Unsupervised Learning Movie Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d0e0f3e5",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f616615",
   "metadata": {},
   "source": [
    "### 1.Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "84db330e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "import scipy.cluster.hierarchy as shc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d4e1ec",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6d5234",
   "metadata": {},
   "source": [
    "### 2. Dataframe preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a34a22bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load csv into dataframe\n",
    "df = pd.read_csv(\"tmdb_5000_movies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "58a0f81a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4803 entries, 0 to 4802\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   budget                4803 non-null   int64  \n",
      " 1   genres                4803 non-null   object \n",
      " 2   homepage              1712 non-null   object \n",
      " 3   id                    4803 non-null   int64  \n",
      " 4   keywords              4803 non-null   object \n",
      " 5   original_language     4803 non-null   object \n",
      " 6   original_title        4803 non-null   object \n",
      " 7   overview              4800 non-null   object \n",
      " 8   popularity            4803 non-null   float64\n",
      " 9   production_companies  4803 non-null   object \n",
      " 10  production_countries  4803 non-null   object \n",
      " 11  release_date          4802 non-null   object \n",
      " 12  revenue               4803 non-null   int64  \n",
      " 13  runtime               4801 non-null   float64\n",
      " 14  spoken_languages      4803 non-null   object \n",
      " 15  status                4803 non-null   object \n",
      " 16  tagline               3959 non-null   object \n",
      " 17  title                 4803 non-null   object \n",
      " 18  vote_average          4803 non-null   float64\n",
      " 19  vote_count            4803 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(13)\n",
      "memory usage: 750.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#see the column information in the\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "967849d8",
   "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>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000000</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
       "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
       "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
       "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
       "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
       "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
       "2  [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   880674609   \n",
       "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16  1084939099   \n",
       "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   284139100   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title  vote_average  vote_count  \n",
       "0                                    Avatar           7.2       11800  \n",
       "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
       "2                                   Spectre           6.3        4466  \n",
       "3                     The Dark Knight Rises           7.6        9106  \n",
       "4                               John Carter           6.1        2124  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see the first 5 rows, notice that genres row and keywords row are dictionaries inside a list (next line of code)\n",
    "#in fact it is in json format embedded and will require processing to extract just the genre names\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2d78ef18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]\n",
      "[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 28, \"name\": \"Action\"}]\n",
      "[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 80, \"name\": \"Crime\"}]\n",
      "[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"name\": \"Crime\"}, {\"id\": 18, \"name\": \"Drama\"}, {\"id\": 53, \"name\": \"Thriller\"}]\n",
      "[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]\n"
     ]
    }
   ],
   "source": [
    "# we see that the json format is a list and inside the list we have dictionaries\n",
    "for i in df[\"genres\"].head():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ad28fa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use the json loads to parse a json string into a python dictionary\n",
    "# we initiate a loop with an inner loop, where i represents the value of genres column for a specific row\n",
    "# inside the inner loop we iterate with j through the list of dictionairies, and i[j] gives us a single dictionary from the list and i[j](\"name\") retrieves the value associated with the key name from the dictionary.\n",
    "# we then append this value to list 1\n",
    "# after we process all dictionaries for a row we update the genres colum for that row using df.loc & convert it to a string\n",
    "df['genres'] = df['genres'].apply(json.loads)\n",
    "for index,i in zip(df.index,df['genres']):\n",
    "    list1 = []\n",
    "    for j in range(len(i)):\n",
    "        list1.append((i[j]['name']))\n",
    "    df.loc[index,'genres'] = str(list1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a1c62f71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Action', 'Adventure', 'Fantasy', 'Science Fiction']\n",
      "['Adventure', 'Fantasy', 'Action']\n",
      "['Action', 'Adventure', 'Crime']\n",
      "['Action', 'Crime', 'Drama', 'Thriller']\n",
      "['Action', 'Adventure', 'Science Fiction']\n",
      "['Fantasy', 'Action', 'Adventure']\n",
      "['Animation', 'Family']\n",
      "['Action', 'Adventure', 'Science Fiction']\n",
      "['Adventure', 'Fantasy', 'Family']\n",
      "['Action', 'Adventure', 'Fantasy']\n"
     ]
    }
   ],
   "source": [
    "# we preview for the first ten rows of the genres column the value output\n",
    "for i in df[\"genres\"].head(10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eede0b9a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# we preview the type which is a string\n",
    "for i in df[\"genres\"].head(4):\n",
    "    print(type(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4838cb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b605ed66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We remove the square brackets from the beginning and end of each string in the \"genres\" column. \n",
    "# We remove any extra spaces from the strings & any single quotes\n",
    "# We then split each string into a listo of individual genre names using the comma as the delimiter\n",
    "# The purpose is to be able to have an output where for each row the genres are in a list format to be used for MultilabelBinarizer\n",
    "df['genres'] = df['genres'].str.strip('[]').str.replace(' ','').str.replace(\"'\",'')\n",
    "df['genres'] = df['genres'].str.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2fb7e387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Action, Adventure, Fantasy, ScienceFiction]\n",
       "1                       [Adventure, Fantasy, Action]\n",
       "2                         [Action, Adventure, Crime]\n",
       "3                   [Action, Crime, Drama, Thriller]\n",
       "4                [Action, Adventure, ScienceFiction]\n",
       "                            ...                     \n",
       "4798                       [Action, Crime, Thriller]\n",
       "4799                               [Comedy, Romance]\n",
       "4800               [Comedy, Drama, Romance, TVMovie]\n",
       "4801                                              []\n",
       "4802                                   [Documentary]\n",
       "Name: genres, Length: 4803, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previous of the list format\n",
    "df[\"genres\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5196ab9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "#confirming the list format\n",
    "for i in df[\"genres\"].head(4):\n",
    "    print(type(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b54995",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f8a588",
   "metadata": {},
   "source": [
    "### 3. Creating dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c4b50e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an instance of MultiLabelBinarizer, used to convert the list into a format where each genre is its own column and has a value of either 1 or 0 (1 means the movie has this genre, 0)\n",
    "mlb = MultiLabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7581c3d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we apply the MultiLabelBinarizer to the genres column and create a new dataframe with the output.\n",
    "# For each column we use the unique names of the genres & for index we use the same index as the original dataframe\n",
    "one_hot_df =pd.DataFrame(mlb.fit_transform(df[\"genres\"]),columns=mlb.classes_,index=df.index)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d3d5dd1b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         Action  Adventure  Animation  Comedy  Crime  Documentary  Drama  \\\n",
       "0     0       1          1          0       0      0            0      0   \n",
       "1     0       1          1          0       0      0            0      0   \n",
       "2     0       1          1          0       0      1            0      0   \n",
       "3     0       1          0          0       0      1            0      1   \n",
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       "...  ..     ...        ...        ...     ...    ...          ...    ...   \n",
       "4798  0       1          0          0       0      1            0      0   \n",
       "4799  0       0          0          0       1      0            0      0   \n",
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       "4801  1       0          0          0       0      0            0      0   \n",
       "4802  0       0          0          0       0      0            1      0   \n",
       "\n",
       "      Family  Fantasy  Foreign  History  Horror  Music  Mystery  Romance  \\\n",
       "0          0        1        0        0       0      0        0        0   \n",
       "1          0        1        0        0       0      0        0        0   \n",
       "2          0        0        0        0       0      0        0        0   \n",
       "3          0        0        0        0       0      0        0        0   \n",
       "4          0        0        0        0       0      0        0        0   \n",
       "...      ...      ...      ...      ...     ...    ...      ...      ...   \n",
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       "4799       0        0        0        0       0      0        0        1   \n",
       "4800       0        0        0        0       0      0        0        1   \n",
       "4801       0        0        0        0       0      0        0        0   \n",
       "4802       0        0        0        0       0      0        0        0   \n",
       "\n",
       "      ScienceFiction  TVMovie  Thriller  War  Western  \n",
       "0                  1        0         0    0        0  \n",
       "1                  0        0         0    0        0  \n",
       "2                  0        0         0    0        0  \n",
       "3                  0        0         1    0        0  \n",
       "4                  1        0         0    0        0  \n",
       "...              ...      ...       ...  ...      ...  \n",
       "4798               0        0         1    0        0  \n",
       "4799               0        0         0    0        0  \n",
       "4800               0        1         0    0        0  \n",
       "4801               0        0         0    0        0  \n",
       "4802               0        0         0    0        0  \n",
       "\n",
       "[4803 rows x 21 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we notice before the column action we have another column which seems to be empty\n",
    "one_hot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "93b41245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['', 'Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
       "       'Documentary', 'Drama', 'Family', 'Fantasy', 'Foreign', 'History',\n",
       "       'Horror', 'Music', 'Mystery', 'Romance', 'ScienceFiction', 'TVMovie',\n",
       "       'Thriller', 'War', 'Western'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we use the below command to get the column names and identify columns that are to be removed. In this case column ''\n",
    "one_hot_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ff84e060",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the drop command to remove it\n",
    "one_hot_df.drop(\"\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bbcb05d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we concatenate the original df with the multilabeled df\n",
    "new_df = pd.concat([df,one_hot_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b162a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d0c71e76",
   "metadata": {},
   "outputs": [
    {
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       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                        genres  \\\n",
       "0  237000000  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "1  300000000                  [Adventure, Fantasy, Action]   \n",
       "\n",
       "                                       homepage     id  \\\n",
       "0                   http://www.avatarmovie.com/  19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/    285   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "\n",
       "                                      title  vote_average  vote_count  Action  \\\n",
       "0                                    Avatar           7.2       11800       1   \n",
       "1  Pirates of the Caribbean: At World's End           6.9        4500       1   \n",
       "\n",
       "   Adventure  Animation  Comedy  Crime  Documentary  Drama  Family  Fantasy  \\\n",
       "0          1          0       0      0            0      0       0        1   \n",
       "1          1          0       0      0            0      0       0        1   \n",
       "\n",
       "   Foreign  History  Horror  Music  Mystery  Romance  ScienceFiction  TVMovie  \\\n",
       "0        0        0       0      0        0        0               1        0   \n",
       "1        0        0       0      0        0        0               0        0   \n",
       "\n",
       "   Thriller  War  Western  \n",
       "0         0    0        0  \n",
       "1         0    0        0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm the result of the new dataframe, review which columns we want to drop\n",
    "new_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "972bfdd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4803 entries, 0 to 4802\n",
      "Data columns (total 40 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   budget                4803 non-null   int64  \n",
      " 1   genres                4803 non-null   object \n",
      " 2   homepage              1712 non-null   object \n",
      " 3   id                    4803 non-null   int64  \n",
      " 4   keywords              4803 non-null   object \n",
      " 5   original_language     4803 non-null   object \n",
      " 6   original_title        4803 non-null   object \n",
      " 7   overview              4800 non-null   object \n",
      " 8   popularity            4803 non-null   float64\n",
      " 9   production_companies  4803 non-null   object \n",
      " 10  production_countries  4803 non-null   object \n",
      " 11  release_date          4802 non-null   object \n",
      " 12  revenue               4803 non-null   int64  \n",
      " 13  runtime               4801 non-null   float64\n",
      " 14  spoken_languages      4803 non-null   object \n",
      " 15  status                4803 non-null   object \n",
      " 16  tagline               3959 non-null   object \n",
      " 17  title                 4803 non-null   object \n",
      " 18  vote_average          4803 non-null   float64\n",
      " 19  vote_count            4803 non-null   int64  \n",
      " 20  Action                4803 non-null   int32  \n",
      " 21  Adventure             4803 non-null   int32  \n",
      " 22  Animation             4803 non-null   int32  \n",
      " 23  Comedy                4803 non-null   int32  \n",
      " 24  Crime                 4803 non-null   int32  \n",
      " 25  Documentary           4803 non-null   int32  \n",
      " 26  Drama                 4803 non-null   int32  \n",
      " 27  Family                4803 non-null   int32  \n",
      " 28  Fantasy               4803 non-null   int32  \n",
      " 29  Foreign               4803 non-null   int32  \n",
      " 30  History               4803 non-null   int32  \n",
      " 31  Horror                4803 non-null   int32  \n",
      " 32  Music                 4803 non-null   int32  \n",
      " 33  Mystery               4803 non-null   int32  \n",
      " 34  Romance               4803 non-null   int32  \n",
      " 35  ScienceFiction        4803 non-null   int32  \n",
      " 36  TVMovie               4803 non-null   int32  \n",
      " 37  Thriller              4803 non-null   int32  \n",
      " 38  War                   4803 non-null   int32  \n",
      " 39  Western               4803 non-null   int32  \n",
      "dtypes: float64(3), int32(20), int64(4), object(13)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0fb9edfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Released           4795\n",
       "Rumored               5\n",
       "Post Production       3\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see the results of value counts\n",
    "new_df[\"status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cee45374",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2a8c2487",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we decide to drop the following columns; reminder we now have multi labelled genres so we can drop the original \n",
    "new_df.drop([\"homepage\",\"genres\",\"id\",\"keywords\",\"original_title\",\"overview\",\n",
    "             \"production_countries\",\"spoken_languages\",\"tagline\",\"title\",\"production_companies\",\"status\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f8c966fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we transform the original language into dummy variables ands save it to a dataframe\n",
    "original_language = pd.get_dummies(df[\"original_language\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8f0bd788",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   ar  cn  cs  da  de  el  en  es  fa  fr  he  hi  hu  id  is  it  ja  ko  ky  \\\n",
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       "\n",
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      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we confirm the result of the dataframe\n",
    "original_language.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "03c57207",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we concatenate the dummy variables created into a new df\n",
    "new_df = pd.concat([new_df,original_language],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1b09266e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we drop the original language column since we have transformed it into dummy variables\n",
    "new_df.drop(\"original_language\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d8e7e4ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 63)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check our new dataframe rows and columns \n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "77e8f8fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['budget', 'popularity', 'release_date', 'revenue', 'runtime',\n",
       "       'vote_average', 'vote_count', 'Action', 'Adventure', 'Animation',\n",
       "       'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy',\n",
       "       'Foreign', 'History', 'Horror', 'Music', 'Mystery', 'Romance',\n",
       "       'ScienceFiction', 'TVMovie', 'Thriller', 'War', 'Western', 'ar', 'cn',\n",
       "       'cs', 'da', 'de', 'el', 'en', 'es', 'fa', 'fr', 'he', 'hi', 'hu', 'id',\n",
       "       'is', 'it', 'ja', 'ko', 'ky', 'nb', 'nl', 'no', 'pl', 'ps', 'pt', 'ro',\n",
       "       'ru', 'sl', 'sv', 'ta', 'te', 'th', 'tr', 'vi', 'xx', 'zh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the column names\n",
    "new_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e6a77c",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b550aee",
   "metadata": {},
   "source": [
    "### 4. NA handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "deb82476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have one row with na release date so we drop it\n",
    "new_df.dropna(subset = [\"release_date\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bfeb9608",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have two rows with NA so we fill them in with the median value to avoid outlier effect verus dropping them\n",
    "# since we have less than 5k entries the throught process is to salvage these rows instead of dropping them\n",
    "new_df.fillna(df[\"runtime\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c37c1701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4802 entries, 0 to 4802\n",
      "Data columns (total 63 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   budget          4802 non-null   int64  \n",
      " 1   popularity      4802 non-null   float64\n",
      " 2   release_date    4802 non-null   object \n",
      " 3   revenue         4802 non-null   int64  \n",
      " 4   runtime         4802 non-null   float64\n",
      " 5   vote_average    4802 non-null   float64\n",
      " 6   vote_count      4802 non-null   int64  \n",
      " 7   Action          4802 non-null   int32  \n",
      " 8   Adventure       4802 non-null   int32  \n",
      " 9   Animation       4802 non-null   int32  \n",
      " 10  Comedy          4802 non-null   int32  \n",
      " 11  Crime           4802 non-null   int32  \n",
      " 12  Documentary     4802 non-null   int32  \n",
      " 13  Drama           4802 non-null   int32  \n",
      " 14  Family          4802 non-null   int32  \n",
      " 15  Fantasy         4802 non-null   int32  \n",
      " 16  Foreign         4802 non-null   int32  \n",
      " 17  History         4802 non-null   int32  \n",
      " 18  Horror          4802 non-null   int32  \n",
      " 19  Music           4802 non-null   int32  \n",
      " 20  Mystery         4802 non-null   int32  \n",
      " 21  Romance         4802 non-null   int32  \n",
      " 22  ScienceFiction  4802 non-null   int32  \n",
      " 23  TVMovie         4802 non-null   int32  \n",
      " 24  Thriller        4802 non-null   int32  \n",
      " 25  War             4802 non-null   int32  \n",
      " 26  Western         4802 non-null   int32  \n",
      " 27  ar              4802 non-null   uint8  \n",
      " 28  cn              4802 non-null   uint8  \n",
      " 29  cs              4802 non-null   uint8  \n",
      " 30  da              4802 non-null   uint8  \n",
      " 31  de              4802 non-null   uint8  \n",
      " 32  el              4802 non-null   uint8  \n",
      " 33  en              4802 non-null   uint8  \n",
      " 34  es              4802 non-null   uint8  \n",
      " 35  fa              4802 non-null   uint8  \n",
      " 36  fr              4802 non-null   uint8  \n",
      " 37  he              4802 non-null   uint8  \n",
      " 38  hi              4802 non-null   uint8  \n",
      " 39  hu              4802 non-null   uint8  \n",
      " 40  id              4802 non-null   uint8  \n",
      " 41  is              4802 non-null   uint8  \n",
      " 42  it              4802 non-null   uint8  \n",
      " 43  ja              4802 non-null   uint8  \n",
      " 44  ko              4802 non-null   uint8  \n",
      " 45  ky              4802 non-null   uint8  \n",
      " 46  nb              4802 non-null   uint8  \n",
      " 47  nl              4802 non-null   uint8  \n",
      " 48  no              4802 non-null   uint8  \n",
      " 49  pl              4802 non-null   uint8  \n",
      " 50  ps              4802 non-null   uint8  \n",
      " 51  pt              4802 non-null   uint8  \n",
      " 52  ro              4802 non-null   uint8  \n",
      " 53  ru              4802 non-null   uint8  \n",
      " 54  sl              4802 non-null   uint8  \n",
      " 55  sv              4802 non-null   uint8  \n",
      " 56  ta              4802 non-null   uint8  \n",
      " 57  te              4802 non-null   uint8  \n",
      " 58  th              4802 non-null   uint8  \n",
      " 59  tr              4802 non-null   uint8  \n",
      " 60  vi              4802 non-null   uint8  \n",
      " 61  xx              4802 non-null   uint8  \n",
      " 62  zh              4802 non-null   uint8  \n",
      "dtypes: float64(3), int32(20), int64(3), object(1), uint8(36)\n",
      "memory usage: 844.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#no other NA values are present\n",
    "# note release date is an object vs date\n",
    "new_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "74da2f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "# to display a max of 500 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b45bd24a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget  popularity release_date     revenue  runtime  vote_average  \\\n",
       "0  237000000  150.437577   2009-12-10  2787965087    162.0           7.2   \n",
       "1  300000000  139.082615   2007-05-19   961000000    169.0           6.9   \n",
       "2  245000000  107.376788   2015-10-26   880674609    148.0           6.3   \n",
       "3  250000000  112.312950   2012-07-16  1084939099    165.0           7.6   \n",
       "4  260000000   43.926995   2012-03-07   284139100    132.0           6.1   \n",
       "\n",
       "   vote_count  Action  Adventure  Animation  Comedy  Crime  Documentary  \\\n",
       "0       11800       1          1          0       0      0            0   \n",
       "1        4500       1          1          0       0      0            0   \n",
       "2        4466       1          1          0       0      1            0   \n",
       "3        9106       1          0          0       0      1            0   \n",
       "4        2124       1          1          0       0      0            0   \n",
       "\n",
       "   Drama  Family  Fantasy  Foreign  History  Horror  Music  Mystery  Romance  \\\n",
       "0      0       0        1        0        0       0      0        0        0   \n",
       "1      0       0        1        0        0       0      0        0        0   \n",
       "2      0       0        0        0        0       0      0        0        0   \n",
       "3      1       0        0        0        0       0      0        0        0   \n",
       "4      0       0        0        0        0       0      0        0        0   \n",
       "\n",
       "   ScienceFiction  TVMovie  Thriller  War  Western  ar  cn  cs  da  de  el  \\\n",
       "0               1        0         0    0        0   0   0   0   0   0   0   \n",
       "1               0        0         0    0        0   0   0   0   0   0   0   \n",
       "2               0        0         0    0        0   0   0   0   0   0   0   \n",
       "3               0        0         1    0        0   0   0   0   0   0   0   \n",
       "4               1        0         0    0        0   0   0   0   0   0   0   \n",
       "\n",
       "   en  es  fa  fr  he  hi  hu  id  is  it  ja  ko  ky  nb  nl  no  pl  ps  pt  \\\n",
       "0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "   ro  ru  sl  sv  ta  te  th  tr  vi  xx  zh  \n",
       "0   0   0   0   0   0   0   0   0   0   0   0  \n",
       "1   0   0   0   0   0   0   0   0   0   0   0  \n",
       "2   0   0   0   0   0   0   0   0   0   0   0  \n",
       "3   0   0   0   0   0   0   0   0   0   0   0  \n",
       "4   0   0   0   0   0   0   0   0   0   0   0  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d7b666",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e9ff90",
   "metadata": {},
   "source": [
    "### 5. Datetime object to date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f228dbac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from object to date\n",
    "new_df[\"release_date\"] = pd.to_datetime(new_df[\"release_date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4cff385c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the month from each column\n",
    "new_df[\"release_month\"] = new_df[\"release_date\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5d0abf38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop release date as no other info here is deemed necessary to retain\n",
    "new_df.drop(\"release_date\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1262df6c",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477d1b8e",
   "metadata": {},
   "source": [
    "### 6. Correlations & data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c21ff049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pVLCjfv36WLx4Mfr06QMTExOYmJgI7UddXR3Vq1eXWtVXHFrDjxBCCCGEEEIIIYSQr2zXrl1wcHBA165d0bVrVzg6OmLHjh1Ct0lNTUVOTs5Xb5tG+FUAb9++hYODA8aPH48//vgDwJdhnr/88guOHz+O5cuXQ01NDadPnwaPx0N2djYcHR3h5eWFhQsXlnPvCSGEEEIIIYQQ8rNjlXgNv2+lSpUq2Llzp9TbyHrcFFm3rzQa4VcBmJqaIjQ0FEFBQUhKSsLHjx8xdOhQjBkzBt26dUNERASuXLmCNWvWAABGjx4NMzMzBAUFlW/HCSGEEEIIIYQQQkiFQyP8KogePXpg5MiR8PT0RIsWLaClpYUlS5YAAGrWrIlNmzbBy8sLr1+/xvHjx3H9+nWoq6uL3VdBQQEKCgqEthWxYqjxVL/5/SCEEEIIIYQQQggh5YtG+FUgK1asQFFREfbv349du3ZBS0tLcF3//v3Rt29fLF68GCtXroStra3E/SxevBiGhoZCl/icO9/jLhBCCCGEEEIIIYSQckYJvwrk0aNHePHiBfh8Pp48eSJ0XV5eHpKTk6Gqqoq0tDSp+5k5cyZycnKELu0MG37LrhNCCCGEEEIIIeQnxgertJcfEU3prSA+f/4MT09PDBw4EPXr14efnx9u3boFMzMzAMCUKVOgoqKC06dPo0ePHnB3d0fHjh3F7ktTUxOamppC22g6LyGEEEIIIYQQQsjPgRJ+FcSsWbOQk5ODNWvWQE9PD6dPn4afnx9OnDiBkydPIjQ0FImJiWjWrBlmzJiBYcOGISUlBcbGxuXddUIIIYQQQgghhBBSgdCU3gogJiYGwcHB2LFjBwwMDKCiooIdO3YgLi4O8+fPh5+fH4KCgtCsWTMAwJw5c1CjRg2MHj26nHtOCCGEEEIIIYQQAjDGKu3lR0Qj/CoAFxcXFBYWCm2ztLREdnY2ACAwMFDoOjU1NVy+fPl7dY8QQgghhBBCCCGEVCI0wo8QQgghhBBCCCGEkB8IjfD7SRRzrDpTxDFeBTxO8YZM+dx0Ice+54PPKd66mFvBlDwODx3XF3guj9tjx7V9LkeNNodjBgAKeNyed12O7XNhXsQt/p56Mad4NY6vdy6PvDbj1nYBx2MePG7tc3m/qs7ndsxlqXA75rne958Vn+PDFpOfySm+t1ZtTvFc+s+5Ih4dc4QQ8kOb7xQo+0ZSBCbN5xS/sDm39gkpb5TwI4QQQgghhBBCCCGc8H/QtfAqK5rSW8GFh4fDyMiovLtBCCGEEEIIIYQQQioJSvgRQgghhBBCCCGEEPIDoSm9hBBCCCGEEEIIIYQTxnV9XvJV0Qi/CiAjIwM8Hk/k4uLiIrhNVFQUGjRoAD09Pbi5ueHly5fl12FCCCGEEEIIIYQQUmFRwq8CsLCwwMuXLwWX69evw8TEBM7OzgCAvLw8rFixAjt27MCFCxeQmZmJgICAcu41IYQQQgghhBBCCKmIaEpvBaCqqorq1asDAPLz8+Hh4YE2bdogKCgI27dvR2FhITZu3Ig6deoAAMaNG4d58+ZJ3F9BQQEKCgqEthWxYqjxVL/dnSCEEEIIIYQQQgghFQKN8Ktg/Pz88N9//2H37t1QUfny9Ojo6AiSfQBgbm6ON2/eSNzH4sWLYWhoKHRJyLn7zftOCCGEEEIIIYSQnxOfsUp7+RFRwq8CWbBgAc6cOYNjx45BX19fsF1dXV3odjweD0zKATlz5kzk5OQIXdoa2n+zfhNCCCGEEEIIIYSQioOm9FYQBw8exLx583D69Gmh0XzK0NTUhKamptA2ms5LCCGEEEIIIYQQ8nOghF8FcPv2bXh7e2P69Olo2LAhXr16BQDQ0NAo554RQgghhBBCCCGEkMqGpvRWAElJScjLy8OCBQtgbm4uuPTt27e8u0YIIYQQQgghhBAiE2Os0l5+RJTwqwB8fHzEHnAxMTHw8fFBdna20O09PDx+2AOSEEIIIYQQQgghhHBDCT9CCCGEEEIIIYQQQn4gtIbfT6IKv3xzu1xbt/rMVzo2R5Vb61mq3AqeZKgWc4rXZ8r3Xx08Tm3nQvnHHQD0we2x4xKtxXEQrDbj9tgVgVsH8njKx+urcDvmuR2xgDGHYxYAcnjKH3fcjlhwfMVwP274POXjuT5vXPtewCG8kOPrxYDjMZfN4/bocXmvVeF41PXTrM0pnl+OEwaKOb7gVDj2net3k08c3qe5vt7KG5dj/iOH93gA0OT42HF95Lm8W3zieN/1OL7XFXA4ZgFu953r88bn+MSpcXy/4NI8189nrtQ5xn/keNxwocfxuFnYPJBT/Kzk+Zzif0aM43c68nV98yyQi4sLJk6c+K2b+WFZW1sjODi4vLtBCCGEEEIIIYQQQioJmtJbQYSHh8PIyKi8u0EIIYQQQgghhBBCKjlOCb/Pnz9/rX4QQgghhBBCCCGEEEK+AoUSfi4uLhg3bhwmT56MqlWrokuXLrh79y569OgBPT09mJmZwcvLC+/evZO4j8+fP2PatGmoWbMmdHV10apVK8TExAiuz8rKwuDBg1GrVi3o6OjAwcEBe/bsEdpHZGQkHBwcoK2tDRMTE3Tu3Bm5ubmC68PCwtCgQQNoaWmhfv36WL9+vVz3LyMjAzweD/v378cvv/wCbW1ttGjRAg8ePMDVq1fh5OQEPT09uLm54e3bt4I4Pp+PefPmoVatWtDU1ESTJk1w5swZkf0eOnQIrq6u0NHRQePGjZGYmAgAiImJwfDhw5GTkwMejwcej4egoCBBfF5eHnx9faGvrw9LS0ts3rxZrvtDCCGEEEIIIYQQ8j0wxirt5Uek8Ai/iIgIqKmpIT4+HkuWLEGHDh3QpEkTJCUl4cyZM3j9+jUGDBggMX748OGIj4/H3r17kZKSgv79+8PNzQ1paWkAgPz8fDRv3hwnTpzA7du3MWrUKHh5eeHy5csAgJcvX2Lw4MHw9fXFvXv3EBMTg759+wqeoC1btmDWrFlYuHAh7t27h0WLFiEwMBARERFy38c5c+bgzz//xLVr16CmpobBgwdj2rRpCAkJwcWLF5Geno7Zs2cLbh8SEoKVK1dixYoVSElJQbdu3dCrVy/BfSoxa9YsBAQE4MaNG7C1tcXgwYNRVFSEtm3bIjg4GAYGBnj58iVevnyJgIAAQdzKlSvh5OSE69evY8yYMfj9999x//59ue8PIYQQQgghhBBCCPl58JgCqUwXFxfk5OTg+vXrAIDZs2fj8uXLiIqKEtzm2bNnsLCwQGpqKmxtbeHi4oImTZogODgY6enpqFevHp49e4YaNWoIYjp37oyWLVti0aJFYtt1d3dHgwYNsGLFCly7dg3NmzdHRkYGrKysRG5raWmJpUuXYvDgwYJtCxYswKlTp5CQkCD1/mVkZKB27drYunUr/Pz8AAB79+7F4MGDce7cOXTs2BEAsGTJEoSHhwuSbjVr1sTYsWPxxx9/CPbVsmVLtGjRAuvWrRO737t376Jhw4a4d+8e6tevj/DwcEycOBHZ2dlCfbK2tsYvv/yCHTt2APiSMa9evTrmzp2L0aNHi70fBQUFKCgoENq2oeFvUONxq5jKReWu0sspHG9Uyq9KrxrHWnTPeYWc4suzSm/1Yo6VYjmWf+RaLZZLld6aHO8718rSVRm3551LlV6uVQCLOFai0+VYQTGXw3034liNnWv1RqrSqxyuVXq5Vp7k+l7FRSHHY06d4+udqvQqj6r0Koeq9CqPqvQq72eu0svtlwy3Kr3qVW04tl45aWjWKu8uKO1zwbPy7sJXp6ZogJOTk+DfycnJiI6Ohp6ensjt0tPTYWtrK7Tt2rVrYIyJbC8oKICJiQkAoLi4GEuWLMG+ffvw/PlzQfJKV1cXANC4cWN06tQJDg4O6NatG7p27Yr//e9/MDY2xtu3b/H06VP4+flh5MiRgv0XFRXB0NBQ7vvo6Ogo+LeZmRkAwMHBQWjbmzdvAAAfPnzAixcv0K5dO6F9tGvXDjdv3pS4X3NzcwDAmzdvUL9+fbn7w+PxUL16dUH74ixevBhz584V2tbFwAHdDB0lRBBCCCGEEEIIIYQo70edGltZKZzwK0m8AV/Wrvv111+xdOlSkduVJLRK4/P5UFVVRXJyMlRVhUeAlCQNV65cidWrVyM4OBgODg7Q1dXFxIkTBQVCVFVV8ffffyMhIQFnz57F2rVrMWvWLFy+fBk6OjoAvkzrbdWqldD+y7Ynjbr6/50H4fF4Yrfx+cJn6EpuV4IxJrJN3H7L7kdWfyS1X9rMmTMxefJkoW0bGv4msx1CCCGEEEIIIYQQUvkpnPArrVmzZjh48CCsra2hpiZ7V02bNkVxcTHevHmDX375RextLl68iN69e2Po0KEAviTE0tLS0KBBA8FteDwe2rVrh3bt2mH27NmwsrLC4cOHMXnyZNSsWROPHj2Cp6cnl7smNwMDA9SoUQNxcXFwdnYWbE9ISEDLli3l3o+GhgaKi7/OgG9NTU1oamoKbSvP6byEEEIIIYQQQggh5PvhlPAbO3YstmzZgsGDB2Pq1KmoWrUqHj58iL1792LLli0io+psbW3h6ekJb29vrFy5Ek2bNsW7d+9w/vx5ODg4oEePHqhbty4OHjyIhIQEGBsbY9WqVXj16pUg4Xf58mWcO3cOXbt2RbVq1XD58mW8fftWcH1QUBDGjx8PAwMDdO/eHQUFBUhKSsL79+9FRr19LVOnTsWcOXNQp04dNGnSBGFhYbhx4wZ27dol9z6sra3x8eNHnDt3Do0bN4aOjo5gxCIhhBBCCCGEEEIIIfLilPCrUaMG4uPjMX36dHTr1g0FBQWwsrKCm5sbVFTELyobFhaGBQsWYMqUKXj+/DlMTEzQpk0b9OjRAwAQGBiIx48fo1u3btDR0cGoUaPg4eGBnJwcAF9G1F24cAHBwcH48OEDrKyssHLlSnTv3h0AMGLECOjo6GD58uWYNm0adHV14eDggIkTJ3K5q1KNHz8eHz58wJQpU/DmzRvY29vj2LFjqFevntz7aNu2LUaPHo2BAwciKysLc+bMQVBQ0DfrMyGEEEIIIYQQQsjXQiv4VSwKVekllddKy6Hl2j5V6VUeVelVDlXpVR5V6VUeVelVDlXpVR5V6VUeVelVHlXpVQ5V6VUeVelVHlXpVR5V6VWcmkbN8u6C0oo+Py/vLnx1XL/rEEIIIYQQQgghhBBCKpCfKuG3aNEi6Onpib2UTAkmhBBCCCGEEEIIIaRSYz+RrKwslpaWJvby7Nmz8u5eucnPz2dz5sxh+fn5lS6+Mveda3xl7jvX+Mrcd67xlbnvXOMrc9+5xlfmvnONr8x95xpfmfvONb4y951rfGXuO9f4ytx3rvGVue9c4ytz37nGU98rZzzXtgn5Xn6qhB8RLycnhwFgOTk5lS6+Mveda3xl7jvX+Mrcd67xlbnvXOMrc9+5xlfmvnONr8x95xpfmfvONb4y951rfGXuO9f4ytx3rvGVue9c4ytz37nGU98rZzzXtgn5Xn6qKb2EEEIIIYQQQgghhPzoKOFHCCGEEEIIIYQQQsgPhBJ+hBBCCCGEEEIIIYT8QCjhR6CpqYk5c+ZAU1Oz0sVX5r5zja/MfecaX5n7zjW+Mveda3xl7jvX+Mrcd67xlbnvXOMrc9+5xlfmvnONr8x95xpfmfvONb4y951rfGXuO9d46nvljOfaNiHfC48xxsq7E4QQQgghhBBCCCGEkK+DRvgRQgghhBBCCCGEEPIDoYQfIYQQQgghhBBCCCE/EEr4EUIIIYQQQgghhBDyA6GEHyGEEEIIIYQQQgghPxBK+BFCCCHfSHZ2NrZu3YqZM2fi33//BQBcu3YNz58/lxhTVFQENTU13L59W+l2i4uLERsbi/fv3yu9D0IIIYSQEvTdgpDKhxJ+5Kf1+fNnpKamoqioSO6YefPmIS8vT2T7p0+fMG/evK/ZPamU6TshymCM4cmTJ/j06VN5d6XSSUlJga2tLZYuXYoVK1YgOzsbAHD48GHMnDlTYpyamhqsrKxQXFysdNuqqqro1q2boM3vqaioCBEREXj16tV3b1ua8ngsytPnz5/x7NkzZGZmCl0qqsLCQri6uuLBgwfl3RWihLt37+LMmTM4duyY0IUQ8m08fPgQUVFRgu9njDGF4pX5jCjP7xaEEOXwmKLvDoQAiImJgYuLy1fZV35+PrS0tL7KvuSRl5cHf39/REREAAAePHgAGxsbjB8/HjVq1MCMGTMkxqqqquLly5eoVq2a0PasrCxUq1ZNrh/o2dnZuHLlCt68eQM+ny90nbe39zfre3FxMcLDw3Hu3DmxbZ8/f15m311cXODr64v+/ftDW1tb5u3LSk9PR1hYGNLT0xESEoJq1arhzJkzsLCwQMOGDRXe3/fE9ZjPzc2Frq7u1+vQd8Ln86GlpYU7d+6gXr165d2dcnHu3DmJr5vQ0FCJcZ07d0azZs2wbNky6Ovr4+bNm7CxsUFCQgKGDBmCjIwMibFhYWE4cOAAdu7ciSpVqijV7xYtWmDJkiXo1KmTUvHK3m8A0NHRwb1792BlZaVU28CXY+/hw4di23d2dpYau3TpUlhbW2PgwIEAgAEDBuDgwYOoXr06Tp06hcaNG8vVh7y8PGRmZuLz589C2x0dHaXGKfpe9+HDB7n6AwAGBgZSr09LS4Ovry8SEhKEtjPGwOPxZH5ORUREoGrVqnB3dwcATJs2DZs3b4a9vT327Nkj13N69+5dsY9br169pMaZmpoiISGB03tNdnY2IiMjkZ6ejqlTp6JKlSq4du0azMzMULNmTaX3W9l8+PAB58+fh52dHRo0aPDN2nn06BH69OmDW7dugcfjCZIOPB4PABQ+cfH27VukpqaCx+PB1tYWpqamMmO+5nfS8pScnIx79+6Bx+OhQYMGaNas2Xfvw/c6bsR59uwZeDzeN3ud9u3bV+7bHjp0SOr1rq6uGDp0KP73v//B0NBQqf5ERkZi//79Yt8rr127JjEuKysLAwcOxPnz58Hj8ZCWlgYbGxv4+fnByMgIK1eulNou188IZb5bNGvWDOfOnYOxsTGaNm0qeH8QR9p9DwwMRFBQEFRVVYW25+TkYPTo0dizZ4/Ufrx+/RoBAQGC7zZl0yBcTrQSUlGplXcHSPngmrhyc3NDzZo1MXz4cAwbNgwWFhYKtc/n87Fw4UJs3LgRr1+/FiSuAgMDYW1tDT8/P5n72LFjBzZu3IjHjx8jMTERVlZWCA4ORu3atdG7d2+JcTNnzsTNmzcRExMDNzc3wfbOnTtjzpw5UpNmJR+GZd28eVOuH+XHjx+Hp6cncnNzoa+vL7QvHo8nM+HHpe8TJkxAeHg43N3d0ahRI6kftpI0b94c06ZNg7+/PwYMGAA/Pz+0bt1artjY2Fh0794d7dq1w4ULF7Bw4UJUq1YNKSkp2Lp1KyIjI2Xuw9raGr6+vvDx8YGlpaXC/S8uLsbq1aslfsEqmXIpDtdj3szMDAMGDICvry/at2+vcN8BbsliZZO1KioqqFevHrKyspT+EX7q1CnBWeHSoqKiwOfz0b1792/S99KUfb+YO3cu5s2bBycnJ5ibmyv0url69So2bdoksr1mzZoyR7+tWbMGDx8+RI0aNWBlZSWSLJb2hbjEwoULERAQgPnz56N58+Yi+5CWOOJyvwGgVatWuHHjhtIJv0uXLmHIkCF48uSJyBdyeX6QbNq0CTt37gQA/P333/j7779x+vRp7N+/H1OnTsXZs2elxr99+xbDhw/H6dOnxV4vrX1l3uuMjIxkPsby/hjz8fGBmpoaTpw4odRzt2jRImzYsAEAkJiYiL/++gvBwcE4ceIEJk2aJPWHMNfkj7e3N7Zt24YlS5Yo1OcSKSkp6Ny5MwwNDZGRkYGRI0eiSpUqOHz4MJ48eYLt27eLxBw7dgzdu3eHurq6zBFpshKWFy5ckHq9uET1mjVrpMaUNn78eInXDRgwAM7Ozhg3bhw+ffoEJycnZGRkgDGGvXv3ol+/flL3rez3wgkTJqB27dr4559/YGNjgytXriArKwtTpkzBihUr5L5vubm58Pf3x44dOwRtqaqqwtvbG2vXroWOjo7EWK6fzwCQmpqKtWvXChJu9evXh7+/P+zs7MTeXpHRi7KOmzdv3mDQoEGIiYmBkZERGGPIycmBq6sr9u7dK5L0/FrHDFB+x00JPp+PBQsWYOXKlfj48SMAQF9fH1OmTMGsWbOgoiJ5MpqirzdlE3PiODg44M8//8S4cePQo0cPeHl5oUePHtDQ0JArfs2aNZg1axaGDRuGo0ePYvjw4UhPT8fVq1cxduxYqbGTJk2CmpoaMjMzhZKyAwcOxKRJk2Qm/Lh+Rijz3aJ3797Q1NQEAHh4eCjUXmnbt2/H33//jV27dqFOnToAviT8vb295UoU+/j4IDMzE4GBgUrd9xIPHjxATEyM2O/js2fPVmqfhHwzjPyUeDwee/36tcj258+fMy0tLZnxWVlZLCQkhDVt2pSpqqqyrl27sn379rGCggK52p87dy6zsbFhO3fuZNra2iw9PZ0xxti+fftY69atZcavX7+eVa1alS1YsEAoPiwsjLm4uEiNtbS0ZImJiYwxxvT09ASxaWlpTF9fX2yMkZERMzY2ZioqKoJ/l1wMDAyYiooKGzNmjMx+16tXj02YMIHl5ubKvO3X6nsJExMTdvLkSaXaLa2oqIgdOXKE9e7dm6mrq7MGDRqw5cuXs1evXkmNa926NVu5cqVI369cucJq1KghV9tr1qxhzZo1Y6qqqqxz585sz549LD8/X+6+BwYGMnNzc7Z8+XKmpaXF5s+fz/z8/JiJiQkLCQmRGsv1mD927Bjr27cv09DQYPXq1WOLFy9mz58/l7vvx44dY/r6+kxFRYUZGhoyIyMjwcXY2Fhm/OTJk5mZmRkzMDBgI0aMEBxH8jhx4gRr3749u3XrltwxpTk4OIg99k6fPs0cHR1lxnPpO2Pc3i+qV6/Otm/frlB7JapVq8auXbvGGBM+5qOiolitWrWkxgYFBUm9yIPH4wkuKioqgkvJ39Jwud+MMbZ//35mY2PD1q5dyxISEtjNmzeFLrI0btyY9e/fn929e5e9f/+eZWdnC11k0dLSYpmZmYwxxsaPH89GjRrFGGMsNTWVGRkZyYwfMmQIa9u2Lbty5QrT1dVlZ8+eZTt27GB2dnbsxIkTUmOVea+LiYmR+yKLjo4Ou3fvnszbSaKtrc2ePHnCGGNs2rRpzMvLizHG2O3bt1nVqlWlxvbs2ZP17t2bvXnzhunp6bG7d++yixcvspYtW7ILFy7IbHvcuHHMwMCANWvWjI0aNYpNmjRJ6CJLp06d2NSpUxljwo99fHw8s7KyEhtT+vtQ6ddM2Yus14yk+NKvPXGsra2FLrq6uozH4wm+Y/B4PKarq8tq164ttW0zMzN248YNxhhju3btYnXr1mW5ubls/fr1rEmTJnL1XZnvhSYmJoLXtIGBAbt//z5jjLFz587J1W6JUaNGMRsbG3bq1CmWk5PDcnJy2MmTJ1mdOnXY6NGjpcZy/Xw+cOAAU1NTY61btxYca23atGFqamps//79YmOkHSuKHjcDBgxgzZs3Z3fv3hVsu3PnDnNycmKDBg0SuX3ZY0bSRdYxw1j5HTclZsyYwUxNTdn69evZzZs32Y0bN9i6deuYqakp++OPP2S2rejrrUReXh77+PGj4O/Hjx+z1atXszNnzsjsc4ni4mIWFRXFhg0bxgwMDJixsTEbOXKkXO/TdnZ2bPfu3Ywx4feqwMBANnbsWKmxpZ+z0rGPHj1iurq6Mtvm+hnB5bsFY4z5+Piwv//+m/H5fIXbzs7OZgMHDmR6enps8+bNLCAggKmrq7PAwEBWVFQkM15PT49dv35d4XZL27x5M1NVVWVmZmascePGrEmTJoJL06ZNOe2bkG+BEn4/mZCQEBYSEsJUVFTYwoULBX+HhISwVatWMQ8PD4W+oDHG2PXr15m/vz+rWrUqq1KlCvP39xd8EElSp04d9s8//zDGhD+s7t27J9ePsQYNGrDDhw+LxN+6dYuZmJhIjS39g7907I0bN5iBgYHYmPDwcBYWFsZ4PB4LCQlh4eHhgsvu3btZQkKCzD4z9uVDtqQ9ZSjT9xLm5uYsNTVV6bbFefPmDZs/fz7T0tJi6urqrHfv3uzcuXNib6urq8sePXok0vfHjx8zTU1Nhdq9ceMGGz9+PDM1NWXGxsZs7NixLDk5WWacjY2N4Me6np4ee/jwIWPsy+ti8ODBcrevzDFf4t27d2zVqlXM0dGRqampMXd3d3bw4EFWWFgoNY5rspgx5ZO1RkZGTENDg6moqDAtLS2hhLc8yUYtLS32+PFjke2PHz9mOjo637TvjHF7v6hSpYrgOFHUyJEjmYeHB/v8+TPT09Njjx49Yk+ePGFNmzZlEyZMUGqfiuCSOOJyvxnjnjjR0dFhaWlpSrdvbm7O4uPjGWOM2draCn60379/X+bJEca+JDwvX77MGGNMX19f8N559OhR1q5dO6mxX+O97sKFC8zT05O1bt2aPXv2jDHG2Pbt29nFixdlxjo5Ocl1O0lMTU0FieomTZqwiIgIxhhjDx8+lPljUpnkz82bN1lxcTFjjDEXFxeJF1dXV5l9NzAwEBy3pR/7jIwMhT9nlFE2Mf327Vt29uxZ1qpVK8F3Hml27drF2rVrJ3jcGPtyzP7yyy9s586dUmNLJ7m9vLzY9OnTGWOMPXnyROrzxvV7oZGRkeBxtrGxYefPn2eMfTletLW1Zd7nEiYmJiw6Olpk+/nz52UmmktT5vO5du3aLDAwUGT77Nmz5UqacWVgYMCuXLkisv3y5cvM0NBQ7v3w+XyFkyjlddyUMDc3Z0ePHhXZfuTIEZkng7m83rp06cI2bNjAGGPs/fv3zMzMjNWqVYtpaWmx9evXy+x3WZ8+fWL79+9njRs3luszTltbm2VkZDDGvrznlhyjDx48YFWqVJEaq6enxx48eCD4d+mTSrJiGeP+GREeHs7OnTsn8p3i/PnzLDw8XGb8r7/+yjQ1NVmNGjXYlClTlErA/fHHH4zH4zF1dXW53ltLNGjQQPD5pixLS0u2ZMkSTvsg5HuihN9PpuSsH4/HYxYWFkJnAm1tbVnXrl3ZpUuXFN7v8+fP2Zw5c5impibT1dVlqqqqrH379uz27dtib6+lpSX4oCv9YXXnzh25zk5Jin/w4IHMM4rOzs5szZo1gtiSH2Zjx45l3bp1kxobExPDPn/+LLN/kvTp04ft27dP6XgufV+xYgUbM2aMUmfUxLl8+TIbPXo0MzQ0ZJaWlmz27Nls5MiRTEdHh02ZMkXk9jVr1hT8AC/9nB06dIjZ2Ngo1YfPnz+z4OBgpqmpyVRUVJijoyPbtm2bxPuoo6MjGLlSvXp1QZIwPT1dZsK0LEWPeXHWrFnDNDU1GY/HY6ampiwwMFBiQo9rsrgsRZK1pRPc4i6ymJmZid3333//zUxNTb9p3xnj9n4xbdo0Nm/ePIX7yBhjOTk5rF27dszIyIipqqoyCwsLpq6uzpydnYVGFkjy/v17tmXLFjZjxgyWlZXFGGMsOTlZkAD6lrjcb8a+JFikXWRxdXVlp0+fVrr9sWPHMisrK9a5c2dmYmLC/vvvP8YYY3v37pXrDLy+vr4gSW1lZcXi4uIYY19GUMhKYnB9r4uMjGTa2tpsxIgRTFNTUxC/bt061r17d5nx586dY23atGHR0dHs3bt3gtFSJRdZhgwZwpo1a8b8/PyYjo4Oe/fuHWPsS7LT3t5eaqwyyR8VFRXBCKHatWsL2lMGl1G1Jf755x82c+ZM5ufnx4YPHy64+Pr6Kt2v2NhY1qxZM5m3s7GxEftjNCkpiVlbW0uNrVevHtu3bx/7+PEjMzU1Fbwn3rhxQ+qJDa7fC9u3by84oTJ48GDm5ubG4uLimLe3N2vYsKHM+1xCW1tbaIRbidu3b8t9YqiEop/P2traYk8wPHjwQO6kJZfjRtKoo2vXrsl1gmLr1q2sYcOGTENDg2loaLCGDRuyLVu2yNXv8jpuSmhqaoo9GX3//n25RgiKI8/rzcTERHA8bNmyhTk6OrLi4mK2f/9+Vr9+fYXae/nyJVu9ejVr3rw54/F4rGXLljJjateuLfgO6uTkxDZu3MgY+/JeJetEao8ePdiff/7JGPu/3wLFxcWsf//+rF+/fmJjSn8GcP2MKP2eXdq7d+/kSnYy9uX7zaZNm1iHDh2YiooKa9CgAVu4cKHYk8NlrVmzhmlra7MhQ4YwOzs7Zm9vL/dJ96ioKNa1a1e52pFEX1//q34fJ+Rbo4TfT8rFxYX9+++/nPbx+fNnduDAAda9e3fBVIgtW7awjx8/sszMTDZ48GDWoEEDsbHNmzdnO3bsYIwJfykPCgpi7du3l9l2gwYN2JEjR0TiQ0JCZH7Ix8fHM319fTZ69GimpaXFJkyYwDp37sx0dXVZUlKSzLaLi4tZamoqu3jxIouNjRW6yLJ161ZmaWnJ5syZwyIjI9nRo0eFLrJw6buHhwczNDRktWvXZj179mR9+vQRusjj9evXbMWKFYIvlv369WOnT58WSrD9/fffYpO2U6dOZe3bt2cvX75k+vr6LC0tjcXFxTEbGxu5pyiW+Pz5M9u3bx9zc3NjqqqqrF27diw0NJQtWLCAVa9eXeJoPVtbW8EX0Pbt27PFixczxr4kAeRJPHE55ku8fPmSLV26lNWvX5/p6OgwT09Pdv78ebZz507WqFEj1qVLF7FxXJPFpSmarOVq5MiRzMHBQWjEWFpaGnN0dGR+fn4K7UuZvnN5vxg/fjwzMjJizs7ObNy4cQpPMWTsSwJm+fLlbOnSpezvv/+WK+bmzZvM1NSU1a1bl6mpqQn6/OeffwqmWMorNzeX3bt3T6FptV/jfjP25STO6dOnhd7njh07JjPu0KFDzN7enoWFhbGkpCSFpwR//vyZrVixgo0fP14ogbJ69Wq2detWmfFOTk6CqV29e/dmXl5e7NmzZ2zatGkyk3Zc3+tKj6orfbxev36dmZmZyYznOt3q/fv3bNy4cax3795C09tmz57NFixYIDVWmeRPlSpVBO/LPB6PvXnzRmYfJeE6qjYoKIipqKiwli1bst69ezMPDw+hi7Lu3r0r18lMbW1twcjS0i5fviwz8bRu3TqmpqbGjIyMBMkLxr78OJa1dAFjX74Xvn//Xubtyjpz5gw7ePAgY+xLYrdBgwaMx+OxqlWrKjTyxtXVlfXv3599+vRJsC0vL4/179+fdezYUWY8l8/n7t27s9DQUJHtoaGhrGvXrjLb5nrc9OrVizk7Owst8/Hs2TPWoUMHmfF//vkn09XVZTNmzBC8x86YMYPp6emxWbNmyWz7ax43yowwbNmyJfP39xfZPm7cONaqVSuF9lVCntdb6aUL+vfvL3hvzszMlCvJm5OTw0JDQ1nnzp2Zmpoas7W1ZUFBQXKPTPfz8xO0uWHDBqatrc06d+7MjIyMZCaJ79y5w0xNTZmbmxvT0NBg//vf/1iDBg2YmZmZxJH5kj4PlPmMkPQ+nZGRoXBynjHGnj59ypYtW8bq16/PVFVVpd7Wzc2NmZiYsAMHDjDGvrxHlPwmWrp0qdiYsksxlcxY0dPTU3jGCmOM+fr6CkaHElIZUJXen9znz5/x+PFj1KlTB2pq8tdw8ff3F1RCGjp0KEaMGIFGjRoJ3SYzMxPW1tYii5kCX4pXeHl5YebMmZg3bx7mzp2L1NRUbN++HSdOnECXLl2kth8WFobAwECsXLkSfn5+2Lp1K9LT07F48WJs3boVgwYNkhp/69YtrFixAsnJyeDz+WjWrBmmT58OBwcHqXFcF5KXtviwPPEAcPv2bSxfvlzhvg8fPlzq9WFhYTLb1tDQQJ06dQSFM8RVz/vw4QN69+6N6Ohooe2FhYXw8fHB3r17wRiDmpoaiouLMWTIEISHh4tU3BLn2rVrCAsLw549e6CqqgovLy+MGDEC9evXF9zm6tWrcHZ2xqdPn0TiZ8yYAQMDA/zxxx+IjIzE4MGDYW1tjczMTEyaNEnqQvFcj/lDhw4hLCwMUVFRsLe3x4gRIzB06FAYGRkJbnPnzh00bdpUpJgIAGzbtg3z5s3D8OHD4eDgAHV1daHr5VkUfMeOHQgLC0NaWhp+/fVXjBgxAt26dRMsWvzPP//Aw8NDsHB2acXFxThy5IhgQXN7e3v06tVLructJycHbm5uSEpKQq1atQB8qcb3yy+/4NChQ0KPwbfoO5f3C1dXV4nX8Xg8uapbK4NLhd8SXApPcL3fXIs3iHuvLNmPPO+VixcvhpmZGXx9fYW2h4aG4u3bt5g+fbrU+F27dgnes65fv45u3brh3bt30NDQQEREhKD6rzji3uuKiorg6ekp13udjo4O7t69C2tra6Hn/tGjR7C3t0d+fr7U+NjYWKnXd+jQQer1wJfF8Ddt2oRHjx4hMjISNWvWxPbt22FjYyNSdCglJQWNGjWCiooKoqKikJeXhz59+uDRo0fo2bMn7t+/DxMTE+zbtw8dO3YUaWvUqFHYvn07zM3NkZmZiVq1akl8jB49eiS13x8+fECPHj1w584d/Pfff6hRowZevXqF1q1b4/Tp0zIrpZubm2PZsmXw8vKS8QiJl5KSIvQ3YwwvX77EkiVLUFhYiPj4eKnxv/76KzIzM7Ft2zY0b94cPB4PSUlJGDlyJCwsLGQWikhOTkZmZia6du0quK8nT56EsbEx2rZtK3L7yZMnY/78+dDV1cWkSZOkLmC/atUqqW2X9u+//8LY2FihBfFv374NNzc35Ofno3HjxuDxeLhx4wY0NTVx9uxZsdWtS4wfPx67d+8GIP/nc+nH8sWLF5g9ezYGDBggKER26dIlHDhwAHPnzsXo0aOl9p3rcfP06VP07t0bt2/fhoWFBXg8Hp48eQJHR0ccOXJEahGSqlWrYu3atRg8eLDQ9j179sDf3x/v3r2T2X7JcdO5c2fo6emBx+Ph5MmTMDIyQrt27WTGb9u2DatXr0ZaWhoAoF69epg4cSJGjBghM/bChQvo0aMHLC0t0aZNG/B4PCQkJODp06c4deoUfvnlF4mxXF5vjo6OGDFiBPr06YNGjRrhzJkzaNOmDZKTk+Hu7i6zsJa2tjaMjY0xYMAAeHp6okWLFjLva2mPHz9GzZo1BUU+9u/fj7i4ONStWxfdu3eXWiQtMzMTampq2LRpk9BvgbFjx6KwsFBsUbvSnwsZGRmwsLAQeZ/l8/nIzMzEsGHDxLY7efJkAEBISAhGjhwpVEinuLgYly9fhqqqqsz3udIKCwtx8uRJ7Ny5EydPnkSVKlXw/Plzibfv0qULIiIiUKNGDaHtJ0+exMiRI/HixQuRmIiICLn7I+m+ly6Uk5ubi1WrVsHd3V3s93FZhXII+e7KLdVIylVeXh7z9fVlqqqqTFVVVTCCwN/fXzDqSZqOHTuy3bt3S10QubCwUOo6UWfOnGHOzs5MV1eXaWtrs3bt2rGoqCi578PmzZuZpaWlYCRDrVq15Bq5wQXXheS5+Pz5M/Px8SnXYeTyLLouy8OHD9mBAwfYvn37BGuQyEtFRYV169aN7d+/X+LU6o8fPzIfHx+59peYmMhWrlwp1+hKrse8gYEBGzVqlNh1ekrk5eVJHAHEdU00dXV1Vr9+fbZs2TKJI2hycnLEntFPS0tj9erVYzo6Oqxp06asSZMmTEdHh9nZ2cm9zhufz2dRUVFs2bJlbO3atXKNiP0afS/xvd4vQkJCBCNUSq9pJO4izddYi0zZwhNFRUUsJiZGMI1YGWWLN9y5c0eh4g1cpwRbWVkJptWWdunSJZlTI8XJzc1lycnJ7O3bt3LHpKenK/VeZ2NjIxgJWvq5j4iIkDmCuASXNQAVnVIsa0puVlaWzFE/p0+fZmvXrmU8Ho/Nnz+fBQcHi73I6/z58wqPqmXs66xdWTJSpvSlTZs2ci2S/+bNG9a9e3fG4/EE0zN5PB7r3r272PVKJ02aJFgeoOwoXHlG5ZYenaXI+ol9+vQRTP0rO1ug7MXLy4stWbJE5nekHTt2sLy8PLZ582Y2efJkNmnSJLZlyxaWl5fHAgICpMaqqKiwPXv2iP18Lokt+/n8NYtucD1uSvz9999szZo1LCQkRO7j1sjISOz7S2pqqtzr/3GZEhwYGKj0CMPPnz8zFxcXFh8fz/744w/Wt29f1qdPHzZr1iy5ippxeb0dOHCAqaurMxUVFaGZFYsWLWJubm4y2z569KjQ9NeMjAyFin5wmRbLdUqtsvEl7wc8Ho+1bdtW6D2ia9eubNSoUXJ/1p0/f56NGDFCUPywpJBHyQhTSc6ePSvxOlkj0Bn7MpJY3Pfsf//9V+o6sV+zUA4h3xuN8PtJTZgwAfHx8QgODoabmxtSUlJgY2ODY8eOYc6cObh+/brU+AsXLqBt27YiowKLioqQkJAAZ2fnb9l9Ie/evQOfz0e1atXkuv2HDx/EbufxeNDU1BScbRNHV1cXN2/eRN26dZXqK1dGRka4du0abGxslN7H27dvkZqaCh6PB1tbW7Gj9Cqi4uJi7NixA7169UKVKlXKuzsKy8vLEzob+j0xxnDx4kU4OTkp1YcePXqAMYZdu3YJHvusrCwMHToUKioqOHny5NfusgDXvpel6PtFac+ePQOPx0PNmjUl3qZ27dpISkqCiYkJateuLfF2PB5P6mglMzMznDlzBk2bNhUa5XX27Fn4+fnh6dOnMvtrbm6Oo0ePomXLljAwMEBSUhJsbW1x7NgxLFu2DHFxcRJjtbS0cO/ePan3QZqqVavi/PnzcHR0hKGhIa5cuQI7OzucP38eU6ZMkfkZw3WEnqT+SxslVzJ6QR5lRztxiS1r2bJliIiIQGhoKLp06YJTp07hyZMnmDRpEmbPno1x48ZJjT948CC8vLzg6emJHTt24O7du7CxscH69etx4sQJnDp1Smp806ZNMWnSJHh7ewsdezdu3ICbm5vIyBcTExOcOnUKrVq1goqKCl6/fq3058rw4cOxZs0a6OvrKxUPAOfOncO5c+fw5s0bkdHWoaGhUmOnT58OPT09BAYGKtX2kydPhP5WUVGBqakptLS0FNpPWloa7t27B8YYGjRoAFtbW7G3c3V1xeHDh2FkZPRdRyOXfp5kzR4oKChAYmIiHBwcpI5QNDIyws6dO9GzZ0+h7ZMnT8aePXvw8uVLhWMnTZqEvXv3So39GrgeN4Dyx62/vz/U1dVF3lcCAgLw6dMnrFu3Tmq7gYGBWL16Nfz9/dGmTRsAQGJiIv766y9MmDABCxYskBrPdYShqakpEhISpI5oE6ewsBD16tVDVFSU4PWl6Ovt1atXePnyJRo3biwYVX7lyhUYGBgIzRwRp2vXrujbty9Gjx6N7Oxs2NnZQUNDA+/evcOqVavw+++/S41XUVHBq1evRL6LPHnyBPb29sjNzf0msSXx4t6n5Y0fPnw4QkJCYGBgIPV2ktSqVQtZWVno1q0bPD098euvv8r9nGlqamLcuHFYvHix4Pfa27dv4evri/j4ePz7779S41VUVGBiYoJ27dph165dgpHQr1+/Ro0aNeSaaUVIZSP/HE7yQzly5Aj27duH1q1bC025sLe3R3p6usx4V1dXvHz5UuTDJicnB66urt/1DbNq1aoK3d7IyEjqNJNatWrBx8cHc+bMEZlW1qpVKzx8+JBTwi82NhYrVqwQTI1s0KABpk6dKnXaQok+ffrgyJEjCv24LJGbmwt/f39s375d8GVSVVUV3t7eWLt2rcRkStOmTeWelnPt2jWJ1zHGEBkZiejoaLFfaA8dOiR136qqqhg9ejQ6dOjAKeH3/PlzxMfHi+1D2WH4sqZPlSZrSu39+/ehrq4umHp99OhRhIWFwd7eHkFBQVITzVwxxtC5c2fcuXNH4S/VwJdj9tKlS0KPu4mJCZYsWSJxus+aNWswatQoaGlpCU2FEEfa9AeufQe+TJ0pKipCvXr1hN4v0tLSoK6uDmtra4mxfD4fCxYswMqVKwXThfX19TFlyhTMmjVL5D3i8ePHYv+tqN69e2PevHnYv38/gC8/2DMzMzFjxgz069dPrn3k5uYK3qOrVKmCt2/fwtbWFg4ODlJfqwDg4OCAR48eKZ3wKy4uhp6eHoAv79EvXryAnZ0drKyskJqaKjN+06ZNgil6pTVs2BCDBg2SmfCzsLBAfHy8SP/j4+NFpgKVKJuETE5ORnFxMezs7AAADx48gKqqKpo3b/5VY8uaNm2a4LM0Pz8fzs7O0NTUREBAgMxkHwAsWLAAGzduhLe3N/bu3SvY3rZtW8ybN09mfGpqqtiTdgYGBsjOzhbZ3q9fP3To0AHm5ubg8XhwcnJSekquPEtLSDN37lzMmzcPTk5Ogv7IUvrzlM/nY/Pmzfjnn3/g6OgoMlVLVrLWyspK4cRN6Wm14j7bS0/FK9t+6aUzyi6j8S2Vfp7kec7u3r0rc8rj3r17MWjQIBw7dkxw/I0fP17wvUHRWH9/fxw6dEjux0XS88bj8bBt2zapsfn5+ZyOG2WO29K2bduGs2fPCk1Hfvr0Kby9vYWOKXH92LBhA7Zs2SKUsOvVqxccHR3h7+8vM+FXXFwMJycnke3NmzdHUVGRzL57e3tj27ZtUpdUEUddXR25ublQVVWFlZWVQrElqlevjurVqwtta9mypVyx165dw+rVqwEAkZGRqF69Oq5fv46DBw9i9uzZEhN+Jc8Hj8fD7NmzxU6LbdKkyVePLRsfGBiocHwJru/Ts2fPRv/+/WFsbKxw7IULF+Dl5YV//vkHu3fvRkZGBnx9fWFvb4+bN2/KtY9//vkHv/32G1q3bo3jx49L/Q4ozrx58xAQECDyu+nTp09Yvnw5Zs+erdD+CPnWKOH3k3r79q3YES65ublyfdFg/38dpbKysrJkrpEDfDnDIq0dWQnDrKwszJ49W2LySNoZnvDwcMyaNQs+Pj5o2bIlGGO4evUqIiIi8Oeff+Lt27dYsWIFNDU18ccffwjF+vv7Y8qUKXj16pXYdRscHR2l9nvnzp0YPnw4+vbti/Hjx4MxhoSEBHTq1Anh4eEYMmSI1Pi6deti/vz5SEhIQPPmzUUea2mJk8mTJyM2NhbHjx8XJGni4uIwfvx4TJkyBRs2bBAb5+HhIfh3fn4+1q9fD3t7e8GZ4EuXLuHOnTsYM2aM1L5PmDABmzdvhqurK8zMzBT+QgtwT0KEhYVh9OjR0NDQgImJiVAfeDyeyONX+r5LI8+aYr/99htmzJghuA+DBg1Cnz59cODAAeTl5SE4OFhqvKSkGY/Hg5aWFurWrQtnZ2exP7RVVFRQr149ZGVlKZU009TUxH///Sey/ePHjxITlatXr4anpye0tLQEX4ol9V/accu17wDg4+MDX19fkfjLly9j69atiImJkRg7a9YswY+Rdu3agTGG+Ph4BAUFIT8/HwsXLpQYGxsbK9d6aeKsWLECPXr0QLVq1fDp0yd06NABr169Qps2baS2WZqdnR1SU1NhbW2NJk2aYNOmTbC2tsbGjRthbm4uNXbhwoUICAjA/Pnzxb7XyDqz36hRI8HI8VatWmHZsmXQ0NDA5s2b5Rqh/OrVK7F9NDU1lWu0zogRIzBx4kQUFhYK1o07d+4cpk2bhilTpoiNKZ0YWLVqFfT19RERESH4UfL+/XsMHz5c7MkZLrHiLFy4ELNmzcLdu3fB5/Nhb28vSKDKomjCrixzc3M8fPhQ5EdQXFyc2Odu8+bN6Nu3Lx4+fIjx48dj5MiRnEbocbFx40aEh4crtJZa2WRtyQ/e27dvC22X5zNLmcTN9evXUVhYKLYvirZfUdnZ2SEhIUHqbdzc3LBx40Z4eHjg7NmzCA0NxdGjRxETEyNxhKOs2OjoaJmxAPeEW0pKCqfjRpnjtsTt27fRrFkzABCcsDc1NYWpqalQXyT1g2vCbujQodiwYYNIMnHz5s3w9PSUGf/582ds3boVf//9N5ycnEQ+a6QlS729vbF161aFk4VfQ15enuB97uzZs+jbty9UVFTQunVrkZG+pZW8xhljuHXrltB3KA0NDTRu3BgBAQFfPfZrxH8to0aNUjq2VatWuH79OkaPHo3mzZsLTspOnTpV7tetubk5YmNj4evrixYtWuDAgQNo0KCB3H0oWdezbMIvLy8Pc+fOpYQfqXi+/yxiUhE4OzuzNWvWMMb+r6Q7Y4yNHTuWdevWTWJcyZosKioqrEePHkLrtPTq1YtZW1tLjS9x5MgRocuBAwfYH3/8wWrWrCnXulpubm6sXr16bMmSJSwsLIyFh4cLXaTp2LGj2Gqn+/btE1SC2759O7OzsxO5jaT1XeRd56V+/fps1apVIttXrlzJ6tevLzOey7oRJiYmLDo6WmT7+fPnWdWqVWW2zdiXqmJ//vmnyPbZs2ez4cOHS401NjZmJ0+elKsdSaKioliTJk3Y8ePH2YsXL1hOTo7QRZZatWqxBQsWyFwj5FsovSbbkiVLBJX/4uLiWK1atWTGW1tbM11dXcbj8ViVKlWYsbEx4/F4TFdXl5mZmTEej8fq1KnDMjMzxcafOHGCtW/fnt26dUvhvnt5ebGGDRuyS5cuCarwJSYmskaNGrFhw4YpvD9Fcek7Y0xQKbWstLQ0mWscmZubi13j8ciRI6xGjRpSY9XV1ZmFhQWbPn06S0lJUajPJZSp8FtyX3fu3MnCwsIYY4xdu3aNmZqaMhUVFaalpcX27t0rdR9cK72WrtyZnp4uVLnz3LlzMuPr1q0rqORe2vbt2+VaI4fP57Np06YxLS0tQd91dHTY3LlzZcYyxliNGjXY7du3RbbfunWLmZubf7PYr4HrGoBLly5l9vb27NKlS0xfX59dvHiR7dy5k5mamrK1a9dKjfXx8WEfPnzgfieU9LXWUlNW9erV2fbt28ut/R/B+vXrmaamJqtVq5bcFU+5xpb381aex21JFfaypkyZwsaMGSNXvIGBAWvYsCHz8/Njfn5+rGHDhszAwECkwrs4iqwdKantZs2asVGjRildTV4ZDg4OLCQkhGVmZjIDAwOWkJDAGGMsKSlJrmrqPj4+cn1v/dqxXyO+vCUnJzM7OztWp04dpq2tzYYPHy5Yy1SWsusXzp8/n2lqarLZs2fL9d2GMclVis+dOyf37ylCvidaw+8nlZCQADc3N0HVwN9++w137txBYmIiYmNjJU47KlmrJSIiAgMGDIC2trbgOg0NDVhbW2PkyJEKT7MtsXv3buzbtw9Hjx6Vejt9fX3ExcWhcePGCreho6ODmzdvioz2SUtLQ+PGjZGXl4fHjx+jYcOGyMvLE7qNtLN2AGROK9DU1MSdO3dEpgQ/fPgQjRo1kll9kQsdHR0kJyeLnMW6c+cOWrZsKXPNDgAwNDREUlKS2MfOyckJOTk5EmNr166N06dPy1wXRZrS0ydLn8ljclbuNDExwZUrV1CnTh2l+6AsAwMDJCcno169eujSpQt69uyJCRMmIDMzE3Z2dmKrCpe2Z88ebN68GVu3bhX0/+HDh/jtt98watQotGvXDoMGDUL16tURGRkpEm9sbIy8vDwUFRVBQ0ND6LULSB8Vm52djWHDhuH48eOCUa1FRUXo1asXwsPDYWhoqNBjUVxcjFu3bsHKykquKR1c+g58OW5jYmLQtGlToe3JyclwcXERO3qxhJaWFlJSUkRGiaSmpqJJkyZSn7d3795h79692LNnDxITE9GoUSMMHToUQ4YMEVQr/hZUVFRQs2ZNuLq6Ci7W1tbIy8vD/fv3YWlpKfM9+mtUei1LkcqdS5cuxfLly7F8+XKxI/RmzpwpV5sfP37EvXv3oK2tjXr16kFTU1OuOH19fRw9elSkquz58+fRu3dvqccMl9ivgesagMCXka2rV68WfCaVTCmeP3/+N+07V19jLTUuyvMzpjKStDxJZGQkmjZtKvQ4yrtupjyxZZX381aex23JUi8WFhZipwSXnski7nGUtnZkad+iqv33XLeyrMjISAwZMgTFxcXo1KkTzp49C+DL+rMXLlzA6dOnv1nbP7MlS5Zgzpw5GDVqFJYvX4709HQMHToUHz58wM6dOwWzjyQRt/7hwYMHMWzYMHz69Enq74iS7y85OTkwMDAQ+i5TXFyMjx8/YvTo0TLXzSTke6OE30/s1q1bWLFihVBJ9+nTpwvWGJNm7ty5CAgIkGv6riLS09Ph6OgoM/nUokULrF27VvDlRBG2trbo27evyBSAGTNm4PDhw0hNTUVSUhJ69+4ttTS8MurWrYupU6fit99+E9q+adMmrFixAmlpaV+1vdI6deoEExMTbN++XbA47qdPnzBs2DD8+++/+Oeff2Tuo3r16li8eLHIIt1hYWGYMWMGXr9+LTE2IiICZ86cQWhoqEjCRl5ckxDTpk1DlSpVMGPGDLna+1rr0AFAx44dYWFhgc6dO8PPzw93795F3bp1ERsbi2HDhiEjI0NqfJ06dXDw4EGR9VWuX7+Ofv364dGjR0hISEC/fv3ETnmMiIiQuv9hw4aJ3c4YQ2ZmJkxNTfHixQvBQvL29vZyr2U5ceJEODg4wM/PD8XFxXB2dkZiYiJ0dHRw4sQJuLi4SI1Xtu8levbsCR0dHezZs0cw5bm4uBgDBw5Ebm6u1C/mrVq1QqtWrUSef39/f1y9ehWXLl2S2naJx48fY/fu3dizZw/u378PZ2dnkR8jso6x0qQdbxcvXkRsbCxiYmKQmJiI/Px8WFpaomPHjoIEoLTCIxUBYwwzZszAmjVr8PnzZwBfkq/Tp0//LtNlvL29ERsbi5UrVwr9CJ46dSqcnZ2lHpNcYr+Wr5Gwy8vLU2pK8fdWdg2+iIgIODo6KrWWGlflnXCsbLgki75moqm8n7cJEyZg+/bt5XLclmfCrrLjUvSDKMfc3ByhoaHo3r27YFthYSH++OMPrFmzBgUFBVLjnzx5AktLS5ETj3fu3EFSUpLU75MRERFgjMHX1xfBwcFCJ7tLBr3ISjgSUh4o4UcqjE+fPmHmzJk4ffq0zEXdr169ihkzZmD27Nlo1KiRyJcjaetLHTt2DP3790f9+vXRokUL8Hg8XL16Fffv30dkZCR69uyJDRs2IC0tTeRL1vbt26X2y9vbW+r1GzZswMSJE+Hr64u2bduCx+MhLi4O4eHhCAkJEUkEllW2YmVZ0iq53b59G25ubsjPz0fjxo3B4/Fw48YNaGlpISoqCg0bNpS6b+DLmbWgoCCMGDFC6IdsaGgoZs+eLTWRlpeXh759+yI+Ph7W1tYiz5msIgJfQ3FxMXr27IlPnz6JXYOx7PP9tSquAl/W+PH09ERmZiYmT56MOXPmAPiSOMrKyhJboKA0HR0dXLhwQWStnatXr6JDhw7Iy8tDRkYGGjVqJCgu8TXw+XxoaWlxKppRq1YtHDlyBE5OTjhy5AjGjh2L6OhobN++HdHR0YiPj/9q/RXn7t27cHZ2hpGRkWANtYsXL+LDhw84f/48GjVqJDE2NjYW7u7usLS0RJs2bcDj8ZCQkICnT5/i1KlTcq/JBnw5/k6fPo3AwECkpKSInEmWd21KeY63EoWFhUhMTERMTAxiYmJw6dIlFBQUoG7dujLfZy9evIhNmzbh0aNHOHDgAGrWrIkdO3agdu3aaN++vVztc6XsCD2u8vLyEBAQgNDQUMH6ampqavDz88Py5culnuziEvu170NlSNhxVd4Ji4qUcCTKKc+EG1C+I9UIqUzevXsncYYCl3WTFREbG4u2bduKvE8QUlFRwu8n9eHDB7HbeTweNDU1xS7E36xZM5w7dw7GxsYyK7fKSt6UndbFGMN///0HHR0d7Ny5U2bF07S0NAwePFhkgWt5p3Y+efIEGzduRGpqKhhjqF+/Pn777TeZlZrKTj8sLCxEXl4eNDQ0oKOjI3NqIQAcPnwYK1euxL179wBAUKW3d+/eMmP79Okj0v7t27eRnZ2Njh07yqx0++nTJ+zcuRP3798XjNLy9PRUaMTd/v37ERISItT/CRMmYMCAAVLjBgwYgOjoaPzvf/8TW7SjJAEmj7y8PGRmZgpG/pSQVTRl/vz5mDNnDuzs7ET6UF5fqvPz86Gqqirzi4O7uztevXqFrVu3CqamXr9+HSNHjkT16tVx4sQJHD9+HH/88Qdu3boldh/FxcU4cuSIoEK0vb09evXqJbGiZomGDRti27ZtSo2oBb6MzHr48CFq1aqFUaNGQUdHB8HBwXj8+DEaN24s8f2otPT0dISFhSE9PR0hISGoVq0azpw5AwsLC7mS1S9evMBff/2FmzdvQltbG46Ojhg3bpxcFZ9fvHiBdevWCb1uxowZI7Haa1nx8fHYtWsXIiMjkZ+fj169esHT01PoDPW39unTJ8TFxSEqKgpbtmzBx48fpb5PHjx4EF5eXvD09MSOHTtw9+5d2NjYYP369Thx4gROnTr13fpennJzc5Geng7GGOrWratQso5LLKk8yjvhSLijhBshRBF8Ph8PHz4UWzhSXNEsQsoTJfx+UrKq5NaqVQs+Pj6YM2eOYJj63LlzMXXqVOjo6GDu3LlS9y8reVN2SpOKigpMTU3RqlUrudb0atmyJdTU1DBhwgSxyaPvcYanRFpaGn7//XdMnToV3bp1+27tluDz+RgzZgxsbGwwbdq0796+vHR1dREVFcVpZNDbt28xfPhwiVMwZSV6jY2NsXr1avj4+Cjdh/Ly6tUreHl54dy5c0Lr6HXq1Ak7duyAmZkZoqOjUVhYiK5du4rEP3z4ED169MDz589hZ2cHxhgePHgACwsLnDx5UuraRSdPnsSSJUuwYcMGqaPhJLGyssKWLVvQqVMn1K5dG+vXr0fPnj1x584dtG/fHu/fv5caHxsbi+7du6Ndu3a4cOEC7t27BxsbGyxbtgxXrlwRu2ZhRfDHH39gz549ePHiBTp37gxPT094eHiIVHb7FvLz85GQkIDo6GjExMTg6tWrqF27Njp06ABnZ2d06NBB6rTepk2bYtKkSfD29oa+vj5u3rwJGxsb3LhxA25ubnj16tU3vw+EEEIIIRXJpUuXMGTIEDx58gRl0yjyDDoh5HujhN9Pavv27Zg1axZ8fHzQsmVLMMZw9epVRERE4M8//8Tbt2+xYsUKTJ06FX/88YdQbHFxMeLi4uDo6ChXcu5b0NHRwfXr12FnZ6f0PpQdJSZOUlIShg4divv37yvdHy5SU1Ph4uIisnbbsWPH0L17d6irq+PYsWNS9yFrVGWJ7OxsREZG4tGjRwgICECVKlVw7do1mJmZSU0g1K9fH/v371fq8S3h6emJjIwMBAcHw9XVFYcPH8br16+xYMECrFy5Eu7u7lLjq1evjosXLyo1NZUxhsjISERHR4s9oydrdGVxcTFWr16N/fv3iz3u5BkdCgD379/HgwcPBCNT5X0N9OjRA4wx7Nq1SzCqLSsrC0OHDoWKigpOnjwpMZZr0YygoCAEBwfD3NwceXl5ePDgATQ1NREaGootW7YgMTFRanybNm3Qv39/TJ48WSj5dPXqVXh4eMi11mZ2djauXLki9rkrOxU/JSUFjRo1goqKClJSUqTuV9rx3LZtW3h6emLgwIFyFTKStAi9ONKmmHXo0AFXr15FnTp1BMm9Dh06wMzMTO796+jo4O7du7C2thZ6zB89egR7e/tvWmCIEEIIIaQiatKkCWxtbTF37lyYm5uLDDpRtJAdId+aWnl3gJSPiIgIrFy5UmgaZq9eveDg4IBNmzbh3LlzsLS0xMKFC0USfqqqqujWrRvu3bvHKeGnyA/wspycnPD06VOlEn5cR4mJo6qqihcvXoi9rkqVKnjw4AGqVq0qs0KlvEmfstLT01FUVCSy3cPDQ1CNysPDQ2K8vGekUlJS0LlzZxgaGiIjIwMjRoxAlSpVcPjwYTx58kTqGocrV67EtGnTsHHjRplTpyU5f/48jh49ihYtWkBFRQVWVlbo0qULDAwMsHjxYpkJvwkTJmDt2rUKFUcoHbt582a4urqKHVUqy9y5c7F161ZMnjwZgYGBmDVrFjIyMnDkyBGFihDUr19fqcWgY2NjcenSJaEprCYmJliyZAnatWsnNTY4OFjh9koLCgpCo0aN8PTpU/Tv31+wDpuqqqpcBVRu3boldo1DU1NTZGVlyYw/fvw4PD09kZubC319fZGp3GXfb5o0aSJ43TRp0gQ8Hk/kLG5JrLTXTUJCgsy+lVZ2iQJJZB17CQkJMDc3h6urK1xcXODs7Kxw5XRzc3M8fPhQ5LUaFxcHGxsbhfZFCCGEEPIjSEtLQ2RkpNyF6wgpb5Tw+0klJiZi48aNItubNm0qGG3Tvn17ZGZmio13cHDAo0eP5F5kvixFf4CX5e/vjwkTJmDq1Kliiy9IG3UzceJEvH//HpcuXRI7SkyasqPkGGN4+fIl/vrrL4lJk9WrV0NfX1/wb0UTRaWVHQFU0v7JkyfFVpYqnUgtm1RVtn0fHx8sW7ZMcJ8AoHv37hgyZIjU2KFDhyIvLw916tSBjo6OyHMmT7IzNzcX1apVA/Alkfr27VvY2trCwcFBrqIfV65cwfnz53HixAk0bNhQpA/SRunt3LkThw4dQo8ePWS2I86uXbuwZcsWuLu7Y+7cuRg8eDDq1KkDR0dHXLp0SWzV1cmTJ2P+/PnQ1dWVOfpL1qLimpqa+O+//0S2f/z4UeyanSUKCwsRExODwMBATome//3vfyLbZFXXLWFkZISXL1+KvN9cv35drmqzU6ZMga+vLxYtWiTXdNrHjx/D1NRU8G8u0tPTERwcLFg3sWTNS3FTqKOjozm1VSI7OxsXL15ETEwMli5disGDB8PW1hYdOnSAi4sLOnToILh/kvz222+YMGECQkNDwePx8OLFCyQmJiIgIOC7VMklhBBCCKloWrVqhYcPH1LCj1QalPD7SdWqVQvbtm3DkiVLhLZv27YNFhYWAL5M95M0gm/hwoUICAjA/Pnz0bx5c5HFyKVVyQUU/wFe1sCBAwEIV60tGYUja9QNl1FiZUfJ8Xg8mJqaomPHjhKThaWTGlzXjis7Aqhk7cOVK1fKrOC7fft2DBw4UKTK5efPn7F3716ZSVbgS0XYTZs2iWyvWbOmzDW9uI4SAwA7OzukpqbC2toaTZo0waZNm2BtbY2NGzfC3NxcZryRkRH69u2rVNuGhoacEl6vXr2Cg4MDAEBPTw85OTkAgJ49eyIwMFBszPXr1wVVPqWN/pInidyzZ0+MGjUK27ZtQ8uWLQEAly9fxujRo6VO51ZXV8fhw4cl9lEe8+bNk3q9rATSkCFDMH36dBw4cAA8Hg98Ph/x8fEICAiQ67h9/vw5xo8fL/d7jZWVleDfT548Qdu2baGmJvxxWVRUhISEBKHblhUVFYVevXqhSZMmaNeuHRhjSEhIQMOGDXH8+HF06dJFrv4oSldXF25ubnBzcwMA/Pfff4iLi0N0dDSWLVsGT09P1KtXD7dv35a4j2nTpiEnJweurq7Iz8+Hs7MzNDU1ERAQgHHjxn2TfhNCCCGEVGT+/v6YMmWK4Hu9IoNOCCkPtIbfT+rYsWPo378/6tevjxYtWoDH4+Hq1au4f/8+IiMj0bNnT2zYsAFpaWliRw6VFPIAhJMN8lbJ1dXVxa1bt5ROoDx58kTq9dJ+hBsYGCAlJQXW1tawtrbGrl270K5dOzx+/BgNGzZEXl6eUn2Sh6qqKl6+fCkYpVYiKysL1apV+6YLvX6Nts3MzHDmzBk0bdpUaF2vs2fPws/PD0+fPv1W3QfwZZRcYWEhfHx8cP36dXTr1g1ZWVnQ0NBAeHi4IBH8LURERODMmTMIDQ1VqKpxCTs7O2zfvh2tWrXCL7/8And3d8yYMQP79u2Dv78/3rx58w16/X+ys7MxbNgwHD9+XKjoR69evRAWFgYjIyOJscOHD4eDg4NCa8yVVlJVuERhYSEeP34MNTU11KlTR+bozJLnfO/evWCMQU1NDcXFxRgyZAjCw8NlVhnu27cvBg0aJLOStDhcXjdNmzZFt27dRE6szJgxA2fPnhW533379kV4eDgMDAxkJqZlrRlZGp/Px9WrVxEdHY3o6GjExcUhPz9frtd8Xl4e7t69Cz6fD3t7e+jp6cndLiGEEELIj6T0b+AS8g46IaQ80Ai/n1SvXr2QmpqKjRs3CgoAdO/eHUeOHBGs2fT7779LjOc69axbt25ISkpSOuEnLaEnC9dRYiVKcuWKTNGVlF8vKCiQOq2yRMeOHXHo0CGR5MyHDx/g4eGB8+fPS21bXF+fPXsm9wKzvXv3xrx587B//34AX+57ZmYmZsyYgX79+onc/sOHD4LRnh8+fJC6b1mjQoEvRTtKNG3aFBkZGbh//z4sLS0VXqNMUf3798eePXtQrVo1WFtbi5zRk5W06tOnD86dO4dWrVphwoQJGDx4MLZt24bMzExMmjRJ7n48fPgQ6enpcHZ2hra2tsTntSwjIyMcPXoUDx8+xL1798AYg729vVxTEurWrYv58+cjISFB7IhecdORSxM3OvHDhw/w8fFBnz59ZLavrq6OXbt2Yf78+bh27Rr4fD6aNm0qd/EVd3d3TJ06FXfv3hV7NlbaCEdJj29WVpbI41DWvXv3BK+V0nx9fcWOeDU0NBS0xWXRZz6fj6SkJMTExCA6Ohrx8fHIzc1FzZo14erqinXr1sHV1VXqPnx9fRESEgJ9fX04OTkJtufm5sLf3x+hoaFK948QQgghpDLiutQLId8bjfAj5WLbtm2YN2+eYOSQPD/Av1bFWa6jxLZv347ly5cjLS0NAGBra4upU6fCy8tLYkxJkYhJkyZh/vz5QqNkiouLceHCBWRkZMhctF9FRUVQTKC0N2/eoGbNmoLpn6U1bdoUPB4PN2/eRMOGDYWmJhYXF+Px48dwc3MTm5go68OHD+jRowfu3LmD//77DzVq1MCrV6/Qpk0bnDp1SiQBUnp0lIqKitjEiSJnxObNm4eAgACRqZmfPn3C8uXL5VpbLDIyUmKlXGlJuwEDBiA6Ohr/+9//xBbtmDNnjsy2S7t8+TLi4+NRt25duSokZ2VlCfrA4/GQlpYGGxsb+Pn5wcjISOb6k1weO2lrdfJ4PDx69Ehm/8W5ffs2evbsiYyMDKm34/q8izsbW0LSsVcywu7o0aNwc3MTmgpfXFyMlJQU2NnZ4cyZMxL3bWFhgVWrVqF///5C2/fv34+AgACJa6QyxpCZmQlTU1OlljwwMDBAbm4uzM3N4eLiAhcXF7i6uopdN1ASSSMb3717h+rVq4stEkQIIYQQQgipOCjh9xNJSUmR+7byrj+Ql5cnNnEiK16ZH+Clk13KxEuSl5cn9yixVatWITAwEOPGjROsyRUfH49169ZhwYIFEkdqlSRMnjx5glq1aglNQdTQ0IC1tTXmzZuHVq1aiY0vee6aNGmC8+fPC1VaLS4uxpkzZ7Bp0yaxiZO5c+cK/j9lyhShZGNJ2/369ZNrhGGJ8+fPC0ZaNWvWDJ07dxZ7u9jYWLRr1w5qamqIjY2Vus8OHTrIbJfrtOQ1a9Zg1qxZGDZsGLZs2YLhw4cjPT0dV69exdixY7Fw4UKJsbq6uoiKikL79u1l9lOcCxcuSF0LztnZWWq8t7c33rx5g61bt6JBgwZC06knTZqEO3fuSI0vz+nkksTFxeHXX3/F+/fvpd6uPPo+fPhwAF+mcg8YMEBoGnfJ62bkyJFS3zPmzZuH1atXY8aMGWjbti14PB7i4uKwdOlSTJkyBX/++afYOD6fDy0tLdy5c0fuUYylbdq0Ca6urrC1tVU49sOHD2CMwdjYGGlpaULFPYqLi3H8+HHMmDFDYlVyQgghhJAfmSIF2Qgpd4z8NHg8HlNRURH8v+TC4/FEtsny5s0b5u7uLhSjSHx5+fz5M6tduza7c+eOUvHW1tYsIiJCZHt4eDiztraWGe/i4sL+/fdfhdst/fyUPF+lLzo6Omzbtm1S9xEeHs4+ffqkcNtfQ2FhIQsKCmKZmZmc9sPj8dibN29Etp87d45VrVpVZrydnR3bvXs3Y4wxPT09lp6ezhhjLDAwkI0dO1Zm7M2bN5Xo9RcqKirs9evXItvfvXsn12vGzMyM3bhxgzEm3PdHjx4xXV1dmfFcH7sSfD6f8fl8uW/PGGMhISFCl+DgYDZ9+nRWo0YNNmjQoO/Wd2UEBQWx3NxcpWL5fD5btWoVq1mzpuC1WrNmTRYcHCzzMbS3t2eJiYlKtctF2c+CshdVVVW2YMGC794vQgghhJDydubMGaahocFatmzJJk2axCZOnMhatmzJNDU12dmzZ8u7e4SIoDX8fiKl1xy4fv06AgICMHXqVLRp0wYAkJiYiJUrV2LZsmUy9zVx4kS8f/8ely5dgqurKw4fPozXr19jwYIFMqcWlpWfnw8tLS2FYpStOKuuro6CggKF1t0r7eXLl2jbtq3I9rZt2+Lly5cy45Vd+/Dx48dgjMHGxgZXrlwRGnWjoaGBatWqySxcULpaMBdXrlxBTEwM3rx5Az6fL3SduAIvAKCmpoYVK1Yo3QdjY2PweDzweDzY2toKPX/FxcX4+PEjRo8eLXM/mZmZgudPW1sb//33HwDAy8sLrVu3xl9//SUxduXKlZg2bRo2btwoWOdSEYzDWnDAl7XTxE3vfPfuncjroLSv9dgpM5W9xOrVq4X+LqkuPWzYMMycOfOb9x348vjFxsaKHZEsbQ1Cb29vPH/+XGSkXVpaGtTV1aUeCzweD5MmTcKkSZMEx5q+vr5c/V22bBmmTp2KDRs2oFGjRnLFfA3R0dFgjKFjx444ePCg0GhiDQ0NWFlZoUaNGt+tP4QQQgghFcWMGTMwadIksQXZpk+fji5dupRTzwgRj6b0/qRatmyJoKAg9OjRQ2j7qVOnEBgYiOTkZKnx5ubmOHr0KFq2bAkDAwMkJSXB1tYWx44dw7JlyxAXFyc1vri4GIsWLcLGjRvx+vVrPHjwADY2NggMDIS1tTX8/PykxnOZ4rdkyRLcv38fW7duFZleKUujRo0wZMgQ/PHHH0LbFyxYgH379uHWrVtS4//3v//ByckJM2bMENq+fPlyXLlyBQcOHFCoP4ooLi7G6tWrJa5f9++//8rcx6JFi/Dnn3/Czs5OZB07Ho8ntWiIh4cHPDw84OPjo3DfIyIiwBgTFDsoXdCgZHplSeJaGhsbG0RGRqJZs2Zo0aIFRowYgd9++w1nz57FoEGDpD4GxsbGyMvLQ1FREXR0dETWnZQU+zXWggO+FJ5o1qwZ5s+fD319faSkpMDKygqDBg0Cn89HZGSk2Liv8dgpO5Wdq6/1vF+/fh09evRAXl4ecnNzUaVKFbx79w46OjqoVq2a1DUIO3ToAF9fX5Fk9c6dO7F161bExMQoff+kKX28aWhoiFSGluf1ysWTJ09gaWmp9MkRQgghhJAfjZaWFm7duiVyIvjBgwdwdHREfn5+OfWMEPFohN9P6tatW2IX4q9duzbu3r0rMz43N1eQbKtSpQrevn0LW1tbODg4yKxWCgALFy5EREQEli1bhpEjRwq2Ozg4YPXq1TITfpJGS8lTcfby5cs4d+4czp49CwcHB5HRVYcOHZIYO3fuXAwcOBAXLlxAu3btBGtynTt3Tq6iF7GxsWKLO7i5uWHFihUy44EvHyiSRthJK14wd+5cbN26FZMnT0ZgYCBmzZqFjIwMHDlyRK5iFwAQEhKC0NBQpZJ23bt3x8yZM3H79m2xlV6lFa4oSbbUrl0bbdu2FUm2yatjx444fvw4mjVrBj8/P0yaNAmRkZFISkoSJOYkEVdVVR4lxyNjDPr6+iJrwbVu3VroNSDJ8uXL4eLigqSkJHz+/BnTpk3DnTt38O+//yI+Pl5iXOnHrmQ9RUWtXbsWGzZsEBo527t3bzRs2BBBQUESE36+vr5y7V9Sxdev0XfgS7GcX3/9FRs2bICRkREuXboEdXV1DB06FBMmTJAae/36dbRr105ke+vWrTFu3DixMR07dpSrX9IS5Moeb1/LvXv38PTpU8GalevWrcOWLVtgb2+PdevWwdjYuFz7RwghhBDyvZmamuLGjRsiCb8bN26IDEQhpCKgEX4/qWbNmqFBgwbYtm2bYDptQUEBfH19ce/ePZlJuxYtWmDBggXo1q0bPDw8YGBggMWLF2PNmjWIjIxEenq61Pi6deti06ZN6NSpE/T19QUFCO7fv482bdpIXMT/a1ScLVmMX5KwsDCp1ycnJ2P16tW4d+8eGGOwt7fHlClT0LRpU6lxwJdppDdu3ICdnZ3Q9vv376Np06b49OmT1PgtW7bg999/R9WqVVG9enWREXbSnrc6depgzZo1cHd3h76+Pm7cuCHYdunSJezevVtm/83NzXHhwgWlCgl8rUIrfD4fDx8+FJvwlFX4gs/ng8/nC46b/fv3Iy4uDnXr1sXo0aMVKlyiqLlz5yIgIECu6buSvHr1Chs2bEBycrKgYMrYsWNhbm4uM/batWtQV1eHg4MDgC8jDsPCwmBvb4+goCCp911LSwu3b99G3bp1hbanpaXBwcFB4tlMFRUVWFlZoWnTppD2UXP48OFv1ncAMDIywuXLl2FnZwcjIyMkJiaiQYMGuHz5MoYNG4b79+9LjDU0NERMTIzI6zs5ORkuLi6Cqbqlldxvd3d3qcnpslOdKxIHBwcsXboUPXr0wK1bt+Dk5IQpU6bg/PnzaNCggcz3SUIIIYSQH42yBdkIKTflsG4gqQAuX77MqlWrxqpWrco6derEOnXqxKpWrcpMTU3Z5cuXZcbv3LmThYWFMcYYu3btGjM1NWUqKipMS0uL7d27V2a8lpYWy8jIYIwJFyC4c+eO1AIEQUFBLCgoiPF4PBYQECD4OygoiC1atIjt3r2bFRQUyPEIlA8nJyc2d+5cke1z5sxhzZo1kxlvaWnJlixZolTbOjo67MmTJ4wxxqpXr86Sk5MZY4ylp6czAwMDufaxdOlSNmHCBKXa/xoSExNZ7dq1xRYvkVX44msVDmGMsby8PJaTkyN0+ZY+f/7MXFxcWGpqqtL7cHJyYpGRkYyxL8+5pqYmGzx4MKtbt67M57Rhw4Zs4cKFItvnz5/PGjVqJDHu999/Z8bGxqxx48YsJCSEZWVlffe+M8ZY1apVBY+dra0tO3PmDGOMsXv37jFtbW2pse7u7qx///6sqKhIsK2oqIj169ePubm5iY1ZunQpa9CgAatWrRqbNGkSu3Xrljx3U0RxcTFLTU1lFy9eZLGxsUKXb01XV5c9fvyYMfbl/alfv36MMcaSk5OZmZnZN2+fEEIIIaSi4VKQjZDyQAm/n1hubi7btGmToMLQ5s2b2cePH5XeV3JyMnv79q1ct2/evDnbsWMHY0w44RcUFMTat28vM55rxdnCwkL2999/s40bN7IPHz4wxhh7/vw5+++//6TGnTx5UpAsKO3MmTPs1KlTMts9evQoU1NTY97e3iw8PJyFh4czLy8vpqqqyg4fPiwzXl9fX/BYKcrW1pZdunSJMcZY+/bt2eLFixljjO3du5eZmprKtY/i4mLm5ubGbGxsWM+ePVmfPn2ELuKcO3eONWjQQGxSLDs7m9nb27MLFy7I1X7jxo1Z//792d27d9n79+9Zdna20EWW0kkMRX38+JGNHTtWkNxWtDL1q1ev2NChQ5m5uTlTVVVVOL5q1arswYMHSvWdMcYMDAzYw4cPGWOMLVmyhHXt2pUxxlhcXByrVauW1NjIyEimqqrKunXrxubNm8fmz5/PunXrxtTU1NihQ4ekxubn57Pdu3ezzp07Mx0dHda/f3925swZhb4Ucek7Y4x16dKF7dq1izHG2G+//cZatmzJdu7cybp168ZatmwpNfbOnTvMxMSE1alTh/n4+DAfHx9Wp04dZmpqKjORl5CQwEaMGMEMDAxYixYt2IYNG+RODnNJbn8NxsbGgmrm7dq1Y5s2bWKMMfb48WOZSVJCCCGEkB/dhw8fBL8jCamoaA2/n5iOjg5GjRr11fbVrFkzuW8/Z84ceHl54fnz5+Dz+Th06BBSU1Oxfft2nDhxQmZ8ydpenz9/Fju109LSUmLskydP4ObmhszMTBQUFKBLly7Q19fHsmXLkJ+fj40bN0qMnTFjhkhVJuDL+mwzZsxA9+7dpfa7V69eOHLkCBYtWoTIyEhoa2ujcePGOH/+PAwMDKTGAkD//v1x9uxZuSuTltanTx+cO3cOrVq1woQJEzB48GBs27YNmZmZchdd8Pf3R3R0NFxdXWFiYiLXgv7BwcEYOXKk2PtnaGiI3377DatWrcIvv/wic19paWmIjIwUmVoqr86dOyMmJkapNQinTZuG6OhorF+/Ht7e3li3bh2eP3+OTZs2iT0myvLx8UFmZiYCAwNhbm6ucDEEb29vbNu2Ta62xGGMCV4n//zzD3r27AkAsLCwwLt376TG9uvXD5cvX8bq1atx5MgRwVT2K1euyJzKrqmpicGDB2Pw4MF48uQJwsPDMWbMGBQWFuLu3bvQ09P7pn0HvhSbKZl6O3/+fAwbNgy///476tatK3Nqqr29PVJSUvDXX3/h5s2b0NbWhre3N8aNGydUwVacNm3aoE2bNggJCcGBAwewbt06BAQE4MWLFzJf76NHj4aTkxNOnjyp1PHCVfv27TF58mS0a9cOV65cwb59+wB8WUO0Vq1a37UvhBBCCCEVjb6+fnl3gRCZaA2/n9T27dulXl96cf4SkydPlnv/q1atknmbqKgoLFq0SGg9stmzZ6Nr164yY9PS0uDr64uEhASh7ez/F/OQth6ch4cH9PX1sW3bNpiYmAjWD4yNjcWIESOQlpYmMVZbWxv37t2DtbW10PaMjAw0bNgQubm5MvteWnZ2Nnbt2oVt27bh5s2bMtexW7x4MVatWgV3d3c4ODiIrA82fvx4udu+dOkSEhISULduXakFM0rT19fH3r174e7uLnc7VlZWOHPmDBo0aCD2+vv376Nr167IzMyUua+OHTti2rRpcHNzk7v90jZt2oSgoCB4enoqXDjE0tIS27dvh4uLCwwMDHDt2jXUrVsXO3bswJ49e3Dq1Cmpbevr6+PixYto0qSJUn339/fH9u3bUbduXTg5OYn0XdZrrmPHjrCwsEDnzp3h5+eHu3fvom7duoiNjcWwYcOQkZEhEvPhwwe5+iZPshoAMjMzER4ejvDwcHz+/Bn379+XK+GnTN8rkri4OISGhuLAgQNo2LAhoqOjRarulqWrq4ubN28qndzmKjMzE2PGjMHTp08xfvx4QSGlSZMmobi4GGvWrCmXfhFCCCGElJesrCzMnj0b0dHRYged/Pvvv+XUM0LEoxF+P6mylSkLCwuRl5cHDQ0N6OjoiE34Xb9+Xa59yzsSpVu3bujWrZtcty3Lx8cHampqOHHihMKjX+Li4hAfHy+y0L+VlRWeP38uNdbQ0BCPHj0SSfg9fPhQoWIM58+fR2hoKA4dOgQrKyv069cP27Ztkxm3efNm6OnpITY2FrGxsULX8Xg8hRJ+rVu3RuvWreW+PfClInOdOnUUinn9+rXUwgVqamp4+/atXPvy9/fHlClT8OrVK7EJT0dHR6nxv//+OwDxyTFZieJ///1XUNnawMBA8IHevn17wX6lsbCwkFq4Qpbbt28LRtE+ePBA6Dp5R1p6enriyJEjmDVrliCRFBkZibZt24qNMTIykmvf0h63goICHDp0CKGhoYiLi0PPnj3x119/wc3NTWohl9JWr16tcN+5SElJQaNGjaCiooKUlBSpt5V0zL148UKQ3Pzw4QOGDh2Ky5cvw97eXq4+tGrVCg8fPiy3hJ+lpaXY0dYVudAIIYQQQsi3NHToUKSnp8PPzw9mZmbffQYGIYqihN9PSlwV3LS0NPz++++YOnWq2Jjo6Oiv1v7w4cMxdOhQdOzYUak3yhs3biA5ORn169dXOJbP54tNUDx79kzm0OxevXph4sSJOHz4sCDx9fDhQ0yZMkXmKLlnz54hPDwcoaGhyM3NxYABA1BYWIiDBw/KnQR4/PixXLeT5Pnz54iPjxd7RkqeZGFQUBDmzJmDsLAw6OjoyNVmzZo1cevWLYmJi5SUFLmqzAJfppYCgK+vr2Abj8eTa2QnAJH7rAgbGxtkZGTAysoK9vb22L9/P1q2bInjx4/DyMhIZnxwcDBmzJiBTZs2iSSM5cH19efo6Ihbt26JbF++fDlUVVVltskYQ48ePbB161bUrFlTrjbHjBmDvXv3wtLSEsOHD8fevXthYmKicN8bN26M27dvK9T30hQ9G9ukSRO8evUK1apVQ5MmTQTHWFmSjrkePXogOjoaXbt2xfLly+Hu7i5UUVyS0slFrsntryE9PR1hYWFIT09HSEgIqlWrhjNnzsDCwgINGzb85u0TQgghhFQkcXFxiIuLQ+PGjcu7K4TIhab0EiFJSUkYOnQo7t+//03b6dWrF86ePQsTExMMGjQIQ4cOlbkWWGktWrTA6tWr0b59e4XbHjhwIAwNDbF582bo6+sjJSUFpqam6N27NywtLaWu6ZWTkwM3NzckJSUJ1rF69uwZfvnlFxw6dEhi4qdHjx6C0U2enp5wc3ODqqoq1NXVcfPmTbkTfiU+f/6Mx48fo06dOnIlEgAgLCwMo0ePhoaGhsj6ezweD48ePZK5j6ZNmyI9PR2MMVhbW4skIa5duyYS4+/vj5iYGFy9ehVaWlpC13369AktW7aEq6urXFMEnzx5IvV6Kysridfx+XyEh4fj0KFDyMjIAI/Hg42NDfr16wcvLy+JieeSEZ0hISFQVVXF+PHjER0dDXd3dxQXF6OoqAirVq0SGTVblrGxMfLy8lBUVAQdHR2Rx06RKQDPnj0Dj8eTO/H2tejr6wumwMtDRUUFlpaWaNq0qdTE/qFDh6Tux8fHB76+vnB2dlaovyW6d+8u9WxsyZqgJZ48eQJLS0vweDyljjkVFRWYm5ujWrVqUu932deLioqKxOQioFhym6vY2Fh0794d7dq1w4ULF3Dv3j3Y2Nhg2bJluHLlCiIjI79p+4QQQgghFU2LFi2wdu1ahWdJEVJeKOFHhFy/fh0dOnSQuXaXq6ur1B+y58+fl9lWdnY29u/fj927d+PixYuws7PD0KFDMWTIEJkjoM6fP48///wTixYtEjv6RdqaYi9evICrqytUVVWRlpYGJycnpKWloWrVqrhw4QKqVasmtW3GGP7++2/BAv6Ojo4yExFqamoYP348fv/9d9SrV0+wXdGEX15eHvz9/REREQHgy9ROGxsbjB8/HjVq1MCMGTMkxlpYWGD06NGYOXOm3FMpy5o7d67U6+fMmSOy7fXr12jWrBlUVVUxbtw42NnZgcfj4d69e1i3bh2Ki4tx7do1mJmZKdUneTDG8Ouvv+LUqVNo3Lgx6tevD8YY7t27h1u3bgmKqYijqqqKly9fCo6LgQMHYs2aNSgoKEBSUhLq1Kkj11m+kudMkrJJp7L4fD4WLFiAlStX4uPHjwC+JOCmTJmCWbNmiX1Oq1SpggcPHqBq1aowNjaW+pqVJ+GoaMLPx8dHrhG8sgpn9OvXDydPnoSFhQWGDx+OYcOGKZTs1NfXV/hsrLOzM44dOyZI4h87dgxdunSRufYe8GUkrDz3u+zrRVZysTRpye2voU2bNujfvz8mT54s9LxfvXoVHh4eMpc/IIQQQgj50Vy9ehUzZszA7Nmz0ahRI4V+gxJSHijh95M6duyY0N+MMbx8+RJ//fUXLCwscPr0aanxZau6FhYW4saNG7h9+zaGDRuGkJAQhfrz7Nkz7NmzB6GhoUhLS0NRUZHU25ckN8r+qJZ39MunT5+wZ88eXLt2TVAwxNPTU64f8/JwcHDAqVOnYGFhAQBITExEaGgo9u/fj/r168PLywsDBw5EjRo1FEr4TZgwAfHx8QgODoabmxtSUlJgY2ODY8eOYc6cOVLXWTQxMcGVK1cUXoPva3jy5Al+//13REVFCUYv8Xg8dOvWDevXr1doiuuOHTuwceNGPH78GImJibCyskJwcDBq166N3r17i40JCwvDhAkTcPToUbi6ugpdd/78eXh4eOCvv/4Su3alioqKYHonoHjS62uZOXMmtm3bhrlz56Jdu3ZgjCE+Ph5BQUEYOXIkFi5cKBITERGBQYMGQVNTE+Hh4VKTULISjkD53Xfgy7TcnTt3Ijw8HLdv30bnzp3h6+sLDw8PqWtEAsqdjS37vBsYGODGjRvf/L77+voiJCSk3Cu/6enp4datW6hdu7bQ856RkYH69esjPz+/XPtHCCGEEPK9paWlYfDgwSK/ub7XDAxCFEUJv59U2dFAPB4Ppqam6NixI1auXCn3mmplBQUF4ePHj1ixYoXcMYWFhTh58iR27tyJkydPokqVKjJHj5QtWFFWhw4dJF6Xl5cn9/pzypKUGMnLy8PevXsRGhqKK1euoLi4GKtWrYKvr69cP/CtrKywb98+tG7dWqiNhw8folmzZlJHZk6bNg1VqlSROgpQXsnJybh37x54PB7s7e3lno79/v17PHz4EIwx1KtXD8bGxgq1u2HDBsyePRsTJ07EwoULcfv2bdjY2CA8PBwRERES17nr2rUrOnbsKPG+L1q0CLGxsYiKihK57lsk/D59+oTCwkKhbbLOCNaoUQMbN24UWSvy6NGjGDNmjMTXzNestFsyBb6keEl5uX79OkJDQ7F161bo6elh6NChGDNmjNDo2dKUORvL5Xlv0qQJRowYAU9PT4WP8bIjSstLrVq1sH//frRt21bovh8+fBgBAQFIT08v1/4RQgghhHxvLVu2hJqaGiZMmCB2mRhpv0EJKQ+U8COCBeyVneZZ2sOHD9GyZUu5pgdGR0dj9+7dOHjwIIqLi9G3b194enqiY8eOX6Uvkujp6cHDwwNeXl7o0qXLN2lLnuRAamoqtm3bhh07diA7OxtdunQRGXlZlo6OjiDJVbqNmzdvwtnZGTk5ORJji4uL0bNnT3z69EnsNGhxlWvLevPmDQYNGoSYmBgYGRmBMYacnBy4urpi7969MDU1lbkPLuzt7bFo0SJ4eHgI3f/bt2/DxcUF7969ExtXvXp1nDlzBk2aNBF7/fXr19G9e3e8evVK5DpVVVW8evVKcN+UTXrl5uZi+vTp2L9/P7KyskSul3VGUEtLCykpKbC1tRXanpqaiiZNmuDTp09i40rWhZNFXPt9+/YV+vv48ePo2LGjSEVqWWvwfU0vX77E9u3bERoaiufPn6Nfv354+fIloqOjsWzZMpHRx4ByZ2O5JPx+++037Nu3DwUFBfDw8MCIESPQqVMnue5f2XbLy7Rp05CYmIgDBw7A1tYW165dw+vXr+Ht7Q1vb2+x0/cJIYQQQn5kOjo6uH79Ouzs7Mq7K4TIhar0/sS2bduG1atXIy0tDQBQr149TJw4ESNGjFB6n4mJiSKFGcSpVasWsrKy0K1bN2zatAm//vqrXHElLly4IPV6aWvqbd++HXv27EGfPn1gYGCAgQMHYujQoWjRooXc7X8NdnZ2WLZsGRYvXozjx48jNDRUZkyLFi1w8uRJ+Pv7A/i/Kc1btmxBmzZtpMYuWrQIUVFRgg+oskU75OHv748PHz7gzp07aNCgAQDg7t27GDZsGMaPH489e/bItR9lPX78WOxoQk1NTeTm5kqM+/fff6WuEWhmZia2cjXwJSnk4+MDTU1NAEB+fj5Gjx6tcNJr2rRpiI6Oxvr16+Ht7Y1169bh+fPn2LRpE5YsWSI1FvhSqfavv/4SKW7y119/SV2bjkulXUNDQ6G/hw4dKjPmWygsLMSxY8cQFhaGs2fPwtHREZMmTYKnp6dgZOzevXvx+++/i034eXp6QkNDA7t37xZ7NlaSqKgowWPA5/Nx7tw5kWrB4qpzb9q0CSEhIThw4ADCwsLQtWtXWFhYwNfXFz4+PrC0tJTarjKVy7+Whw8fom7duli4cCGGDx+OmjVrgjEGe3t7FBcXY8iQIfjzzz/LrX+EEEIIIeXFyckJT58+pYQfqTRohN9PKjAwEKtXr4a/v78gUZSYmIi//voLEyZMwIIFC6TGlx35U7IGYFJSEgIDA2WO/ti8eTP69++v8HS3EuJG5ZX+kSzP+gn//fcfIiMjsWfPHkRHR6N27doYOnQoZs+erVSfSvtWa50lJCTAzc0Nnp6eCA8Px2+//YY7d+4gMTERsbGxaN68ucRYY2NjrF69Gj4+Pkq3b2hoiH/++UckOXrlyhV07doV2dnZSu9bHvb29li8eDF69+4t9BivWbMGERERSE5OFhtXdpReWa9fv0aNGjXEHjfDhw+Xq2+yCk9YWlpi+/btcHFxgYGBAa5du4a6detix44d2LNnD06dOiU1PjY2Fu7u7rC0tESbNm3A4/GQkJCAp0+f4tSpU/jll1/k6md5rsOnrKpVqwqSTSNHjhQ7UvP9+/do1qwZHj9+LHKdMmdj5Rn5K+9aLY8fP0ZoaCi2b9+O58+fo1OnTvDz88OAAQPEtmtoaCgz6adIVWdFqKiooGbNmnB1dYWrqys6dOggWOu0adOmEqdNE0IIIYT86A4cOICgoCBMnTpV7IwpR0fHcuoZIeJRwu8nVbVqVaxduxaDBw8W2r5nzx74+/tLnBpZomwSREVFRbAGYNeuXeXux8OHD5Geng5nZ2doa2sLptjJUnbqamFhIa5fv47AwEAsXLhQ7ulzJe7evQtPT0+kpKR8lcVWv2VS5datW1ixYgWSk5MFBUemT58OBwcHqXHVq1fHxYsXOf1g19fXx8WLF0USLvJWd+YqLCwMgYGBWLlyJfz8/LB161akp6dj8eLF2Lp1KwYNGiQ2TkVFBd27dxeM0iuroKAAZ86c+aYL7erp6eHOnTuwsrJCrVq1cOjQIbRs2RKPHz+Gg4ODoPJuWY8ePULt2rXB4/Hw4sULrF+/Hvfu3ROMuhozZgxq1Kghdz8qY8Jvx44d6N+/v0KjgEtzdnbG7Nmz0blz56/cM8UwxnDw4EH89ttvyM7OljiVODg4WGR0ZVnyFFlRxsWLFxEbG4uYmBgkJiYiPz8flpaW6NixoyAJqEiFZEIIIYSQH4WkQSdUtINUVJTw+0kZGxvjypUrIsmfBw8eoGXLlt98pFZWVhYGDBiA6Oho8Hg8pKWlwcbGBn5+fjAyMsLKlSuV2u+FCxcwadIkiSO9SsvPz8exY8ewe/dunDlzBtWqVcPgwYOxdOlSpdourSImVRYvXoyXL1+KTAlVRO/evZGdnY09e/YIkkzPnz8XFCc4fPjw1+quRFu2bMGCBQvw9OlTAEDNmjURFBQEPz8/iTFfa5QeF46Ojli7di06dOiArl27wtHREStWrMCaNWuwbNkyPHv2TGxc2SIOAwcOxJo1a6ROUZamIh6bkvj6+sp1O1nT4SvC2djo6GiEhYXh0KFDUFNTw6BBg7Bx40aR21WUNfyALydSEhMTERMTg5iYGFy6dAkFBQWoW7cuUlNTy7t7hBBCCCHf1ZMnT6Reb2Vl9Z16Qoh8KOH3k/L394e6urpIoYaAgAB8+vQJ69atk2s/SUlJgmqtDRo0kDqltDRvb2+8efMGW7duRYMGDQQJiLNnz2LSpEm4c+eOwvcJAO7du4cWLVpIHC0FAGfPnsWuXbtw5MgRqKqq4n//+x88PT0VrqqUn58vccTR7t270bt3b5F13rhydXXF0KFD8b///U/mCKCy+vTpg/Pnz8PExAQNGzYUSXrIU3jh6dOn6N27N27fvg0LCwvweDxkZmbCwcEBR48eRa1atRTqExfv3r0Dn8+vEIkRaR49egRra2uEhIRAVVUV48ePR3R0NNzd3VFcXIyioiKsWrUKEyZMEBtfNgFkYGCAGzduKJ2wqyiVduWhoqICKysrNG3aFNI+qmQlmrmejd2xYwc2btyIx48fIzExEVZWVli9ejVsbGzQu3dviXGZmZkIDw9HeHg4MjIy8Msvv8DPzw/9+/eHtra22JiKUqW3tE+fPiEuLg5RUVHYsmULPn78SGewCSGEEEIkcHd3x9atW2Fubl7eXSE/OSra8ROZPHmy4N88Hg9bt27F2bNn0bp1awDApUuX8PTpU3h7e8vc17NnzzB48GDEx8fDyMgIAJCdnY22bdtiz549sLCwkBp/9uxZREVFiSSI6tWrJ/PMCQCkpKQI/V2yhuCSJUukFjAAAA8PD7i7uyMiIgLu7u4iiS9p+Hw+Fi5ciI0bN+L169d48OABbGxsEBgYCGtra8EosyFDhsi9T0U4ODjgzz//xLhx49CjRw94eXmhR48e0NDQkBlrZGQksvaioiwsLHDt2jX8/fffuH//vmBaaXlMlaxatep3b1MZ9erVw8uXLwXFJEpG6N2/fx9JSUmoU6eOzGO2NEXP0ZR9zpUtOlIeRo8ejb179+LRo0fw9fXF0KFDUaVKFYX3I25dP3lt2LABs2fPxsSJE7Fw4UJBosvY2BjBwcFiE367d+9GWFgYoqOjYWZmBm9vb/j5+aFu3boy26sI5+Dy8/ORkJCA6OhoxMTE4OrVq6hduzY6dOiADRs2KHxyhBBCCCHkZ3LhwgV8+vSpvLtBCI3w+5m4urrKdTsej4fz589LvU3Xrl3x4cMHRERECBbCT01Nha+vL3R1dXH27Fmp8fr6+rh27Rrq1asnNMXw6tWrcHNzQ1ZWltR4FRUVwQid0lq3bo3Q0FDUr19fYuyHDx9gYGAgdf+SzJs3DxEREZg3bx5GjhyJ27dvw8bGBvv378fq1auRmJio1H4Vwefz8c8//2D37t04fPgwp1GK8jp//jzGjRuHS5cuiTx2OTk5aNu2LTZu3Ch34QhlZWVlYfbs2YiOjsabN2/A5/OFrv9WhQy4KDtCT9EptWULjig6Qq8iTGfmoqCgAIcOHUJoaCgSEhLg7u4OPz8/dO3a9btUs7W3t8eiRYvg4eEh9Nzdvn0bLi4uYtc71dDQEPSzR48echUAqSg6dOiAq1evok6dOnB2dkaHDh3QoUMHpaeQE0IIIYT8bCrTEjrkx0YJP6IUbW1tJCQkoGnTpkLbr127hnbt2sk8o+Hu7o5mzZph/vz5ggSGlZUVBg0aBD6fj8jISImxhYWFqFevHqKiogRTakuKhii6qP+nT59QWFgotE1aMrBu3brYtGkTOnXqJPRGfv/+fbRp0wbv379XqH2u8vPzcfz4cSxcuBC3bt36ZtPsevXqBVdXV8EotbLWrFmD6Ojob76GX/fu3ZGeng4/Pz+YmZmJJHy+VSEDLrgm/MoWHDl+/Dg6duxYKUbofW1PnjxBeHg4tm/fjsLCQty9exd6enpyxaanpyM4OFhoCYIJEyagTp06UuO0tbVx//59WFlZCT13aWlpcHR0FPte16NHD+zbtw/6+vpK3c/ypK6uDnNzc3h4eMDFxQXOzs6VZjQtIYQQQkhFQAk/UlHQlF6iFEtLS5FEGQAUFRXJVcFxxYoV6NChA5KSkvD582dMmzYNd+7cwb///ov4+Hipserq6sjNzYWqqqpSC6Pm5uZi+vTp2L9/v9iRhNKSZs+fPxc7LY/P54t9PL6lV69eYe/evdi5cydSUlLQokULkds0a9YM586dg7GxMZo2bSp1RNS1a9ckXnfz5k2pxUy6du2KFStWKHYHlBAXF4e4uDiFpsCWNx6PJ/K4KzIyrWwSc+jQoV+lX5VRyWPJGBMZ3SlNVFQUevXqhSZNmqBdu3ZgjCEhIQENGzbE8ePH0aVLF4mxtWvXxo0bN0Tea06fPg17e3uJ7X369KlSJvyys7Nx8eJFxMTEYOnSpRg8eDBsbW3RoUMHuLi4oEOHDoLRpoQQQgghhJCKixJ+RCnLli2Dv78/1q1bh+bNm4PH4yEpKQkTJkyQmfgpLCzEmDFjcOzYMZw+fRqqqqrIzc1F3759MXbsWLkWN/X29sbWrVuxZMkShfs+bdo0REdHY/369fD29sa6devw/PlzbNq0Seb+GjZsiIsXL4r8+D9w4IDIaMdv4cOHDzh48CB2796NmJgY2NjYYMiQIdi7d6/YRGTv3r0FI8M8PDyUbvf169dS1zpUU1PD27dvld6/vOrXr1/p1sNgjMHHx0fwPCi6hl5FnWr7vZSe0hsXF4eePXvir7/+gpubm9xTZWfMmIFJkyaJvL5nzJiB6dOnS034TZ06FWPHjkV+fj4YY7hy5Qr27NmDxYsXY+vWrWJjKvPAeV1dXbi5ucHNzQ3/r737Dory2t8A/qwIiB2VeC10EF3FdoljGQsWMBawRI2AKEa9sQAaS3B+EcUCGUORaGIDRDTCxVxwgg0UkIAVCEFFVFCwlxFcpYjg7v7+cNxxXZoovLo8nxln2PPuefdhsO13zzlfACgqKkJKSgoSExOxefNmODo6wtzcHJcvXxY4KRERERERVYdbeqlOdHV1UVpailevXqFp09d14zdfv1vIqOxcNT09PZw5cwbm5uZ1en1XV1eEhYXBzMwMVlZWKq/5bvfhtxkYGCAsLAwjRoxA69at8ffff8PMzAz79u1DeHg4jh49WuXcmJgYzJo1C6tXr8b69evh5eWFa9euISwsDIcPH662cPAx6OjoQFdXF9OnT4ejo2Olq/rqg6mpKXx9fTF58uRKr0dFRWHFihW4efNmveZITU2Fh4cHPD090atXL5UiZF3PZqxPn/sZekJatGgRIiIiYGBgABcXFzg5OaF9+/bvfZ9mzZrh0qVLKn/fXL9+Hb1790ZZWVm183fv3o2NGzfizp07AIAuXbpg3bp1iiY972rSpAkePXqkFivhZDIZUlNTkZiYiMTERKSkpKCsrIxdeomIiIiqwC299KlgwY/qZO/evbV+bmXnqi1fvhyampp1WqEHVN+ApKamIy1btkRWVhYMDQ3RtWtXREVFYcCAAcjLy4OlpSWKi4urfe3Y2Fh4e3sjPT0dMpkM/fv3h6enJ2xsbOr0vbyPuLg4jB49+oOaAJSXl1fa8MLAwKDKOa6uropune+ek/jixQsMGDAA1tbW+OWXX+qcqzZycnIwc+ZMZGRkKI3L5XKIRCIWIdRMkyZNYGBgUON29JrOL9TX14e/vz+mTZumNB4ZGYkVK1bg9u3btcrz5MkTyGQyxXmM1eVu06ZNjVu3P8UmMzKZDGlpaTh16hQSExNx+vRplJSUoEuXLrC2tlb8qstxCkRERESNgY+PDxYuXIi2bdsKHYUaOW7ppTr50OYI5eXlCAoKwokTJ957hR4AJCYm1vm1TUxMkJ+fD0NDQ4jFYkRGRmLAgAGIiYmp1V/Ktra2sLW1rfPrf4gPKSpev34d3377Lc6cOaM0Xpti2Y8//oioqCh069YNS5YsgYWFBUQiEbKzs/Hrr79CKpXi//7v/+qcrbYcHR2hpaWFAwcOVNq0g9SLs7PzR/kZz58/HwsWLMDNmzcxePBgiEQipKSk4KeffsKKFSuqnZuXl4dXr17B3NxcqXlFTk4ONDU1YWRkVOk8Ly8vtGnT5oOzN7S2bduipKQEnTp1wogRI+Dv7w9ra+sam5sQERERNQb79u3Djh07kJeXh7Nnz8LQ0BBbtmyBsbEx7O3tAQCrV68WOCXRa1zhR3UmlUpx6NAhRddLsVgMOzs7aGho1Dj3Q1bofaiAgABoaGjAzc0NiYmJGD9+PKRSKV69egV/f3+4u7tXOdfExASpqakq2wolEgn69+9fL1taa1rd9LbqGm8MGTIETZs2hYeHBzp16qRyz5oaYdy6dQsLFy5EbGys4owykUgEW1tb/Pbbb1UWPj6m5s2bIyMjAxYWFvX+WqQ+5HI5tmzZAj8/P9y/fx8A0LlzZ6xatQqTJ0+Gvr5+lXOHDx+OuXPnqnzIsX//fgQFBeHUqVMqc97tzPw52blzJ6ytrdGtWzehoxARERF9UrZv3w5PT08sXboUmzZtwuXLl2FiYoLQ0FDs3bv3gxalENUHFvyoTnJzczFu3Djcu3cPFhYWkMvluH79OvT19XHkyJHPajXI7du3kZaWBlNT0xqLXlW9kX/06BEMDAzw8uXLj57Py8tL8XVZWRl+++03iMViDBo0CABw7tw5ZGVlYdGiRfDx8anyPi1atEB6ejq6d+/+QXmePn2K3NxcyOVymJubQ1dX94Pu9z6GDRsGT09PjB49usFek9RLUVERAKC4uBje3t4ICgqqthHM2+d8vi03NxdWVlaQSCQqczQ0NPDgwYPPsuBHRERERJUTi8Xw9vbGpEmTlM7pu3z5MkaMGIEnT54IHZFICbf0Up24ubnB1NQU586dQ7t27QAABQUFcHJygpubG44cOSJwwtozMDCo9Pw6S0tLHD16FPr6+vjzzz8V47GxsUpb9aRSKeLj4+tthdvatWsVX8+bNw9ubm7YsGGDynPeNBSoilgs/ij/COnq6jZYs5B3ubq6wt3dHStXroSlpaVK047evXsLkos+TRKJBIsXL0ZcXBw0NTXh4eGBJUuWwMvLC76+vhCLxQgJCan2HiKRSFEkfNuzZ8+q3AbPz9GIiIiI1E9eXh769eunMq6trY2SkhIBEhFVjyv8qE5atGiBc+fOwdLSUmk8MzMTQ4YMqbHxxefg7U9t3jTJEIlEKm/m35zj5efnhwkTJtRrpjZt2iAtLU2l22hOTg6srKzw7NmzKucmJCTgxx9/hLe3d6XFsk+xw+27KmtW8uZnwqYd9K5FixYhJiYGM2bMwPHjx5GdnQ1bW1uUlZVh7dq1GD58eI33mDBhApo3b47w8HDFcQVSqRQzZsxASUkJjh07Vt/fBhERERF9AsRiMXx8fGBvb6/0XvGXX37B3r17kZ6eLnREIiVc4Ud1oq2tXemql+LiYmhpaQmQqH696WhrbGyM1NRUpcP7G5KOjg5SUlJUCn4pKSkq3XPf9WYb7KhRo5TGP6diWV5entAR6DNy5MgR7NmzB6NHj8aiRYtgZmaGbt26YcuWLbW+x+bNmzFs2DBYWFhg6NChAIDk5GQ8f/68Xs8aJSIiIqJPy8qVK7F48WKUlZVBLpfjwoULCA8Ph4+PD4KCgoSOR6SCBT+qkwkTJmDBggUIDg7GgAEDAADnz5/Hd999Bzs7O4HT1R+hC05Lly7FwoULkZ6ejoEDBwJ4fYZfcHCw0tbfylR3iGxGRsZHzVlfDA0NhY5An5H79+9DLBYDeN1wp1mzZpg3b9573UMsFuPixYvYtm0bMjMzoaOjA2dnZyxZskRxnAERERERqT8XFxe8evUKq1atQmlpKRwcHNClSxcEBgbim2++EToekQpu6aU6kUgkmD17NmJiYhRbQysqKmBvb4/Q0FClM+4+V28v035bUlISfH19Fd2Je/TogZUrVypW/9S3yMhIBAYGIjs7G8DrgoS7uzvMzc3Rt2/fWt/n2bNn+P333xEUFITMzMzPYoVfWFhYtdednZ0bKAl9DjQ0NPDw4UPo6ekBeP1n+uLFizA2NhY4GRERERF9zp48eQKZTMYmbfRJY8GPPkhubi6uXLkC4HXh6d1Olp+zygp++/fvh4uLC6ZMmYIhQ4ZALpfjzJkziI6ORmhoKBwcHBo0o0Qiwe+//47g4OBaF+0SEhIQEhKCqKgoGBoaYurUqZg6dWqlB9B+at7tCFxRUYHS0lJoaWmhefPmKCwsFCgZfYqaNGmCr776Ctra2gCAmJgYjBw5Ei1atFB6XlRUVLX3kUgkuHDhAh4/fqzY3v8Gi8xEREREjcOLFy8gl8vRvHlzAMCtW7cQHR0NsVgMGxsbgdMRqWLBj+osODgYAQEByMnJAQCYm5tj6dKl771l7lNVWcGvR48eWLBgAZYtW6b0XH9/f+zevVux6q6+vW/R7u7duwgNDUVISAhKSkowffp07NixA5mZmYotj5+rnJwcLFy4ECtXroStra3QcegT4uLiUqvn7dmzp8prMTExcHR0RElJCVq1agWRSKS4JhKJWGQmIiIiaiRsbGwwZcoUfPfdd5BIJLCwsICWlhaePHkCf39/LFy4UOiIREpY8KM6WbNmDQICAuDq6opBgwYBAM6ePYtt27bB3d0dGzduFDhh7ZSVlVXZ7OLAgQOwt7dXWg2kra2NrKwslZWMubm56NWrF8rKyuota12LduPGjUNKSgomTJgAR0dHjB07FhoaGtDU1FSLgh8ApKWlwcnJCVevXhU6CqmZbt26Ydy4cfD29lZ8mktEREREjU+HDh2QlJSEnj17IigoCFu3bkVGRgb+97//wdPTs8EWfxDVVhOhA9Dnafv27di9ezd8fHxgZ2cHOzs7+Pj4YNeuXdixY4fQ8aolk8mwYcMGdOnSBS1btsTNmzcBvC5iBgcHK57n4OCgsvVPX18f8fHxKveMj4+Hvr5+vWUeN24cxGIxrly5gq1bt+L+/fvYunVrrebGxcVh3rx58PLywvjx46GhoVFvOYWioaGB+/fvCx2D1NC9e/fg5ubGYh8RERFRI1daWopWrVoBeP0ea8qUKWjSpAkGDhyIW7duCZyOSBW79FKdSKVSWFlZqYz/+9//xqtXrwRIVHsbN27E3r17sXnzZsyfP18xbmlpiYCAAHz77bdVzl2+fDnc3Nzwzz//YPDgwRCJREhJSUFoaCgCAwPrLXNcXBzc3NywcOFCmJubv9fc5ORkhISEwMrKCt27d8esWbMwY8aMekpav/7880+lx3K5HA8ePMC2bdswZMgQgVKROrO1tUVaWppK8x4iIiIialzMzMxw6NAhTJ48GbGxsYpjnh4/fozWrVsLnI5IFbf0Up24urpCU1MT/v7+SuMrVqzAixcv8OuvvwqUrGZmZmbYuXMnRo0apXRO39WrVzFo0CA8ffq02vnR0dHw8/NTLNl+06XX3t6+3jKfPXsWISEhiIyMVCrade7cudbbcktLSxEREYGQkBBcuHABUqkU/v7+mDt3ruKTqk9dkybKi5JFIhH09PQwcuRI+Pn5oVOnTgIlI3UVHByM9evXw8XFBZaWloqu5G/Y2dkJlIyIiIiIGtIff/wBBwcHSKVSjBo1CnFxcQAAHx8f/PXXXzh27JjACYmUseBHdeLq6oqwsDDo6+tj4MCBAIBz587hzp07cHZ2VnpT/G5RUGg6Ojq4evUqDA0NlQp+V65cwYABA1BcXFzlXBcXFzg5OWHkyJFKh/c3lI9VtLt27RqCg4Oxb98+SCQSjBkzRmX1HBGpFpnfJhKJatUZm4iIiIjUw8OHD/HgwQP06dNH8f/ECxcuoHXr1ujevbvA6YiUseBHdWJtbV2r54lEIiQkJNRzmvdjZWWFpUuXwsnJSang5+XlhZMnTyI5ObnKuXZ2doiLi0P79u0xc+ZMODk5oW/fvg0X/i0fo2gnlUoRExODkJAQFvyIiIiIiIiI1AQLftToxMTEYNasWVi9ejXWr18PLy8vXLt2DWFhYTh8+DDGjBlT7XyJRILIyEgcOHAAycnJsLCwgJOTExwcHGBkZNQw38RbGlPR7uuvv4aVlRU8PDyUxn/++WdcuHABBw8eFCgZNQbVdfUmIiIiIvWXmpqKgwcP4vbt2ygvL1e6FhUVJVAqosqx4EeNUmxsLLy9vZGeng6ZTIb+/fvD09MTNjY273Wfu3fvIjw8HCEhIcjJyfnkG5Z87vT09JCQkABLS0ul8UuXLmH06NF49OiRQMlIXUmlUnh7e2PHjh149OgRrl+/DhMTE6xZswZGRkbVNvkhIiIiIvUREREBZ2dn2NjY4MSJE7CxsUFOTg4ePnyIyZMnY8+ePUJHJFJS9eFERGrM1tYWSUlJKC4uRmlpKVJSUt672FdRUYG0tDScP38e+fn56NixYz2lpTeKi4uhpaWlMq6pqYnnz58LkIjU3aZNmxAaGorNmzcr/d6ztLREUFCQgMmIiIiIqCF5e3sjICAAhw8fhpaWFgIDA5GdnY3p06fDwMBA6HhEKljwo0bHxMQEBQUFKuMSiQQmJiY1zk9MTMT8+fPRsWNHzJ49G61atUJMTAzu3LlTH3HpLb169cJ///tflfGIiIhadSomel9hYWHYtWsXHB0doaGhoRjv3bs3rl69KmAyIiIiImpIN27cwPjx4wEA2traKCkpgUgkwrJly7Br1y6B0xGpaip0AKKGlp+fX2lnzZcvX+LevXvVzu3atSsKCgpga2uLnTt3YuLEiTzTqwGtWbMGU6dOxY0bNzBy5EgAQHx8PMLDw3l+H9WLe/fuwczMTGVcJpOhoqJCgEREREREJIR27dqhqKgIANClSxdcvnwZlpaWkEgkKC0tFTgdkSoW/KjReLuhRWxsLNq0aaN4LJVKER8fX2PTDU9PT0ybNg26urr1FZOqYWdnh0OHDsHb2xt//PEHdHR00Lt3b5w8eRLDhw8XOh6poZ49eyI5ORmGhoZK4wcPHkS/fv0ESkVEREREDW3o0KE4ceIELC0tMX36dLi7uyMhIQEnTpzAqFGjhI5HpIJNO6jRaNLk9Q52kUiEd3/ba2pqwsjICH5+fpgwYYIQ8YjoE/ShXb2JiIiISD0UFhairKwMnTt3hkwmg6+vL1JSUmBmZoY1a9ZwUQh9cljwo0bH2NgYqamp6NChg9BRqI7S09ORnZ0NkUgEsVjMlVZUrz5WV28iIiIiIqKGwoIfEX02Hj9+jG+++QanTp1C27ZtIZfL8ezZM1hbWyMiIgJ6enpCRyQiIiIiIjV148YN7NmzBzdu3EBgYCC++OILHD9+HPr6+ujZs6fQ8YiUsEsvNUpJSUmYOHEizMzMYG5uDjs7OyQnJwsdi2rg6uqK58+fIysrC4WFhXj69CkuX76M58+fw83NTeh4RERERESkppKSkmBpaYnz588jKioKxcXFAICLFy9i7dq1AqcjUsUVftTo7N+/Hy4uLpgyZQqGDBkCuVyOM2fOIDo6GqGhoXBwcBA6IlWhTZs2OHnyJL788kul8QsXLsDGxgYSiUSYYKRW2rVrh+vXr6NDhw7Q1dWFSCSq8rmFhYUNmIyIiIiIhDJo0CBMmzYN33//PVq1aoXMzEyYmJggNTUVkyZNwr1794SOSKSEXXqp0dm0aRM2b96MZcuWKcbc3d3h7++PDRs2sOD3CZPJZNDU1FQZ19TUhEwmEyARqaOAgAC0atVK8XV1BT8iIiIiahwuXbqEAwcOqIzr6emhoKBAgERE1eMKP2p0tLW1kZWVBTMzM6Xx3Nxc9OrVC2VlZQIlo5rY29tDIpEgPDwcnTt3BgDcu3cPjo6O0NXVRXR0tMAJiYiIiIhIHXXt2hWRkZEYPHiw0gq/6OhorFixAjdu3BA6IpESnuFHjY6+vj7i4+NVxuPj46Gvry9AIqqtbdu2oaioCEZGRjA1NYWZmRmMjY1RVFSErVu3Ch2P1NDRo0cRGxurMh4XF4djx44JkIiIiIiIhODg4IAffvgBDx8+hEgkgkwmw+nTp7FixQo4OzsLHY9IBVf4UaOzfft2LF26FHPnzsXgwYMhEomQkpKC0NBQBAYG4j//+Y/QEakGJ0+eRHZ2NuRyOcRiMUaPHi10JFJTvXv3xk8//YRx48YpjR8/fhw//PADMjMzBUpGRERERA2poqICc+bMQUREBORyOZo2bQqpVAoHBweEhoZCQ0ND6IhESljwo0YpOjoafn5+yM7OBgD06NEDK1euhL29vcDJqCoymQyhoaGIiopCfn4+RCIRjI2N8fXXX2PWrFk8Z43qhY6ODrKzs2FkZKQ0np+fj549e6KkpESYYEREREQkiBs3biAjIwMymQz9+vWDubm50JGIKsWmHdTouLi4wMnJCcnJySwSfSbkcjns7Oxw9OhR9OnTB5aWlpDL5cjOzsacOXMQFRWFQ4cOCR2T1FCbNm1w8+ZNlYJfbm4uWrRoIUwoIiIiIhKMqakpTE1NhY5BVCMW/KjRKSgowPjx49G+fXvMnDkTTk5O6Nu3r9CxqBqhoaH466+/EB8fD2tra6VrCQkJmDRpEsLCwnh2Bn10dnZ2WLp0KaKjoxX/scvNzcXy5cthZ2cncDoiIiIiqk/ff/99rZ/r7+9fj0mI3h+39FKjJJFIEBkZiQMHDiA5ORkWFhZwcnKCg4ODykoeEp6NjQ1GjhwJDw+PSq97e3sjKSmp0uYKRB/i2bNnGDt2LNLS0tC1a1cAwJ07dzBs2DBERUWhbdu2wgYkIiIionrz7mKDqohEIiQkJNRzGqL3w4IfNXp3795FeHg4QkJCkJOTg1evXgkdid7xr3/9C8ePH69yJWZGRga++uorPHz4sGGDUaMgl8tx4sQJZGZmQkdHB3369MHQoUOFjkVERERERFSlJkIHIBJSRUUF0tLScP78eeTn56Njx45CR6JKFBYWVvuz6dixI54+fdqAiUjdnT9/HseOHQPw+hNbGxsbfPHFF/D19cXUqVOxYMECvHz5UuCURERERNTQcnNzERsbixcvXgB4/eEw0aeIBT9qlBITEzF//nx07NgRs2fPRqtWrRATE4M7d+4IHY0qIZVK0bRp1UeOamhocGUmfVTr1q3DxYsXFY8vXbqE+fPnY8yYMfDw8EBMTAx8fHwETEhEREREDamgoACjRo1Ct27dMG7cODx48AAAMG/ePCxfvlzgdESq2LSDGp2uXbuioKAAtra22LlzJyZOnIhmzZoJHYuqIZfLMWfOHGhra1d6nSut6GP7559/sGHDBsXjiIgIDBgwALt37wYA6OvrY+3atVi3bp1ACYmIiIioIS1btgyampq4ffs2evTooRifMWMGli1bBj8/PwHTEaliwY8aHU9PT0ybNg26urpCR6Famj17do3PYYde+piePn2qtI08KSkJY8eOVTz+8ssvuSKYiIiIqBGJi4tDbGysopHbG+bm5rh165ZAqYiqxoIfNToLFiwQOgK9pz179ggdgRqZjh07Ii8vD/r6+igvL8fff/8NLy8vxfWioiJoamoKmJCIiIiIGlJJSQmaN2+uMv7kyZMqdyIRCYln+BEREb1j7Nix8PDwQHJyMlavXo3mzZsrdea9ePEiTE1NBUxIRERERA1p2LBhCAsLUzwWiUSQyWT4+eefYW1tLWAyospxhR8REdE7Nm7ciClTpmD48OFo2bIl9u7dCy0tLcX1kJAQ2NjYCJiQiIiIiBqSr68vhg8fjrS0NJSXl2PVqlXIyspCYWEhTp8+LXQ8IhUiOXtIExERVerZs2do2bIlNDQ0lMYLCwvRsmVLpSIgEREREamniooK2NjYwMfHB8eOHUN6ejpkMhn69++PxYsXo1OnTkJHJFLBgh8RERERERERUTX09PRw5swZmJubCx2FqFZ4hh8RERERERERUTWcnZ0RHBwsdAyiWuMZfkRERERERERE1SgvL0dQUBBOnDgBKysrtGjRQum6v7+/QMmIKseCHxERERERERFRNS5fvoz+/fsDAK5fv650TSQSCRGJqFo8w4+IiIiIiIiIiEiN8Aw/IiIiIiIiIiIiNcKCHxERERERERERkRphwY+IiIiIiIiIiEiNsOBHRERERERERESkRljwIyIiIiIiIiIiUiMs+BEREREREREREakRFvyIiIiIiIiIiIjUyP8DuP9cAWk49LIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a correlation heatmap using plt.figure to adjust the size and sns heatmap to visualize the correlation matrix\n",
    "plt.figure(figsize=(16,12))\n",
    "sns.heatmap(new_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3db69290",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c8cbd829",
   "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>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Foreign</th>\n",
       "      <th>History</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>ScienceFiction</th>\n",
       "      <th>TVMovie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>ar</th>\n",
       "      <th>cn</th>\n",
       "      <th>cs</th>\n",
       "      <th>da</th>\n",
       "      <th>de</th>\n",
       "      <th>el</th>\n",
       "      <th>en</th>\n",
       "      <th>es</th>\n",
       "      <th>fa</th>\n",
       "      <th>fr</th>\n",
       "      <th>he</th>\n",
       "      <th>hi</th>\n",
       "      <th>hu</th>\n",
       "      <th>id</th>\n",
       "      <th>is</th>\n",
       "      <th>it</th>\n",
       "      <th>ja</th>\n",
       "      <th>ko</th>\n",
       "      <th>ky</th>\n",
       "      <th>nb</th>\n",
       "      <th>nl</th>\n",
       "      <th>no</th>\n",
       "      <th>pl</th>\n",
       "      <th>ps</th>\n",
       "      <th>pt</th>\n",
       "      <th>ro</th>\n",
       "      <th>ru</th>\n",
       "      <th>sl</th>\n",
       "      <th>sv</th>\n",
       "      <th>ta</th>\n",
       "      <th>te</th>\n",
       "      <th>th</th>\n",
       "      <th>tr</th>\n",
       "      <th>vi</th>\n",
       "      <th>xx</th>\n",
       "      <th>zh</th>\n",
       "      <th>release_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505364</td>\n",
       "      <td>0.730806</td>\n",
       "      <td>0.269811</td>\n",
       "      <td>0.092644</td>\n",
       "      <td>0.593148</td>\n",
       "      <td>0.310219</td>\n",
       "      <td>0.406209</td>\n",
       "      <td>0.207959</td>\n",
       "      <td>-0.068633</td>\n",
       "      <td>-0.012146</td>\n",
       "      <td>-0.099260</td>\n",
       "      <td>-0.196895</td>\n",
       "      <td>0.184034</td>\n",
       "      <td>0.263739</td>\n",
       "      <td>-0.058881</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>-0.123760</td>\n",
       "      <td>-0.064609</td>\n",
       "      <td>0.011624</td>\n",
       "      <td>-0.102655</td>\n",
       "      <td>0.198388</td>\n",
       "      <td>-0.027990</td>\n",
       "      <td>0.043049</td>\n",
       "      <td>0.026906</td>\n",
       "      <td>-0.006384</td>\n",
       "      <td>-0.014563</td>\n",
       "      <td>-0.022524</td>\n",
       "      <td>-0.014563</td>\n",
       "      <td>-0.018117</td>\n",
       "      <td>-0.037736</td>\n",
       "      <td>-0.010296</td>\n",
       "      <td>0.128891</td>\n",
       "      <td>-0.046672</td>\n",
       "      <td>-0.020426</td>\n",
       "      <td>-0.061440</td>\n",
       "      <td>-0.017428</td>\n",
       "      <td>-0.042310</td>\n",
       "      <td>-0.010296</td>\n",
       "      <td>-0.014037</td>\n",
       "      <td>-0.010296</td>\n",
       "      <td>-0.034637</td>\n",
       "      <td>-0.020954</td>\n",
       "      <td>-0.017369</td>\n",
       "      <td>-0.010296</td>\n",
       "      <td>-0.009056</td>\n",
       "      <td>-0.015370</td>\n",
       "      <td>-0.010013</td>\n",
       "      <td>-0.009531</td>\n",
       "      <td>-0.010280</td>\n",
       "      <td>-0.029709</td>\n",
       "      <td>-0.014349</td>\n",
       "      <td>-0.017743</td>\n",
       "      <td>-0.010296</td>\n",
       "      <td>-0.019069</td>\n",
       "      <td>-0.014563</td>\n",
       "      <td>0.003881</td>\n",
       "      <td>-0.014870</td>\n",
       "      <td>-0.006752</td>\n",
       "      <td>-0.009836</td>\n",
       "      <td>-0.006043</td>\n",
       "      <td>-0.012974</td>\n",
       "      <td>0.050281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>0.505364</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644700</td>\n",
       "      <td>0.225398</td>\n",
       "      <td>0.273990</td>\n",
       "      <td>0.778114</td>\n",
       "      <td>0.166947</td>\n",
       "      <td>0.247866</td>\n",
       "      <td>0.123190</td>\n",
       "      <td>-0.076988</td>\n",
       "      <td>0.017554</td>\n",
       "      <td>-0.084467</td>\n",
       "      <td>-0.112325</td>\n",
       "      <td>0.068876</td>\n",
       "      <td>0.145651</td>\n",
       "      <td>-0.055234</td>\n",
       "      <td>-0.026342</td>\n",
       "      <td>-0.035022</td>\n",
       "      <td>-0.052821</td>\n",
       "      <td>0.027149</td>\n",
       "      <td>-0.083200</td>\n",
       "      <td>0.166444</td>\n",
       "      <td>-0.019398</td>\n",
       "      <td>0.055971</td>\n",
       "      <td>0.012603</td>\n",
       "      <td>-0.013508</td>\n",
       "      <td>-0.010762</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>-0.012967</td>\n",
       "      <td>-0.004603</td>\n",
       "      <td>-0.026815</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>0.083775</td>\n",
       "      <td>-0.021023</td>\n",
       "      <td>-0.014369</td>\n",
       "      <td>-0.042806</td>\n",
       "      <td>-0.012678</td>\n",
       "      <td>-0.036654</td>\n",
       "      <td>-0.008887</td>\n",
       "      <td>0.001982</td>\n",
       "      <td>-0.009018</td>\n",
       "      <td>-0.006252</td>\n",
       "      <td>0.007573</td>\n",
       "      <td>-0.003024</td>\n",
       "      <td>-0.009716</td>\n",
       "      <td>-0.003862</td>\n",
       "      <td>-0.015181</td>\n",
       "      <td>-0.004668</td>\n",
       "      <td>-0.003734</td>\n",
       "      <td>-0.009095</td>\n",
       "      <td>-0.013875</td>\n",
       "      <td>-0.010713</td>\n",
       "      <td>-0.020978</td>\n",
       "      <td>-0.009724</td>\n",
       "      <td>-0.014358</td>\n",
       "      <td>-0.013556</td>\n",
       "      <td>-0.002378</td>\n",
       "      <td>-0.011212</td>\n",
       "      <td>-0.009087</td>\n",
       "      <td>-0.009719</td>\n",
       "      <td>-0.006786</td>\n",
       "      <td>-0.029546</td>\n",
       "      <td>0.046648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>0.730806</td>\n",
       "      <td>0.644700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251209</td>\n",
       "      <td>0.197153</td>\n",
       "      <td>0.781475</td>\n",
       "      <td>0.203543</td>\n",
       "      <td>0.344369</td>\n",
       "      <td>0.199317</td>\n",
       "      <td>-0.050452</td>\n",
       "      <td>-0.040771</td>\n",
       "      <td>-0.068107</td>\n",
       "      <td>-0.177352</td>\n",
       "      <td>0.170037</td>\n",
       "      <td>0.212262</td>\n",
       "      <td>-0.042475</td>\n",
       "      <td>-0.031439</td>\n",
       "      <td>-0.082793</td>\n",
       "      <td>-0.041573</td>\n",
       "      <td>-0.006826</td>\n",
       "      <td>-0.065422</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>-0.020639</td>\n",
       "      <td>-0.004552</td>\n",
       "      <td>0.002028</td>\n",
       "      <td>-0.029163</td>\n",
       "      <td>-0.010313</td>\n",
       "      <td>-0.014918</td>\n",
       "      <td>-0.010313</td>\n",
       "      <td>-0.012289</td>\n",
       "      <td>-0.031544</td>\n",
       "      <td>-0.007282</td>\n",
       "      <td>0.100748</td>\n",
       "      <td>-0.032000</td>\n",
       "      <td>-0.014548</td>\n",
       "      <td>-0.052142</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>-0.028961</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.010028</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.026314</td>\n",
       "      <td>-0.005769</td>\n",
       "      <td>-0.016748</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>-0.013403</td>\n",
       "      <td>-0.007116</td>\n",
       "      <td>-0.006343</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.020822</td>\n",
       "      <td>-0.010239</td>\n",
       "      <td>-0.021410</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.016311</td>\n",
       "      <td>-0.010313</td>\n",
       "      <td>0.001571</td>\n",
       "      <td>-0.010784</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>-0.007235</td>\n",
       "      <td>-0.002394</td>\n",
       "      <td>-0.018720</td>\n",
       "      <td>0.055232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>0.269811</td>\n",
       "      <td>0.225398</td>\n",
       "      <td>0.251209</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.370979</td>\n",
       "      <td>0.272057</td>\n",
       "      <td>0.090961</td>\n",
       "      <td>0.087283</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.227629</td>\n",
       "      <td>0.050567</td>\n",
       "      <td>-0.088228</td>\n",
       "      <td>0.272309</td>\n",
       "      <td>-0.147176</td>\n",
       "      <td>0.005268</td>\n",
       "      <td>0.013932</td>\n",
       "      <td>0.266793</td>\n",
       "      <td>-0.168948</td>\n",
       "      <td>0.026872</td>\n",
       "      <td>0.033405</td>\n",
       "      <td>0.052648</td>\n",
       "      <td>0.009137</td>\n",
       "      <td>-0.038526</td>\n",
       "      <td>0.017271</td>\n",
       "      <td>0.194395</td>\n",
       "      <td>0.061110</td>\n",
       "      <td>-0.013482</td>\n",
       "      <td>-0.004763</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>0.024618</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>-0.008252</td>\n",
       "      <td>-0.069301</td>\n",
       "      <td>0.007979</td>\n",
       "      <td>-0.005948</td>\n",
       "      <td>-0.009611</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>0.088681</td>\n",
       "      <td>0.021182</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>-0.014011</td>\n",
       "      <td>0.016174</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.035756</td>\n",
       "      <td>0.017983</td>\n",
       "      <td>-0.002493</td>\n",
       "      <td>0.014534</td>\n",
       "      <td>-0.010172</td>\n",
       "      <td>-0.017210</td>\n",
       "      <td>-0.015291</td>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.020885</td>\n",
       "      <td>-0.015291</td>\n",
       "      <td>0.017325</td>\n",
       "      <td>0.050322</td>\n",
       "      <td>0.033340</td>\n",
       "      <td>0.025980</td>\n",
       "      <td>0.009664</td>\n",
       "      <td>0.017983</td>\n",
       "      <td>-0.004413</td>\n",
       "      <td>0.030598</td>\n",
       "      <td>0.157606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>0.092644</td>\n",
       "      <td>0.273990</td>\n",
       "      <td>0.197153</td>\n",
       "      <td>0.370979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313263</td>\n",
       "      <td>-0.049063</td>\n",
       "      <td>0.023660</td>\n",
       "      <td>0.047116</td>\n",
       "      <td>-0.092795</td>\n",
       "      <td>0.062445</td>\n",
       "      <td>0.018602</td>\n",
       "      <td>0.237234</td>\n",
       "      <td>-0.018524</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.018393</td>\n",
       "      <td>0.108741</td>\n",
       "      <td>-0.136408</td>\n",
       "      <td>0.044061</td>\n",
       "      <td>0.021226</td>\n",
       "      <td>0.045878</td>\n",
       "      <td>-0.026105</td>\n",
       "      <td>-0.014776</td>\n",
       "      <td>-0.041588</td>\n",
       "      <td>0.091567</td>\n",
       "      <td>0.009361</td>\n",
       "      <td>0.020673</td>\n",
       "      <td>0.017080</td>\n",
       "      <td>-0.007598</td>\n",
       "      <td>0.033197</td>\n",
       "      <td>0.014674</td>\n",
       "      <td>0.009771</td>\n",
       "      <td>-0.086184</td>\n",
       "      <td>0.038908</td>\n",
       "      <td>0.031059</td>\n",
       "      <td>0.034359</td>\n",
       "      <td>0.027420</td>\n",
       "      <td>-0.004386</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>0.022386</td>\n",
       "      <td>0.009771</td>\n",
       "      <td>0.042444</td>\n",
       "      <td>0.046424</td>\n",
       "      <td>0.023299</td>\n",
       "      <td>-0.073816</td>\n",
       "      <td>0.007348</td>\n",
       "      <td>0.026212</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.012193</td>\n",
       "      <td>0.002502</td>\n",
       "      <td>0.010746</td>\n",
       "      <td>-0.041865</td>\n",
       "      <td>0.010502</td>\n",
       "      <td>-0.001132</td>\n",
       "      <td>0.026193</td>\n",
       "      <td>-0.004171</td>\n",
       "      <td>0.017039</td>\n",
       "      <td>-0.002661</td>\n",
       "      <td>-0.021726</td>\n",
       "      <td>-0.013246</td>\n",
       "      <td>0.012193</td>\n",
       "      <td>0.013037</td>\n",
       "      <td>0.108011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>-0.006752</td>\n",
       "      <td>-0.009087</td>\n",
       "      <td>-0.007292</td>\n",
       "      <td>0.009664</td>\n",
       "      <td>-0.021726</td>\n",
       "      <td>-0.007989</td>\n",
       "      <td>0.025660</td>\n",
       "      <td>0.032524</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.005942</td>\n",
       "      <td>-0.002210</td>\n",
       "      <td>0.015072</td>\n",
       "      <td>-0.004991</td>\n",
       "      <td>-0.004491</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>-0.005024</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>-0.004034</td>\n",
       "      <td>-0.006903</td>\n",
       "      <td>-0.005110</td>\n",
       "      <td>-0.000590</td>\n",
       "      <td>-0.008673</td>\n",
       "      <td>-0.002538</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000722</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.056108</td>\n",
       "      <td>-0.001182</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.001755</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000834</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000466</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.020216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vi</th>\n",
       "      <td>-0.009836</td>\n",
       "      <td>-0.009719</td>\n",
       "      <td>-0.007235</td>\n",
       "      <td>0.017983</td>\n",
       "      <td>-0.013246</td>\n",
       "      <td>-0.008059</td>\n",
       "      <td>-0.008117</td>\n",
       "      <td>-0.006404</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.005942</td>\n",
       "      <td>-0.002210</td>\n",
       "      <td>0.015072</td>\n",
       "      <td>-0.004991</td>\n",
       "      <td>-0.004491</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>-0.005024</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>-0.004034</td>\n",
       "      <td>-0.006903</td>\n",
       "      <td>-0.005110</td>\n",
       "      <td>-0.000590</td>\n",
       "      <td>-0.008673</td>\n",
       "      <td>-0.002538</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000722</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.056108</td>\n",
       "      <td>-0.001182</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.001755</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000834</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000466</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.020216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xx</th>\n",
       "      <td>-0.006043</td>\n",
       "      <td>-0.006786</td>\n",
       "      <td>-0.002394</td>\n",
       "      <td>-0.004413</td>\n",
       "      <td>0.012193</td>\n",
       "      <td>-0.006796</td>\n",
       "      <td>-0.008117</td>\n",
       "      <td>0.032524</td>\n",
       "      <td>-0.003266</td>\n",
       "      <td>-0.010791</td>\n",
       "      <td>-0.005942</td>\n",
       "      <td>-0.002210</td>\n",
       "      <td>0.015072</td>\n",
       "      <td>-0.004991</td>\n",
       "      <td>-0.004491</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>-0.002985</td>\n",
       "      <td>-0.005024</td>\n",
       "      <td>-0.002889</td>\n",
       "      <td>-0.004034</td>\n",
       "      <td>-0.006903</td>\n",
       "      <td>-0.005110</td>\n",
       "      <td>-0.000590</td>\n",
       "      <td>-0.008673</td>\n",
       "      <td>-0.002538</td>\n",
       "      <td>-0.001902</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000722</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.056108</td>\n",
       "      <td>-0.001182</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.001755</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000780</td>\n",
       "      <td>-0.000834</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000692</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000466</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>-0.000208</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>0.021936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zh</th>\n",
       "      <td>-0.012974</td>\n",
       "      <td>-0.029546</td>\n",
       "      <td>-0.018720</td>\n",
       "      <td>0.030598</td>\n",
       "      <td>0.013037</td>\n",
       "      <td>-0.033121</td>\n",
       "      <td>0.075033</td>\n",
       "      <td>0.041753</td>\n",
       "      <td>0.008852</td>\n",
       "      <td>-0.027190</td>\n",
       "      <td>-0.015137</td>\n",
       "      <td>-0.011514</td>\n",
       "      <td>0.039500</td>\n",
       "      <td>-0.007974</td>\n",
       "      <td>0.025678</td>\n",
       "      <td>0.026866</td>\n",
       "      <td>0.110818</td>\n",
       "      <td>-0.026176</td>\n",
       "      <td>-0.015052</td>\n",
       "      <td>-0.021019</td>\n",
       "      <td>0.006964</td>\n",
       "      <td>-0.017775</td>\n",
       "      <td>-0.003072</td>\n",
       "      <td>-0.026263</td>\n",
       "      <td>0.052097</td>\n",
       "      <td>-0.009911</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.003764</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.002873</td>\n",
       "      <td>-0.005654</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.292339</td>\n",
       "      <td>-0.006159</td>\n",
       "      <td>-0.002171</td>\n",
       "      <td>-0.009146</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>-0.004739</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.004066</td>\n",
       "      <td>-0.004348</td>\n",
       "      <td>-0.003603</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.002171</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.003258</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.003603</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.002428</td>\n",
       "      <td>-0.001535</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_month</th>\n",
       "      <td>0.050281</td>\n",
       "      <td>0.046648</td>\n",
       "      <td>0.055232</td>\n",
       "      <td>0.157606</td>\n",
       "      <td>0.108011</td>\n",
       "      <td>0.042652</td>\n",
       "      <td>-0.031244</td>\n",
       "      <td>0.012833</td>\n",
       "      <td>0.023096</td>\n",
       "      <td>-0.064752</td>\n",
       "      <td>-0.019516</td>\n",
       "      <td>-0.007135</td>\n",
       "      <td>0.072785</td>\n",
       "      <td>0.017070</td>\n",
       "      <td>0.014905</td>\n",
       "      <td>-0.020356</td>\n",
       "      <td>0.070592</td>\n",
       "      <td>-0.036257</td>\n",
       "      <td>0.028997</td>\n",
       "      <td>-0.022751</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>-0.012336</td>\n",
       "      <td>0.023315</td>\n",
       "      <td>-0.008541</td>\n",
       "      <td>0.041863</td>\n",
       "      <td>0.017715</td>\n",
       "      <td>-0.001764</td>\n",
       "      <td>-0.018945</td>\n",
       "      <td>0.025064</td>\n",
       "      <td>-0.024824</td>\n",
       "      <td>-0.007719</td>\n",
       "      <td>-0.003355</td>\n",
       "      <td>0.008870</td>\n",
       "      <td>-0.000351</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>-0.013557</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>-0.020216</td>\n",
       "      <td>0.010160</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>0.009992</td>\n",
       "      <td>0.023500</td>\n",
       "      <td>-0.013683</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>0.013506</td>\n",
       "      <td>-0.000387</td>\n",
       "      <td>-0.024431</td>\n",
       "      <td>0.013506</td>\n",
       "      <td>-0.007570</td>\n",
       "      <td>-0.018511</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>-0.006050</td>\n",
       "      <td>0.009291</td>\n",
       "      <td>0.015126</td>\n",
       "      <td>0.004198</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.018530</td>\n",
       "      <td>-0.020216</td>\n",
       "      <td>-0.020216</td>\n",
       "      <td>0.021936</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 budget  popularity   revenue   runtime  vote_average  \\\n",
       "budget         1.000000    0.505364  0.730806  0.269811      0.092644   \n",
       "popularity     0.505364    1.000000  0.644700  0.225398      0.273990   \n",
       "revenue        0.730806    0.644700  1.000000  0.251209      0.197153   \n",
       "runtime        0.269811    0.225398  0.251209  1.000000      0.370979   \n",
       "vote_average   0.092644    0.273990  0.197153  0.370979      1.000000   \n",
       "...                 ...         ...       ...       ...           ...   \n",
       "tr            -0.006752   -0.009087 -0.007292  0.009664     -0.021726   \n",
       "vi            -0.009836   -0.009719 -0.007235  0.017983     -0.013246   \n",
       "xx            -0.006043   -0.006786 -0.002394 -0.004413      0.012193   \n",
       "zh            -0.012974   -0.029546 -0.018720  0.030598      0.013037   \n",
       "release_month  0.050281    0.046648  0.055232  0.157606      0.108011   \n",
       "\n",
       "               vote_count    Action  Adventure  Animation    Comedy     Crime  \\\n",
       "budget           0.593148  0.310219   0.406209   0.207959 -0.068633 -0.012146   \n",
       "popularity       0.778114  0.166947   0.247866   0.123190 -0.076988  0.017554   \n",
       "revenue          0.781475  0.203543   0.344369   0.199317 -0.050452 -0.040771   \n",
       "runtime          0.272057  0.090961   0.087283  -0.170325 -0.227629  0.050567   \n",
       "vote_average     0.313263 -0.049063   0.023660   0.047116 -0.092795  0.062445   \n",
       "...                   ...       ...        ...        ...       ...       ...   \n",
       "tr              -0.007989  0.025660   0.032524  -0.003266 -0.010791 -0.005942   \n",
       "vi              -0.008059 -0.008117  -0.006404  -0.003266 -0.010791 -0.005942   \n",
       "xx              -0.006796 -0.008117   0.032524  -0.003266 -0.010791 -0.005942   \n",
       "zh              -0.033121  0.075033   0.041753   0.008852 -0.027190 -0.015137   \n",
       "release_month    0.042652 -0.031244   0.012833   0.023096 -0.064752 -0.019516   \n",
       "\n",
       "               Documentary     Drama    Family   Fantasy   Foreign   History  \\\n",
       "budget           -0.099260 -0.196895  0.184034  0.263739 -0.058881  0.004330   \n",
       "popularity       -0.084467 -0.112325  0.068876  0.145651 -0.055234 -0.026342   \n",
       "revenue          -0.068107 -0.177352  0.170037  0.212262 -0.042475 -0.031439   \n",
       "runtime          -0.088228  0.272309 -0.147176  0.005268  0.013932  0.266793   \n",
       "vote_average      0.018602  0.237234 -0.018524  0.000851  0.018393  0.108741   \n",
       "...                    ...       ...       ...       ...       ...       ...   \n",
       "tr               -0.002210  0.015072 -0.004991 -0.004491 -0.001219 -0.002985   \n",
       "vi               -0.002210  0.015072 -0.004991 -0.004491 -0.001219 -0.002985   \n",
       "xx               -0.002210  0.015072 -0.004991 -0.004491 -0.001219 -0.002985   \n",
       "zh               -0.011514  0.039500 -0.007974  0.025678  0.026866  0.110818   \n",
       "release_month    -0.007135  0.072785  0.017070  0.014905 -0.020356  0.070592   \n",
       "\n",
       "                 Horror     Music   Mystery   Romance  ScienceFiction  \\\n",
       "budget        -0.123760 -0.064609  0.011624 -0.102655        0.198388   \n",
       "popularity    -0.035022 -0.052821  0.027149 -0.083200        0.166444   \n",
       "revenue       -0.082793 -0.041573 -0.006826 -0.065422        0.152590   \n",
       "runtime       -0.168948  0.026872  0.033405  0.052648        0.009137   \n",
       "vote_average  -0.136408  0.044061  0.021226  0.045878       -0.026105   \n",
       "...                 ...       ...       ...       ...             ...   \n",
       "tr            -0.005024 -0.002889 -0.004034 -0.006903       -0.005110   \n",
       "vi            -0.005024 -0.002889 -0.004034 -0.006903       -0.005110   \n",
       "xx            -0.005024 -0.002889 -0.004034 -0.006903       -0.005110   \n",
       "zh            -0.026176 -0.015052 -0.021019  0.006964       -0.017775   \n",
       "release_month -0.036257  0.028997 -0.022751  0.004289       -0.012336   \n",
       "\n",
       "                TVMovie  Thriller       War   Western        ar        cn  \\\n",
       "budget        -0.027990  0.043049  0.026906 -0.006384 -0.014563 -0.022524   \n",
       "popularity    -0.019398  0.055971  0.012603 -0.013508 -0.010762 -0.017094   \n",
       "revenue       -0.020639 -0.004552  0.002028 -0.029163 -0.010313 -0.014918   \n",
       "runtime       -0.038526  0.017271  0.194395  0.061110 -0.013482 -0.004763   \n",
       "vote_average  -0.014776 -0.041588  0.091567  0.009361  0.020673  0.017080   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000590 -0.008673 -0.002538 -0.001902 -0.000295 -0.000722   \n",
       "vi            -0.000590 -0.008673 -0.002538 -0.001902 -0.000295 -0.000722   \n",
       "xx            -0.000590 -0.008673 -0.002538 -0.001902 -0.000295 -0.000722   \n",
       "zh            -0.003072 -0.026263  0.052097 -0.009911 -0.001535 -0.003764   \n",
       "release_month  0.023315 -0.008541  0.041863  0.017715 -0.001764 -0.018945   \n",
       "\n",
       "                     cs        da        de        el        en        es  \\\n",
       "budget        -0.014563 -0.018117 -0.037736 -0.010296  0.128891 -0.046672   \n",
       "popularity    -0.012967 -0.004603 -0.026815  0.003339  0.083775 -0.021023   \n",
       "revenue       -0.010313 -0.012289 -0.031544 -0.007282  0.100748 -0.032000   \n",
       "runtime       -0.002169  0.024618  0.002938 -0.008252 -0.069301  0.007979   \n",
       "vote_average  -0.007598  0.033197  0.014674  0.009771 -0.086184  0.038908   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000295 -0.000551 -0.001085 -0.000208 -0.056108 -0.001182   \n",
       "vi            -0.000295 -0.000551 -0.001085 -0.000208 -0.056108 -0.001182   \n",
       "xx            -0.000295 -0.000551 -0.001085 -0.000208 -0.056108 -0.001182   \n",
       "zh            -0.001535 -0.002873 -0.005654 -0.001085 -0.292339 -0.006159   \n",
       "release_month  0.025064 -0.024824 -0.007719 -0.003355  0.008870 -0.000351   \n",
       "\n",
       "                     fa        fr        he        hi        hu        id  \\\n",
       "budget        -0.020426 -0.061440 -0.017428 -0.042310 -0.010296 -0.014037   \n",
       "popularity    -0.014369 -0.042806 -0.012678 -0.036654 -0.008887  0.001982   \n",
       "revenue       -0.014548 -0.052142 -0.012063 -0.028961 -0.007292 -0.010028   \n",
       "runtime       -0.005948 -0.009611  0.001223  0.088681  0.021182  0.000999   \n",
       "vote_average   0.031059  0.034359  0.027420 -0.004386  0.004925  0.022386   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000417 -0.001755 -0.000361 -0.000910 -0.000208 -0.000295   \n",
       "vi            -0.000417 -0.001755 -0.000361 -0.000910 -0.000208 -0.000295   \n",
       "xx            -0.000417 -0.001755 -0.000361 -0.000910 -0.000208 -0.000295   \n",
       "zh            -0.002171 -0.009146 -0.001880 -0.004739 -0.001085 -0.001535   \n",
       "release_month  0.001721 -0.013557  0.001490  0.005695 -0.020216  0.010160   \n",
       "\n",
       "                     is        it        ja        ko        ky        nb  \\\n",
       "budget        -0.010296 -0.034637 -0.020954 -0.017369 -0.010296 -0.009056   \n",
       "popularity    -0.009018 -0.006252  0.007573 -0.003024 -0.009716 -0.003862   \n",
       "revenue       -0.007292 -0.026314 -0.005769 -0.016748 -0.007292 -0.006923   \n",
       "runtime       -0.014011  0.016174  0.040000  0.035756  0.017983 -0.002493   \n",
       "vote_average   0.009771  0.042444  0.046424  0.023299 -0.073816  0.007348   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000208 -0.000780 -0.000834 -0.000692 -0.000208 -0.000208   \n",
       "vi            -0.000208 -0.000780 -0.000834 -0.000692 -0.000208 -0.000208   \n",
       "xx            -0.000208 -0.000780 -0.000834 -0.000692 -0.000208 -0.000208   \n",
       "zh            -0.001085 -0.004066 -0.004348 -0.003603 -0.001085 -0.001085   \n",
       "release_month  0.005075  0.009992  0.023500 -0.013683  0.005075  0.013506   \n",
       "\n",
       "                     nl        no        pl        ps        pt        ro  \\\n",
       "budget        -0.015370 -0.010013 -0.009531 -0.010280 -0.029709 -0.014349   \n",
       "popularity    -0.015181 -0.004668 -0.003734 -0.009095 -0.013875 -0.010713   \n",
       "revenue       -0.013403 -0.007116 -0.006343 -0.007292 -0.020822 -0.010239   \n",
       "runtime        0.014534 -0.010172 -0.017210 -0.015291  0.005749  0.001451   \n",
       "vote_average   0.026212  0.000079  0.012193  0.002502  0.010746 -0.041865   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000417 -0.000208 -0.000208 -0.000208 -0.000625 -0.000295   \n",
       "vi            -0.000417 -0.000208 -0.000208 -0.000208 -0.000625 -0.000295   \n",
       "xx            -0.000417 -0.000208 -0.000208 -0.000208 -0.000625 -0.000295   \n",
       "zh            -0.002171 -0.001085 -0.001085 -0.001085 -0.003258 -0.001535   \n",
       "release_month -0.000387 -0.024431  0.013506 -0.007570 -0.018511  0.001217   \n",
       "\n",
       "                     ru        sl        sv        ta        te        th  \\\n",
       "budget        -0.017743 -0.010296 -0.019069 -0.014563  0.003881 -0.014870   \n",
       "popularity    -0.020978 -0.009724 -0.014358 -0.013556 -0.002378 -0.011212   \n",
       "revenue       -0.021410 -0.007292 -0.016311 -0.010313  0.001571 -0.010784   \n",
       "runtime        0.020885 -0.015291  0.017325  0.050322  0.033340  0.025980   \n",
       "vote_average   0.010502 -0.001132  0.026193 -0.004171  0.017039 -0.002661   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "tr            -0.000692 -0.000208 -0.000466 -0.000295 -0.000208 -0.000361   \n",
       "vi            -0.000692 -0.000208 -0.000466 -0.000295 -0.000208 -0.000361   \n",
       "xx            -0.000692 -0.000208 -0.000466 -0.000295 -0.000208 -0.000361   \n",
       "zh            -0.003603 -0.001085 -0.002428 -0.001535 -0.001085 -0.001880   \n",
       "release_month -0.006050  0.009291  0.015126  0.004198  0.000860  0.018530   \n",
       "\n",
       "                     tr        vi        xx        zh  release_month  \n",
       "budget        -0.006752 -0.009836 -0.006043 -0.012974       0.050281  \n",
       "popularity    -0.009087 -0.009719 -0.006786 -0.029546       0.046648  \n",
       "revenue       -0.007292 -0.007235 -0.002394 -0.018720       0.055232  \n",
       "runtime        0.009664  0.017983 -0.004413  0.030598       0.157606  \n",
       "vote_average  -0.021726 -0.013246  0.012193  0.013037       0.108011  \n",
       "...                 ...       ...       ...       ...            ...  \n",
       "tr             1.000000 -0.000208 -0.000208 -0.001085      -0.020216  \n",
       "vi            -0.000208  1.000000 -0.000208 -0.001085      -0.020216  \n",
       "xx            -0.000208 -0.000208  1.000000 -0.001085       0.021936  \n",
       "zh            -0.001085 -0.001085 -0.001085  1.000000      -0.000398  \n",
       "release_month -0.020216 -0.020216  0.021936 -0.000398       1.000000  \n",
       "\n",
       "[63 rows x 63 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the correlations as a table\n",
    "# revenue and budget have a high correlation which intuitively may make sense more spending = more resources for a better movie\n",
    "# popularity and revenue have a high correlation, more people watch a movie the higher its revenue\n",
    "new_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9d20fede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x257e51bd790>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"budget\"])\n",
    "#sns.histplot(df[\"budget\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4f9553aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x257e5132af0>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"revenue\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "402b0952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x257e200ac70>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"popularity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c7975423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x257e5182c10>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"vote_count\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e85e30",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3271ba61",
   "metadata": {},
   "source": [
    "#### 7. Scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f717676e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will use a robust scaler in order to not be affected by outliers for movies which may have huge budgets or huge vote counts\n",
    "# from the above graphs we can see that the columns do not appear to have a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "3e129883",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initiate the scaler\n",
    "robust = RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "17ba5260",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit transform into a new dataframe\n",
    "new_df2 = pd.DataFrame(robust.fit_transform(new_df),columns = new_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "4d392484",
   "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>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Foreign</th>\n",
       "      <th>History</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>ScienceFiction</th>\n",
       "      <th>TVMovie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>ar</th>\n",
       "      <th>cn</th>\n",
       "      <th>cs</th>\n",
       "      <th>da</th>\n",
       "      <th>de</th>\n",
       "      <th>el</th>\n",
       "      <th>en</th>\n",
       "      <th>es</th>\n",
       "      <th>fa</th>\n",
       "      <th>fr</th>\n",
       "      <th>he</th>\n",
       "      <th>hi</th>\n",
       "      <th>hu</th>\n",
       "      <th>id</th>\n",
       "      <th>is</th>\n",
       "      <th>it</th>\n",
       "      <th>ja</th>\n",
       "      <th>ko</th>\n",
       "      <th>ky</th>\n",
       "      <th>nb</th>\n",
       "      <th>nl</th>\n",
       "      <th>no</th>\n",
       "      <th>pl</th>\n",
       "      <th>ps</th>\n",
       "      <th>pt</th>\n",
       "      <th>ro</th>\n",
       "      <th>ru</th>\n",
       "      <th>sl</th>\n",
       "      <th>sv</th>\n",
       "      <th>ta</th>\n",
       "      <th>te</th>\n",
       "      <th>th</th>\n",
       "      <th>tr</th>\n",
       "      <th>vi</th>\n",
       "      <th>xx</th>\n",
       "      <th>zh</th>\n",
       "      <th>release_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.663265</td>\n",
       "      <td>5.811961</td>\n",
       "      <td>29.797827</td>\n",
       "      <td>2.484211</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>16.931918</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.270408</td>\n",
       "      <td>5.332045</td>\n",
       "      <td>10.135958</td>\n",
       "      <td>2.778947</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>6.243777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.867347</td>\n",
       "      <td>3.992000</td>\n",
       "      <td>9.271493</td>\n",
       "      <td>1.894737</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>6.193997</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.994898</td>\n",
       "      <td>4.200627</td>\n",
       "      <td>11.469795</td>\n",
       "      <td>2.610526</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>12.987555</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>1.310300</td>\n",
       "      <td>2.851554</td>\n",
       "      <td>1.221053</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>2.765007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4797</th>\n",
       "      <td>-0.377041</td>\n",
       "      <td>0.056840</td>\n",
       "      <td>-0.184397</td>\n",
       "      <td>-0.926316</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.003660</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4798</th>\n",
       "      <td>-0.382423</td>\n",
       "      <td>-0.519114</td>\n",
       "      <td>-0.206362</td>\n",
       "      <td>-0.757895</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>-0.337482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4799</th>\n",
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       "      <td>-0.206362</td>\n",
       "      <td>0.715789</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>-0.336018</td>\n",
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       "    <tr>\n",
       "      <th>4800</th>\n",
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       "      <td>-0.206362</td>\n",
       "      <td>-0.210526</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4802 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        budget  popularity    revenue   runtime  vote_average  vote_count  \\\n",
       "0     5.663265    5.811961  29.797827  2.484211      0.833333   16.931918   \n",
       "1     7.270408    5.332045  10.135958  2.778947      0.583333    6.243777   \n",
       "2     5.867347    3.992000   9.271493  1.894737      0.083333    6.193997   \n",
       "3     5.994898    4.200627  11.469795  2.610526      1.166667   12.987555   \n",
       "4     6.250000    1.310300   2.851554  1.221053     -0.083333    2.765007   \n",
       "...        ...         ...        ...       ...           ...         ...   \n",
       "4797 -0.377041    0.056840  -0.184397 -0.926316      0.333333    0.003660   \n",
       "4798 -0.382423   -0.519114  -0.206362 -0.757895     -0.250000   -0.337482   \n",
       "4799 -0.382653   -0.485221  -0.206362  0.715789      0.666667   -0.336018   \n",
       "4800 -0.382653   -0.510050  -0.206362 -0.210526     -0.416667   -0.334553   \n",
       "4801 -0.382653   -0.464705  -0.206362 -0.547368      0.083333   -0.321376   \n",
       "\n",
       "      Action  Adventure  Animation  Comedy  Crime  Documentary  Drama  Family  \\\n",
       "0        1.0        1.0        0.0     0.0    0.0          0.0    0.0     0.0   \n",
       "1        1.0        1.0        0.0     0.0    0.0          0.0    0.0     0.0   \n",
       "2        1.0        1.0        0.0     0.0    1.0          0.0    0.0     0.0   \n",
       "3        1.0        0.0        0.0     0.0    1.0          0.0    1.0     0.0   \n",
       "4        1.0        1.0        0.0     0.0    0.0          0.0    0.0     0.0   \n",
       "...      ...        ...        ...     ...    ...          ...    ...     ...   \n",
       "4797     1.0        0.0        0.0     0.0    1.0          0.0    0.0     0.0   \n",
       "4798     0.0        0.0        0.0     1.0    0.0          0.0    0.0     0.0   \n",
       "4799     0.0        0.0        0.0     1.0    0.0          0.0    1.0     0.0   \n",
       "4800     0.0        0.0        0.0     0.0    0.0          0.0    0.0     0.0   \n",
       "4801     0.0        0.0        0.0     0.0    0.0          1.0    0.0     0.0   \n",
       "\n",
       "      Fantasy  Foreign  History  Horror  Music  Mystery  Romance  \\\n",
       "0         1.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "1         1.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "2         0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "3         0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "4         0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "...       ...      ...      ...     ...    ...      ...      ...   \n",
       "4797      0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "4798      0.0      0.0      0.0     0.0    0.0      0.0      1.0   \n",
       "4799      0.0      0.0      0.0     0.0    0.0      0.0      1.0   \n",
       "4800      0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "4801      0.0      0.0      0.0     0.0    0.0      0.0      0.0   \n",
       "\n",
       "      ScienceFiction  TVMovie  Thriller  War  Western   ar   cn   cs   da  \\\n",
       "0                1.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "1                0.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "2                0.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "3                0.0      0.0       1.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "4                1.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "...              ...      ...       ...  ...      ...  ...  ...  ...  ...   \n",
       "4797             0.0      0.0       1.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "4798             0.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "4799             0.0      1.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "4800             0.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "4801             0.0      0.0       0.0  0.0      0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       de   el   en   es   fa   fr   he   hi   hu   id   is   it   ja   ko  \\\n",
       "0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4797  0.0  0.0 -1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4798  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4799  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4800  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4801  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       ky   nb   nl   no   pl   ps   pt   ro   ru   sl   sv   ta   te   th  \\\n",
       "0     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4797  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4798  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4799  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4800  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4801  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       tr   vi   xx   zh  release_month  \n",
       "0     0.0  0.0  0.0  0.0       0.833333  \n",
       "1     0.0  0.0  0.0  0.0      -0.333333  \n",
       "2     0.0  0.0  0.0  0.0       0.500000  \n",
       "3     0.0  0.0  0.0  0.0       0.000000  \n",
       "4     0.0  0.0  0.0  0.0      -0.666667  \n",
       "...   ...  ...  ...  ...            ...  \n",
       "4797  0.0  0.0  0.0  0.0       0.333333  \n",
       "4798  0.0  0.0  0.0  0.0       0.833333  \n",
       "4799  0.0  0.0  0.0  0.0       0.500000  \n",
       "4800  0.0  0.0  0.0  0.0      -0.333333  \n",
       "4801  0.0  0.0  0.0  0.0       0.166667  \n",
       "\n",
       "[4802 rows x 63 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0534397",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3c737a9d",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c608b9",
   "metadata": {},
   "source": [
    "### 8. Using PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e64840ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will use pca for dimensionality reduction creating 5 columns that combine our current columns\n",
    "pca = PCA(n_components=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e12da0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=5)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we fit the pca into our new dataframe\n",
    "pca.fit(new_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "e6d3700e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8117788414100637"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we see the sum of variance explained by all components in the data\n",
    "pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "464bb743",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we create a new dataframe where we have fit and transformed the original data using pca\n",
    "pca_df2 = pd.DataFrame(pca.fit_transform(new_df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acd48a15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "ceeb0157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8117788414100641"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6b26f4",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83811842",
   "metadata": {},
   "source": [
    "### 9. Hierarchical clustering, Inertia and Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5788bf7f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c902c029",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform hierarchical clustering on your reduced data, this will allow us to see the ideal number of clusters\n",
    "Z = linkage(pca_df2, method='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d2ab2f08",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# applying a line at y value =120 we can see three distinct clusters forming with the maximum distance between them and their further subsections\n",
    "dendrogram(Z, truncate_mode='lastp', p=30)\n",
    "plt.axhline(y = 120, color = 'r', linestyle = '-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "46ec5db7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb12ff36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can also use the kmeans elbow method and silhouette scores to attempt to find the perfect clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a0f94387",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we create an empty list both for the inertias and the silhouettes. \n",
    "#Inertia will count the intra cluster distance while the silhouette will count a combination of both intra and inter cluster distance\n",
    "# we initiate a loop that will have values of k being 2-7\n",
    "# we fit the kmeans algorithm with number of clusters being the iteration value\n",
    "# we append the inertia and the silhouette score to the list\n",
    "inertias = []\n",
    "silhouettes = []\n",
    "for k in range(2, 8):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0).fit(pca_df2)\n",
    "    inertias.append(kmeans.inertia_)\n",
    "    silhouettes.append(silhouette_score(pca_df2, kmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8bfb7939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Silhouette Method')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mTZqEZ555BpcuXcKCBQuq3etq417g6ekJAPjhhx9gYWEBExMTuLm5wcbGBv/73//w4osvIjc3F88++yzs7e2Rk5ODo0ePIicnB4sWLXqgczB69OhqlSdv17p1a7z//vt466238M8//2DQoEGwsrJCVlYWDh48CHNzc90IFC8vLwDAxx9/jMGDB0Mul8Pb2xsKhaLGMUVGRmLgwIF44oknMHv2bCgUCixcuBAnTpzAmjVrdA9o3377bWzcuBFPPvkk/u///g9mZmb49ttva/weHTVgUlcVIWoM7lVlEYC4ePFiXdu7VVlcsWKFOG3aNNHOzk5UKpVinz59xMOHD1f7rg0bNoi+vr6iiYmJaG5uLvbv3188cOBAtXZ5eXmiTCYTzc3NxbKyMt36VatWiQDEp59+uka/7dYqi/dyp+pYRUVF4ttvvy26u7uLCoVCVKlUopeXlzhjxgy9KlDJycliQECAaGZmplftsOq83l6dq+qc3VpxS6PRiB9//LHYvn170djYWLS1tRVHjx4tpqWl6e2r1WrFyMhI0dXVVVQoFKK3t7e4adOmO1YqIyKSwtq1a8VRo0aJ7dq1E5s1ayYaGxuLLVu2FMPDw8VTp07ptX2QKou1fS1dsGCB2KZNG9HExETs3r27uHv37jvG86j3AlEUxS+//FJ0c3MT5XK5CEBcunSpbtu+ffvEIUOGiNbW1qKxsbHYokULcciQIeKvv/56z/N8a5XFe7lbpcQNGzaITzzxhGhpaSkqlUqxVatW4rPPPivu3LlT16a0tFScMGGCaGdnJwqCoFftEIA4efLkase9UwXL/fv3i08++aRobm4umpqain5+fuKmTZuq7XvgwAHRz89PVCqVoqOjozhnzhzxhx9+YJXFRk4QxZsTRBAREREREVG9Ytl7IiIiIiIiiTAhIyIiIiIikggTMiIiIiIiIokwISMiIiIiIpIIEzIiIiIiIiKJMCEjIiIiIiKSCCeGrkVarRbp6emwsLDQTeJHRER1TxRFFBYWwtnZGTIZnzVW4X2JiEg6Nb03MSGrRenp6XB1dZU6DCIig5WWlgYXFxepw2gweF8iIpLe/e5NTMhqkYWFBYDKk25paSlxNEREhqOgoACurq666zBV4n2JiEg6Nb03MSGrRVXDQSwtLXnjIyKSAIfl6eN9iYhIeve7N3GgPRERERERkUQkTcgWLVoEb29v3ZM7f39/bNu2TbddFEW89957cHZ2hqmpKfr164eTJ0/qHaO0tBRTp06Fra0tzM3NMXToUFy+fFmvTV5eHsLDw6FSqaBSqRAeHo78/Hy9NqmpqQgJCYG5uTlsbW0xbdo0lJWV1dlvJyIiIiIikjQhc3Fxwfz583H48GEcPnwYTz75JIYNG6ZLuhYsWIDPP/8c33zzDQ4dOgRHR0cMHDgQhYWFumNMnz4d69evR1RUFGJiYlBUVITg4GBoNBpdm1GjRiE5ORnR0dGIjo5GcnIywsPDdds1Gg2GDBmC4uJixMTEICoqCuvWrcOsWbPq72QQEREREZHBEURRFKUO4lbW1tb45JNPMG7cODg7O2P69Ol44403AFT2hjk4OODjjz/GxIkToVarYWdnhxUrViAsLAzAvxWltm7diqCgIKSkpMDDwwPx8fHw9fUFAMTHx8Pf3x+nT5+Gu7s7tm3bhuDgYKSlpcHZ2RkAEBUVhbFjxyI7O7vG4+4LCgqgUqmgVqs5Vp+IqB7x+ntnPC9ERNKp6TW4wbxDptFoEBUVheLiYvj7++PChQvIzMxEYGCgro1SqUTfvn0RGxsLAEhMTER5ebleG2dnZ3h6euraxMXFQaVS6ZIxAPDz84NKpdJr4+npqUvGACAoKAilpaVITEy8a8ylpaUoKCjQW4iIiIiIiGpK8oTs+PHjaNasGZRKJV555RWsX78eHh4eyMzMBAA4ODjotXdwcNBty8zMhEKhgJWV1T3b2NvbV/tee3t7vTa3f4+VlRUUCoWuzZ1ERkbq3ktTqVSc64WIiIiIiB6I5AmZu7s7kpOTER8fj1dffRUvvvgiTp06pdt+e5lIURTvWzry9jZ3av8wbW43d+5cqNVq3ZKWlnbPuIiIiIiIiG4leUKmUCjw2GOPoXv37oiMjETnzp3x1VdfwdHREQCq9VBlZ2frerMcHR1RVlaGvLy8e7bJysqq9r05OTl6bW7/nry8PJSXl1frObuVUqnUVYjkHC9ERERERPSgJE/IbieKIkpLS+Hm5gZHR0fs2LFDt62srAz79u1Dr169AAA+Pj4wNjbWa5ORkYETJ07o2vj7+0OtVuPgwYO6NgkJCVCr1XptTpw4gYyMDF2b7du3Q6lUwsfHp05/LxERERERGS4jKb/8P//5DwYPHgxXV1cUFhYiKioKe/fuRXR0NARBwPTp0/HRRx+hXbt2aNeuHT766COYmZlh1KhRAACVSoXx48dj1qxZsLGxgbW1NWbPng0vLy8MGDAAANCxY0cMGjQIERER+P777wEAL7/8MoKDg+Hu7g4ACAwMhIeHB8LDw/HJJ58gNzcXs2fPRkRERL31emm0Ig5eyEV2YQnsLUzQ080actm9h2YSERHVFd6XiIjqh6QJWVZWFsLDw5GRkQGVSgVvb29ER0dj4MCBAIDXX38dN27cwKRJk5CXlwdfX19s374dFhYWumN88cUXMDIywogRI3Djxg30798fy5Ytg1wu17VZtWoVpk2bpqvGOHToUHzzzTe67XK5HFu2bMGkSZMQEBAAU1NTjBo1Cp9++mm9nIfoExmYt+kUMtQlunVOKhO8G+KBQZ5O9RIDERFRFd6XiIjqT4Obh6wxe5j5XqJPZODVlUdw+z9C1TPIRaO78eZHRHQfnG/rznhfIiKSTqObh8wQabQi5m06Ve2mB0C3bt6mU9BomTMTEVHd432JiKj+MSGT0MELuXrDQW4nAshQl+Dghdz6C4qIiAwW70tERPWPCZmEsgvvftN7mHZERESPgvclIqL6x4RMQvYWJrXajoiI6FHwvkREVP+YkEmop5s1nFQmuFsRYQGVVa16ulnXZ1hERGSgeF8iIqp/TMgkJJcJeDfEAwDuevN7N8SD874QEVG94H2JiKj+MSGT2CBPJywa3Q2OKv3hH2YKOUsLExFRvbvbfQkA3hjcgfclIqJaJunE0FRpkKcTBno44uCFXMT8nYNv95yHAKB3OzupQyMiIgN0630pu7AEvxxKw4Hz13Dp2nWpQyMianLYQ9ZAyGUC/NvaYHagO9rYmaO4TIP1Ry5LHRYRERmoqvvSsC4tMOXJdgCAjclXUFxaIXFkRERNCxOyBkYQBIT7tQIALI+7BFHk5JtERCQtvzbWcLOtfFi4+Vi61OEQETUpTMgaoGd8XGCmkONcdhESOPkmERFJTBAEhPVwBQCsPpgmcTRERE0LE7IGyNLEGKFdWwAAVsRdkjgaIiIi4FkfFxjLBRxNy0dKRoHU4RARNRlMyBqoqmGLf57MRFZBicTREBGRobNtpsRADwcAQNTBVImjISJqOpiQNVAdnSzRo7UVKrQiVifwxkdERNIb2aMlAGB90hXcKNNIHA0RUdPAhKwBC/dvDQBYczAV5RqttMEQEZHB6/2YLVysTFFQUoGtxzOkDoeIqElgQtaADerkCNtmSmQXlmL7ySypwyEiIgMnkwkI615Z3CPqEEdvEBHVBiZkDZjCSIZRPStvfMvjLkobDBEREYDnurtCLhNw6GIe/s4ulDocIqJGjwlZA/e8b0vIZQISLuTiTCZvfEREJC1HlQmecLcHAESxBD4R0SNjQtbAOalMMbBjZVWrFfEXpQ2GiIgIwPM3R2+sO3IZpRUs7kFE9CiYkDUCY/wrS+CvP3IFhSXlEkdDRESGrm97OzhamiDvejn+5DvORESPhAlZI+Df1gZt7cxRXKbB+qQrUodDREQGzkguw4geN4t7cE4yIqJHwoSsERAEQTdR9PK4SxBFUeKIiIjI0I3o7gJBAGLPX8Ola8VSh0NE1GgxIWsknvZxgZlCjr+zixD3zzWpwyEiIgPnYmWGx9vZAQCiDrG4BxHRw2JC1khYmhhjeNcWAICV8ZckjoaIiOjf4h6/Hr6Mco1W4miIiBonJmSNyBj/1gCAP09mIVNdIm0wRERk8Pp3dIBtMyWuFpViVwqLexARPQwmZI2Iu6MFerpZQ6MVsZovURMRkcSM5TI8190FALCGc5IRET0UJmSNTFVxjzUHU1FWweEhREQkrZE3qy3+dS4Hl/OuSxwNEVHjw4SskQnq5Ag7CyVyCkvx58lMqcMhIiID18rGHL3a2kAUgV9Y3IOI6IExIWtkFEYyPN+zJQBgBYt7EBFRAzDy5n3pl8OXUcHiHkRED4QJWSM0qmdLyGUCDl7IxenMAqnDISIiAxfUyQFWZsbILCjBvrM5UodDRNSoMCFrhBxVJgj0cAAArIhjLxkREUlLaSTHM91Y3IOI6GEwIWukwv0ri3usT7qCgpJyiaMhIiJDN/LmnGR7zmRzahYiogfAhKyR8m9jg3b2zXC9TIP1R65IHQ4RERm4x+wt0KO1FTRaEb8eZi8ZEVFNMSFrpARB0PWSrYi/BFEUJY6IiIgMXVXRqbWH06DV8r5ERFQTkiZkkZGR6NGjBywsLGBvb4/Q0FCcOXNGr01RURGmTJkCFxcXmJqaomPHjli0aJFem9LSUkydOhW2trYwNzfH0KFDcfnyZb02eXl5CA8Ph0qlgkqlQnh4OPLz8/XapKamIiQkBObm5rC1tcW0adNQVlZWJ7+9Ngzv2gLmCjn+zi5C3PlrUodDREQG7ikvJ1iaGOFy3g3E/H1V6nCIiBoFSROyffv2YfLkyYiPj8eOHTtQUVGBwMBAFBcX69rMmDED0dHRWLlyJVJSUjBjxgxMnToVf/zxh67N9OnTsX79ekRFRSEmJgZFRUUIDg6GRqPRtRk1ahSSk5MRHR2N6OhoJCcnIzw8XLddo9FgyJAhKC4uRkxMDKKiorBu3TrMmjWrfk7GQ7AwMcbwbi0AAMtZ3IOIiCRmYizH8K6V96WoQ6kSR0NE1DgIYgMa65aTkwN7e3vs27cPjz/+OADA09MTYWFheOedd3TtfHx88NRTT+G///0v1Go17OzssGLFCoSFhQEA0tPT4erqiq1btyIoKAgpKSnw8PBAfHw8fH19AQDx8fHw9/fH6dOn4e7ujm3btiE4OBhpaWlwdnYGAERFRWHs2LHIzs6GpaXlfeMvKCiASqWCWq2uUfvacDarEIFf/AW5TEDMG0/ASWVaL99LRNSQSHH9bQykOC8pGQUY/NV+GMkExM3tDzsLZb18LxFRQ1PTa3CDeodMrVYDAKytrXXrevfujY0bN+LKlSsQRRF79uzB2bNnERQUBABITExEeXk5AgMDdfs4OzvD09MTsbGxAIC4uDioVCpdMgYAfn5+UKlUem08PT11yRgABAUFobS0FImJiXeMt7S0FAUFBXpLfWvvYAFfN2totCLWJPBpJBERSaujkyU6uzZHhVbEuiOX778DEZGBazAJmSiKmDlzJnr37g1PT0/d+q+//hoeHh5wcXGBQqHAoEGDsHDhQvTu3RsAkJmZCYVCASsrK73jOTg4IDMzU9fG3t6+2nfa29vrtXFwcNDbbmVlBYVCoWtzu8jISN07aSqVCq6urg9/Ah7BGP/WAIDVB9NQVqGVJAYiIqIqo26WwF97KI1Fp4iI7qPBJGRTpkzBsWPHsGbNGr31X3/9NeLj47Fx40YkJibis88+w6RJk7Bz5857Hk8URQiCoPt8638/SptbzZ07F2q1WrekpUlT5jewkwPsLZS4WlSK6JN3Th6JiIjqS7C3M8wVcly4Woz4f3KlDoeIqEFrEAnZ1KlTsXHjRuzZswcuLi669Tdu3MB//vMffP755wgJCYG3tzemTJmCsLAwfPrppwAAR0dHlJWVIS8vT++Y2dnZuh4vR0dHZGVlVfvenJwcvTa394Tl5eWhvLy8Ws9ZFaVSCUtLS71FCsZyma7U8Iq4i5LEQEREVMVcaYShXVjcg4ioJiRNyERRxJQpU/D7779j9+7dcHNz09teXl6O8vJyyGT6Ycrlcmi1lUPzfHx8YGxsjB07dui2Z2Rk4MSJE+jVqxcAwN/fH2q1GgcPHtS1SUhIgFqt1mtz4sQJZGRk6Nps374dSqUSPj4+tfvD68Ao35Ywkgk4dDEPKRn1/y4bERHRrZ6/OWxx2/FM5BU33ClkiIikJmlCNnnyZKxcuRKrV6+GhYUFMjMzkZmZiRs3bgAALC0t0bdvX8yZMwd79+7FhQsXsGzZMixfvhzDhw8HAKhUKowfPx6zZs3Crl27kJSUhNGjR8PLywsDBgwAAHTs2BGDBg1CREQE4uPjER8fj4iICAQHB8Pd3R0AEBgYCA8PD4SHhyMpKQm7du3C7NmzERER0SgqdjlYmiCokyOAyomiiYiIpOTVQoVOzpYo02jxe9IVqcMhImqwJE3IFi1aBLVajX79+sHJyUm3rF27VtcmKioKPXr0wAsvvAAPDw/Mnz8fH374IV555RVdmy+++AKhoaEYMWIEAgICYGZmhk2bNkEul+varFq1Cl5eXggMDERgYCC8vb2xYsUK3Xa5XI4tW7bAxMQEAQEBGDFiBEJDQ3VDIxuD0X6tAAAbkq6goKRc4miIiMiQCYKAkTeH00cdTGVxDyKiu2hQ85A1dlLPgyOKIoK+/Atns4rwbogHXgpwu/9ORERNgNTX34ZK6vNSUFIO3w934Ua5Bute9YdPK+v770RE1EQ0ynnI6NEIgoDwm71kK+Iv8WkkERFJytLEGEO8nQAAqxOkqURMRNTQMSFrYoZ3c0EzpRH+ySlG7PlrUodDREQGrqoK8Jbj6VDf4HB6IqLbMSFrYpopjfB0t8pSw8tZAp+IiCTWrWVztHdohpJyLTYms7gHEdHtmJA1QVXFPXacykJ6/g2JoyEiIkMmCAJG9qjsJVt9MI3D6YmIbsOErAlq72ABvzbW0IrA6gROyElERNJ6ulsLKIxkSMkowLHLaqnDISJqUJiQNVFj/FsDAKIOpaKsQittMEREZNCamykw2LNyrsyoQ3xQSER0KyZkTdRADwc4WCpxtagM205kSB0OEREBWLhwIdzc3GBiYgIfHx/s37//nu1LS0vx1ltvoVWrVlAqlWjbti2WLFlST9HWrqriHhuT01FcWiFxNEREDQcTsibKWC7T3fxWxF2SOBoiIlq7di2mT5+Ot956C0lJSejTpw8GDx6M1NS79xiNGDECu3btwk8//YQzZ85gzZo16NChQz1GXXt83azRxtYcxWUabDqaLnU4REQNBhOyJmxUz5Ywkgk4fCkPp9ILpA6HiMigff755xg/fjwmTJiAjh074ssvv4SrqysWLVp0x/bR0dHYt28ftm7digEDBqB169bo2bMnevXqVc+R1w5BEBDWwxUAsOYQ5yQjIqrChKwJs7c0QdDNMfsr4i9KGwwRkQErKytDYmIiAgMD9dYHBgYiNjb2jvts3LgR3bt3x4IFC9CiRQu0b98es2fPxo0bd6+eW1paioKCAr2lIXnGxwXGcgFH0/L5oJCI6CYmZE3cmJsl8DckcUJOIiKpXL16FRqNBg4ODnrrHRwckJmZecd9/vnnH8TExODEiRNYv349vvzyS/z222+YPHnyXb8nMjISKpVKt7i6utbq73hUts2UCPRgcQ8iolsxIWvierpZw93BAjfKNViXeFnqcIiIDJogCHqfRVGstq6KVquFIAhYtWoVevbsiaeeegqff/45li1bdtdesrlz50KtVuuWtLSGNzRwZM/KJHF90hXcKNNIHA0RkfSYkDVxgiBgtH9lL9nK+EvQajkhJxFRfbO1tYVcLq/WG5adnV2t16yKk5MTWrRoAZVKpVvXsWNHiKKIy5fv/IBNqVTC0tJSb2loAtrawtXaFIUlFdh6nFWAiYiYkBmA4V1boJnSCP9cLcaB81elDoeIyOAoFAr4+Phgx44deut37Nhx1yIdAQEBSE9PR1FRkW7d2bNnIZPJ4OLiUqfx1iWZTEBY95vFPQ5y2CIRERMyA9BMaYRnurUAwBL4RERSmTlzJn788UcsWbIEKSkpmDFjBlJTU/HKK68AqBxuOGbMGF37UaNGwcbGBi+99BJOnTqFv/76C3PmzMG4ceNgamoq1c+oFc91d4X8ZhXgc1mFUodDRCQpJmQGIvzmsMWdKVm4kn/3Cl1ERFQ3wsLC8OWXX+L9999Hly5d8Ndff2Hr1q1o1ary+pyRkaE3J1mzZs2wY8cO5Ofno3v37njhhRcQEhKCr7/+WqqfUGscLE3wZAd7AEAUS+ATkYETRFHkS0W1pKCgACqVCmq1ukGO23/+h3jE/XMNk59oizlBjXNiUSKiO2no11+pNOTzsvt0FsYtOwwrM2PEze0PE2O51CEREdWqml6D2UNmQMbc7CWLOpiG0gpWtiIiIun0bW8PJ5UJ8q6X48+Tdy79T0RkCJiQGZCBHg5wtDTBteIybDvOmx8REUlHLhPw3M3iHlEHOWyRiAwXEzIDYiSXYZRvSwDAingW9yAiImmF9XCFIABx/1zDxavFUodDRCQJJmQGZmRPVxjJBCReysPJdLXU4RARkQFr0dwUfdvbAWBxDyIyXEzIDIy9hQkGeToCYAl8IiKS3sgelSM3fktMQ1mFVuJoiIjqHxMyAzTGvzUAYEPyFaivl0sbDBERGbT+He1h20yJq0Vl2JWSJXU4RET1jgmZAerR2godHC1QUq7Fb0cuSx0OEREZMGO5DCO6uwAA1nDYIhEZICZkBkgQBN1E0SvjL0Gr5VR0REQknbAeldUW95/LQVrudYmjISKqX0zIDFRolxawUBrhwtVixPx9VepwiIjIgLWyMUfAYzYQReDXw+wlIyLDwoTMQJkrjfCMT+UQkeUs7kFERBKrKu6x9nAaKjQs7kFEhoMJmQEb7Vc5bHH36SxczuMQESIikk5gJwdYmRkjq6AUe8/kSB0OEVG9YUJmwB6zb4aAx2ygFYHVCalSh0NERAZMaSTHszdHbkQd4j2JiAwHEzIDF36zl2ztoTSUVmgkjoaIiAxZ2M1hi7tPZyNTXSJxNERE9YMJmYEb0NEBTioTXCsuw9bjGVKHQ0REBuwx+2bo2doaWhH4hcU9iMhAMCEzcEZyGUb1rHwiyeIeREQktZE9K0vgrz2UxmlZiMggMCEjjOzZEsZyAUmp+ThxRS11OEREZMCe8nKCpYkRruTfwH5Oy0JEBoAJGcHOQonBnk4AgBXsJSMiIgmZGMvxdLebxT0OsrgHETV9TMgIABDuX1nc44+jV6C+Xi5xNEREZMiqhi3uOJWFnMJSiaMhIqpbkiZkkZGR6NGjBywsLGBvb4/Q0FCcOXOmWruUlBQMHToUKpUKFhYW8PPzQ2rqv0/NSktLMXXqVNja2sLc3BxDhw7F5cuX9Y6Rl5eH8PBwqFQqqFQqhIeHIz8/X69NamoqQkJCYG5uDltbW0ybNg1lZWV18tsbmu6trNDB0QIl5Vr8msgXqYmISDodHC3RxbU5KrQifku8fP8diIgaMUkTsn379mHy5MmIj4/Hjh07UFFRgcDAQBQXF+vanD9/Hr1790aHDh2wd+9eHD16FO+88w5MTEx0baZPn47169cjKioKMTExKCoqQnBwMDSaf8u4jxo1CsnJyYiOjkZ0dDSSk5MRHh6u267RaDBkyBAUFxcjJiYGUVFRWLduHWbNmlU/J0NigiBgjH9rAMCK+Et8kZqIiCT1vK64RypEkfckImq6BLEBXeVycnJgb2+Pffv24fHHHwcAjBw5EsbGxlixYsUd91Gr1bCzs8OKFSsQFhYGAEhPT4erqyu2bt2KoKAgpKSkwMPDA/Hx8fD19QUAxMfHw9/fH6dPn4a7uzu2bduG4OBgpKWlwdnZGQAQFRWFsWPHIjs7G5aWlveNv6CgACqVCmq1ukbtG5rrZRXw/XAXCksr8PO4nujb3k7qkIiIaqSxX3/rSmM+L8WlFfD9aBeKSiuwOsIXvdraSh0SEdEDqek1uEG9Q6ZWV1b4s7a2BgBotVps2bIF7du3R1BQEOzt7eHr64sNGzbo9klMTER5eTkCAwN165ydneHp6YnY2FgAQFxcHFQqlS4ZAwA/Pz+oVCq9Np6enrpkDACCgoJQWlqKxMTEO8ZbWlqKgoICvaUxM1MY4RmfyhepV8RdlDYYIiIyaOZKIwztcvMB6UEOpSeipqvBJGSiKGLmzJno3bs3PD09AQDZ2dkoKirC/PnzMWjQIGzfvh3Dhw/H008/jX379gEAMjMzoVAoYGVlpXc8BwcHZGZm6trY29tX+057e3u9Ng4ODnrbraysoFAodG1uFxkZqXsnTaVSwdXV9dFOQgNQVdxj1+lspOVelzgaIiIyZM/3qJwnM/pEJnKLDeOdbiIyPA0mIZsyZQqOHTuGNWvW6NZptVoAwLBhwzBjxgx06dIFb775JoKDg/Hdd9/d83iiKEIQBN3nW//7Udrcau7cuVCr1bolLa3xP8Fra9cMvR+zhSgCqxJYbpiIiKTj5aJCJ2dLlGm0+P0Ii3sQUdPUIBKyqVOnYuPGjdizZw9cXFx0621tbWFkZAQPDw+99h07dtRVWXR0dERZWRny8vL02mRnZ+t6vBwdHZGVlVXte3NycvTa3N4TlpeXh/Ly8mo9Z1WUSiUsLS31lqagqpds7aFUlJRr7tOaiIio7jzfs7KXLOpQGot7EFGTJGlCJooipkyZgt9//x27d++Gm5ub3naFQoEePXpUK4V/9uxZtGpVmTT4+PjA2NgYO3bs0G3PyMjAiRMn0KtXLwCAv78/1Go1Dh48qGuTkJAAtVqt1+bEiRPIyMjQtdm+fTuUSiV8fHxq94c3cP072MNZZYK86+XYejzj/jsQERHVkWFdnGFqLMff2UVIvJR3/x2IiBoZSROyyZMnY+XKlVi9ejUsLCyQmZmJzMxM3LhxQ9dmzpw5WLt2LRYvXoy///4b33zzDTZt2oRJkyYBAFQqFcaPH49Zs2Zh165dSEpKwujRo+Hl5YUBAwYAqOxRGzRoECIiIhAfH4/4+HhEREQgODgY7u7uAIDAwEB4eHggPDwcSUlJ2LVrF2bPno2IiIgm0/NVU0ZyGUb5Vj6RXB53SeJoiIjIkFmYGCPY2wkAsIbFPYioCZI0IVu0aBHUajX69esHJycn3bJ27Vpdm+HDh+O7777DggUL4OXlhR9//BHr1q1D7969dW2++OILhIaGYsSIEQgICICZmRk2bdoEuVyua7Nq1Sp4eXkhMDAQgYGB8Pb21iulL5fLsWXLFpiYmCAgIAAjRoxAaGgoPv300/o5GQ1MWI+WMJYLSE7Lx/HLaqnDISIiAzby5rDFLcfTob5RLnE0RES1q0HNQ9bYNeb5Xu7ktagk/JGcjud8XPDJc52lDoeI6K6a2vW3tjSV8yKKIgZ9uR9nsgrx/rBOGOPfWuqQiIjuq1HOQ0YNy5ibxT02Hk1H/nWWGyYiImkIgoCRPSunlllzkMU9iKhpYUJGd9WtpRU6OlmitEKLXw+z3DAREUlneNcWUBjJkJJRgGMcSk9ETQgTMrorQRB0vWQrEy5Bq+UTSSIikkZzMwWe8nQEAKw5yHkyiajpYEJG9zSsizMsTIxw6dp17DuXI3U4RERkwKqKe2w8mo6i0gqJoyEiqh1MyOiezBRGeM6nctz+CpbAJyIiCfm6WaONrTmul2mw6Wi61OEQEdUKJmR0X6P9Kp9I7jmTjbTc6xJHQ0REhurW4h5RHLZIRE0EEzK6rzZ2zdCnnS1EsfJdMiIiIqk8080FxnIBRy+rcTKdxT2IqPFjQkY1Eu5XWdzjl0NpKCnXSBwNEREZKptmSgR6VBb3iDqYJnE0RESPjgkZ1Uj/jg5o0dwUedfLsflYhtThEBGRAXv+ZnGPDclXcKOMDwmJqHFjQkY1IpcJGOVbeQNcEc9hi0REJJ1ebW3gam2KwpIKbDnOh4RE1LgxIaMaC+vhCoVchqNp+Th2OV/qcIiIyEDJZAJG9qh8SMjiHkTU2DEhoxqzbabEU16V4/aXswQ+ERFJ6DkfF8hlAg5fysPZrEKpwyEiemhMyOiBhPu3BgBsOpqOvOIyaYMhIiKDZW9pgv4d7AGwuAcRNW5MyOiBdGvZHJ2cLVFaocUvh3kDJCIi6VQV9/g96TIrABNRo8WEjB6IIAi6EvgrEy5BqxUljoiIiAzV4+3t4KwyQf71cvx5MlPqcIiIHgoTMnpgw7q0gKWJEdJyb2Df2RypwyEiIgMllwl4rrsrAGANi3sQUSPFhIwemKlCrrsBLo+7KG0wRERk0Eb0cIUgAPH/5OLC1WKpwyEiemBMyOihjL45bHHv2RykXrsucTRERGSoWjQ3Rb/2dgCAqEPsJSOixocJGT0UN1tzPN7eDqIIrEpgCXwiIpLOyJvFPdYlXkZZhVbiaIiIHgwTMnpoVcU91h5OY3UrIiKSzJMd7GFnocTVojLsTMmSOhwiogfChIwe2pMd7NGiuSnyr5dj09F0qcMhIiIDZSyX4TkfFwAs7kFEjQ8TMnpocpmAF/wqh4msiOewRSIiks7IHpX3o5i/ryItl+82E1HjwYSMHklYd1co5DIcu6xGclq+1OEQEZGBamljht6P2UIUgV8Op0kdDhFRjTEho0di00yJId5OAIAVcewlIyIi6YzsWTklyy+H01ChYXEPImocmJDRIwv3ryzuselYOnKLyySOhoiIDNVADwdYmyuQVVCKPWdypA6HiKhGmJDRI+vq2hyeLSxRVqHlMBEiIpKM0kiOZ7q1AABEsbgHETUSTMjokQmCgDF+rQEAK+MvQaMVpQ2IiIgMVtWcZHvOZCNDfUPiaIiI7o8JGdWKkM7OUJka43LeDew7my11OEREZKDa2jVDTzdraEXg18OXpQ6HiOi+mJBRrTBVyHVzwCxncQ8iIpLQ8zeLe6w9lMZRG0TU4DEho1oz2q+yuMe+szm4dK1Y4miIiMhQDfZ0gqWJEa7k38D+cyzuQUQNGxMyqjWtbc3Rt70dRLHyXTIiIiIpmBjL8XS3ylEbUQdZbIqIGjYmZFSrxtwsgf/L4cu4UaaROBoiIjJUz98s7rEzJQvZhSUSR0NEdHdMyKhW9XO3R4vmplDfKMemY+lSh0NE9MgqKiqwc+dOfP/99ygsLAQApKeno6ioSOLI6F7cHS3QtWVzVGhF/JbI4h5E1HAxIaNaJZcJunfJVsRdgijyZWoiarwuXboELy8vDBs2DJMnT0ZOTuX7SAsWLMDs2bMljo7u5/kelb1kaw+lQcviHkTUQDEho1oX1sMVCiMZjl9RIzktX+pwiIge2muvvYbu3bsjLy8PpqamuvXDhw/Hrl27JIyMaiK4sxOaKY1w6dp1xP9zTepwiIjuSNKELDIyEj169ICFhQXs7e0RGhqKM2fO3LX9xIkTIQgCvvzyS731paWlmDp1KmxtbWFubo6hQ4fi8mX94Ql5eXkIDw+HSqWCSqVCeHg48vPz9dqkpqYiJCQE5ubmsLW1xbRp01BWVlZbP9dgWJsrEOztBKCyl4yIqLGKiYnB22+/DYVCobe+VatWuHLlikRRUU2ZKYwwrIszAGDNIRb3IKKGSdKEbN++fZg8eTLi4+OxY8cOVFRUIDAwEMXF1Uumb9iwAQkJCXB2dq62bfr06Vi/fj2ioqIQExODoqIiBAcHQ6P5t6jEqFGjkJycjOjoaERHRyM5ORnh4eG67RqNBkOGDEFxcTFiYmIQFRWFdevWYdasWXXz45u48JvDFjcfy0BuMZNaImqctFqt3r2kyuXLl2FhYSFBRPSgqop7/Hkik/cjImqQJE3IoqOjMXbsWHTq1AmdO3fG0qVLkZqaisTERL12V65cwZQpU7Bq1SoYGxvrbVOr1fjpp5/w2WefYcCAAejatStWrlyJ48ePY+fOnQCAlJQUREdH48cff4S/vz/8/f2xePFibN68Wdcjt337dpw6dQorV65E165dMWDAAHz22WdYvHgxCgoK6ueENCFdXJvDq4UKZRot1vKpJBE1UgMHDtQblSEIAoqKivDuu+/iqaeeki4wqjHPFip4trBEmUaL34+wuAcRNTwN6h0ytVoNALC2ttat02q1CA8Px5w5c9CpU6dq+yQmJqK8vByBgYG6dc7OzvD09ERsbCwAIC4uDiqVCr6+vro2fn5+UKlUem08PT31euCCgoJQWlpaLUGsUlpaioKCAr2FKgmCgPCbJfBXxl+Chi9TE1Ej9Pnnn2Pfvn3w8PBASUkJRo0ahdatW+PKlSv4+OOPpQ6PamjkzeIeaw6mstgUETU4DSYhE0URM2fORO/eveHp6alb//HHH8PIyAjTpk27436ZmZlQKBSwsrLSW+/g4IDMzExdG3t7+2r72tvb67VxcHDQ225lZQWFQqFrc7vIyEjdO2kqlQqurq41/8EGYGhnZzQ3M8aV/BvYczpb6nCIiB5YixYtkJycjDlz5mDixIno2rUr5s+fj6SkpDveV6hhGtbFGabGcpzPKcbhS3lSh0NEpKfBJGRTpkzBsWPHsGbNGt26xMREfPXVV1i2bBkEQXig44miqLfPnfZ/mDa3mjt3LtRqtW5JS+PQvFuZGMsxontlkro8nsU9iKhxKS8vR5s2bXDhwgW89NJL+Oabb7Bw4UJMmDBBr+Lig1i4cCHc3NxgYmICHx8f7N+//65t9+7dC0EQqi2nT59+2J9ksCxMjBHSubLY1JqDqRJHQ0Skr0EkZFOnTsXGjRuxZ88euLi46Nbv378f2dnZaNmyJYyMjGBkZIRLly5h1qxZaN26NQDA0dERZWVlyMvTf+KVnZ2t6/FydHREVlZWte/NycnRa3N7T1heXh7Ky8ur9ZxVUSqVsLS01FtI3wu+LSEIwF9nc3DxavViLUREDZWxsTFKS0sf+IHg3axduxbTp0/HW2+9haSkJPTp0weDBw9Gauq9E4QzZ84gIyNDt7Rr165W4jE0I28W99hyLAPq6+USR0NE9C9JEzJRFDFlyhT8/vvv2L17N9zc3PS2h4eH49ixY0hOTtYtzs7OmDNnDv78808AgI+PD4yNjbFjxw7dfhkZGThx4gR69eoFAPD394darcbBgwd1bRISEqBWq/XanDhxAhkZGbo227dvh1KphI+PT52dg6aulY05+ra3A1D5LhkRUWMydepUfPzxx6ioqHjkY33++ecYP348JkyYgI4dO+LLL7+Eq6srFi1adM/97O3t4ejoqFvkcvkjx2KIuro2h7uDBUortNiQzCkLiKjhMJLyyydPnozVq1fjjz/+gIWFha6HSqVSwdTUFDY2NrCxsdHbx9jYGI6OjnB3d9e1HT9+PGbNmgUbGxtYW1tj9uzZ8PLywoABAwAAHTt2xKBBgxAREYHvv/8eAPDyyy8jODhYd5zAwEB4eHggPDwcn3zyCXJzczF79mxERESw5+sRjfFvhb1ncvDL4TTMCnSHqYJ/TBBR45CQkIBdu3Zh+/bt8PLygrm5ud7233//vUbHKSsrQ2JiIt5880299YGBgbriUnfTtWtXlJSUwMPDA2+//TaeeOKJu7YtLS1FaWmp7jOLTf1LEAQ839MV7206hTUHUzHGv1Wt9X4SET0KSXvIFi1aBLVajX79+sHJyUm3rF279oGO88UXXyA0NBQjRoxAQEAAzMzMsGnTJr2niKtWrYKXlxcCAwMRGBgIb29vrFixQrddLpdjy5YtMDExQUBAAEaMGIHQ0FB8+umntfZ7DVXf9vZwtTZFQUkFNh7lU0kiajyaN2+OZ555BkFBQXB2dtYr5KRSqWp8nKtXr0Kj0VQbAn9rAarbOTk54YcffsC6devw+++/w93dHf3798dff/111+9hsal7G97VBUojGU5nFuLoZbXU4RARAQAEkfVfa01BQQFUKhXUajV71W7z3b7zmL/tNDo5W2Lz1N58KklEtaqhX3/T09PRokULxMbGwt/fX7f+ww8/xIoVK2pcqCMkJASCIGDjxo133H6nHjJXV9cGe16kMGNtMtYnXUFYd1d8/Ky31OEQURNW03tTgyjqQU3fiO6uUBjJcDK9AElp+VKHQ0T0QHJychATE4MDBw4gJyfngfe3tbWFXC6v1ht2awGqmvDz88O5c+fuup3Fpu5vZI/KXsNNx9JRVPro7wYSET0qJmRUL6zNFQjxrpx0e0Uci3sQUeNQXFyMcePGwcnJCY8//jj69OkDZ2dnjB8/HtevX6/xcRQKBXx8fPQKUAHAjh07dMWlaiIpKQlOTk41bk/V9XSzRhs7c1wv02BjcrrU4RARMSGj+jPGvxWAypLDV4tK79OaiEh6M2fOxL59+7Bp0ybk5+cjPz8ff/zxB/bt24dZs2Y98LF+/PFHLFmyBCkpKZgxYwZSU1PxyiuvAKic23LMmDG69l9++SU2bNiAc+fO4eTJk5g7dy7WrVuHKVOm1OpvNDSCIOD5HpUl8KMOcU4yIpKepFUWybB0dm2Ozi4qHL2sxtpDaZj8xGNSh0REdE/r1q3Db7/9hn79+unWPfXUUzA1NcWIESPuW7L+VmFhYbh27Rref/99ZGRkwNPTE1u3bkWrVpUPqzIyMvTmJCsrK8Ps2bNx5coVmJqaolOnTtiyZQueeuqpWvt9hurpbi2w4M/TOHZZjRNX1PBsUfMCLUREtY1FPWpRQ3+pvCH49XAa5vx2DC2am+Kv15+AXMbiHkT06Orq+mtmZobExER07NhRb/3JkyfRs2dPFBc37AnveV+6u8mrj2DLsQyM9muJD0K9pA6HiJogFvWgBimkszOamxnjSv4N7D6dLXU4RET35O/vj3fffRclJSW6dTdu3MC8efP0qiVS41M1bPGPpHRcL2NxDyKSDhMyqlcmxnKEda+scLU87qK0wRAR3cdXX32F2NhYuLi4oH///hgwYABcXV0RGxuLr776Surw6BH0amuDltZmKCytwJZjGVKHQ0QGjAkZ1bsXfFtBEID9567in5wiqcMhIrorT09PnDt3DpGRkejSpQu8vb0xf/58nDt3Dp06dZI6PHoEMpmAsJsl8KMOpUkcDREZMhb1oHrX0sYM/drbYc+ZHKxKSMU7wR5Sh0REdFempqaIiIiQOgyqA8/5uODzHWeReCkPZ7MK0d7BQuqQiMgAsYeMJDHGvzWAyiIfN8o00gZDRHQXkZGRWLJkSbX1S5YswccffyxBRFSb7C1N0L+DPQBgzUGWwCciaTAhI0n0bW+HltZmKCipwB/JV6QOh4jojr7//nt06NCh2vpOnTrhu+++kyAiqm3P+1YW91ifdAUl5XxASET1jwkZSUImEzDar/ImuDzuEjj7AhE1RJmZmXBycqq23s7ODhkZLATRFDzezg4tmpsi/3o5/jyZKXU4RGSAmJCRZJ7zcYXSSIZTGQU4kpondThERNW4urriwIED1dYfOHAAzs7OEkREtU0uE/BcdxcAHLZIRNJgQkaSsTJXIKRz5R80K+IuSRwNEVF1EyZMwPTp07F06VJcunQJly5dwpIlSzBjxgwW+mhCRnR3hUwA4v/JZfVfIqp3rLJIkhrj3wq/JV7GlmMZGOTpiNIKLewtTNDTzRpymSB1eERk4F5//XXk5uZi0qRJKCsrAwCYmJjgjTfewNy5cyWOjmqLc3NT9L1Z/XftoTTMfaqj1CERkQERRL68U2sKCgqgUqmgVqthaWkpdTiNRt9P9uDStet665xUJng3xAODPKu/u0FEdLu6vv4WFRUhJSUFpqamaNeuHZRKZa1/R13gfanmtp/MxMsrEmFjrkDc3P5QGHEQERE9mppeg3m1IUlFn8iolowBQKa6BK+uPILoE3xpnoik16xZM/To0QMtW7bEtm3bkJKSInVIVMue7GAPewslrhWXYWdKltThEJEBYUJGktFoRczbdOqO26q6bedtOgWNlp24RCSNESNG4JtvvgEA3LhxA927d8eIESPg7e2NdevWSRwd1SYjuYzFPYhIEkzISDIHL+QiQ11y1+0igAx1CQ5eyK2/oIiIbvHXX3+hT58+AID169dDFEXk5+fj66+/xgcffCBxdFTbwrpXTsey/9xVpOVWH71BRFQXmJCRZLIL756MPUw7IqLaplarYW1tDQCIjo7GM888AzMzMwwZMgTnzp2TODqqbS1tzNCnnS0AYO2hNImjISJDwYSMJGNvYVKr7YiIapurqyvi4uJQXFyM6OhoBAYGAgDy8vJgYsJrU1M0skdlL9kvh9NQodFKHA0RGQImZCSZnm7WcFKZ4F7F7R0slejpZl1vMRER3Wr69Ol44YUX4OLiAmdnZ/Tr1w9A5VBGLy8vaYOjOjHQwwE25gpkF5Zi9+lsqcMhIgPw0POQHTp0CL/++itSU1N1c7NU+f333x85MGr65DIB74Z44NWVRyDg30IeekTgalEpHCz5JJqI6t+kSZPg6+uL1NRUDBw4EDJZ5XPMNm3a8B2yJkphJMMzPi744a9/sGjvedwo13B+TCKqUw/VQxYVFYWAgACcOnUK69evR3l5OU6dOoXdu3dDpVLVdozUhA3ydMKi0d3gqNJPuOwtlLA2UyCrsBQjf4hH5j2KfxAR1SUfHx8MHz4czZo1060bMmQIAgICJIyK6pJz88p7UlJaPl6LSsbzi+PR++PdnIqFiOrEQ00M7e3tjYkTJ2Ly5MmwsLDA0aNH4ebmhokTJ8LJyQnz5s2ri1gbPE7A+fA0WhEHL+Qiu7BE9yQyPf8GRv4Qjyv5N9DaxgxrXvaDk8pU6lCJqAHi9ffOeF4eXPSJDLy68ki1URtVfWOLRnfDIE+n+g6LiBqhOp0Y+vz58xgyZAgAQKlUori4GIIgYMaMGfjhhx8eLmIyaHKZAP+2NhjWpQX829pALhPgam2GqJf90KK5KS5eu46RP8QjPf+G1KESEVETVTU/5p2eVHN+TCKqKw+VkFlbW6OwsBAA0KJFC5w4cQIAkJ+fj+vXOW8H1R5XazOsnegHFytTXLqZlF1hUkZERHWA82MSkRQeKiHr06cPduzYAQAYMWIEXnvtNUREROD5559H//79azVAIhcrM6yd6A9Xa1Ok5l7HyB/icDmPiT8REdUuzo9JRFJ4qITsm2++wciRIwEAc+fOxezZs5GVlYWnn34aP/30U60GSAQALZqbYu3L/mhpbYa03Mp3y9JymZQRUd3bv38/Ro8eDX9/f1y5cgUAsGLFCsTExEgcGdU2zo9JRFJ46CGLzs7OlQeQyfD6669j48aN+Pzzz2FlZVWrARJVcW5uirUT/dDKxgyX85iUEVHdW7duHYKCgmBqaoqkpCSUlpYCAAoLC/HRRx9JHB3VtvvNjykAcFKZcH5MIqpVNU7ICgoK9P77XgtRXXFSVfaUudma48rNKoyp15iUEVHd+OCDD/Ddd99h8eLFMDY21q3v1asXjhw5ImFkVBeq5scEcNek7N0QD85HRkS1qsYJmZWVFbKzK2esb968OaysrKotVeuJ6pKjygRrIvzQRpeUxeHStWKpwyKiJujMmTN4/PHHq623tLREfn5+/QdEde5u82MCwBdhXVjynohqnVFNG+7evRvW1pVd9Hv27KmzgIhqwlFlgjUv++H5xfH4J6cYI3+Ix5oIP7S2NZc6NCJqQpycnPD333+jdevWeutjYmLQpk0baYKiOjfI0wkDPRwr58csKMFH21KQVVAKgR1jRFQHapyQ9e3bV/ffbm5ucHV1hXDblUkURaSlpdVedET34GBpgqiIyqTsfFVS9rIf3JiUEVEtmThxIl577TUsWbIEgiAgPT0dcXFxmD17Nv7v//5P6vCoDlXNjwkA57KL8M2ev7H5WAaGdWkhcWRE1NQ8VFEPNzc35OTkVFufm5sLNze3Rw6KqKbsLSt7yh6zb4bMghKM/CEO/+QUSR0WETURr7/+OkJDQ/HEE0+gqKgIjz/+OCZMmICJEydiypQpUodH9SS4c+UwxX1nclBQUi5xNETU1DxUQiaKYrXeMQAoKiqCiUnNS8FGRkaiR48esLCwgL29PUJDQ3HmzBnd9vLycrzxxhvw8vKCubk5nJ2dMWbMGKSnp+sdp7S0FFOnToWtrS3Mzc0xdOhQXL58Wa9NXl4ewsPDoVKpoFKpEB4eXm38f2pqKkJCQmBubg5bW1tMmzYNZWVlNf49JA17i8p3ytrZN0NWQSlG/hCP80zKiKiWfPjhh7h69SoOHjyI+Ph45OTk4L///a/UYVE9cnewwGP2zVCm0WLHySypwyGiJuaBErKZM2di5syZEAQB77zzju7zzJkz8dprryEsLAxdunSp8fH27duHyZMnIz4+Hjt27EBFRQUCAwNRXFxZoOH69es4cuQI3nnnHRw5cgS///47zp49i6FDh+odZ/r06Vi/fj2ioqIQExODoqIiBAcHQ6PR6NqMGjUKycnJiI6ORnR0NJKTkxEeHq7brtFoMGTIEBQXFyMmJgZRUVFYt24dZs2a9SCniCRiZ6HEmpf94O5ggezCyqTs72wmZUT0aMaNG4fCwkKYmZmhe/fu6NmzJ5o1a4bi4mKMGzdO6vCongiCgGDvyl6yTcfS79OaiOjBCKIoijVt/MQTTwCoTKT8/f2hUCh02xQKBVq3bo3Zs2ejXbt2DxVMTk4O7O3tsW/fvjtWtQKAQ4cOoWfPnrh06RJatmwJtVoNOzs7rFixAmFhYQCA9PR0uLq6YuvWrQgKCkJKSgo8PDwQHx8PX19fAEB8fDz8/f1x+vRpuLu7Y9u2bQgODkZaWppujrWoqCiMHTsW2dnZsLS0rBZLaWmpbk4aoHI6AFdXV6jV6ju2p7p3ragUL/yYgNOZhbBtpkTUy754zN5C6rCIqI4VFBRApVLV+vVXLpcjIyMD9vb2euuvXr0KR0dHVFRU1Np31YW6Oi+G6O/sIgz4fB+MZAIOvTUAVuaK++9ERAatptfgGhf1AP6trjh27Fj873//g4VF7f6hq1arAUBXzfFubQRBQPPmzQEAiYmJKC8vR2BgoK6Ns7MzPD09ERsbi6CgIMTFxUGlUumSMQDw8/ODSqVCbGws3N3dERcXB09PT10yBgBBQUEoLS1FYmKiLhm9VWRkJObNm/eoP5tqkU0zJVZH+GHU4niczizUVV9s58CkjIhqrqCgAKIoQhRFFBYW6g3H12g02Lp1a7UkjZq2x+yboaOTJVIyCvDnyUyM7NlS6pCIqIl44HfIKioqsHLlSly6dKlWAxFFETNnzkTv3r3h6el5xzYlJSV48803MWrUKF2WmZmZCYVCUW3+MwcHB2RmZura3OnGaW9vr9fGwcFBb7uVlRUUCoWuze3mzp0LtVqtW1hhsmGwNldgTYQfPJwscbWoDCN/iMeZzEKpwyKiRqR58+awtraGIAho37693pybtra2GDduHCZPnix1mFTPqoYtbj6WIXEkRNSUPFAPGQAYGRmhVatWeu9n1YYpU6bg2LFjiImJueP28vJyjBw5ElqtFgsXLrzv8W4vPHKnIiQP0+ZWSqUSSqXyvrFQ/bMyV2DVBF+M/ikBJ9MLMGpxPFZF+KKDI4fsENH97dmzB6Io4sknn8S6dev0Rm4oFAq0atVKb0QFGYYQb2d88ucZxJ6/iqtFpbBtxr8BiOjRPVSVxbfffhtz585Fbm5urQQxdepUbNy4EXv27IGLi0u17eXl5RgxYgQuXLiAHTt26I3BdHR0RFlZGfLy8vT2yc7O1vV4OTo6IiurelWknJwcvTa394Tl5eWhvLy8Ws8ZNQ5VSZlnC0tcKy7DqMUJSMkokDosImoE+vbti379+uHChQsIDQ1F3759dYu/vz+cnZ2RmpoqdZhUz1ramMHbRQWtCGw7zl4yIqodD5WQff3119i/fz+cnZ3h7u6Obt266S01JYoipkyZgt9//x27d+++4xxmVcnYuXPnsHPnTtjY2Oht9/HxgbGxMXbs2KFbl5GRgRMnTqBXr14AAH9/f6jVahw8eFDXJiEhAWq1Wq/NiRMnkJHx7wV2+/btUCqV8PHxqfFvooaluZkCq8b7wauFCrnFZRi1OB6n0pmUEVHNtGnT5o7zbl67do3zbhqof6stMiEjotrxwEMWASA0NLRWvnzy5MlYvXo1/vjjD1hYWOh6qFQqFUxNTVFRUYFnn30WR44cwebNm6HRaHRtrK2toVAooFKpMH78eMyaNQs2NjawtrbG7Nmz4eXlhQEDBgAAOnbsiEGDBiEiIgLff/89AODll19GcHAw3N3dAQCBgYHw8PBAeHg4PvnkE+Tm5mL27NmIiIhgZapGTmVmjJUTfDHmpwQcvazGqB/jsWqCLzo5q6QOjYgauLsVIn7QeTep6Rji7YyPtp7GoYu5yCoogYMl/3dARI/mgcre1/qX3+XdrKVLl2Ls2LG4ePHiXZ9A7tmzB/369QNQWexjzpw5WL16NW7cuIH+/ftj4cKFcHV11bXPzc3FtGnTsHHjRgDA0KFD8c033+iqNQKVE0NPmjQJu3fvhqmpKUaNGoVPP/20xu+Jsbxww6a+UY4xSw7iaFo+VKbGN4czMikjagpq+/o7c+ZMAMBXX32FiIgImJmZ6bZpNBokJCRALpfjwIEDj/xddYn3pbrxzKJYJF7Kw/8Fe2Bcb/aUEtGd1fQa/NAJWX5+Pn777TecP38ec+bMgbW1NY4cOQIHBwe0aNHioQNvzHjja/gKSsox5qeDSL6ZlK0c7wsvFyZlRI1dbV9/63rezfrC+1LdWHrgAuZtOoWuLZtj/aQAqcMhogaqTuYhq3Ls2DEMGDAAKpUKFy9eREREBKytrbF+/XpcunQJy5cvf+jAieqSpYkxVozviReXHMSR1Hy88GM8Vk7whbdLc6lDI6IGpGrezZdeeglfffUVkxnS85SXE97ffApJqfm4nHcdLlZm99+JiOguHqqox8yZMzF27FicO3dObwz94MGD8ddff9VacER1wcLEGD+P6wmfVlYoKKnACz8m4GhavtRhEVEDtHTpUlhaWuLvv//Gn3/+iRs3bgC4+7tlZBgcLE3Qs3XlVAhbWNyDiB7RQyVkhw4dwsSJE6utb9GixV0nUSZqSKqSsu6trFBYUoHRPyYgKTXv/jsSkUHJzc1F//790b59ezz11FO6SrwTJkzArFmzJI6OpBTSuXIeOk4STUSP6qESMhMTExQUVC8dfubMGdjZ2T1yUET1oZnSCMvG9UTP1tYoLK3AmJ8O4giTMiK6xfTp02FsbIzU1FS9wh5hYWGIjo6WMDKS2mBPR8hlAo5fUePi1WKpwyGiRuyhErJhw4bh/fffR3l5OYDKaompqal488038cwzz9RqgER1qZnSCEtf6oGebv8mZYmXmJQRUaXt27fj448/houLi976du3a4dKlSxJFRQ2BTTMlerWtnBt187F0iaMhosbsoRKyTz/9FDk5ObC3t8eNGzfQt29fPPbYY7CwsMCHH35Y2zES1SlzpRGWvdQDfm2sUVRagTE/JeDwxVypwyKiBqC4uFivZ6zK1atXazwlCjVdVZNEc9giET2Kh0rILC0tERMTg3Xr1mH+/PmYMmUKtm7din379sHc3Ly2YySqc2YKIywZ2wP+bWxQXKbBi0sO4hCTMiKD9/jjj+tVDhYEAVqtFp988omuND4ZrqBOjjCSCTidWYi/swulDoeIGilJJ4ZuajjfS+N3o0yDCcsP4cDf12CmkGPZSz3R081a6rCI6D7q6vp76tQp9OvXDz4+Pti9ezeGDh2KkydPIjc3FwcOHEDbtm1r7bvqAu9LdW/cskPYfTobr/VvhxkD20sdDhE1IHU6DxkA7Nq1C7t27UJ2dja0Wq3etiVLljzsYYkkZaqQ46cXeyBi+WHsP3cVY5cexJKxPeDXxkbq0IhIAh4eHjh27BgWLVoEuVyO4uJiPP3005g8eTKcnJykDo8agGBvJ+w+nY3Nx9IxfUA7CIIgdUhE1Mg8VEI2b948vP/+++jevTucnJx48aEmxcRYjsVjuuuSspeWHqocztiWSRmRIXJ0dMS8efOkDoMaqIEeDlAYyXA+pxgpGYXwcGZPJBE9mIdKyL777jssW7YM4eHhtR0PUYNQlZRNXJGIfWdz8NKyg1jyYg/0esxW6tCIqB799ddf99z++OOP11Mk1FBZmBijX3s7bD+Vhc3H0pmQEdEDe6iErKysDL169artWIgaFBNjOb4P98ErKxOx90wOXlp2CD+92AO92zEpIzIU/fr1q7bu1lEhGo2mHqOhhiq4s/PNhCwDc4LcOXKIiB7IQ1VZnDBhAlavXl3bsRA1OFVJ2ZMd7FFaocX4nw9h/7kcqcMionqSl5ent2RnZyM6Oho9evTA9u3bpQ6PGogBHe1haixHau51HL+iljocImpkHqqHrKSkBD/88AN27twJb29vGBsb623//PPPayU4ooZAaSTHotHdMGnlEew6nY3xPx/G4jHd0be9ndShEVEdU6lU1dYNHDgQSqUSM2bMQGJiogRRUUNjpjDCkx3tseVYBjYfy4C3S3OpQyKiRuShesiOHTuGLl26QCaT4cSJE0hKStJbiJoapZEcC0d3w4CODiir0CJi+WHsPZMtdVhEJBE7OzucOXNG6jCoAQmpmiT6aDq0Ws4oREQ191A9ZHv27KntOIgaPKWRHAtf6IbJq49gx6ksvLw8Ed+H++CJDvZSh0ZEdeTYsWN6n0VRREZGBubPn4/OnTtLFBU1RP3c7WGukCNdXYKktDz4tOIclkRUMw+UkD399NP3bSMIAtatW/fQARE1ZAojGb4d1Q1T1xzBnyezMHFFIr4L74YnOzhIHRoR1YEuXbpAEASIon6Ph5+fH+fcJD0mxnIEdnLE+qQr2HQ0gwkZEdXYAyVkdxpLT2RoFEYyfDOqG6atScK2E5mYuCIRi17wwQAPJmVETc2FCxf0PstkMtjZ2cHExESiiKghC/Z2wvqkK9h6PAPvBHtALmO1RSK6vwdKyJYuXVpXcRA1KsZyGb5+vitei0rC1uOZeHVVIha+4IOBTMqImpRWrVpJHQI1In3a2cHSxAjZhaU4dDEXfm1spA6JiBqBhyrqQUSVSdlXI7tiiLcTyjUiJq1KxJ8nM6UOi4hq2b59+xASEoLHHnsM7dq1w9ChQ7F//36pw6IGSGEkQ1AnRwDApqPpEkdDRI0FEzKiR2Asl+GrsC4I6eyMco2IyauOIPoEkzKipmLlypUYMGAAzMzMMG3aNEyZMgWmpqbo378/5+OkOwru7AwAiD6RiQqNVuJoiKgxEMTb31Smh1ZQUACVSgW1Wg1LS0upw6F6VKHRYuYvR7HxaDqMZAL+93xXDPZykjosIoNRV9ffjh074uWXX8aMGTP01n/++edYvHgxUlJSau276gLvS/WvXKOF70e7kFtchhXje6JPO85ZSWSoanoNZg8ZUS0wksvw+YjOCO3ijAqtiClrkrDlWIbUYRHRI/rnn38QEhJSbf3QoUOrFfwgAipHTgzyrBy2uPko7wNEdH9MyIhqiZFchs9GdMHTXVtAoxUxLSoJm4/xHQKixszV1RW7du2qtn7Xrl1wdXWVICJqDIJvThIdfTITZRUctkhE9/ZQE0MT0Z3JZQI+ea5z5Xx8Ry7jtahkaEVg6M13CoiocZk1axamTZuG5ORk9OrVC4IgICYmBsuWLcNXX30ldXjUQPm62cDOQomcwlLE/J3DuSqJ6J6YkBHVMrlMwIJnvSETgF8TL2N6VBJEUcSwLi2kDo2IHtCrr74KR0dHfPbZZ/jll18AVL5XtnbtWgwbNkzi6KihkssEPOXpiJ/jLmHz0QwmZER0T0zIiOqAXCbg42e8IRMErD2chhlrkyGKQGhXJmVEjc3w4cMxfPhwqcOgRiakszN+jruE7aeyUFKugYmxXOqQiKiBYkJGVEdkMgGRT3tBEICoQ2mY+UsytKKIp7u5SB0aET2gsrIyZGdnQ6vVfx+oZcuWEkVEDV23llZwUpkgQ12CfWdzdPOTERHdjkU9iOqQTCbgo+FeeL5nS2hFYNavR/Fb4mWpwyKiGjp37hz69OkDU1NTtGrVCm5ubnBzc0Pr1q3h5uYmdXjUgMlkAobcnP6Ek0QT0b0wISOqYzKZgA9DPfGCb0uIIjDnt6P49XCa1GERUQ2MHTsWMpkMmzdvRmJiIo4cOYIjR44gKSkJR44ceeDjLVy4EG5ubjAxMYGPjw/2799fo/0OHDgAIyMjdOnS5YG/k6RTNUn0rpRsXC+rkDgaImqoOGSRqB7IZAI+CPWETBCwIv4SXl93DKIIjOjBstlEDVlycjISExPRoUOHRz7W2rVrMX36dCxcuBABAQH4/vvvMXjwYJw6deqeQx/VajXGjBmD/v37Iysr65HjoPrT2UUFV2tTpOXewO7T2Qj2ZsVdIqqOPWRE9UQQBLw/rBNe9G8FUQReX3cMUQdTAQAarYi489fwR/IVxJ2/Bo1WlDhaIgIADw8PXL16tVaO9fnnn2P8+PGYMGECOnbsiC+//BKurq5YtGjRPfebOHEiRo0aBX9///t+R2lpKQoKCvQWko4gCLokjJNEE9HdsIeMqB4JgoD3hnaCIAhYFnsRb/5+HMcuq7HnTDYy1CW6dk4qE7wb4oFBnk4SRktkmG5NYj7++GO8/vrr+Oijj+Dl5QVjY2O9tpaWljU6ZllZGRITE/Hmm2/qrQ8MDERsbOxd91u6dCnOnz+PlStX4oMPPrjv90RGRmLevHk1ionqR7C3ExbtPY89Z7JRWFIOCxPj++9ERAaFCRlRPRMEAe+GeEAQgKUHLmL1zV6yW2WqS/DqyiNYNLobkzKieta8eXMIgqD7LIoi+vfvr9dGFEUIggCNRlOjY169ehUajQYODvrzUTk4OCAzM/OO+5w7dw5vvvkm9u/fDyOjmt2u586di5kzZ+o+FxQUwNWVQ6Ol5OFkiTa25vjnajF2pmRheFdW2iUifZIOWYyMjESPHj1gYWEBe3t7hIaG4syZM3ptRFHEe++9B2dnZ5iamqJfv344efKkXpvS0lJMnToVtra2MDc3x9ChQ3H5sn4lu7y8PISHh0OlUkGlUiE8PBz5+fl6bVJTUxESEgJzc3PY2tpi2rRpKCsrq5PfToZNEAS89VRHmCvuPC9N1YDFeZtOcfgiUT3bs2cPdu/erVtu/3zrugd1a6IH/JvY3U6j0WDUqFGYN28e2rdvX+PjK5VKWFpa6i0krcphi5UP1jhskYjuRNIesn379mHy5Mno0aMHKioq8NZbbyEwMBCnTp2Cubk5AGDBggX4/PPPsWzZMrRv3x4ffPABBg4ciDNnzsDCwgIAMH36dGzatAlRUVGwsbHBrFmzEBwcjMTERMjllX/wjho1CpcvX0Z0dDQA4OWXX0Z4eDg2bdoEoPLmN2TIENjZ2SEmJgbXrl3Diy++CFEU8b///U+Cs0NN3aGLeSguu/vTdRFAhroEBy/kwr+tTf0FRmTg+vbtW+vHtLW1hVwur9Yblp2dXa3XDAAKCwtx+PBhJCUlYcqUKQAArVYLURRhZGSE7du348knn6z1OKluBHd2xte7/8Zf53Kgvl4OlRmHLRLRvyRNyKqSoypLly6Fvb09EhMT8fjjj0MURXz55Zd466238PTTTwMAfv75Zzg4OGD16tWYOHEi1Go1fvrpJ6xYsQIDBgwAAKxcuRKurq7YuXMngoKCkJKSgujoaMTHx8PX1xcAsHjxYvj7++PMmTNwd3fH9u3bcerUKaSlpcHZufIF3M8++wxjx47Fhx9+yKeMVOuyC0vu3+gB2hFR7Th27FiN23p7e9eonUKhgI+PD3bs2IHhw4fr1u/YsQPDhg2r1t7S0hLHjx/XW7dw4ULs3r0bv/32G+dAa2TaO1jA3cECZ7IK8eepTIzozmGkRPSvBvUOmVqtBgBYW1sDAC5cuIDMzEwEBgbq2iiVSvTt2xexsbGYOHEiEhMTUV5ertfG2dkZnp6eiI2NRVBQEOLi4qBSqXTJGAD4+flBpVIhNjYW7u7uiIuLg6enpy4ZA4CgoCCUlpYiMTERTzzxRLV4S0tLUVpaqvvMalb0IOwtTGq1HRHVji5dukAQBIjivYcLP8g7ZAAwc+ZMhIeHo3v37vD398cPP/yA1NRUvPLKKwAq3/+6cuUKli9fDplMBk9PT7397e3tYWJiUm09NQ7B3k44s6MQm49lMCEjIj0NJiETRREzZ85E7969dTebqqEdd3oJ+tKlS7o2CoUCVlZW1dpU7Z+ZmQl7e/tq32lvb6/X5vbvsbKygkKhuOsL16xmRY+ip5s1nFQmyFSX4G5/9tlbKNHTzbpe4yIydBcuXKiT44aFheHatWt4//33kZGRAU9PT2zduhWtWrUCAGRkZCA1tXqRH2oagjs747MdZ3Hg76u4VlQKm2ZKqUMiogaiwSRkU6ZMwbFjxxATE1NtW01fgr5Xmzu1f5g2t2I1K3oUcllltcVXVx6BANwxKSsp1+BkuhreLs3rOToiw1WVINWFSZMmYdKkSXfctmzZsnvu+9577+G9996r/aCoXrjZmqOTsyVOphcg+mQmXvCtu/+dEVHj0iASsqlTp2Ljxo3466+/4OLybzlYR0dHAJW9V05O/5b+vvUlaEdHR5SVlSEvL0+vlyw7Oxu9evXStcnKyqr2vTk5OXrHSUhI0Nuel5eH8vLyO75wDVQOn1Qq+YSLHt4gTycsGt0N8zad0puHzN5CCWO5gCv5JRjxfRy+DOuKQZ6OEkZKZDg2btyIwYMHw9jYGBs3brxn26FDh9ZTVNQUBHs742R6ATYfzWBCRkQ6kiZkoihi6tSpWL9+Pfbu3VvtJWU3Nzc4Ojpix44d6Nq1K4DKyTX37duHjz/+GADg4+MDY2Nj7NixAyNGjABQOezjxIkTWLBgAQDA398farUaBw8eRM+ePQEACQkJUKvVuqTN398fH374ITIyMnTJ3/bt26FUKuHj41P3J4MM1iBPJwz0cMTBC7nILiyBvYUJerpZ43pZBaasTsK+szl4dVUi3hzUAS8/3ua+vcNE9GhCQ0N1Q91DQ0Pv2u5B3yEjCvZ2wsfRp5Fw4Zruek9EJIj3e2u5Dk2aNAmrV6/GH3/8AXd3d916lUoFU1NTAMDHH3+MyMhILF26FO3atcNHH32EvXv36pW9f/XVV7F582YsW7YM1tbWmD17Nq5du6ZX9n7w4MFIT0/H999/D6Cy7H2rVq30yt536dIFDg4O+OSTT5Cbm4uxY8ciNDS0xmXvCwoKoFKpoFarWZWRakWFRov3N5/C8rjKdyZH9nDFf0M9YSyXdApBogaH198743lpeEK/PYDktHzMG9oJL/ZqLXU4RFSHanoNlvSvukWLFkGtVqNfv35wcnLSLWvXrtW1ef311zF9+nRMmjQJ3bt3x5UrV7B9+3ZdMgYAX3zxBUJDQzFixAgEBATAzMwMmzZt0iVjALBq1Sp4eXkhMDAQgYGB8Pb2xooVK3Tb5XI5tmzZAhMTEwQEBGDEiBEIDQ3Fp59+Wj8ng+gOjOQyvD/ME++GeEAmAFGH0jB26UGor5dLHRoRET2EqkmiNx1NlzgSImooJO0ha2r4JJLq0q6ULExdk4TrZRq0tTPHkrE90MrGXOqwiBqE2r7+JiQkIDc3F4MHD9atW758Od59910UFxfrRk809PeIeV9qeDLUN+AfuRsAEPvmk3BubipxRERUVxpFDxkR1Vz/jg747ZVecFKZ4HxOMYYvjMXhi7lSh0XUJL333nt6E0QfP34c48ePx4ABA/Dmm29i06ZNiIyMlDBCaqycVKbo0bqyCNnW4xkSR0NEDQETMqJGxMPZEn9MDoBXCxVyi8swanEC/ki+InVYRE1OcnIy+vfvr/scFRUFX19fLF68GDNnzsTXX3+NX375RcIIqTEL6ewMANh0jAkZETEhI2p07C1NsHaiHwI9HFCm0eK1qGR8ufMsOPqYqPbk5eXpTXmyb98+DBo0SPe5R48eSEtLkyI0agIGezpBJgBH0/KRlntd6nCISGJMyIgaITOFEb4b7YOJj7cBAHy58xxmrE1GSTlLcBPVBgcHB1y4cAFA5XQrR44cgb+/v257YWEhjI2NpQqPGjk7CyX82tgAADYdY3EPIkPHhIyokZLJBMx9qiMin/aCkUzAhuR0jP4xAdeKSqUOjajRGzRoEN58803s378fc+fOhZmZGfr06aPbfuzYMbRt21bCCKmxC/auHLa4+SiHLRIZOiZkRI3c8z1bYtlLPWFhYoTDl/IwfGEs/s4ukjosokbtgw8+gFwuR9++fbF48WIsXrwYCoVCt33JkiUIDAyUMEJq7AZ5OsJIJuBURgH+yeE1m8iQMSEjagJ6t7PF+km94GptitTc63h64QEc+Puq1GERNVp2dnbYv38/8vLykJeXh+HDh+tt//XXX/Huu+9KFB01BdbmCgQ8ZgsA2MziHkQGjQkZURPxmL0FNkwKgE8rKxSUVODFJQex9lCq1GERNWoqlQpyubzaemtra70eM6KHUTVJ9Ga+R0Zk0JiQETUhNs2UWDXBF0M7O6NCK+KNdccRuS0FWi0rMBIRNTSBnRyhkMtwNqsIZzILpQ6HiCTChIyoiTExluOrkV3wWv92AIDv9/2DSauO4EYZKzASETUkKlNjPN6+atgie8mIDBUTMqImSBAEzBjYHl+GdYFCLkP0yUyE/RCH7IISqUMjIqJbVE0SvflYBueTJDJQTMiImrDQri2wKsIXVmbGOHZZjdBvD+BUeoHUYRER0U39OzpAaSTDhavFOMnrM5FBYkJG1MT1aG2NDZMD0MbOHOnqEjz3XSz2nM6WOiwiIgLQTGmEJzvYA+Ak0USGigkZkQFoZWOO9a8GwL+NDYrLNBj/8yEsO3BB6rCIiAj/ThK9hcMWiQwSEzIiA6EyM8bP43piRHcXaEXgvU2n8O4fJ1Ch0UodGhGRQXuygz3MFHJczruB5LR8qcMhonrGhIzIgCiMZPj4GW+8MagDAODnuEuIWH4YRaUVEkdGRGS4TBVyDOjoAICTRBMZIiZkRAZGEAS82q8tFr3QDSbGMuw5k4NnF8XiSv4NqUMjIjJYVZNEbzmWwbkjiQwMEzIiAzXYywlrX/aHnYUSpzMLMeybAzjKoTJERJLo624HC6URMgtKcPhSntThEFE9YkJGZMA6uzbHhskB6OBogatFpQj7IQ7RJzhchoiovimN5BjYqWrYIqstEhkSJmREBq5Fc1P8+oo/+rnboaRci1dWHsF3+86z0hcRUT0LuVltcevxTGg4bJHIYDAhIyJYmBjjxzHd8aJ/KwDA/G2n8ea64yirYAVGIqL6EvCYLZqbGeNqUSkS/rkmdThEVE+YkBERAMBILsO8YZ54L8QDMgFYezgNY5cehPp6udShEREZBIWRDIM6OQIANrHaIpHBYEJGRHrGBrjhxxe7w1whR+z5axi+6AAuXSuWOiwiIoNQNUn0thMZKOc8kUQGgQkZEVXzZAcH/PpKLzipTPBPTjFCvz2AQxdzpQ6LiKjJ82tjDRtzBfKvl+PA31elDoeI6gETMiK6Iw9nS/wxOQBeLVTIu16OFxYnYEPSFanDIiJq0ozkMgz2qhy2yEmiiQwDEzIiuit7SxOsneiHoE4OKNNoMX1tMr7YcZYVGImI6lBVtcU/T2aitEIjcTREVNeYkBHRPZkpjLDoBR9M7NsGAPDVrnN4LSoZJeX8I4GIqC70aG0NB0slCksqsP8shy0SNXVMyIjovmQyAXMHd8T8p71gJBOw8Wg6XvgxAdeKSqUOjYioyZHJBDzl5QQA2MRJoomaPCZkRFRjI3u2xM/jesLCxAiJl/IQuvAA/s4ulDosIqImp6ra4s5TWRyRQNTEMSEjogcS8Jgt1k/qhZbWZkjLvYHhC2NZCYyIqJZ1a9kcLZqborhMgz2ns6UOh4jqEBMyInpgj9lbYP2kXvBpZYXCkgq8uOQg1hxMlTosIqImQxAEBHtXDltktUWipo0JGRE9FJtmSqya4IthXZxRoRUx9/fjiNyaAq2WFRiJiGpD1bDFXaezUFxaIXE0RFRXmJAR0UMzMZbjy7AumD6gHQDg+7/+waurEnG9jH84EBE9Ks8WlmhlY4aSci12pmRJHQ4R1REmZET0SARBwPQB7fFlWBco5DL8eTILYd/HI6ugROrQiIgaNQ5bJDIMTMiIqFaEdm2BVRG+sDZX4PgVNUK/PYBT6QVSh0VE1KhVDVvcdyYHBSXlEkdDRHVB0oTsr7/+QkhICJydnSEIAjZs2KC3vaioCFOmTIGLiwtMTU3RsWNHLFq0SK9NaWkppk6dCltbW5ibm2Po0KG4fPmyXpu8vDyEh4dDpVJBpVIhPDwc+fn5em1SU1MREhICc3Nz2NraYtq0aSgrK6uLn03UZPVobY31k3qhjZ05MtQleO67WOw+zWE2REQPq4OjBR6zb4YyjRY7TvJ6StQUSZqQFRcXo3Pnzvjmm2/uuH3GjBmIjo7GypUrkZKSghkzZmDq1Kn4448/dG2mT5+O9evXIyoqCjExMSgqKkJwcDA0mn/n7Bg1ahSSk5MRHR2N6OhoJCcnIzw8XLddo9FgyJAhKC4uRkxMDKKiorBu3TrMmjWr7n48URPVysYc618NQK+2Nigu02DCz4ex9MAFqcMiImqU9IctcpJooqZIEEWxQZREEwQB69evR2hoqG6dp6cnwsLC8M477+jW+fj44KmnnsJ///tfqNVq2NnZYcWKFQgLCwMApKenw9XVFVu3bkVQUBBSUlLg4eGB+Ph4+Pr6AgDi4+Ph7++P06dPw93dHdu2bUNwcDDS0tLg7Fw5NCAqKgpjx45FdnY2LC0ta/QbCgoKoFKpoFara7wPUVNVrtHi7fUnsPZwGgBgjH8r/F+wB4zkHClNtY/X3zvjeWka/s4uwoDP98FIJuDQWwNgZa6QOiQiqoGaXoMb9F9GvXv3xsaNG3HlyhWIoog9e/bg7NmzCAoKAgAkJiaivLwcgYGBun2cnZ3h6emJ2NhYAEBcXBxUKpUuGQMAPz8/qFQqvTaenp66ZAwAgoKCUFpaisTExLvGV1paioKCAr2FiCoZy2WY/4wX3hzcAQCwPO4SJiw/jEK+A0FE9EAes2+GDo4WqNCK+PNkptThEFEta9AJ2ddffw0PDw+4uLhAoVBg0KBBWLhwIXr37g0AyMzMhEKhgJWVld5+Dg4OyMzM1LWxt7evdmx7e3u9Ng4ODnrbraysoFAodG3uJDIyUvdemkqlgqur6yP9XqKmRhAEvNK3Lb4b3Q0mxjLsPZOD576Lw5X8G1KHRkTUqIR0rnxozGqLRE1Pg0/I4uPjsXHjRiQmJuKzzz7DpEmTsHPnznvuJ4oiBEHQfb71vx+lze3mzp0LtVqtW9LS0mrys4gMziBPJ6x92R92FkqczizEsG8OIDktX+qwiIgajZCb1RZjz1/F1aJSiaMhotrUYBOyGzdu4D//+Q8+//xzhISEwNvbG1OmTEFYWBg+/fRTAICjoyPKysqQl5ent292draux8vR0RFZWdWrEuXk5Oi1ub0nLC8vD+Xl5dV6zm6lVCphaWmptxDRnXV2bY4NkwPQwdECV4tKEfZ9HLYer3zSq9GKiDt/DX8kX0Hc+WvQaBvEq61ERA1GSxszeLuooBWBbSc4bJGoKWmwCVl5eTnKy8shk+mHKJfLodVqAVQW+DA2NsaOHTt02zMyMnDixAn06tULAODv7w+1Wo2DBw/q2iQkJECtVuu1OXHiBDIy/h0GsH37diiVSvj4+NTZbyQyNC2am+K3V3uhn7sdSiu0mLTqCKZHJSHg4914fnE8XotKxvOL49H7492IPsFhOUREt6qqtrjpKKstEjUlkiZkRUVFSE5ORnJyMgDgwoULSE5ORmpqKiwtLdG3b1/MmTMHe/fuxYULF7Bs2TIsX74cw4cPBwCoVCqMHz8es2bNwq5du5CUlITRo0fDy8sLAwYMAAB07NgRgwYNQkREBOLj4xEfH4+IiAgEBwfD3d0dABAYGAgPDw+Eh4cjKSkJu3btwuzZsxEREcFeL6Ja1kxphB/HdMfYXq0BABuS05GpLtFrk6kuwasrjzApIyK6xZCbwxYPXcxFVkHJfVoTUWMhaUJ2+PBhdO3aFV27dgUAzJw5E127dsX//d//AagsPd+jRw+88MIL8PDwwPz58/Hhhx/ilVde0R3jiy++QGhoKEaMGIGAgACYmZlh06ZNkMvlujarVq2Cl5cXAgMDERgYCG9vb6xYsUK3XS6XY8uWLTAxMUFAQABGjBiB0NBQ3dBIIqpdRnIZ3gn2gKWJ0R23Vw1YnLfpFIcvEhHd1KK5KXxaWUEUgS0s7kHUZDSYeciaAs73QlRzceev4fnF8fdttybCD/5tbeohImrMeP29M56XpmfpgQuYt+kUurVsjt8nBUgdDhHdQ5OYh4yImq7swpoNt6lpOyIiQ/CUlxMEATiSmo/LedelDoeIagETMiKShL2FSa22IyIyBA6WJujZ2hoAhy0SNRVMyIhIEj3drOGkMsHdZ/oD5DIBFnd5z4yIyFAFc5JooiaFCRkRSUIuE/BuiAcA3DUp02hFPLMoFlEHU8HXXYmIKg32dIRcJuD4FTUuXi2WOhwiekRMyIhIMoM8nbBodDc4qvSHJTqpTPDJs966+cre/P04Zv5yFMWlFRJFSkTUcNg2U6LXzWJHW46zl4yoseNYICKS1CBPJwz0cMTBC7nILiyBvYUJerpZQy4T8Ew3F3z313l8tv0s1iddwbHL+Vj4gg/cHS2kDpuISFLB3k7Yf+4qNh1Nx+QnHpM6HCJ6BOwhIyLJyWUC/NvaYFiXFvBvawO5rHIQo0wmYFK/x7B6gi/sLZQ4n1OMYd/G4NfDaRJHTEQkraBOjjCSCTidWYi/swulDoeIHgETMiJq8Hzb2GDra33Qp50tSsq1mPPbMcz+9ShulGmkDo2ISBLNzRTo084WALDpKIctEjVmTMiIqFGwbabEzy/1xKyB7SETgN8SL2PYtzF8MkxEBitEV20xnYWPiBoxJmRE1GjIZAKm9m+HlRN8YdtMibNZRRj6zQGsT7osdWhERPVuoIcDFEYynM8pxulMPpwiaqyYkBFRo9OrrS22vtYb/m1scL1Mgxlrj2Lu78dQUs4hjERkOCxMjNGvvR0AYNPRdImjIaKHxYSMiBolewsTrJzgi2n920EQgDUH0xD67QH8k1MkdWhERPXm1kmiOWyRqHFiQkZEjZZcJmDmwPZYPq4nbMwVOJ1ZiJD/xfBJMREZjP4d7GFiLENq7nUcv6KWOhwieghMyIio0evTzg5bX+uDnm7WKC7TYOqaJLy94TiHMBJRk2euNEL/jg4AKnvJiKjxYUJGRE2Cg6UJVk/wxeQn2gIAVsan4plFsbh0rVjiyIiI6laItxMAYMuxDGi1HLZI1NgwISOiJsNILsOcoA5Y9lIPWJkZ42R6AYK/jsG243xqTERNVz93e5gr5LiSfwNJaXlSh0NED4gJGRE1Of3c7bH1tT7o3soKhaUVeHXVEby38SRKKziEkYiaHhNjOQZ6VA5b5CTRRI0PEzIiapKcVKZY87IfJvZtAwBYFnsRI76LQ1rudYkjIyKqfcHeldUWtx7PgIbDFokaFSZkRNRkGctlmDu4I356sTtUpsY4elmNIV/vx/aTmVKHRgZq4cKFcHNzg4mJCXx8fLB///67to2JiUFAQABsbGxgamqKDh064IsvvqjHaKkx6dPeFpYmRsguLMWhi7lSh0NED4AJGRE1ef07OmDLtN7o4tocBSUVeHlFIv67+RTKKrRSh0YGZO3atZg+fTreeustJCUloU+fPhg8eDBSU1Pv2N7c3BxTpkzBX3/9hZSUFLz99tt4++238cMPP9Rz5NQYKI3kCOrkCADYfIxTfxA1JoLIWQRrTUFBAVQqFdRqNSwtLaUOh4huU1ahxcfRp/FTzAUAQBfX5vj2hW5o0dxU4sjoUTWG66+vry+6deuGRYsW6dZ17NgRoaGhiIyMrNExnn76aZibm2PFihU1at8YzgvVnn1nc/DikoOwMVcg4T/9YSTnc3ciKdX0Gsz/TyUig6EwkuGdYA98H+4DCxMjJKflY8jX+7H7dJbUoVETV1ZWhsTERAQGBuqtDwwMRGxsbI2OkZSUhNjYWPTt2/eubUpLS1FQUKC3kOHo1dYGVmbGuFZchrh/rkkdDhHVEBMyIjI4QZ0csXVaH3i7qJB/vRzjlh1G5LYUlGs4hJHqxtWrV6HRaODg4KC33sHBAZmZ936n0cXFBUqlEt27d8fkyZMxYcKEu7aNjIyESqXSLa6urrUSPzUOxnIZBnlWzkm2mdUWiRoNJmREZJBcrc3w6yv+GNurNQDg+33/4Pkf4pGhviFtYNSkCYKg91kUxWrrbrd//34cPnwY3333Hb788kusWbPmrm3nzp0LtVqtW9LS0molbmo8QjpXJmTRJzP5nixRI8GEjIgMltJIjveGdsLCF7rBQmmEw5fyMOTrGOw9ky11aNTE2NraQi6XV+sNy87OrtZrdjs3Nzd4eXkhIiICM2bMwHvvvXfXtkqlEpaWlnoLGRZfNxvYWSihvlGOA39flTocIqoBJmREZPCe8nLCpqm94eFkidziMoxdegif/HkaFRzCSLVEoVDAx8cHO3bs0Fu/Y8cO9OrVq8bHEUURpaWltR0eNSFymYCnPCurLW46ymqLRI0BEzIiIgCtbc3x+6ReGO3XEgDw7Z7zeOHHBGQVlEgcGTUVM2fOxI8//oglS5YgJSUFM2bMQGpqKl555RUAlcMNx4wZo2v/7bffYtOmTTh37hzOnTuHpUuX4tNPP8Xo0aOl+gnUSAR3rpwkevupLJSUaySOhojux0jqAIiIGgoTYzk+CPVCTzcbzF13DAkXcvHUV/vx1ciu6N3OVurwqJELCwvDtWvX8P777yMjIwOenp7YunUrWrVqBQDIyMjQm5NMq9Vi7ty5uHDhAoyMjNC2bVvMnz8fEydOlOonUCPh09IKjpYmyCwowb6zObr5yYioYeI8ZLWI870QNR3nc4owedURnM4shCAA055sh2n920Euu3cBBpIGr793xvNiuD7YfAo/xlxASGdn/O/5rlKHQ2SQOA8ZEdEjaGvXDBsmB2BkD1eIIvDVrnMI/ykB2YUcwkhEDV/VsMWdp7JwvaxC4miI6F6YkBER3YWJsRzzn/HGF2GdYWosR+z5axjydQxiz7NyGRE1bJ1dVHC1NsWNcg12n2blWKKGjAkZEdF9DO/qgk1TA9DeoRlyCksx+scE/G/XOWi1HPFNRA2TIAgY4lXZS8ZJookaNiZkREQ18Ji9BTZMDsCzPi7QisBnO87ixaUHca2IJciJqGGqmiR6z5lsFJVy2CJRQ8WEjIiohswURvj0uc745FlvmBjLsP/cVTz19X4cvJArdWhERNV4OFmija05Siu02HkqS+pwiOgumJARET2g57q74o/JvdHWzhxZBaV4fnE8Fu79m0MYiahBEQQBwd6VvWScJJqo4ZI0Ifvrr78QEhICZ2dnCIKADRs2VGuTkpKCoUOHQqVSwcLCAn5+fnrztJSWlmLq1KmwtbWFubk5hg4disuXL+sdIy8vD+Hh4VCpVFCpVAgPD0d+fr5em9TUVISEhMDc3By2traYNm0aysrK6uJnE1ET4O5ogY1TeiO0izM0WhELos9g/M+HkFfM6wYRNRxV1Rb/OpcD9fVyiaMhojuRNCErLi5G586d8c0339xx+/nz59G7d2906NABe/fuxdGjR/HOO+/AxMRE12b69OlYv349oqKiEBMTg6KiIgQHB0Oj+Xdm+lGjRiE5ORnR0dGIjo5GcnIywsPDdds1Gg2GDBmC4uJixMTEICoqCuvWrcOsWbPq7scTUaNnrjTCF2FdMP9pLyiMZNhzJgdPfb0fiZc4hJGIGob2DhZo79AM5RoRf57KlDocIrqDBjMxtCAIWL9+PUJDQ3XrRo4cCWNjY6xYseKO+6jVatjZ2WHFihUICwsDAKSnp8PV1RVbt25FUFAQUlJS4OHhgfj4ePj6+gIA4uPj4e/vj9OnT8Pd3R3btm1DcHAw0tLS4Oxc+SQpKioKY8eORXZ2do0n0+QEnESG61R6ASavPoILV4thJBPw+iB3RPRpA0HgRNL1gdffO+N5IQD4365z+GzHWTze3g7Lx/WUOhwig9HoJ4bWarXYsmUL2rdvj6CgINjb28PX11dvWGNiYiLKy8sRGBioW+fs7AxPT0/ExsYCAOLi4qBSqXTJGAD4+flBpVLptfH09NQlYwAQFBSE0tJSJCYm3jXG0tJSFBQU6C1EZJg8nC2xaWpvhHR2RoVWxEdbTyNi+WHkX+cQRiKSVtWwxQN/X0Uuh1UTNTgNNiHLzs5GUVER5s+fj0GDBmH79u0YPnw4nn76aezbtw8AkJmZCYVCASsrK719HRwckJmZqWtjb29f7fj29vZ6bRwcHPS2W1lZQaFQ6NrcSWRkpO69NJVKBVdX10f6zUTUuDVTGuHrkV3w31BPKOQy7EzJxpCvY5CUmid1aERkwNxszdHJ2RIarYhtJzgnGVFD02ATMq1WCwAYNmwYZsyYgS5duuDNN99EcHAwvvvuu3vuK4qi3jChOw0Zepg2t5s7dy7UarVuSUtLu+/vIqKmTRAEhPu1wu+TeqGltRmu5N/AiO/jsCTmAhrICHEiMkDB3pwkmqiharAJma2tLYyMjODh4aG3vmPHjroqi46OjigrK0Nenv7T5+zsbF2Pl6OjI7Kyqs+9kZOTo9fm9p6wvLw8lJeXV+s5u5VSqYSlpaXeQkQEAJ4tVNg8rTcGezqiXCPi/c2n8OrKI1DfYJUzIqp/VeXvEy5cQ3ZhicTRENGtGmxCplAo0KNHD5w5c0Zv/dmzZ9GqVSsAgI+PD4yNjbFjxw7d9oyMDJw4cQK9evUCAPj7+0OtVuPgwYO6NgkJCVCr1XptTpw4gYyMf58abd++HUqlEj4+PnX2G4moabM0McbCF7rhvRAPGMsFRJ/MRPD/9uP4ZbXUoRGRgXG1NkMX1+bQisC246y2SNSQGEn55UVFRfj77791ny9cuIDk5GRYW1ujZcuWmDNnDsLCwvD444/jiSeeQHR0NDZt2oS9e/cCAFQqFcaPH49Zs2bBxsYG1tbWmD17Nry8vDBgwAAAlT1qgwYNQkREBL7//nsAwMsvv4zg4GC4u7sDAAIDA+Hh4YHw8HB88sknyM3NxezZsxEREcFeLyJ6JIIgYGyAG7q2tMLk1UeQlnsDzyyKxdvBHRHu1wqCIECjFXHwQi6yC0tgb2GCnm7WkMtYnZGIalewtxOS0/Kx+Vg6XuzVWupwiOgmScve7927F0888US19S+++CKWLVsGAFiyZAkiIyNx+fJluLu7Y968eRg2bJiubUlJCebMmYPVq1fjxo0b6N+/PxYuXKhXYCM3NxfTpk3Dxo0bAQBDhw7FN998g+bNm+vapKamYtKkSdi9ezdMTU0xatQofPrpp1AqlTX+PSwvTET3or5ejtm/HcWOU5XDqId4OWFAR3ss+PMMMtT/DiFyUpng3RAPDPJ0kirURofX3zvjeaFbZahvwD9yNwAg9s0n4dzcVOKIiJq2ml6DG8w8ZE0Bb3xEdD+iKOKnmAuYv+00KrR3vvxW9Y0tGt2NSVkN8fp7ZzwvdLvnvovFoYt5eHtIR0zo00bqcIiatEY/DxkRUVMkCAIm9GmDNS/74W6jEqvStHmbTkFzl6SNiOhhVFVb3HSM1RaJGgomZEREEqjQiLhXriUCyFCX4OCF3HqLiYiavsFejpAJwNG0fKTlXpc6HCICEzIiIknUtOw0y1MTUW2ytzCBXxsbAMBm9pIRNQhMyIiIJGBvYVKjdheuFnPYIhHVKt2wxaPpEkdCRP/f3r3HRVHv/wN/ze7CchFWuYsgeANFEO8KeCsNLMX81VE7x0izMou8JJraOYV2KqTTRU/HLK008xh9v78Tlh0Fs8K7gih5QRCRELm4ishNXWB3vn8gGwuLgq476L6ej8c+aGc+M7w/kztv3juf+QzAgoyISBJDuzmhs8oGt5vcftWuHIz7cDcSUs9DU6c1S2xE9GAbH+gBuUxAZnEFzl2qkjocIovHgoyISAJymYDYyAAAaFaUCTdfE4I6Q2VrhbzL1Vj63QmMjP8V6/bkokpTZ+5wiegB4mRvjbCeLgA4bJGoPWBBRkQkkfGBnbH26YHwUBkOX/RQ2WDt0wOxZvpAHFj6MP42oQ88HG2grtTg3e1ZCI37Ge8nZ6O0SiNR5ER0v4vsV/9IjR+Pc9gikdT4HDIT4vNeiOhOaHUiUvOuQF15A24ONhjazQnyJnPi19TpsDWjEJ/uzsW5S9UAABsrGaYN9sbzI7vD28lOitDbDZ5/jeNxoZaUX6/FkLd3oUarQ/KCUfD3cJA6JKJ2pTW5+XZaew5W3G2wRER0d+QyASE9nG/Zxlohw9TB3vjTQC/szLyItSln8duFcnx1MB+bD5/HpGBPvDi6O3p78I9uIro9la0VRvm5YNdpNX48XgR/D3+pQyJqN5JOFmPFtkwUl/8x03FnlQ1iIwMwPrCzyX8fhywSEd1HZDIB4wM9sDU6DFteGIaRvVyg1YlIPFaI8av24rmNaTjyO59dRkS31zDb4o/Hi8EBU0T1kk4W46XNRw2KMQAoKb+BlzYfRdJJ0993yYKMiOg+JAgCQnu44OvnhmHbKyMwIagzBAH4OUuNP316EFM+PYBfsi7yjywiatG4AHcoFTLkXa7GqaIKqcMhkpxWJ2LFtkwYy5wNy1ZsyzT542hYkBER3eeCvFRYM30gfokZgz8P9Ya1XIa038swa+MRPLp6L7YeK0SdVid1mETUznRQKvBwbzcAnG2RLJMoirh6rQZnLlZi/9nL+PCn7GZXxgzaAyguv4HUPNOOROE9ZERED4huLvaIe6IfFozzw5f78rD5UD6ySiqx4NsMvL8zGy+O6o4pg71hYyWXOlQiaicm9vPEjpMl+PF4EZaM94cgtG3SAqL26EatFpcqNVBXanCpUoNLVTd/Vt64+fOP5bXatl/tUle2XLTdCRZkREQPGHdHGyx7rA9eHtMTmw/n48t9ebhQdh1vfH8Kq3blYNaIbnh6uA9UtlZSh0pEEnu4txvsrOW4UHYdGQVXMaBrJ6lDonbCFLMMmjqeK9U1BgWW2kiBdalSg8obbXteZ0c7K7h2UMJaIWvV8F03B5vbtmkLFmRERA8olZ0Voh/qiedGdMP/HinAZ3vO4ULZdfwjORtrU3IxfVhXzBrRDe6Opk0sRHT/sLWWY2wfd2z7rQg/Hi9mQdZIeytIzMlcswyKoojqGq2+oGqpwFJXalBapUFbbt2yVsjg5qCEq4MSrh3qf7o52NS/b/Ry6WANpaJ+5IhWJ2JE/C8oKb9h9D4yAfXPCh3azckk/dfvl88hMx0+74WI2rM6rQ7/PVGMtSm5yCqpBABYy2V4clAXzB7VA91c7CWO8M7x/Gscjwu1RvKpErz4dTo8HG1wYOnDkFlI0XEr5p72vD1pmGWwaYHQ8K9i7dMDb3sMarU6lFbV3LLAalh2vVbb6tgEAXC2t4ZLSwVWw3JHJRyUijsagtvQfwAGx6At/W/Q2nMwCzITYuIjovuBKIr4NVuNtSm5SPu9DEB9knsssDPmjO6BIC+VxBG2Hc+/xvG4UGvcqNViyNu7UKmpw//OCcEQX9N++3+/MUVBcr9quELU0sQWAgCXDkp8NDUYpddqGl3ZMiy6rlTXtOn32lvLb1lg1a9TwsneGgr5vZ+T0FQFOQsyCTDxEdH9Ju33K/g0JRc/Z6n1y0b2csFLo3sgpIfzfXODP8+/xvG4UGst/J8MfHe0EM+E+OCtxwOlDkcyrSlIPFQ22LfkYZMMXxRFEVqdiLqbL61WRK1OB61ORK224WdDGx3qtDfbNqzTidDqdPo2DdvUt2myTqe7uf/65Q37arxd0dXr2JNz+a77BQAKmaC/kmWsuPpjyKAS9sr2dxeVKYastvYc3P56T0REZjPE1wlDZjohq6QCn+0+hx9+K8LenMvYm3MZwd4d8dLoHggPcOcQJqIHXGQ/T3x3tBDbT5QgNrKvxdwr1VRq3pVWTXs+7bODcLS10hdHBkVS4wLqZmFUpzNc17hwuh+5OyrR3aWD0QKrofjqZGd9X+cOuUxASA9ns/wuFmRERITeHo74aFp/LHzED5/vPYeEtAL8VnAVczano7urPeaM7oHJ/bvAWsHHVxI9iMJ6ukBla4XLVRocPleK0J4uUodkFpo6Lc6UVOFEYTlOFJZjX86lVm13JL/snsYllwlQNLzkMihkAuQyAVZyWf06ecN6WbP/ljfZTv9Tv53s5r4EyGWyRssFFF69jm9SC24b36ppA8xWrFgCFmRERKTn7WSHFY8HYu7YXti4/3dsOvg7zl2qxmv//zg+3HkGz4/shj8P7douh5cQ0Z2zVsjwaKAHEtIKsO148QNZkGnqtMguqcSJwnKcvFmAZZdU3tFzqJ4b4Qt/d8dGxdEfhVHjwql50SNroTBqVGzJBMmGi2t1IlKyL5l9lkFLx3vITIhj9YnoQVN5oxbfpJ7H53vzoK7UAABUtlaYEeqLmaG+cLK3ljjCejz/GsfjQm2xL+cynv7iMDrZWSH1r+NgZYbJE+6VG7XNi68zF40XXypbKwR1USGwiwp9OzvirR8zcblKc8uCxFT3kLVHppxl0NLxHjIiIrprDjZWmD2qB2aE+iLxaCE+23MOeZer8c+fc7B+zzk8NdQbz4/sji4dbaUOlYju0vDuTnC2t0ZpdQ32n72MMf5uUofUKjdqtchqKL4u/FF8Gbs/q6PdH8VX0M2XVydbgytSVgoBL20+CgHGC5LYyIAHthgDgPGBnbH26YHNZhn0sJBp/6XAK2QmxG8iiehBp9WJSD5Vgk9SzuJkYQWA+pm0Hu/fBXNGd0cvdwdJ4uL51zgeF2qrv209gc2HzuNPg7zw/pRgqcNp5katFqeLK/RXvU4UViCnheLLyd76ZuHlqC/CunS0bdVwQEt+DlkDS34wtqlw2nsJMPERkaUQRRH7z5Zi7e6z2H+2VL/8kQB3vDSmBwZ27WTWeHj+NY7Hhdrq0LlSPLXuEBxsFDjyt3FQKuSSxXKjVovMhuLr5pWvHHUVtEaKL2d98XXz6peXCp4qm7u6F4sFCd0tDlkkIqJ7RhAEjOjlghG9XPBbwVWsTclFcmYJfsq8iJ8yL2JYNye8NKYHRvu53jfPMiOi+kdhuDsqcbFCg71nLmNcgLtZfu/1mkbF1837vloqvlw6NCm+uqjQ+S6LL2PMOe05WTYWZEREdFeCvTvi06hBOKuuwro9uUg8VojDeVdwOO8K+nR2xEtjeuCxQA8o7uMJAogshVwm4LGgztiw/3f8eLzonhRk9cVXw1WvipvFVyWMPZLLpYPSYMhhkJcKHo6mL76IpMQhiybEoSFEREBx+XV8sTcPW1LP41qNFgDQ1ckOs0d1x58GecHGynAIlCmGBfH8axyPC92J9PwyPLn2AGwUMrz9/wLRpaPdHQ/Xu1ZTh8yiCv1zvk4WluOsuspo8eXqoGw24Ya7o5LFF923eA+ZBJj4iIj+cPVaDTYdzMeG/Xkou1YLoP7b7lkjfPH0cB842liZ7MZ5nn+N43GhO7HjRDGitxw1KJpa87ms1tQhs7gCJy78MdV87iXjxZdb0+LLSwV3R5t70Bsi6bAgkwATHxFRc9dq6vA/aQVYvzcPhVevAwAclAqE9nRG8qmLzdrfybNueP41jseF2qrhGVRN/zhs+rms1tTh1M0rX42LL2N/Vbo7Nr/y5cbiiywACzIJMPEREbWsVqvDtt+KsDYlFznqqlu2bevDV3n+NY7HhdpCqxMxIv4XgyvWTdlaydBZZYO80mtGiy8PR5tGV70cEdhFBTcHFl9kmTjLIhERtStWchmeGOiFyf274JOUs3h/55kW24oAistvIDXvCmc5IzKT1LwrtyzGAOB6rQ7nLl8DUD+MsfFVr8AuKrg6KM0RKtEDhQUZERGZlUwmwNvJrlVt1ZW3/uOQiEyntZ+3F0d3xwsju8OlA4svIlPgHMRERGR2rR3CxKFORObT2s/bGD83FmNEJiRpQbZnzx5ERkbC09MTgiBg69atLbZ98cUXIQgCVq1aZbBco9Fg7ty5cHFxgb29PSZNmoQLFy4YtCkrK0NUVBRUKhVUKhWioqJw9epVgzbnz59HZGQk7O3t4eLignnz5qGmpsZEPSUiosaGdnOqf5BrC+sF1A+HGtrNyZxhEVk0fi6JpCFpQVZdXY3g4GD861//umW7rVu34vDhw/D09Gy2bsGCBUhMTERCQgL27duHqqoqTJw4EVqtVt/mL3/5CzIyMpCUlISkpCRkZGQgKipKv16r1WLChAmorq7Gvn37kJCQgP/85z+IiYkxXWeJiEhPLhMQGxkAAM3++Gt4HxsZcEfPPSKiO8PPJZE02s0si4IgIDExEZMnTzZYXlhYiGHDhiE5ORkTJkzAggULsGDBAgBAeXk5XF1d8fXXX2PatGkAgKKiInh7e2P79u2IiIjA6dOnERAQgEOHDmHYsGEAgEOHDiEkJARZWVnw9/fHjh07MHHiRBQUFOiLvoSEBMycORNqtbrVM1NxNisiorbhc8juLR4XuhOm+lwSWboHYpZFnU6HqKgoLF68GH379m22Pj09HbW1tQgPD9cv8/T0RGBgIA4cOICIiAgcPHgQKpVKX4wBwPDhw6FSqXDgwAH4+/vj4MGDCAwMNLgCFxERAY1Gg/T0dDz00ENG49NoNNBoNPr3FRUVpug2EZHFGB/YGY8EeCA17wrUlTfg5lA/HIrfwBNJh59LIvNq1wVZfHw8FAoF5s2bZ3R9SUkJrK2t0alTJ4Pl7u7uKCkp0bdxc3Nrtq2bm5tBG3d3d4P1nTp1grW1tb6NMXFxcVixYkWb+kRERIbkMoFT2xO1M/xcEplPu51lMT09HatXr8bGjRshCG37RkYURYNtjG1/J22aWrZsGcrLy/WvgoKCNsVJRERERESWrd0WZHv37oVarUbXrl2hUCigUCiQn5+PmJgY+Pr6AgA8PDxQU1ODsrIyg23VarX+ipeHhwcuXrzYbP+XLl0yaNP0SlhZWRlqa2ubXTlrTKlUwtHR0eBFRERERETUWu22IIuKisLx48eRkZGhf3l6emLx4sVITk4GAAwaNAhWVlb46aef9NsVFxfj5MmTCA0NBQCEhISgvLwcqamp+jaHDx9GeXm5QZuTJ0+iuLhY32bnzp1QKpUYNGiQObpLREREREQWSNJ7yKqqqnD27Fn9+7y8PGRkZMDJyQldu3aFs7Ph2GUrKyt4eHjA398fAKBSqfDcc88hJiYGzs7OcHJywqJFixAUFIRx48YBAPr06YPx48fjhRdewGeffQYAmD17NiZOnKjfT3h4OAICAhAVFYV//OMfuHLlChYtWoQXXniBV72IiIiIiOiekfQK2ZEjRzBgwAAMGDAAALBw4UIMGDAAb775Zqv38dFHH2Hy5MmYOnUqwsLCYGdnh23btkEul+vb/Pvf/0ZQUBDCw8MRHh6Ofv364euvv9avl8vl+O9//wsbGxuEhYVh6tSpmDx5Mt5//33TdZaIiIiIiKiJdvMcsgcBn/dCRCQNnn+N43EhIpJOa8/B7fYeMiIiIiIiogcdCzIiIiIiIiKJsCAjIiIiIiKSCAsyIiIiIiIiiUg67f2DpmF+lIqKCokjISKyLA3nXc5TZYh5iYhIOq3NTSzITKiyshIA4O3tLXEkRESWqbKyEiqVSuow2g3mJSIi6d0uN3HaexPS6XQoKiqCg4MDBEFo8/YVFRXw9vZGQUGBRU5PzP6z/+w/+3+n/RdFEZWVlfD09IRMxtH4DZiX7g77b9n9B3gM2H/z5CZeITMhmUwGLy+vu96Po6OjRf6jb8D+s//sP/t/J3hlrDnmJdNg/y27/wCPAft/b3MTv0YkIiIiIiKSCAsyIiIiIiIiibAga0eUSiViY2OhVCqlDkUS7D/7z/6z/5ba//bK0v+/sP+W3X+Ax4D9N0//OakHERERERGRRHiFjIiIiIiISCIsyIiIiIiIiCTCgoyIiIiIiEgiLMiIiIiIiIgkwoJMYnFxcRgyZAgcHBzg5uaGyZMnIzs7W+qwzGbt2rXo16+f/oF7ISEh2LFjh9RhSSYuLg6CIGDBggVSh2IWy5cvhyAIBi8PDw+pwzKrwsJCPP3003B2doadnR369++P9PR0qcMyG19f32b/BgRBQHR0tNShWTTmJuamxpibmJssKTdJkZcU92zP1Cq7d+9GdHQ0hgwZgrq6Ovz1r39FeHg4MjMzYW9vL3V495yXlxdWrlyJnj17AgC++uorPP744zh27Bj69u0rcXTmlZaWhnXr1qFfv35Sh2JWffv2xa5du/Tv5XK5hNGYV1lZGcLCwvDQQw9hx44dcHNzQ25uLjp27Ch1aGaTlpYGrVarf3/y5Ek88sgjmDJlioRREXMTc1MD5qZ6zE2Wk5skyUsitStqtVoEIO7evVvqUCTTqVMn8fPPP5c6DLOqrKwUe/XqJf7000/i6NGjxfnz50sdklnExsaKwcHBUochmSVLlogjRoyQOox2Zf78+WKPHj1EnU4ndSjUCHMTcxNzk+VgbjJkjrzEIYvtTHl5OQDAyclJ4kjMT6vVIiEhAdXV1QgJCZE6HLOKjo7GhAkTMG7cOKlDMbucnBx4enqiW7dueOqpp3Du3DmpQzKbH374AYMHD8aUKVPg5uaGAQMGYP369VKHJZmamhps3rwZs2bNgiAIUodDjTA3MTdZGuYm5ibAfHmJBVk7IooiFi5ciBEjRiAwMFDqcMzmxIkT6NChA5RKJebMmYPExEQEBARIHZbZJCQk4OjRo4iLi5M6FLMbNmwYNm3ahOTkZKxfvx4lJSUIDQ1FaWmp1KGZxblz57B27Vr06tULycnJmDNnDubNm4dNmzZJHZoktm7diqtXr2LmzJlSh0KNMDcxN1ka5ibmpgZmy0v37NobtdnLL78s+vj4iAUFBVKHYlYajUbMyckR09LSxKVLl4ouLi7iqVOnpA7LLM6fPy+6ubmJGRkZ+mWWNCykqaqqKtHd3V384IMPpA7FLKysrMSQkBCDZXPnzhWHDx8uUUTSCg8PFydOnCh1GNQEcxNzkygyNzE3WWZuMlde4hWydmLu3Ln44Ycf8Ouvv8LLy0vqcMzK2toaPXv2xODBgxEXF4fg4GCsXr1a6rDMIj09HWq1GoMGDYJCoYBCocDu3bvxz3/+EwqFwuCmUktgb2+PoKAg5OTkSB2KWXTu3LnZN+59+vTB+fPnJYpIOvn5+di1axeef/55qUOhRpibmJuYm5ibAMvMTebMS5xlUWKiKGLu3LlITExESkoKunXrJnVIkhNFERqNRuowzGLs2LE4ceKEwbJnn30WvXv3xpIlSyxqVicA0Gg0OH36NEaOHCl1KGYRFhbWbCrxM2fOwMfHR6KIpLNhwwa4ublhwoQJUodCYG4yhrmJuYm5ybJykznzEgsyiUVHR2PLli34/vvv4eDggJKSEgCASqWCra2txNHde6+//joeffRReHt7o7KyEgkJCUhJSUFSUpLUoZmFg4NDs3sy7O3t4ezsbBH3aixatAiRkZHo2rUr1Go13n77bVRUVGDGjBlSh2YWr776KkJDQ/Huu+9i6tSpSE1Nxbp167Bu3TqpQzMrnU6HDRs2YMaMGVAomJbaA+Ym5ibmJuYmS85NZs9L93xQJN0SAKOvDRs2SB2aWcyaNUv08fERra2tRVdXV3Hs2LHizp07pQ5LUpY0Tn/atGli586dRSsrK9HT01N84oknLOYejQbbtm0TAwMDRaVSKfbu3Vtct26d1CGZXXJysghAzM7OljoUuom5ibmpKeYm5iZLYu68JIiiKN77so+IiIiIiIia4qQeREREREREEmFBRkREREREJBEWZERERERERBJhQUZERERERCQRFmREREREREQSYUFGREREREQkERZkREREREREEmFBRkREREREJBEWZETtxO+//w5BEJCRkSF1KHpZWVkYPnw4bGxs0L9//zZv3x77RERErdMez+HMS/QgYkFGdNPMmTMhCAJWrlxpsHzr1q0QBEGiqKQVGxsLe3t7ZGdn4+eff5Y6HGzcuBEdO3aUOgwiIrNgXmqOeYkeRCzIiBqxsbFBfHw8ysrKpA7FZGpqau5429zcXIwYMQI+Pj5wdnY2YVTS0mq10Ol0UodBRHRbzEuGmJfoQcSCjKiRcePGwcPDA3FxcS22Wb58ebNhEqtWrYKvr6/+/cyZMzF58mS8++67cHd3R8eOHbFixQrU1dVh8eLFcHJygpeXF7788stm+8/KykJoaChsbGzQt29fpKSkGKzPzMzEY489hg4dOsDd3R1RUVG4fPmyfv2YMWPwyiuvYOHChXBxccEjjzxitB86nQ5vvfUWvLy8oFQq0b9/fyQlJenXC4KA9PR0vPXWWxAEAcuXL29xP/Hx8ejZsyeUSiW6du2Kd955x2hbY98kNv2m97fffsNDDz0EBwcHODo6YtCgQThy5AhSUlLw7LPPory8HIIgGMRUU1OD1157DV26dIG9vT2GDRtmcNwafu+PP/6IgIAAKJVK5OfnIyUlBUOHDoW9vT06duyIsLAw5OfnG42diEgKzEvMS8xLDz4WZESNyOVyvPvuu/j4449x4cKFu9rXL7/8gqKiIuzZswcffvghli9fjokTJ6JTp044fPgw5syZgzlz5qCgoMBgu8WLFyMmJgbHjh1DaGgoJk2ahNLSUgBAcXExRo8ejf79++PIkSNISkrCxYsXMXXqVIN9fPXVV1AoFNi/fz8+++wzo/GtXr0aH3zwAd5//30cP34cERERmDRpEnJycvS/q2/fvoiJiUFxcTEWLVpkdD/Lli1DfHw83njjDWRmZmLLli1wd3e/4+M2ffp0eHl5IS0tDenp6Vi6dCmsrKwQGhqKVatWwdHREcXFxQYxPfvss9i/fz8SEhJw/PhxTJkyBePHj9f3BQCuXbuGuLg4fP755zh16hScnJwwefJkjB49GsePH8fBgwcxe/Zsix0GRETtE/MS8xLzkgUQiUgURVGcMWOG+Pjjj4uiKIrDhw8XZ82aJYqiKCYmJoqNPyqxsbFicHCwwbYfffSR6OPjY7AvHx8fUavV6pf5+/uLI0eO1L+vq6sT7e3txW+++UYURVHMy8sTAYgrV67Ut6mtrRW9vLzE+Ph4URRF8Y033hDDw8MNfndBQYEIQMzOzhZFURRHjx4t9u/f/7b99fT0FN955x2DZUOGDBFffvll/fvg4GAxNja2xX1UVFSISqVSXL9+vdH1DX06duyYKIqiuGHDBlGlUhm0aXp8HRwcxI0bNxrdn7Htz549KwqCIBYWFhosHzt2rLhs2TL9dgDEjIwM/frS0lIRgJiSktJi/4iIpMS8xLxEloFXyIiMiI+Px1dffYXMzMw73kffvn0hk/3xEXN3d0dQUJD+vVwuh7OzM9RqtcF2ISEh+v9WKBQYPHgwTp8+DQBIT0/Hr7/+ig4dOuhfvXv3BlA/rr7B4MGDbxlbRUUFioqKEBYWZrA8LCxM/7ta4/Tp09BoNBg7dmyrt7mdhQsX4vnnn8e4ceOwcuVKg34Zc/ToUYiiCD8/P4Pjsnv3boNtra2t0a9fP/17JycnzJw5ExEREYiMjMTq1atRXFxssn4QEZkS81LrMC/R/YgFGZERo0aNQkREBF5//fVm62QyGURRNFhWW1vbrJ2VlZXBe0EQjC5rzU28DcMVdDodIiMjkZGRYfDKycnBqFGj9O3t7e1vu8/G+20gimKbhkbY2tq2ui3QumO3fPlynDp1ChMmTMAvv/yCgIAAJCYmtrhPnU4HuVyO9PR0g2Ny+vRprF692iDWpn3bsGEDDh48iNDQUHz77bfw8/PDoUOH2tQnIiJzYF5qHeYluh+xICNqwcqVK7Ft2zYcOHDAYLmrqytKSkoMTuCmfJ5J4xNvXV0d0tPT9d82Dhw4EKdOnYKvry969uxp8GptsgMAR0dHeHp6Yt++fQbLDxw4gD59+rR6P7169YKtrW2rpx52dXVFZWUlqqur9cuMHTs/Pz+8+uqr2LlzJ5544gls2LABQP23iVqt1qDtgAEDoNVqoVarmx0TDw+P28Y0YMAALFu2DAcOHEBgYCC2bNnSqr4QEZkb89LtMS/R/YgFGVELgoKCMH36dHz88ccGy8eMGYNLly7hvffeQ25uLtasWYMdO3aY7PeuWbMGiYmJyMrKQnR0NMrKyjBr1iwAQHR0NK5cuYI///nPSE1Nxblz57Bz507MmjWrWUK4ncWLFyM+Ph7ffvstsrOzsXTpUmRkZGD+/Pmt3oeNjQ2WLFmC1157DZs2bUJubi4OHTqEL774wmj7YcOGwc7ODq+//jrOnj2LLVu2YOPGjfr1169fxyuvvIKUlBTk5+dj//79SEtL0ydjX19fVFVV4eeff8bly5dx7do1+Pn5Yfr06XjmmWfw3XffIS8vD2lpaYiPj8f27dtbjD0vLw/Lli3DwYMHkZ+fj507d+LMmTNtSvxERObEvHR7zEt0P2JBRnQLf//735sNZejTpw8++eQTrFmzBsHBwUhNTW1xpqc7sXLlSsTHxyM4OBh79+7F999/DxcXFwCAp6cn9u/fD61Wi4iICAQGBmL+/PlQqVQG9wW0xrx58xATE4OYmBgEBQUhKSkJP/zwA3r16tWm/bzxxhuIiYnBm2++iT59+mDatGnN7j9o4OTkhM2bN2P79u0ICgrCN998YzBtsVwuR2lpKZ555hn4+flh6tSpePTRR7FixQoAQGhoKObMmYNp06bB1dUV7733HoD6IR7PPPMMYmJi4O/vj0mTJuHw4cPw9vZuMW47OztkZWXhySefhJ+fH2bPno1XXnkFL774Ypv6T0RkTsxLt8e8RPcbQWz6qSYiIiIiIiKz4BUyIiIiIiIiibAgIyIiIiIikggLMiIiIiIiIomwICMiIiIiIpIICzIiIiIiIiKJsCAjIiIiIiKSCAsyIiIiIiIiibAgIyIiIiIikggLMiIiIiIiIomwICMiIiIiIpIICzIiIiIiIiKJ/B9P269ATjpoawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we plot the result diving the space into two subsplots, one for the elbow method and the other for the silhouette method\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ax1.plot(range(2, 8), inertias, marker='o')\n",
    "ax1.set_xlabel('Number of clusters')\n",
    "ax1.set_ylabel('Inertia')\n",
    "ax1.set_title('Elbow Method')\n",
    "\n",
    "ax2.plot(range(2, 8), silhouettes, marker='o')\n",
    "ax2.set_xlabel('Number of clusters')\n",
    "ax2.set_ylabel('Silhouette score')\n",
    "ax2.set_title('Silhouette Method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6530006c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see that for the elbow method the elbow appears to begin at three clusters,\n",
    "# we can see that for the silhouette the ideal number of clusters is 2, and the next optimal is 3\n",
    "# since from the hierarchical clustering we also have the cutoff point at three, and these methods also more or less confirm it we will proceed with three"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbe1e1a",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25258883",
   "metadata": {},
   "source": [
    "### 10. Final cluster and aggregated metrics per cluster(ROI,majority genre representation per cluster &moreabs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "275ccacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we create a variable with the number of clusters and fit it with the k means algorithm\n",
    "# we then create a new dataframe which will contain a new column that is the prediction of the algo for that cluster (the value will be either 0,1,2)\n",
    "number_of_clusters = 3\n",
    "\n",
    "km = KMeans(n_clusters = number_of_clusters,random_state = 22)\n",
    "km.fit(pca_df2)\n",
    "\n",
    "result = pca_df2.copy()\n",
    "result[\"cluster\"] = km.predict(pca_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0637e613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3767\n",
       "2     874\n",
       "1     161\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[\"cluster\"].value_counts()\n",
    "# we see the size of each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5635ee0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e76c9779",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we add the column with the results to our original dataframe\n",
    "new_df[\"result\"] = result[\"cluster\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5f1dd756",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>popularity</th>\n",
       "      <th>revenue</th>\n",
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       "      <th>vote_average</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4798</th>\n",
       "      <td>220000</td>\n",
       "      <td>14.269792</td>\n",
       "      <td>2040920</td>\n",
       "      <td>81.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4799</th>\n",
       "      <td>9000</td>\n",
       "      <td>0.642552</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4800</th>\n",
       "      <td>0</td>\n",
       "      <td>1.444476</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4801</th>\n",
       "      <td>0</td>\n",
       "      <td>0.857008</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4802</th>\n",
       "      <td>0</td>\n",
       "      <td>1.929883</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4802 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         budget  popularity     revenue  runtime  vote_average  vote_count  \\\n",
       "0     237000000  150.437577  2787965087    162.0           7.2       11800   \n",
       "1     300000000  139.082615   961000000    169.0           6.9        4500   \n",
       "2     245000000  107.376788   880674609    148.0           6.3        4466   \n",
       "3     250000000  112.312950  1084939099    165.0           7.6        9106   \n",
       "4     260000000   43.926995   284139100    132.0           6.1        2124   \n",
       "...         ...         ...         ...      ...           ...         ...   \n",
       "4798     220000   14.269792     2040920     81.0           6.6         238   \n",
       "4799       9000    0.642552           0     85.0           5.9           5   \n",
       "4800          0    1.444476           0    120.0           7.0           6   \n",
       "4801          0    0.857008           0     98.0           5.7           7   \n",
       "4802          0    1.929883           0     90.0           6.3          16   \n",
       "\n",
       "      Action  Adventure  Animation  Comedy  Crime  Documentary  Drama  Family  \\\n",
       "0          1          1          0       0      0            0      0       0   \n",
       "1          1          1          0       0      0            0      0       0   \n",
       "2          1          1          0       0      1            0      0       0   \n",
       "3          1          0          0       0      1            0      1       0   \n",
       "4          1          1          0       0      0            0      0       0   \n",
       "...      ...        ...        ...     ...    ...          ...    ...     ...   \n",
       "4798       1          0          0       0      1            0      0       0   \n",
       "4799       0          0          0       1      0            0      0       0   \n",
       "4800       0          0          0       1      0            0      1       0   \n",
       "4801       0          0          0       0      0            0      0       0   \n",
       "4802       0          0          0       0      0            1      0       0   \n",
       "\n",
       "      Fantasy  Foreign  History  Horror  Music  Mystery  Romance  \\\n",
       "0           1        0        0       0      0        0        0   \n",
       "1           1        0        0       0      0        0        0   \n",
       "2           0        0        0       0      0        0        0   \n",
       "3           0        0        0       0      0        0        0   \n",
       "4           0        0        0       0      0        0        0   \n",
       "...       ...      ...      ...     ...    ...      ...      ...   \n",
       "4798        0        0        0       0      0        0        0   \n",
       "4799        0        0        0       0      0        0        1   \n",
       "4800        0        0        0       0      0        0        1   \n",
       "4801        0        0        0       0      0        0        0   \n",
       "4802        0        0        0       0      0        0        0   \n",
       "\n",
       "      ScienceFiction  TVMovie  Thriller  War  Western  ar  cn  cs  da  de  el  \\\n",
       "0                  1        0         0    0        0   0   0   0   0   0   0   \n",
       "1                  0        0         0    0        0   0   0   0   0   0   0   \n",
       "2                  0        0         0    0        0   0   0   0   0   0   0   \n",
       "3                  0        0         1    0        0   0   0   0   0   0   0   \n",
       "4                  1        0         0    0        0   0   0   0   0   0   0   \n",
       "...              ...      ...       ...  ...      ...  ..  ..  ..  ..  ..  ..   \n",
       "4798               0        0         1    0        0   0   0   0   0   0   0   \n",
       "4799               0        0         0    0        0   0   0   0   0   0   0   \n",
       "4800               0        1         0    0        0   0   0   0   0   0   0   \n",
       "4801               0        0         0    0        0   0   0   0   0   0   0   \n",
       "4802               0        0         0    0        0   0   0   0   0   0   0   \n",
       "\n",
       "      en  es  fa  fr  he  hi  hu  id  is  it  ja  ko  ky  nb  nl  no  pl  ps  \\\n",
       "0      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4      1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "4798   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4799   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4800   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4801   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4802   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "      pt  ro  ru  sl  sv  ta  te  th  tr  vi  xx  zh  release_month  result  \n",
       "0      0   0   0   0   0   0   0   0   0   0   0   0             12     1.0  \n",
       "1      0   0   0   0   0   0   0   0   0   0   0   0              5     1.0  \n",
       "2      0   0   0   0   0   0   0   0   0   0   0   0             10     1.0  \n",
       "3      0   0   0   0   0   0   0   0   0   0   0   0              7     1.0  \n",
       "4      0   0   0   0   0   0   0   0   0   0   0   0              3     2.0  \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..            ...     ...  \n",
       "4798   0   0   0   0   0   0   0   0   0   0   0   0              9     0.0  \n",
       "4799   0   0   0   0   0   0   0   0   0   0   0   0             12     0.0  \n",
       "4800   0   0   0   0   0   0   0   0   0   0   0   0             10     0.0  \n",
       "4801   0   0   0   0   0   0   0   0   0   0   0   0              5     0.0  \n",
       "4802   0   0   0   0   0   0   0   0   0   0   0   0              8     NaN  \n",
       "\n",
       "[4802 rows x 64 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5814b258",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>release_month</th>\n",
       "      <th>popularity</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0.0</th>\n",
       "      <td>103.848911</td>\n",
       "      <td>5.930828</td>\n",
       "      <td>239.686405</td>\n",
       "      <td>1.631461e+07</td>\n",
       "      <td>2.616286e+07</td>\n",
       "      <td>9</td>\n",
       "      <td>11.586709</td>\n",
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       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>127.919255</td>\n",
       "      <td>7.077640</td>\n",
       "      <td>5493.608696</td>\n",
       "      <td>1.384699e+08</td>\n",
       "      <td>7.210830e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>114.496050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>116.175057</td>\n",
       "      <td>6.612586</td>\n",
       "      <td>1748.250572</td>\n",
       "      <td>6.380876e+07</td>\n",
       "      <td>2.064922e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>47.089452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           runtime  vote_average   vote_count        budget       revenue  \\\n",
       "result                                                                      \n",
       "0.0     103.848911      5.930828   239.686405  1.631461e+07  2.616286e+07   \n",
       "1.0     127.919255      7.077640  5493.608696  1.384699e+08  7.210830e+08   \n",
       "2.0     116.175057      6.612586  1748.250572  6.380876e+07  2.064922e+08   \n",
       "\n",
       "        release_month  popularity  \n",
       "result                             \n",
       "0.0                 9   11.586709  \n",
       "1.0                 6  114.496050  \n",
       "2.0                12   47.089452  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we groupby the cluster and aggregate column metrics\n",
    "new_df.groupby(\"result\").agg({\n",
    "    \"runtime\": np.mean,\n",
    "    \"vote_average\": np.mean,\n",
    "    \"vote_count\":np.mean,\n",
    "    \"budget\":np.mean,\n",
    "    \"revenue\":np.mean,\n",
    "    \"release_month\": pd.Series.mode,\n",
    "    \"popularity\":np.mean\n",
    "                               })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "22306aa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we save tge above into variable agg\n",
    "agg = new_df.groupby(\"result\").agg({\n",
    "    \"runtime\": np.mean,\n",
    "    \"vote_average\": np.mean,\n",
    "    \"vote_count\":np.mean,\n",
    "    \"budget\":np.mean,\n",
    "    \"revenue\":np.mean,\n",
    "    \"release_month\": pd.Series.mode,\n",
    "    \"popularity\":np.mean\n",
    "                               })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1f19f853",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we keep only budget and revenue and save it to a new variable\n",
    "new_agg = agg[[\"budget\",\"revenue\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "e23025a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicolas.margos\\AppData\\Local\\Temp\\ipykernel_24680\\339332241.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_agg[\"ROI in %\"]=round((new_agg[\"revenue\"] - new_agg[\"budget\"])/(new_agg[\"budget\"])*100,0)\n"
     ]
    }
   ],
   "source": [
    "# we create a new columnf for the ROI\n",
    "new_agg[\"ROI in %\"]=round((new_agg[\"revenue\"] - new_agg[\"budget\"])/(new_agg[\"budget\"])*100,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "1f7ddbca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>ROI in %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0.0</th>\n",
       "      <td>1.631461e+07</td>\n",
       "      <td>2.616286e+07</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1.384699e+08</td>\n",
       "      <td>7.210830e+08</td>\n",
       "      <td>421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>6.380876e+07</td>\n",
       "      <td>2.064922e+08</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              budget       revenue  ROI in %\n",
       "result                                      \n",
       "0.0     1.631461e+07  2.616286e+07      60.0\n",
       "1.0     1.384699e+08  7.210830e+08     421.0\n",
       "2.0     6.380876e+07  2.064922e+08     224.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we visualize the budget, the rev and the ROI per cluster\n",
    "new_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c7fb0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "8135c000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='result'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we visualize the above graph for budget and revenue\n",
    "new_agg[[\"budget\",\"revenue\"]].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a5a2ac95",
   "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>runtime</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>release_month</th>\n",
       "      <th>popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>103.848911</td>\n",
       "      <td>5.930828</td>\n",
       "      <td>239.686405</td>\n",
       "      <td>1.631461e+07</td>\n",
       "      <td>2.616286e+07</td>\n",
       "      <td>9</td>\n",
       "      <td>11.586709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>127.919255</td>\n",
       "      <td>7.077640</td>\n",
       "      <td>5493.608696</td>\n",
       "      <td>1.384699e+08</td>\n",
       "      <td>7.210830e+08</td>\n",
       "      <td>6</td>\n",
       "      <td>114.496050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>116.175057</td>\n",
       "      <td>6.612586</td>\n",
       "      <td>1748.250572</td>\n",
       "      <td>6.380876e+07</td>\n",
       "      <td>2.064922e+08</td>\n",
       "      <td>12</td>\n",
       "      <td>47.089452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           runtime  vote_average   vote_count        budget       revenue  \\\n",
       "result                                                                      \n",
       "0.0     103.848911      5.930828   239.686405  1.631461e+07  2.616286e+07   \n",
       "1.0     127.919255      7.077640  5493.608696  1.384699e+08  7.210830e+08   \n",
       "2.0     116.175057      6.612586  1748.250572  6.380876e+07  2.064922e+08   \n",
       "\n",
       "        release_month  popularity  \n",
       "result                             \n",
       "0.0                 9   11.586709  \n",
       "1.0                 6  114.496050  \n",
       "2.0                12   47.089452  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.groupby(\"result\").agg({\n",
    "    \"runtime\": np.mean,\n",
    "    \"vote_average\": np.mean,\n",
    "    \"vote_count\":np.mean,\n",
    "    \"budget\":np.mean,\n",
    "    \"revenue\":np.mean,\n",
    "    \"release_month\": pd.Series.mode,\n",
    "    \"popularity\":np.mean\n",
    "                               })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0a5852ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    3766\n",
       "2.0     874\n",
       "1.0     161\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[\"result\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2829e4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['budget', 'popularity', 'revenue', 'runtime', 'vote_average',\n",
       "       'vote_count', 'Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
       "       'Documentary', 'Drama', 'Family', 'Fantasy', 'Foreign', 'History',\n",
       "       'Horror', 'Music', 'Mystery', 'Romance', 'ScienceFiction', 'TVMovie',\n",
       "       'Thriller', 'War', 'Western', 'ar', 'cn', 'cs', 'da', 'de', 'el', 'en',\n",
       "       'es', 'fa', 'fr', 'he', 'hi', 'hu', 'id', 'is', 'it', 'ja', 'ko', 'ky',\n",
       "       'nb', 'nl', 'no', 'pl', 'ps', 'pt', 'ro', 'ru', 'sl', 'sv', 'ta', 'te',\n",
       "       'th', 'tr', 'vi', 'xx', 'zh', 'release_month', 'result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d564f91e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicolas.margos\\AppData\\Local\\Temp\\ipykernel_24680\\4184419309.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Foreign</th>\n",
       "      <th>History</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Music</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>ScienceFiction</th>\n",
       "      <th>TVMovie</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>740</td>\n",
       "      <td>436</td>\n",
       "      <td>116</td>\n",
       "      <td>1423</td>\n",
       "      <td>537</td>\n",
       "      <td>108</td>\n",
       "      <td>1931</td>\n",
       "      <td>343</td>\n",
       "      <td>232</td>\n",
       "      <td>34</td>\n",
       "      <td>159</td>\n",
       "      <td>452</td>\n",
       "      <td>165</td>\n",
       "      <td>263</td>\n",
       "      <td>755</td>\n",
       "      <td>326</td>\n",
       "      <td>8</td>\n",
       "      <td>960</td>\n",
       "      <td>103</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>89</td>\n",
       "      <td>110</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>46</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>325</td>\n",
       "      <td>244</td>\n",
       "      <td>86</td>\n",
       "      <td>267</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>333</td>\n",
       "      <td>124</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>132</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>283</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Action  Adventure  Animation  Comedy  Crime  Documentary  Drama  \\\n",
       "result                                                                    \n",
       "0.0        740        436        116    1423    537          108   1931   \n",
       "1.0         89        110         32      32     12            0     33   \n",
       "2.0        325        244         86     267    147            1    333   \n",
       "\n",
       "        Family  Fantasy  Foreign  History  Horror  Music  Mystery  Romance  \\\n",
       "result                                                                       \n",
       "0.0        343      232       34      159     452    165      263      755   \n",
       "1.0         46       50        0        2       2      0       10        7   \n",
       "2.0        124      142        0       36      65     20       75      132   \n",
       "\n",
       "        ScienceFiction  TVMovie  Thriller  War  Western  \n",
       "result                                                   \n",
       "0.0                326        8       960  103       69  \n",
       "1.0                 58        0        31    5        2  \n",
       "2.0                151        0       283   36       11  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we group by cluster to see the sum of movie genres that belong in each cluster\n",
    "new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
    "       'Documentary', 'Drama', 'Family', 'Fantasy', 'Foreign', 'History',\n",
    "       'Horror', 'Music', 'Mystery', 'Romance', 'ScienceFiction', 'TVMovie',\n",
    "       'Thriller', 'War', 'Western'].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66827124",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7577bde8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nicolas.margos\\AppData\\Local\\Temp\\ipykernel_24680\\1807596194.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  movie_percents = new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
      "C:\\Users\\nicolas.margos\\AppData\\Local\\Temp\\ipykernel_24680\\1807596194.py:4: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  'Thriller', 'War', 'Western'].sum() / new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n"
     ]
    }
   ],
   "source": [
    "# we turn these into percentages per genre to see for example the representation of Action per cluster\n",
    "movie_percents = new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
    "       'Documentary', 'Drama', 'Family', 'Fantasy', 'Foreign', 'History',\n",
    "       'Horror', 'Music', 'Mystery', 'Romance', 'ScienceFiction', 'TVMovie',\n",
    "       'Thriller', 'War', 'Western'].sum() / new_df.groupby(\"result\")['Action', 'Adventure', 'Animation', 'Comedy', 'Crime',\n",
    "       'Documentary', 'Drama', 'Family', 'Fantasy', 'Foreign', 'History',\n",
    "       'Horror', 'Music', 'Mystery', 'Romance', 'ScienceFiction', 'TVMovie',\n",
    "       'Thriller', 'War', 'Western'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "3cea9215",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.115773</td>\n",
       "      <td>0.030802</td>\n",
       "      <td>0.377854</td>\n",
       "      <td>0.142592</td>\n",
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       "      <td>0.042220</td>\n",
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       "      <td>0.086564</td>\n",
       "      <td>0.002124</td>\n",
       "      <td>0.254912</td>\n",
       "      <td>0.027350</td>\n",
       "      <td>0.018322</td>\n",
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       "      <td>0.552795</td>\n",
       "      <td>0.683230</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.062112</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.360248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.192547</td>\n",
       "      <td>0.031056</td>\n",
       "      <td>0.012422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.371854</td>\n",
       "      <td>0.279176</td>\n",
       "      <td>0.098398</td>\n",
       "      <td>0.305492</td>\n",
       "      <td>0.168192</td>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.381007</td>\n",
       "      <td>0.141876</td>\n",
       "      <td>0.162471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>0.074371</td>\n",
       "      <td>0.022883</td>\n",
       "      <td>0.085812</td>\n",
       "      <td>0.151030</td>\n",
       "      <td>0.172769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.323799</td>\n",
       "      <td>0.041190</td>\n",
       "      <td>0.012586</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          Action  Adventure  Animation    Comedy     Crime  Documentary  \\\n",
       "result                                                                    \n",
       "0.0     0.196495   0.115773   0.030802  0.377854  0.142592     0.028678   \n",
       "1.0     0.552795   0.683230   0.198758  0.198758  0.074534     0.000000   \n",
       "2.0     0.371854   0.279176   0.098398  0.305492  0.168192     0.001144   \n",
       "\n",
       "           Drama    Family   Fantasy   Foreign   History    Horror     Music  \\\n",
       "result                                                                         \n",
       "0.0     0.512746  0.091078  0.061604  0.009028  0.042220  0.120021  0.043813   \n",
       "1.0     0.204969  0.285714  0.310559  0.000000  0.012422  0.012422  0.000000   \n",
       "2.0     0.381007  0.141876  0.162471  0.000000  0.041190  0.074371  0.022883   \n",
       "\n",
       "         Mystery   Romance  ScienceFiction   TVMovie  Thriller       War  \\\n",
       "result                                                                     \n",
       "0.0     0.069835  0.200478        0.086564  0.002124  0.254912  0.027350   \n",
       "1.0     0.062112  0.043478        0.360248  0.000000  0.192547  0.031056   \n",
       "2.0     0.085812  0.151030        0.172769  0.000000  0.323799  0.041190   \n",
       "\n",
       "         Western  \n",
       "result            \n",
       "0.0     0.018322  \n",
       "1.0     0.012422  \n",
       "2.0     0.012586  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we have the results for each table\n",
    "movie_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "63615674",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mystery           2.0\n",
       "War               2.0\n",
       "Crime             2.0\n",
       "Thriller          2.0\n",
       "Action            1.0\n",
       "Animation         1.0\n",
       "Family            1.0\n",
       "Fantasy           1.0\n",
       "ScienceFiction    1.0\n",
       "Adventure         1.0\n",
       "TVMovie           0.0\n",
       "Romance           0.0\n",
       "History           0.0\n",
       "Music             0.0\n",
       "Horror            0.0\n",
       "Foreign           0.0\n",
       "Drama             0.0\n",
       "Documentary       0.0\n",
       "Comedy            0.0\n",
       "Western           0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use the following command to find which genre has the highest representation in each cluster\n",
    "movie_percents.idxmax().sort_values(ascending=False)"
   ]
  }
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