spotify_trend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee0e5b02-355f-48e8-8ebb-6dfea02bdd33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9228b0c-7a7a-419a-bd18-5dc118e2eb93",
   "metadata": {},
   "outputs": [],
   "source": [
    "spotify = pd.read_csv(\"C:/Users/Hp Pc/Downloads/Most Streamed Spotify Songs 2024.csv\", encoding = \"unicode_escape\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc8c32cc-ea49-4c3b-af69-6d73416e4121",
   "metadata": {
    "scrolled": true
   },
   "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>Track</th>\n",
       "      <th>Album Name</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>ISRC</th>\n",
       "      <th>All Time Rank</th>\n",
       "      <th>Track Score</th>\n",
       "      <th>Spotify Streams</th>\n",
       "      <th>Spotify Playlist Count</th>\n",
       "      <th>Spotify Playlist Reach</th>\n",
       "      <th>...</th>\n",
       "      <th>SiriusXM Spins</th>\n",
       "      <th>Deezer Playlist Count</th>\n",
       "      <th>Deezer Playlist Reach</th>\n",
       "      <th>Amazon Playlist Count</th>\n",
       "      <th>Pandora Streams</th>\n",
       "      <th>Pandora Track Stations</th>\n",
       "      <th>Soundcloud Streams</th>\n",
       "      <th>Shazam Counts</th>\n",
       "      <th>TIDAL Popularity</th>\n",
       "      <th>Explicit Track</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MILLION DOLLAR BABY</td>\n",
       "      <td>Million Dollar Baby - Single</td>\n",
       "      <td>Tommy Richman</td>\n",
       "      <td>4/26/2024</td>\n",
       "      <td>QM24S2402528</td>\n",
       "      <td>1</td>\n",
       "      <td>725.4</td>\n",
       "      <td>390,470,936</td>\n",
       "      <td>30,716</td>\n",
       "      <td>196,631,588</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>62.0</td>\n",
       "      <td>17,598,718</td>\n",
       "      <td>114.0</td>\n",
       "      <td>18,004,655</td>\n",
       "      <td>22,931</td>\n",
       "      <td>4,818,457</td>\n",
       "      <td>2,669,262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not Like Us</td>\n",
       "      <td>Not Like Us</td>\n",
       "      <td>Kendrick Lamar</td>\n",
       "      <td>5/4/2024</td>\n",
       "      <td>USUG12400910</td>\n",
       "      <td>2</td>\n",
       "      <td>545.9</td>\n",
       "      <td>323,703,884</td>\n",
       "      <td>28,113</td>\n",
       "      <td>174,597,137</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>67.0</td>\n",
       "      <td>10,422,430</td>\n",
       "      <td>111.0</td>\n",
       "      <td>7,780,028</td>\n",
       "      <td>28,444</td>\n",
       "      <td>6,623,075</td>\n",
       "      <td>1,118,279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i like the way you kiss me</td>\n",
       "      <td>I like the way you kiss me</td>\n",
       "      <td>Artemas</td>\n",
       "      <td>3/19/2024</td>\n",
       "      <td>QZJ842400387</td>\n",
       "      <td>3</td>\n",
       "      <td>538.4</td>\n",
       "      <td>601,309,283</td>\n",
       "      <td>54,331</td>\n",
       "      <td>211,607,669</td>\n",
       "      <td>...</td>\n",
       "      <td>536</td>\n",
       "      <td>136.0</td>\n",
       "      <td>36,321,847</td>\n",
       "      <td>172.0</td>\n",
       "      <td>5,022,621</td>\n",
       "      <td>5,639</td>\n",
       "      <td>7,208,651</td>\n",
       "      <td>5,285,340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Flowers</td>\n",
       "      <td>Flowers - Single</td>\n",
       "      <td>Miley Cyrus</td>\n",
       "      <td>1/12/2023</td>\n",
       "      <td>USSM12209777</td>\n",
       "      <td>4</td>\n",
       "      <td>444.9</td>\n",
       "      <td>2,031,280,633</td>\n",
       "      <td>269,802</td>\n",
       "      <td>136,569,078</td>\n",
       "      <td>...</td>\n",
       "      <td>2,182</td>\n",
       "      <td>264.0</td>\n",
       "      <td>24,684,248</td>\n",
       "      <td>210.0</td>\n",
       "      <td>190,260,277</td>\n",
       "      <td>203,384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11,822,942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Houdini</td>\n",
       "      <td>Houdini</td>\n",
       "      <td>Eminem</td>\n",
       "      <td>5/31/2024</td>\n",
       "      <td>USUG12403398</td>\n",
       "      <td>5</td>\n",
       "      <td>423.3</td>\n",
       "      <td>107,034,922</td>\n",
       "      <td>7,223</td>\n",
       "      <td>151,469,874</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>82.0</td>\n",
       "      <td>17,660,624</td>\n",
       "      <td>105.0</td>\n",
       "      <td>4,493,884</td>\n",
       "      <td>7,006</td>\n",
       "      <td>207,179</td>\n",
       "      <td>457,017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>For the Last Time</td>\n",
       "      <td>For the Last Time</td>\n",
       "      <td>$uicideboy$</td>\n",
       "      <td>9/5/2017</td>\n",
       "      <td>QM8DG1703420</td>\n",
       "      <td>4,585</td>\n",
       "      <td>19.4</td>\n",
       "      <td>305,049,963</td>\n",
       "      <td>65,770</td>\n",
       "      <td>5,103,054</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14,217</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20,104,066</td>\n",
       "      <td>13,184</td>\n",
       "      <td>50,633,006</td>\n",
       "      <td>656,337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>Dil Meri Na Sune</td>\n",
       "      <td>Dil Meri Na Sune (From \"Genius\")</td>\n",
       "      <td>Atif Aslam</td>\n",
       "      <td>7/27/2018</td>\n",
       "      <td>INT101800122</td>\n",
       "      <td>4,575</td>\n",
       "      <td>19.4</td>\n",
       "      <td>52,282,360</td>\n",
       "      <td>4,602</td>\n",
       "      <td>1,449,767</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193,590</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4597</th>\n",
       "      <td>Grace (feat. 42 Dugg)</td>\n",
       "      <td>My Turn</td>\n",
       "      <td>Lil Baby</td>\n",
       "      <td>2/28/2020</td>\n",
       "      <td>USUG12000043</td>\n",
       "      <td>4,571</td>\n",
       "      <td>19.4</td>\n",
       "      <td>189,972,685</td>\n",
       "      <td>72,066</td>\n",
       "      <td>6,704,802</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74</td>\n",
       "      <td>6.0</td>\n",
       "      <td>84,426,740</td>\n",
       "      <td>28,999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,135,998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>Nashe Si Chadh Gayi</td>\n",
       "      <td>November Top 10 Songs</td>\n",
       "      <td>Arijit Singh</td>\n",
       "      <td>11/8/2016</td>\n",
       "      <td>INY091600067</td>\n",
       "      <td>4,591</td>\n",
       "      <td>19.4</td>\n",
       "      <td>145,467,020</td>\n",
       "      <td>14,037</td>\n",
       "      <td>7,387,064</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6,817,840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448,292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>Me Acostumbre (feat. Bad Bunny)</td>\n",
       "      <td>Me Acostumbre (feat. Bad Bunny)</td>\n",
       "      <td>Arcï¿½ï¿½</td>\n",
       "      <td>4/11/2017</td>\n",
       "      <td>USB271700107</td>\n",
       "      <td>4,593</td>\n",
       "      <td>19.4</td>\n",
       "      <td>255,740,653</td>\n",
       "      <td>32,138</td>\n",
       "      <td>14,066,526</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>127,479</td>\n",
       "      <td>4.0</td>\n",
       "      <td>69,006,739</td>\n",
       "      <td>11,320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>767,006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Track                        Album Name  \\\n",
       "0                 MILLION DOLLAR BABY      Million Dollar Baby - Single   \n",
       "1                         Not Like Us                       Not Like Us   \n",
       "2          i like the way you kiss me        I like the way you kiss me   \n",
       "3                             Flowers                  Flowers - Single   \n",
       "4                             Houdini                           Houdini   \n",
       "...                               ...                               ...   \n",
       "4595                For the Last Time                 For the Last Time   \n",
       "4596                 Dil Meri Na Sune  Dil Meri Na Sune (From \"Genius\")   \n",
       "4597            Grace (feat. 42 Dugg)                           My Turn   \n",
       "4598              Nashe Si Chadh Gayi             November Top 10 Songs   \n",
       "4599  Me Acostumbre (feat. Bad Bunny)   Me Acostumbre (feat. Bad Bunny)   \n",
       "\n",
       "              Artist Release Date          ISRC All Time Rank  Track Score  \\\n",
       "0      Tommy Richman    4/26/2024  QM24S2402528             1        725.4   \n",
       "1     Kendrick Lamar     5/4/2024  USUG12400910             2        545.9   \n",
       "2            Artemas    3/19/2024  QZJ842400387             3        538.4   \n",
       "3        Miley Cyrus    1/12/2023  USSM12209777             4        444.9   \n",
       "4             Eminem    5/31/2024  USUG12403398             5        423.3   \n",
       "...              ...          ...           ...           ...          ...   \n",
       "4595     $uicideboy$     9/5/2017  QM8DG1703420         4,585         19.4   \n",
       "4596      Atif Aslam    7/27/2018  INT101800122         4,575         19.4   \n",
       "4597        Lil Baby    2/28/2020  USUG12000043         4,571         19.4   \n",
       "4598    Arijit Singh    11/8/2016  INY091600067         4,591         19.4   \n",
       "4599       Arcï¿½ï¿½    4/11/2017  USB271700107         4,593         19.4   \n",
       "\n",
       "     Spotify Streams Spotify Playlist Count Spotify Playlist Reach  ...  \\\n",
       "0        390,470,936                 30,716            196,631,588  ...   \n",
       "1        323,703,884                 28,113            174,597,137  ...   \n",
       "2        601,309,283                 54,331            211,607,669  ...   \n",
       "3      2,031,280,633                269,802            136,569,078  ...   \n",
       "4        107,034,922                  7,223            151,469,874  ...   \n",
       "...              ...                    ...                    ...  ...   \n",
       "4595     305,049,963                 65,770              5,103,054  ...   \n",
       "4596      52,282,360                  4,602              1,449,767  ...   \n",
       "4597     189,972,685                 72,066              6,704,802  ...   \n",
       "4598     145,467,020                 14,037              7,387,064  ...   \n",
       "4599     255,740,653                 32,138             14,066,526  ...   \n",
       "\n",
       "      SiriusXM Spins Deezer Playlist Count Deezer Playlist Reach  \\\n",
       "0                684                  62.0            17,598,718   \n",
       "1                  3                  67.0            10,422,430   \n",
       "2                536                 136.0            36,321,847   \n",
       "3              2,182                 264.0            24,684,248   \n",
       "4                  1                  82.0            17,660,624   \n",
       "...              ...                   ...                   ...   \n",
       "4595             NaN                   2.0                14,217   \n",
       "4596             NaN                   1.0                   927   \n",
       "4597             NaN                   1.0                    74   \n",
       "4598             NaN                   NaN                   NaN   \n",
       "4599             NaN                   4.0               127,479   \n",
       "\n",
       "     Amazon Playlist Count Pandora Streams Pandora Track Stations  \\\n",
       "0                    114.0      18,004,655                 22,931   \n",
       "1                    111.0       7,780,028                 28,444   \n",
       "2                    172.0       5,022,621                  5,639   \n",
       "3                    210.0     190,260,277                203,384   \n",
       "4                    105.0       4,493,884                  7,006   \n",
       "...                    ...             ...                    ...   \n",
       "4595                   NaN      20,104,066                 13,184   \n",
       "4596                   NaN             NaN                    NaN   \n",
       "4597                   6.0      84,426,740                 28,999   \n",
       "4598                   7.0       6,817,840                    NaN   \n",
       "4599                   4.0      69,006,739                 11,320   \n",
       "\n",
       "     Soundcloud Streams  Shazam Counts TIDAL Popularity Explicit Track  \n",
       "0             4,818,457      2,669,262              NaN              0  \n",
       "1             6,623,075      1,118,279              NaN              1  \n",
       "2             7,208,651      5,285,340              NaN              0  \n",
       "3                   NaN     11,822,942              NaN              0  \n",
       "4               207,179        457,017              NaN              1  \n",
       "...                 ...            ...              ...            ...  \n",
       "4595         50,633,006        656,337              NaN              1  \n",
       "4596                NaN        193,590              NaN              0  \n",
       "4597                NaN      1,135,998              NaN              1  \n",
       "4598                NaN        448,292              NaN              0  \n",
       "4599                NaN        767,006              NaN              1  \n",
       "\n",
       "[4600 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "191a6d2b-93cb-4153-909e-db418eaff00a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Track',\n",
       " 'Album Name',\n",
       " 'Artist',\n",
       " 'Release Date',\n",
       " 'ISRC',\n",
       " 'All Time Rank',\n",
       " 'Track Score',\n",
       " 'Spotify Streams',\n",
       " 'Spotify Playlist Count',\n",
       " 'Spotify Playlist Reach',\n",
       " 'Spotify Popularity',\n",
       " 'YouTube Views',\n",
       " 'YouTube Likes',\n",
       " 'TikTok Posts',\n",
       " 'TikTok Likes',\n",
       " 'TikTok Views',\n",
       " 'YouTube Playlist Reach',\n",
       " 'Apple Music Playlist Count',\n",
       " 'AirPlay Spins',\n",
       " 'SiriusXM Spins',\n",
       " 'Deezer Playlist Count',\n",
       " 'Deezer Playlist Reach',\n",
       " 'Amazon Playlist Count',\n",
       " 'Pandora Streams',\n",
       " 'Pandora Track Stations',\n",
       " 'Soundcloud Streams',\n",
       " 'Shazam Counts',\n",
       " 'TIDAL Popularity',\n",
       " 'Explicit Track']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotify.columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68327b92-0295-4ad0-83c0-83788c00d0e2",
   "metadata": {},
   "source": [
    "PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d13c1051-2d58-42e5-9faa-7948bc2389f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4370"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spotify.Track.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b058f0ef-718f-4245-a73f-da05eb970a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4005"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spotify['Album Name'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "523feff0-d208-4892-8120-aff19b002a3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spotify.Artist.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c784f7ab-aa11-4b96-b157-f53c415fd343",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = spotify.filter(regex = 'Stream|Views')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adc89c63-e882-427c-af82-9a2987caa339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams',\n",
       "       'Soundcloud Streams'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fb58e517-0e2f-4de3-9091-583f4f3e86fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spotify Streams        113\n",
       "YouTube Views          308\n",
       "TikTok Views           981\n",
       "Pandora Streams       1106\n",
       "Soundcloud Streams    3333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eeaaee7b-f818-4669-bbed-450fc8463069",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = popularity.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0aabae92-59c5-4cf3-9b90-4fef2c4a3599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spotify Streams       0\n",
       "YouTube Views         0\n",
       "TikTok Views          0\n",
       "Pandora Streams       0\n",
       "Soundcloud Streams    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "254066b6-2b3b-4948-9778-49aec86c70e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#popularity = popularity.replace(\",\", \" \")\n",
    "popularity = popularity.replace( regex= \",\", value= \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10773daf-892e-4092-92da-e817e494a0ea",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Spotify Streams YouTube Views TikTok Views Pandora Streams  \\\n",
       "0          390470936      84274754   5332281936        18004655   \n",
       "1          323703884     116347040    208339025         7780028   \n",
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       "...              ...           ...          ...             ...   \n",
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       "4599       255740653     866300755     70196388        69006739   \n",
       "\n",
       "     Soundcloud Streams  \n",
       "0               4818457  \n",
       "1               6623075  \n",
       "2               7208651  \n",
       "3                     0  \n",
       "4                207179  \n",
       "...                 ...  \n",
       "4595           50633006  \n",
       "4596                  0  \n",
       "4597                  0  \n",
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       "\n",
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47dab3b0-155f-4090-9c5b-a699494bafaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = popularity.apply(lambda x: pd.to_numeric(x, errors = 'coerce'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65da4190-19bc-476a-a4c6-f5cee408d923",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = popularity.replace([float('inf'), -float('inf')], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "162f48e5-98e1-4c11-a6b1-15064eb06c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spotify Streams       int64\n",
       "YouTube Views         int64\n",
       "TikTok Views          int64\n",
       "Pandora Streams       int64\n",
       "Soundcloud Streams    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff0bb995-98c2-4bbc-bb6f-3033de73a99b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      Spotify Streams  YouTube Views  TikTok Views  Pandora Streams  \\\n",
       "0           390470936       84274754    5332281936         18004655   \n",
       "1           323703884      116347040     208339025          7780028   \n",
       "2           601309283      122599116    3369120610          5022621   \n",
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       "4           107034922       77373957             0          4493884   \n",
       "...               ...            ...           ...              ...   \n",
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       "4598        145467020     1118595159      37757301          6817840   \n",
       "4599        255740653      866300755      70196388         69006739   \n",
       "\n",
       "      Soundcloud Streams  \n",
       "0                4818457  \n",
       "1                6623075  \n",
       "2                7208651  \n",
       "3                      0  \n",
       "4                 207179  \n",
       "...                  ...  \n",
       "4595            50633006  \n",
       "4596                   0  \n",
       "4597                   0  \n",
       "4598                   0  \n",
       "4599                   0  \n",
       "\n",
       "[4600 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "897e574f-da52-4310-adf7-d55586612be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach  = spotify.filter(regex = 'Reach')\n",
    "social_reach['Amazon Reach'] = spotify['Amazon Playlist Count']\n",
    "social_reach['Apple Reach'] = spotify['Apple Music Playlist Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bff2c3d0-a702-4f1a-b64e-9e53dcd5e8f0",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Spotify Playlist Reach YouTube Playlist Reach Deezer Playlist Reach  \\\n",
       "0               196,631,588            150,597,040            17,598,718   \n",
       "1               174,597,137            156,380,351            10,422,430   \n",
       "2               211,607,669            373,784,955            36,321,847   \n",
       "3               136,569,078          3,351,188,582            24,684,248   \n",
       "4               151,469,874            112,763,851            17,660,624   \n",
       "...                     ...                    ...                   ...   \n",
       "4595              5,103,054                 53,016                14,217   \n",
       "4596              1,449,767             24,973,048                   927   \n",
       "4597              6,704,802             29,253,152                    74   \n",
       "4598              7,387,064             25,150,516                   NaN   \n",
       "4599             14,066,526            175,831,376               127,479   \n",
       "\n",
       "      Amazon Reach  Apple Reach  \n",
       "0            114.0        210.0  \n",
       "1            111.0        188.0  \n",
       "2            172.0        190.0  \n",
       "3            210.0        394.0  \n",
       "4            105.0        182.0  \n",
       "...            ...          ...  \n",
       "4595           NaN          3.0  \n",
       "4596           NaN          1.0  \n",
       "4597           6.0         19.0  \n",
       "4598           7.0          1.0  \n",
       "4599           4.0         11.0  \n",
       "\n",
       "[4600 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dcb32369-0912-4a6a-8c8e-0bcf50a51f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach = social_reach.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a3453132-a998-45df-82df-08ad85fc9742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spotify Playlist Reach    0\n",
       "YouTube Playlist Reach    0\n",
       "Deezer Playlist Reach     0\n",
       "Amazon Reach              0\n",
       "Apple Reach               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_reach.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9f36873-2a5e-4136-bff1-6f4b4884fcd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach = social_reach.apply(lambda x: x.replace(regex= \",\", value= \"\")) #remove all the commas\n",
    "social_reach = social_reach.apply(lambda x: pd.to_numeric(x, errors = 'coerce'))\n",
    "#social_reach = social_reach.replace([float('inf'), -float('inf')], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd9cd06f-61a6-4d03-8cef-1261be884cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spotify Playlist Reach</th>\n",
       "      <th>YouTube Playlist Reach</th>\n",
       "      <th>Deezer Playlist Reach</th>\n",
       "      <th>Amazon Reach</th>\n",
       "      <th>Apple Reach</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196631588</td>\n",
       "      <td>150597040</td>\n",
       "      <td>17598718</td>\n",
       "      <td>114.0</td>\n",
       "      <td>210.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174597137</td>\n",
       "      <td>156380351</td>\n",
       "      <td>10422430</td>\n",
       "      <td>111.0</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>211607669</td>\n",
       "      <td>373784955</td>\n",
       "      <td>36321847</td>\n",
       "      <td>172.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>136569078</td>\n",
       "      <td>3351188582</td>\n",
       "      <td>24684248</td>\n",
       "      <td>210.0</td>\n",
       "      <td>394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151469874</td>\n",
       "      <td>112763851</td>\n",
       "      <td>17660624</td>\n",
       "      <td>105.0</td>\n",
       "      <td>182.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>14217</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>4596</th>\n",
       "      <td>1449767</td>\n",
       "      <td>24973048</td>\n",
       "      <td>927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4597</th>\n",
       "      <td>6704802</td>\n",
       "      <td>29253152</td>\n",
       "      <td>74</td>\n",
       "      <td>6.0</td>\n",
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       "      <th>4598</th>\n",
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       "      <td>25150516</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>14066526</td>\n",
       "      <td>175831376</td>\n",
       "      <td>127479</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Spotify Playlist Reach  YouTube Playlist Reach  Deezer Playlist Reach  \\\n",
       "0                  196631588               150597040               17598718   \n",
       "1                  174597137               156380351               10422430   \n",
       "2                  211607669               373784955               36321847   \n",
       "3                  136569078              3351188582               24684248   \n",
       "4                  151469874               112763851               17660624   \n",
       "...                      ...                     ...                    ...   \n",
       "4595                 5103054                   53016                  14217   \n",
       "4596                 1449767                24973048                    927   \n",
       "4597                 6704802                29253152                     74   \n",
       "4598                 7387064                25150516                      0   \n",
       "4599                14066526               175831376                 127479   \n",
       "\n",
       "      Amazon Reach  Apple Reach  \n",
       "0            114.0        210.0  \n",
       "1            111.0        188.0  \n",
       "2            172.0        190.0  \n",
       "3            210.0        394.0  \n",
       "4            105.0        182.0  \n",
       "...            ...          ...  \n",
       "4595           0.0          3.0  \n",
       "4596           0.0          1.0  \n",
       "4597           6.0         19.0  \n",
       "4598           7.0          1.0  \n",
       "4599           4.0         11.0  \n",
       "\n",
       "[4600 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f30c2097-c0d0-4786-86b8-3090b060cba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams',\n",
       "       'Soundcloud Streams'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6c7b2ce-e3cf-4e24-987b-61dc314c16a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity['pop_score'] = (popularity['Spotify Streams'] + \n",
    "                           popularity['YouTube Views'] + \n",
    "                           popularity['TikTok Views'] +\n",
    "                           popularity['Pandora Streams'] + \n",
    "                           popularity['Soundcloud Streams'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b156b43-38b0-40fc-bfe2-6286f510d225",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity['pop_score'] = np.log(popularity['pop_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "74d3744b-af03-47e1-91d8-f474270f5a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(popularity[popularity['pop_score'] <= 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "469e0ba6-af49-43a1-96d9-e6049ba3b1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity = popularity[popularity['pop_score'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f42088c-8c16-4573-a480-5278f0143986",
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity['pop_score'] = popularity['pop_score'].apply(lambda x: round(x,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92f73f40-acdc-4695-aba2-452a73fab5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spotify Streams</th>\n",
       "      <th>YouTube Views</th>\n",
       "      <th>TikTok Views</th>\n",
       "      <th>Pandora Streams</th>\n",
       "      <th>Soundcloud Streams</th>\n",
       "      <th>pop_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>390470936</td>\n",
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       "      <td>5332281936</td>\n",
       "      <td>18004655</td>\n",
       "      <td>4818457</td>\n",
       "      <td>22.49</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323703884</td>\n",
       "      <td>116347040</td>\n",
       "      <td>208339025</td>\n",
       "      <td>7780028</td>\n",
       "      <td>6623075</td>\n",
       "      <td>20.31</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>601309283</td>\n",
       "      <td>122599116</td>\n",
       "      <td>3369120610</td>\n",
       "      <td>5022621</td>\n",
       "      <td>7208651</td>\n",
       "      <td>22.14</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2031280633</td>\n",
       "      <td>1096100899</td>\n",
       "      <td>14603725994</td>\n",
       "      <td>190260277</td>\n",
       "      <td>0</td>\n",
       "      <td>23.61</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>77373957</td>\n",
       "      <td>0</td>\n",
       "      <td>4493884</td>\n",
       "      <td>207179</td>\n",
       "      <td>19.06</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>20.72</td>\n",
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       "      <td>189972685</td>\n",
       "      <td>201027333</td>\n",
       "      <td>36849005</td>\n",
       "      <td>84426740</td>\n",
       "      <td>0</td>\n",
       "      <td>20.05</td>\n",
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       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>145467020</td>\n",
       "      <td>1118595159</td>\n",
       "      <td>37757301</td>\n",
       "      <td>6817840</td>\n",
       "      <td>0</td>\n",
       "      <td>20.99</td>\n",
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       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>255740653</td>\n",
       "      <td>866300755</td>\n",
       "      <td>70196388</td>\n",
       "      <td>69006739</td>\n",
       "      <td>0</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4578 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Spotify Streams  YouTube Views  TikTok Views  Pandora Streams  \\\n",
       "0           390470936       84274754    5332281936         18004655   \n",
       "1           323703884      116347040     208339025          7780028   \n",
       "2           601309283      122599116    3369120610          5022621   \n",
       "3          2031280633     1096100899   14603725994        190260277   \n",
       "4           107034922       77373957             0          4493884   \n",
       "...               ...            ...           ...              ...   \n",
       "4595        305049963      149247747     143934379         20104066   \n",
       "4596         52282360      943920245         19935                0   \n",
       "4597        189972685      201027333      36849005         84426740   \n",
       "4598        145467020     1118595159      37757301          6817840   \n",
       "4599        255740653      866300755      70196388         69006739   \n",
       "\n",
       "      Soundcloud Streams  pop_score  \n",
       "0                4818457      22.49  \n",
       "1                6623075      20.31  \n",
       "2                7208651      22.14  \n",
       "3                      0      23.61  \n",
       "4                 207179      19.06  \n",
       "...                  ...        ...  \n",
       "4595            50633006      20.32  \n",
       "4596                   0      20.72  \n",
       "4597                   0      20.05  \n",
       "4598                   0      20.99  \n",
       "4599                   0      20.96  \n",
       "\n",
       "[4578 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c51f6810-5f67-4d1d-a2dd-f3be7560ab64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Spotify Playlist Reach', 'YouTube Playlist Reach',\n",
       "       'Deezer Playlist Reach', 'Amazon Reach', 'Apple Reach'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_reach.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c7d9f9c-e764-425f-9e06-3978820aea51",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach['reach'] = (social_reach['Spotify Playlist Reach'] +\n",
    "                        social_reach['YouTube Playlist Reach'] +\n",
    "                        social_reach['Deezer Playlist Reach'] +\n",
    "                        social_reach['Amazon Reach'] +\n",
    "                        social_reach['Apple Reach'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c52513-b7f2-4bd9-8a82-79f9857a2457",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach['reach'] = np.log(social_reach['reach'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9fe895a0-dd4c-4720-9fcb-82e4e4d529b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(social_reach[social_reach['reach'] <= 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b5583cc-6441-4000-b5c7-569f265c54d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach = social_reach[social_reach['reach'] >0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b792b505-d5e5-4aae-b741-b82e02df6770",
   "metadata": {},
   "outputs": [],
   "source": [
    "social_reach['reach'] = social_reach['reach'].apply(lambda x: round(x,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c63fee87-8b6d-41bb-bb6a-fe5aff2d7d0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Spotify Playlist Reach</th>\n",
       "      <th>YouTube Playlist Reach</th>\n",
       "      <th>Deezer Playlist Reach</th>\n",
       "      <th>Amazon Reach</th>\n",
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       "      <td>196631588</td>\n",
       "      <td>150597040</td>\n",
       "      <td>17598718</td>\n",
       "      <td>114.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174597137</td>\n",
       "      <td>156380351</td>\n",
       "      <td>10422430</td>\n",
       "      <td>111.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>19.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>211607669</td>\n",
       "      <td>373784955</td>\n",
       "      <td>36321847</td>\n",
       "      <td>172.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>20.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>136569078</td>\n",
       "      <td>3351188582</td>\n",
       "      <td>24684248</td>\n",
       "      <td>210.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>21.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151469874</td>\n",
       "      <td>112763851</td>\n",
       "      <td>17660624</td>\n",
       "      <td>105.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19.46</td>\n",
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       "      <td>...</td>\n",
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       "      <th>4595</th>\n",
       "      <td>5103054</td>\n",
       "      <td>53016</td>\n",
       "      <td>14217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.46</td>\n",
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       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>1449767</td>\n",
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       "      <td>927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.09</td>\n",
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       "      <th>4597</th>\n",
       "      <td>6704802</td>\n",
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       "      <td>74</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.40</td>\n",
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       "    <tr>\n",
       "      <th>4598</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.30</td>\n",
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       "    <tr>\n",
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       "      <td>14066526</td>\n",
       "      <td>175831376</td>\n",
       "      <td>127479</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4540 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Spotify Playlist Reach  YouTube Playlist Reach  Deezer Playlist Reach  \\\n",
       "0                  196631588               150597040               17598718   \n",
       "1                  174597137               156380351               10422430   \n",
       "2                  211607669               373784955               36321847   \n",
       "3                  136569078              3351188582               24684248   \n",
       "4                  151469874               112763851               17660624   \n",
       "...                      ...                     ...                    ...   \n",
       "4595                 5103054                   53016                  14217   \n",
       "4596                 1449767                24973048                    927   \n",
       "4597                 6704802                29253152                     74   \n",
       "4598                 7387064                25150516                      0   \n",
       "4599                14066526               175831376                 127479   \n",
       "\n",
       "      Amazon Reach  Apple Reach  reach  \n",
       "0            114.0        210.0  19.71  \n",
       "1            111.0        188.0  19.65  \n",
       "2            172.0        190.0  20.25  \n",
       "3            210.0        394.0  21.98  \n",
       "4            105.0        182.0  19.46  \n",
       "...            ...          ...    ...  \n",
       "4595           0.0          3.0  15.46  \n",
       "4596           0.0          1.0  17.09  \n",
       "4597           6.0         19.0  17.40  \n",
       "4598           7.0          1.0  17.30  \n",
       "4599           4.0         11.0  19.06  \n",
       "\n",
       "[4540 rows x 6 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7757c5a-feb3-4e9f-91e0-986f37a85688",
   "metadata": {},
   "outputs": [],
   "source": [
    "track_stats = popularity.merge(social_reach,left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f3e5a9e3-6f18-4a97-8839-1740f7cd115a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Spotify Streams</th>\n",
       "      <th>YouTube Views</th>\n",
       "      <th>TikTok Views</th>\n",
       "      <th>Pandora Streams</th>\n",
       "      <th>Soundcloud Streams</th>\n",
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       "      <td>0</td>\n",
       "      <td>20.05</td>\n",
       "      <td>6704802</td>\n",
       "      <td>29253152</td>\n",
       "      <td>74</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
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      ],
      "text/plain": [
       "      Spotify Streams  YouTube Views  TikTok Views  Pandora Streams  \\\n",
       "0           390470936       84274754    5332281936         18004655   \n",
       "1           323703884      116347040     208339025          7780028   \n",
       "2           601309283      122599116    3369120610          5022621   \n",
       "3          2031280633     1096100899   14603725994        190260277   \n",
       "4           107034922       77373957             0          4493884   \n",
       "...               ...            ...           ...              ...   \n",
       "4595        305049963      149247747     143934379         20104066   \n",
       "4596         52282360      943920245         19935                0   \n",
       "4597        189972685      201027333      36849005         84426740   \n",
       "4598        145467020     1118595159      37757301          6817840   \n",
       "4599        255740653      866300755      70196388         69006739   \n",
       "\n",
       "      Soundcloud Streams  pop_score  Spotify Playlist Reach  \\\n",
       "0                4818457      22.49               196631588   \n",
       "1                6623075      20.31               174597137   \n",
       "2                7208651      22.14               211607669   \n",
       "3                      0      23.61               136569078   \n",
       "4                 207179      19.06               151469874   \n",
       "...                  ...        ...                     ...   \n",
       "4595            50633006      20.32                 5103054   \n",
       "4596                   0      20.72                 1449767   \n",
       "4597                   0      20.05                 6704802   \n",
       "4598                   0      20.99                 7387064   \n",
       "4599                   0      20.96                14066526   \n",
       "\n",
       "      YouTube Playlist Reach  Deezer Playlist Reach  Amazon Reach  \\\n",
       "0                  150597040               17598718         114.0   \n",
       "1                  156380351               10422430         111.0   \n",
       "2                  373784955               36321847         172.0   \n",
       "3                 3351188582               24684248         210.0   \n",
       "4                  112763851               17660624         105.0   \n",
       "...                      ...                    ...           ...   \n",
       "4595                   53016                  14217           0.0   \n",
       "4596                24973048                    927           0.0   \n",
       "4597                29253152                     74           6.0   \n",
       "4598                25150516                      0           7.0   \n",
       "4599               175831376                 127479           4.0   \n",
       "\n",
       "      Apple Reach  reach  \n",
       "0           210.0  19.71  \n",
       "1           188.0  19.65  \n",
       "2           190.0  20.25  \n",
       "3           394.0  21.98  \n",
       "4           182.0  19.46  \n",
       "...           ...    ...  \n",
       "4595          3.0  15.46  \n",
       "4596          1.0  17.09  \n",
       "4597         19.0  17.40  \n",
       "4598          1.0  17.30  \n",
       "4599         11.0  19.06  \n",
       "\n",
       "[4534 rows x 12 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_stats"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3c5f3409-de03-4ffe-8eea-f2b657d409ef",
   "metadata": {},
   "source": [
    "spotify.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "27ee904c-3182-4f43-9367-54418a7d33b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "track_stats = track_stats.merge(spotify[['Track', 'Album Name', 'Artist', 'Release Date', 'Track Score', 'Explicit Track']], \n",
    "                                left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cdf49d0-7d6c-4848-afcd-019a92ce758f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19.71</td>\n",
       "      <td>MILLION DOLLAR BABY</td>\n",
       "      <td>Million Dollar Baby - Single</td>\n",
       "      <td>Tommy Richman</td>\n",
       "      <td>4/26/2024</td>\n",
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       "      <td>0</td>\n",
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       "      <td>111.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>19.65</td>\n",
       "      <td>Not Like Us</td>\n",
       "      <td>Not Like Us</td>\n",
       "      <td>Kendrick Lamar</td>\n",
       "      <td>5/4/2024</td>\n",
       "      <td>545.9</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>172.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>20.25</td>\n",
       "      <td>i like the way you kiss me</td>\n",
       "      <td>I like the way you kiss me</td>\n",
       "      <td>Artemas</td>\n",
       "      <td>3/19/2024</td>\n",
       "      <td>538.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2031280633</td>\n",
       "      <td>1096100899</td>\n",
       "      <td>14603725994</td>\n",
       "      <td>190260277</td>\n",
       "      <td>0</td>\n",
       "      <td>23.61</td>\n",
       "      <td>136569078</td>\n",
       "      <td>3351188582</td>\n",
       "      <td>24684248</td>\n",
       "      <td>210.0</td>\n",
       "      <td>394.0</td>\n",
       "      <td>21.98</td>\n",
       "      <td>Flowers</td>\n",
       "      <td>Flowers - Single</td>\n",
       "      <td>Miley Cyrus</td>\n",
       "      <td>1/12/2023</td>\n",
       "      <td>444.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107034922</td>\n",
       "      <td>77373957</td>\n",
       "      <td>0</td>\n",
       "      <td>4493884</td>\n",
       "      <td>207179</td>\n",
       "      <td>19.06</td>\n",
       "      <td>151469874</td>\n",
       "      <td>112763851</td>\n",
       "      <td>17660624</td>\n",
       "      <td>105.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19.46</td>\n",
       "      <td>Houdini</td>\n",
       "      <td>Houdini</td>\n",
       "      <td>Eminem</td>\n",
       "      <td>5/31/2024</td>\n",
       "      <td>423.3</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4595</th>\n",
       "      <td>305049963</td>\n",
       "      <td>149247747</td>\n",
       "      <td>143934379</td>\n",
       "      <td>20104066</td>\n",
       "      <td>50633006</td>\n",
       "      <td>20.32</td>\n",
       "      <td>5103054</td>\n",
       "      <td>53016</td>\n",
       "      <td>14217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.46</td>\n",
       "      <td>For the Last Time</td>\n",
       "      <td>For the Last Time</td>\n",
       "      <td>$uicideboy$</td>\n",
       "      <td>9/5/2017</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4596</th>\n",
       "      <td>52282360</td>\n",
       "      <td>943920245</td>\n",
       "      <td>19935</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.72</td>\n",
       "      <td>1449767</td>\n",
       "      <td>24973048</td>\n",
       "      <td>927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.09</td>\n",
       "      <td>Dil Meri Na Sune</td>\n",
       "      <td>Dil Meri Na Sune (From \"Genius\")</td>\n",
       "      <td>Atif Aslam</td>\n",
       "      <td>7/27/2018</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4597</th>\n",
       "      <td>189972685</td>\n",
       "      <td>201027333</td>\n",
       "      <td>36849005</td>\n",
       "      <td>84426740</td>\n",
       "      <td>0</td>\n",
       "      <td>20.05</td>\n",
       "      <td>6704802</td>\n",
       "      <td>29253152</td>\n",
       "      <td>74</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.40</td>\n",
       "      <td>Grace (feat. 42 Dugg)</td>\n",
       "      <td>My Turn</td>\n",
       "      <td>Lil Baby</td>\n",
       "      <td>2/28/2020</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4598</th>\n",
       "      <td>145467020</td>\n",
       "      <td>1118595159</td>\n",
       "      <td>37757301</td>\n",
       "      <td>6817840</td>\n",
       "      <td>0</td>\n",
       "      <td>20.99</td>\n",
       "      <td>7387064</td>\n",
       "      <td>25150516</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.30</td>\n",
       "      <td>Nashe Si Chadh Gayi</td>\n",
       "      <td>November Top 10 Songs</td>\n",
       "      <td>Arijit Singh</td>\n",
       "      <td>11/8/2016</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>255740653</td>\n",
       "      <td>866300755</td>\n",
       "      <td>70196388</td>\n",
       "      <td>69006739</td>\n",
       "      <td>0</td>\n",
       "      <td>20.96</td>\n",
       "      <td>14066526</td>\n",
       "      <td>175831376</td>\n",
       "      <td>127479</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.06</td>\n",
       "      <td>Me Acostumbre (feat. Bad Bunny)</td>\n",
       "      <td>Me Acostumbre (feat. Bad Bunny)</td>\n",
       "      <td>Arcï¿½ï¿½</td>\n",
       "      <td>4/11/2017</td>\n",
       "      <td>19.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4534 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Spotify Streams  YouTube Views  TikTok Views  Pandora Streams  \\\n",
       "0           390470936       84274754    5332281936         18004655   \n",
       "1           323703884      116347040     208339025          7780028   \n",
       "2           601309283      122599116    3369120610          5022621   \n",
       "3          2031280633     1096100899   14603725994        190260277   \n",
       "4           107034922       77373957             0          4493884   \n",
       "...               ...            ...           ...              ...   \n",
       "4595        305049963      149247747     143934379         20104066   \n",
       "4596         52282360      943920245         19935                0   \n",
       "4597        189972685      201027333      36849005         84426740   \n",
       "4598        145467020     1118595159      37757301          6817840   \n",
       "4599        255740653      866300755      70196388         69006739   \n",
       "\n",
       "      Soundcloud Streams  pop_score  Spotify Playlist Reach  \\\n",
       "0                4818457      22.49               196631588   \n",
       "1                6623075      20.31               174597137   \n",
       "2                7208651      22.14               211607669   \n",
       "3                      0      23.61               136569078   \n",
       "4                 207179      19.06               151469874   \n",
       "...                  ...        ...                     ...   \n",
       "4595            50633006      20.32                 5103054   \n",
       "4596                   0      20.72                 1449767   \n",
       "4597                   0      20.05                 6704802   \n",
       "4598                   0      20.99                 7387064   \n",
       "4599                   0      20.96                14066526   \n",
       "\n",
       "      YouTube Playlist Reach  Deezer Playlist Reach  Amazon Reach  \\\n",
       "0                  150597040               17598718         114.0   \n",
       "1                  156380351               10422430         111.0   \n",
       "2                  373784955               36321847         172.0   \n",
       "3                 3351188582               24684248         210.0   \n",
       "4                  112763851               17660624         105.0   \n",
       "...                      ...                    ...           ...   \n",
       "4595                   53016                  14217           0.0   \n",
       "4596                24973048                    927           0.0   \n",
       "4597                29253152                     74           6.0   \n",
       "4598                25150516                      0           7.0   \n",
       "4599               175831376                 127479           4.0   \n",
       "\n",
       "      Apple Reach  reach                            Track  \\\n",
       "0           210.0  19.71              MILLION DOLLAR BABY   \n",
       "1           188.0  19.65                      Not Like Us   \n",
       "2           190.0  20.25       i like the way you kiss me   \n",
       "3           394.0  21.98                          Flowers   \n",
       "4           182.0  19.46                          Houdini   \n",
       "...           ...    ...                              ...   \n",
       "4595          3.0  15.46                For the Last Time   \n",
       "4596          1.0  17.09                 Dil Meri Na Sune   \n",
       "4597         19.0  17.40            Grace (feat. 42 Dugg)   \n",
       "4598          1.0  17.30              Nashe Si Chadh Gayi   \n",
       "4599         11.0  19.06  Me Acostumbre (feat. Bad Bunny)   \n",
       "\n",
       "                            Album Name          Artist Release Date  \\\n",
       "0         Million Dollar Baby - Single   Tommy Richman    4/26/2024   \n",
       "1                          Not Like Us  Kendrick Lamar     5/4/2024   \n",
       "2           I like the way you kiss me         Artemas    3/19/2024   \n",
       "3                     Flowers - Single     Miley Cyrus    1/12/2023   \n",
       "4                              Houdini          Eminem    5/31/2024   \n",
       "...                                ...             ...          ...   \n",
       "4595                 For the Last Time     $uicideboy$     9/5/2017   \n",
       "4596  Dil Meri Na Sune (From \"Genius\")      Atif Aslam    7/27/2018   \n",
       "4597                           My Turn        Lil Baby    2/28/2020   \n",
       "4598             November Top 10 Songs    Arijit Singh    11/8/2016   \n",
       "4599   Me Acostumbre (feat. Bad Bunny)       Arcï¿½ï¿½    4/11/2017   \n",
       "\n",
       "      Track Score  Explicit Track  \n",
       "0           725.4               0  \n",
       "1           545.9               1  \n",
       "2           538.4               0  \n",
       "3           444.9               0  \n",
       "4           423.3               1  \n",
       "...           ...             ...  \n",
       "4595         19.4               1  \n",
       "4596         19.4               0  \n",
       "4597         19.4               1  \n",
       "4598         19.4               0  \n",
       "4599         19.4               1  \n",
       "\n",
       "[4534 rows x 18 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ac32ab-77d4-43aa-a0c7-ebe6548a3d20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b50f238d-e8b8-4fdd-89b0-1b3c30e7e8c8",
   "metadata": {},
   "source": [
    "EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a8bcd8bd-bdcf-4f05-a533-39203820670c",
   "metadata": {},
   "outputs": [],
   "source": [
    "track_stats['Release Date'] = pd.to_datetime(track_stats['Release Date'])\n",
    "\n",
    "track_stats['year'] = track_stats['Release Date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c16a5f79-6d8b-47af-934b-75a1ac32c4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top Artists')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_artists = track_stats['Artist'].value_counts().head(15)\n",
    "\n",
    "top_artists.plot(kind ='bar')\n",
    "plt.title('Top Artists')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "209ab7ba-4031-4485-aa37-764ed3b2d9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Song Release by Year')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hg = track_stats.groupby('year')['Release Date'].count().tail(20)\n",
    "\n",
    "hg.plot(kind='bar')\n",
    "plt.title('Song Release by Year')\n",
    "\n",
    "#songs released each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "99e10784-b724-4861-b26e-84e6f15d8666",
   "metadata": {},
   "outputs": [],
   "source": [
    "track_stats['track_perf'] = (track_stats['pop_score']+\n",
    "                            track_stats['reach'])\n",
    "\n",
    "\n",
    "\n",
    "track_stats['track_perf'] = (track_stats['pop_score']/track_stats['track_perf']) * 100 #percent popularity/performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f11999da-b9e4-48dd-b7da-07771b2cafd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       53.293839\n",
       "1       50.825826\n",
       "2       52.229299\n",
       "3       51.787673\n",
       "4       49.480789\n",
       "          ...    \n",
       "4595    56.791504\n",
       "4596    54.800317\n",
       "4597    53.538051\n",
       "4598    54.818490\n",
       "4599    52.373813\n",
       "Name: track_perf, Length: 4534, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_stats['track_perf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eecc5042-8b69-403c-85e8-2a4a7acc36cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compare the distribution of the custom Track_perf and Track_score\n",
    "\n",
    "track_stats['ori_track_perf'] = (track_stats['Track Score']/10) \n",
    "#reduce the track_Score to the same range as the track_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a499d4db-73ee-4faa-a962-30d38035d38c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of custom Track Score')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,7))\n",
    "\n",
    "ax1.boxplot(track_stats['ori_track_perf'])\n",
    "ax1.set_title(\"Distribution of Original Track Score\")\n",
    "\n",
    "ax2.boxplot(track_stats['track_perf'])\n",
    "ax2.set_title(\"Distribution of custom Track Score\")\n",
    "\n",
    "#the Custom Track Score is more tightly distributed and thus be used to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e89d63-a2a5-4da0-b721-36b29eaa45e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c85d7d3d-10c8-4002-ad7e-6f42ae6a04d8",
   "metadata": {},
   "source": [
    "MODELLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f04dacf7-6f09-4492-a8d8-ea73d50b07a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "02899b0c-8a69-48fd-8ece-c9f105b0fd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "track_stats['Track_Code'] = track_stats['Track'].astype('category').cat.codes\n",
    "track_stats['Artist_Code'] = track_stats['Artist'].astype('category').cat.codes\n",
    "\n",
    "\n",
    "predictors = ['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams',\n",
    "       'Soundcloud Streams', 'Spotify Playlist Reach',\n",
    "       'YouTube Playlist Reach', 'Deezer Playlist Reach', 'Amazon Reach',\n",
    "       'Apple Reach', 'Explicit Track', 'year', 'Track_Code', 'Artist_Code']\n",
    "\n",
    "target = ['track_perf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f3afbed9-c8de-411d-ae52-6433993b079f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = track_stats[track_stats['year'] <= 2023]\n",
    "test = track_stats[track_stats['year'] > 2023 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bb920f16-fa0a-4b58-bb05-09e498afde68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3863, 23), (671, 23))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d14075d1-02ee-4831-b7ba-2282c86923c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test different estimators to obtain the best.\n",
    "\n",
    "model_params = {\n",
    "    \"svr\" : {\n",
    "        \"model\" : LinearSVR(random_state=42),\n",
    "        \"params\" : {\n",
    "            \"C\" : [1,3,5],\n",
    "            \"max_iter\" : [1000, 1500, 2000]\n",
    "        }\n",
    "    },\n",
    "\n",
    "    \"hst\" : {\n",
    "        \"model\" : HistGradientBoostingRegressor(random_state=42),\n",
    "        \"params\" : {\n",
    "            \"max_iter\" : [75, 100, 150, 200],\n",
    "            \"max_depth\" : [5, 10,15,20],\n",
    "            \"max_features\" : [0.2, 0.5, 0.7, 1.0],\n",
    "            \"min_samples_leaf\": [5,10,15,20]\n",
    "        }\n",
    "    },\n",
    "\n",
    "    \"rfc\" :{\n",
    "        \"model\" : RandomForestRegressor(random_state=42, n_jobs=2),\n",
    "        \"params\" : {\n",
    "            \"n_estimators\" : [50, 100, 200, 300],\n",
    "            \"max_depth\": [5,10,15,20],\n",
    "            \"max_leaf_nodes\": [2,5,7],\n",
    "            \"min_samples_leaf\": [2,4,7,10]\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8f57262f-c9d5-4df0-a60f-189c478a561b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\svm\\_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=3)\n",
    "\n",
    "scores = []\n",
    "\n",
    "#ct = ColumnTransformer([\n",
    "    #('encode', OneHotEncoder(dtype=int), ['Track', 'Artist'])\n",
    "#], \n",
    "  #remainder ='passthrough')\n",
    "\n",
    "#preds_trans = ct.fit_transform(track_stats[predictors]) #transform the object columns\n",
    "\n",
    "#preds_trans = preds_trans.toarray()   #convert sparse matrix to dense\n",
    "\n",
    "for model_name, mp in model_params.items():\n",
    "    #use pipeline to incorprate OneHotEncoding\n",
    "    rgrs = RandomizedSearchCV(mp['model'], mp['params'], cv=tscv, random_state=42, n_iter=5, error_score ='raise')\n",
    "    #pipe = make_pipeline(ct, rgrs)\n",
    "    rgrs.fit(train[predictors], train[target])\n",
    "    scores.append({\n",
    "        'model': model_name,\n",
    "        'best_score': rgrs.best_score_,\n",
    "        'best_params': rgrs.best_params_\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e7b74119-8f4a-463d-8a93-56a16c196a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models: svr\n",
      " mps: {'model': LinearSVR(random_state=42), 'params': {'C': [1, 3, 5], 'max_iter': [1000, 1500, 2000]}}\n",
      "models: hst\n",
      " mps: {'model': HistGradientBoostingRegressor(random_state=42), 'params': {'max_iter': [75, 100, 150, 200], 'max_depth': [5, 10, 15, 20], 'max_features': [0.2, 0.5, 0.7, 1.0], 'min_samples_leaf': [5, 10, 15, 20]}}\n",
      "models: rfc\n",
      " mps: {'model': RandomForestRegressor(n_jobs=2, random_state=42), 'params': {'n_estimators': [50, 100, 200, 300], 'max_depth': [5, 10, 15, 20], 'max_leaf_nodes': [2, 5, 7], 'min_samples_leaf': [2, 4, 7, 10]}}\n"
     ]
    }
   ],
   "source": [
    "for model_name, mp in model_params.items():\n",
    "    print(f\"models: {model_name}\")\n",
    "    print(f\" mps: {mp}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "450f23e8-c607-4fd1-a262-4bd96522842d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'model': 'svr',\n",
       "  'best_score': np.float64(-71199941.88136497),\n",
       "  'best_params': {'max_iter': 1000, 'C': 1}},\n",
       " {'model': 'hst',\n",
       "  'best_score': np.float64(0.9800061608525166),\n",
       "  'best_params': {'min_samples_leaf': 5,\n",
       "   'max_iter': 100,\n",
       "   'max_features': 0.7,\n",
       "   'max_depth': 20}},\n",
       " {'model': 'rfc',\n",
       "  'best_score': np.float64(0.8931868616448608),\n",
       "  'best_params': {'n_estimators': 50,\n",
       "   'min_samples_leaf': 7,\n",
       "   'max_leaf_nodes': 7,\n",
       "   'max_depth': 15}}]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b421ca9c-13a7-4caa-8d06-5de7c0789a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp Pc\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HistGradientBoostingRegressor(max_depth=20, max_features=0.7,\n",
       "                              min_samples_leaf=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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;HistGradientBoostingRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html\">?<span>Documentation for HistGradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>HistGradientBoostingRegressor(max_depth=20, max_features=0.7,\n",
       "                              min_samples_leaf=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "HistGradientBoostingRegressor(max_depth=20, max_features=0.7,\n",
       "                              min_samples_leaf=5)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = HistGradientBoostingRegressor(\n",
    "    min_samples_leaf=5, max_iter =100, max_features = 0.7, max_depth =20)\n",
    "\n",
    "model.fit(train[predictors], train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "46c1d0bb-c5e8-475b-bbed-1eadfaf72bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2c7c11a7-0ab0-4e1e-b69c-df6af5590cf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.DataFrame(preds, columns = ['predicted score'], index = test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9e171076-1365-463f-aabc-a1a0f053d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "finals = pd.concat([test[['Artist', 'Track', 'year', 'track_perf']], preds], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7bbeb6f5-1c51-4911-a773-c01396f36882",
   "metadata": {},
   "outputs": [],
   "source": [
    "finals['var'] = (finals['track_perf'] - finals['predicted score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1b3dc186-ad52-460e-a411-8477bec4905d",
   "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>Artist</th>\n",
       "      <th>Track</th>\n",
       "      <th>year</th>\n",
       "      <th>track_perf</th>\n",
       "      <th>predicted score</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tommy Richman</td>\n",
       "      <td>MILLION DOLLAR BABY</td>\n",
       "      <td>2024</td>\n",
       "      <td>53.293839</td>\n",
       "      <td>53.370084</td>\n",
       "      <td>-0.076245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kendrick Lamar</td>\n",
       "      <td>Not Like Us</td>\n",
       "      <td>2024</td>\n",
       "      <td>50.825826</td>\n",
       "      <td>50.936971</td>\n",
       "      <td>-0.111145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Artemas</td>\n",
       "      <td>i like the way you kiss me</td>\n",
       "      <td>2024</td>\n",
       "      <td>52.229299</td>\n",
       "      <td>52.377572</td>\n",
       "      <td>-0.148273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eminem</td>\n",
       "      <td>Houdini</td>\n",
       "      <td>2024</td>\n",
       "      <td>49.480789</td>\n",
       "      <td>49.710014</td>\n",
       "      <td>-0.229225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Benson Boone</td>\n",
       "      <td>Beautiful Things</td>\n",
       "      <td>2024</td>\n",
       "      <td>48.876662</td>\n",
       "      <td>48.967758</td>\n",
       "      <td>-0.091096</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4544</th>\n",
       "      <td>Nemzzz</td>\n",
       "      <td>ATM</td>\n",
       "      <td>2024</td>\n",
       "      <td>49.717691</td>\n",
       "      <td>52.651130</td>\n",
       "      <td>-2.933438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4552</th>\n",
       "      <td>ian</td>\n",
       "      <td>AirBnb</td>\n",
       "      <td>2024</td>\n",
       "      <td>54.519774</td>\n",
       "      <td>57.302970</td>\n",
       "      <td>-2.783196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4561</th>\n",
       "      <td>Diljit Dosanjh</td>\n",
       "      <td>Tu Juliet Jatt Di ( From \"Jatt &amp; Juliet 3\")</td>\n",
       "      <td>2024</td>\n",
       "      <td>48.959904</td>\n",
       "      <td>50.867733</td>\n",
       "      <td>-1.907830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4593</th>\n",
       "      <td>Thaman S</td>\n",
       "      <td>Jaragandi (From \"Game Changer\") (Telugu)</td>\n",
       "      <td>2024</td>\n",
       "      <td>55.344117</td>\n",
       "      <td>56.452174</td>\n",
       "      <td>-1.108058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4594</th>\n",
       "      <td>BUSHIDO ZHO</td>\n",
       "      <td>ýýýýýýýýýýýý (ýýýýýýýýýýýýýý ýýýýýýýýýýýý) [pr...</td>\n",
       "      <td>2024</td>\n",
       "      <td>57.720460</td>\n",
       "      <td>59.690075</td>\n",
       "      <td>-1.969615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Artist                                              Track  year  \\\n",
       "0      Tommy Richman                                MILLION DOLLAR BABY  2024   \n",
       "1     Kendrick Lamar                                        Not Like Us  2024   \n",
       "2            Artemas                         i like the way you kiss me  2024   \n",
       "4             Eminem                                            Houdini  2024   \n",
       "6       Benson Boone                                   Beautiful Things  2024   \n",
       "...              ...                                                ...   ...   \n",
       "4544          Nemzzz                                                ATM  2024   \n",
       "4552             ian                                             AirBnb  2024   \n",
       "4561  Diljit Dosanjh        Tu Juliet Jatt Di ( From \"Jatt & Juliet 3\")  2024   \n",
       "4593        Thaman S           Jaragandi (From \"Game Changer\") (Telugu)  2024   \n",
       "4594     BUSHIDO ZHO  ýýýýýýýýýýýý (ýýýýýýýýýýýýýý ýýýýýýýýýýýý) [pr...  2024   \n",
       "\n",
       "      track_perf  predicted score       var  \n",
       "0      53.293839        53.370084 -0.076245  \n",
       "1      50.825826        50.936971 -0.111145  \n",
       "2      52.229299        52.377572 -0.148273  \n",
       "4      49.480789        49.710014 -0.229225  \n",
       "6      48.876662        48.967758 -0.091096  \n",
       "...          ...              ...       ...  \n",
       "4544   49.717691        52.651130 -2.933438  \n",
       "4552   54.519774        57.302970 -2.783196  \n",
       "4561   48.959904        50.867733 -1.907830  \n",
       "4593   55.344117        56.452174 -1.108058  \n",
       "4594   57.720460        59.690075 -1.969615  \n",
       "\n",
       "[671 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db9e51fa-25fe-4be6-a448-7492743527ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1e8abc80-9f7d-4ceb-bce0-636005ec2468",
   "metadata": {},
   "source": [
    "METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fe4e718f-9114-459b-9342-b21ca828d161",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from above df, the predicted score in most cases are higher than the Test Score\n",
    "#Therefore, we will use Absolute Error to check the differences between predicted and True score\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3e732c9e-3d4c-4ef7-8648-4cb02530f804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.8939504125148874), np.float64(3.3564956845289555))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Try out AVerage Precision metric\n",
    "\n",
    "error = mean_absolute_error(finals['track_perf'], finals['predicted score'])\n",
    "mse = mean_squared_error(finals['track_perf'], finals['predicted score'])\n",
    "\n",
    "\n",
    "error, mse  #-- very high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d175c847-2e33-4328-8bb9-5dcf6642f191",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To visually understand our model's predictions better\n",
    "\n",
    "artist_score = finals.groupby('Artist')['track_perf'].sum()\n",
    "artist_score = artist_score.sort_values(ascending=False).head(25)\n",
    "\n",
    "artist_score_preds = finals.groupby(\"Artist\")['predicted score'].sum()\n",
    "artist_score_preds = artist_score_preds.sort_values(ascending=False).head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f5b9df52-2381-443e-ba84-4a2da871c293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Artist'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "artist_score.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0a858976-6a19-4bc6-abf3-31ffc6c825a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Artist'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I6NMzeALTqFEjTJo0Ce7u7ihSpAjOnz+PGTNmICAgAACgUqnQt29fTJw4EQULFkS+fPkwatQouLq6omnTpgAAb29v1KtXD507d8aCBQsQHR2Nnj17olWrVlrNQCIiIqLPm8ETmDlz5mDUqFHo3r07nj9/DldXV/z4448YPXq0ss/gwYMRFRWFLl26IDQ0FJUrV8aOHTtgYWGh7LNq1Sr07NkTtWrVglqtRosWLTB79mxDh0tERERGyOB1YLKKT1UHhpeQiIiIDCfT6sAQERERZTQmMERERGR0mMAQERGR0WECQ0REREbH4LOQSH8cEExERKQd9sAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZnQxJYJ48eYI2bdogR44csLS0RLFixXDmzBllu4hg9OjRyJ07NywtLeHr64ugoCCNNl69egV/f3/Y2dnBwcEBHTt2RGRkZEaES0REREbG4AnM69evUalSJWTLlg3//vsvrl27hunTp8PR0VHZZ9q0aZg9ezYWLFiAkydPwtraGnXr1sW7d++Uffz9/XH16lXs3r0bf//9Nw4dOoQuXboYOlwiIiIyQqaGbvCnn36Cm5sbli5dqjyWL18+5d8igpkzZ2LkyJFo0qQJAGD58uVwdnbGli1b0KpVK1y/fh07duzA6dOnUbZsWQDAnDlz0KBBA/zyyy9wdXU1dNhERERkRAzeA/PXX3+hbNmy+N///gcnJyeUKlUKixYtUrbfu3cPwcHB8PX1VR6zt7dH+fLlcfz4cQDA8ePH4eDgoCQvAODr6wu1Wo2TJ08m+7rv379HeHi4xo2IiIg+TwZPYO7evYv58+ejYMGC2LlzJ7p164bevXtj2bJlAIDg4GAAgLOzs8bznJ2dlW3BwcFwcnLS2G5qaors2bMr+yQ2ZcoU2NvbKzc3NzdDvzUiIiLKIgyewMTFxaF06dKYPHkySpUqhS5duqBz585YsGCBoV9Kw7BhwxAWFqbcHj16lKGvR0RERJnH4AlM7ty54ePjo/GYt7c3Hj58CABwcXEBAISEhGjsExISomxzcXHB8+fPNbbHxMTg1atXyj6JmZubw87OTuNGREREnyeDJzCVKlXCzZs3NR67desWPDw8AHwc0Ovi4oK9e/cq28PDw3Hy5ElUrFgRAFCxYkWEhobi7Nmzyj779u1DXFwcypcvb+iQiYiIyMgYfBZSv3798M0332Dy5Mlo2bIlTp06hd9//x2///47AEClUqFv376YOHEiChYsiHz58mHUqFFwdXVF06ZNAXzssalXr55y6Sk6Oho9e/ZEq1atOAOJiIiIDJ/AfP3119i8eTOGDRuG8ePHI1++fJg5cyb8/f2VfQYPHoyoqCh06dIFoaGhqFy5Mnbs2AELCwtln1WrVqFnz56oVasW1Go1WrRogdmzZxs6XCIiIjJCBk9gAKBhw4Zo2LBhittVKhXGjx+P8ePHp7hP9uzZsXr16owIj4iIiIwc10IiIiIio8MEhoiIiIwOExgiIiIyOkxgiIiIyOgwgSEiIiKjwwSGiIiIjA4TGCIiIjI6TGCIiIjI6DCBISIiIqPDBIaIiIiMDhMYIiIiMjpMYIiIiMjoZMhijpQ1eA7drtV+96f6ZXAkREREhsUeGCIiIjI6TGCIiIjI6PASEmmNl6SIiCirYA8MERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkd08wOgL5cnkO3a7Xf/al+GRwJEREZG/bAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREYnwxOYqVOnQqVSoW/fvspj7969Q48ePZAjRw7Y2NigRYsWCAkJ0Xjew4cP4efnBysrKzg5OWHQoEGIiYnJ6HCJiIjICGRoAnP69GksXLgQxYsX13i8X79+2LZtG/78808cPHgQT58+RfPmzZXtsbGx8PPzw4cPH3Ds2DEsW7YMgYGBGD16dEaGS0REREYiwxKYyMhI+Pv7Y9GiRXB0dFQeDwsLwx9//IEZM2agZs2aKFOmDJYuXYpjx47hxIkTAIBdu3bh2rVrWLlyJUqWLIn69etjwoQJmDdvHj58+JBRIRMREZGRyLAEpkePHvDz84Ovr6/G42fPnkV0dLTG44ULF4a7uzuOHz8OADh+/DiKFSsGZ2dnZZ+6desiPDwcV69eTfb13r9/j/DwcI0bERERfZ4yZCmBtWvX4ty5czh9+nSSbcHBwTAzM4ODg4PG487OzggODlb2SZi8xG+P35acKVOmYNy4cQaInoiIiLI6g/fAPHr0CH369MGqVatgYWFh6OZTNGzYMISFhSm3R48efbLXJiIiok/L4AnM2bNn8fz5c5QuXRqmpqYwNTXFwYMHMXv2bJiamsLZ2RkfPnxAaGioxvNCQkLg4uICAHBxcUkyKyn+fvw+iZmbm8POzk7jRkRERJ8ngycwtWrVwuXLl3HhwgXlVrZsWfj7+yv/zpYtG/bu3as85+bNm3j48CEqVqwIAKhYsSIuX76M58+fK/vs3r0bdnZ28PHxMXTIREREZGQMPgbG1tYWRYsW1XjM2toaOXLkUB7v2LEj+vfvj+zZs8POzg69evVCxYoVUaFCBQBAnTp14OPjgx9++AHTpk1DcHAwRo4ciR49esDc3NzQIRMREZGRyZBBvGn59ddfoVar0aJFC7x//x5169bFb7/9pmw3MTHB33//jW7duqFixYqwtrZGu3btMH78+MwIl4iIiLKYT5LAHDhwQOO+hYUF5s2bh3nz5qX4HA8PD/zzzz8ZHBkREREZI66FREREREaHCQwREREZHSYwREREZHSYwBAREZHRyZRZSEQZwXPodq32uz/VL4MjISKijMYeGCIiIjI6TGCIiIjI6DCBISIiIqPDBIaIiIiMDgfxEqXA0IOCtWmPA4yJiLTDHhgiIiIyOkxgiIiIyOgwgSEiIiKjwwSGiIiIjA4TGCIiIjI6TGCIiIjI6DCBISIiIqPDBIaIiIiMDhMYIiIiMjpMYIiIiMjoMIEhIiIio8MEhoiIiIwOExgiIiIyOlyNmsgIGXqlbCIiY8MeGCIiIjI6TGCIiIjI6DCBISIiIqPDBIaIiIiMDgfxEhEHBROR0WEPDBERERkdJjBERERkdJjAEBERkdFhAkNERERGh4N4icjgOCiYiDIaExgiyvKYEBFRYryEREREREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERoezkIjoi8NZTUTGjz0wREREZHSYwBAREZHRYQJDRERERsfgCcyUKVPw9ddfw9bWFk5OTmjatClu3rypsc+7d+/Qo0cP5MiRAzY2NmjRogVCQkI09nn48CH8/PxgZWUFJycnDBo0CDExMYYOl4iIiIyQwQfxHjx4ED169MDXX3+NmJgYDB8+HHXq1MG1a9dgbW0NAOjXrx+2b9+OP//8E/b29ujZsyeaN2+Oo0ePAgBiY2Ph5+cHFxcXHDt2DM+ePUPbtm2RLVs2TJ482dAhExGlizaDgjkgmMiwDJ7A7NixQ+N+YGAgnJyccPbsWVStWhVhYWH4448/sHr1atSsWRMAsHTpUnh7e+PEiROoUKECdu3ahWvXrmHPnj1wdnZGyZIlMWHCBAwZMgRjx46FmZmZocMmIsoSDD1DijOu6HOV4WNgwsLCAADZs2cHAJw9exbR0dHw9fVV9ilcuDDc3d1x/PhxAMDx48dRrFgxODs7K/vUrVsX4eHhuHr1arKv8/79e4SHh2vciIiI6POUoXVg4uLi0LdvX1SqVAlFixYFAAQHB8PMzAwODg4a+zo7OyM4OFjZJ2HyEr89fltypkyZgnHjxhn4HRARUULs0aGsIkN7YHr06IErV65g7dq1GfkyAIBhw4YhLCxMuT169CjDX5OIiIgyR4b1wPTs2RN///03Dh06hLx58yqPu7i44MOHDwgNDdXohQkJCYGLi4uyz6lTpzTai5+lFL9PYubm5jA3NzfwuyAiIqKsyOA9MCKCnj17YvPmzdi3bx/y5cunsb1MmTLIli0b9u7dqzx28+ZNPHz4EBUrVgQAVKxYEZcvX8bz58+VfXbv3g07Ozv4+PgYOmQiIiIyMgbvgenRowdWr16NrVu3wtbWVhmzYm9vD0tLS9jb26Njx47o378/smfPDjs7O/Tq1QsVK1ZEhQoVAAB16tSBj48PfvjhB0ybNg3BwcEYOXIkevTowV4WIiIiMnwCM3/+fABA9erVNR5funQp2rdvDwD49ddfoVar0aJFC7x//x5169bFb7/9puxrYmKCv//+G926dUPFihVhbW2Ndu3aYfz48YYOl4iIiIyQwRMYEUlzHwsLC8ybNw/z5s1LcR8PDw/8888/hgyNiIiIPhMZOo2aiIgoNZyWTfriYo5ERERkdNgDQ0REnw0uxfDlYAJDRET0CTC5MiwmMERERGR0CRbHwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERkdJjBERERkdJjAEBERkdFhAkNERERGhwkMERERGR0mMERERGR0mMAQERGR0WECQ0REREaHCQwREREZHSYwREREZHSYwBAREZHRYQJDRERERocJDBERERmdLJ3AzJs3D56enrCwsED58uVx6tSpzA6JiIiIsoAsm8CsW7cO/fv3x5gxY3Du3DmUKFECdevWxfPnzzM7NCIiIspkWTaBmTFjBjp37owOHTrAx8cHCxYsgJWVFZYsWZLZoREREVEmM83sAJLz4cMHnD17FsOGDVMeU6vV8PX1xfHjx5N9zvv37/H+/XvlflhYGAAgPDxcY7+492+0iiHx81JiyPaycmxs79O0l5VjY3vpay8rx8b2Pk17WTm2rNRe/H0RSf2JkgU9efJEAMixY8c0Hh80aJCUK1cu2eeMGTNGAPDGG2+88cYbb5/B7dGjR6nmClmyB0Yfw4YNQ//+/ZX7cXFxePXqFXLkyAGVSpXi88LDw+Hm5oZHjx7Bzs4u3XEYsr2sHBvb4/8t2/s07WXl2Nge/28zoj0RQUREBFxdXVNtL0smMDlz5oSJiQlCQkI0Hg8JCYGLi0uyzzE3N4e5ubnGYw4ODlq/pp2dnUH+gzKivawcG9vLOm2xvc+7vawcG9vLOm19Lu3Z29un2U6WHMRrZmaGMmXKYO/evcpjcXFx2Lt3LypWrJiJkREREVFWkCV7YACgf//+aNeuHcqWLYty5cph5syZiIqKQocOHTI7NCIiIspkWTaB+e677/DixQuMHj0awcHBKFmyJHbs2AFnZ2eDvo65uTnGjBmT5PJTVmgvK8fG9rJOW2zv824vK8fG9rJOW19ieyqRtOYpEREREWUtWXIMDBEREVFqmMAQERGR0WECQ0REREaHCQwREREZnS8ugRk/fjzevEm6PsPbt28xfvz4TIjo/zx+/DjFbSdOnPiEkRi/d+/eZXYIX6zo6GgEBATg3r17mR0KZaAPHz7g5s2biImJyexQ6Av1xSUw48aNQ2RkZJLH37x5g3HjxmVCRP+nTp06ePXqVZLHjx49inr16unV5ooVK1CpUiW4urriwYMHAICZM2di69atesf54cMHPH78GA8fPtS4Zba4uDhMmDABefLkgY2NDe7evQsAGDVqFP74449Mji5rMzExwfPnz5M8/vLlS5iYmOjUVrZs2bBx40ZDhWYU7ty5g5EjR6J169bK3/Hff//F1atXMzmy/3PixAmULVsWAwcOVJIOLy8v5bigrTdv3qBjx46wsrJCkSJFlO9+r169MHXqVK3aOHXqFGJjY1Pc/v79e6xfv16nuDKKIb8bGendu3cIDw/XuH3usmwdmIwiIsmujXTx4kVkz549EyL6PxUqVECdOnWwf/9+2NraAgAOHTqERo0aYezYsTq3N3/+fIwePRp9+/bFpEmTlAOGg4MDZs6ciSZNmujUXlBQEAICAnDs2DGNx+P/pqkdkFJz5swZrF+/Hg8fPsSHDx80tm3atEnrdiZOnIhly5Zh2rRp6Ny5s/J40aJFMXPmTHTs2FGv+K5du5ZsbI0bN07zuWq1OtW1uABApVJl+llsStUU3r9/DzMzM53ba9q0KbZs2YJ+/fqlN7QMERMTg9WrV6Nu3brpri118OBB1K9fH5UqVcKhQ4cwadIkODk54eLFi/jjjz+wYcMGnduMjY1FYGAg9u7di+fPnyMuLk5j+759+3Ruc9SoURg/fjwuXbqExo0b46+//sKLFy90/t4OGzYMFy9exIEDBzROrHx9fTF27FgMHTo0zTYqVqyIZ8+ewcnJCcDH0vIXLlyAl5cXACA0NBStW7dGy5YtdYrt/fv3UKvVyJYtG4CPieWSJUvw8OFDeHh4oGPHjsiXL59ObRr6u2FIb968weDBg7F+/Xq8fPkyyfbMPCZre1Lr7u6uc3zxvpgExtHRESqVCiqVCl999ZXGj0psbCwiIyPRtWtXvdsPDQ3Fhg0bcOfOHQwaNAjZs2fHuXPn4OzsjDx58mjVxuLFi/Htt9+iUaNG2LlzJ44dO4bGjRtj4sSJ6NOnj84xzZkzB4sWLULTpk01zoziz8J01b59e5iamuLvv/9G7ty50/xh1sbatWvRtm1b1K1bF7t27UKdOnVw69YthISEoFmzZjq1tXz5cvz++++oVauWxv9liRIlcOPGDZ1ju3v3Lpo1a4bLly9DpVIpB7L4963NwWHz5s0pbjt+/Dhmz56d5MdJFwEBAaluX7JkSarbZ8+eDeDje1q8eDFsbGyUbbGxsTh06BAKFy6sc1wFCxbE+PHjcfToUZQpUwbW1tYa23v37q1zm2mJjY3FmjVrUKNGjTS/c6ampujatSuuX7+e7tcdOnQoJk6ciP79+ysnHgBQs2ZNzJ07V682+/Tpg8DAQPj5+aFo0aI6f9eOHDmCmJgYVK9eXXksNjYWDRo0QIMGDeDs7Iy2bdvqFduWLVuwbt06VKhQQSOuIkWK4M6dO1q1kTgpSC5J0KdEWd26ddGzZ098++23OHr0KGrVqoVChQrB29sb//zzD3799Vfs2bNHqyVpMuq7kdCHDx+STVC1/VEfNGgQ9u/fj/nz5+OHH37AvHnz8OTJEyxcuFDr3rDEDHVM9vT0TPZzm7ATId0nb6muVf0ZCQwMlKVLl4pKpZJZs2ZJYGCgclu9erUcO3ZM77YvXrwouXLlkgIFCoipqancuXNHRERGjBghP/zwg05tvX//Xnx9feWbb74RGxsbmTNnjt5xWVhYyP3790VExMbGRonr1q1bYmFhoXN7VlZWcv36db3jSU6xYsVk7ty5GjHGxcVJ586dZfTo0Tq1ldL7vXr1qlhbW+scW8OGDaVJkyby4sULsbGxkWvXrsnhw4elXLlycujQIZ3bi3fjxg1p2rSpmJiYSNu2bZWY9dG0aVONm5+fn3h4eIi9vb00a9Yszed7enqKp6enqFQqcXNzU+57enrKV199JXXq1JETJ07oHFfCdhLf8uXLp89b1Uq7du0kICBAq32rVasmW7ZsSfdrWltby927d0VE83N37949MTc316vNHDlyyPbt2/WOKSgoSMqUKSOtW7eW4OBgERGZNm2arFy5Utln8uTJolKplHi1ZWlpqTwn4fu9cOGC2NnZadWGSqWSkJAQ5X7CdkREgoODRa1W6xSXiIidnZ3cunVLRD7+//br109j+8iRI6VSpUpatZVR3w2Rj8fgypUri1qt1ripVCqd3rebm5vs379fRERsbW0lKChIRESWL18u9evX1ys2Qx2TL1y4kOzt/PnzMmTIELG0tJRcuXLpFWO8LyKBKVWqlLx69UpERKpXry4REREGbb9WrVoyaNAgEdH8Ih49elQ8PDxSfe7FixeT3I4cOSJubm7StWtXjcd15e3trRygE8Y1e/ZsKVWqlM7tlS1bVg4fPqzz81JjZWUl9+7dExGR7Nmzy6VLl0RE5Nq1a+Li4qJTW6VLl5YVK1aIiOb7HTdunFSuXFnn2HLkyKH83e3s7OTGjRsiIrJ3714pWbKkzu09efJEOnXqJNmyZZOGDRvK5cuXdW5DG7GxsdKlSxf56aeftH5O9erVle9IVlW9enVp3769hIaGStOmTaVNmzayYcMGjX2OHj0qefLk0aq9devWiZeXl8yZM0eOHTuW5HuorTx58sjRo0dFRPNzt2nTJvHy8tK6nYRy584tN2/e1Ou58WJjY2XOnDni5uYmM2fOlA8fPkhYWJjGPg8ePJCYmBid2q1SpYrMnj1bRD6+3/jkrWfPnlK3bl2t2sioBMba2lo5yXJ2dpYLFy5obL99+7bY2Njo1GZGfDe++eYbqVq1qvzzzz9y/vz5JD/y2rK2tpYHDx6IyMfP4cmTJ0VE5O7du3qdtIkY9pic2O7du6VMmTJia2srY8aMkfDw8HS190UkMBYWFvLo0SMREVGr1fL8+XODtm9nZye3b98WEc0v4v3799M8A4vPuFUqlXJLeF+frDzeokWLJE+ePLJ27VqxtraWNWvWyMSJE5V/ayMsLEy57d27VypWrCj79++X//77T2Nb4gOjtvLkyaN8QYoVKyarV68WEZFjx45pfTYXb8uWLWJvby9Tp04VKysr+fnnn6VTp05iZmYmu3bt0jk2BwcH5eDs5eUl+/btE5GPB0FLS0ut2wkNDZXBgweLpaWlVKxYMV29N9q6ceNGug42MTExcv78+XQfuN+/fy83btyQ6OjodLUjItK3b1+ZMmWKRERESN++faVdu3ZibW0tQ4cOVfY5fvy42NraatVewu9cer5vAwYMkMqVK8uzZ8+Us+AjR46Il5eXjB07Vuf3KSLyyy+/SPfu3SUuLk6v5ycUHBwsbdu2lRIlSsiRI0fS3d7hw4fFxsZGunbtKhYWFtKnTx+pXbu2WFtby5kzZ7RqQ6VSyf79+5Vk0draWrZv367c37t3r17HvJo1a8q0adNE5GOSsGzZMo3tGzZsEHd3d53aHDdunERFRSV5/M2bNzJu3DidYxQxXG92sWLF5MCBAyLy8UR6wIABIiIya9YsrRP5xAx5TI539uxZ8fX1FXNzc+nRo4dG8poeX0QCU6FCBfH19ZWxY8eKSqWSQYMGybhx45K96SNXrlxy7tw5EdFMYHbt2iV58+ZN9bn379/X+qaPlStXSoECBZQDdJ48eWTx4sVaPz/+YJ64izM93Z4JtW7dWqZPny4iIuPHj5dcuXJJp06dxMPDQ6tLIIkdOnRIfH19JVeuXGJpaSmVKlWSnTt36hVb5cqVZfPmzUqc9erVkyNHjkjbtm2lSJEiWrXx008/Sfbs2cXHx8cglyu0tX37dsmZM6fW+/fp00f5XMTExMg333wjKpVKrK2tlS5qXURFRUlAQICYmJiIiYmJ8p3o2bOnTJkyRef2UnLmzBnJlSuXtGjRQlauXCnlypWTevXqafVcQ33f3r9/L506dRJTU1NRqVSSLVs2UavV0qZNG516N5o1a6Zxs7e3l3z58knDhg2TbNPHwYMHpVixYtKuXTt58eKFXm3Eu337tnTq1Em+/vpr8fb2Fn9/f+VHTxvJnbgZ4qTt2LFjYm9vL2PGjJE5c+ZIzpw5ZeTIkbJq1SoZPXq0ODg46NQzKfLxpDe5H9z//vtP7+OeoXqzZ8yYIbNmzRKRj70bFhYWYm5uLmq1WmbOnKlXm4Y8Jt++fVtatmwpJiYm0rp1a50vV6bli1jM8ebNmxgzZgzu3LmDc+fOwcfHB6amSccvq1QqnDt3Tuf2O3XqhJcvX2L9+vXInj07Ll26BBMTEzRt2hRVq1bFzJkz02wjOjoaP/74I0aNGqXzKHltvHnzBpGRkcqof20dPHhQ632rVauma1h49eoV3r17B1dXV8TFxWHatGk4duwYChYsiJEjR8LR0VHnNg1l586diIqKQvPmzXH79m00bNgQt27dQo4cObBu3TrUrFkzzTbUajUsLS3h6+ub6pRLXWZbJdS/f3+N+yKCZ8+eYfv27WjXrp3Wg0jz5MmDrVu3omzZstiyZQt69OiB/fv3Y8WKFdi3bx+OHj2qU1x9+vTB0aNHMXPmTNSrVw+XLl2Cl5cXtm7dirFjx+L8+fM6tZeaoKAgdOrUCWfOnEH58uWxdOlSeHh4GKx9bT18+BBXrlxBZGQkSpUqhYIFC+r0/A4dOmi979KlS3UND8DHGNevX4/Zs2dj6NCh6Natm0EG4+tK26nb+vw/Hj9+HP3798fJkyc1Hnd1dcWgQYN0nhChVqsREhKCXLlyaTy+b98+fPfdd3jx4oXOMe7btw8jR47E5MmTUaxYMWXWVDw7Ozud2wQ+/l3Pnj2LAgUKoHjx4nq1Yahjcvfu3fHHH3+gRo0amDp1KkqWLKlXPKn5IhKYhNRqNYKDg3X+IU9NWFgYvv32W5w5cwYRERFwdXVFcHAwKlasiH/++SfJDIyU2Nvb48KFCxmSwJBhvHr1SpnRpo327dtrta++P0g1atTQuK9Wq5ErVy7UrFkTAQEBySbqybGwsMDt27eRN29edOnSBVZWVpg5cybu3buHEiVK6FxTwsPDQ5mpYmtri4sXL8LLywu3b99G6dKltW7v4cOHcHNzy7Af2Tt37mDmzJnKbCQfHx/06dMH+fPnz5DXy2w1a9bEvn378OTJE/Tv3x+3b9/Gvn37YG9vr1M7d+7cwdKlS3H37l3MnDkTTk5O+Pfff+Hu7o4iRYqkO87Q0FD8888/+P777/Vu48WLF7h79y7i4uKQO3dueHp66vT8+O95WFgY7OzsUpy5Om/ePJ1jU6s/lmBL/LmWdJakyErUajUsLCzSnKmlT6dBvC9mGnW89ExZTYm9vT12796No0eP4uLFi4iMjETp0qXh6+urUzuGrp1RqlSpZA/8KpUKFhYWKFCgANq3b5/kRzAlO3bsgI2NDSpXrgwAmDdvHhYtWgQfHx/MmzdP796SuLg43L59O9nphFWrVk31ubokE8kVCdSVrrWCAgMD0/2aqdm/f79B2nF2dsa1a9eQO3du7NixA/PnzwfwsedOn2JdL168SPYkISoqSqdkxNPT0+AnHPF27tyJxo0bo2TJkqhUqRKAj0UjixQpgm3btqF27dopPjdxz1dqZsyYoXeMz58/x82bNwEAhQoV0vrvsGfPHkyePFnjMRFBSEgIgI89buvWrcOePXsQHR2tU0yJ695MnDgx3XVvEnvw4AF++OGHdCUwuXLlStJroouZM2dCRBAQEIBx48ZpJHlmZmbw9PTUajp2cgz1ve3duzcKFCiQpCzB3Llzcfv2ba16/wHodIKibe/QmDFjtG5TX19EAvPXX3+hfv36yJYtG/76669U99WmOFlC0dHRsLS0xIULF1CpUiXlQKgPQ9fOqFevHubPn49ixYqhXLlyAIDTp0/j0qVLaN++Pa5duwZfX19s2rRJq6J2gwYNwk8//QQAuHz5Mvr3748BAwZg//796N+/v169CCdOnMD333+PBw8eJKn7oM2ZSMIv6MuXLzFx4kTUrVtXObAcP34cO3fuxKhRo3SOLSoqClOnTk2xmFh8pd+03L9/H7t378aHDx9QvXp1g5yhGlqHDh3QsmVLpb5PfPJ98uRJvWpdlC1bFtu3b0evXr0A/N+Z5uLFi3U66J8/fx45c+YEkHJCHk/XM7mhQ4eiX79+SeplDB06FEOGDEk1gdH2Epi+PUfh4eHo0aMH1q5dq3wHTExM8N1332HevHlp9pi8ffsW+fLl06j1IiIYPny4xn66nmQBGVP3xpD+++8/LFmyBMePH0dwcDAAwMXFBRUrVkSHDh20TmratWsHAMiXLx+++eabJJd50kOfy+3J2bhxY7K/ad988w2mTp2qdQLj4OCQ5mdV196hT5HAfBGXkBJeNorvukuOvl13Xl5e2Lx5M0qUKJGeMFO9dKRSqbT+wYzXuXNnuLu7J/nxnjhxIh48eIBFixZhzJgx2L59O86cOZNmezY2Nrhy5Qo8PT0xduxYXLlyBRs2bMC5c+fQoEED5WChi5IlS+Krr77CuHHjki2Op0vXdosWLVCjRg307NlT4/G5c+diz5492LJli06xtW7dGgcPHsQPP/yQbGzaXEvfv38/GjZsiLdv3wL4WEBtyZIlaNOmjU6xpCRfvnypHnh0+cxs2LABjx49wv/+9z/kzZsXALBs2TI4ODjoXLX5yJEjqF+/Ptq0aYPAwED8+OOPuHbtGo4dO4aDBw+iTJkyWrUze/ZsdOnSBRYWFmku9aHrAdPCwgKXL19OMlbl1q1bKF68eKaup/Xdd9/h/PnzmDNnjkYy3qdPH5QsWRJr165N9fkPHz7EihUrMGLECI3Hf/rpJwwZMiRdsdnY2ODy5cvIly+fxuXB+/fvo3Dhwgb5u128eBGlS5fW+Xh8+vRp1K1bF1ZWVvD19VWqLIeEhGDv3r148+YNdu7cibJly+rUbmxsLLZs2aJcaixSpAgaN26c7qUE3rx5k2y1W23Hr1hYWODKlSsoUKCAxuO3b99G0aJFtf6/yIixjs+fP0+1xzAmJgbnzp1TTq71YtAhwV+oxYsXS4MGDeTly5eZHYoGOzs7pbBRQkFBQcp0uOvXr2tdF8HR0VGuXr0qIiKVKlWShQsXisjHgl26TCtOyMrKKtkY9WFtbZ3i+9WnJoK9vX26p51WqlRJmjRpIk+fPpVXr15J9+7dJXfu3OlqM6GZM2dq3H7++Wf5/vvvJXv27HrP9nn79q1BYkvvTBWRj8XE/vvvP4PEk1jevHll/fr1SR5ft26duLm56dXmo0ePlJIN6WFlZZXsLJVDhw6JlZVVuttPj4yoe5PYhQsX9JrhU758eenSpUuy08/j4uKkS5cuUqFCBZ3aDAoKkoIFC4qVlZWUKlVKSpUqJVZWVlKoUCGlfIaunj9/Ln5+fklmdMbftFWkSJFki53Onj1bvL299YrNUBLP3ipatKg8fPhQua9vrZ+EvohLSAm9e/cOFhYWBm0z/nqjq6srPDw8klz6Sc8gpfSwsLDAsWPHkmTnx44dU/4GcXFxWv89KleujP79+6NSpUo4deoU1q1bB+DjGWv8Gbuuypcvj9u3byeJUR85cuTA1q1bMWDAAI3Ht27dihw5cujcnqOjY7rXx7py5QqOHTuG3LlzAwB+/vlnLFy4EC9fvtQrpsRS6gWaN2+eVr1q8WJjYzF58mQsWLAAISEhuHXrFry8vDBq1Ch4enrqtY5U/vz5sWjRIp2fl1DCFa1Pnz6NuLg4lC9fXmOfkydPwsTEROez6s6dO6NLly64e/cuvvnmGwAfx8D89NNPOo1xiYuLw8SJEzF9+nRloVhbW1sMGDAAI0aMSLXXNyU5cuRItvfR3t7e4DPzYmNjcezYMRQvXlyrHs9WrVphyJAh+PPPP6FSqRAXF4ejR49i4MCBWi9PEF+mPyVPnjzRqp3ELl68iMDAwBTH/vXr1w+lSpXSqc3evXsjf/78OHHihHI8ePnyJdq0aYPevXtj+/btOsfZt29fhIaG4uTJk6hevTo2b96MkJAQ5XOkrf79+6Nnz5548eKFMity7969mD59utaXjwDg0qVLWu+rbe+QJLq4c//+/STjrRLvo7N0pT9GyNzcXKpUqSIjR46UPXv2yJs3b9Ld5tixY1O96eLRo0cyb948GTJkiPTr10/jpqsJEyaIpaWl9O7dW1asWCErVqyQ3r17i5WVlUycOFFEPtYR8PX11aq9Bw8eiJ+fnxQvXlyjlkzfvn2lV69eOscn8vGszcfHR5YuXSpnzpzRuxqqiMjSpUvFxMREGjZsKBMmTJAJEyZIw4YNxdTUVJYuXapzbCtWrJBvv/022SJW2kpccVQkadXRjHDnzh2tC7qJfCzW5eXlJStXrtQoFb927Vqdz1hFMqZ2xtdffy1//vlnksc3btwo5cqV07m9uLg4mTFjhuTJk0ejTtLMmTN1KiA3dOhQyZUrl/z222/K53bevHmSK1cuGT58uM5xiYgsXLhQfH195dmzZ8pjz549kzp16siCBQv0ajM1jRo10voYY4i6N6ktNZHwpitPT88kxesSWrZsWZrV0ROzsrJKtufwwoULele7dXFxUarm2traKlWXt27dqvVSB/F+++03jc9wvnz5Uv0bJCe1ujyJa/To0mZGVFtO6ItLYA4fPiyTJk1SKkeam5tLpUqVZPjw4XpVazWkPXv2iJWVlRQtWlRMTU2lZMmS4uDgIPb29lKjRg292ly5cqVUqFBBHB0dxdHRUSpUqCCrVq1Str9588Zglwz0YehCVidOnJDvv/9e6er9/vvv9V6vpGTJkmJrays2NjZStGhRpc34mzZUKpUsX75ctm7dqtysrKzk999/13jM0H766SedDtT58+eXPXv2iIjmgeb69evi4OCg8+snl7iJfFxOQZ91uEQ+XiJMLvG7e/euzuXhEwsPD9e7rHnu3LmT/T/csmWLuLq6at1OyZIlNT5fNjY2ki1bNsmfP7/kz59fsmXLJjY2NjovA9KhQwcZPny4RERESL9+/WTkyJFy6tQpjX327dun8xpVDx8+lO3bt8u6deuU9Ycy29y5c8Xc3Fx69+4tW7dulRMnTsiJEydk69at0rt3b7G0tJR58+bp1Kajo6NyySyhI0eOiKOjo15x2traKuX63d3dlUvVd+/e1fty/PPnz/VeJicjCqp+igTmi7uEVLlyZVSuXBnDhw9HTEwMTp8+jYULF2LatGmYOnVqps6/HzZsGAYOHIhx48bB1tYWGzduhJOTE/z9/TWWrdeFv78//P39U9xuaWmZ6vPDw8OVaXNpTbXTp/hSwksEhlC+fHmsWrXKIG01bdrUIO3Ez2ZI6Mcff1T+nZ66D4ln5ogIgoOD8eLFC/z2229at/PkyZNkL+PFxcXpNM02I1fwNTc3R0hICLy8vDQef/bsmdb1blKScDaNrl69epXseypcuLBOU/cN9XlL7PXr17CxsUFsbCzu3buH169fY9q0aZg1a5ayaruDgwOeP3+eZlvh4eGwsbGBWq2Gm5sb3NzcAHz8nCQ8VmSWHj16IGfOnPj111/x22+/aczgKlOmDAIDA9GyZUud2mzYsCG6dOmCP/74QxlwevLkSXTt2lXnWavxChUqhJs3b8LT0xMlSpTAwoUL4enpiQULFiiXm7Vx7949xMTEoGDBghqzq4KCgpAtWzata99kROFHlUqFiIgIWFhYKDOYIiMjld8RXWtLJfsaIp//LKTEbt26hQMHDii39+/fo2rVqqhevbrOVRqBj7OcUpsJou2Pk62tLS5cuID8+fPD0dERR44cQZEiRXDx4kU0adIE9+/f1zm29DIxMcGzZ8+UGVypLY+eFYovpaemjDFKPDMnvpBd9erVdUoUypQpg379+qFNmzYaM0vGjx+P3bt34/Dhw1q1Ez+T7sGDB8ibN6/GLI342hnjx49PMo5FG61bt8azZ8+wdetWZaxGaGgomjZtCicnJ6xfv16n9kJCQjBw4EBlmnziQ6G2n+fy5cujfPnyScZ19OrVC6dPn8aJEyd0iutT2L59O7777jt07doVzZo1w+TJkxEeHp7q//PmzZsxZMgQXLhwAVZWVhrboqKiULp0afzyyy9o1KhRmq+f1hiYeLqWjkgoOjoa//33HwAgZ86cek+DDg0NRbt27bBt2zaljZiYGDRu3BiBgYE6FwEEgJUrVyImJgbt27fH2bNnUa9ePbx8+RJmZmZYtmwZvvvuO63aqVatGgICApKcJK1cuRKLFy/GgQMHtGonI0qNJP69iP+dSHw/Pb8bX1wCkydPHrx9+xbVq1dH9erVUa1aNRQvXjxdlT63bt2qcT86Ohrnz5/HsmXLMG7cOK0HQLq4uGD//v3w9vaGj48Ppk6disaNG+PixYuoVKmSMkBQW7Gxsfj111+xfv36ZKfqaXN2ePDgQVSqVAmmpqZpTrXTt7bBihUrsGDBAty7dw/Hjx+Hh4cHZs6ciXz58uk0fTe9NWVS8uHDh2QTInd3d73ay4q2bt2Kdu3aYdiwYRg/fjzGjRuHmzdvYvny5fj7779TrYmSnBo1amDTpk0GHXD65MkTVK1aFS9fvlQGYl64cAHOzs7YvXu30hugrfr16+Phw4fo2bNnstPktf3sHTx4EH5+fnB3d9eY8vzo0SP8888/qFKlik5xfSrHjx9HmzZtcO/ePXh6eqZZCqJOnTpo2bIlOnXqlOz2JUuWYN26ddi5c2ear61NtXF9SkfEe/HiRYr1Xi5fvoxixYrp3Obt27eVadTe3t4GmXgQ782bN7hx4wbc3d2VukfasLOzw7lz55KdRl22bFmEhoZq1U5GlBrRdmp2emrifHEJTMmSJXHjxg2ULl1aSWIqV66c5IzCEFavXo1169YlSXBS0rRpU/j5+aFz584YOHAgtm7divbt2ys/BHv27NHp9UePHo3FixdjwIABGDlyJEaMGIH79+9jy5YtGD16dLrObgxl/vz5GD16NPr27YtJkybhypUr8PLyQmBgIJYtW6ZTxUpD1pQBPvbUdezYEceOHdN4XJczh7TOZoCPtWFcXFxQtGhRmJmZ6RQjYLhep8OHD2P8+PEa1aRHjx6NOnXq6BxTRomKisKqVatw8eJFWFpaonjx4mjdurVeZ9e2trY4fPiwQdZoefr0KebNm4cbN24A+PgD1717d7i6uurVnqF6ddMiIvjvv/+0Ku7m6uqKQ4cOpfjDffv2bVStWhVPnz41SGzp4eLigj/++AN+fn4aj//yyy8YNWqUUpfpU+rfvz8mTJgAa2vrNGe5aVu92d7eHgcOHEgys+rs2bOoXr06IiIi9I7XGHxxCQzwsUvw0KFDOHjwIA4ePIhr166hZMmSqFGjBiZNmmSw17l79y6KFy+udc/J3bt3ERkZieLFiyMqKgoDBgxQFtGaMWOGztcp8+fPj9mzZ8PPz0/j8tTs2bNx4sQJrF69Os02MmJ6XUI+Pj6YPHkymjZtqnHp4sqVK6hevbrSBawNa2trXLx40WBnRvE9T0OHDk02IdKmcKEuU2hdXFywbt06nc7Y09vrtHTpUtSsWdPg18BjY2MRGBiYYhXjffv2GfT19OHj44NVq1bpPK32U0hvr27CS7+GYmlpifPnz6d4afL69esoXbp0piQHiU2bNg2jR49Ghw4dMGPGDLx69Qpt27bF5cuXsXDhQjRr1kzrtlq0aIFy5colKQA4bdo0nD59Gn/++adW7dSoUQObN2+Gg4NDqsu3qFQqrb8fjRo1gqWlJdasWaNcro2NjcV3332HqKgo/Pvvv1q1o60nT54gT548Wu27fv16NG3aVDkpe/z4MVxdXZVj4ps3bzB37lwMHjxY73i+yAQm3suXL3HgwAFs3boVa9asQVxcnMHObN6+fYthw4bh33//VdYySU14eDhOnjyJDx8+oFy5culawyOetbU1rl+/Dnd3d+TOnRvbt29H6dKlcffuXZQqVQphYWFpthF/JpjWx0TfSzSWlpa4ceMGPDw8NBKYoKAgFC9eXKeDYc2aNTF48GC9BzwnZm1tjbNnz+o96FRb8v/XqJk4cSKOHTumU92g9PY6WVpa4sOHD/Dw8ECNGjVQs2ZN1KhRQ++eg3g9e/ZEYGAg/Pz8ko3r119/1avdoKAg7N+/P9mkaPTo0Tq1tWvXLkyfPl0ZQKlPLKNHj8bChQuTDF4NCwtDt27dMHHixCSDjtND217drVu3okGDBsiWLRuaN2+e6r7aroTu7e2NESNGpFhFesWKFZg0aZLSC5Wa5cuXa/Wa2taVSc758+fxww8/4P3793j16hXKly+PJUuWwMXFRad2cuXKhX379iW57HT58mX4+voq60tlhmvXrqFq1apwcHBQTnwOHz6M8PBw7Nu3D0WLFjXI6wQHB2PSpEn4448/8ObNG62ekziJtrOzw4ULF5TvQ0hICFxdXdP1m/vFzULatGmTMnj32rVryJ49OypXrozp06frfS0u8YKCIoKIiAhYWVlh5cqVaT7/woULaNCgAUJCQiAisLW1xfr161G3bl294omXN29ePHv2DO7u7sifPz927dqF0qVL4/Tp0zA3N9eqDUPPEkosX758uHDhQpIegB07dsDb21untnr16oUBAwYgODg42SXqde0h8vHx0akHSF8qlQouLi4YOHCgzslSUFAQNmzYoHevU2hoqFLef//+/Vi9ejU+fPiAAgUKoEaNGqhRowaqV6+ulGTX1tq1a7F+/Xo0aNBAr7iSs2jRInTr1g05c+aEi4uLxndOpVLpnMB89913ePPmDfLnzw8rK6skn5e0xoj9/PPPcHNzS3bmjb29Pdzc3PDzzz8rC2MaQoUKFdClS5c090v4t9FnkGlymjdvjhEjRqB27dpJPg/BwcEYOXKk1ktkpDZZQqVSISoqCjExMelKYAoUKICiRYti48aNAD7+f+uavABAZGRkspd2s2XLZpCZNOnh4+ODS5cuYe7cucpl1bZt26Jnz546F+F8/fo1unfvjt27d8PMzAxDhw5Fz549MXbsWPzyyy8oXry4TuvdJT7pzZC+knRNwjZCuXLlkhYtWsicOXN0LmuekqVLl0pgYKByW758ufz777/y6tUrrZ5fp04d+eabb+TYsWNy7tw5adasmRQoUCDdcQ0ZMkQmTZokIh8LkpmamkqBAgXEzMxMhgwZku72DWHRokWSJ08eWbt2rVhbW8uaNWtk4sSJyr91YeiaMnv37pWKFSvK/v375b///pOwsDCNW0YIDQ3Vaf8aNWrIv//+a7DXf/v2rezbt09GjRolVapUEQsLCzExMdG5ndy5cyvFuQzF3d1dpk6darD2En5nk7ul5auvvkpSTyWhM2fOyFdffWWweN+8eSN9+vTRqs2U6vCkR3h4uBQpUkRsbW2lW7duyvIVXbt2FVtbW/Hx8dG7lk68p0+fyo8//ijZsmWTunXr6t3OkSNHxNPTU0qXLi3Xrl2TRYsWia2trbRs2VLr43K8r7/+WsaNG5fk8TFjxkjp0qX1jjGr6dKli7i7u8uAAQOkaNGiolarpX79+uLn5yfHjx/XuT0WsjMSDx48SLFy54MHD9J8fo4cOeTs2bPK/devX4tKpTL4j+Tx48dl+vTp8tdff2n9nK1bt8qHDx+Uf6d209fKlSulQIECGtVQE1b61ZahCjDFS5gEJbzpmxAZSsJKxYasZCzyscrqgQMHZPTo0VK1alUxNzfXucCZiMgvv/wi3bt316mibVpsbW0zvIKxLiwsLFL9XN2/f1/vomQODg5K8UlHR0dxcHAQExMTsbW1zZDCh9oKDQ2Vbt26Sfbs2ZXvh6Ojo3Tr1k3nxCCh8PBwGTFihNjY2Ej58uVl37596Yoz/iQt/tgl8nFtrgoVKkiePHl0auuvv/4SU1NTadu2rZLc/vDDD2JqaiqbN29OV5yGcOjQIfH395eKFSvK48ePRURk+fLlya6llRo3NzfZu3eviHxc306lUsmwYcP0jutTJDBfzBiYmJgYxMbGalw6CQkJwYIFCxAVFYVGjRrpPd0xpQFzL1++hJOTU5rX+BJOYYtna2uLS5cuaTXdMCWGWPcpo1fyTujNmzeIjIw06MDD9MioaePplda4pPht2vyffPjwASdOnMCBAwewb98+nDx5Eh4eHqhatSqqVq2KatWq6Tw9GQCaNWuG/fv3I3v27ChSpEiSyzPajrtIqGPHjvj666+V4muGcOfOHSxduhR37tzBrFmz4OTkhH///Rfu7u4oUqRIqs91cXHB6tWrlTVoEtu7dy/8/f31WqV92bJlGvfj6/uUL19eq6nppUuXxt69e+Ho6Jik2GFi+qzVJv9/9pKIIFeuXHqXoYiOjsacOXMwefJk5MiRA5MmTcK3336rV1sJHTx4MNnvZ1xcHCZNmoRRo0bp1N727dsxefJkXLhwQZn9NmbMmEw7BsTbuHEjfvjhB/j7+2PFihW4du0avLy8MHfuXPzzzz/4559/tG7L1NQUjx49UgrpWVlZ4cyZM/Dx8dErNrVajWXLlimXMFu3bo2ZM2cqlx9DQ0PRoUMH1oHRRocOHWBmZoaFCxcCACIiIlCkSBG8e/cOuXPnxrVr15SBb7pKLgEBPhbz8vHxQVRUVJrP37dvn8Y1y2+++Qbr16/XWCRR1zEcdnZ2aNasGdq0aYNatWrptaicMbp27VqydW/0rZqZ1Tx48EDrfdOaXWRpaQknJyc0atQI1apVQ5UqVfQaJ5BYhw4dUt2uy7X0eFOmTMGMGTPg5+eX7BgnXcsCHDx4EPXr10elSpVw6NAhXL9+HV5eXpg6dSrOnDmDDRs2pPr8li1bIjo6Gps3b052e5MmTWBmZqb1LBVDGjduHAYNGgQrK6skxQ4TGzNmzCeK6v+ICJYvX47Ro0cjJiYGY8aMQceOHTUKH1LaSpUqhX79+qFt27YakyDOnz+P+vXr65Q8m5iYIDg4WJlAkt6TaG1+b1jITktfffUV5s6dq9S0mDdvHiZPnoxr167B3t4eQ4YMwalTp3SqOxI/l3/WrFno3LmzRi2Z2NhYZZXco0ePptpOamfUupxNJ7Z582asXr0a27dvh729Pb777ju0adNG51V7DS2tM8KEdDk7vHv3Lpo1a4bLly9r/D3jX0vXv9+hQ4dS3Z6eyr53797F27dv4e3tnamJZYUKFXD+/HkUKlRIKexYvXp1g6yUbWipHUj1KXpWsWJF/O9//0P//v01Dv6nTp1C8+bN8fjx41Sff/78eVSsWBENGzbE4MGDUahQIQDAjRs3MG3aNGzfvh3Hjh1D6dKldYorXmhoKE6dOpXsjKv0DG7NCooVK4a7d++iV69e6Nu3b4p1uPRZmuDt27c4e/YssmfPnqT34N27d1i/fr3ef793795h3bp1iIqKQu3atVGwYEG92jEUKysrXLt2DZ6enhqf4bt378LHxwfv3r3Tui21Wo2iRYsqy3JcunQJhQsXTjKAWZ8eu4zyxSQw1tbWuHLlinIQbN68OfLmzauUtL527RqqV6+u1Xog8eLn8h88eBAVK1bU+I+OL5s+cODAND/k2p5R61urIyIiAhs2bMCaNWuwb98+eHl5oU2bNlrP2mjQoAHWrFmjdAVOnToVXbt2hYODA4CPl8qqVKmCa9euadVeWmeECelydtioUSOYmJhg8eLFyJcvH06dOoWXL19iwIAB+OWXX3S+RJhcYpEw8dImIYqOjsbEiRNx7tw5VKhQAUOHDkWbNm2UsveFChXCP//8o9c0XuBjr4SzszMCAgI0Hl+yZAlevHiRpHZFciIjI3HkyBHs378fBw4cwPnz5/HVV18pCU21atX0uqwXExODAwcO4M6dO/j+++9ha2uLp0+fws7OTmONpMxiY2ODy5cvI1++fBoH//v376Nw4cJaHfz//vtvBAQE4OXLlxqP58iRA4sXL9a712/btm3w9/dHZGQk7Ozsksy40mWNpUePHkGlUim9uadOncLq1avh4+Oj1YymjJDwu2XI5Ulu3bqFOnXq4OHDh1CpVKhcuTLWrl2rXBbRZepu//79lUtcAJQSF9euXYOVlRViYmKwe/dupfpyZvDy8sLvv/8OX19fjc/w8uXLMXXqVK2PyYD2x2Vtj8kBAQGYNWtWutYZS1O6RtAYkezZs8vVq1eV+7lz55aVK1cq9+/cuaP3gLv27dtn2KwUQ7t69aqULFlSp8FTarVaYzBW4sGUhhiMZQg5cuRQBq7a2dnJjRs3ROTjbKKSJUvq3F5oaKjG7cWLF7Jr1y4pX768snJzWvr37y+5cuWSTp06iZeXlzRu3FgKFSoka9eulfXr10uxYsXk+++/1zm2eB4eHsmulHvixAnx9PTUq83w8HDZvn279O3bV+zt7fWahXT//n0pXLiwWFlZiYmJifJ56d27t/z44496xZVQXFxcugcI58mTR/nbJRxguGnTJvHy8tK6nTdv3simTZtk2rRp8tNPP8nmzZslKioqXbEVLFhQ+vTpk+52REQqV64sy5cvFxGRZ8+eia2trVSsWFFy5syZ7OyaT+HAgQNa3XTVtGlT8fPzkxcvXkhQUJD4+flJvnz5lMkUuhyrihQpojFgesmSJeLo6Cj379+XuLg4ad++vTRo0EDnGA1h2bJl8u7dO5k8ebL4+PjIiRMnxNbWVg4fPiwrV66UXLlyyezZszMltniJfzcywheTwNSsWVOGDh0qIh9HbavVann69KmyfdeuXZI/f/7MCi9DvX37VtatWydNmjQRc3NzcXd312kadUaPJn/9+rUsWrRIhg4dKi9fvhQRkbNnzyoj6rXl4OAgd+/eFRERLy8vZSbD7du39U5Ok3PgwAGtp0+6u7vL9u3bRUTk5s2bolKp5J9//tFoS9dZEQmZm5sr7zmhO3fuiLm5uU5txcbGyokTJ2Tq1KlSt25dsbGxEZVKpVci1KRJE2nTpo28f/9e4/Oyf//+dJUIWLZsmRQtWlTMzc3F3NxcihUrpvw462rAgAFSuXJl5Uc9KChIjhw5Il5eXjJ27Fi9YzQEKysrg824cnBwUJL5WbNmyTfffCMiIjt37tRrhllW5uTkpFEeIy4uTrp27Sru7u5y584dnY5V8Z+JeK1atZLOnTsr98+fPy+5c+c2XPA6iE8O4uLilLIT8bPCLCwsZOTIkel+jefPn8vhw4fl8OHD8vz5c52fnxFT+RP7YgrZjR49GvXr18f69evx7NkztG/fXmPZ8s2bN6NSpUp6tZ3SLIR4mVU2fefOnVi9ejW2bNkCU1NTfPvtt9i1a1eWWpX50qVL8PX1hb29Pe7fv4/OnTsje/bs2LRpEx4+fKh1xU4AKFq0KC5evIh8+fKhfPnymDZtGszMzPD7778btBqqs7OzVtWVgY9r5MQvOfDVV1/B3Nxco+jcV199pdcslXhubm44evRokvEhR48e1aqa7qlTp5TCjkeOHEFkZCTy5s2L6tWrY/bs2ahRo4Zel7cOHz6MY8eOJbl+7unpiSdPnujcHvBxfZhRo0ahZ8+eynf1yJEj6Nq1K/777z/069dPp/YmT56MHj16wM3NDbGxsfDx8UFsbCy+//57jBw5Uq8YDaVu3bo4c+aMQT630dHRyuzLPXv2KJe1ChcujGfPnmnVhrarRwPaDabOqDLzb9++VcZwAB8vT82fPx89e/ZEtWrVtFo+JZ5ardYYl3jixAmN2UsODg54/fq1TvEZiiQY3zdixAgMGjQIt2/fRmRkJHx8fNJ1iTYqKgq9evXCihUrlEttJiYmaNu2LebMmaPTuoERERFpzoTVZ5xTvC8mgalWrRrOnj2LXbt2wcXFBf/73/80tpcsWRLlypXTq+3Ea+JER0fjwoULuHLlSpJlzj+lZs2aoWHDhli+fLlSVlwfKpUqyXXq9KzenVD//v3Rvn17TJs2TeNaaYMGDfD999/r1NbIkSOVGV/jx49Hw4YNUaVKFeTIkQPr1q3TObbE60CJCJ49e4apU6dqvQBgbGysxt/d1NRUY6ZF4oOkrjp37oy+ffsiOjpaSaT37t2LwYMHY8CAAWk+v0KFCnBxcUGNGjUwY8YM1KhRA/nz59c7nngpLcvx+PFjva+Jz5kzB/Pnz9cYgNm4cWMUKVIEY8eO1TmBMTMzw6JFizBq1ChcuXIFkZGRKFWqVKYNzEy48Kefnx8GDRqEa9euJTvjSpexNUWKFMGCBQvg5+eH3bt3Y8KECQA+JtfaDtbWdukHlUqlVQLTunVrjdITPj4+GmXmIyIiMGzYMJ0TmMKFC+PMmTNJqnjPnTsXgG5/N29vb2zbtg39+/fH1atX8fDhQ401jB48eKBzhWpDSngMNjMz03u6c2L9+/fHwYMH8ddff2mcKPTu3RsDBgzQqbL0V199leI20XOcU0JfzCDezDB27FhERkbil19+yZTXj4iIMMgAKrVajfr16ytncdu2bUPNmjVhbW0NAHj//j127Nih1wfR3t4e586dQ/78+TUGoT148ACFChXSaRR9cl69epVkqQdtpTQ7rEKFCliyZIlWZf8zuhaCiGDo0KGYPXu2Mm3cwsICQ4YM0WqQ9s2bN5XZM4b03Xffwd7eHr///rsyHTNXrlxo0qQJ3N3d9ZpGbWFhgStXriRZNiEoKAjFihVL92cls2k7G03Xg/6BAwfQrFkzhIeHo127dliyZAkAYPjw4bhx44ZeNXnSK3HpiYTffUD/dXKmTJmCw4cPp1j/pHv37liwYEGSWV3J2bx5M1q1aoXKlSvj6tWr+Prrr7Ft2zZl+5AhQ3Dv3j1lQP6nlHjGUEr0mTGUM2dObNiwAdWrV9d4fP/+/WjZsiVevHihdYwbN25Mc0mD9NTSYQKTgW7fvo1y5crpNGMgo7x79y5JXRRtu+7SqukRT58fJScnJ+zcuROlSpXSOIjt3r0bAQEBePTokVbtREdHw9LSEhcuXDDYAmaJZ4fFFxPTpTigtj9K2hxQUxMZGYnr16/D0tISBQsW1Hqtq4zy+PFj1K1bFyKCoKAglC1bFkFBQciZMycOHTqk16ymokWL4vvvv8fw4cM1Hp84cSLWrVuHy5cva9XO+PHjtdpP17WVsrLY2FiEh4drFMG7f/8+rKysMqVwZEYlMIa2d+9e/P3333BxcUGvXr00Lp+MGzdOKTvwqanVagwYMCDNS0X61PixsrLC2bNnk/RiXb16FeXKlUuzrlnCGJOrj2ZITGAy0IoVKzBkyBA8ffpUq/1DQkIwcOBA7N27F8+fP09y5q/rlzkqKgpDhgzB+vXrk0zz1Ke9jNCpUye8fPkS69evR/bs2XHp0iWYmJigadOmqFq1KmbOnKl1W15eXti8eXOSS3q6evv2Lfbu3YuGDRsCAIYNG4b3798r201NTTF+/Ph0Vzk2tPi6JQmLH2ammJgYrF27FpcuXUJkZCRKly4Nf39/WFpa6tXexo0b8d1338HX11fp2j569Cj27t2L9evXo1mzZlq1o1ar4erqCicnp1SrGWdGvYvjx4/j5cuXymcP+Lhy85gxYxAVFYWmTZtizpw5WSJB/euvv5ItGDljxow0n28sCUxWlZHJQa1atZAjRw4sX75cOca9ffsW7dq1w6tXr7Bnz55MjzHeFzMGJiMlXq4+fqzEmTNndCpZ3b59ezx8+BCjRo1C7ty50z3OZPDgwdi/fz/mz5+PH374AfPmzcOTJ0+wcOFCTJ06NV1tG8r06dPx7bffwsnJCW/fvkW1atXw7NkzVKxYEZMmTdKprREjRmD48OFYsWKFziuxJrRs2TJs375d+RGZO3cuihQpovzw3rhxA7lz59Z5zEVy4uLi8M8//2j8YOn6/IkTJ2L69OmIjIwE8PHHYMCAARgxYkSmFskzNTXVenVibbRo0QInT57Er7/+ii1btgD4OE7h1KlTKFWqlNbt1K9fH/v27UPZsmUREBCAhg0bZsjfydfXF3fv3tWpwN64ceNQo0YN5fNw+fJldOzYEe3bt4e3tzd+/vlnuLq6YuzYsam2k5FLCezduxeNGzeGl5cXbty4gaJFi+L+/fsQEZ2K9u3cuVO5tBoXF4e9e/fiypUrAD5eWtVV8+bNERgYCDs7uyTH5MQy47KZIRlqDGJyZs2ahbp16yJv3rzKyeDFixdhYWGBnTt3at2Oh4dHhldW/qJ6YGJjY3H06FEUL15cKcKWHnfv3oWnpyc6duyo8Xj8pYaaNWsqlX+1YWtri8OHD2s9QDQt7u7uWL58OapXrw47OzucO3cOBQoUwIoVK7BmzRqd1snIaEeOHFHO1MuUKYNatWrp3EapUqVw+/ZtREdHw8PDQxmjE0/bA3WVKlUwePBgNGrUCEDSs8OVK1di3rx5OH78uM4xxrt9+zaWLFmCwMBAvHjxAtHR0Xq1M2zYMPzxxx8YN26cxoC7sWPHonPnzjongYZ08+ZNzJkzB9evXwfwMdno2bOnVmOHMtrTp0+xbNkyBAYGIjw8HG3btkVAQIBBxwPNnTsXL1++1KkbP3fu3Ni2bZtSLXvEiBE4ePAgjhw5AgD4888/MWbMmDQLlGXkUgLlypVD/fr1MW7cOOW74eTkBH9/f9SrVw/dunVLs42MKDPfoUMHzJ49G7a2tmjfvn2qP/L6XO7OSjK6d+PNmzdYtWoVbty4AeDjdzc9vacZ5YtKYICPAwGvX7+erkUS4yVexPG7777D7Nmz9R6Z7uPjg1WrVul0NpkaGxsbXLt2De7u7sibNy82bdqEcuXK4d69eyhWrJhyxp4ZkusqX7ZsGcaMGYM3b97o1VVuqAN17ty5cfz4cWX6cK5cuXD69Gnl/q1bt/D1118jLCxM69iAj92wf/75JxYvXoyjR4+iSpUqaNWqFZo1a6b3Z8bV1RULFixIMrti69at6N69u85Tlj98+IB79+4hf/78aQ4QTM3GjRvRqlUrlC1bVqlUeuLECZw+fRpr165FixYtdG6zWrVq6NixI/73v/8Z9EB66NAhLF26FBs3bkSxYsWwZ8+eTDtQW1hYICgoSFlAs3Llyqhfvz5GjBgB4OO4lWLFiiEiIiJT4gM+JvQXLlxA/vz54ejoiCNHjqBIkSK4ePEimjRpgvv372dabF+KBw8ewN3dPUN7YtIrrfIiwMckde/evXq/xhd3Calo0aK4e/euQRKYxLnfv//+q/UAp+TMnDkTQ4cOxcKFC/UuLZ+Ql5cX7t27B3d3dxQuXBjr169HuXLlsG3bNoP0QKXH+PHjUb16dY2u8s6dO6Ndu3Y6dZUnZKhF6UJDQzXGvCQedR8XF6exPS2nT5/G4sWLsXbtWuTPnx/+/v44duwYfvvtt3RPfXz16lWyPRqFCxfWafD4mzdv0KtXL2UV5Fu3bsHLywu9evVCnjx5MHToUJ3iGjx4MIYNG5ZkwOyYMWMwePBgvRKYUqVKYeDAgejVqxdatmyJjh07okKFCjq3k9jXX3+N+/fv49q1azh//rwyIDwtaV2mAD5eRnNxcUHt2rWVHr3UODs74969e3Bzc8OHDx9w7tw5jcQ8IiJC73IIhmJtba2Me8mdOzfu3LmjrNz933//adXG33//DT8/vwz7Aa5ZsyY2bdqU5DgXHh6Opk2bZlptLkPRd1mZlCScwp8WbaeipzYWMSIiAqtXr9bpOJqsDC2TlwX9+++/UrJkSdm2bZs8ffpUwsLCNG66SKtCra4cHBzEzMxM1Gq12NjYiKOjo8ZNVzNmzJBZs2aJiMju3bvFwsJCzM3NRa1Wy8yZM/WO0xBcXFzk9OnTyv3hw4dLpUqVlPvr168Xb29vvdoODw/X+D+NiIjQ6fkFChSQDRs2pLh93bp1WldtLlasmHh4eMiwYcPkypUryuOmpqYaS1voq1y5ctKrV68kj/fs2VPKly+vdTu9e/eWMmXKyOHDh8Xa2lr5HG/ZskWvZRgsLS01qpjGu3XrVrqqIkdHR8vGjRulcePGki1bNvH29paff/5ZgoODdW7r2LFj0qlTJ7Gzs5OyZcvKvHnz5PXr11o/v3379mne2rZtK/Xq1RNLS0sZNWpUmm127dpVKlasKIcOHZL+/ftLjhw55P3798r2lStXStmyZbWKL1++fFrddNWkSRP5/fffReRjNeMCBQrIxIkTpXTp0lKrVi2t2jAxMRFXV1cZPnx4sp+T9EqpCmxISIiYmprq1NabN280lnS4f/++/Prrr7Jz5850x5lVxFfxTeuW3iVjoqOjZebMmZIrVy4pUKCArFmzJl3tfXEJTOL/jPibPv85arVao8SyjY1NsmXdtRUYGJjqLb3u3bsnGzduVNYL0sfy5cvlm2++kdy5c8v9+/dFROTXX3+VLVu26NSOubm5PHz4ULlfqVIlmThxokasNjY2WrV1/vx5qV+/vnLfxsZG4//WxMRETp06pXVsvXv3Fh8fH3n79m2SbW/evBEfHx/p3bu3Vm2ZmZnJDz/8ILt27dJYu8dQCcyBAwfE2tpavL29JSAgQAICAsTb21tsbGzk0KFDWrfj7u4ux48fFxHNRDwoKEhsbW11jqt+/fqyZMmSJI8vWbJE6tSpo3N7yQkJCZEJEyaIhYWFZMuWTZo0aSJ79+5N83k//fSTeHt7S65cuaRv377p+j5oa9u2beLm5pbmfi9evJAqVaqISqUSW1tb2bRpk8b2mjVryvDhw7V6zfhlIIYPHy4zZ85M8aarO3fuKH+zyMhI+fHHH6VYsWLSvHlz5ZiQlocPH8q4cePEy8tL1Gq1VK1aVZYvXy5v3rzROZ6ELl68KBcvXhSVSiX79+9X7l+8eFHOnTsnkydPFg8PD53arF27tsyfP19EPi574uzsLHnz5hULCwv57bff0hXvl2TlypXi5eUluXPnlnnz5kl0dHS62/zixsAcPHgw1e26FNVJq8BbPGMf8R5v/vz5GD16NPr27YtJkybhypUr8PLyQmBgIJYtW4b9+/dr3ZaHhwdWrFiBqlWr4sOHD3BwcMC2bduUwbuXL19GtWrVtLoM0rFjR+TPn1+pD2Jra4uFCxciT548EBEsWbIEIoIVK1ZoFVtISAhKliwJMzMz9OzZU6kmefPmTcydOxcxMTE4f/68VuNWnjx5gsDAQCxduhRv375F69at4e/vj/Lly+PChQsGqZ759OlTzJs3T2PAXffu3bVaSiCelZWV8v+ZcNDyxYsXUbVqVZ3H+yxYsACjR49Gy5Ytlcs8J06cwJ9//olx48ZpxKbPis2nTp3C0qVLsXbtWtjZ2aF9+/Z48uQJVq9eje7du6daPFKtVsPd3R0NGzZMstRBQtpMB9ZWaGgoAgICtD4WhIWFwcbGJsksjlevXsHGxibVuOP9+eefWLJkCQ4cOID69esjICAADRo0SNeMK0NPhAA+FkgLDAzExo0bYWpqilatWqFjx474+uuvdW4rvvgkkPQSPwBYWlpizpw5SVZvT03OnDlx8OBBFClSBIsXL8acOXNw/vx5bNy4EaNHj1YGqRu7d+/eYc+ePQYvH7Fjxw4MHToU9+7dw8CBA9G/f/8kv5H6+uISGEMyRIG38PBwpaBceHh4qu1oW3guo2pJ+Pj4YPLkyWjatKnGj9yVK1dQvXp1ra9/A0C3bt1w8eJF/PTTT9iyZQuWLVuGp0+fKgfmVatWYebMmTh9+nSabXl7e2P16tXK4OfEs4ZOnjyJli1bJilMl5p79+6hW7du2L17t8a6I7Vr18Zvv/2m1xo1+/btw5IlS7Bp0ya8e/cOAwcORKdOnVItt52a6Oho1KtXDwsWLEh3+fuqVavif//7H3r16qVUzs2XLx969eqFoKAg7NixQ6f2MqKq7PPnz7FixQosXboUQUFBaNSoETp16oS6desqP1pHjhxBvXr1Uh2gXr169TTHXqhUKqMfJxEvPokODAzEmzdv8MMPP6Bjx456f2YMOREioYiICKxduxaBgYE4ceKEsraZLh48eAARgZeXF06dOoVcuXIp28zMzODk5KTz1F4rKyvcuHED7u7uaNmyJYoUKYIxY8bg0aNHKFSoEN68eaNTe+mVeImT1BQvXlzrfRcsWIDt27cr1YZtbW2TlI8YNGgQ+vfvr1V7p06dwpAhQ3DixAl07doVI0aMQM6cObWORyvp7sMxQq9fv5ZffvlFOnbsKB07dpQZM2ZIaGhopsSScMnxxJe19L28Va9ePZk6dapy/9KlS2JqaiqdOnWS6dOni4uLi4wZM0bnWC0sLJQu4oSXGW7duiUWFhY6tWXIrnJLS0t59OiRcn/GjBka45kePHig88rM8V6+fCknT56UkydPKitlp1doaKjMmzdPypQpIyqVSooVK6Z3Wzlz5pRbt26lO6bDhw+LjY2NdO3aVSwsLKRPnz5Su3Ztsba2ljNnzqS7fUPIli2bFC5cWKZNm5bi6rhhYWFSvXr1TxyZ8Thw4IBUr15d1Gq1vHr1Sq82ypQpI3v27DFwZB/duXNHRowYIdmzZ9d5rEpyrl69Kv/++69s3bpV46aLYsWKyaxZs+Thw4diZ2cnx44dExGRM2fOiLOzc7pj1FX870Fq41T0GRJRuXJl+euvv5T7icd0rlixQipUqKBTnFZWVtK3b1+ZNWtWirf0+OISmNOnT0v27NklT5480qxZM2nWrJnkzZtXcuTIIWfPnv3k8Rw4cEC5Frh//345cOBAijdtZdQAWW9vb2WsS8IP9+zZs6VUqVI6tyfy8cc8JiYmyeMvX77UGLyYGkdHRzly5EiK248cOaLXIOhP4fz588kOwtVW3759ZciQIQaJ5fbt29KpUyf5+uuvxdvbW/z9/eXSpUs6t/PhwwepWbOmQRKrhHQZ06ONffv2GbS9rOzt27eyYsUKqVGjhlhaWsp3330n796906stQ06EEPk4rmzZsmVSrVo1UavVkj9/fpk4caI8fvxYr/hERO7evSslSpRI8mMff1Koiz///FOyZcsmarVaateurTw+efJkqVevnt4x6uv+/fta33Th4uIi9+7dU+7nzJlT4/7NmzfFzs5O6/Y8PDzE09Mz1Zs+g8gT+uISmMqVK0v79u01BhBFR0dLu3btpEqVKpkY2ccDf0pevHihdTuGHCCb0KJFiyRPnjyydu1asba2ljVr1sjEiROVf2eWmjVrysCBA1Pc3r9/f6lZs+YnjEh7jx49ks6dO+v9/J49e4qdnZ2UKVNGunTpIv369dO4ZRZD9QwlFh0dLbt375YFCxZIeHi4iIg8efJE55lmIh8HWHt5ecmECRM0vi+fkxMnTkjnzp3F3t5eSpUqJXPmzNG75yWeoSZCHD9+XInN0tJS/P39DZZUNmzYUJo0aSIvXrwQGxsbuXr1qhw+fFjKlSunVyL87NkzOXfunMTGxiqPnTx5Uq5fv26QeLMCCwsLuXHjRorbr1+/rndPdkb54hIYCwuLZD90V69eTdf0TkNo3ry5xkyVeMHBwVKkSBGt23F3d5eDBw+KiMj79+/F0tJSo8v30qVLevdIrFy5UgoUKKAcwPLkySOLFy/Wqy1D2bBhg5iamsrcuXM1DjAxMTEye/ZsyZYtm/z555+ZGGHKLly4kK6pidWrV0/xVqNGDa3bqVq1qixbtizds0DiGbJnKN79+/elcOHCYmVlJSYmJkoPYO/eveXHH3/Uub0XL17IjBkzpESJEmJqaip16tSRdevWad3zl9X5+PhIzpw5pXfv3nLhwgWDtZtaL7G2PcXe3t6iVqulTJky8ttvvxn8En6OHDmUmVJ2dnbKD/PevXv1KguQlWzdulU52U18aSw9l8oMWT7iU/niEhgnJ6dk5+/v2LFDnJycMiGi/1O2bFkJCAjQeOzp06dSuHBhadGihdbtGLKWREqioqKSrbOQWQYPHiwqlUrs7OykZMmSUrJkSbGzsxO1Wp1q70xmS28CYyh9+vSRXLlyiZ2dnXTq1EmZUq2vjOgZatKkibRp00bev3+vcQlz//79UqBAgXTFe/bsWenZs6fkyJFDcuTIIb169TLoj35mUKlUYmNjIw4ODklqSqWnvtSDBw+SPdGKi4uTBw8eaNVGRv99HRwclJIWXl5eSs/O7du3tTpRbdasmXI5LH6oQUq3Ty1hjRtD1mwxZPkIkY+lFBImplOmTNGos/Tff//pXesr3hc3C6l3797YvHkzfvnlF3zzzTcAPq5oO2jQILRo0UKn1Y8N7cWLF6hatSrq16+PGTNm4OnTp6hRowZKlCiBtWvXaj2z47///kPz5s1x5MgR2NjYYNmyZRor9daqVQsVKlTI1HVyMsKJEyewZs0aBAUFAQAKFiyI1q1bG6Raa0a5ePEiSpcunSVW3Y2JicFff/2FZcuW4d9//0WBAgUQEBCAH374QeelDmrUqJHiNn1n+OTIkQPHjh1DoUKFNGaa3b9/Hz4+PumeDfL06VP8/vvvmDp1KkxNTfHu3TtUrFgRCxYsUCrNGpP4qsppadeunU7tJl5CJd7Lly/h5OSUJT7LVapUwYABA9C0aVN8//33eP36NUaOHInff/8dZ8+eVRaNTEnCdZXSmm1q7OsqxTNk+Qgg6efEzs4OFy5cMOyK4+lKf4zQ+/fvpXfv3krFW7VaLebm5tK3b1+9B7UZ0sOHD8Xd3V369esnBQsWlO+++y7ZQa7aMMQA2VKlSinXzEuWLCmlSpVK8Ua6M8QlpBo1aqR405e+ReIykoODg1L8L2EPzOHDh/XuPf3w4YP8+eefUr9+fTE1NZUKFSrIokWLJDIyUu7duyf+/v7pPkv83KhUqmRngd2/f1+srKy0aiOlXiFPT0+pU6eO7Nq1K10x7tixQzZu3CgiH4sxFipUSFQqleTMmTPTP8efgr5j6+7evSt169ZNMvC5bt26OleZT6tSfXBwcLp7n7+4tZDMzMwwa9YsTJkyBXfu3AEA5M+fH1ZWVpkc2Udubm7YvXs3qlSpgtq1a2PFihV6rxcSv1R9YtmzZ9e6jSZNmij1Ypo2bapXHF+ytNbLCQ0NTVf7iVcuj46OxoULF3DlyhWdz6zjJSwS5+TkpBSJa9iwYZpF4jJSnTp1MHPmTPz+++8APvbkREZGYsyYMWjQoIHO7fXq1Qtr1qyBiOCHH37AtGnTULRoUWW7tbU1fvnlF50KAn7O4ut/qFQqjBo1SuOYGRsbi5MnTyb5PKYkpZ7u0NBQnD17Fg0bNsSGDRu0Wj8qOXXr1lX+XaBAAdy4cQOvXr2Co6OjzsfTiRMnwt/f3+B1bzLSy5cv8ccffyjfFW3ly5cPO3bswKtXr3D79m0AH/9+uvxmfEpf3CWkrCalL9SbN29gbm6uUXRJl8X5KGswRLFDfYwdOxaRkZFaJxuGKhKX0JkzZ7B+/Xo8fPhQWfwvnj7VqR8/foy6detCRBAUFISyZcsiKCgIOXLkwOHDh5Nc0khLrVq10KlTJzRv3jzFoo4xMTE4evSoThW6P1fxlwUPHjyIihUralQDNjMzg6enJwYOHJjuoorAxyrIGzZswLFjx9LdVnqVKFECV65cQfny5dGmTRu0bNnS8AXZDCwrXJo2MTFBcHCwUkwwYYFMwDCXkL6IBEabVWPjfeqy/9pepwZ0v1ZNX67bt2+jXLlyWie9ZmZmyJ8/PwICAtC+fXuNCqbxwsPD0aRJE62WjFi7di3atm2LunXrYteuXahTpw5u3bqFkJAQNGvWTO+ELSYmBmvXrsWlS5cQGRmJ0qVLw9/fX6vVoxOLiooyWEnzL0mHDh0wa9YsrSuD6+PWrVuoUKFCljlpu3r1KlatWoW1a9fi8ePHqF27Nvz9/dG0adMs03ufUFZIYNJaauf9+/fYsWMHE5i0aHsWDHw+A7IMRZcu16xysCFgxYoVGDx4MJ49e6bV/ocPH0aVKlUM9vrFixfHjz/+iB49eigDbvPly4cff/wRuXPnxrhx43Ru8+XLl8iRIwcA4NGjR1i0aBHevn2Lxo0b6xW7jY0NWrZsiYCAAFSuXFnn51PGuXz5MmrXro3g4ODMDiWJo0ePYvXq1fjzzz/x7t27NJeAyQxZIYFp3769Vr8d6fnN/SLGwBhTUhIXF4fbt2/j+fPniIuL09hWtWrVTx5PZs7KorQl7l0UETx79gynT59G3759tW7HkMkLANy5cwd+fn4APvbuREVFQaVSoV+/fqhZs6ZOCczly5fRqFEjPHr0CAULFsTatWtRr149REVFQa1W49dff8WGDRt0HqO1cuVKBAYGombNmvD09ERAQADatm3LMS9piIqKwtSpU7F3795kj1N3795N92v88ccfWo+n+dSsra1haWkJMzMzREREZEoMGT22zhACAwMz/DW+iAQmoTFjxiAgIAAeHh6ZHUoSJ06cwPfff68sSJaQLovepeX169fYtm0b2rZtm+a+xnbZasOGDSmOuzh37lwmRWV4v/76K/r16wc7OzuNsxy1Wo1ChQph+PDh+Omnn7RuLyQkBAMHDlR+lBJ//nT97Dk6OioH9zx58uDKlSsoVqwYQkNDdZ7uPHjwYBQrVgyrVq3CihUr0LBhQ/j5+WHRokUAPg7GnTp1qs4JTNOmTdG0aVO8ePECK1asQGBgIEaNGoW6desiICAAjRs3hqnp53GIfPz4Mf76669kvxe6rrrdqVMnHDx4ED/88ANy586t1ySDlBYEDAsLw7lz53Dr1i0cOnRI53Yzyr1797B69WqsXr0aN2/eRLVq1TBu3Dh8++23mRJPShM0Em7X5viekb799tsk4+gMLl1zmIxQiRIlxMTERGrWrCmrVq3KElOn45UoUUL+97//ybVr1+T169cSGhqqcTMUXabuJl7nJLVbZps1a5bY2NhIz549xczMTH788Ufx9fUVe3t7rReGNBYWFhaybNmyZLdFRETIN998I4UKFdK6vXr16omPj4/89ttvsnnzZtmyZYvGTVetW7eW6dOni4jI+PHjJVeuXNKpUyfx8PDQufhXwqqqERERolKpNBaYvH79utjb2+scY3Jmz54t5ubmolKpJFeuXDJq1CiJiooySNuZZc+ePWJlZSVFixYVU1NTKVmypDg4OIi9vb1eU+3t7e1TXXtMGylVj27cuLEMGjRIKUKXFZQvX17UarWULFlSfv7553St0fQlqVmzpqjVasmbN6+MGjVK52nY2vjiEhgRkXPnzkmvXr0kZ86c4uDgIF27dpVTp05ldlhiZWUlQUFB6W4nrUTj8OHDWicwKa2QnZ41UDJKoUKFZPXq1SKiWXNg1KhR0qNHj8wMzeD+/PNPsbCwSFIuPDIyUipXrixfffWVPHnyROv2bGxs5Pz58waL7+XLl8rrx8bGypQpU6RRo0bSv39/ndfiyeh6EsHBwfLTTz+Jt7e3WFlZKWvyLF++XIoUKaKxgJ8x+vrrr2X06NEi8n9/u4iICGncuLH89ttvOrfn6ekp165dM3SYWdbw4cOV+kOkm/v378uYMWMkX758olarpUaNGgbtOPgiBvGmJDo6Gtu2bcPSpUuxc+dOFC5cGB07dkT79u3T7KLLCDVr1sTgwYNRr169dLWjVqtT7bITEa0vSR08eFDr183sqaZWVla4fv06PDw84OTkhN27d6NEiRIICgpChQoV8PLly08e09mzZzFw4EBs3bo1yayNsLAwNG3aFDNnzkSJEiV0bnvx4sXo06cPtm/fjurVqyMqKgr16tVDcHAwDhw4gDx58mjdlo+PD1atWoVSpUrpHEdGU6vVCAkJMfh0zE2bNinffR8fH3Tq1Alt2rSBg4ODss+dO3fg7e2d5LKLMbG1tcWFCxeQP39+ODo64siRIyhSpAguXryIJk2a4P79+zq1t3LlSmzduhXLli3TewaONjNDTU1N4eLigtq1a+tdD4ayjn379mHJkiXYvHkzzM3N0bp1awQEBKBMmTJ6t/l5XODVk4ggOjoaHz58gIjA0dERc+fOxahRo7Bo0SJ89913nzSeXr16YcCAAQgODkaxYsWQLVs2je3FixfXqh1bW1uMGDEC5cuXT3Z7UFAQfvzxR63ayuykRBcuLi549eoVPDw84O7ujhMnTqBEiRK4d+9ekjEdn8r06dNRs2bNZKec2tvbo3bt2vj555+xcuVKndvu1KkTXr16hSZNmmDr1q0YPXo0nj59ioMHD+qUvAAfB2sPHToUCxcuhKenp86xxHv69ClmzJiB0aNHJ5uwTZw4EQMHDtR5aYL27dsr0zHfvXuHrl27akzH1EeHDh3QqlUrHD16FF9//XWy+7i6umLEiBF6tZ9VWFtbKwlY7ty5cefOHWVphP/++0/n9qZPn447d+7A2dkZnp6eSY5T2ow10+YEMS4uDkFBQVi8eDEGDhyI8ePH6xyroRhyDNGXqmbNmqhZsyYiIiKwevVqDB8+HAsXLkRMTIzebX6RCczZs2exdOlSrFmzBubm5mjbti3mzZuHAgUKAADmzJmD3r17f/IEpkWLFgCAgIAA5TGVSqVTjwkAlC5dGkDKyYeDg4PWP+iXLl1C0aJFoVarcenSpVT31TbByig1a9bEX3/9hVKlSqFDhw7o168fNmzYgDNnzuhUC8iQTp48iaFDh6a4vVGjRli8eLHe7Q8ePBivXr1CrVq14OnpiQMHDiBv3rw6t/Pdd9/hzZs3SlXqxD9K2k6RnzFjBsLDw1NM2CIiIjBjxgydBhgnHkjepk2bJPvoM2Dx2bNnafYgWFpaYsyYMTq3nZVUqFABR44cgbe3Nxo0aIABAwbg8uXL2LRpk17rhBmiIrcuM0P//vtvdO/ePdMSmL1796Jx48bw8vLCjRs3ULRoUdy/fx8iohxrSTv37t1DYGAgAgMDERYWBl9f33S198VdQipWrBhu3LiBOnXqoHPnzmjUqJFGtVvg41mJk5NTkumBGe3Bgwepbtd25lR8fYzevXsnuz0kJAQLFizQ6sCsVqsRHBwMJycn5dJUch8ZQ86S0ldcXBzi4uKUmSNr167FsWPHULBgQfz4448alUM/FQsLC1y/fj3FMuT37t2Dj48P3r59q1O7iROyf/75ByVKlEjS86JtYca0CipqOxutaNGiWLBgQYp1VY4dO4bOnTvj6tWrWrWX0WJjY7FlyxZcv34dwMdLaU2aNElyTDBmd+/eRWRkJIoXL46oqCgMGDBA+V7MmDEjS87ITCg0NBQBAQGfvMhovHLlyqF+/foYN26cUtPIyckJ/v7+qFevHrp165YpcRmLd+/eYcOGDViyZAkOHToENzc3dOjQAR06dICbm1u62v7iEpgJEyYgICBA5y72L9WDBw/g7u4OlUplsATrS+Lm5oZFixalOK7p33//RZcuXfDo0SOd2s2sJQrSYm1tjevXr8Pd3T3Z7Q8fPoS3tzeioqI+aVzJuX37Nho0aIAnT56gUKFCAD6uvOvm5obt27cjf/78mRxh1hUaGooNGzbgzp07GDRoELJnz45z587B2dn5szu2GnoM0Zfi1KlTWLJkCdatW4d3796hWbNmCAgIQK1atQw2rfqLS2Cymr/++gv169dHtmzZ8Ndff6W6b+PGjT9RVMYrNDQUp06dSrbAVmbURejQoQNu376Nw4cPJ9kmIqhSpQoKFiyYpYotvnv3Lsl1fm3LxufMmRObNm1KsejioUOH0Lx5c73GXhhagwYNICJYtWqVsljdy5cv0aZNG6jVamzfvj2TI8yaLl26BF9fX9jb2+P+/fu4efMmvLy8MHLkSDx8+BDLly/P7BANysXFBfv374e3tzd8fHwwdepUNG7cGBcvXkSlSpW0XhvsS6NWq1GiRAl07NgR/v7+cHR0NPhrfJEJTFYakJX4Ek1KdL1E8/btW5w9exbZs2eHj4+PxrZ3795h/fr1Ov+gG7qUu6Ft27YN/v7+iIyMTFLgTaVSZcpSB3fu3EGZMmVQqFAhDBgwQDnTv3HjBqZPn45bt27hzJkzyvirzBIVFYUhQ4Zg/fr1yc7W0vaz5+fnB1dXV6XIXGKdOnXC06dP8c8//6QrXkOwtrbGiRMnUKxYMY3HP4cfpoxcAsTX1xelS5fGtGnTlEsqXl5eOHbsGL7//vvPrkeiadOm8PPzQ+fOnZUZhe3bt8emTZvg6OiIPXv2ZHaIWdK5c+cyfoyQQSZjGxFDF3XKim7evCkeHh5KfZaqVavK06dPle261s24dOmSeHh4iFqtlkKFCsn58+fF2dlZbGxsxM7OTkxMTGTz5s0Z8E50U7BgQenTp0+WKzx2+vRpKVKkiEZNHZVKJUWKFMkS9YdERLp37y7e3t6yYcMGsbS0lCVLlsiECRMkb968snLlSq3b2bdvn5iYmMiAAQMkODhYeTw4OFj69+8vJiYmsnfv3ox4CzpzdHSUo0ePJnn8yJEj4ujomAkRGU5gYKBymz59ujg6OkqrVq1k1qxZMmvWLGnVqpU4OjrKjBkzdG7bzs5Obt++LSKaNXnu378v5ubmBn0fWcGdO3eUQoqRkZHy448/SrFixaR58+Zy//79TI4u6/rpp5/kzZs3yv0jR45o1H8JDw+Xbt26pes1vrgExtBFnT6FR48eSefOnbXev2nTpuLn5ycvXryQoKAg8fPzk3z58smDBw9ERPcEpl69etKwYUM5cuSI/Pjjj5InTx4JCAiQ2NhYiY2Nle7du0v58uV1fl+GZmVllSHVHg3l3Llzsn79elm3bp1Bi8YZgpubm+zfv19ERGxtbZWCisuXL5f69evr1NaCBQvE3Nxc1Gq1ODg4iKOjo6jVajE3N89S37EffvhBihQpIidOnJC4uDiJi4uT48ePS9GiRaVdu3aZHZ7BNG/eXObMmZPk8Tlz5kiTJk10bi9Xrlxy7tw5EdFMYHbt2iV58+ZNV6xZTUxMjBw8eFBev36d2aEYHbVarVGA0tbW1qAFKEW+wATGxsZGOXtwcHCQK1euiMjH8voeHh6ZGFnKdCn9LyLi5OQkly5dUu7HxcVJ165dxd3dXe7cuaPzB+dTlnJPj2bNmsm6desyOwyjZG1trSS4efLkkZMnT4qIyN27d8Xa2lrn9h4/fiwzZsyQ7t27S7du3eTXX3+VR48eGTTm9Hr9+rU0btxYVCqVmJmZiZmZmajVamnatKlBl+7IbNbW1slW+A4KCtLr/7Zjx47StGlT+fDhg9jY2Mjdu3flwYMHUqpUKenTp48BIs5azM3Ns9TSBsYioytoi4h8cXVgDF3UKSt6+/atxiJ0KpUK8+fPR8+ePVGtWjWsXr1ap/ZevXoFFxcXAICNjQ2sra01BmQlXLgvM/n5+WHQoEG4du1asoUAM2MQdEqL1iWW2cWwvLy8cO/ePbi7u6Nw4cJYv349ypUrh23btmlUptVWnjx50K9fP8MHakAODg7YunUrgoKCcOPGDQCAt7d3po9HMrQcOXJg69atGDBggMbjW7duVca06WL69On49ttv4eTkhLdv36JatWoIDg5GxYoVMWnSJEOFnWUULVoUd+/eTbEUAmWeLyaBGT9+PAYMGGDwok5ZUeHChXHmzBl4e3trPD537lwA+v2QJx4QmGGri6ZD586dASDZgleZVafm/Pnzae6TFf6WHTp0wMWLF1GtWjUMHToUjRo1wty5cxEdHZ3pyVVGK1iwIAoWLJjZYWSYcePGoVOnTjhw4IBSnfvkyZPYsWNHioOtU2Nvb4/du3fj6NGjuHjxIiIjI1G6dOl0FyXLquKrR0+YMAFlypRRKkDH03aGHhneFzMLycTEBM+ePUNkZKTRFXW6ePEiSpcurfUP8JQpU3D48OEUZ3p0794dCxYs0LpQn1qtRv369ZVS7tu2bUPNmjU1Srnv2LEj0wvZkeE8ePAAZ8+eRYECBeDl5QUbG5vMDskgtO0RAzK/V8yQTp48idmzZysF+7y9vdG7d+8Ulxuh/zvptbW1VR5LeLIhOlZI/9Ko1WpMnDhROXYMGTIEgwYNQs6cOQEAERERGD16dLr+fl9MApNwunJWk1aZ+9DQUBw8eDDTvihZtWiaMYuJicG7d+8yPTH49ddfU73UExERgXr16uHo0aOfMKqMU6NGDa32U6lU2LdvXwZHY1z27duHnj174sSJE8muc/XNN99gwYIFWaKkgiHEn/TGJ30pMab14j4lT09PrXqX7927p/drfFEJTMIVbbMSJgiGExUVhYMHDyZb4yelpRUy0rZt2/Dy5Uu0b99eeWzSpEmYMGECYmJiULNmTaxbty5Dijxpw9LSEgsXLky2JlBUVBTq1KmDly9fKmNEPgd3796Fp6dnqnWXPjdxcXG4fft2sgUeUyo6mFjjxo1Ro0aNFBPe2bNnY//+/di8eXO6480KsvJJL/1/6RoCbERUKpUypTO1Gxmvc+fOiYuLi1KbJleuXKJSqcTa2lry5cuXKTFVr15d5s6dq9w/evSoqNVqmThxomzcuFEKFy4s/fr1y5TYRET+/PNPsbCwkK1bt2o8HhERIZUqVZKCBQtq1BD6HCSe3tmyZUuNmjWfm+PHj0u+fPmU+kMJb7rMAnF3d5dr166luP369evi5uZmiJCzBJVKJc+fP8/sMIzWsWPHZNu2bRqPLVu2TDw9PSVXrlzSuXNnjbow+vhiBvECHwezabOMOxmnfv36oVGjRliwYAHs7e1x4sQJZMuWDW3atEGfPn0yJaarV69qjKXYsGEDateujREjRgD4uNhjnz59Mm28xbfffovQ0FC0bt0a27dvR/Xq1REVFYX69esjJCQEBw8eRO7cuXVuNzY2Fr/++ivWr1+fbG9YZlRFjieJOp3/+ecfTJkyJZOiyXhdu3ZF2bJlsX37duTOnVvvQeMhISFJZvYlZGpqihcvXugbZpb01Vdfpfn3yszPclY2btw41KhRAw0bNgQAXL58GR07dkT79u3h7e2Nn3/+Ga6urhg7dqzer/FFJTCtWrVid+Bn7MKFC1i4cCHUajVMTEzw/v17eHl5Ydq0aWjXrl2aY40yQkREhMZU1SNHjuB///ufcr9IkSJ4+vTpJ48roU6dOuHVq1do0qQJtm7ditGjR+Pp06c4ePAgXF1d9Wpz3LhxWLx4MQYMGICRI0dixIgRuH//PrZs2YLRo0cb+B1QaoKCgrBhw4Z0Tw/PkycPrly5kmI7ly5d0ivZzcp40qu/ixcvYuLEicr9tWvXonz58srMNzc3N4wZM4YJjDaywlRVyljZsmVTxjU4OTkpKx/b29vrvNqzoeTJk0dZnTkyMhIXL17Er7/+qmx/+fIlrKysMiW2hAYPHoxXr16hVq1a8PT0xIEDB5A3b16921u1ahUWLVoEPz8/jB07Fq1bt0b+/PlRvHhxnDhxIlPGI8VTqVRGURbAUMqXL4/bt2+nO4Fp0KABRo0ahXr16sHCwkJj29u3bzFmzBjlbPtzwZNe/b1+/RrOzs7K/YMHD6J+/frK/a+//jrdx+UvJoFJ3G1Mn59SpUrh9OnTKFiwIKpVq4bRo0fjv//+w4oVK1C0aNFMiel///sf+vbti+HDh+Off/6Bi4uLRr2hM2fOKAs8ZobEvVLZsmVDzpw5k1xy27Rpk07tBgcHK4sk2tjYICwsDADQsGFDjBo1Kh0Rp5+IoH379kpZgHfv3qFr165J6nvo+p6zql69emHAgAHK/0niy0DFixfXqp2RI0di06ZN+Oqrr9CzZ0+NhUnnzZuH2NhY5dLo5+BzTmo/BWdnZ9y7dw9ubm748OEDzp07h3HjxinbIyIiUr0kqY0vJoHRtuYJGa/JkycrFYEnTZqEtm3bolu3bihYsCCWLFmSKTGNHj0aT548Qe/eveHi4oKVK1fCxMRE2b5mzRo0atQoU2IDkKR7vHXr1gZpN2/evHj27Bnc3d2RP39+7Nq1C6VLl8bp06eVxCGztGvXTuN+mzZtMimST6NFixYAgICAAOUxlUqlcx0TZ2dnHDt2DN26dcOwYcOUk0KVSoW6deti3rx5Gmfcxo4nvenToEEDDB06FD/99BO2bNkCKysrjSn2ly5dQv78+dP1Gl/MNGoi+nSGDh0KOzs7DB8+HOvWrUObNm3g6emJhw8fol+/fpg6dWpmh/jFePDgQarb9Sne+fr1a9y+fRsigoIFC2ZaGQDKuv777z80b94cR44cgY2NDZYtW4ZmzZop22vVqoUKFSqka/kJJjBElOGOHz+O48ePo2DBgpna40REn1ZYWBhsbGw0ep6Bj7O3bGxsYGZmpnfbTGDIqJUuXRp79+6Fo6MjSpUqlep163Pnzn3CyD5ydHRMNiZ7e3t89dVXGDhwIGrXrv3J46Ivx/Lly1PdnlwRQyJj8MWMgaHPU5MmTZQxFU2bNs3cYJIxc+bMZB8PDQ3F2bNn0bBhQ2zYsOGz7JUICgrC/v37k63+yqnUn07iAdnR0dF48+YNzMzMYGVlxQSGjBZ7YOizEBsbi6NHj6J48eJwcHDI7HC0NmPGDGzYsAHHjh3L7FAMatGiRejWrRty5swJFxcXjV4olUqVKb1h9H+CgoLQrVs3DBo0CHXr1s3scIj0wgSGPhsWFha4fv068uXLl9mhaO3WrVuoUKHCZ1fN08PDA927d8eQIUMyOxRKwZkzZ9CmTZvPap0r+rJ8OauZ0WevaNGiuHv3bmaHoZP379+naxBbVvX69WuNisOU9ZiammZ6FWii9OAYGPpsTJw4EQMHDsSECRNQpkyZJIXJ7OzsMimylP3xxx8oWbJkZodhcP/73/+wa9cudO3aNbND+eL99ddfGvdFBM+ePcPcuXNRqVKlTIqKKP14CYk+G/HLCACaVTR1LdhlSP3790/28bCwMJw7dw63bt3CoUOHUKZMmU8cWcaaMmUKZsyYAT8/v2Srv2bmUgJfmoTfC+DjdyNXrlyoWbMmpk+f/tmtX0RfDiYw9Nk4ePBgqturVav2iSL5PzVq1Ej2cTs7OxQqVAjdunUzqjE72krtPalUKqO71EdEWQ8TGPoiXLlyJdPWQyIiIsPjGBj6bEVERGDNmjVYvHgxzp49mymXkIgyW0qXMVUqFSwsLFCgQAE0adIE2bNn/8SREaUPe2Dos3Po0CH88ccf2LhxI1xdXdG8eXO0aNECX3/99SePJeECeqnJrMUmDal///6YMGECrK2tU/zRjDdjxoxPFBXVqFED586dQ2xsrLKC9K1bt2BiYoLChQvj5s2bUKlUOHLkCHx8fDI5WiLtsQeGPgvBwcEIDAzEH3/8gfDwcLRs2RLv37/Hli1bMvWgHBgYCA8PD5QqVeqzX932/PnziI6OBvBx2YaUlnVIbbkHMrz43pWlS5cqM/HCwsLQqVMnVK5cGZ07d8b333+Pfv36YefOnZkcLZH22ANDRq9Ro0Y4dOgQ/Pz84O/vj3r16sHExATZsmXDxYsXMzWB6dGjB9asWQMPDw906NABbdq0YVc9fVJ58uTB7t27k3wPrl69ijp16uDJkyc4d+4c6tSpg//++y+ToiTSHQvZkdH7999/0bFjR4wbNw5+fn5JVj3NTPPmzcOzZ88wePBgbNu2DW5ubmjZsiV27tz52fbIREdHw9TUFFeuXMnsUAgfe1ueP3+e5PEXL14gPDwcAODg4IAPHz586tCI0oUJDBm9I0eOICIiAmXKlEH58uUxd+7cLHUmaW5ujtatW2P37t24du0aihQpgu7du8PT0xORkZGZHZ7BZcuWDe7u7hw0nUU0adIEAQEB2Lx5Mx4/fozHjx9j8+bN6Nixo7IA6qlTp/DVV19lbqBEOmICQ0avQoUKWLRoEZ49e4Yff/wRa9euhaurK+Li4rB7925ERERkdogKtVoNlUoFEfmsf+BHjBiB4cOHf3ZrPBmjhQsXolatWmjVqhU8PDzg4eGBVq1aoVatWliwYAEAoHDhwli8eHEmR0qkG46Boc/SzZs38ccff2DFihUIDQ1F7dq1k5RU/1Tev3+PTZs2YcmSJThy5AgaNmyIDh06oF69ekmqpH4uSpUqhdu3byM6OhoeHh5JlnXgatSfXmRkpFJA0MvLCzY2NpkcEVH6cBYSfZYKFSqEadOmYcqUKdi2bVumTVPu3r071q5dCzc3NwQEBGDNmjXImTNnpsTyKcVfmqCsIzg4GM+ePUPVqlVhaWmpLLFBZKzYA0OUgdRqNdzd3VGqVKlUfyw2bdr0/9q7/9go6wOO45+nP6GlheKobflR2oItHdbxw2oGqPwYwaqo4CigDAJkmZvAHISUqEzAQCzQLcRVgxw0VcIPcTGUAEWsgGtdoMC1rECBtQ4QCJPTKidwba/7w3jzUt307trHe+79SprwfJ/r8WnSNJ/7Pt/v83RiKoSSa9euacqUKXr//fdlGIbOnj2r9PR0zZ49WwkJCVq7dq3ZEQGfMAMDdKBf/epXIf0pt7q6WqdOnZIkZWdnW+6hlcHg2WefVWRkpM6fP69BgwZ5xvPz8/WHP/yBAoOgxQwMgIC7ePGipk2bpsrKSvXo0UOS9Nlnn+nnP/+5tm7dqj59+pgbMIQkJSWpvLxcd911l+Li4lRTU6P09HQ1NDQoJyfHkjvhEBqsuYIQgKnmzp2r5uZmnTp1Sg6HQw6HQ6dOnZLb7dbcuXPNjhdSnE6nYmJi2o07HA5FR0ebkAgIDGZgAARc165dVVVVpSFDhniNHz16VKNGjdKXX35pUrLQk5eXp2HDhmnFihWKi4tTbW2tZyu12+3Wjh07zI4I+IQ1MAACrm/fvp7nIn1Ta2urUlJSTEgUugoLCzV27FhVV1fL5XJp8eLFqqurk8PhUGVlpdnxAJ9xCQlAwK1evVrz5s1TdXW1Z6y6uloLFizQmjVrTEwWegYPHqwzZ85o5MiRevTRR+V0OjVp0iQdP35cGRkZZscDfMYlJAABkZCQ4LXjyul0qqWlRRERX030fv3v2NhY7tALwG9cQgIQEH/+85/NjoBv+OSTT+R0OpWamuoZq6ur05o1a+R0OvXYY49p+vTpJiYE/MMMDABY0LRp05SSkuK5z8vVq1eVlZWllJQUZWRkaM+ePbLZbJoxY4bJSQHfMAMDoEPdvHlTLpfLayw+Pt6kNKHj73//u0pKSjzHpaWl6tmzp+x2uyIiIrRmzRr95S9/ocAgaLGIF0DAOZ1OPfPMM0pMTFRsbKwSEhK8vtDxrly5ov79+3uOKyoqNGnSJM+apIkTJ+rs2bMmpQP8R4EBEHCLFy9WRUWFXn31VUVHR2vDhg1atmyZUlJSVFpaana8kBAfH6/PPvvMc3z48GHdc889nmPDMHTr1i0TkgGBQYEBEHBlZWUqLi7W5MmTFRERoVGjRun555/XypUrtXnzZrPjhYR7771X69at89ys7osvvtCYMWM858+cOaO+ffuamBDwDwUGQMA5HA6lp6dL+mom4Ott0yNHjtShQ4fMjBYyVqxYoZ07d6pr167Kz8/X4sWLvS7fbd26Vffff7+JCQH/sIgXQMClp6ersbFR/fr1U1ZWlrZv367c3FyVlZV5Hu6IjpWTk6NTp06psrJSSUlJXpePJGnq1KnKzs42KR3gP7ZRAwi4P/3pTwoPD9f8+fO1f/9+PfLII2pra1Nzc7OKioq0YMECsyMCCHIUGAAd7qOPPtKxY8c0YMAA5eTkmB0HgAVQYAAAQNBhES+AgPnwww+1a9cur7HS0lKlpaUpMTFRv/71r9m6CyAgKDAAAmb58uWqq6vzHJ84cUJz5szRuHHjVFBQoLKyMq1atcrEhACsgktIAAImOTlZZWVlGj58uCTpueee08GDB/W3v/1NkvTWW2/pj3/8o06ePGlmTAAWwAwMgID59NNPdfvtt3uODx48qAcffNBzfPfdd+vChQtmRAtpo0eP1qxZs7zGZs6c6XVjOyDYUGAABMztt9+uxsZGSZLL5dKxY8d07733es5/8cUXioyMNCteyEpNTVVKSorXWO/evZWammpSIsB/3MgOQMDk5eWpoKBAL7/8st555x3FxMRo1KhRnvO1tbXKyMgwMWHoaWho8Hoq9ddWrlzZ+WGAAGIGBkDArFixQhEREbr//vv1+uuv6/XXX1dUVJTn/MaNGzV+/HgTE4aeAQMGaMyYMXrzzTd18+ZNs+MAAcMiXgAB19TUpG7duik8PNxr3OFwqFu3bl6lBh3Lbrdr06ZN2rJli1wul/Lz8zVnzhzl5uaaHQ3wCwUGAEJAS0uLdu7cqZKSEu3du1d33HGHZs+erRkzZqhXr15mxwN+MAoMAISQW7duqbi4WEuWLJHL5VJUVJSmTJmil19+WcnJyWbHA7431sAAQAiorq7Wb3/7WyUnJ6uoqEiLFi3SP//5T7377ru6dOmSHn30UbMjAj8IMzAAYGFFRUXatGmT6uvrlZeXp7lz5yovL09hYf/9/Hrx4kX1799fLS0tJiYFfhi2UQOAhb366quaPXu2Zs2a9Z2XiBITE2Wz2To5GeAfZmAAAEDQYQYGACzu5s2bqq2t1dWrV+V2u73OTZw40aRUgH8oMABgYXv37tWMGTN07dq1ducMw1Bra6sJqQD/sQsJACxs3rx5mjJlii5fviy32+31RXlBMGMNDABYWHx8vI4fP84zqGA5zMAAgIU98cQTOnDggNkxgIBjBgYALOzLL7/UL3/5S/Xq1Ut33nmnIiMjvc7Pnz/fpGSAfygwAGBhNptNv/nNb9SlSxfddtttMgzDc84wDDU0NJiYDvAdBQYALCwpKUnz589XQUGB1913gWDHbzMAWJjL5VJ+fj7lBZbDbzQAWNjMmTO1bds2s2MAAceN7ADAwlpbW1VYWKjy8nLl5OS0W8RbVFRkUjLAP6yBAQALGz169HeeMwxDFRUVnZgGCBwKDAAACDqsgQGAEHDu3DmVl5frxo0bkiQ+uyLYUWAAwMKuXbumsWPH6o477lBeXp4uX74sSZozZ44WLlxocjrAdxQYALCwZ599VpGRkTp//rxiYmI84/n5+dq7d6+JyQD/sAsJACxs3759Ki8vV58+fbzGBw4cqH/9618mpQL8xwwMAFiY0+n0mnn5msPhUHR0tAmJgMCgwACAhY0aNUqlpaWeY8Mw5Ha7VVhY+D+3WAM/dmyjBgAL+8c//qGxY8dq6NChqqio0MSJE1VXVyeHw6HKykplZGSYHRHwCQUGACyuqalJr7zyimpqanT9+nUNHTpUv/vd75ScnGx2NMBnFBgAABB02IUEABZTW1v7vV+bk5PTgUmAjsMMDABYTFhYmAzDUFtbmwzD8Ix//ef+m2Otra2dng8IBHYhAYDFNDY2qqGhQY2NjXr77beVlpam4uJi2e122e12FRcXKyMjQ2+//bbZUQGfMQMDABaWm5urF198UXl5eV7ju3fv1gsvvKCjR4+alAzwDzMwAGBhJ06cUFpaWrvxtLQ0nTx50oREQGBQYADAwgYNGqRVq1bJ5XJ5xlwul1atWqVBgwaZmAzwD5eQAMDCDh8+rEceeURtbW2eHUe1tbUyDENlZWXKzc01OSHgGwoMAFic0+nU5s2bdfr0aUlfzcpMnz5dsbGxJicDfEeBAQAAQYcb2QFACDh58qTOnz/vtRZGkiZOnGhSIsA/FBgAsLCGhgY9/vjjOnHihOfmdtJ/b2bHjewQrNiFBAAWtmDBAqWlpenq1auKiYlRXV2dDh06pOHDh+vAgQNmxwN8xhoYALCwn/zkJ6qoqFBOTo66d++uw4cPKzMzUxUVFVq4cKGOHz9udkTAJ8zAAICFtba2Ki4uTtJXZebSpUuSpNTUVNXX15sZDfALa2AAwMIGDx6smpoapaWl6Z577lFhYaGioqK0fv16paenmx0P8BmXkADAwsrLy+V0OjVp0iSdO3dODz/8sM6cOaPbbrtN27Zt05gxY8yOCPiEAgMAIcbhcCghIcGzEwkIRhQYALCwpqYmtba2qmfPnl7jDodDERERio+PNykZ4B8W8QKAhU2dOlVbt25tN759+3ZNnTrVhERAYDADAwAW1rNnT1VWVrZ78vTp06c1YsQIXbt2zaRkgH+YgQEAC7t165ZaWlrajTc3N+vGjRsmJAICgwIDABaWm5ur9evXtxt/7bXXNGzYMBMSAYHBfWAAwMJeeukljRs3TjU1NRo7dqwk6b333tORI0e0b98+k9MBvmMNDABYnN1u1+rVq2W329W1a1fl5ORoyZIlGjhwoNnRAJ9RYAAgBLndbu3evVsPP/yw2VEAn3AJCQBCyLlz57Rx40aVlJTo3//+t5qbm82OBPiERbwAYHE3btxQaWmp7rvvPmVmZqqqqkpLly7VxYsXzY4G+IwZGACwqCNHjmjDhg3aunWrMjIy9OSTT6qqqkrFxcXKzs42Ox7gFwoMAFhQTk6OPv/8c02fPl1VVVX66U9/KkkqKCgwORkQGFxCAgALqq+v13333afRo0cz2wJLosAAgAU1NDQoMzNTTz/9tPr06aNFixbp+PHjPIEalkGBAQAL6t27t5577jmdO3dOb7zxhq5cuaIRI0aopaVFJSUlOnPmjNkRAb9wHxgACBFNTU3avHmzNm7cqGPHjmnw4MGqra01OxbgEwoMAIQgu92ujRs3at26dWZHAXxCgQEAAEGHNTAAACDoUGAAAEDQocAAAICgQ4EBgBBx8+ZNsyMAAUOBAQALc7vdWrFihXr37q1u3bqpoaFBkvTCCy/IZrOZnA7wHQUGACzspZdeUklJiQoLCxUVFeUZHzx4sDZs2GBiMsA/FBgAsLDS0lKtX79eTz75pMLDwz3jd911l06fPm1iMsA/FBgAsLCPP/5YAwYMaDfudrvV3NxsQiIgMCgwAGBh2dnZ+uCDD9qN79ixQ0OGDDEhERAYEWYHAAB0nKVLl2rmzJn6+OOP5Xa79de//lX19fUqLS3Vrl27zI4H+IxHCQCAxX3wwQdavny5ampqdP36dQ0dOlRLly7V+PHjzY4G+IwCAwAAgg6XkAAgBLhcLl29elVut9trvF+/fiYlAvxDgQEACzt79qxmz56tqqoqr/G2tjYZhqHW1laTkgH+ocAAgIXNmjVLERER2rVrl5KTk2UYhtmRgIBgDQwAWFhsbKyOHj2qrKwss6MAAcV9YADAwrKzs/XJJ5+YHQMIOGZgAMBiPv/8c8+/q6ur9fzzz2vlypW68847FRkZ6fXa+Pj4zo4HBAQFBgAsJiwszGuty9cLdr+JRbwIdiziBQCLef/9982OAHQ4ZmAAwIKWL1+uRYsWKSYmxuwoQIegwACABYWHh+vy5ctKTEw0OwrQIdiFBAAWxGdTWB0FBgAsipvWwcq4hAQAFhQWFqbu3bv/3xLjcDg6KREQWOxCAgCLWrZsmbp37252DKBDMAMDABYUFhamK1eusIgXlsUaGACwINa/wOooMABgQUyuw+q4hAQAAIIOMzAAACDoUGAAAEDQocAAAICgQ4EBAABBhwIDIKgZhqF33nnH7BgAOhkFBoApPvzwQ4WHh+uhhx76Xq9/8cUX9bOf/azd+OXLl/Xggw9+r/eg7ADWQYEBYAqbzaZ58+bp0KFDunTp0ne+rq2tTS0tLd95PikpSdHR0R0REcCPGAUGQKe7fv26tm3bpqeffloPPfSQSkpKPOcOHDggwzC0Z88eDRs2TNHR0XrzzTe1bNky1dTUyDAMGYbh+Z5vzqq4XC4988wzSk5OVpcuXZSamqpVq1ZJkvr37y9Jevzxx2UYhucYQHDiYY4AOt327duVlZWlzMxMPfXUU/r973+vJUuWeN3+vqCgQGvWrFF6erq6dOmihQsXau/evdq/f78kfetDCtetW6edO3dq+/bt6tevny5cuKALFy5Iko4cOaLExERt2rRJEyZMUHh4eOf8sAA6BAUGQKez2Wx66qmnJEkTJkxQU1OTDh48qAceeMDzmuXLl+sXv/iF57hbt26KiIhQUlLSd77v+fPnNXDgQI0cOVKGYSg1NdVzrlevXpKkHj16/M/3ABAcuIQEoFPV19fr8OHDmjZtmiQpIiJC+fn5stlsXq8bPnz4D37vWbNmyW63KzMzU/Pnz9e+ffsCkhnAjw8zMAA6lc1mU0tLi1JSUjxjbW1tio6O1iuvvOIZi42N/cHvPXToUDU2NmrPnj3av3+/pkyZonHjxmnHjh0ByQ7gx4MCA6DTtLS0qLS0VGvXrtX48eO9zj322GPasmWLsrKyvvV7o6Ki1Nra+n//j/j4eOXn5ys/P19PPPGEJkyYIIfDoZ49eyoyMvJ7vQeAHz8KDIBOs2vXLn366aeaM2dOu0W4kydPls1m0+rVq7/1e/v376/GxkbZ7Xb16dNHcXFx7bZPFxUVKTk5WUOGDFFYWJjeeustJSUlqUePHp73eO+99zRixAhFR0crISGhQ35OAB2PNTAAOo3NZtO4ceO+dQfR5MmTVV1drdra2m/93smTJ2vChAkaPXq0evXqpS1btrR7TVxcnAoLCzV8+HDdfffd+uijj7R7926FhX31p27t2rV699131bdvXw0ZMiSwPxyATmW0tbW1mR0CAADgh2AGBgAABB0KDAAACDoUGAAAEHQoMAAAIOhQYAAAQNChwAAAgKBDgQEAAEGHAgMAAIIOBQYAAAQdCgwAAAg6FBgAABB0/gPW7Tr0rcRILgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "artist_score_preds.plot(kind='bar')\n",
    "plt.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "451e1bd8-290d-4fc9-99a0-7f7758ff2a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Predicted Track Score')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(15,7))\n",
    "\n",
    "ax1.boxplot(finals['track_perf'])\n",
    "ax1.set_title(\"Distribution of Original Track Score\")\n",
    "\n",
    "ax2.boxplot(finals['predicted score'])\n",
    "ax2.set_title(\"Distribution of Predicted Track Score\")\n",
    "\n",
    "#the Custom Track Score is more tightly distributed and thus be used to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a3257b9-f3bd-45bb-b3d9-2f2a7090baad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}