n",
       "3      512.0            3.93        29532.0   \n",
       "4      170.0            4.15        33684.0   \n",
       "5      176.0            4.09        37569.0   \n",
       "\n",
       "                                           thumbnail  \n",
       "0  http://books.google.com/books/content?id=KQZCP...  \n",
       "1  http://books.google.com/books/content?id=gA5GP...  \n",
       "3  http://books.google.com/books/content?id=FKo2T...  \n",
       "4  http://books.google.com/books/content?id=XhQ5X...  \n",
       "5  http://books.google.com/books/content?id=Kk-uV...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books_final = books_final.drop(['isbn10', 'title', 'subtitle', 'missing_desc', 'age_of_book', 'words_in_desc'], axis=1)\n",
    "\n",
    "books_final = books_final[['isbn13', 'full_title', 'authors', 'categories', \n",
    "                           'description', 'full_desc',\n",
    "                           'published_year', 'num_pages', 'average_rating', 'ratings_count', \n",
    "                           'thumbnail']]\n",
    "\n",
    "books_final.to_csv('./data/books_cleaned.csv', index=False)\n",
    "\n",
    "books_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5197 entries, 0 to 6808\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   isbn13          5197 non-null   int64  \n",
      " 1   full_title      5197 non-null   object \n",
      " 2   authors         5165 non-null   object \n",
      " 3   categories      5167 non-null   object \n",
      " 4   description     5197 non-null   object \n",
      " 5   full_desc       5197 non-null   object \n",
      " 6   published_year  5197 non-null   float64\n",
      " 7   num_pages       5197 non-null   float64\n",
      " 8   average_rating  5197 non-null   float64\n",
      " 9   ratings_count   5197 non-null   float64\n",
      " 10  thumbnail       5031 non-null   object \n",
      "dtypes: float64(4), int64(1), object(6)\n",
      "memory usage: 487.2+ KB\n"
     ]
    }
   ],
   "source": [
    "books_final.info()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "book_rcm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
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 },
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}