IMDb-Data-Analysis-Part-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science Exercise- Part 1\n",
    "### Jesus Cantu Jr. \n",
    "### Last Updated: June 6, 2023\n",
    "![Image](IMDb.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Aim\n",
    "The purpose of this project is to begin exploring the data on IMDb (https://www.imdb.com/) and some of the associations that may exist between the various variables available. The hope is to take the findings from this report into consideration when creating a recommender system for TV shows/movies or revenue prediction models using IMDb data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# IMDb Datasets\n",
    "\n",
    "IMDb (an acronym for Internet Movie Database)is an online database of information related to films, television series, podcasts, home videos, video games, and streaming content online – including cast, production crew and personal biographies, plot summaries, trivia, ratings, and fan and critical reviews. As of March 2022, the database contained some 10.1 million titles (including television episodes) and 11.5 million person records.\n",
    "\n",
    "Subsets of IMDb data are available for access to customers for personal and non-commercial use. The dataset files can be accessed and downloaded from https://www.imdb.com/interfaces/. The data is refreshed daily.\n",
    "\n",
    "Each dataset is contained in a gzipped, tab-separated-values (TSV) formatted file in the UTF-8 character set. The first line in each file contains headers that describe what is in each column. A ‘\\N’ is used to denote that a particular field is missing or null for that title/name. The available datasets are as follows:\n",
    "  1) title.akas.tsv.gz \n",
    "  2) title.basics.tsv.gz \n",
    "  3) title.crew.tsv.gz\n",
    "  4) title.episode.tsv.gz\n",
    "  5) title.principals.tsv.gz\n",
    "  6) title.ratings.tsv.gz  \n",
    "  7) name.basics.tsv.gz. \n",
    "\n",
    "For this particulat project we will be analyzing data from: \n",
    "\n",
    "__title.basics.tsv.gz__ which contains the following information for titles:\n",
    " - tconst (string) - alphanumeric unique identifier of the title\n",
    " - titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)\n",
    " - primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release\n",
    " - originalTitle (string) - original title, in the original language\n",
    " - isAdult (boolean) - 0: non-adult title; 1: adult title\n",
    " - startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year\n",
    " - endYear (YYYY) – TV Series end year. ‘\\N’ for all other title types\n",
    " - runtimeMinutes – primary runtime of the title, in minutes\n",
    " - genres (string array) – includes up to three genres associated with the title\n",
    "\n",
    "__title.ratings.tsv.gz__ which contains the IMDb rating and votes information for titles: \n",
    "  - tconst (string) - alphanumeric unique identifier of the title\n",
    "  - averageRating – weighted average of all the individual user ratings\n",
    "  - numVotes - number of votes the title has received\n",
    "\n",
    "__title.episode.tsv.gz__  which contains the tv episode information. Fields include:\n",
    " - tconst (string) - alphanumeric identifier of episode\n",
    " - parentTconst (string) - alphanumeric identifier of the parent TV Series\n",
    " - seasonNumber (integer) – season number the episode belongs to\n",
    "- episodeNumber (integer) – episode number of the tconst in the TV series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported required libraries for data wrangling.\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries \n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import urllib.request\n",
    "import gzip\n",
    "import io\n",
    "import os\n",
    "print(\"Imported required libraries for data wrangling.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My working directory: /Users/Jesse/Desktop/Workspace/JESUSC1-GitHub/Data-Science-Exercise-Python.\n"
     ]
    }
   ],
   "source": [
    "# Set the working directory to the desired path\n",
    "root_dir = os.path.expanduser(\"/Users/Jesse/Desktop/Workspace/JESUSC1-GitHub/Data-Science-Exercise-Python\")\n",
    "os.chdir(root_dir)\n",
    "\n",
    "print(f'My working directory: {root_dir}.')\n",
    "\n",
    "# By setting the working directory to the root directory, \n",
    "# we can easily access other files and directories within our repository using relative paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                    genres  \n",
       "0       0      1894      \\N              1         Documentary,Short  \n",
       "1       0      1892      \\N              5           Animation,Short  \n",
       "2       0      1892      \\N              4  Animation,Comedy,Romance  \n",
       "3       0      1892      \\N             12           Animation,Short  \n",
       "4       0      1893      \\N              1              Comedy,Short  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from local file \n",
    "# You can download the files from the links described above. \n",
    "basics_tsv_file = f'{root_dir}/Raw_Data/title.basics.tsv.gz'\n",
    "basics = pd.read_csv(basics_tsv_file, sep='\\t',low_memory = False)\n",
    "basics.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>tconst</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>5.8</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>5.6</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  averageRating  numVotes\n",
       "0  tt0000001            5.7      1964\n",
       "1  tt0000002            5.8       263\n",
       "2  tt0000003            6.5      1808\n",
       "3  tt0000004            5.6       178\n",
       "4  tt0000005            6.2      2606"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from local file\n",
    "ratings_tsv_file = f'{root_dir}/Raw_Data/title.ratings.tsv.gz'\n",
    "ratings = pd.read_csv(ratings_tsv_file, sep='\\t',low_memory = False)\n",
    "ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>tconst</th>\n",
       "      <th>parentTconst</th>\n",
       "      <th>seasonNumber</th>\n",
       "      <th>episodeNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0042816</td>\n",
       "      <td>tt0989125</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0042889</td>\n",
       "      <td>tt0989125</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0043426</td>\n",
       "      <td>tt0040051</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0043631</td>\n",
       "      <td>tt0989125</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst parentTconst seasonNumber episodeNumber\n",
       "0  tt0041951    tt0041038            1             9\n",
       "1  tt0042816    tt0989125            1            17\n",
       "2  tt0042889    tt0989125           \\N            \\N\n",
       "3  tt0043426    tt0040051            3            42\n",
       "4  tt0043631    tt0989125            2            16"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from local file\n",
    "episodes_tsv_file = f'{root_dir}/Raw_Data/title.episode.tsv.gz'\n",
    "episodes = pd.read_csv(episodes_tsv_file, sep='\\t',low_memory = False)\n",
    "episodes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                    genres  \n",
       "0       0      1894      \\N              1         Documentary,Short  \n",
       "1       0      1892      \\N              5           Animation,Short  \n",
       "2       0      1892      \\N              4  Animation,Comedy,Romance  \n",
       "3       0      1892      \\N             12           Animation,Short  \n",
       "4       0      1893      \\N              1              Comedy,Short  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from URL\n",
    "# Use this if running code from GitHub repo! \n",
    "# Note, data sets might be updated later. There is no guarantee variable names will remain unchanged. \n",
    "basics_url = 'https://datasets.imdbws.com/title.basics.tsv.gz'\n",
    "\n",
    "# Download the gzipped TSV file\n",
    "response = urllib.request.urlopen(basics_url)\n",
    "compressed_file = response.read()\n",
    "\n",
    "# Decompress the gzipped file\n",
    "uncompressed_file = gzip.decompress(compressed_file)\n",
    "\n",
    "# Decode the file contents\n",
    "file_content = uncompressed_file.decode('utf-8')\n",
    "\n",
    "# Read the TSV file into a pandas DataFrame\n",
    "basics = pd.read_csv(io.StringIO(file_content), delimiter='\\t', low_memory = False)\n",
    "basics.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>tconst</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>5.8</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  averageRating  numVotes\n",
       "0  tt0000001            5.7      1978\n",
       "1  tt0000002            5.8       265\n",
       "2  tt0000003            6.5      1831\n",
       "3  tt0000004            5.6       179\n",
       "4  tt0000005            6.2      2621"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from URL\n",
    "# Use this if running code from GitHub repo! \n",
    "ratings_url = 'https://datasets.imdbws.com/title.ratings.tsv.gz'\n",
    "\n",
    "# Download the gzipped TSV file\n",
    "response = urllib.request.urlopen(ratings_url)\n",
    "compressed_file = response.read()\n",
    "\n",
    "# Decompress the gzipped file\n",
    "uncompressed_file = gzip.decompress(compressed_file)\n",
    "\n",
    "# Decode the file contents\n",
    "file_content = uncompressed_file.decode('utf-8')\n",
    "\n",
    "# Read the TSV file into a pandas DataFrame\n",
    "ratings = pd.read_csv(io.StringIO(file_content), delimiter='\\t', low_memory = False)\n",
    "ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>tconst</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>5.8</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  averageRating  numVotes\n",
       "0  tt0000001            5.7      1978\n",
       "1  tt0000002            5.8       265\n",
       "2  tt0000003            6.5      1831\n",
       "3  tt0000004            5.6       179\n",
       "4  tt0000005            6.2      2621"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read IMDb datasets from URL\n",
    "# Use this if running code from GitHub repo! \n",
    "episodes_url = 'https://datasets.imdbws.com/title.ratings.tsv.gz'\n",
    "\n",
    "# Download the gzipped TSV file\n",
    "response = urllib.request.urlopen(episodes_url)\n",
    "compressed_file = response.read()\n",
    "\n",
    "# Decompress the gzipped file\n",
    "uncompressed_file = gzip.decompress(compressed_file)\n",
    "\n",
    "# Decode the file contents\n",
    "file_content = uncompressed_file.decode('utf-8')\n",
    "\n",
    "# Read the TSV file into a pandas DataFrame\n",
    "episodes= pd.read_csv(io.StringIO(file_content), delimiter='\\t', low_memory = False)\n",
    "episodes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9798553 entries, 0 to 9798552\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Dtype \n",
      "---  ------          ----- \n",
      " 0   tconst          object\n",
      " 1   titleType       object\n",
      " 2   primaryTitle    object\n",
      " 3   originalTitle   object\n",
      " 4   isAdult         object\n",
      " 5   startYear       object\n",
      " 6   endYear         object\n",
      " 7   runtimeMinutes  object\n",
      " 8   genres          object\n",
      "dtypes: object(9)\n",
      "memory usage: 672.8+ MB\n"
     ]
    }
   ],
   "source": [
    "basics.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1304129 entries, 0 to 1304128\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count    Dtype  \n",
      "---  ------         --------------    -----  \n",
      " 0   tconst         1304129 non-null  object \n",
      " 1   averageRating  1304129 non-null  float64\n",
      " 2   numVotes       1304129 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 29.8+ MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7438416 entries, 0 to 7438415\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   tconst         object\n",
      " 1   parentTconst   object\n",
      " 2   seasonNumber   object\n",
      " 3   episodeNumber  object\n",
      "dtypes: object(4)\n",
      "memory usage: 227.0+ MB\n"
     ]
    }
   ],
   "source": [
    "episodes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb data has been merged.\n"
     ]
    }
   ],
   "source": [
    "# Merge IMDb Basics, Ratings & Episodes data sets by tconst\n",
    "data_merged_basics_ratings = pd.merge(basics, ratings, on = \"tconst\")\n",
    "data_merged_basics_ratings_episodes = pd.merge(data_merged_basics_ratings, episodes, on = \"tconst\")\n",
    "print(\"IMDb data has been merged.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1304129, 11)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_basics_ratings.shape # This new dataset has ~1 million rows and 11 columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>5.8</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>\\N</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>5.6</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes                    genres  \\\n",
       "0       0      1894      \\N              1         Documentary,Short   \n",
       "1       0      1892      \\N              5           Animation,Short   \n",
       "2       0      1892      \\N              4  Animation,Comedy,Romance   \n",
       "3       0      1892      \\N             12           Animation,Short   \n",
       "4       0      1893      \\N              1              Comedy,Short   \n",
       "\n",
       "   averageRating  numVotes  \n",
       "0            5.7      1964  \n",
       "1            5.8       263  \n",
       "2            6.5      1808  \n",
       "3            5.6       178  \n",
       "4            6.2      2606  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_basics_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(637624, 14)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_basics_ratings_episodes.shape #This new dataset has ~637K rows and 14 columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>parentTconst</th>\n",
       "      <th>seasonNumber</th>\n",
       "      <th>episodeNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>\\N</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0043631</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Life of King Henry V</td>\n",
       "      <td>The Life of King Henry V</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>\\N</td>\n",
       "      <td>133</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11</td>\n",
       "      <td>tt0989125</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0044093</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Three Musketeers</td>\n",
       "      <td>The Three Musketeers</td>\n",
       "      <td>0</td>\n",
       "      <td>1950</td>\n",
       "      <td>\\N</td>\n",
       "      <td>60</td>\n",
       "      <td>Drama</td>\n",
       "      <td>4.5</td>\n",
       "      <td>17</td>\n",
       "      <td>tt0959862</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0045960</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>\\N</td>\n",
       "      <td>75</td>\n",
       "      <td>Drama,History,Music</td>\n",
       "      <td>6.9</td>\n",
       "      <td>183</td>\n",
       "      <td>tt0044284</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0046150</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>Othello</td>\n",
       "      <td>Othello</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>\\N</td>\n",
       "      <td>84</td>\n",
       "      <td>Drama</td>\n",
       "      <td>8.2</td>\n",
       "      <td>11</td>\n",
       "      <td>tt0341798</td>\n",
       "      <td>\\N</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  titleType              primaryTitle             originalTitle  \\\n",
       "0  tt0041951  tvEpisode            The Tenderfeet            The Tenderfeet   \n",
       "1  tt0043631  tvEpisode  The Life of King Henry V  The Life of King Henry V   \n",
       "2  tt0044093  tvEpisode      The Three Musketeers      The Three Musketeers   \n",
       "3  tt0045960  tvEpisode                 King Lear                 King Lear   \n",
       "4  tt0046150  tvEpisode                   Othello                   Othello   \n",
       "\n",
       "  isAdult startYear endYear runtimeMinutes               genres  \\\n",
       "0       0      1949      \\N             30              Western   \n",
       "1       0      1951      \\N            133                Drama   \n",
       "2       0      1950      \\N             60                Drama   \n",
       "3       0      1953      \\N             75  Drama,History,Music   \n",
       "4       0      1953      \\N             84                Drama   \n",
       "\n",
       "   averageRating  numVotes parentTconst seasonNumber episodeNumber  \n",
       "0            7.5        81    tt0041038            1             9  \n",
       "1            6.8        11    tt0989125            2            16  \n",
       "2            4.5        17    tt0959862            1             6  \n",
       "3            6.9       183    tt0044284            2             3  \n",
       "4            8.2        11    tt0341798           \\N            \\N  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_merged_basics_ratings_episodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no duplicate rows in the dataframe: data_merged_basics_ratings\n",
      " \n",
      "There are no duplicate rows in the dataframe: data_merged_basics_ratings_episodes\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicate rows in merged data sets \n",
    "duplicate_rows = data_merged_basics_ratings[data_merged_basics_ratings.duplicated()]\n",
    "\n",
    "if len(duplicate_rows) > 0:\n",
    "    print(f'There are {len(duplicate_rows)} duplicate rows in the dataframe: data_merged_basics_ratings')\n",
    "else:\n",
    "    print('There are no duplicate rows in the dataframe: data_merged_basics_ratings')\n",
    "\n",
    "print(\" \")\n",
    "duplicate_rows2 = data_merged_basics_ratings_episodes[data_merged_basics_ratings_episodes.duplicated()]\n",
    "\n",
    "if len(duplicate_rows2) > 0:\n",
    "    print(f'There are {len(duplicate_rows2)} duplicate rows in the dataframe: data_merged_basics_ratings_episodes')\n",
    "else:\n",
    "    print('There are no duplicate rows in the dataframe: data_merged_basics_ratings_episodes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe data_merged_basics_ratings has 2 missing values\n",
      "tconst            0\n",
      "titleType         0\n",
      "primaryTitle      0\n",
      "originalTitle     0\n",
      "isAdult           0\n",
      "startYear         0\n",
      "endYear           0\n",
      "runtimeMinutes    0\n",
      "genres            2\n",
      "averageRating     0\n",
      "numVotes          0\n",
      "dtype: int64\n",
      " \n",
      "The dataframe data_merged_basics_ratings_episodes has 2 missing values\n",
      "tconst            0\n",
      "titleType         0\n",
      "primaryTitle      0\n",
      "originalTitle     0\n",
      "isAdult           0\n",
      "startYear         0\n",
      "endYear           0\n",
      "runtimeMinutes    0\n",
      "genres            2\n",
      "averageRating     0\n",
      "numVotes          0\n",
      "parentTconst      0\n",
      "seasonNumber      0\n",
      "episodeNumber     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values in merged data sets \n",
    "missing_values = data_merged_basics_ratings.isnull().sum()\n",
    "\n",
    "if missing_values.sum() > 0:\n",
    "    print(f'The dataframe data_merged_basics_ratings has {missing_values.sum()} missing values')\n",
    "    print(missing_values)\n",
    "else:\n",
    "    print('The dataframe data_merged_basics_ratings has no missing values')\n",
    "    \n",
    "print(\" \")\n",
    "missing_values2 = data_merged_basics_ratings_episodes.isnull().sum()\n",
    "\n",
    "if missing_values2.sum() > 0:\n",
    "    print(f'The dataframe data_merged_basics_ratings_episodes has {missing_values2.sum()} missing values')\n",
    "    print(missing_values2)\n",
    "else:\n",
    "    print('The dataframe data_merged_basics_ratings_episodes has no missing values')\n",
    "\n",
    "# Note, looking at the data it appears that some missing values might be coded as \\N and thus might not be read as NaN\n",
    "data_merged_basics_ratings = data_merged_basics_ratings.replace('\\\\N', np.nan) # replace all occurrences of '\\\\N' with NaN\n",
    "data_merged_basics_ratings_episodes = data_merged_basics_ratings_episodes.replace('\\\\N', np.nan) # replace all occurrences of '\\\\N' with NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe data_merged_basics_ratings has 1649437 missing values\n",
      "tconst                  0\n",
      "titleType               0\n",
      "primaryTitle            0\n",
      "originalTitle           0\n",
      "isAdult                 0\n",
      "startYear             258\n",
      "endYear           1258832\n",
      "runtimeMinutes     370670\n",
      "genres              19677\n",
      "averageRating           0\n",
      "numVotes                0\n",
      "dtype: int64\n",
      " \n",
      "The dataframe data_merged_basics_ratings_episodes has 914464 missing values\n",
      "tconst                 0\n",
      "titleType              0\n",
      "primaryTitle           0\n",
      "originalTitle          0\n",
      "isAdult                0\n",
      "startYear            144\n",
      "endYear           637624\n",
      "runtimeMinutes    245167\n",
      "genres              1095\n",
      "averageRating          0\n",
      "numVotes               0\n",
      "parentTconst           0\n",
      "seasonNumber       15217\n",
      "episodeNumber      15217\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Re-check for missing values in merged data set \n",
    "missing_values = data_merged_basics_ratings.isnull().sum()\n",
    "\n",
    "if missing_values.sum() > 0:\n",
    "    print(f'The dataframe data_merged_basics_ratings has {missing_values.sum()} missing values')\n",
    "    print(missing_values)\n",
    "else:\n",
    "    print('The dataframe data_merged_basics_ratings has no missing values')\n",
    "    \n",
    "print(\" \")\n",
    "missing_values2 = data_merged_basics_ratings_episodes.isnull().sum()\n",
    "\n",
    "if missing_values2.sum() > 0:\n",
    "    print(f'The dataframe data_merged_basics_ratings_episodes has {missing_values2.sum()} missing values')\n",
    "    print(missing_values2)\n",
    "else:\n",
    "    print('The dataframe data_merged_basics_ratings_episodes has no missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 1st file has been saved to: /Users/Jesse/Desktop/Workspace/JESUSC1-GitHub/Data-Science-Exercise-Python/Processed_Data/IMDb_basic_ratings_data.csv\n",
      "The 2nd file has been saved to: /Users/Jesse/Desktop/Workspace/JESUSC1-GitHub/Data-Science-Exercise-Python/Processed_Data/IMDb_basic_ratings_episodes_data.csv\n"
     ]
    }
   ],
   "source": [
    "# Save the merged dataframes to a CSV file for future use\n",
    "# At this point, we will not remove any missing values \n",
    "path = f'{root_dir}/Processed_Data'\n",
    "\n",
    "filename1 = 'IMDb_basic_ratings_data.csv'\n",
    "filename2 = 'IMDb_basic_ratings_episodes_data.csv'\n",
    "\n",
    "data_merged_basics_ratings.to_csv(f'{path}/{filename1}', index = False)\n",
    "data_merged_basics_ratings_episodes.to_csv(f'{path}/{filename2}', index = False)\n",
    "\n",
    "# print a message when done saving\n",
    "print(f'The 1st file has been saved to: {path}/{filename1}')\n",
    "print(f'The 2nd file has been saved to: {path}/{filename2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will begin our investigation by identifying the number of years included in the data set, as well as the different tile types and genres associated with all the titles in the data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported required libraries for EDA.\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries \n",
    "import zipfile\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "print(\"Imported required libraries for EDA.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb merged data has finished loading from CSV files.\n"
     ]
    }
   ],
   "source": [
    "# Load the merged dataframes from local CSV files \n",
    "\n",
    "# Basics & Ratings data for IMDb titles (from CSV file)\n",
    "IMDb_df = pd.read_csv(f'{root_dir}/Processed_Data/IMDb_basic_ratings_data.csv', low_memory = False)\n",
    "\n",
    "# Basics, Ratings & Episodes data for IMDb titles (from CSV file)\n",
    "IMDb_df2 = pd.read_csv(f'{root_dir}/Processed_Data/IMDb_basic_ratings_episodes_data.csv', low_memory = False)\n",
    "\n",
    "print(\"IMDb merged data has finished loading from CSV files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb merged data has finished loading from zip files.\n"
     ]
    }
   ],
   "source": [
    "# Load the merged dataframes from local zip files\n",
    "# Use this if using code from GitHub repo!  \n",
    "\n",
    "# Specify the paths to the zip files\n",
    "zip_path1 = f'{root_dir}/Processed_Data/IMDb_basic_ratings_data.csv.zip'\n",
    "zip_path2 = f'{root_dir}/Processed_Data/IMDb_basic_ratings_episodes_data.csv.zip'\n",
    "\n",
    "# Specify the names of the CSV files inside the zip files\n",
    "csv_filename1 = 'IMDb_basic_ratings_data.csv'\n",
    "csv_filename2 = 'IMDb_basic_ratings_episodes_data.csv'\n",
    "\n",
    "# Extract the first CSV file from the first zip file\n",
    "with zipfile.ZipFile(zip_path1, 'r') as zip_ref:\n",
    "    zip_ref.extract(csv_filename1, path='temp_folder')  # Extract to a temporary folder\n",
    "\n",
    "# Extract the second CSV file from the second zip file\n",
    "with zipfile.ZipFile(zip_path2, 'r') as zip_ref:\n",
    "    zip_ref.extract(csv_filename2, path='temp_folder')  # Extract to the same temporary folder\n",
    "\n",
    "# Load the first CSV file into a pandas DataFrame\n",
    "csv_path1 = 'temp_folder/' + csv_filename1\n",
    "IMDb_df = pd.read_csv(csv_path1, low_memory = False) \n",
    "\n",
    "\n",
    "# Load the second CSV file into a pandas DataFrame\n",
    "csv_path2 = 'temp_folder/' + csv_filename2\n",
    "IMDb_df2 = pd.read_csv(csv_path2, low_memory = False)\n",
    "\n",
    "# Do further processing with the DataFrames...\n",
    "\n",
    "# Cleanup: Remove the temporary folder\n",
    "import os\n",
    "os.remove(csv_path1)\n",
    "os.remove(csv_path2)\n",
    "os.rmdir('temp_folder')\n",
    "\n",
    "print(\"IMDb merged data has finished loading from zip files.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the merged data sets \n",
    "\n",
    "# Remove any rows in IMDb_df where startYear, genres, and runtimeMinutes is NaN\n",
    "IMDb_df = IMDb_df.dropna(subset=['startYear', 'genres', 'runtimeMinutes'])\n",
    "\n",
    "# Remove any rows in IMDb_df2 where startYear, seasonNumber, and episodeNumber is NaN\n",
    "IMDb_df2 = IMDb_df2.dropna(subset=['startYear', 'seasonNumber', 'episodeNumber'])\n",
    "\n",
    "# Convert seasonNumber and episodeNumber to int\n",
    "IMDb_df2 ['seasonNumber'] = IMDb_df2['seasonNumber'].astype(int)\n",
    "IMDb_df2 ['episodeNumber'] = IMDb_df2['episodeNumber'].astype(int)\n",
    "\n",
    "# Convert startYear & endYear to datetime\n",
    "IMDb_df['startYear'] = pd.to_datetime(IMDb_df['startYear'], format='%Y', errors='coerce')\n",
    "IMDb_df2['startYear'] = pd.to_datetime(IMDb_df2['startYear'], format='%Y', errors='coerce')\n",
    "\n",
    "# Keep year only and convert to integer (to subset later based on year)\n",
    "IMDb_df['startYear'] = IMDb_df['startYear'].dt.year.fillna(0).astype(int)\n",
    "IMDb_df2['startYear'] = IMDb_df2['startYear'].dt.year.fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0000001</td>\n",
       "      <td>short</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>Carmencita</td>\n",
       "      <td>0</td>\n",
       "      <td>1894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Documentary,Short</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0000002</td>\n",
       "      <td>short</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>Le clown et ses chiens</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>5.8</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0000003</td>\n",
       "      <td>short</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>Pauvre Pierrot</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Animation,Comedy,Romance</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0000004</td>\n",
       "      <td>short</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>Un bon bock</td>\n",
       "      <td>0</td>\n",
       "      <td>1892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>Animation,Short</td>\n",
       "      <td>5.6</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0000005</td>\n",
       "      <td>short</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>Blacksmith Scene</td>\n",
       "      <td>0</td>\n",
       "      <td>1893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy,Short</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst titleType            primaryTitle           originalTitle  \\\n",
       "0  tt0000001     short              Carmencita              Carmencita   \n",
       "1  tt0000002     short  Le clown et ses chiens  Le clown et ses chiens   \n",
       "2  tt0000003     short          Pauvre Pierrot          Pauvre Pierrot   \n",
       "3  tt0000004     short             Un bon bock             Un bon bock   \n",
       "4  tt0000005     short        Blacksmith Scene        Blacksmith Scene   \n",
       "\n",
       "   isAdult  startYear  endYear runtimeMinutes                    genres  \\\n",
       "0        0       1894      NaN              1         Documentary,Short   \n",
       "1        0       1892      NaN              5           Animation,Short   \n",
       "2        0       1892      NaN              4  Animation,Comedy,Romance   \n",
       "3        0       1892      NaN             12           Animation,Short   \n",
       "4        0       1893      NaN              1              Comedy,Short   \n",
       "\n",
       "   averageRating  numVotes  \n",
       "0            5.7      1964  \n",
       "1            5.8       263  \n",
       "2            6.5      1808  \n",
       "3            5.6       178  \n",
       "4            6.2      2606  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 923886 entries, 0 to 1304128\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          923886 non-null  object \n",
      " 1   titleType       923886 non-null  object \n",
      " 2   primaryTitle    923886 non-null  object \n",
      " 3   originalTitle   923886 non-null  object \n",
      " 4   isAdult         923886 non-null  int64  \n",
      " 5   startYear       923886 non-null  int64  \n",
      " 6   endYear         29263 non-null   float64\n",
      " 7   runtimeMinutes  923886 non-null  object \n",
      " 8   genres          923886 non-null  object \n",
      " 9   averageRating   923886 non-null  float64\n",
      " 10  numVotes        923886 non-null  int64  \n",
      "dtypes: float64(2), int64(3), object(6)\n",
      "memory usage: 84.6+ MB\n"
     ]
    }
   ],
   "source": [
    "IMDb_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>parentTconst</th>\n",
       "      <th>seasonNumber</th>\n",
       "      <th>episodeNumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0043631</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Life of King Henry V</td>\n",
       "      <td>The Life of King Henry V</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.8</td>\n",
       "      <td>11</td>\n",
       "      <td>tt0989125</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0044093</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Three Musketeers</td>\n",
       "      <td>The Three Musketeers</td>\n",
       "      <td>0</td>\n",
       "      <td>1950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Drama</td>\n",
       "      <td>4.5</td>\n",
       "      <td>17</td>\n",
       "      <td>tt0959862</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0045960</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>King Lear</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>Drama,History,Music</td>\n",
       "      <td>6.9</td>\n",
       "      <td>183</td>\n",
       "      <td>tt0044284</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tt0046855</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>A Christmas Carol</td>\n",
       "      <td>A Christmas Carol</td>\n",
       "      <td>0</td>\n",
       "      <td>1954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Adventure,Drama,Family</td>\n",
       "      <td>5.8</td>\n",
       "      <td>157</td>\n",
       "      <td>tt0046643</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  titleType              primaryTitle             originalTitle  \\\n",
       "0  tt0041951  tvEpisode            The Tenderfeet            The Tenderfeet   \n",
       "1  tt0043631  tvEpisode  The Life of King Henry V  The Life of King Henry V   \n",
       "2  tt0044093  tvEpisode      The Three Musketeers      The Three Musketeers   \n",
       "3  tt0045960  tvEpisode                 King Lear                 King Lear   \n",
       "5  tt0046855  tvEpisode         A Christmas Carol         A Christmas Carol   \n",
       "\n",
       "   isAdult  startYear  endYear runtimeMinutes                  genres  \\\n",
       "0        0       1949      NaN             30                 Western   \n",
       "1        0       1951      NaN            133                   Drama   \n",
       "2        0       1950      NaN             60                   Drama   \n",
       "3        0       1953      NaN             75     Drama,History,Music   \n",
       "5        0       1954      NaN             60  Adventure,Drama,Family   \n",
       "\n",
       "   averageRating  numVotes parentTconst  seasonNumber  episodeNumber  \n",
       "0            7.5        81    tt0041038             1              9  \n",
       "1            6.8        11    tt0989125             2             16  \n",
       "2            4.5        17    tt0959862             1              6  \n",
       "3            6.9       183    tt0044284             2              3  \n",
       "5            5.8       157    tt0046643             1              4  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDb_df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 622266 entries, 0 to 637623\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          622266 non-null  object \n",
      " 1   titleType       622266 non-null  object \n",
      " 2   primaryTitle    622266 non-null  object \n",
      " 3   originalTitle   622266 non-null  object \n",
      " 4   isAdult         622266 non-null  int64  \n",
      " 5   startYear       622266 non-null  int64  \n",
      " 6   endYear         0 non-null       float64\n",
      " 7   runtimeMinutes  387720 non-null  object \n",
      " 8   genres          621346 non-null  object \n",
      " 9   averageRating   622266 non-null  float64\n",
      " 10  numVotes        622266 non-null  int64  \n",
      " 11  parentTconst    622266 non-null  object \n",
      " 12  seasonNumber    622266 non-null  int64  \n",
      " 13  episodeNumber   622266 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(7)\n",
      "memory usage: 71.2+ MB\n"
     ]
    }
   ],
   "source": [
    "IMDb_df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique years: 144\n",
      "Range of years: 1874 - 2023\n",
      "Number of unique title types: 10\n",
      "Number of unique genres: 1950\n"
     ]
    }
   ],
   "source": [
    "# Get the number of unique years, title types, and genres in the IMDb dataset\n",
    "num_years = IMDb_df['startYear'].nunique()\n",
    "min_year = IMDb_df['startYear'].min()\n",
    "max_year = IMDb_df['startYear'].max()\n",
    "\n",
    "num_title_types = IMDb_df['titleType'].nunique()\n",
    "num_genres = IMDb_df['genres'].nunique()\n",
    "\n",
    "print(\"Number of unique years:\", num_years)\n",
    "print(\"Range of years:\", min_year, \"-\", max_year)\n",
    "print(\"Number of unique title types:\", num_title_types)\n",
    "print(\"Number of unique genres:\", num_genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama                         71721\n",
       "Comedy                        69478\n",
       "Documentary                   55343\n",
       "Action,Adventure,Animation    29925\n",
       "Drama,Short                   21926\n",
       "Crime,Drama,Mystery           20633\n",
       "Short                         20188\n",
       "Reality-TV                    19895\n",
       "Comedy,Drama                  19502\n",
       "Drama,Romance                 18357\n",
       "Name: genres, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check genres \n",
    "IMDb_df['genres'].value_counts().head(10) # displays counts for top 10 genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, each title in the data set can be associated with at most three different genres!\n",
    "We will move on to visualizing the number of titles played each year by title type and genre. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal bar chart showing total number of titles by type across the full data set \n",
    "ax = IMDb_df['titleType'].value_counts().plot(kind = 'barh', color = '#5CACEE')\n",
    "\n",
    "# Format the x-axis tick labels with commas\n",
    "fmt = '{x:,.0f}'\n",
    "tick = mtick.StrMethodFormatter(fmt)\n",
    "ax.xaxis.set_major_formatter(tick)\n",
    "\n",
    "# Add axis labels and a title\n",
    "plt.xlabel('Number of Titles')\n",
    "plt.ylabel('Title Type')\n",
    "plt.title('Total Number of Titles by IMDb Title Type (1974-2023)')\n",
    "\n",
    "# Rotate the y-axis labels for better readability\n",
    "plt.yticks(rotation = 0)\n",
    "\n",
    "# Add a grid to make it easier to compare the different categories\n",
    "plt.grid(axis = 'x')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the counts of different titles by year and title type \n",
    "\n",
    "# Create a subset of the data with startYear >= 1940\n",
    "IMDb_df_subset = IMDb_df[IMDb_df['startYear'] >= 1940] # Number of titles before 1940 is relatively small \n",
    "\n",
    "# Group the data by year and title type\n",
    "grouped = IMDb_df_subset.groupby(['startYear', 'titleType'])\n",
    "\n",
    "# Create a table of counts of different titles by year and title type\n",
    "count_by_year_type = grouped['primaryTitle'].nunique().unstack()\n",
    "\n",
    "# Split the count_by_year_type table into two separate tables\n",
    "first_table = count_by_year_type[['movie', 'short', 'tvEpisode', 'tvMiniSeries', 'tvMovie']]\n",
    "second_table = count_by_year_type[['tvSeries', 'tvShort', 'tvSpecial', 'video', 'videoGame']]\n",
    "\n",
    "# Plot the counts of different titles by year and title type\n",
    "fig, axes = plt.subplots(nrows = 2, ncols = 1, figsize = (8, 8))\n",
    "\n",
    "first_table.plot(kind = 'bar', stacked = True, ax = axes[0])\n",
    "axes[0].set_title('Number of IMDb Titles Released Each Year by Type')\n",
    "axes[0].set_xlabel(' ')\n",
    "axes[0].set_ylabel('Number of Titles')\n",
    "axes[0].set_xticks(range(0, len(first_table.index), 10))\n",
    "axes[0].set_xticklabels(first_table.index[::10])\n",
    "legend = axes[0].legend(title = 'Title Types')\n",
    "plt.setp(legend.get_title(), fontsize = 'medium')\n",
    "\n",
    "second_table.plot(kind = 'bar', stacked = True, ax = axes[1])\n",
    "axes[1].set_title(' ')\n",
    "axes[1].set_xlabel('Year')\n",
    "axes[1].set_ylabel('Number of Titles')\n",
    "axes[1].set_xticks(range(0, len(second_table.index), 10))\n",
    "axes[1].set_xticklabels(second_table.index[::10])\n",
    "legend = axes[1].legend(title = 'Title Types')\n",
    "plt.setp(legend.get_title(), fontsize = 'medium')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems the IMDb title types with the greatest number of titles released between 1974 and 2023 are TV episodes, followed by movies and short films. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal bar plot of the top 15 IMDb genres by number of titles\n",
    "ax = IMDb_df['genres'].value_counts().head(15).sort_values().plot(kind='barh', figsize = (10, 6), color = '#5CACEE')\n",
    "\n",
    "# Set plot title and axis labels\n",
    "plt.title('Total Number of Titles by IMDb Genre (1974-2023)')\n",
    "plt.xlabel('Number of Titles')\n",
    "plt.ylabel('Top 15 IMDb Genres')\n",
    "\n",
    "# Customize x-axis tick labels to show values as thousands\n",
    "fmt = '{x:,.0f}'\n",
    "tick = mtick.StrMethodFormatter(fmt)\n",
    "ax.xaxis.set_major_formatter(tick)\n",
    "\n",
    "# Remove spines and add grid lines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_linewidth(0.5)\n",
    "ax.grid(axis='x', linestyle = '--', alpha = 0.5)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems the IMDb genres with the highest total number of titles released between 1974 and 2023 are Drama, Comedy, and Documentary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1910s\n",
      "   averageRating  numVotes                    genres\n",
      "0            8.2      11.0   Biography,Romance,Short\n",
      "1            7.7    3028.0  Animation,Comedy,Fantasy\n",
      "2            7.7    1891.0          Drama,Horror,War\n",
      "3            7.6      14.0          Comedy,Drama,War\n",
      "4            7.4      65.0    Animation,Comedy,Drama\n",
      "\n",
      "\n",
      "1920s\n",
      "   averageRating  numVotes                   genres\n",
      "0            8.4   26396.0        Documentary,Music\n",
      "1            8.1   21540.0   Action,Comedy,Thriller\n",
      "2            8.1   10327.0   Drama,Thriller,Western\n",
      "3            8.0    5453.5  Adventure,Drama,Fantasy\n",
      "4            7.9   59288.0   Drama,History,Thriller\n",
      "\n",
      "\n",
      "1930s\n",
      "   averageRating  numVotes                    genres\n",
      "0           8.00      10.0     Comedy,Short,Thriller\n",
      "1           7.90     597.0   Adventure,Comedy,Horror\n",
      "2           7.90      68.0  Comedy,Documentary,Drama\n",
      "3           7.90      49.0                     Sport\n",
      "4           7.75    4588.5         Documentary,Sport\n",
      "\n",
      "\n",
      "1940s\n",
      "   averageRating  numVotes                     genres\n",
      "0            8.5      17.0  Adventure,Fantasy,Mystery\n",
      "1            8.3      59.0    Biography,Drama,Fantasy\n",
      "2            8.3      30.0           Animation,Family\n",
      "3            8.1      55.0           Documentary,News\n",
      "4            8.0      67.0     Family,History,Musical\n",
      "\n",
      "\n",
      "1950s\n",
      "   averageRating  numVotes                        genres\n",
      "0            9.0      32.0        Family,Game-Show,Music\n",
      "1            8.8      14.0              Biography,Comedy\n",
      "2            8.6      22.0  Action,Adventure,Documentary\n",
      "3            8.4      64.0     Animation,Musical,Romance\n",
      "4            8.4      16.0        Action,Fantasy,Romance\n",
      "\n",
      "\n",
      "1960s\n",
      "   averageRating  numVotes                     genres\n",
      "0            9.8      85.0    Comedy,Family,Talk-Show\n",
      "1            9.5      18.0              Crime,History\n",
      "2            9.3       9.0     Documentary,Drama,News\n",
      "3            9.0      57.0  Adventure,Mystery,Romance\n",
      "4            9.0      11.0  Documentary,Drama,History\n",
      "\n",
      "\n",
      "1970s\n",
      "   averageRating  numVotes                       genres\n",
      "0            9.3     144.0  Biography,History,Talk-Show\n",
      "1            8.9     158.0  Documentary,Horror,Thriller\n",
      "2            8.8      65.0  Family,Reality-TV,Talk-Show\n",
      "3            8.7     110.0          Drama,Fantasy,Sport\n",
      "4            8.7      68.0                  Short,Sport\n",
      "\n",
      "\n",
      "1980s\n",
      "   averageRating  numVotes                      genres\n",
      "0            9.2      72.0     Fantasy,Musical,Mystery\n",
      "1            9.2      32.0   Comedy,Documentary,Family\n",
      "2            9.2      31.0      Family,Game-Show,Music\n",
      "3            9.2      14.0    Biography,Comedy,Fantasy\n",
      "4            9.0       6.0  Documentary,Family,Western\n",
      "\n",
      "\n",
      "1990s\n",
      "   averageRating  numVotes                     genres\n",
      "0            9.4      11.0           Reality-TV,Sport\n",
      "1            9.2      40.0   Adventure,Horror,Mystery\n",
      "2            9.0      99.0  Adventure,Fantasy,Musical\n",
      "3            9.0      62.0   Documentary,Family,Sport\n",
      "4            9.0      30.0    Documentary,News,Sci-Fi\n",
      "\n",
      "\n",
      "2000s\n",
      "   averageRating  numVotes                    genres\n",
      "0          10.00      7.00         Comedy,News,Short\n",
      "1           9.50   3218.00  Family,Reality-TV,Sci-Fi\n",
      "2           9.42     32.11  Comedy,Horror,Reality-TV\n",
      "3           9.40     14.00        Family,History,War\n",
      "4           9.30     14.00  Adventure,Short,Thriller\n",
      "\n",
      "\n",
      "2010s\n",
      "   averageRating  numVotes                           genres\n",
      "0           10.0       7.0            Music,News,Reality-TV\n",
      "1            9.8     100.5            News,Reality-TV,Short\n",
      "2            9.8       9.0                Fantasy,Talk-Show\n",
      "3            9.6      14.0  Adventure,Documentary,Talk-Show\n",
      "4            9.5     183.0             Romance,Thriller,War\n",
      "\n",
      "\n",
      "2020s\n",
      "   averageRating  numVotes                       genres\n",
      "0           10.0       5.0       Comedy,Musical,Mystery\n",
      "1           10.0       5.0         Family,Musical,Short\n",
      "2            9.9      60.0  Documentary,News,Reality-TV\n",
      "3            9.8      29.0  Adventure,Documentary,Short\n",
      "4            9.7      26.0         Comedy,Fantasy,Music\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Find the top 5 genres based on averageRating and numVotes for each decade\n",
    "\n",
    "# Create a new column 'decade' based on 'startYear' column\n",
    "IMDb_df['decade'] = IMDb_df['startYear'] // 10 * 10\n",
    "\n",
    "# Convert 'runtimeMinutes' column to float and replace missing values with NaN\n",
    "IMDb_df['runtimeMinutes'] = IMDb_df['runtimeMinutes'].replace('\\\\N', np.nan).astype(float)\n",
    "\n",
    "# Group the data by decade and genres\n",
    "grouped = IMDb_df.groupby(['decade', 'genres'])\n",
    "\n",
    "# Calculate the mean of 'averageRating' and 'numVotes' columns for each group\n",
    "grouped_mean = round(grouped[['averageRating', 'numVotes']].mean(),2)\n",
    "\n",
    "# Sort the data in descending order based on 'averageRating' and 'numVotes'\n",
    "sorted_mean = grouped_mean.sort_values(['decade', 'averageRating', 'numVotes'], ascending = [True, False, False])\n",
    "\n",
    "# Select the top 5 genres based on 'averageRating' and 'numVotes' for each decade\n",
    "top_genres = {}\n",
    "for decade in range(1910, 2030, 10):\n",
    "    top_genres[str(decade) + 's'] = sorted_mean.loc[decade].nlargest(5, ['averageRating', 'numVotes'])\n",
    "    top_genres[str(decade) + 's']['genres'] = top_genres[str(decade) + 's'].index.get_level_values('genres')\n",
    "    top_genres[str(decade) + 's'].reset_index(drop = True, inplace = True)\n",
    "\n",
    "# Print the results\n",
    "for decade in top_genres.keys():\n",
    "    print(decade)\n",
    "    print(top_genres[decade])\n",
    "    print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets dig in a little further into how titles have changed with time, particulary in regards to adult content. Across titles, is more adult or non-adult content created each year?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a dictionary to map the binary values (isAdult) to categorical values\n",
    "isAdult_map = {0: 'non-adult', 1: 'adult'}\n",
    "\n",
    "# Use the map() function to recode the binary variable as a categorical variable\n",
    "IMDb_df['isAdult_cat'] = IMDb_df['isAdult'].map(isAdult_map)\n",
    "\n",
    "# Group the data by year and adult status\n",
    "grouped = IMDb_df.groupby(['startYear', 'isAdult_cat'])\n",
    "\n",
    "# Count the number of titles for each group\n",
    "counted = grouped.size().reset_index(name = 'count')\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.lineplot(x = 'startYear', y = 'count', hue = 'isAdult_cat', data = counted)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Titles')\n",
    "plt.title('Number of Adult and Non-Adult Titles by Year')\n",
    "plt.legend(title = ' ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems more non-adult content is created each year. Notice, the sharp increase in non-adult content in the early 2000s and the significant drop in 2020, following the start of the COVID-19 pandemic. Lets look at runtime now, particularly for movies and TV episodes, and see how that has changed with time.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data to only include movies\n",
    "IMDb_movies = IMDb_df[IMDb_df['titleType'] == 'movie']\n",
    "\n",
    "# Filter the data to only include titles with runtime information\n",
    "IMDb_movies = IMDb_movies.dropna(subset=['runtimeMinutes'])\n",
    "\n",
    "# Group the data by year and calculate the average runtime\n",
    "runtime_by_year = IMDb_movies.groupby('startYear')['runtimeMinutes'].mean()\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(runtime_by_year.index, runtime_by_year.values)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Runtime (minutes)')\n",
    "plt.title('Average Runtime of Movies Each Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data to only include tvEpisode\n",
    "IMDb_tvEpisode = IMDb_df[IMDb_df['titleType'] == 'tvEpisode']\n",
    "\n",
    "# Filter the data to only include titles with runtime information\n",
    "IMDb_tvEpisode = IMDb_tvEpisode.dropna(subset=['runtimeMinutes'])\n",
    "\n",
    "# Group the data by year and calculate the average runtime\n",
    "runtime_by_year = IMDb_tvEpisode.groupby('startYear')['runtimeMinutes'].mean()\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(runtime_by_year.index, runtime_by_year.values, color = 'green')\n",
    "plt.ylim(0, 60)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Runtime (minutes)')\n",
    "plt.title('Average Runtime of TV Episodes Each Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Averaging between 80-100 minutes, it seems the current runtime for movies has decreased by 20-40 minutes from that of the early 1900s. However, the average runtime for TV episodes has increased to be more than 30 minutes long. Has the new era of streaming services redefined the current runtime and lenght of TV series?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets dig a little deeper into viewer preferences and see what types of titles and genres have higher ratings among viewers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>tconst</th>\n",
       "      <th>titleType</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>originalTitle</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>startYear</th>\n",
       "      <th>endYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>parentTconst</th>\n",
       "      <th>seasonNumber_x</th>\n",
       "      <th>episodeNumber</th>\n",
       "      <th>seasonNumber_y</th>\n",
       "      <th>totalEpisodes</th>\n",
       "      <th>totalSeasons</th>\n",
       "      <th>avgEpisodesPerSeason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0041951</td>\n",
       "      <td>tvEpisode</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>The Tenderfeet</td>\n",
       "      <td>0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>Western</td>\n",
       "      <td>7.5</td>\n",
       "      <td>81</td>\n",
       "      <td>tt0041038</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst  titleType    primaryTitle   originalTitle  isAdult  startYear  \\\n",
       "0  tt0041951  tvEpisode  The Tenderfeet  The Tenderfeet        0       1949   \n",
       "1  tt0041951  tvEpisode  The Tenderfeet  The Tenderfeet        0       1949   \n",
       "2  tt0041951  tvEpisode  The Tenderfeet  The Tenderfeet        0       1949   \n",
       "3  tt0041951  tvEpisode  The Tenderfeet  The Tenderfeet        0       1949   \n",
       "4  tt0041951  tvEpisode  The Tenderfeet  The Tenderfeet        0       1949   \n",
       "\n",
       "   endYear runtimeMinutes   genres  averageRating  numVotes parentTconst  \\\n",
       "0      NaN             30  Western            7.5        81    tt0041038   \n",
       "1      NaN             30  Western            7.5        81    tt0041038   \n",
       "2      NaN             30  Western            7.5        81    tt0041038   \n",
       "3      NaN             30  Western            7.5        81    tt0041038   \n",
       "4      NaN             30  Western            7.5        81    tt0041038   \n",
       "\n",
       "   seasonNumber_x  episodeNumber  seasonNumber_y  totalEpisodes  totalSeasons  \\\n",
       "0               1              9               1             52             5   \n",
       "1               1              9               2             26             5   \n",
       "2               1              9               3             52             5   \n",
       "3               1              9               4             52             5   \n",
       "4               1              9               5             39             5   \n",
       "\n",
       "   avgEpisodesPerSeason  \n",
       "0                  44.0  \n",
       "1                  44.0  \n",
       "2                  44.0  \n",
       "3                  44.0  \n",
       "4                  44.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average number of episodes per season for all unique shows in the data set \n",
    "\n",
    "# Filter the data to only include tvEpisode\n",
    "IMDb_tvEpisode = IMDb_df2[IMDb_df2['titleType'] == 'tvEpisode']\n",
    "\n",
    "# Find the total number of episodes per season\n",
    "episodes_per_season = IMDb_tvEpisode.groupby(['parentTconst', 'seasonNumber'])['episodeNumber'].max().reset_index(name='totalEpisodes')\n",
    "\n",
    "# Count the total number of seasons for each show\n",
    "season_counts = episodes_per_season.groupby(['parentTconst']).size().reset_index(name = 'totalSeasons')\n",
    "\n",
    "# Calculate the average number of episodes per season for all unique shows\n",
    "avg_episodes_per_season = episodes_per_season.groupby(['parentTconst'])['totalEpisodes'].mean().reset_index(name = 'avgEpisodesPerSeason')\n",
    "avg_episodes_per_season['avgEpisodesPerSeason'] = avg_episodes_per_season['avgEpisodesPerSeason'].round(decimals = 0)\n",
    "\n",
    "# Merge all the data frames together\n",
    "merged_data = pd.merge(IMDb_tvEpisode, episodes_per_season, on = 'parentTconst', how = 'left')\n",
    "merged_data = pd.merge(merged_data, season_counts, on = 'parentTconst', how = 'left')\n",
    "merged_data = pd.merge(merged_data, avg_episodes_per_season, on='parentTconst', how = 'left')\n",
    "\n",
    "merged_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot showing the average number of episodes per season for each year\n",
    "\n",
    "# Plot the results in a line graph\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10, 8))\n",
    "\n",
    "# Average episodes per season per year\n",
    "avg_episodes_per_season = merged_data.groupby('startYear')['avgEpisodesPerSeason'].mean()\n",
    "ax.plot(avg_episodes_per_season.index, avg_episodes_per_season.values, color = 'darkblue')\n",
    "ax.set_title('Average Total Number Episodes per Season (TV Series)')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Average Number of Episodes per Season')\n",
    "ax.set_ylim([10,60])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot helps us visualize the trend of the average number of episodes per season for TV series over the years. By grouping the data by startYear and calculating the average number of episodes per season for each year, we can see if there is any change in the trend over time. The plot shows that the average number of episodes per season has decreased slightly from the early 2000s to the present day. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The three title types with the highest average rating are:\n",
      "\n",
      "1. tvEpisode (avg rating = 7.40)\n",
      "2. tvMiniSeries (avg rating = 7.14)\n",
      "3. tvSpecial (avg rating = 6.97)\n"
     ]
    }
   ],
   "source": [
    "# Create a boxplot that displays the distribution of average ratings by title type \n",
    "\n",
    "# Calculate the average rating by title type\n",
    "avg_ratings_by_title_type = IMDb_df.groupby('titleType')['averageRating'].mean().sort_values(ascending=False)\n",
    "\n",
    "# Set the style and color palette\n",
    "sns.set(style=\"ticks\", palette=\"pastel\")\n",
    "\n",
    "# Create the boxplot\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.boxplot(x=\"titleType\", y=\"averageRating\", data=IMDb_df, palette=\"pastel\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Title Type', fontsize=14)\n",
    "plt.ylabel('Average Rating', fontsize=14)\n",
    "plt.title('Distribution of Average Ratings by Title Type', fontsize=16)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Print the three title types with the highest average rating\n",
    "print(\"The three title types with the highest average rating are:\\n\")\n",
    "for i, title_type in enumerate(avg_ratings_by_title_type.index[:3]):\n",
    "    print(\"{}. {} (avg rating = {:.2f})\".format(i+1, title_type, avg_ratings_by_title_type[title_type]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top three genres by average rating are:\n",
      "\n",
      "1. Crime,Drama,Mystery (avg rating = 7.40)\n",
      "2. Action,Adventure,Animation (avg rating = 7.39)\n",
      "3. Documentary (avg rating = 7.25)\n"
     ]
    }
   ],
   "source": [
    "# Create a boxplot that displays the distribution of average ratings by genre (top 10)\n",
    "\n",
    "# Get the top 10 genres by number of titles\n",
    "top_genres = IMDb_df['genres'].value_counts().head(10).index.tolist()\n",
    "\n",
    "# Create a subset DataFrame with only the top 10 genres\n",
    "top_genres_df = IMDb_df[IMDb_df['genres'].isin(top_genres)]\n",
    "\n",
    "# Calculate the average rating for each genre\n",
    "avg_ratings_by_genre = top_genres_df.groupby('genres')['averageRating'].mean().sort_values(ascending=False)\n",
    "\n",
    "# Set the style and color palette\n",
    "sns.set(style = \"ticks\", palette = \"pastel\")\n",
    "\n",
    "# Create the boxplot\n",
    "plt.figure(figsize = (10,8))\n",
    "sns.boxplot(x = \"genres\", y = \"averageRating\", data = top_genres_df, order = IMDb_df['genres'].value_counts().index[:10], palette = \"pastel\")\n",
    "\n",
    "# Rotate x-axis labels\n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Genres', fontsize = 14)\n",
    "plt.ylabel('Average Rating', fontsize = 14)\n",
    "plt.title('Distribution of Average Ratings by Top 10 Genres', fontsize = 16)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "# Print the top three genres by average rating\n",
    "print(\"The top three genres by average rating are:\\n\")\n",
    "for i, genre in enumerate(avg_ratings_by_genre.index[:3]):\n",
    "    print(\"{}. {} (avg rating = {:.2f})\".format(i+1, genre, avg_ratings_by_genre[genre]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets find the highest rated movie each year in the 2000s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>startYear</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141368</th>\n",
       "      <td>2000</td>\n",
       "      <td>Long Sleepless Nights</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165145</th>\n",
       "      <td>2001</td>\n",
       "      <td>Faber</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397417</th>\n",
       "      <td>2002</td>\n",
       "      <td>Desh Devi</td>\n",
       "      <td>9.8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217778</th>\n",
       "      <td>2003</td>\n",
       "      <td>Don: Plain &amp; Tall</td>\n",
       "      <td>9.9</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239847</th>\n",
       "      <td>2004</td>\n",
       "      <td>Alex</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015374</th>\n",
       "      <td>2005</td>\n",
       "      <td>Efunsetan Aniwura</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076170</th>\n",
       "      <td>2006</td>\n",
       "      <td>Long Road Home</td>\n",
       "      <td>9.9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496642</th>\n",
       "      <td>2007</td>\n",
       "      <td>Petersburg's tram</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653406</th>\n",
       "      <td>2008</td>\n",
       "      <td>Hearing Everett: The Rancho Sordo Mudo Story</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803576</th>\n",
       "      <td>2009</td>\n",
       "      <td>Max Ernst Hanging</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875484</th>\n",
       "      <td>2010</td>\n",
       "      <td>Townbiz</td>\n",
       "      <td>9.4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820566</th>\n",
       "      <td>2011</td>\n",
       "      <td>Live 2 Tell: The Lucas Torres Story</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916993</th>\n",
       "      <td>2012</td>\n",
       "      <td>Heal America</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999258</th>\n",
       "      <td>2013</td>\n",
       "      <td>Lesser of Two Evils</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972294</th>\n",
       "      <td>2014</td>\n",
       "      <td>Musica Maestros</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925342</th>\n",
       "      <td>2015</td>\n",
       "      <td>Breaking the Dwarf Wall</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985497</th>\n",
       "      <td>2016</td>\n",
       "      <td>The World Awaits</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146625</th>\n",
       "      <td>2017</td>\n",
       "      <td>TransPARENT</td>\n",
       "      <td>9.8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573070</th>\n",
       "      <td>2018</td>\n",
       "      <td>Zayed's Antarctic Lights</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832106</th>\n",
       "      <td>2019</td>\n",
       "      <td>Pelkie: 100 Years of Finnishness in Michigan's...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476278</th>\n",
       "      <td>2020</td>\n",
       "      <td>Halkara</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584417</th>\n",
       "      <td>2021</td>\n",
       "      <td>The End of Blindness</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535547</th>\n",
       "      <td>2022</td>\n",
       "      <td>The Fragile King</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583722</th>\n",
       "      <td>2023</td>\n",
       "      <td>Rudy</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         startYear                                       primaryTitle  \\\n",
       "141368        2000                              Long Sleepless Nights   \n",
       "165145        2001                                              Faber   \n",
       "397417        2002                                          Desh Devi   \n",
       "217778        2003                                  Don: Plain & Tall   \n",
       "239847        2004                                               Alex   \n",
       "1015374       2005                                  Efunsetan Aniwura   \n",
       "1076170       2006                                     Long Road Home   \n",
       "496642        2007                                  Petersburg's tram   \n",
       "653406        2008       Hearing Everett: The Rancho Sordo Mudo Story   \n",
       "803576        2009                                  Max Ernst Hanging   \n",
       "875484        2010                                            Townbiz   \n",
       "820566        2011                Live 2 Tell: The Lucas Torres Story   \n",
       "916993        2012                                       Heal America   \n",
       "999258        2013                                Lesser of Two Evils   \n",
       "972294        2014                                    Musica Maestros   \n",
       "925342        2015                            Breaking the Dwarf Wall   \n",
       "985497        2016                                   The World Awaits   \n",
       "1146625       2017                                        TransPARENT   \n",
       "573070        2018                           Zayed's Antarctic Lights   \n",
       "832106        2019  Pelkie: 100 Years of Finnishness in Michigan's...   \n",
       "476278        2020                                            Halkara   \n",
       "584417        2021                               The End of Blindness   \n",
       "535547        2022                                   The Fragile King   \n",
       "583722        2023                                               Rudy   \n",
       "\n",
       "         averageRating  numVotes  \n",
       "141368             9.7        10  \n",
       "165145             9.8         9  \n",
       "397417             9.8        11  \n",
       "217778             9.9       246  \n",
       "239847             9.7         7  \n",
       "1015374           10.0         6  \n",
       "1076170            9.9        12  \n",
       "496642            10.0        27  \n",
       "653406             9.8         6  \n",
       "803576            10.0         7  \n",
       "875484             9.4        10  \n",
       "820566            10.0         7  \n",
       "916993            10.0         6  \n",
       "999258             9.8         6  \n",
       "972294            10.0         5  \n",
       "925342            10.0         8  \n",
       "985497            10.0         9  \n",
       "1146625            9.8         8  \n",
       "573070            10.0         8  \n",
       "832106            10.0        13  \n",
       "476278            10.0         5  \n",
       "584417            10.0         9  \n",
       "535547            10.0         6  \n",
       "583722            10.0         5  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the highest rated movie each year in the 2000s based on average ratings\n",
    "\n",
    "# Subset the data to only include movies from the 2000s\n",
    "movies_2000s = IMDb_df[(IMDb_df['startYear'] >= 2000) & (IMDb_df['titleType'] == 'movie')]\n",
    "\n",
    "# Group the subset by year using the 'startYear' column\n",
    "grouped = movies_2000s.groupby('startYear')\n",
    "\n",
    "# For each year group, find the movie with the highest 'averageRating'\n",
    "highest_rated = []\n",
    "for year, group in grouped:\n",
    "    highest_rated.append(group.loc[group['averageRating'].idxmax()])\n",
    "\n",
    "# Select the relevant columns and display the result\n",
    "result = pd.DataFrame(highest_rated, columns=['startYear', 'primaryTitle', 'averageRating', 'numVotes'])\n",
    "result.head(24)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as the the highest rated TV series each year in the 2000s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>startYear</th>\n",
       "      <th>primaryTitle</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>728554</th>\n",
       "      <td>2000</td>\n",
       "      <td>Nas Garras da Patrulha</td>\n",
       "      <td>9.4</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095638</th>\n",
       "      <td>2001</td>\n",
       "      <td>Amrutham</td>\n",
       "      <td>9.6</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225477</th>\n",
       "      <td>2002</td>\n",
       "      <td>Fashiontrance</td>\n",
       "      <td>9.9</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832981</th>\n",
       "      <td>2003</td>\n",
       "      <td>Mundo da Leitura</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570420</th>\n",
       "      <td>2004</td>\n",
       "      <td>Offside</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953329</th>\n",
       "      <td>2005</td>\n",
       "      <td>Khawatir</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397888</th>\n",
       "      <td>2006</td>\n",
       "      <td>My Two Cents</td>\n",
       "      <td>9.6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241142</th>\n",
       "      <td>2007</td>\n",
       "      <td>I stin Alpha i sti Vita oloi pan gia ti marmita</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899049</th>\n",
       "      <td>2008</td>\n",
       "      <td>Coke Studio</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718980</th>\n",
       "      <td>2009</td>\n",
       "      <td>Siubhlachan</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817392</th>\n",
       "      <td>2010</td>\n",
       "      <td>John Wants Answers</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780817</th>\n",
       "      <td>2011</td>\n",
       "      <td>Safe at Home</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867864</th>\n",
       "      <td>2012</td>\n",
       "      <td>Couchers</td>\n",
       "      <td>9.8</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493774</th>\n",
       "      <td>2013</td>\n",
       "      <td>Retro bus. Soviet buses</td>\n",
       "      <td>9.7</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840192</th>\n",
       "      <td>2014</td>\n",
       "      <td>Uncle Samsonite</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047705</th>\n",
       "      <td>2015</td>\n",
       "      <td>Yabanci: Hayati Kesfet</td>\n",
       "      <td>9.6</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157367</th>\n",
       "      <td>2016</td>\n",
       "      <td>Going Postal</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522145</th>\n",
       "      <td>2017</td>\n",
       "      <td>Blind Wave: Fullmetal Alchemist Brotherhood Re...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281592</th>\n",
       "      <td>2018</td>\n",
       "      <td>Just Jen</td>\n",
       "      <td>9.7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669314</th>\n",
       "      <td>2019</td>\n",
       "      <td>The Krusty Kooch Chronicles</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545446</th>\n",
       "      <td>2020</td>\n",
       "      <td>Ice Fishing Hardcore</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650348</th>\n",
       "      <td>2021</td>\n",
       "      <td>I tehni tou dromou</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751911</th>\n",
       "      <td>2022</td>\n",
       "      <td>Background Extras</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928937</th>\n",
       "      <td>2023</td>\n",
       "      <td>Agnisakshi... Ek Samjhauta</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         startYear                                       primaryTitle  \\\n",
       "728554        2000                             Nas Garras da Patrulha   \n",
       "1095638       2001                                           Amrutham   \n",
       "225477        2002                                      Fashiontrance   \n",
       "832981        2003                                   Mundo da Leitura   \n",
       "570420        2004                                            Offside   \n",
       "953329        2005                                           Khawatir   \n",
       "397888        2006                                       My Two Cents   \n",
       "1241142       2007    I stin Alpha i sti Vita oloi pan gia ti marmita   \n",
       "899049        2008                                        Coke Studio   \n",
       "718980        2009                                        Siubhlachan   \n",
       "817392        2010                                 John Wants Answers   \n",
       "780817        2011                                       Safe at Home   \n",
       "867864        2012                                           Couchers   \n",
       "493774        2013                            Retro bus. Soviet buses   \n",
       "840192        2014                                    Uncle Samsonite   \n",
       "1047705       2015                             Yabanci: Hayati Kesfet   \n",
       "1157367       2016                                       Going Postal   \n",
       "522145        2017  Blind Wave: Fullmetal Alchemist Brotherhood Re...   \n",
       "1281592       2018                                           Just Jen   \n",
       "669314        2019                        The Krusty Kooch Chronicles   \n",
       "545446        2020                               Ice Fishing Hardcore   \n",
       "650348        2021                                 I tehni tou dromou   \n",
       "751911        2022                                  Background Extras   \n",
       "928937        2023                         Agnisakshi... Ek Samjhauta   \n",
       "\n",
       "         averageRating  numVotes  \n",
       "728554             9.4        57  \n",
       "1095638            9.6       775  \n",
       "225477             9.9        14  \n",
       "832981            10.0        11  \n",
       "570420             9.7         7  \n",
       "953329             9.5      3218  \n",
       "397888             9.6        17  \n",
       "1241142            9.6      1129  \n",
       "899049             9.6      1250  \n",
       "718980             9.5         7  \n",
       "817392             9.5        15  \n",
       "780817             9.8        10  \n",
       "867864             9.8       204  \n",
       "493774             9.7        28  \n",
       "840192             9.7         7  \n",
       "1047705            9.6       105  \n",
       "1157367           10.0        10  \n",
       "522145            10.0         9  \n",
       "1281592            9.7        17  \n",
       "669314            10.0         8  \n",
       "545446            10.0         7  \n",
       "650348            10.0         8  \n",
       "751911            10.0        11  \n",
       "928937            10.0         8  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the highest rated tvSeries each year in the 2000s based on average ratings\n",
    "\n",
    "# Subset the data to only include movies from the 2000s\n",
    "movies_2000s = IMDb_df[(IMDb_df['startYear'] >= 2000) & (IMDb_df['titleType'] == 'tvSeries')]\n",
    "\n",
    "# Group the subset by year using the 'startYear' column\n",
    "grouped = movies_2000s.groupby('startYear')\n",
    "\n",
    "# For each year group, find the movie with the highest 'averageRating'\n",
    "highest_rated = []\n",
    "for year, group in grouped:\n",
    "    highest_rated.append(group.loc[group['averageRating'].idxmax()])\n",
    "\n",
    "# Select the relevant columns and display the result\n",
    "result = pd.DataFrame(highest_rated, columns=['startYear', 'primaryTitle', 'averageRating', 'numVotes'])\n",
    "result.head(24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression can help us understand the relationship between two variables, such as average rating and runtime, by identifying if there is a significant linear relationship between them. A linear regression model will estimate the slope and intercept of the linear relationship between the two variables; we can use this information to predict the value of one variable based on the other.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported required libraries for data modeling.\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "print(\"Imported required libraries for data modeling.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 923886 entries, 0 to 1304128\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   tconst          923886 non-null  object \n",
      " 1   titleType       923886 non-null  object \n",
      " 2   primaryTitle    923886 non-null  object \n",
      " 3   originalTitle   923886 non-null  object \n",
      " 4   isAdult         923886 non-null  int64  \n",
      " 5   startYear       923886 non-null  int64  \n",
      " 6   endYear         29263 non-null   float64\n",
      " 7   runtimeMinutes  923886 non-null  float64\n",
      " 8   genres          923886 non-null  object \n",
      " 9   averageRating   923886 non-null  float64\n",
      " 10  numVotes        923886 non-null  int64  \n",
      " 11  decade          923886 non-null  int64  \n",
      " 12  isAdult_cat     923886 non-null  object \n",
      "dtypes: float64(3), int64(4), object(6)\n",
      "memory usage: 98.7+ MB\n"
     ]
    }
   ],
   "source": [
    "IMDb_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adjust variable types for regression analysis\n",
    "\n",
    "# Convert genres to categorical\n",
    "IMDb_df['genres'] = IMDb_df['genres'].astype('category')\n",
    "\n",
    "# Convert averageRating to integer\n",
    "IMDb_df['averageRating'] = IMDb_df['averageRating'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 923886 entries, 0 to 1304128\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count   Dtype   \n",
      "---  ------          --------------   -----   \n",
      " 0   tconst          923886 non-null  object  \n",
      " 1   titleType       923886 non-null  object  \n",
      " 2   primaryTitle    923886 non-null  object  \n",
      " 3   originalTitle   923886 non-null  object  \n",
      " 4   isAdult         923886 non-null  int64   \n",
      " 5   startYear       923886 non-null  int64   \n",
      " 6   endYear         29263 non-null   float64 \n",
      " 7   runtimeMinutes  923886 non-null  float64 \n",
      " 8   genres          923886 non-null  category\n",
      " 9   averageRating   923886 non-null  int64   \n",
      " 10  numVotes        923886 non-null  int64   \n",
      " 11  decade          923886 non-null  int64   \n",
      " 12  isAdult_cat     923886 non-null  object  \n",
      "dtypes: category(1), float64(2), int64(5), object(5)\n",
      "memory usage: 93.5+ MB\n"
     ]
    }
   ],
   "source": [
    "IMDb_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Mean Squared Error:  1.86\n",
      "Linear Regression R^2 Score:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Perform linear regression analysis to explore the relationship \n",
    "# between the average rating of titles and their runtime\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X = IMDb_df['runtimeMinutes'].values.reshape(-1, 1)\n",
    "y = IMDb_df['averageRating'].values.reshape(-1, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 0)\n",
    "\n",
    "# Run Linear Regression\n",
    "\n",
    "# Train the model\n",
    "lin_reg = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set results\n",
    "y_pred = lin_reg.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Linear Regression Mean Squared Error: \", round(mse, 2))\n",
    "print(\"Linear Regression R^2 Score: \", round(r2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MSE represents the average squared difference between the predicted ratings and the actual ratings. A lower value indicates better accuracy. The R-squared score represents the proportion of variance in the data that is explained by the model. A score of 1 indicates a perfect fit, while a score of 0 indicates that the model does not explain any of the variability in the data.\n",
    "\n",
    "The linear regression model used above shows a mean squared error of 1.86, which means that the average squared difference between the predicted and actual average ratings is 1.86. The R^2 score of 0.0 indicates that the model does not fit the data well and does not explain much of the variance in the data. Therefore, the relationship between average rating and runtime is weak. We cannot be accurately predicted using a linear regression model with only runtime as the predictor variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Mean Squared Error:  1.71\n",
      "Linear Regression R^2 Score:  0.08\n"
     ]
    }
   ],
   "source": [
    "# Perform linear regression analysis to explore the relationship \n",
    "# between the average rating of titles and their runtime and genre\n",
    "\n",
    "# Create the OneHotEncoder object\n",
    "encoder = OneHotEncoder()\n",
    "\n",
    "# Fit the encoder to the genres column and transform it\n",
    "X_genres = encoder.fit_transform(IMDb_df['genres'].str.split(',', expand = True).fillna(''))\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X = np.hstack((IMDb_df['runtimeMinutes'].values.reshape(-1, 1), X_genres.toarray()))\n",
    "y = IMDb_df['averageRating'].values.reshape(-1, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)\n",
    "\n",
    "# Run Linear Regression\n",
    "\n",
    "# Train the model\n",
    "lin_reg = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set results\n",
    "y_pred = lin_reg.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Linear Regression Mean Squared Error: \", round(mse, 2))\n",
    "print(\"Linear Regression R^2 Score: \", round(r2, 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Linear Regression Mean Squared Error of 1.71 indicates that the average difference between the predicted values and actual values is 1.71 units, which is relatively low. \n",
    "\n",
    "The R^2 Score of 0.08 indicates that the model explains only 8% of the variance in the target variable. This is a relatively low value, which suggests that the model is not very accurate in predicting the average rating based on the runtime and genre. It is likely that there are other important factors for predicting average rating of titles that are not included in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMmUK3t7ehISEULhwYQICAkyn9VepUoVBgwaxYMECfv75Z1MYbNmy5QtrqVChApUrV2bChAns3LmTmTNnvnI/IpZiMOqqZiKSSH744QeKFy9OwYIFTW1bt26lS5curF69OtZRExGR+FCwEZFE8/nnn3Py5En69OlDzpw5+e+//5g6dSr58uVjwYIFli5PRGyAgo2IJJpbt24xYcIEfv31V27evEmWLFmoW7cuvXr1wsnJydLliYgNsKpgExQUxM6dO1/4n9uQIUP4/fff+eWXXxK5MhEREUkKrOZ07/nz5zN16tQX3r9p0yaWLVuWiBWJiIhIUmPxs6KuXLmCt7c3f/zxBwUKFIi1z9WrVxk6dCjly5fnwoULiVyhiIiIJBUWH7E5fPgwGTJkYM2aNZQuXfq5+41GI4MHD6Zx48axrjgqIiIiEs3iIzbu7u6mC+XFZv78+Vy7do3p06czY8aMOG3zZetLXLhwAQcHhxden0ZERESsz7Vr13BwcGDfvn0v7WfxYPMyx44dIzAwkO+++w4HBwezbPPpqyOLiIhI0hAeHh6nC8tabbAJDQ2lf//+dO3aNd6Ldm3evPmF90WP5rysj4iIiFiXuK72bfE5Ni9y8OBB/v33XwIDA3Fzc8PNzY0ZM2Zw8eJF3NzcWLNmjaVLFBEREStjtSM2pUqV4ueff47RFn1tkwULFuDs7GyhykRERMRaWW2wSZ06Nfny5YvRliFDBlKkSPFcu4iIiAhY8aEoERERkfiyqksqJAZNHhYRWxEREUFYWJilyxB5YylTpsTe3v6lfeL6/m21h6JERCR2RqORy5cvc/v2bUuXImI2GTNmJEeOHBgMhjfajoKNiEgSEx1qsmXLhqOj4xu/EYhYktFo5OHDh1y9ehWAnDlzvtH2FGxERJKQiIgIU6jR2aFiK9KkSQNEXRsyW7Zsrzws9TKaPCwikoREz6lxdHS0cCUi5hX9M/2m88YUbEREkiAdfhJbY66faQUbERERsRkKNiIiImIzFGxERMQi3N3dcXV1NX0UK1aMsmXL0rp1a/bt22fWfe3evRtXV1fOnz8PQOvWrRk8eHCcHvvw4UO+++67N9r/+fPncXV1Zffu3W+0HXk1nRUlIpKMRUTA9u1w6RLkzAnVqsEbnJASbx06dKBDhw5A1Gm/t2/fZuLEiXTq1ImffvqJHDlyJMh+AwIC4nzmzdy5c1mxYgWtWrVKkFrEvDRiIyKSTK1YAfnzQ61a8OmnUZ/z549qTyyOjo5kzZqVrFmzki1bNooUKcLw4cMJCQl57kLI5pQxY0bSpUsXp77JbIH+JE/BRkQkGVqxApo1gydHZkwuXIhqT8xw86wUKaIOJjg4OODu7o6/vz/16tWjQoUK7Nq1C6PRyKxZs6hduzalS5emcePGrFmzJsY29u3bR/PmzSlVqhRNmjThn3/+iXH/s4eiDh06RPv27XFzc6Ny5cr4+Pjw8OFDAgICCAwM5MKFCzEOZQUHB/Phhx9SqlQpPvzwQ7755hsiIyNN2zt+/Dht2rShTJky1K1bl127diXUyyXP0KEoEZFkJiICeveG2AYijEYwGKBPH2jcOHEPSwFcuXIFf39/HB0dqV69OjNnzmTRokXMmDGDdOnS4erqyqRJk1i7di0+Pj64uLiwd+9ehg0bxr1792jVqhXnzp2jQ4cONGnShDFjxnDixAl8fHxeuM/z58/TunVr3N3dWbJkCffv38fT0xMfHx+GDx/Ow4cP+fHHH1m+fDmZM2dmyZIlTJgwAR8fH0qXLs2RI0cYOXIkV65cYeDAgdy7d4927dpRpkwZli1bxtWrVxk6dGgivorJm4KNiEgys3378yM1TzMa4dy5qH41ayZsLTNmzGDu3LkAhIeH8/jxY1xcXJg8eTJvvfUWADVq1KBy5cpA1ETe+fPn89VXX1GrVi0A8ubNy4ULF5gzZw6tWrVi6dKlZMmSBV9fX+zt7XFxceHSpUuMHj061hqWLl1KhgwZGDNmDClTpgRg1KhR7NmzBycnJxwdHbG3tydr1qwABAUF0aVLFxo0aABAnjx5uH//PsOHD6d3796sW7eOkJAQxo4dS7p06ShcuDBeXl5079494V5IMVGwERFJZi5dMm+/N9GyZUtat24NgJ2dXaxzX/Lly2f6+sSJE4SGhjJo0CA8PT1N7dGh6NGjRxw/fpzixYvHmBz8zjvvvLCGf/75hxIlSphCDUC5cuUoV67cc31v3rzJ5cuXmTJlCoGBgab2yMhIQkNDOX/+PMePHyd//vwxnoebm1tcXg4xAwUbEZFkJq7XGHzDaxHGSYYMGWIEl9ikTp3a9HX0RN7JkydTsGDB5/o6ODjE6Bctet5ObFKkSBHnVW+j59F4enqaRpGeFn0Bx/jsX8xLk4dFRJKZatUgd+6ouTSxMRggT56oftamYMGCpEiRgosXL5IvXz7Tx7Zt25gzZw52dnYUK1aMv//+m8ePH5se9/fff79wm4UKFeLIkSNERESY2jZu3Ej16tUJCQmJEXqcnZ1xdnbm7NmzMfZ/+PBhJk+eDECxYsU4ffo0N2/ejNP+xbwUbEREkhl7e5gyJerrZ8NN9O3JkxN/4nBcpEuXjpYtWzJ58mRWrVrFuXPnWLlyJePGjSNLliwAfPLJJ4SEhODl5cXJkyfZsmVLjMNGz/r000+5desWvr6+nDx5kn379jF+/HiqVKlCmjRpcHR05M6dO5w+fZrw8HA6derEggULWLBgAWfPnmXTpk0MHz4cBwcHHBwcqF+/Ps7Oznz55ZccO3aMPXv24O/vn1gvUbKnYCMikgx5eMDy5ZArV8z23Lmj2j08LFNXXHh6etKuXTumTp3Khx9+yLRp0+jRowc9e/YEIHv27HzzzTdcvnyZpk2bMmbMGLp27frC7WXPnp25c+dy+vRpmjZtSp8+fahRowa+vr4A1KlTh6xZs9KoUSOOHDlChw4d8PT05LvvvqNevXqMHDkSDw8PRo4cCUStzfPtt9+SMmVKPvnkEwYOHEjnzp0T/oURAAzGZLbyUO3atQHYvHmzhSsREYm/R48ecfr0aQoUKBBj7snrsvTKwyLRXvWzHdf3b81mEhFJxuztE/6UbpHEpENRIiIiYjMUbERERMRmKNiIiIiIzVCwEREREZuhYCMiIiI2Q8FGREREbIaCjYiIiNgMBRsRERGxGQo2IiIiYjMUbERExOLu379P6dKlqVy5coyrcsfFv//+y9atW81aj7u7OwEBAWbdpiQOBRsRkeQsIgK2boVFi6I+R0RYpIx169bh7OzM/fv32bhxY7we26VLF/7+++8EqkySGgUbEZHkasUKyJ8fatWCTz+N+pw/f1R7IgsODqZq1apUqlSJxYsXJ/r+xXYo2IiIJEcrVkCzZnD+fMz2Cxei2hMx3Jw8eZKDBw9SpUoVPvjgA/bs2cPJkydj9FmwYAF169alVKlS1KtXj9WrVwNRh4wuXLhAYGAgrVu3BsDV1ZUVz9T/7KGl4OBgmjRpQqlSpShTpgytW7fm8OHDCfxMJTEo2IiIJDcREdC7NxiNz98X3danT6Idllq+fDmOjo5Ur16d9957DwcHBxYtWmS6f86cOYwfP56OHTvyww8/0KpVKzw9PdmxYwfLly8nR44cdOjQIc5zYjZu3Iivry/t2rVj/fr1fPPNNzx69Ahvb++EeoqSiBRsRESSm+3bnx+peZrRCOfORfVLYOHh4axdu5ZatWqRJk0a0qVLR40aNVi9ejUhISEAzJ8/nzZt2vDxxx+TN29eWrVqxZdffklERASZM2fG3t4eR0dHMmbMGKd9ZsyYkVGjRtGkSRNy5cpF6dKlad68Of/8808CPlNJLCksXYCIiCSyS5fM2+8NbNu2jWvXrlGvXj1TW7169di4cSPr1q3D3d2dq1evUrp06RiP69ix42vvs1y5cmTOnJmgoCDOnDnD6dOnOXr0KJGRka+9TbEeCjYiIslNzpzm7fcGoufC9OrV67n7Fi9ezAcffACAwWCI13aNzxxmCwsLM329bt06Bg4cSIMGDShVqhTNmjXj+PHjjBgxIr7lixVSsBERSW6qVYPcuaMmCsc2z8ZgiLq/WrUELePmzZts27YNDw8P2rdvH+O+b775huXLl3PmzBmyZcvG33//Te3atU339+rVi2zZsjFkyJDntpsyZUru3btnun3//n1u3rxpuj19+nSaNWvG8OHDTW2bN28GogJRfEOUWBcFGxGR5MbeHqZMiTr7yWCIGW6i39QnT47ql4BWr15NeHg4nTp1wsXFJcZ9X3zxBStXrmTRokV8/vnnTJw4kfz58/POO++wfft2Nm/ezJw5cwBwcnLiv//+4/r162TJkgU3NzeWLFlCuXLlSJkyJZMnTyZFiv+/3eXMmZM///yTw4cPky5dOn755RcWLlwIwOPHj0mVKlWCPm9JWJo8LCKSHHl4wPLlkCtXzPbcuaPaPTwSvIQVK1ZQuXLl50INQJ48eXj//fdZt24dTZo0oXv37kydOpX69euzePFiJk2aRMWKFQFo3bo1W7dupUOHDgAMGzaMrFmz0rJlSzp37kz58uVxc3MzbXvo0KFkyZKFzz77jObNm7Nlyxa++uorAA4ePJjgz1sSlsH47IFIGxc9lBk97CgikpQ8evSI06dPU6BAAVKnTv3mG4yIiDr76dKlqDk11aol+EiNSGxe9bMd1/dvqzoUFRQUxM6dO1mwYIGp7ZdffmHatGmcOnWKTJkyUbduXXr37m2eX2gRkeTO3h5q1rR0FSJmYzWHoubPn8/UqVNjtO3bt48ePXpQt25dVq1axbBhw1i/fn2MCV8iIiIi0SwebK5cuUKnTp2YMmUKBQoUiHHf4sWLqVixIp9//jn58uWjevXq9O3blzVr1sT76q8iIiJi+yx+KOrw4cNkyJCBNWvWMG3aNC5cuGC6r0OHDtjZPZ+9wsPDuX//PpkzZ451m0+fEvisS5cukTMR1mYQERGRxGfxYOPu7o67u3us9xUvXjzG7cePHzNv3jxKlCjxwlAjIiIiyZfFg01chYeHM3DgQE6cOMF333330r4vmzH9stEcERERSdqSRLC5f/8+ffr0Yffu3UydOvW5a4aIiIiIQBIINlevXqVz586cP3+eWbNmmRZkEhEREXmWVQebO3fu0LZtW+7fv8/333+Pq6urpUsSERERK2bx071fZvTo0Zw7d45x48aROXNmrl27ZvqIiIiwdHkiIvIG3N3dcXV1NX0UK1aMsmXL0rp1a/bt2/fG21+1ahXVqlWjZMmSfPvtt2+8vYcPH750jufu3btjPB9XV1dKlChBzZo1GTJkCHfv3n3jGp41ePBgWrduDcD58+dxdXVl9+7dcXrsv//+y9atW023XV1dTVdbT8qsdsQmMjKSH3/8kbCwMNq2bfvc/Zs3byZ37twWqExERMylQ4cOpms8GY1Gbt++zcSJE+nUqRM//fQTOXLkeO1t+/n54e7uTq9evUifPv0b1zp37lxWrFhBq1atXtpv2bJlpmVFIiIi+Oeffxg8eDDXr19n+vTpb1zHi+TMmZPffvuNDBkyxKl/ly5daNq0KTWfrDz922+/kS5dugSrL7FYVbAZM2aM6Ws7Ozv++usvC1YjIiIJzdHRkaxZs5puZ8uWjeHDh1O9enV+/vln2rRp89rbvnv3LuXLlyfXsxf6fE1xvbRi5syZYzynHDly0LZtWyZPnsy9e/cSLDzY29vH2G98vcljrYlVH4oSEZG4MRqNPHjwwGIf5ryecooUUf9zOzg4AFFrmI0bN45q1arh5ubGxx9/zG+//Wbqv2LFCtzd3fHz86Ns2bJ88cUXpjmZXl5epq9ftR2AQ4cO0b59e9zc3KhcuTI+Pj48fPiQgIAAAgMDuXDhAq6urpw/fz5ez8ne3h6DwUCKFClMh6xmzZpFhQoVaNq0KREREVy5coW+fftStmxZKlSowBdffMF///1n2obRaCQoKIjq1atTpkwZvL29CQ0NNd0f26GoBQsWULduXUqVKkW9evVYvXo1EHUY8MKFCwQGBpoOZT17KGrVqlU0atSIUqVK4e7uzvTp04mMjIyxr/Xr19O8eXNKlixJ7dq1Wb58uenxN27coFevXlSoUIFSpUrRsmVL9uzZE6/X7XVY1YiNiIjEn9FopGrVqvz+++8Wq6FKlSps374dg8HwRtu5cuUK/v7+ODo6Ur16dQA8PT35999/GTduHDly5GDLli188cUXBAYGmg6jXLhwgStXrrBy5UoePXrEyJEjqVq1Kl5eXtSrVy9O2zl//jytW7fG3d2dJUuWcP/+fTw9PfHx8WH48OE8fPiQH3/8keXLl8d5kdjw8HAOHDjAt99+S40aNUiTJo3pvq1bt7JkyRJCQkIIDQ2ldevWFC1alIULF2JnZ8e8efP4+OOPWbt2LdmzZ2fmzJnMnj2bESNGULx4cZYsWcLy5cspX758rPueM2cOU6dOxdvbm4oVK7J9+3Y8PT3JkiULy5cvp2nTptSrV48uXbo899j58+czYcIEBg8eTJUqVfj7778ZMWIEt2/fZvDgwaZ+Y8aMwcfHh/z58zNv3jyGDh1KhQoVyJMnD8OGDSM0NJSFCxfi4ODA9OnT6datG7/++iuOjo5xev1eh4KNiIgNeNNAYSkzZsxg7ty5QFQIePz4MS4uLkyePJm33nqLM2fO8MMPP7B8+XJKliwJQPv27Tl27Bhz5swxBRuAbt26kSdPnhjbT5cuHVmzZo3TdpYuXUqGDBkYM2YMKVOmBGDUqFHs2bMHJycnHB0d43S4p0GDBqbvx6NHj7C3t6dGjRqMGDEiRr8OHTqQP39+IGpezq1bt5gwYYJp335+fuzevZulS5fSo0cPFixYQJs2bWjQoAEQFdReNlF4/vz5tGnTho8//hiAVq1a8ejRIyIiIsicOTP29vY4OjqSMWPGGI8zGo3MmjWLzz77zDSfKH/+/Ny+fZuxY8fSvXt3U9/27dubFr4dNGgQy5Yt4+DBg+TJk4ezZ89SpEgR8ubNS6pUqfD29qZhw4bY29u/9PV7Uwo2IiJJnMFgYPv27Tx8+NBiNTg6Or5WuGrZsqXpUIidnR0ZM2aMMQflyJEjAM/NtQkLC3tuQnB0SIhNXLbzzz//UKJECVOwAChXrhzlypWL13OaOXMm2bNnB6IOpzk7O5sOq72o3iNHjnD//v3nRl9CQ0M5efIkt27d4tq1a6ZQFq1MmTKcPHnyuW3fvHmTq1evPregbceOHV9Z/82bN7l+/TrvvvtujPZy5coRFhbGqVOncHZ2BsDFxcV0f/T3LSwsDIAePXowYMAANm7cSNmyZalatSr16tUjVapUr6zhTSjYiIjYAIPBgJOTk6XLiLcMGTKQL1++F94fPXfnu+++e+75PXuR5NSpU7/RdlKkSGGWka+33norTmftPv0GHxkZSYECBfj666+f6/f0YZtn5zJFz0d6VnSQep3n86L5UtHLrDy9z9gCW/Tj33//fbZv38727dv5/fffmT17NlOmTGHp0qUULlw43nXFlSYPi4iI1Yp+A7x69Sr58uUzfaxYsYLg4GCzbqdQoUIcOXIkxjppGzdupHr16oSEhCTo4b4iRYpw8eJF0qVLZ6otV65cTJgwgb1795I5c2Zy5szJH3/8EeNxhw4dinV7adOmJVu2bPz9998x2nv16sWoUaNeWouzszPOzs7P7Wvfvn2kTJmSvHnzvvL5PH782LQWXb169Rg1ahQbN27Ezs4uxto5CUHBRkRErFbhwoWpVasWvr6+bN68mXPnzjFnzhxmzJjx3HyaN93Op59+yq1bt/D19eXkyZPs27eP8ePHU6VKFdKkSYOjoyN37tzh9OnTpsMt5tKoUSMyZMhAjx49OHDgACdPnsTT05Nt27aZQlnnzp357rvvWLZsGadPn2by5MkvXRbl888/55tvvmHVqlWcPXuW7777js2bN/Pee+8B4OTkxH///cf169djPM5gMNChQwcWLlzId999x5kzZ1i7di2BgYG0aNEiTqerOzg4cPDgQYYOHcqBAwc4f/48K1as4MGDB7i5ub3BK/VqOhQlIiJWbdKkSUyaNAlfX1/u3LlDnjx5GDlyJB999JFZt5M9e3bmzp3L+PHjadq0KenTp6devXr069cPgDp16rB06VIaNWrEwoULzXpB5nTp0rFw4UK++uorOnXqREREBMWKFWPOnDmmYNOqVSsiIyP5+uuvuX79OtWqVaNZs2acPn061m1+9tlnhIaGMnXqVK5du0b+/PmZNGmS6ZqLrVu3ZuzYsfz777+sWbMmxmM7deqEg4MD33zzDaNHjyZHjhx07tw5TnN0ok2ZMoXRo0fTtWtX7t27R8GCBZkwYQJly5Z9zVcpbgxGcy4+kAREz97evHmzhSsREYm/R48ecfr0aQoUKPDSOSUiSc2rfrbj+v6tQ1EiIiJiMxRsRERExGYo2IiIiIjNULARERERm6FgIyIiIjZDwUZEJAlKZie0SjJgrp9pBRsRkSQk+jpGlrwulEhCiP6ZfvpaXa9DC/SJiCQh9vb2ZMyYkatXrwKvf/FJEWthNBp5+PAhV69eJWPGjG989W8FGxGRJCZHjhwApnAjYgsyZsxo+tl+Ewo2IiJJjMFgIGfOnGTLls3s1ywSsYSUKVO+8UhNNAUbEZEkyt7e3mxvBiK2QpOHRURExGYo2IiIiIjNULARERERm6FgIyIiIjZDwUZERERshoKNiIiI2AwFGxEREbEZCjYiIiJiMxRsRERExGYo2IiIiIjNULARERERm6FgIyIiIjZDwUZERERshoKNiIiI2AwFGxEREbEZCjYiIiJiMxRsRERExGYo2IiIiIjNULARERERm6FgIyIiIjYjhaULEBERERtgMDzfZjQmehlWNWITFBRE69atY7QdPXqUzz77jDJlylCzZk3mzJljoepEREQkVrGFmpe1JyCrCTbz589n6tSpMdpu3bpF+/btyZ8/P8HBwfTs2ZMpU6YQHBxsoSpFREQkhleFl0QONxY/FHXlyhW8vb35448/KFCgQIz7li5dioODA8OGDSNFihS4uLhw5swZZs2axUcffWShikVERASIe2gxGBLtsJTFg83hw4fJkCEDa9asYdq0aVy4cMF03759+yhXrhwpUvy/zIoVKzJjxgxu3LiBs7NzrNusXbv2C/d36dIlcubMab4nICIiIlwA/IBjwGIgm4XqsHiwcXd3x93dPdb7Ll++TJEiRWK0ZcsW9VJdvHjxhcFGREREEscNYAwQCDwiao7LVZJxsHmZR48e4eDgEKMtVapUAISGhr7wcZs3b37hfS8bzREREZG4uXfvHpOA8cC9J21ViQo5b1usKiuaPByb1KlT8/jx4xht0YHG0dHREiWJiIgka48ePWLy5Mm4uLjgS1SoKQ2sA34Fqli0OisPNjly5ODq1asx2qJvZ8+e3RIliYiIJEvh4eHMnTuXIkWK0LdvX65du0ahQoVYBPwJ1ANeOJU4EdezsepgU65cOf744w8iIiJMbTt37qRAgQKaXyMiIpIIjEYjy5cvp2TJknTs2JFz586RK1cuZsyYwZEjR2hpNL48TCTyIn1WHWw++ugj7t+/j7e3NydOnGDFihV88803dOnSxdKliYiI2DSj0cjPP/9MuXLlaN68OceOHcPZ2Znx48fz77//8vnnn5MyZcrozi/aSOIV/IRVTx52dnZm9uzZ+Pn50bRpU7JmzcrAgQNp2rSppUsTERGxWTt37sTT05Nt27YBkDZtWvr168eXX35J+vTpY3+QBUJMbKwq2IwZM+a5tlKlSrFkyRILVCMiIpK8/P3333h7e7N27VoAHBwc6NatG15eXmTNmtXC1cWNVQUbERERSXynTp3Cx8eH77//HqPRiJ2dHe3bt8fHx4e8efNaurx4UbARERFJpi5dusTIkSOZNWsW4eHhADRv3pyRI0fi6upq4epej4KNiIhIMnPz5k2++uorpk6dSkhICAB169bFz8+Pd99918LVvRkFGxERkWTi/v37TJkyhXHjxnHnzh0AKlWqxOjRo6lRo4aFqzMPBRsREREbFxoaysyZMxk1apRpoduSJUvi5+dHgwYNMMT1Kt1JgIKNiIiIjYqIiGDhwoX4+vpy5swZAAoWLMiIESP45JNPsLOz6uXsXouCjYiIiI0xGo2sWrWKIUOGcOTIEQBy5syJj48PHTt2/P/CejZIwUZERMSGbN68GS8vL/bs2QNApkyZGDx4MD169EgWF5B+rTGoVatWmVYjPHr0KPXr1+edd97By8vruatxi4iISMLbs2cP7733Hu+99x579uzB0dERb29vTp06xcCBA5NFqIHXCDbz5s3D09PTNLQ1fPhw7ty5Q/Pmzdm0aRNTp041e5EiIiISu8OHD9O0aVMqVKjA5s2bSZkyJT179uTUqVOMGjWKjBkzWrrERBXvYLN06VI6depE165duXjxIgcOHKBbt254enry5Zdfsm7duoSoU0RERJ7y33//0bZtW0qWLMmqVauws7Ojbdu2HD9+nKlTp5I9e3ZLl2gR8Z5jc/78eapXrw7Atm3bMBgMuLu7A1EzrW/cuGHeCkVERMTkypUrjBo1ihkzZhAWFgaAh4cHI0eOpHjx4hauzvLiHWwyZ87M9evXAdiyZQsFCxYkR44cAPzzzz9kyZLFvBWKiIgIt2/fZty4cUyePJmHDx8C8N577+Hv70+5cuUsXJ31iHewcXd3Z8KECezcuZNff/2Vvn37AlFzb6ZNm4aHh4fZixQREUmuHj58SEBAAGPHjuXWrVsAlC9fntGjR5uOmMj/xTvYeHp6EhERwd69e2nZsiUdOnQAYPHixdSoUYM+ffqYu0YRERHbFduqv0Yjjx8/Zvbs2YwcOZLLly8DULx4cfz8/GjcuLFNrRZsTvEONg4ODowYMeK59jVr1pAqVSqzFCUiIpIsxBJOIoBFBgO+BQty6tQpAPLnz8/w4cNp1aoV9vb2iVxk0hLvYLN3794X3mcwGHByciJPnjykTZv2jQoTERGxac+EGiOwFvAGDgGcOkX27NkZOnQonTt3xsHBIfFrTILiHWxat25tGv4yGo0Azw2H2dnZ0aRJE0aMGKFkKSIi8qxn3je3Ap7Arie3MwIDgV5XruDUvXtiVpbkxTvYfP311/Tt25fGjRvToEEDsmTJwo0bN9iwYQOLFy9mwIAB2NvbM3nyZHLnzk3Xrl0Tom4REZEk7w/AC/j5ye00QG+iQk0mSxWVxMU72MyaNYtPPvmEQYMGmdoKFChA2bJlcXR0ZOPGjSxYsIDIyEgWLlyoYCMiIvKMY8AQIPjJ7RTA50/aclqqKBsR75WHDx8+TLVq1WK9r0KFChw8eBCAYsWKcenSpTerTkRExIacPXuWDh06UIKoUGMAPgP+AaahUGMO8Q42WbNmZffu3bHet3v3bpydnQG4desW6dOnf7PqREREbMDVq1fp06cPhQsXZt68eUQCjYGDwAKgoGXLsynxPhT1ySefMGHCBEJCQqhbty7Ozs7cuHGDjRs3snDhQnr06MHly5f5+uuvqVChQkLULCIikiTcuXOHCRMmMHHiRB48eABArVq18Pf3p2KlSq/ewJOTdCTu4h1sOnbsSEhICLNnz2bBggVA1NlR6dKlo2fPnnTp0oVVq1bx+PFj+vXrZ/aCRURErF1ISAjTpk1j9OjR3Lx5E4CyZcvi7+/Pe++9F3U2sdEY++J80RRqXovBaHy9Vy4kJIT9+/dz69YtsmfPTrFixXBycgIgIiLCak/zrl27NgCbN2+2cCUiImJrwsLCmDt3LiNGjODixYsAFC1alFGjRuHh4RH7asEvWHlYYorr+3e8R2yipUmThsqVK8d6n7WGGhERkYQQGRnJkiVL8PHx4cSJEwDkzZuXYcOG0bp1a1KkeMnbrUKMWcU72Ny8eRM/Pz+2bt1KSEgIzw74GAwGjhw5YrYCRURErJXRaOTHH3/E29vbdFZw1qxZ8fb25osvvtClhiwg3sFm2LBhbNu2jfr165MjRw7s7OJ9YpWIiEiSt337djw9PdmxYwcA6dOnZ8CAAfTp00eXFbKgeAeb7du34+XlRYsWLRKiHhEREau2f/9+vLy8+OmnnwBInTo1PXv2ZNCgQaYlT8RyXuvq3nny5EmIWkRERKzW8ePH8fHxYcmSJQCkSJGCjh07MnToUHLlymXh6iRavI8jvf/++/zwww8JUYuIiIjVOX/+PJ07d6Z48eKmUPPJJ59w9OhRpk+frlBjZeI9YlO8eHEmT57MuXPnKF26NKlTp45xv8FgoLuuRCoiIknc9evXGT16NNOmTSM0NBSA+vXr4+fnR+nSpS1cnbxIvIPNiBEjANi7dy979+597n4FGxERScru3bvHxIkTmTBhAvfu3QOgevXq+Pv7U6VKFQtXJ68S72Bz7NixhKhDRETEoh49ekRQUBCjR4/m+vXrALi5ueHv70/dunVjX1xPrM5rL9AnIiJiC8LDw5k/fz7Dhw/n/PnzABQpUoSRI0fSrFkzLWuSxMQp2LRp0wZfX19cXFxo06bNS/saDAa++eYbsxQnIiKSUCIjI1m+fDlDhw7l+PHjAOTOnRtfX1/atWv38tWCxWrF6bv29OrCr7q01GteekpERCRRGI1GNmzYgJeXF/v37wfA2dkZLy8vunXr9txJMZK0xCnYRF/F+9mvRUREkpLff/8dT09Pfv31VwDSpUvHl19+Sd++fUmfPr2FqxNziPeBwzZt2nDy5MlY7zt27BgNGzZ846JERETM6eDBgzRs2JAqVarw66+/kipVKvr168epU6fw9fVVqLEhcRqx2bdvn+kQ0549e9i7dy83b958rt+WLVs4d+6ceSsUERF5TSdOnMDHx4fFixdjNBqxt7enffv2+Pj4aBV9GxWnYLN8+XJWrVqFwWDAYDAwfPjw5/pEB58GDRqYt0IgLCyMwMBAVq9ezZ07dyhWrBj9+/fnnXfeMfu+REQkiYjt9Osn70UXL15kxIgRzJkzh/DwcABatGjBiBEjKFKkSGJWKYksTsHG29sbDw8PjEYjbdu2xcfHh0KFCsXoY2dnR/r06SlcuLDZi/z6668JDg5mzJgx5MmTh1mzZtG5c2d+/PFHsmfPbvb9iYiIlXvBmjI3DAbGDhhAQEAAjx49AuCDDz7A398fNze3xKxQLCROwSZdunSUL18egG+//ZYSJUrg5OSUoIU9bfPmzTRo0ICqVasCMHjwYJYtW8aBAweoW7duotUhIiJWIJZQcx+YDIwD7o4bB0DlypUZPXo01atXT8zqxMLifZJ++fLluXz5Mtu2bePx48em9sjISEJCQti3bx+TJk0ya5EZM2Zky5YtfPbZZ+TMmZMlS5bg4OBAsWLFzLofERGxcs+EmlBgBuAHXH3SVgrwB+r99ptWC06G4h1s1q9fz4ABAwgPDzf9wBiNRtPXBQsWNG+FRB0K69u3L7Vr18be3h47OzumTJlC3rx5Y+1fu3btF27r0qVL5MyZ0+w1iohI4gkHFgDDgLNP2lyAkUALnpzyq1CTLMX7dO8ZM2ZQvHhxVqxYgYeHB40aNWLdunUMGDCAFClS4OXlZfYiT548Sfr06Zk2bRpLlizBw8ODQYMG6bpVIiLJjBEIBkoCHYgKNW8B04GjwCe8xhub2JR4j9icPn2a8ePHU7x4cSpVqsTs2bNxcXHBxcWFGzduMH36dLNe/fTChQsMGDCA+fPnU7ZsWQBKlizJiRMnCAgIYNq0ac89ZvPmzS/c3stGc0RExDoZjUY2bdqEF7DvSVtmwBPoDqSxWGVibeIdbO3s7MiYMSMA+fPn59SpU0RGRgJQrVo1Tpw4YdYC//rrL8LCwihZsmSM9tKlS/Pff/+ZdV8iImJ9du3aRe3atalTpw77ACdgKHAK6I9CjcQU72BTsGBB/vjjDyAq2ISFhXH06FEA7t69G2NCsTlEz4f5559/YrQfP36cfPnymXVfIiJiPQ4dOkSTJk2oVKkSW7ZswcHBgd69e3MKGAFkeNmDdd3CZCvewaZly5ZMnTqViRMnkjZtWipUqICXlxcLFixgwoQJlChRwqwFlipVirJlyzJo0CB27drFf//9x+TJk9m5cyeff/65WfclIiKWd+rUKVq3bk2pUqVYvXo1dnZ2tG/fnuPHjzN58mSyvSq0KNQka/EONs2bN8fb25uwsDAARo4cSWhoKH5+foSHh5t98rCdnR1BQUFUrFgRT09PPDw82LVrF/Pnz6dMmTJm3ZeIyMscOBB1ok30x4EDlq7o9ezcGfN57Nxp6YqiXLp0ie7du1O0aFEWLlyI0Wjko48+4tChQ8ydOzfmKL3RyDUgAoh88vnak3ZJ3gxG45v/FBiNRm7dukXmzJnNUVOCip48/LIJxiJiXhERsH07XLoEOXNCtWpgb2/pquLnZWcOJ6X3Umt8Hrdu3eKrr75iypQphISEAFCnTh38/PxMJ408K2NGuHPn+fYMGeD27YSrNSHYwu9HYojr+7dZzoozGAxkzpyZCxcu0KNHD3NsUkSI+oO3dSssWhT1OSLC0hXF34oVkC8f1KoFn34a9Tlfvqj2pOJVy6EkleVSrO15PHjwgNGjR1OwYEHGjBlDSEgIFStWZMuWLWzYsCHeoQai2p+c35IkrFgB+fPH/P3Inz9p/X5YmzgFm4iICCZNmkSVKlWoUqUK48aNI+Kpv7CPHz8mMDCQ+vXrayRExExs4Q/eihXw0Udw4ULM9gsXotqTwnOJ6+Emaz8sFdfDTYlxWOrx48dMmzYNFxcXvLy8uH37Nm+//TarV6/m999/p2bNmi987LVrLw410e7ciepn7aJ/P86fj9l+/nzS+f2wRnEKNkFBQcyYMYM8efJQrFgx5s6dy6xZswDYt28fDRo0IDAwkOzZszN9+vQELVgkOVixApo1e/4P3oULUe1J4Q9eRAS0avXyPp99Zv2jUHG9bqK1X1+xcmXz9nsdERERfPvtt7i6utKjRw+uXLlCgQIFWLBgAQcOHKBRo0avvATCCwZxXrufpUREwKvOf/n8c+v//bBGcVqgb/369TRs2JBxTy4sNm/ePBYsWEDhwoXp3bs3KVOm5Msvv6Rdu3akTJkyQQsWsXUREdC7d+zzHaLb+vSBxo2t+zj8pk3w5OLKLxQSEtVP17K1bUajkdWrVzNkyBAOHz4MQI4cORg6dCidOnXCwcEhzts6e/bVfeLTz1K2boUbN17e58aNqH5aVzZ+4jRic+nSJRo0aGC63aBBAy5evMjgwYMpX74869evp3Pnzgo1ImawffvzIzXPOncuqp81GzPGvP0kafrll1+oVKkSTZs25fDhw2TMmJExY8Zw4sQJunXrFq9QY0t++MG8/eT/4hRsQkJCyJQpk+l29MrDFSpUYM6cOeTIkSNBipPEZQsTVaOFhECPHlEjAT16RN1OKs6dM28/S9m1y7z9JGnZu3cv77//PrVr12b37t04Ojri5eXF6dOnGTRoEE5OTpYu0aK+/da8/eT/XuusKDu7qIe1adNGl4S3EbYwUTVakybg6AjTpsHPP0d9dnSMak8Kdu82bz9LedVhqPj2k6Th6NGjfPTRR5QvX55NmzaRMmVKevTowcmTJ/Hz8zP9Y5zc3bxp3n7yf290undyT9y2whYmqkZr0gRWr479vtWrk0a4ietaIklp7RSxff/99x/t27fn7bffZsWKFRgMBtq0acM///xDQECARvYl0bxRsNFoTdIX14mqSeGwVEjIi0NNtNWrrf+wVFz/odU/vmINrly5Qq9evShSpAjz588nMjKSJk2a8Pfff/PNN99QoEABS5coicRapjPE6awogBYtWjzX9tFHHz3XZjAYOHLkyJtVJYnmVRNVjcb/T1R9ydISVmHAgLj3CwxM2FrexMSJce/n55ewtYi8yO3btxk/fjyTJ0/mwYMHALi7u+Pv70+FChUsXJ0kthUroFevmGtW5coFU6eCh0fi1hKnYKPVhG3XpUvm7WdJ//5r3n6WorkpYs0ePnxIYGAgY8aM4datWwCUK1eO0aNHm5a8l+QleqHBZ0UvxBkcnLjhRsHGDB4/hqAgOHkSXFygWzdIKmcwZstm3n6WlD+/eftZip0dREbGrZ9IYgkLC2POnDmMGDGCS0/+0ylevDijRo2iSZMmmpqQTMV1ocHEXHdLfxrf0MCBUWfc9O0bdXijb9+o2wMHWrqyuHn82Lz9LMlWRjreece8/UTeRGRkJN9//z3FihWja9euXLp0iXz58jF//nz++usvmjZtqlCTjMVnocHEomDzBgYOhHHjnp8gFRER1Z4Uws3ChebtZ0nr15u3n6Xs32/efiKvw2g0snbtWsqUKUOrVq04efIk2bJlY+rUqfzzzz+0bdsWe2te+loSxaZN5u1nDgo2r+nx41dP8pw40fpHOv76y7z9LCk83Lz9LCWuZxIkhTPVxDotxUA4BiKefF5KzBGXbdu2UbVqVRo1asTff/9NhgwZGDVqFCdPnqRnz56kSpXKQpWLtdm3z7z9zEHB5jUFBb36jSUiIqqfJI6KFc3bT8QWhWOgGWBP1BuAPdDsSTv8CXxAzZo1+f3330mTJg2DBg3i1KlTeHt7kzZtWgtWLtboyQlxZutnDnE+3VtiOnnSvP0spUgROHQobv2sXYYM5u0nYmvCMcT63+w/gA8A7wKQIkUKOnfuzJAhQ3jrrbcSr0BJckJDzdvPHOIUbFatWhWvjTZJCsu7vqG8ec3bz1Js6Xo+ixfHvd+iRQlbi4i1WfpUqIk+8HQOGA7MByKetFcB5h87houLS6LXKElP6tTm7WcOcQo2gwcPjnE7ega88anlap+eFZ8cgk1cTseNTz9LuXjRvP1ExDp58P9Acw3wB4KA6GmADQE/oDhgr1AjcRTXAb3EHPiLU7DZvHmz6eujR48ycOBAunbtyocffki2bNm4desWv/zyCwEBAYwePTrBirUmv/wS936DBiVsLSIir2IA7gITgInA/SftNYgKOZWf3Lby/8XEyvz9t3n7mUOcgk2uXLlMX/fs2ZOuXbvSuXNnU1v27Nn55JNPCAsLY9y4cdSoUcP8lVoZazzFTUQkNiEhIQQCY4HoJUfeISrQ1IEY50Tp2qoSH//9Z95+5hDvs6JOnjxJsWLFYr2vQIECnH/ZhYdsiK0cihIR2xUWFsbMmTMpXLgwA4kKNa7AMmAfUJf/hxrjk48VFqlUkqrYLqD8Jv3MId7BJn/+/Kx+wSWUlyxZQpGkcPqMiIgNi4yMZPHixZQoUYIuXbpw4cIFIA+zgL+Bj4h9lCYS+FhjNhIPjo7m7WcO8T7du3v37vTu3Zv//vuP2rVrkzlzZq5fv87PP//MiRMnmDVrVkLUKSIir2A0Glm/fj3e3t4cOHAAgCxZsuDt7U3fvl/QmdS0J/bLH0QCKRRqJJ5u3zZvP3OId7CpU6cO06ZNY9q0aUyZMgWj0YidnR1ubm7Mnz+fsmXLJkSdIiLyEr/99huenp789ttvAKRLl47+/fvTt29f0qVLR9++Uf1SYGQpBtNZUtGHnzRSI7bitRboc3d3x93dndDQUO7cuUPGjBlxSCqXsxYRsSEHDhzA29ubH3/8EYBUqVLRo0cPBg8eTJYsWWJ9jEKM2LLXXnn45MmT7Nixg2vXrvHZZ59x7tw5ihYtqiW3RUQSwb///ouPjw+Ln6xMaW9vT8eOHRk6dCi5c+e2cHUilhPvYBMREYGvry/BwcEYjUYMBgMffPAB06ZN49y5cyxcuJAcOXIkRK0iIsne+fPnGTFiBHPnziXiyQXrWrZsyYgRIyhcuLCFqxOxvHifFfX111+zdu1aRo0axY4dO0yrDw8aNIjIyEgmTZpk9iJFRJK7Gzdu0L9/fwoVKsSsWbOIiIigXr167N+/n0WLFinUiDwR72ATHBxMr169+Oijj8iYMaOpvWjRovTq1YsdO3aYsz4RkWTt3r17jBgxggIFCjBhwgRCQ0OpWrUq27dvZ926dZQpU8bSJYpYlXgfirp+/foLF+jLnj07d+/efeOiRESSu0ePHjF9+nT8/f25du0aAKVLl8bf358PP/wwxvX5ROT/4j1iky9fPrZt2xbrfXv27CFfvnxvXJSISHIVHh7O3LlzKVKkCH379uXatWsULlyYxYsX8+eff1KvXj2FGpGXiPeITdu2bfHx8SEsLIxatWphMBg4c+YMu3fvZu7cuc9dCVxERF4tMjKS4OBghg4dyj///ANEXafP19eXdu3akTJlSgtXKJI0xDvYNG/enJs3bzJ9+nQWLVqE0WikX79+pEyZkk6dOvHJJ58kRJ0iIjbJaDTy888/4+XlxZ9//gmAs7Mznp6edOvWjTRp0li4QpGk5bXWsenSpQutWrVi//793L59m/Tp01O6dOkYk4lFROTldu7ciaenp+nwftq0afnyyy/p168f6dOnt3B1IklTvOfYeHp6cu7cOdKmTUu1atVo2LAhNWrUIGPGjJw6dYovvvgiIeoUEbEZf//9N40aNaJy5cps27YNBwcH+vbty6lTpxg2bJhCjcgbiNOIzcWLF01fr1q1ivfeew97e/vn+v3666/8/vvv5qtORMSGnDx5El9fX77//nvTdfbat2+Pj48PefPmtXR5IjYhTsFmxIgRMc6E6tGjR6z9jEYjVapUMU9lIiI24uLFi4wcOZLZs2cTHh4ORM1XHDlyJK6urhauTsS2xCnYDB8+nN9//x2j0YiXlxddu3Z97r8LOzs70qdPT4UKFRKkUBGRpObmzZuMHTuWgIAAQkJCAKhbty5+fn68++67Fq5OxDbFKdhkz56dpk2bAmAwGKhZsyZp06Y1nX4YEhJCaGioJg+LiAD3799nypQpjBs3jjt37gBQqVIlRo8eTY0aNSxcnYhti/fk4fr16zNhwgQ+/vhjU9v+/fupWrUqfn5+pouyiYgkN6GhoQQEBODi4sKQIUO4c+cOpUqVYu3atezYsUOhRiQRxDvYTJ06lR9//JEmTZqY2kqUKMGgQYNYuXIls2bNMmd9JqtWraJevXqULFmS+vXrs379+gTZj4jIiyzFQDgGIp58Xkr0CsARwDe4urrSq1cvrl69SsGCBfnuu+/Yv38/DRo00GrBIokk3sFm3bp1DBo0iLZt25raMmTIQOvWrenbty/BwcFmLRBg9erVeHl50aJFC3744Qfq1atHv3792L9/v9n3JSISm3AMNAPsifrDaQ98BCzDAJQE2nHmzBly5szJ119/zbFjx/j000+xs4v3n1mRJCNVKvP2M4d4/8bdunWL3Llzx3pfgQIFuHLlyhsX9TSj0ciUKVNo27Ytbdu2JV++fHTv3p3KlSuzZ88es+5LRCQ24Rie+2O5CagINAfgKJCJsWPHcuLECb744gtdAkGShWzZzNvPHOK98rCLiwsbNmyI9bTujRs3mv0imKdOneLChQs0bNgwRvucOXPMuh8RkdgsfSrUGIDdgBfwy5M2J6A3UJhbtBs40AIViljO7dvm7WcO8Q42HTp04Msvv+T27du89957ODs7c/PmTTZt2sTPP//M6NGjzVrgf//9B8DDhw/p2LEjR44cIXfu3HTt2hV3d/dYH1O7du0Xbu/SpUvkzJnTrDWKiO3yICrQHAaGAKuetDsAXxAVcrITNctGJLl5+NC8/cwh3sGmfv363Lt3j8DAQH7++WdTe6ZMmRg6dGiMScXmcP/+fQAGDRpEjx496N+/Pxs2bKBbt27MmzePSpUqmXV/IiJPOwMMBxYARqKO37cBhgFPj09rarCIdXiti2C2bNmSFi1acPr0adNFMAsWLJggk+Sij1N37NjRtJZOsWLFOHLkyAuDzebNm1+4vZeN5sRHqlQQGhq3fiKS9Fy+fBk/Pz9mAGFP2jyAkUDxWPobE60yEetRtCgcPhy3fonltYINRC3UV7BgQXPWEqscOXIAUKRIkRjthQoVYuvWrQm+/xeJS6iJTz8RsQ63b9/mq6++YsqUKTx8Mn7+HuAHlI+lf3SgWUH0RGKR5OOff8zbzxziFGyKFSvGkiVLKFWqFEWLFn3pegwGg4EjR46YrcDixYvj5OTEwYMHKVu2rKn9+PHjumiciJjNw4cPmTp1KmPHjuX2k5mO5cuXZ8+e0fxEbeyICjFP//WLDjWRwMcYNWojyc6TS5+ZrZ85xCnYdO/enezZs5u+TsyFplKnTk2nTp2YNm0a2bNnp1SpUqxbt44dO3Ywf/78RKtDRGzT48ePmT17NiNHjuTy5ctA1KKjfn5+NGrUCDs7AykwxnrKN0SFmhSKNCJWI07B5umreffs2TPBinmRbt26kSZNGiZNmsSVK1dwcXEhICBAF9wUkdcWERHBokWL8PHx4fTp0wDkz5+fESNG8Omnn2Jvbx+jfwqMLMVgOkvKSNThp48VakSsSpyCzcWLF+O10bfeeuu1inmZ9u3b0759e7NvV0SSF6PRyNq1a/H29ubQoUNA1IV+hw4dSufOnXFwcHjhYxViRKxfnIKNu7t7vA4/HT169LULEhFJKFu2bMHLy4tdu3YBkDFjRgYOHEivXr1wcnKycHUiSY+jY9zWqHF0TPhaosUp2Pj7+5uCzZ07dxg/fjyVKlXiww8/JGvWrNy+fZtffvmFrVu3Mnjw4AQt2Fpkzgw3b8atn4hY1r59+/Dy8mLjxo0ApEmTht69ezNw4EAyZcpk4epEkq6yZeHXX+PWL7HEKdh4eHiYvu7evTtNmzZl5MiRMfo0bNgQPz8/1q9fT4sWLcxbpRVydYWdO+PWT0Qs4+jRowwdOtR0cd6UKVPy+eef4+3trRXIRcygXr24BZt69RK+lmjxXlFvx44dfPDBB7HeV7NmzWRzxe24fpMS85spIlHOnDlDhw4dePvttwkODsZgMNC6dWuOHTtGYGCgQo2Imbzgmtiv3c8c4h1sMmXKxIEDB2K9b9euXabTwm2dNS5KJJLcXb16ld69e1OkSBHmzZtHZGQkjRs35q+//uLbb79NlEVFRZKTJ2vomq2fOcR75eHmzZsTFBRESEgI7u7uZM6cmevXr/PTTz+xaNEivLy8EqJOq7N8edz7LViQsLWIJHfRc/8mTZrEgwcPAKhVqxb+/v5UrFjRwtWJSGKKd7Dp2rUr9+7dY/78+cyZMweIOn0yderU9O7dm1atWpm9SGv06JF5+4lI/IWEhBAYGMiYMWO4+WQ2f9myZfH39+e9995L1MVERZKjq1fN288c4h1sDAYDgwYNolu3bhw4cIA7d+6QKVMm3NzccEzM87lEJNkKCwtj7ty5jBgxwrTOVtGiRRk1ahQeHh4KNCKJJK7T1RJzWttrXwTTycmJrFmzYjQaKV26NI8fP1awEZEEFRkZyZIlS/Dx8eHEiRMA5M2bl+HDh/PZZ5+RIsVr/0kTkddQrVrUxOALF8AYy/qVBkPU/dWqJV5Nr/VXYPXq1UyYMIFr165hMBhYtmwZAQEBpEyZkgkTJrx05U4RkfgyGo38+OOPeHt7c/DgQQCyZs3KkCFD6NKlC6lSpbJwhSLJk709TJkCzZpFhZinw030wOnkyVH9Eku8z4r68ccfGTRoEBUrVmTixIlERkYCUKdOHX799VeCgoLMXqQ1ius/hvoHUuTNbN++nWrVqtGgQQMOHjxI+vTpGTlyJKdOnaJXr14KNSIW5uERdaJMrlwx23Pnjmp/aim8RBHvt93p06fTsmVLhg0bRkREhKndw8ODGzdusHTpUvr06WPOGq1SunRw61bc+olI/O3fvx8vLy9++uknAFKnTk3Pnj0ZNGgQzs7OFq5ORJ7m4QGNG8P27XDpUtScmmrVEnekJlq8R2xOnz7N+++/H+t9pUuX5sqVK29cVFLw5Zfm7SciUY4fP06LFi145513+Omnn0iRIgVdunThxIkTfPXVVwo1IlbK3h5q1oRPPon6bIlQA68RbJydnTl58mSs9508eTLZ/NEZPdq8/USSu3PnztG5c2eKFy/O0qVLMRgMfPrppxw9epTp06eT69lxbhGRWMQ72NSrV4+pU6fy008/8fjxYyDqFPBDhw4RFBT0wsst2Jona4CZrZ+ILVuKgXAMRDz5vJSnT8e+Rr9+/ShcuDCzZ88mIiKCBg0acODAAb777jsKFSpksbpFJOmJ9xybPn36cPz4cfr06YOdXVQuat26NQ8fPqRs2bL07t3b7EWKSNIVjgE7iBFlmgE3MZAZX2ACkybdB6B69er4+/tTpUoVC1QqIrYg3sHGwcGB2bNns2PHDnbt2sXt27dJly4d5cuXp0aNGloYS0RMokPN0x4B04AxAAwHwM3NDX9/f+rWrau/ISLyRuIdbL744gvatGlDlSpV9F+ViLzQ0qdCjQEIB+YTFWXOP2kvAtQDJuzbZxoBFhF5E/H+S7J3717sLTXVWUSSDA+iAo0RWAqUADoTFWpyA7OBw8B4UKgREbOJ91+TKlWqsGzZMkJDQxOiHhGxIeuBskAL4DiQBZgI/At0JGrIWAeeRMSc4n0oKlWqVKxfv56NGzeSO3fu507vNhgMfPPNN2YrUESSnh07djAY+O3J7XTAl0BfIP0zfWO5vIyIyGuL94jN5cuXcXNzo1SpUmTOnBmj0RjjI/oSC7YubVrz9hOxBQcPHqRBgwZUrVqV34BURIWZU4AvMUON8cnHisQvU8Tismc3bz/5v3iP2CxYsCAh6khywsLM208kKTtx4gQ+Pj4sXrwYo9GIvb09ERHtOcZs8sXSP3qUJhL4GKNGbSTZebIMnNn6yf/Fa8Tmr7/+Yv369Rw5ciSh6kkyFGxE4MKFC3zxxRcUK1aMRYsWYTQaadGixZO/EbMogJEXjeFGAikUaSSZun3bvP3k/+I0YnP37l26dOnCgQMHMBqNGAwGypQpw8SJE8mZM2dC12iV0qaFu3fj1k/E1ty4cYOxY8cSEBDAo0ePAPjwww/x8/PDzc0tRt8UGFmKIcZZUiuIGqkRSa7s7OCp60i/tJ/ET5xessmTJ3PkyBF69uzJzJkzGTRoEKdPn2bo0KEJXZ/VmjnTvP0sJWVK8/YT23b//n1GjhxJwYIFGTduHI8ePaJKlSr8+uuv/Pjjj8+FmmgfYyQFRuyffFaokeSuTBnz9pP/i9OIzZYtW+jXrx9t27YFopY9z549O/379+fhw4c4OjomaJHWqFEj8/azlFq14Oef49bP2uXJA+fOxa2fxE9oaCjTp0/Hz8+Pa9euAVC6dGn8/PyoV6+eVguWRPPXX1CqVNz6WbNateCPP+LWT+InTiM2165do0SJEjHaKlSoQEREBJcuXUqQwqzd+PHm7Wcp6dKZt58lVaxo3n6Wkv7Z86HfsN+bCA8PZ968eRQpUoQ+ffpw7do1ChUqxKJFi/jzzz+pX7/+C0NNxoxx20dc+1lKXA8F6JBB4jh61Lz9LCWub53J9C32jcTpVzE8PBwHB4cYbRkyZABItgv1TZpk3n6WEhxs3n6WtGyZeftZSlzmbsWn3+swGo0EBwdTsmRJOnTowNmzZ3nrrbeYMWMGR44coWXLlq9cLdhWJkfGdQWLZLLShcV17mzefmJ74n2697OMxuR5rNwa3nxEzM1oNLJp0ya8vLzYt28fAJkzZ8bT05Pu3buTJk0aC1coyd2DB+btZyn5YlsH4Q36yf+98eBpcj22niqVefuJWNquXbtwd3enTp067Nu3DycnJ4YOHcqpU6fo37+/Qo1YBScn8/azFHd38/aT/4vziM2wYcNI+9S5y9EjNUOHDsXpqZ+g5HJJhY8/hvnz49ZPxJodOnQIb29v1qxZA4CDgwNdu3bFy8uLbNmyWbg6kZhmzoSWLePWz5rVrAnOznDjxov7ODtH9ZP4idOITbly5XBycopx6YTodkdHx2R5SYWgIPP2s5TAQPP2k6Tj1KlTtG7dmlKlSrFmzRrs7Ozo0KEDx48fZ/LkyQo1TxQqZN5+llKvnnn7WUqzZubtZyn29q8OXzNnRvWTeDImM+7u7kZ3d3ezbKtxY6MRXvzRuLFZdpPgXvYcoj+SgoED4/ZcBg60dKUvN2RI3J7HkCGvt/2LFy8au3XrZkyZMmX05ZqMzZo1Mx49etSsz2P+/Lg9j/nzzbpbs9uwIW7PY8MGS1f6crbyPIxGozE4+OXPITjY0hXGXXCw0Zg7d8z6c+dOWs8hscT1/VsnKL6BVaugcePY72vcOOr+pOBV87+TyvzwOnXM28/W3Lp1C09PT1xcXAgKCiIsLMw0n2bZsmUULVrUrPuzlcmRtWu/egXxtGmj+lkzW3keAB4eUWdqZskSsz1r1qh2Dw/L1PU6PDzgv/9gyxb4/vuoz//9l7Seg7VRsHlDq1bBw4fQvXvUG2b37lG3k0qoiWY0Pn+4KTAw6YQa+P8x65dJCses41pfXPs9ePAAf39/ChQowJgxYwgJCaFixYps2bKFDRs28O67775uqS9VrRrkzv3yPnnyRPWzZvb28Kppg998Y/2HDGzleUTz8IDLl2MGgkuXkmYgsLeP+n3+5JOoz0nle2C1EmH0yKqY81CUWB9bGKIODzcanZ1f/jycnaP6vUxoaKgxICDAmD17dtMhp7ffftu4evVqY2RkZKI8l+Bgo9FgiP05GAxJ4/sRLTjYaHzrrZjPIVeupPUcjEbbeR6S/OhQlCRL0UPUuXLFbM+dO+kMUb/ppMKIiAi+/fZbXF1d6dmzJ1euXKFAgQIsWLCAAwcO0KhRo0RbpsHDA5Yvf37kJk+eqPak8P2I5uEBZ8/GHCE4cyZpPQewnech8iIGozEpHWx4c7WfHEDevHmzhSuRhBQRAdu3Rw1N58wZdbgjqQ3vrlgBvXrBhQv/b8udG6ZMif1NyGg0snr1aoYMGcLhw4cByJEjBz4+PnTs2PG51cMTky18P0TEsuL6/v3GKw+LWKPoY9ZJmYdH1CT0uASCX375BS8vL3bv3g1AxowZGTx4MD179rSKi9TawvdDRJIGBRsRK/aqQLB37168vLzYtGkTAI6OjvTp04cBAwaQ0dqvLikikgCS1Byb06dP4+bmxooVKyxdikjiMBie/wCOHDmCh4cH5cuXZ9OmTaRMmZIePXpw8uRJ/Pz8FGpEJNlKMiM2YWFh9O/fn4cPH1q6FJHEEcsE3/+AYQYDC+zsiIyMxGAw0Lp1a4YNG0aBAgUSvUQREWuTZIJNQEBAjGtSidi0Z0LNFcAPmA6EAURG0rRpU0aOHEmJEiUSvz4RESuVJA5F7d27lyVLljB27FhLlyKS8J4KNbeBIYALEEBUqKkN7AZWrFypUCMi8gyrH7G5e/cuAwcOZMiQIeTMmTNOj6n9kjXBL126FOftiFjKQyAQGAPcetJWDhhNVLAREZHYWX2wGTZsGGXKlKFhw4aWLkUkwYWFhTEbGAlcetJWHBgFNAESZ1k9EZGky6qDzapVq9i3bx9r166N1+NetnjPy0ZzRCwlMjKSRYsW4ePjw6knbfmA4cBngNayExGJG6sONsHBwdy4cYOazyzk4evry5w5c1i3bp1lChMxE6PRyA8//IC3tzd///03ANmJmlfTGUhlyeLkOVpBWcT6WXWwGT9+PI8ePYrRVqdOHXr16kW9evUsVJWIeWzbtg1PT0927twJQIYMGRg4cCC9e/fGKW3aV28geV0NxeJWrIDeveH8+f+3vewSFyJiGVYdbLJnzx5ru7OzM7mevcqhSBLx559/4uXlxYYNGwBIkyYNvXr1YuDAgWTOnDmqk9EY6zo2Jgo1iWrFCmjW7PmX/cKFqPakdkFPEVuWJE73FrEFx44do3nz5rz77rts2LCBFClS0LVrV06ePMmYMWP+H2qivSi8KNQkqoiIqJGa2F726LY+faL6iYjlWfWITWz++ecfS5cgEi9nz55l+PDhzJ8/37Ra8Keffsrw4cNxcXF5+YMVYixu+/aYh5+eZTTCuXNR/XShTxHLS3LBRiSpuHbtGv7+/gQFBfH48WMAGjVqxKhRoyhZsqSFq5O4unTp1X3i009EEpaCjYiZ3blzh4kTJzJx4kTu378PQM2aNfH396dSpUoWrk7iK67reWrdTxHroGAjYiYhISEEBQXh7+/PzZs3AXj33Xfx9/fn/fffx/CyycBitapVizr76cKF2I8MGgxR91erlvi1icjzNHlY5A2FhYUxc+ZMChcuTP/+/bl58yaurq4sW7aMvXv3UqdOHYWaJMzePuqUbnj+RLXo25Mnaz0bEWuhYCPymiIjI1m8eDElSpSgS5cuXLhwgTx58jBnzhwOHTpEs2bNFGhshIdH1Cndz64ykTu3TvUWsTY6FCUST0ajkfXr1+Pt7c2BAwcAyJIlC97e3nzxxRekTp3asgVKgvDwgMaNtfKwiLVTsBGJh99++w1PT09+++03ANKnT0///v3p06cP6dKls3B1ktDs7XVKt4i1U7ARiYMDBw7g5eXF+vXrAUidOjU9evRg8ODBODs7W7g6kfjRNa/ElinYiLzEv//+y9ChQ1myZAkA9vb2dOzYER8fH13WQ5IkXfNKbJ2CjUgszp8/z4gRI5g7dy4RT9bKb9myJSNGjKBw4cIWrk7k9djaNa808iSx0VlRIk+5fv06/fv3p1ChQsyaNYuIiAjq1avH/v37WbRokUKNJFm2ds2rFSsgf36oVQs+/TTqc/78Ue2SvCnYiAD37t1jxIgRFCxYkAkTJhAaGkq1atXYvn0769ato0yZMpYuUeSNxOeaV9YueuTp2ecTPfKkcJO8KdhIsvbo0SMmTZpEwYIF8fX15d69e5QpU4Yff/yRbdu2UbVqVUuXKGIWtnLNK1sbeRLzU7CRZCk8PJw5c+ZQpEgR+vXrx/Xr1ylcuDCLFy/mjz/+4MMPP9TiemJTbOWaV7Y08iQJQ5OHxTbFFkqMRiIjIwkODmbo0KH8888/AOTKlQtfX1/atWtHypQpE7lQkcRhK9e8spWRJ0k4CjZie2IJNUbgZ4MBr3fe4c8//wTA2dkZLy8vunXrptWCxeZFX/OqWbOoX5Gnw01SuuaVrYw8ScLRoSixLbGEmp1ALeAD4M8//yRt2rT4+vpy6tQp+vXrp1AjyYYtXPMqeuTpRUeKDQbIk8f6R54k4WjERmzHM3/p/gKGAGuf3E4FdAM8798n67BhiVqaiLVI6te8spWRJ0k4CjZic04CPsAiog5B2QHtAV8gjwXrSs60kJp1SerXvIoeeYptBeXJk5PGyJMkHAUbsRkXgZHAbCD8SVvzJ22ulipKtIS/JIikPvIkCUfBRpK8mzdvMnbsWAKAkCdtHwB+wDuWK0uwvSX8xbok9ZEnSRiaPCxJ1v379/Hz86NgwYJ89dVXhACVgW3AehRqLE0LqYmIJSjYSJITGhpKQEAALi4uDBkyhDt37lCqVCnWrl3Lb0D1V20gtndaMTstpCYilqBDUZJkREREsGDBAoYNG8aZM2cAcHFxYcSIEbRs2RI7O7uod8uXrRisUJNotJCaiFiCgo1YPaPRyMqVKxkyZAhHjx4FIGfOnPj4+NCxY8fnVwt+UbhRqElUWkhNRCxBwUas2qZNm/Dy8mLv3r0AZMqUCU9PT7p3746jo+OLH6gQY3G2soS/iCQtmmMjVmn37t3Url2b999/n7179+Lk5MSQIUM4ffo0AwYMeHmoEasQvZAaPD+ApoXURCShKNiIVTl8+DBNmzalYsWK/PLLLzg4ONCrVy9OnjzJyJEjyZAhg6VLlHiwhSX8RSRp0aEosQqnT5/G19eXhQsXYjQasbOzo02bNgwbNox8+fJZujx5A1pITUQSk4KNWNTly5cZNWoUM2fOJCwsDAAPDw9GjhxJ8eLFLVydmIsWUhORxKJgIxZx69Ytxo0bx5QpU3j48CEA77//Pn5+fpQrV87C1YmISFKlYCOJ6uHDh0ydOpWxY8dy+/ZtACpUqMDo0aOpVauWZYsTEZEkT8FGEsXjx4+ZPXs2I0eO5PLlywCUKFECPz8/GjVqhOFli+qJiIjEkYKNJKiIiAi+//57fH19OX36NAD58+dnxIgRfPrpp9hrBqmIiJiRgo0kCKPRyJo1axgyZAiHDh0CIHv27AwdOpTOnTvj4OBg4QpFRMQWKdiI2W3ZsgUvLy927doFQMaMGRk4cCC9evXCycnJwtWJiIgtU7ARs9m3bx9eXl5s3LgRAEdHR3r37s2AAQPIlCmThasTEZHkQMFG3tjRo0cZOnQowcHBAKRMmZLPP/+cIUOGkCNHDgtXJyIiyYmCjby2M2fOMHz4cL755hsiIyMxGAx89tlnDB8+nAIFCli6PBERSYYUbCTerl69ip+fH9OnT+fx48cANG7cmFGjRvH2229buDoREUnOFGzk/2JbS8ZoNH15584dxo8fz6RJk3jw4AEAtWrVwt/fn4oVKyZWlSIiIi+UJILN7du3mThxIlu3buX+/fu4urry5ZdfUrZsWUuXZjtetECewUDIw4cEBgYyZswYbt68CUDZsmUZPXo0tWvX1uJ6IiJiNZJEsOnXrx83btxg4sSJZM6cme+//56OHTuyYsUKXFxcLF1e0veCYBIGzAVGODpy8UlbsWLFGDVqFE2bNlWgERERq2Nn6QJe5cyZM+zYsQNfX1/Kli1LwYIF8fb2Jnv27Pzwww+WLi/piyWcRAKLgOLAF8BFIC8wb948/v77bzw8PBRqRETEKln9iE2mTJmYOXNmjEmpBoMBo9HInTt3Yn1M7dq1X7i9S5cukTNnTrPXaQuMwDrAG/jrSVtWYAjQBUjVrp1lChMREYkjqw826dOnp0aNGjHa1q9fz9mzZ6lataqFqrI9vwJewI4nt9MDA4A+QFoL1SQiIhJfVh9snvXHH3/g5eVF7dq1cXd3j7XP5s2bX/j4l43mJEf7iQo0Pz25nRroBQwEnC1VlIiIyGuy+jk2T9u0aRMdO3akVKlSTJw40dLlJGn//PMPLVq04B2iQk0KoubTnATGolAjIiJJU5IJNgsXLqRnz55Ur16dWbNmkTp1akuXlCSdO3eOTp06UaJECZYuXYrBYOBT4CjwNfDWix741Ho2IiIi1ipJBJvvv/+ekSNH0qpVKyZPnoyDg4OlS0pyrl27Rr9+/ShcuDBz5swhIiKChg0bcuDAAb4zGin0sgcr1IiISBJh9XNsTp8+jb+/P++//z5dunThxo0bpvtSp05NunTpLFid9bt79y4TJ05kwoQJ3L9/H4Dq1aszevRoKleu/P+ORuMrVx4WERGxdlYfbDZs2EBYWBgbN25k48aNMe5r2rQpY8aMsVBl1u3Ro0cEBQXh7+9vCoNubm74+/tTt27d2NehUYgREZEkzuqDzRdffMEXX3xh6TKSjPDwcObPn8/w4cM5f/48AEWKFGHUqFF89NFH2NkliaOPIiIir8Xqg43ETWRkJMuWLWPo0KH8+++/AOTOnZthw4bRtm1bUqRIXt/qiAjYvh0uXYKcOaFaNbC3t3RVIiKS0JLXu50NMhqN/PTTT3h7e7N//34AsmTJgpeXF127dk2WZ4+tWAG9e8OTASsAcueGKVPAw8NydYmISMJTsEnCduzYgaenJ9u3bwcgXbp0fPnll/Tr1y/ZTqpesQKaNXt+utCFC1Hty5cr3IiI2DJNuEiCDh48SIMGDahatSrbt28nVapU9OvXj1OnTuHr65tsQ01ERNRITWxzoKPb+vSJ6iciIrZJwSYJOXHiBJ9++illypRh3bp12Nvb07lzZ06cOMGECRPIkiWLpUu0qO3bYx5+epbRCOfORfUTERHbpENRScCFCxcYMWKEaWE9gBYtWjBixAiKFCli4eqsx6VL5u0nIiJJj4KNFbtx4wZjxowhMDCQR48eAfDhhx/i5+eHm5ubhauzPjlzmrefiIgkPQo2Vuj+/ftMmjSJ8ePHc/fuXQCqVKnC6NGjqVatmoWrs17VqkWd/XThQuzzbAyGqPv1EoqI2C7NsbEioaGhTJkyhYIFC+Lj48Pdu3cpXbo069atY/v27Qo1r2BvH3VKNzx/dYjo25Mnaz0bERFbpmBjBcLDw5k3bx5FihShT58+XLt2jUKFCrFo0SL+/PNP6tWrF/slEOQ5Hh5Rp3TnyhWzPXfupHmqd0QEbN0KixZFfdYZXSIiL6dDURZkNBoJDg5m6NChHDt2DIC33noLX19f2rdvT8qUKS1cYdLk4QGNGyf9lYe10KCISPwp2FiA0Whk48aNeHl58ccffwCQOXNmPD096d69O2nSpLFwhUmfvT3UrGnpKl6fFhoUEXk9OhSVyHbt2oW7uzt169bljz/+wMnJiaFDh3Lq1Cn69++vUCNaaFBE5A0o2CSSQ4cO0bhxYypVqsTWrVtxcHCgT58+nDp1ihEjRpAhQwZLlyhWQgsNioi8Ph2KMofYJvY++dc6+jIH3333HUajETs7O9q1a4evry958+ZN5EIlKdBCgyIir0/B5k294GylSwYDo7p1Y+bMmYSHhwPQrFkzRo4cSdGiRROzQklitNCgiMjr06GoNxFLqLkFDAZcgKCgIMLDw6lTpw779u1j2bJlCjXyStELDb7oDH+DAfLk0UKDIiKxUbB5Xc+86zwC/IECwFggBKgIbAE2bNjAu+++m9gVShKlhQZFRF6fgo2ZjAC8gTtASWAN8DtQ04I1SdJlawsNiogkFs2xMZNawB9AG6AloH+m5U3ZykKDIiKJScHGTN5/8iFiTkl9oUERkcSmQ1EiIiJiMxRsXldsy8K+ST8RERF5Ywo2b+JVoUWhRkREJFEp2LypF4UXhRoREZFEp8nD5qAQIyIiYhU0YiMiIiI2Q8FGREREbIaCjYiIiNgMBRsRERGxGQo2IiIiYjMUbERERMRmKNiIiIiIzVCwEREREZuhYCMiIiI2Q8FGREREbIaCjYiIiNgMBRsRERGxGQo2IiIiYjMUbERERMRmKNiIiIiIzUgSwSYyMpKpU6dSrVo1SpcuTYcOHThz5oylyxIRERErkySCTVBQEIsXL2bUqFEsWbIEg8FA586defz4saVLExERESti9cHm8ePHzJ07l549e1KjRg2KFi3KpEmTuHLlChs3brR0eSIiImJFUli6gFc5duwYDx48oGLFiqa29OnTU7x4cfbu3Uv9+vWfe0zt2rVfuL3z589jb2//0j4iIiJiXS5duoS9vf0r+1l9sLl8+TIAOXPmjNGeLVs2Ll26FO/tGQwGUqSw+qdtMdGv6bOvt1iOvifWRd8P66Lvh3VJyO9HihQpcHBweHU/s+/ZzEJCQgCeezKpUqXizp07sT5m8+bNCV6XrYoeydJraD30PbEu+n5YF30/rIs1fD+sfo5N6tSpAZ6bKBwaGkqaNGksUZKIiIhYKasPNtHDWVevXo3RfvXqVXLkyGGJkkRERMRKWX2wKVq0KGnTpmX37t2mtrt373LkyBHKli1rwcpERETE2lj9HBsHBwc+++wzxo8fT+bMmcmVKxfjxo0jR44cvP/++5YuT0RERKyI1QcbgF69ehEeHs6QIUN49OgR5cqVY86cOXGaHS0iIiLJR5IINvb29gwYMIABAwZYuhQRERGxYgaj0Wi0dBEiIiIi5mD1k4dFRERE4krBRkRERGyGgo2IiIjYDAUbERERsRkKNiIiImIzFGzE5Pbt2/j4+FC9enXeeecdPvnkE/bt22fpsgQ4ffo0bm5urFixwtKlJGurVq2iXr16lCxZkvr167N+/XpLl5RshYWFMWnSJGrWrImbmxuffvopf/75p6XLSpaCgoJo3bp1jLajR4/y2WefUaZMGWrWrMmcOXMSrR4FGzHp168fBw8eZOLEiSxfvpwSJUrQsWNHTp48aenSkrWwsDD69+/Pw4cPLV1KsrZ69Wq8vLxo0aIFP/zwA/Xq1aNfv37s37/f0qUlS19//TXBwcGMGjWKVatWUbBgQTp37syVK1csXVqyMn/+fKZOnRqj7datW7Rv3578+fMTHBxMz549mTJlCsHBwYlSk4KNAHDmzBl27NiBr68vZcuWpWDBgnh7e5M9e3Z++OEHS5eXrAUEBODk5GTpMpI1o9HIlClTaNu2LW3btiVfvnx0796dypUrs2fPHkuXlyxt3ryZBg0aULVqVfLly8fgwYO5f/8+Bw4csHRpycKVK1fo1KkTU6ZMoUCBAjHuW7p0KQ4ODgwbNgwXFxc++ugj2rVrx6xZsxKlNgUbASBTpkzMnDmTt99+29RmMBgwGo3cuXPHgpUlb3v37mXJkiWMHTvW0qUka6dOneLChQs0bNgwRvucOXPo0qWLhapK3jJmzMiWLVs4f/48ERERLFmyBAcHB4oVK2bp0pKFw4cPkyFDBtasWUPp0qVj3Ldv3z7KlStHihT/v7hBxYoVOX36NDdu3Ejw2pLEJRUk4aVPn54aNWrEaFu/fj1nz56latWqFqoqebt79y4DBw5kyJAh5MyZ09LlJGv//fcfAA8fPqRjx44cOXKE3Llz07VrV9zd3S1bXDLl7e1N3759qV27Nvb29tjZ2TFlyhTy5s1r6dKSBXd39xf+7F++fJkiRYrEaMuWLRsAFy9exNnZOUFr04iNxOqPP/7Ay8uL2rVr6w+3hQwbNowyZco8N0ogie/+/fsADBo0iAYNGjB37lyqVKlCt27d2Llzp4WrS55OnjxJ+vTpmTZtGkuWLMHDw4NBgwZx7NgxS5eW7D169Oi5i1SnSpUKgNDQ0ATfv0Zs5DmbNm2if//+lC5dmokTJ1q6nGRp1apV7Nu3j7Vr11q6FAFSpkwJQMeOHWnatCkAxYoV48iRI8ybN49KlSpZsrxk58KFCwwYMID58+dTtmxZAEqWLMmJEycICAhg2rRpFq4weUudOjWPHz+O0RYdaBwdHRN8/xqxkRgWLlxIz549qV69OrNmzSJ16tSWLilZCg4O5saNG6ZTWd3c3ADw9fWlfv36Fq4u+cmRIwfAc8PrhQoV4vz585YoKVn766+/CAsLo2TJkjHaS5cubTpsKJaTI0cOrl69GqMt+nb27NkTfP8asRGT77//npEjR9K6dWu8vLyws1PutZTx48fz6NGjGG116tShV69e1KtXz0JVJV/FixfHycmJgwcPmkYIAI4fP645HRYQPefsn3/+oVSpUqb248ePky9fPkuVJU+UK1eOxYsXExERgb29PQA7d+6kQIECCT6/BhRs5InTp0/j7+/P+++/T5cuXWLMXE+dOjXp0qWzYHXJz4v+q3F2diZXrlyJXI2kTp2aTp06MW3aNLJnz06pUqVYt24dO3bsYP78+ZYuL9kpVaoUZcuWZdCgQfj6+pIjRw5WrVrFzp07+f777y1dXrL30UcfMXv2bLy9venUqRN//fUX33zzDcOHD0+U/SvYCAAbNmwgLCyMjRs3snHjxhj3NW3alDFjxlioMhHr0K1bN9KkScOkSZO4cuUKLi4uBAQEUKFCBUuXluzY2dkRFBTE5MmT8fT05M6dOxQpUoT58+dTpkwZS5eX7Dk7OzN79mz8/Pxo2rQpWbNmZeDAgab5aQnNYDQajYmyJxEREZEEpkkUIiIiYjMUbERERMRmKNiIiIiIzVCwEREREZuhYCMiIiI2Q8FGREREbIaCjYiIiNgMBRsRMStbWxrL1p6PiK1TsBERk4EDB+Lq6srMmTPj/djLly/TpUsXLly4YPa6du/ejaurK7t3735hn9atW+Pq6hrjo2jRorz77rs0b96cdevWxXu/J06c4JNPPonR5urqSkBAQLy3JSKJQ5dUEBEA7t+/z88//0yRIkVYunQpnTt3xmAwxPnxv//+O1u3bmXo0KEJWOXLFS9eHF9fX9PtiIgILl++zPz58+nXrx/p0qWjevXqcd7e+vXr2b9/f4y2JUuWmK72LSLWR8FGRABYt24dERERDBkyhDZt2vDbb79RrVo1S5cVL2nTpo31WkE1atSgUqVKBAcHxyvYxEbXIhKxbjoUJSIABAcHU6FCBSpUqECBAgVYvHjxc33WrVuHh4cHpUuXpmbNmowbN47Hjx+zYsUKPD09AahduzaDBw8GwN3d3fR1tBUrVuDq6sr58+dNbZs2beLTTz/Fzc2Nt99+mw8++ICFCxea7bk5ODiQMmXKGG2PHj1iwoQJ1KlTh7fffpt33nmH9u3bc/ToUQACAgIIDAwEYh5+evrr6ENkO3fupEOHDpQuXZrKlSszduxYwsPDTfu6f/8+Pj4+VKpUCTc3N/r27cv8+fNxdXU19Tl37hxdu3alQoUKlC5dmhYtWrBt2zazvQYiyYWCjYhw8uRJDh48aLr6roeHB1u2bOHKlSumPosXL6Zfv34UK1aMwMBAunTpwvfff8+wYcOoWbMmXbt2BSAwMJBu3brFed9bt26le/fulChRgqCgIAICAsiVKxcjR47kzz//jNfzMBqNhIeHmz5CQ0M5c+YMQ4YM4cGDBzRu3NjUd+DAgSxfvpzPP/+cuXPnMnjwYI4fP07fvn0xGo00b96cZs2aAVGHn5o3b/7C/fbv3593332X6dOn07BhQ+bOncvy5ctN93fv3p3169fTs2dPJk2axIMHD5gwYYLp/sjISLp06cLDhw/56quvCAoKImPGjHTr1o0zZ87E6zUQSe50KEpEWL58OenTp+e9994DoEmTJkyePJlly5bRo0cPIiMjCQgI4P3338fPz8/0uNDQUFauXEnatGnJmzcvAMWKFSN37txx3veJEydo0qQJ3t7epjY3NzcqVKjA3r17eeedd+K8rb1791KiRIkYbQaDgSJFijBlyhTc3d0BePz4MQ8ePGDo0KHUq1cPgPLly/PgwQPGjBnDtWvXyJEjh2kuzasOPzVv3pzu3bsDUKlSJTZt2sTWrVtp2bIlO3fuZNeuXQQEBFCnTh0AqlevTsOGDTlx4gQAN27c4OTJk3zxxRfUqFEDgFKlShEYGEhoaGicn7+IKNiIJHvh4eGsWbOG9957j9DQUEJDQ0mdOjUVKlRg2bJldO3alf/++4/r16+bgk+0du3a0a5duzfaf6dOnQB4+PAhZ8+e5fTp0/z9998AhIWFxWtbJUqUYPjw4QBcuXKFKVOmEBYWxqRJk3BxcTH1c3BwYM6cOQBcvXqVM2fOcOrUKbZs2fJa+3Vzc4txO0eOHDx8+BCAXbt2kTJlyhivnZ2dHR9++KHpkFaWLFkoVKgQQ4cO5ffff6d69epUrVrVdHhPROJOwUYkmdu6dSvXr19nxYoVrFix4rn7t2zZQqZMmQBwdnY2+/5v3ryJr68vmzZtwmAwkC9fPt59910g/mvIODk5UbJkSQBKliyJm5sbjRs3pkOHDqxcuZLMmTOb+m7fvh1/f39OnTqFk5MTrq6uODk5vdZ+U6dOHeO2nZ2daRu3bt0iY8aM2NnFPPKfJUsW09cGg4G5c+fy9ddfs3HjRlauXGkKQ8OGDSNjxozxqkckOdMcG5Fkbvny5eTKlYtvv/32uY+MGTOyePFi0qdPD0SFkKfdvn2bHTt28ODBgxduPyIiIsbt6JGMaP379+evv/5i3rx5HDhwgPXr18c4LPUmnJ2d8fHx4fLlyzEOoZ09e5bu3btTtGhRNm7cyJ9//smiRYuoVauWWfb7tOzZs3Pr1i0iIyNjtN+4ceO5fsOGDeO3335j1apVdOzYkZ9//plJkyaZvSYRW6ZgI5KMXb9+ne3bt1O/fn3TGVFPf9SrV48dO3aQKlUqMmXKxObNm2M8fu3atXTu3JnQ0NDnRiQg6vTry5cvx2h7dkLwH3/8Qd26dalYsSIODg4A/PrrrwDPhYHXUadOHapVq8YPP/xgWuDv0KFDhIaG0qVLF9PcIIgaxYH/j9jE9pziq3z58oSHh/PLL7/EaN+0aZPp6/3791O5cmX++usvDAYDxYoVo2/fvhQpUuS5109EXk6HokSSsZUrVxIeHk79+vVjvb9p06Z8//33LFu2jJ49ezJixAiGDRvG+++/z3///cfkyZP55JNPyJw5s2lUZ+PGjVSvXh0XFxdq1arFjBkzmD59OmXKlGHr1q3s3Lkzxj5KlSrF2rVrKVGiBDly5GD//v3MmDEDg8FASEiIWZ6nl5cXjRo1YtSoUaxcuZISJUqQIkUKxo0bR4cOHUynrG/duhX4/6hS9HP64YcfKF26NHny5In3vsuVK0eVKlXw9vbm+vXrvPXWWyxfvpxjx46ZFkAsXrw4qVOnZuDAgfTs2ZMsWbLw+++/c/ToUdq0aWOW10AkudCIjUgytnLlSgoXLkzRokVjvb9UqVIULFiQ4OBgmjdvzpgxY9i3bx9dunRh7ty5dOjQwbROTYUKFahcuTITJkxg7NixAHTp0oXmzZszd+5cunbtypUrV2IcEgIYM2YMpUuXZuTIkXTv3p1NmzYxfPhwqlatyr59+8zyPAsWLEjr1q05fvw4CxcuJF++fEyYMIErV67QtWtXfHx8AFiwYAEGg8G03zp16lCyZEkGDx5smmz8OiZNmoS7uzsTJkygd+/eODg48Mknn+Do6AhAqlSpmDt3LoULF8bPz4+OHTuyefNmRowYgYeHx5u/ACLJiMGoK7yJiCSYCxcucODAAWrXrh1jknGvXr04d+4cK1eutGB1IrZHh6JERBKQnZ0dgwcPpnbt2jRr1gx7e3t+/fVXfv75Z0aPHm3p8kRsjkZsREQS2K5du5g2bRpHjx4lPDwcFxcX2rdvT4MGDSxdmojNUbARERERm6HJwyIiImIzFGxERETEZijYiIiIiM1QsBERERGboWAjIiIiNkPBRkRERGyGgo2IiIjYDAUbERERsRn/A1W6ILYoy4GUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the results\n",
    "\n",
    "plt.scatter(y_test, y_pred, color = 'blue', label = 'Predicted')\n",
    "plt.scatter(y_test, y_test, color = 'red', label = 'Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color = 'black', label = 'Perfect Predictions')\n",
    "plt.ylim(0, 14)\n",
    "plt.xlabel('Actual Ratings')\n",
    "plt.ylabel('Predicted Ratings')\n",
    "plt.title('Linear Regression Results')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Note, can do anything to better capture what appears to be a nonlinear pattern?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A polynomial regression may be better than a linear regression in our case because it allows us to model a nonlinear relationship between the average rating and runtime of titles. In other words, while a linear regression assumes a linear relationship between the two variables, a polynomial regression allows us to fit a curve to the data that better captures any nonlinear patterns.\n",
    "\n",
    "In this case, we are using polynomial regression of degree 3, which means we are fitting a cubic curve to the data. This may be a good choice because it can capture some of the curvature that we may see in the relationship between average rating and runtime.\n",
    "\n",
    "However, it's important to note that while a polynomial regression can fit a more complex curve to the data, it can also be prone to overfitting if the degree is too high. Therefore, it's important to strike a balance between fitting the curve well to the data and avoiding overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial Regression Mean Squared Error: 1.86\n",
      "Polynomial Regression R^2 Score:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Perform polynomial regression analysis to explore the relationship \n",
    "# between the average rating of titles and their runtime\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X = IMDb_df[['runtimeMinutes']]\n",
    "y = IMDb_df['averageRating']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "# Run Polynomial Regression\n",
    "\n",
    "# Create polynomial features of degree 3\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "X_poly = poly.fit_transform(X_train)\n",
    "\n",
    "# Fit linear regression model\n",
    "lin_reg = LinearRegression().fit(X_poly, y_train)\n",
    "\n",
    "# Predict the test set results\n",
    "y_pred = lin_reg.predict(poly.transform(X_test))\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Polynomial Regression Mean Squared Error:\", round(mse, 2))\n",
    "print(\"Polynomial Regression R^2 Score: \", round(r2, 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polynomial regression model in this case has a mean squared error (MSE) of 1.86, which is higher than the linear regression model's MSE of 1.71. This indicates that the polynomial model is less accurate than the linear model in predicting the average rating of titles based on their runtime.\n",
    "\n",
    "However, the polynomial regression model's R-squared (R2) score of 0.0 indicates that the model does not fit the data well. We could play around with the degree to see if a different curvature fits the data better. Overall, the polynomial regression model does not perform well in predicting the average rating of titles based on their runtime in this dataset, and trying a different model might be a better choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the results\n",
    "\n",
    "plt.scatter(y_test, y_pred, color = 'blue', label = 'Predicted')\n",
    "plt.scatter(y_test, y_test, color = 'red', label = 'Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color = 'black', label = 'Perfect Predictions')\n",
    "plt.ylim(-1, 11)\n",
    "plt.xlabel('Actual Ratings')\n",
    "plt.ylabel('Predicted Ratings')\n",
    "plt.title('Polynomial Regression Results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A random forest model could potentially be appropriate for this problem. Random forest is a powerful machine learning algorithm that can handle non-linear relationships between the features and the target variable. It can capture interactions between the features and handle non-linearities that a linear regression model may not be able to capture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Mean Squared Error: 1.61\n",
      "Random Forest R^2 Score:  0.14\n"
     ]
    }
   ],
   "source": [
    "# Random Forest model\n",
    "\n",
    "# Train the model\n",
    "rf_reg = RandomForestRegressor(n_estimators = 100, random_state = 0).fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set results\n",
    "y_pred = rf_reg.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Random Forest Mean Squared Error:\", round(mse, 2))\n",
    "print(\"Random Forest R^2 Score: \", round(r2, 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest model has a Mean Squared Error of 1.61, which means that on average, the predicted ratings are off by 1.61 units compared to the actual ratings. The R^2 score of 0.14 indicates that the model explains only 14% of the variability in the data. While this is better than the results obtained from linear and polynomial regression, the model still does not have a strong predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VUcksOho2bYILFyB/fqhZE1LqgpS2bdvStm1b4L/1eSZNmkT79u355ZdfcHV1TZbzBgQEJPqqmzlz5hAaGkrLli2TpRYREUlb1GOTjEJDoWhReOMN+Phj079Fi5qOpwRnZ2fy5MlDnjx5yJs3LyVKlGDo0KHcu3eP3377LdnOmz17drJkyZKotkajMdnqEBGRtEfBJpmEhsL778PD0Rmzc+dMx1Mq3DwuXTpTJ52TkxNeXl6MGjWK+vXrU6VKFf7880+MRiMzZ86kbt26eHh40KRJE3788cc4z7Fz506aN29OuXLlePfddzl06FCc+x8fitq3bx9t2rTB09OTatWqMWjQIMLDwwkICCAwMJBz587FGcpaunQp77zzDuXKleOdd95h3rx5xMTEmJ/v8OHDfPrpp5QvX57//e9//Pnnn8n15RIRkVRGQ1HJIDoaunWDhDojjEYwGOCLL6BJk5QblgIICwtj1KhRODs7U6tWLWbMmMF3333H9OnTyZIlC+7u7kyePJmffvqJQYMG4ebmxo4dOxgyZAi3b9+mZcuWnDlzhrZt2/Luu+8yZswYjh49yqBBg554zrNnz9KqVSu8vLxYvHgxd+7coV+/fgwaNIihQ4cSHh7Ozz//TEhICDlz5mTx4sVMnDiRQYMG4eHhwf79+xk+fDhhYWH07t2b27dv89lnn1G+fHm+//57Ll26xMCBA1PuiygiIjZNwSYZbNoUv6fmUUYjnDljalenTvLVMX36dObMmQNAVFQUDx48wM3NDT8/P1566SUAateuTbVq1QDTRN65c+cybtw43njjDQAKFy7MuXPnmD17Ni1btmTJkiXkzp2bwYMH4+joiJubGxcuXGD06NEJ1rBkyRKyZcvGmDFjSJ8+PQAjRoxg+/btuLi44OzsjKOjI3ny5AEgODiYjh070rBhQwAKFSrEnTt3GDp0KN26dWPlypXcu3ePsWPHkiVLFl555RX69++Pj49P8n0hRUQk1VCwSQYXLli23fP66KOPaNWqFQAODg4Jzn0pUqSI+fOjR48SERFBnz596Nevn/l4bCi6f/8+hw8fpnTp0nEmB1eoUOGJNRw6dIgyZcqYQw1ApUqVqFSpUry2165d4+LFi/j7+xMYGGg+HhMTQ0REBGfPnuXw4cMULVo0zuvw9PRMzJdDRETSAAWbZJDYPQaTey/CbNmyxQkuCcmYMaP589iJvH5+fhQvXjxeWycnpzjtYsXO20lIUjYRjZ1H069fP3Mv0qNiN29MyvlFRCRt0eThZFCzJhQsaJpLkxCDAQoVMrWzJcWLFyddunScP3+eIkWKmD82bNjA7NmzcXBwoFSpUvzzzz88ePDA/Lh//vnnic/58ssvs3//fqKjo83HVq9eTa1atbh3716c0JMrVy5y5crF6dOn45z/33//xc/PD4BSpUpx4sQJrl27lqjzi4hI2qJgkwwcHcHf3/T54+Em9rafX8pOHE6MLFmy8NFHH+Hn58eyZcs4c+YMP/zwA+PHjzfvdt6iRQvu3btH//79OXbsGOvWrYszbPS4jz/+mOvXrzN48GCOHTvGzp07mTBhAtWrVydTpkw4Oztz8+ZNTpw4QVRUFO3bt2fBggUsWLCA06dPs2bNGoYOHYqTkxNOTk40aNCAXLly8eWXX3Lw4EG2b9/OqFGjUupLJCIiNk7BJpk0awYhIVCgQNzjBQuajjdrZp26nqVfv3589tlnTJkyhXfeeYegoCC6dOlC165dAciXLx/z5s3j4sWLNG3alDFjxuDt7f3E58uXLx9z5szhxIkTNG3alC+++ILatWszePBgAOrVq0eePHlo3Lgx+/fvp23btvTr149vvvmG+vXrM3z4cJo1a8bw4cMB09o88+fPJ3369LRo0YLevXvToUOH5P/CiIhIqmAwprEV0urWrQvA2rVr4913//59Tpw4QbFixeLMPXkR1lx5WORRyfHzLSJiltD8CwtGjKe9fz/KpnpsgoODzVfxxDpw4ACffPIJ5cuXp06dOsyePdtK1T0fR0fTJd0tWpj+VagRERG787RJpSnMZoLN3LlzmTJlSpxj169fp02bNhQtWpSlS5fStWtX/P39Wbp0qZWqFBERkTieFV5SONxY/TrZsLAwBgwYwK5duyhWrFic+5YsWYKTkxNDhgwhXbp0uLm5cerUKWbOnMl7771npYpFREQESHxoMRgsOiz1NFYPNv/++y/ZsmXjxx9/JCgoiHPnzpnv27lzJ5UqVYqzTsnrr7/O9OnTuXr1Krly5UrwOWPH4RJy4cIF83ooIiIiYhlG4FtgDzAYcLFSHVYPNl5eXnh5eSV438WLFylRokScY3nz5gXg/PnzTww2IiIiknL+BToDGx/efg+oYqVarB5snub+/fvm1W5jZciQAYCIiIgnPu5pM6af1psjIiIiiRcODAcmAFGAMzAMqGzFmmw62GTMmDHOCrfwX6Bxdna2RkkiIiICrFixgi7AqYe3mwBTgMLWKwmwoauiEuLq6sqlS5fiHIu9nS9fPmuUJCIikqadPn2apk2b0qhRI05hCjLLgWU8JdSk4JJ5Nh1sKlWqxK5du+LsM7R161aKFSum+TUiIiIpKDIykgkTJlCqVCmWLVtGunTp6NOnD/vv3KHx0x6YwusA23Swee+997hz5w4DBgzg6NGjhIaGMm/ePDp27Gjt0kRERNKMLVu2UKFCBXr16kV4eDg1a9bk77//ZsyYMbi4uDw5vFhhcwObDja5cuVi1qxZ5n2GAgMD6d27N02bNrV2aanKnTt38PDwoFq1avHmLD3LkSNHWL9+vUXr8fLyIiAgwKLPKSIilnf16lXat29PjRo12LdvH7lz5+brr79mw4YNlClTJm5jozH+hxXY1OThMWPGxDtWrlw5Fi9ebIVqLMQGNotauXIluXLl4sqVK6xevZoGDRok+rEdO3akadOm1KlTJ/kKFBERmxITE8PcuXPp3bs3V69eBaB9+/aMGTPG5qeC2HSPTaoXGgpFi8Ibb8DHH5v+LVrUdDwFLV26lBo1alC1alUWLVqUoucWEZHUZd++fdSuXZt27dpx9epVypYty+bNm5k5c6bNhxpQsEk+oaHw/vtw9mzc4+fOmY6nULg5duwYe/bsoXr16rz99tts376dY8eOxWmzYMEC/ve//1GuXDnq16/P8uXLAdOQ0blz5wgMDDRvTuru7k7oY7U/PrS0dOlS3n33XcqVK0f58uVp1aoV//77bzK/UhEReRF3796lT58+eHp6snnzZlxcXJgwYQK7du2ievXq1i4v0RRskkN0NHTrlvD4YuyxL74wtUtmISEhODs7U6tWLd58802cnJz47rvvzPfPnj2bCRMm0K5dO1asWEHLli3p168fW7ZsISQkBFdXV9q2bZvoOTGrV69m8ODBfPbZZ6xatYp58+Zx//59BgwYkFwvUUREXtDy5cspXbo048aNIyoqiqZNm3LgwAG+/PJL0qdPb+3ykkTBJjls2hS/p+ZRRiOcOWNql4yioqL46aefeOONN8iUKRNZsmShdu3aLF++nHv37gGmXdU//fRTPvjgAwoXLkzLli358ssviY6OJmfOnDg6OuLs7Ez27NkTdc7s2bMzYsQI3n33XQoUKICHhwfNmzfn0KFDyfhKRUTkeZw6dYomTZrw7rvvcvr0aYoWLcpPP/1EaGgohQoVsnZ5z8WmJg/bjQsXLNvuOW3YsIHLly9Tv35987H69euzevVqVq5ciZeXF5cuXcLDwyPO49q1a/fc56xUqRI5c+YkODiYU6dOceLECQ4cOEBMTMxzP6eIiFhWZGQkkyZNYtiwYYSHh5M+fXp69uzJV199lepX9lewSQ6J3T08mXcZj50L4+vrG+++RYsW8fbbbwNgSOy28w8ZHxtii4yMNH++cuVKevfuTcOGDSlXrhzvv/8+hw8fZtiwYUktX0REksGmTZvw9vY2z32sXbs2wcHBlC5d2sqVWYaCTXKoWRMKFjRNFE5ono3BYLq/Zs1kK+HatWts2LCBZs2a0aZNmzj3zZs3j5CQEE6dOkXevHn5559/4mwO6uvrS968efnqq6/iPW/69Om5ffu2+fadO3e4du2a+fa0adN4//33GTp0qPlY7KakRqMxySFKREQs4/Lly/Tu3Zu5c+cCkCdPHiZMmECrVq3s6nezgk1ycHQEf3/T1U8GQ9xwE/vD4+eXrOvZLF++nKioKNq3b4+bm1uc+zp16sQPP/zAd999x+eff86kSZMoWrQoFSpUYNOmTaxdu5bZs2cD4OLiwsmTJ7ly5Qq5c+fG09OTxYsXU6lSJdKnT4+fnx/p0v33Y5Q/f37++usv/v33X7JkycLvv//OwoULAXjw4IF5d3YREUkZMTExzJkzhz59+pj/EP38888ZPXo0OXPmtHJ1lqfJw8mlWTMICYECBeIeL1jQdLxZs2Q9fWhoKNWqVYsXagAKFSrEW2+9xcqVK3n33Xfx8fFhypQpNGjQgEWLFjF58mRef/11AFq1asX69etp27YtAEOGDCFPnjx89NFHdOjQgcqVK+Pp6Wl+7oEDB5I7d24++eQTmjdvzrp16xg3bhwAe/bsSdbXLCIicf3zzz/UrFmTDh06cO3aNTw8PPjjjz+YPn26XYYaAIPx8QkTdi52yCV2eORR9+/f58SJExQrVoyMGTNa5oQ2sPKwCCTTz7eI2KQ7d+4wZMgQ/Pz8iI6OJnPmzAwbNoyuXbvG6WVPTZ72/v2o1PnqUhNHR9B2BCIikgKMRiPLli3D19eXsw+XHXnvvffw8/OjYMGCVq4uZSjYiIiI2IETJ07QtWtXVq5cCUCxYsUIDAyMs+RHWqA5NiIiIqnYgwcPGD16NGXKlGHlypWkT5+eAQMGsG/fvjQXakA9NiIiIqnWhg0b8Pb25sCBAwDUqVOHqVOnUrJkSStXZj3qsREREUllLl26ROvWralTpw4HDhwgT548LFiwgN9//z1NhxpQsBEREUk1YmJimDFjBiVLlmT+/PkYDAY6derEoUOH+OSTT+xqob3npaEoERGRVGDPnj14e3uzdetWAMqXL8+0adOoUqWKlSuzLeqxERERsWG3b9+mR48evPbaa2zdupUsWbLg5+fHjh07FGoSoB4bERERa0po+MhoxGg0EhoaSrdu3Th37hwAzZs3Z/LkyRR4fFV7MVOPjZ3y8vLC3d3d/FGqVCkqVqxIq1at2Llz5ws//7Jly6hZsyZly5Zl/vz5L/x84eHhfPPNN0+8f9u2bXFej7u7O2XKlKFOnTp89dVX3Lp164VreFzfvn1p1aoVAGfPnsXd3Z1t27Yl6rFHjhxh/fr15tvu7u7m3dZFRMyeMCfmuMFAgwYNeP/99zl37hzFixdn1apVLFmyRKHmGdRjY8fatm1r3uPJaDRy48YNJk2aRPv27fnll19wdXV97uceOXIkXl5e+Pr6kjVr1heudc6cOYSGhtKyZcuntvv+++/Jnz8/ANHR0Rw6dIi+ffty5coVpk2b9sJ1PEn+/PnZvHkz2bJlS1T7jh070rRpU+o8XHV68+bNZMmSJdnqE5FUKIFQEwFMAEYA91etwsnJiT59+tCvXz8yZcqU0hWmSuqxsWPOzs7kyZOHPHnykDdvXkqUKMHQoUO5d+8ev/322ws9961bt6hcuTIFChSwyBt2Yrcsy5kzp/k1ubq6Urt2bVq3bs369eu5ffv2C9fxJI6OjuTJkwcnJ6fnenyePHm0P5OI/CeBULMO8AC+Au4DXsDeBw8YNmyYQk0SKNgkktFo5O7du1b5sOQ+pbGbn8W+QT948IDx48dTs2ZNPD09+eCDD9i8ebO5fWhoKF5eXowcOZKKFSvSqVMn3N3dAejfv7/582c9D8C+ffto06YNnp6eVKtWjUGDBhEeHk5AQACBgYGcO3cOd3d38/4mieXo6IjBYCBdunTmIauZM2dSpUoVmjZtSnR0NGFhYXTv3p2KFStSpUoVOnXqxMmTJ83PYTQaCQ4OplatWpQvX54BAwYQERFhvj+hoagFCxbwv//9j3LlylG/fn2WL18OmIYBz507R2BgoHko6/GhqGXLltG4cWPKlSuHl5cX06ZNIyYmJs65Vq1aRfPmzSlbtix169YlJCTE/PirV6/i6+tLlSpVKFeuHB999BHbt29P0tdNRGxDGNAKU5A5BOQDvgHWAO5WrCu1UrBJBKPRSI0aNcicObNVPmrWrGmRcBMWFsawYcNwdnamVq1aAPTr149NmzYxfvx4fvjhB9555x06deoUZ37IuXPnCAsL44cffuDLL780B5b+/fubP3/W85w9e5ZWrVqRM2dOFi9eTGBgINu2bWPQoEHmITNXV1c2b95sHmp6lqioKHbu3Mn8+fOpXbt2nL9o1q9fz+LFixk1ahQRERG0atWK6OhoFi5cyIIFC8iRIwcffPABYWFhAMyYMYNZs2bRu3dvQkNDyZw5Mz///PMTzz179mwmTJhAu3btWLFiBS1btqRfv35s2bKFkJAQXF1dadu2LQEBAfEeO3fuXAYOHMiHH37Ijz/+SPfu3Zk9ezbjxo2L027MmDF06tSJZcuWUbVqVQYOHMiZM2cAGDJkCPfv32fhwoX89NNPFCtWjM6dOxMeHp6or52IWF8MMA0oCSwEDEBn4CDw8cPbknSaY5NIqXHRo+nTpzNnzhzAFAIePHiAm5sbfn5+vPTSS5w6dYoVK1YQEhJC2bJlAWjTpg0HDx5k9uzZ5vkhAJ07d6ZQoUJxnj9LlizkyZMnUc+zZMkSsmXLxpgxY0ifPj0AI0aMYPv27bi4uODs7Gwe7nmahg0bmr8X9+/fx9HRkdq1azNs2LA47dq2bUvRokUB07yc69evM3HiRPO5R44cybZt21iyZAldunRhwYIFfPrppzRs2BAwBbWnTRSeO3cun376KR988AEALVu25P79+0RHR5MzZ04cHR1xdnYme/bscR5nNBqZOXMmn3zyiXk+UdGiRblx4wZjx47Fx8fH3LZNmzbUrVsXgD59+vD999+zZ88eChUqxOnTpylRogSFCxcmQ4YMDBgwgEaNGuHo6PjUr5+I2IbdQCcgtp+1AqaQU8lqFdkPBZtEMBgMbNq0yWp/DTs7Oz9XsProo4/MQyEODg5kz549znyY/fv3A/Dpp5/GeVxkZGS8CcGxISEhiXmeQ4cOUaZMGXOwAKhUqRKVKiXtv/GMGTPIly8fYBpOy5UrV4LzXh6td//+/dy5c4fKlSvHaRMREcGxY8e4fv06ly9fNoeyWOXLl+fYsWPxnvvatWtcunQJDw+POMfbtWv3zPqvXbvGlStXeO211+Icr1SpEpGRkRw/fpxcuXIB4ObmZr4/9vsWGRkJQJcuXejVqxerV6+mYsWK1KhRg/r165MhQ4Zn1iAi1nPr1i0GDRpEAKYemyzASEw9NfqzxDIUbBLJYDDg4uJi7TKSJFu2bBQpUuSJ98cOb33zzTfxXpuDQ9xRyqdNfE3M86RLl84ivV4vvfQSBQsWfGa7R9/gY2JiKFasGFOnTo3XztnZ2fz548N9sfORHhcbpJ7n9TxpSDE6OjreORMKbLGPf+utt9i0aRObNm3ijz/+YNasWfj7+7NkyRJeeeWVJNclIsnLaDQSEhLCF198wfnz5wH4EJgEvPT0B6ZAdfZFc2zSsNg3wEuXLlGkSBHzR2hoKEuXLrXo87z88svs37/f/AYOsHr1amrVqsW9e/eSdaivRIkSnD9/nixZsphrK1CgABMnTmTHjh3kzJmT/Pnzs2vXrjiP27dvX4LPlzlzZvLmzcs///wT57ivry8jRox4ai25cuUiV65c8c61c+dO0qdPT+HChZ/5eh48eMDo0aM5c+YM9evXZ8SIEaxevRoHB4c4c6NExDYcO3aMd955hw8++IDz58/z8ssv8+uvv7LIaFSoSQYKNmnYK6+8whtvvMHgwYNZu3YtZ86cYfbs2UyfPj3efJoXfZ6PP/6Y69evM3jwYI4dO8bOnTuZMGEC1atXJ1OmTDg7O3Pz5k1OnDhhHm6xlMaNG5MtWza6dOnC33//zbFjx+jXrx8bNmwwh7IOHTrwzTff8P3333PixAn8/PzYu3fvE5/z888/Z968eSxbtozTp0/zzTffsHbtWt58800AXFxcOHnyJFeuXInzOIPBQNu2bVm4cCHffPMNp06d4qeffiIwMJAPP/wwUZfOOzk5sWfPHgYOHMjff//N2bNnCQ0N5e7du3h6er7AV0pELCkiIoLhw4dTpkwZfv31V5ycnBg8eDD//PMP9erVMzV6UnhRqHluGopK4yZPnszkyZMZPHgwN2/epFChQgwfPpz33nvPos+TL18+5syZw4QJE2jatClZs2alfv369OjRA4B69eqxZMkSGjduzMKFC+PNX3kRWbJkYeHChYwbN4727dsTHR1NqVKlmD17tjnYtGzZkpiYGKZOncqVK1eoWbMm77//PidOnEjwOT/55BMiIiKYMmUKly9fpmjRokyePJnXX38dgFatWjF27FiOHDnCjz/+GOex7du3x8nJiXnz5jF69GhcXV3p0KFDouboxPL392f06NF4e3tz+/ZtihcvzsSJE6lYseJzfpVExJJ+//13vL29OXz4MABvvvkmQUFBlChRIn5jhRiLMhgtuUhKKhB7lcnatWvj3Xf//n1OnDhBsWLFtJia2B39fIskv4sXL/Lll1/y7bffAuDq6srkyZP58MMPU+XVtbbkae/fj9JQlIiIyAuKjo4mODiYkiVL8u233+Lg4ECXLl04ePAgH330kUJNCtJQlIiIyAvYtWsX3t7e7NixA4CKFSsybdq0eMs6SMpQj42IiMhzuHnzJr6+vlSuXJkdO3aQNWtWAgMD+fPPPxVqrEg9NiIiIklgNBpZsmQJ3bt358KFCwC0aNGCiRMnJnpLGEk+CjYiImnMgwcQHAzHjoGbG3TuDM+5cb1VRUfDpk1w4QLkzw81a0Jy7ypy5MgRfHx8WL16NWBa7iI4ONi81INYn4aiEpDGLhSTNEI/1wLQuzc4O0P37hAYaPrX2dl0PDUJDYWiReGNN+Djj03/Fi1qOp4c7t+/z9ChQylbtiyrV68mQ4YMDB06lL179yrU2Bj12Dwiffr0GAwG7t69G2enaBF7ELvX2aP7dUna0rs3jB8f/3h09H/HH9tk3iaFhsL778df/uXcOdPxkBBo1sxy51u9ejU+Pj4cOXIEMK27FRQUxMsvv2y5k4jFaB2bx1y4cIEbN26QNWtWsmbNarE9jkSsxWg0Eh4ezqVLl8iePbvmAKRRDx6YemYe2dUkHkdHCA+37WGp6GhTz8zZswnfbzBAwYJw4sSLD0tduHCBHj16sGjRIgDy58+Pn58fzZs31/uCFSR2HRv12DzG1dWVTJkycenSJW7dumXtckQsJnv27Li6ulrl3NaYC5EcUvPrCA5+eqgB0/3BwfDFFylS0nPZtOnJoQZMvThnzpja1anzfOeIjo5m6tSpDBgwgFu3bpnXpBk+fDhZs2Z9vieVFKNg8xiDwUD27NnJli0b0dHRREVFWbskkReWPn16HK30DhwaCt26xX0zKlgQ/P0tO1yQ3FL76zh2zLLtrOXhRUgWa/e4nTt30qlTJ/NGtZUqVWLatGlUqFDh+Z5QUpyCzRMYDAbSpUtHunT6Eok8r5SeC5Fc7OF1uLlZtp21JHYkNakjrjdu3OCrr74iODgYo9FItmzZGD16NJ9//rnV/iiQ56M5NiKSLFJyLkRyspfXcecOJGLzeG7fhsyZk7+e5xX7/Th3LuG9I5P6/TAajSxatIju3bsTFhYGmDa5nTBhAvny5bNs8fJCtFeUiFhVUuZC2DJ7eR2zZlm2nbU4OpqG/8AUYh4Ve9vPL3Gh5vDhw7z11lt8/PHHhIWF4e7uztq1a1mwYIFCTSqmYCMiySK550KkFHt5HfYyxwZMw34hIVCgQNzjBQsmbljw/v37DB48mLJly7J27VoyZszIiBEj2LNnD15eXslXuKQITSARkWSRXHMhUpq9vA57mWMTq1kzaNIk6Vep/frrr/j4+HDsYYJ7++23CQwMxC21vHB5JvXYiEiyqFnT9Bf0k5b7MBigUCFTO1tmL6+jc+dnv+k7Opra2aPz58/z4Ycf8vbbb3Ps2DFeeuklvv/+e37++WeFGjujYCMiycKScyGsyV5eh5MT9Ojx9DY9etj24nyPSuyWClFRUUyZMoWSJUuyZMkSHBwc+OKLLzh48CDvv/++FtqzQwo2IpJsXnQuhK2wl9cxbhz06hU/hDk6mo6nhu0U4L/L7x+f1B17+X1suNm+fTuVK1emW7du3L59mypVqrBr1y4mT55MlsRcIiapUqq43DsyMpLAwECWL1/OzZs3KVWqFD179nyuBZN0ubdIytNu0rblzh1o1eq/78eCBbZ9ifejHr38fgkGmgEGwAiEAh8ajLz00g0aNerP9OnTMBqNZM+enTFjxtChQwccHGzv73l7+blKbol+/zamAv7+/sbq1asbN23aZDx58qRxwIABxgoVKhgvXryY5Ofy8vIyenl5JUOVIpKQpUuNxgIFjEbThdGmjwIFTMcl5fXqZTQ6Osb9fjg6mo6nBuvWmWqOAmPMoy8CjNFgnA9GyGvElHWMn376qTEsLMzaZT/R0qVGY8GCcb8fBQvq/0dCEvv+bXvRNQFr166lYcOG1KhRgyJFitC3b1/u3LnD33//be3SROQpQkPhvfdMQwSPOnfOdPzx+RCSvGJ39358z6jY3b1797ZOXUlx4QJEYYg3j+Ig8BbwKQCXeOmlkqxbt4558+aRN2/elC4zUWL/fzw+pHb2rP5/vIhUEWyyZ8/OunXrOHv2LNHR0SxevBgnJydKlSpl7dJE5Amio6F166e3ad362RszimU8eACTJj29zaRJpna2LP/H/4UaA3APGAiUA34HMgIjgLnnD1LneXfBTAHR0fD5509v8/nn+v/xPFLFOjYDBgyge/fu1K1bF0dHRxwcHPD396dw4cIJto8dh0vIhQsXyG/rC06I2IG1a01zOZ7mzh1Tu3r1UqamtMxedveuiSnQAKwCugDHH96uDwQCxQBbzwPr18PVq09vc/Wqqd1T3tIkAamix+bYsWNkzZqVoKAgFi9eTLNmzejTpw8HDx60dmki8gQLFli2nS2Ijja90Xz3nenf1PTX9JEjlm1nLQbgHNAcU5A5DhQElgIrMIWa2Ha27PffLdtO/mPzPTbnzp2jV69ezJ07l4oVKwJQtmxZjh49SkBAAEFBQfEe87QZ00/rzRERy7l927LtrC00FLp1izsfomBB0xo3qeFy78Re/2rL18lGRUUxBRgM3AEcgW7AEODxi7dt+GUAcOqUZdvJf2y+x2bv3r1ERkZStmzZOMc9PDw4efKkdYoSSSGpuYfgpZcs286anrRuytmzcddNsWXZs1u2XUr7888/qVSpEl9iCjVVgb+AicQNNbGXQ6WCb4kkE5sPNrHzYQ4dOhTn+OHDhylSpIg1ShJJEYldWdVWVa1q2XbWEh1t6ql5Uk+G0Wiak2LroTOxC+za2kK8169fp1OnTlSrVo2///6bdOlyMA3YBJR9rG3stygGmPGmbffZJPbtS29zSWfzwaZcuXJUrFiRPn368Oeff3Ly5En8/PzYunUrnz9rSrlIKpXYlVVtWaFClm1nLZs2xf8+PO7MGVM7W5Yjh2XbJTej0cj8+fNxd3dn+vTpGI1GWrduTefOh+j0lIGmGCAdRh7OXLBZid1EXJuNJ53NBxsHBweCg4N5/fXX6devH82aNePPP/9k7ty5lC9f3trliVjc03oIYo+lhh6CatUSt+litWopU8/zenwNnhdtZy3Xr1u2XXI6cOAAXl5etG7dmsuXL1O6dGk2bNjA3LlzefvtPIApvIRguvop5uG/IQ+PA9SqZaXiE6lOHciV6+ltcuUytZOksflgA5AtWzYGDx7MunXr+Ouvv1i0aBGVK1e2dlkiyeJZPQRGY+roIfjjj8RdXvzHHylTz/O6fNmy7awlNUweDg8Pp3///nh4eLB+/XoyZcrE6NGj2b17N7UeJpVt2/5r/wFG0mHE8eG/HzzSk/NoO1vk6AgzZjy9zYwZ2lrheaSKYCOSlly4YNl21mIvryNPHsu2s5ZnrZmS1HaWtnLlSsqUKcPo0aOJjIykYcOG7N+/n759++L0yMZiz1pkMKntrKlZM1i6NOHNVZcuTR1X29kim7/cW+R5pOZN5RK7fqStrzOZ2FXsbXS1ezN7eR379lm2naWcPXuWbt26Efpw4lihQoWYMmUKTZo0wZDATObw8MQ9b2LbWVuzZtCkSer9fWWLFGzE7qT29UZq1jTVe+5cwsMCBoPp/po1U762tGjPnsS3e+ut5K3lRdy8adl2LyoqKoopU6YwaNAg7t69i6OjIz169GDQoEFkfspW405OcO/es58/Ne0e7+iouTSWpKEosSv2cDWRo6MphEH8S29jb/v52f5fdJcuWbadtWzebNl21pI+vWXbvYitW7fy2muv8eWXX3L37l2qV6/O7t27GTdu3FNDDUCNGok7R2Lb2YLUvF6VLVKwEbthL1cTgalnKSQk4bH3kJDU0fNkL0Nq9rKCckSEZds9j2vXrvH5559TrVo19u7dS86cOZk1axYbN26Mtwjrk9y9m7hzJbadtaX29apskYKN2A17uZooVrNmcPIkrFsH335r+vfEidQRasA0VJaYy1ltfUjtxg3LtrMWa07mNhqNzJs3D3d3d2bOnAlA27ZtOXToEO3atcPBIfFvRZkyWbadNdlDD7Mt0hwbsRv2chXOo1L72Puz/vp/8CBl6hDImNGy7RLr33//pXPnzmzcuBGAMmXKMG3aNGo851jRK6+YdoRPTDtbltge5iZNbH/Y2daox0bshr0MfdiL9evhzp2nt7l929TOlj0+HPii7awlseuZWmrd0/DwcPr160f58uXZuHEjzs7OjBs3jt27dz93qAFo0MCy7azFXla0tkUKNmI3Yq8metJeNwaDafl+Wx/6eFRqnlSY2MBi68GmWDHLtrOWlJxjs2LFCkqXLs2YMWOIioqiSZMm7N+/n169epH+BWcnr1lj2XbWYi8rWtsiBRuxG7FXEz1ts8LUcDVRrNQ+qTAmxrLtrCWxX29b/75s2GDZdgk5ffo0TZs2pVGjRpw6dYrChQuzfPlyli1bZrFNi7/+2rLtrOXiRcu2k/8o2IjYIHuYVPisicNJbWct9+9btp21JGfQjIyMZMKECZQqVYply5aRLl06+vTpw/79+2ncuHHSn/ApErOGTVLaWcu1a5ZtJ/9RsBGz1DzsAf9NxnsSgyF1XO5tL5etp7bdpJ/E2dmy7ezNli1bqFChAr169SI8PJyaNWuye/duxowZg4uLi7XLs1n20qNpixRsBEj9wx5gP5d728vrWL7csu2sxV72ikrsEGxi2129epX27dtTo0YN9u3bR65cufj666/ZsGEDr7766vMX+gxPmkP3vO2sJTXttp7aKNiIXQx7gP1c7m0vr+PMGcu2s5YDByzbzlostbt3TEwMc+bMwd3dndmzZwPQvn17Dh06xGeffZbg/k6WFBlp2XbWojk2yUfBJo2zl2EPsJ/LvXPmtGw7a7HUG6m1aY7Nf/bt20ft2rVp164dV69epWzZsmzevJmZM2eSK4UmS9nLz9Uzdo5Icjv5j4JNGmcvwx5gP5d728sQjr28AWkuBNy9e5c+ffrg6enJ5s2bcXFxYcKECezatYvq1atbu7xUqUwZy7aT/2jlYQuIjk69W87by7AH/He59/vvm0LMo2+YqWnzyCNHLNvOWuxlj6W0bvny5fj6+nL69GkAmjZtir+/P4UKFbJyZanbP/9Ytp38Rz02Lyg01DQk8Oik25w5U8+8FHsZvokVu3mkk1Pc4xkypJ7NI+3lclZ72d07rTp16hRNmjTh3Xff5fTp0xQpUoSffvqJ0NBQhRoLeJgTLdbOFly7BmXLmpZwKFvWepeqK9i8gNBQeO89uHUr7vFbt0zHU0O4iR2+eZrUMHzzqPfei7966v37puOpQSI3OU50O2tJ7D5Q2i/K1kQyduxYSpcuzY8//ki6dOno168f+/fvp2HDhtYuzm5Ya++u5OLqago0+/aZAs2+fabbrq4pX4uCzXOKjn72G+V779n+pFtHx8TtV2LrwzexnnVBhq1fAgrg7m7ZdtaSIYNl24nlLMFAFAaiH/67hNj/GJsAT/r27Ut4eDi1atViz549jBo1Cue0ulBPMrGXZQTAFF7CwhK+Lyws5cONgs1zmjvXsu2sxZ7GeUNCLNvOWiZPtmw7a3m8J/NF24llRGHgfcAR0xuAI1AbaI0BqAX8S+7cuZk3bx7r16+ndOnSVqzWfi1aZNl21nLt2pNDTaywsJQdljIYjbZ+TYJl1a1bF4C1idn3/imS8pe/LX+F7eV1gP28Fr0O22JPryMKg/mvWQMQA8wB+gD/ve98ztWro8lpo+sJ2NP3I7Fs+XWULp24NZxKlYL9+1/sXIl9/36uHptly5ax4eFOaQcOHKBBgwZUqFCB/v3780AD5iIiNmfJY6HmH6Am0AFTqCkHbAGWMMNmQ43YnqNHLdvOEpIcbL7++mvzRDKAoUOHcvPmTZo3b86aNWuYMmWKxYsUEZEX0wxToLkL9AQ8gT+AzMAkYBdQ7WE7kcRK7DzSlJxvmuRgs2TJEtq3b4+3tzfnz5/n77//pnPnzvTr148vv/ySlStXJkedIiLygn4ASgETgWjgPeAA0J3/FjVLBfPrxYYkdp/TlNwPNckL9J09e5ZatWoBsGHDBgwGA15eXgAUL16cq1evWrZCERF5ISdOnKAL8PPD28WAQKB+Am1teDqH2CBbXIgzyT02OXPm5MqVKwCsW7eO4sWL4/rwWq5Dhw6RO3duy1YoIiLP5cGDB4wePZoyZcrwM5Ae6A/sI36oMT78SAXLb4k8VZJ7bLy8vJg4cSJbt25l48aNdO/eHTDNvQkKCqJZaljaVUTEzm3YsAFvb28OmC9ZqcNfrCehrYdie2ligA8wqtdGUrUk99j069ePatWqsWPHDj766CPatm0LwKJFi6hduzZffPGFpWsUEZFEunTpEq1bt6ZOnTocOHCAPHnyMH/+fOB3ymLkSXt1xgDpFGnEDiS5x8bJyYlhw4bFO/7jjz+SQUuIiohYRUxMDLNmzaJv375cv34dg8FAx44dGTVqFDly5ODTT03t0mFkCQbzVVKxw08fKNSInUhysNmxY8cT7zMYDLi4uFCoUCEyZ878QoWJiEji7NmzB29vb7Zu3QpA+fLlmTZtGlWqVEmwvUKM2LMkB5tWrVpheLhkYuyixYbHllB0cHDg3XffZdiwYTimlk2GRERSmdu3bzN48GCmTJlCdHQ0mTNnZsSIEfj4+JAuXZJ/vYvYhST/5E+dOpXu3bvTpEkTGjZsSO7cubl69Sq//vorixYtolevXjg6OuLn50fBggXx9vZOjrpFRNIso9FIaGgo3bp149y5cwA0b96cyZMnU6BAAStXJ2JdSQ42M2fOpEWLFvTp08d8rFixYlSsWBFnZ2dWr17NggULiImJYeHChQo2IiIWdPz4cbp06cKqVasA0/phQUFBvP3221auTMQ2JPmqqH///ZeaNWsmeF+VKlXYs2cPAKVKleLChQsvVp2IiAAQERHByJEjKVOmDKtWrSJ9+vQMHDiQffv2KdSIPCLJPTZ58uRh27ZtVKtWLd5927ZtI1euXABcv36drFmzvniFIiJp3Lp16/D29ubQoUOAaT2x4OBg3N3drVyZiO1JcrBp0aIFEydO5N69e/zvf/8jV65cXL16ldWrV7Nw4UK6dOnCxYsXmTp16hNn5IuIyLOFhYXRs2dPFi5cCEDevHmZPHkyLVq0iHfRhoiYJDnYtGvXjnv37jFr1iwWLFgAmCayZcmSha5du9KxY0eWLVvGgwcP6NGjh8ULFhGxdzExMcyYMYN+/fpx48YNDAYD3t7ejBw5kuzZs1u7PBGbZjDGXrOdRPfu3WP37t1cv36dfPnyUapUKVwebt8ZHR1ts5d5161bF4C1a9e+0PMk5Y+l5/sKpwx7eR1gP69Fr8O2pPTr2L17N506dWL79u0AVKhQgWnTplGpUqUXel59P2yLXkfSJfb9+7kXOsiUKVOC82wAmw01IiK26tatWwwaNIiAgABiYmLIkiULI0eOpHPnzvqdKpIESQ42165dY+TIkaxfv5579+7xeIePwWBg//79FitQRMSeGY1GQkJC+OKLLzh//jwAH374IZMmTeKll16ycnUiqU+Sg82QIUPYsGEDDRo0wNXVFQeHJF8xLiIiwLFjx/Dx8eHXX38FwM3NjeDgYOrVq2flykRSryQHm02bNtG/f38+/PDD5KhHRMTuRUREMG7cOEaOHElERAROTk7069ePvn37kjFjRmuXJ5KqPdfu3oUKFUqOWkRE7N7vv/+Ot7c3hw8fBuDNN98kKCiIEiVKWLkyEfuQ5HGkt956ixUrViRHLSIiduvixYu0bNmSunXrcvjwYVxdXfnuu+/47bffFGpELCjJPTalS5fGz8+PM2fO4OHhEa/b1GAw4OPjY7ECRURSs+joaKZPn07//v25efOm+XfkiBEjyJYtm7XLE7E7SQ42w4YNA2DHjh3s2LEj3v0KNiIiJrt27cLb29v8u/K1115j2rRpVKxY0cqVidivJAebgwcPJkcdIiJ24+bNmwwcOJCgoCBiYmLImjUro0aNolOnTlqTRiSZPfcCfSIiEpfRaGTJkiV0796dCxcuAP/tr5c/f34rVyeSNiQq2Hz66acMHjwYNzc3Pv3006e2NRgMzJs3zyLFiYikHkf43/98WL16NQCvvPIKwcHBvPnmm1auSyT5ZMkCt28nrl1KSdRVUY+uLmw0Gp/6ERMTkyyFLlu2jPr161O2bFkaNGjAqlWrkuU8ieXsbNl2ImL7lmAgCgPRD/9dggG4DwwFyrJ69WoyZMjA0KFD2bt3r0KN2L3SpS3bzhIS1WMTu4v345+nlOXLl9O/f3/69OlDnTp1WLFiBT169MDV1RVPT88Ur0dE0p4oDDgAj+75lw14hUwceXi7Xr16BAUF8fLLL6d8gSJW8PLLsG1b4tqllCSvY/Ppp59y7NixBO87ePAgjRo1euGiHmU0GvH396d169a0bt2aIkWK4OPjQ7Vq1cy731pDeLhl24mI7YoNNbEuAC2A/wFHANPsmcX88ssvCjWSprRubdl2lpCoHpudO3eah6O2b9/Ojh07uHbtWrx269at48yZMxYt8Pjx45w7dy5eYJo9e7ZFzyMikpAlj4SaGGAqMAC4hekvQx9gOPAbH2IwfGCdIkWspEYNy7azhEQFm5CQEJYtW4bBYMBgMDB06NB4bWKDT8OGDS1a4MmTJwEIDw+nXbt27N+/n4IFC+Lt7Y2Xl1eCj6lbt+4Tn+/ChQu6OkFEEq0ZpuGnnUAnYNfD45WAaUCFR9qJpDXTpye+3RdfJGspZokKNgMGDKBZs2YYjUZat27NoEGD4nW3Ojg4kDVrVl555RWLFnjnzh0A+vTpQ5cuXejZsye//vornTt35uuvv6Zq1aoWPZ+IyKNuAQOBYMCIaV7NaOBz4NEVaQzxHypi9w4dsmw7S0hUsMmSJQuVK1cGYP78+ZQpUwYXF5dkLSxW+vTpAWjXrh1NmzYFoFSpUuzfv/+JwWbt2rVPfL6n9eaIiMQyGo0sWrSIHsDFh8daAhMA14Tap1hlIrbj7FnLtrOEJC/QV7lyZS5evMiGDRt48OCB+XhMTAz37t1j586dTJ482WIFurqafoU8vkncyy+/zPr16y12HhGRWIcPH6Zz587mP5JKYOqxSejPothAEwo0T5nyRGzG5cuWbWcJSQ42q1atolevXkRFRWEwmDpfjUaj+fPixYtbtMDSpUvj4uLCnj174uyvcvjwYQoXLmzRc4lI2nb//n1Gjx7NmDFjePDgARkzZuT+/QHsZiCZMIWYR4ecYkNNDPABRvXaSJpz6pRl21lCki/3nj59OqVLlyY0NJRmzZrRuHFjVq5cSa9evUiXLh39+/e3aIEZM2akffv2BAUFsWLFCk6fPs3UqVPZsmULbdq0sei5RCTt+vXXX3n11VcZNmwYDx484O2332bfvn3AV7hg5ElLj8YA6RRpJI2yxcVqk9xjc+LECSZMmEDp0qWpWrUqs2bNws3NDTc3N65evcq0adOoXr26RYvs3LkzmTJlYvLkyYSFheHm5kZAQABVqlSx6HlEJO05f/483bt3Z8mSJQC89NJL+Pv7895775l7osEUXpZgMF8lZcQ0/PSBQo2kYZGRlm1nCUkONg4ODmTPnh2AokWLcvz4cWJiYnBwcKBmzZqEhoZaukYA2rRpox4aEbGYqKgogoOD+eqrr7h9+zYODg74+voydOhQsmbNmuBjFGJE4npkqq1F2llCkoeiihcvzq5dppUcihYtSmRkJAcOHADg1q1bcSYUi4jYou3bt1O5cmW6devG7du3qVKlivnChyeFGhGJ7+GFyxZrZwlJDjYfffQRU6ZMYdKkSWTOnJkqVarQv39/FixYwMSJEylTpkxy1Cki8sJu3LhB586def3119m9ezfZs2dn2rRp/PHHH9p3TuQ53L9v2XaWkORg07x5cwYMGEDkwwGz4cOHExERwciRI4mKirL45GERkRdlNBr55ptvcHd3Z+rUqRiNRlq1asWhQ4fo2LEjDg5J/lUoIsDNm5ZtZwlJnmMD0LJlS/PnhQoVYtWqVVy/fp2cOXNarDAREUs4ePAgPj4+/P777wCULFmS4OBg3njjDStXJpL62eLkYYv8mWIwGMiZMyfnzp2jS5culnhKEZEXcu/ePQYOHEi5cuX4/fffyZgxIyNHjmTPnj0KNSJ2LFE9NtHR0UyZMoWQkBAA3n33XXr06IGjo2mnlAcPHjBjxgxmzZpFRERE8lUrIpIIq1atokuXLhw/fhyA+vXrExAQYPEFREXSOkdHiI5OXLuUkqgem+DgYKZPn06hQoUoVaoUc+bMYebMmQDs3LmThg0bEhgYSL58+Zg2bVqyFiwi8iTnzp2jefPm1K9fn+PHj1OwYEGWLl3KihUrFGpEkkH+/JZtZwmJ6rFZtWoVjRo1Yvz48QB8/fXXLFiwgFdeeYVu3bqRPn16vvzySz777DPzppUiIiklKiqKwMBABg4cyJ07d3B0dKRbt24MGTKELFmyWLs8EbtVowYsWpS4diklUT02Fy5coGHDhubbDRs25Pz58/Tt25fKlSuzatUqOnTooFAjIinuzz//pFKlSnTv3p07d+5QtWpVdu3axcSJExVqRJJZ27aWbWcJiQo29+7dI0eOHObbsSsPV6lShdmzZ5t34E5LXnvNsu1EJGmuX79Op06dqFatGn///Tc5cuRgxowZbN68GQ8PD2uXJ5ImeHlB5sxPb5Mli6ldSnmuq6Ji13z49NNP4+ylkpasXWvZdtbStatl21lTgQKWbSe2yWg0Mn/+fNzd3Zk+fTpGo5HWrVtz6NAhOnTooDVp7FzVqpZtZy27d1u2nbU4OsK8eU9vM3euDU4efhIXFxdL1ZHqZMsGbm5Pb+PmZmpnyyZPtmw7azp71rLtrGXdOsu2s5bkeB0HDhzAy8uL1q1bc/nyZUqXLs2GDRuYO3cuefLkeb5Cn+HXXy3bzlqMidzmKrHtrOWPPyzbzlrKl7dsO2tq1gyWLo3/R2PBgqbjzZqlbD0vFGzSam9NrKNHnxxu3NxM99s6R0fTD97TLF2asmn7RTzrl7Kt/9IGqFnT9AvhaQoVMrWzZZZ8HeHh4fTv3x8PDw/Wr19PpkyZGD16NLt376ZWrVqWKfgJ6tZ9dld75symdrbOHv5/gF6HLWrWDE6dMv2h8u23pn9Pnkz5UANJWHn4ww8/jHfsvffei3fMYDCwf//+F6sqFTl61LRUdIMGcPo0FC4MK1fafk/No2LTdps2cOvWf8ezZYM5c6zzg/kijEbTG+q5c/8dK1DA9ntqYjk6gr8/vP9+wr/YDAbw87P9sGmp17Fy5Uq6dOnCyZMnAdPFCwEBARQtWtTSJScotqs9gV93ZvPm2f73I5bRaPraJ3Q8NTEaoVo12Lr1v2NVq9p+T83jjEb4+294dKuy3btTR0/N4xwdoU4da1cBBqPx2T/OgYGBSXpSW159uO7DP6vW2vrkFyuIjoZNm+DCBdOaAzVrpp5f1vYoNBS6dYsbyAoVMoWB1BQ2n/d1nD17lm7duhEaGvrwMYWYMmUKTZo0sUpvcWgo+PrGD8xTpqSu74dIapXY9+9EBRt7omAjqYm9hM2kvI6oqCimTJnCoEGDuHv3Lo6OjnTv3p3BgweT+VljQsnMXr4fIqlRYt+/n2sTTBFJGbbStfuiEvs6tm7dSqdOndi7dy8A1atXZ+rUqZQtWzZ5C0wke/l+iNgzXRcpIlZ37do1Pv/8c6pVq8bevXvJmTMns2bNYuPGjTYTakQkdVCPjYgkvyfMVo1dk6Znz55cuXIFgDZt2jBu3Dhy586dwkWKiD1QsBGR5PWEib7/Ggx0rlWLjRs3AlCmTBmmTZtGjZTcVEZE7I6GokQk+SQQasKBfkB5YOPGjTg7OzN27Fh2796tUCMiLyxRPTbLli1L0pO+++67z1GKiNiVBELNCqALcOrh7SaAf3g4RXr3TsHCRMSeJSrY9O3bN87t2DUkHr1S/NF1JRRsRORRp4FuwLKHtwsDAUBjaxUkInYrUcHm0WvGDxw4QO/evfH29uadd94hb968XL9+nd9//52AgABGjx6dbMWKSOoSCfgDgzENQaUDegCDgLS705yIJKdEBZsCj+xs1bVrV7y9venQoYP5WL58+WjRogWRkZGMHz+e2rVrW75SEUlVtgCdgH0Pb9cApgKvWq2iF6cF+kRsX5InDx87doxSpUoleF+xYsU4m1o25BGRZHH16lXat29PDUyhJhcwB9hA6g41oaFQtCi88QZ8/LHp36JFTcdFxHYkOdgULVqU5cuXJ3jf4sWLKVGixAsXJSKpT0xMDHPmzMHd3Z3Zs2cD0B44BLThKb9sUsGuLqGhps08H/+77dw503GFGxHbkeR1bHx8fOjWrRsnT56kbt265MyZkytXrvDbb79x9OhRZs6cmRx1iogN27dvH97e3mzevBmAsmXLMnXqVKpXr/7EdWyAVBFqoqNNm3gmVGrsTtlffAFNmmhYSsQWJDnY1KtXj6CgIIKCgvD398doNOLg4ICnpydz586lYsWKyVGniNigu3fvMmzYMCZNmkRUVBQuLi4MHToUX19f0qdPb2oU++7/uFQQasA0p+ZpI+xGI5w5Y2qnfaRErO+5Vh728vLCy8uLiIgIbt68Sfbs2XFycrJ0bSJiw5YvX46vry+nT58GTMs8+Pv7U7hw4fiNU0mISciFC5ZtJyLJ67m3VDh27Bhbtmzh8uXLfPLJJ5w5c4aSJUuSOXNmS9YnIjbm1KlT+Pr68uOPPwJQpEgRAgMDadiwoZUrSx7581u2nS148ACCg+HYMXBzg86dQX+bir1IcrCJjo5m8ODBLF26FKPRiMFg4O233yYoKIgzZ86wcOFCXF1dk6NWEbGiyMhIJk2axLBhwwgPDyddunT07NmTgQMH4uzsbO3ykk3NmlCwoGmicEIdTwaD6f6aNVO+tufRuzdMmmSaOxSrZ0/o0QPGjbNeXSKWkuSroqZOncpPP/3EiBEj2LJli3n14T59+hATE8PkyZMtXqSIWNemTZvw9PSkb9++hIeHU6tWLfbs2cPo0aPtOtSAaUKwv7/p88enCsXe9vNLHROHe/eG8ePjhhow3R4/3nS/SGqX5GCzdOlSfH19ee+998iePbv5eMmSJfH19WXLli2WrE9ErOjy5cu0adOGWrVq8e+//5I7d27mzp3L+vXrKV26tLXLSzHNmkFICDyyVilg6qkJCTHdb+sePDD11DzNpEmmdiKpWZKDzZUrV564QF++fPm4devWCxclItYVExPDrFmzKFmyJHPnzgWgQ4cOHDp0iNatW8fZGy6taNYMTp6Edevg229N/544kTpCDZjm1DzeU/O46GhTO5HULMlzbIoUKcKGDRuoVq1avPu2b99OkSJFLFKYiFjHP//8Q6dOnfjjjz8AKFeuHNOmTaNq1apWrsz6HB1T7yXdx45Ztp2IrUpysGndujWDBg0iMjKSN954A4PBwKlTp9i2bRtz5syJtxO4iKQOd+7cYciQIfj5+REdHY2LiwvDhg3D19eXdOme+wJKsRFubpZtJ2KrDEZj0heYmD59OtOmTeP+/fvmycPp06enffv2dOvWzeJFWlLdunWBuDuWi6RlRqORZcuW4evra97r7b333sPPz4+CBQtauTqxlAcPwNn56cNRjo4QHq5Lv8U2Jfb9+7n+DOvYsSMtW7Zk9+7d3Lhxg6xZs+Lh4RFnMrGI2L4TJ07QtWtXVq5cCZg2sg0MDKR+/fpWrkwszcnJdEn3+PFPbtOjh0KNpH5Jnjzcr18/zpw5Q+bMmalZsyaNGjWidu3aZM+enePHj9OpU6fkqFNELOjBgweMHj2aMmXKsHLlStKnT0///v3Zt2+fQo0dGzcOevWKf2m6o6PpuNaxEXuQqB6b8+fPmz9ftmwZb775Jo4JLNqwceNG84RDEbFNGzZswNvbmwMHDgBQp04dgoODn3i1o9iXceNgxAitPCz2K1HBZtiwYWzYsMF8u0uXLgm2MxqNpt18RcTmXLp0iV69ejF//nwA8uTJw8SJE/nkk0/S5OXbaZmTk2lHchF7lKhgM3ToUP744w+MRiP9+/fH29s73kZ3Dg4OZM2alSpVqiRLoSLyfGLXpOnbty/Xr1/HYDDw+eefM3r0aHLkyGHt8kRELCpRwSZfvnw0bdoUAIPBQJ06dcicOTPp06cH4N69e0RERGjysIiN2bNnD97e3mzduhWA8uXLM23aNP0BIiJ2K8mThxs0aMDEiRP54IMPzMd2795NjRo1GDlyJNHPWtpSRJLd7du36dGjB6+99hpbt24lc+bMTJ48mR07dijUiIhdS/Ll3lOmTOHnn3+Os15NmTJl6NOnD/7+/uTKlUtXRonVRUfDpk1w4QLkz2/aeTk1bFL4ooxGI6GhoXTr1o1z584B0Lx5cyZPnkyBxzc6kiRLqz9XIqlJkoPNypUr6dOnDx9++KH5WLZs2WjVqhUODg7MnTtXwUasKjQUunWDh2vNAabNCv39U8++Ps/j+PHjdOnShVWrVgFQvHhxgoKCePvtt61cmX1Iqz9XIqlNkoeirl+//sTVSIsVK0ZYWNgLFyXyvEJD4f334775AJw7ZzoeGmqdupJTREQEI0eOpEyZMqxatYr06dMzcOBA9u3bp1BjIWnx50oktUpysHFzc+PXX39N8L7Vq1drE0yxmuho01/UCW0SEnvsiy+evcNxarJu3TrKly/PV199xf379/Hy8uKff/5h2LBhZMqUydrl2YW0+HMlkpolOdi0bduWJUuW4Ovry48//siWLVv46aef6NatG4sWLaJdu3bJUSdgWv7d09OTUP15JAnYtCn+X9SPMhrhzBlTu1TDYIj/AYSFhdGqVSu8vLw4ePAgefPmZeHChaxZswZ3d3crF21f7PLnSsSOJXmOTYMGDbh9+zaBgYH89ttv5uM5cuRg4MCBvPvuu5aszywyMpKePXsSHh6eLM8vqd+FC5ZtZ3UJLJoXA8wwGOiXPTs3btzAYDDg7e3NyJEjtdxCMrG7nysRO/dcm2B+9NFHfPjhh5w4ccK8CWbx4sVxcEhyB1CiBQQE4OLikmzPL6lf/vyWbWdVCYSa3UAnYDvAjRtUqFCBqVOnUrly5RQuLm2xq58rkTTguYINmBbqK168uCVreaIdO3awePFili1bRp06dVLknJL61Kxpukrl3LmE50MYDKb7a9ZM+dqS5LFQcwsYBARg6rHJAowEOv/1F44KNcnObn6uRNKIRAWbUqVKsXjxYsqVK0fJkiWfuq+MwWBg//79Fivw1q1b9O7dm6+++or8ifyTqG7duk+878KFC4l+HkldHB1Nl96+/77pzebRN6HYH1k/v9Sz7ogRCAG+AGK3of0QmAS8ZKWa0iJ7+7kSsXeJCjY+Pj7ky5fP/HlKbpg3ZMgQypcvT6NGjVLsnJJ6NWsGISEJrzfi55d61hs5BnQBfnl42w0IBupZraK0zV5+rkTSgkQFm0d38+7atWuyFfO4ZcuWsXPnTn766ackPW7t2rVPvO9pvTliH5o1gyZNUucKsREREYwDRgH3ASegH9AXyGjNwiRV/1yJpCWJCjbnz59/dqNHvPSSZTrKly5dytWrV+PNqxk8eDCzZ89m5cqVFjmP2B9HR0ht07F+//13vL29Ofzw9ptAEFDCijVJXKnx50okrUlUsPHy8krS8NOBAweeu6BHTZgwgfv378c5Vq9ePXx9falfv75FziFibRcvXuTLL7/k22+/BcDV1ZXJFy/yIfDU/3UJzWQVEUnjEhVsRo0aZQ42N2/eZMKECVStWpV33nmHPHnycOPGDX7//XfWr19P3759LVZc7Lyex+XKlUsb+kmqFx0dzfTp0+nfvz83b97EYDDg4+PDiBEjyJYtW4KXfJsp1IiIJChRwabZIzPjfHx8aNq0KcOHD4/TplGjRowcOZJVq1bF2SBTROLbtWsX3t7e7NixA4DXXnuNadOmUbFixf8aGY0JhxuFGhGRJ0ryinpbtmx54sZ6derUYffu3S9c1NMcOnQoTtASSU1u3ryJr68vlStXZseOHWTNmpXAwEC2bdsWN9TEMhrjf4iIyBMlOdjkyJGDv//+O8H7/vzzzycOH4mkZUajkcWLF1OqVCkCAgKIiYmhRYsWHDx4EB8fHxx1aY2IiEUkeeXh5s2bExwczL179/Dy8iJnzpxcuXKFX375he+++47+/fsnR50iqdaRI0fo0qWLeW+1V155heDgYN58800rVyYiYn+SHGy8vb25ffs2c+fOZfbs2YDpr9GMGTPSrVs3WrZsafEiRVKj+/fvM3bsWEaPHk1ERAQZMmSgf//+9O7dm4wZtSqNiEhySHKwMRgM9OnTh86dO/P3339z8+ZNcuTIgaenJ87OzslRo0iqs3r1anx8fDhy5AhgWqYgKCiIl19+2cqViYjYt+fejtvFxYU8efKQNWtWPDw8ePDggSXrEkmVLly4QIsWLahXrx5Hjhwhf/78LF68mF9++UWhRkQkBTzX7t7Lly9n4sSJXL58GYPBwPfff09AQADp06dn4sSJODk5WbpOEZsWHR3N1KlTGTBgALdu3cLBwYEuXbowfPhwsmbNau3yRETSjCT32Pz888/06dOH119/nUmTJhETEwOYuto3btxIcHCwxYsUsWU7d+6kSpUqdO3alVu3blGpUiV27NiBv7+/Qo2ISApLcrCZNm0aH330EePGjaNevf/2Gm7WrBldunTR/k2SZty8eZMuXbpQuXJldu3aRbZs2QgKCmLr1q1UqFDB2uWJiKRJSQ42J06c4K233krwPg8PD8LCwl64KBFbZjQa+e677yhZsiRBQUEYjUZatmzJwYMH6dy5s9akERGxoiQHm1y5cnHs2LEE7zt27Bi5cuV64aJEbNXhw4d56623+Pjjj7l48SIlSpRgzZo1LFy4EFdXV2uXJyKS5iU52NSvX58pU6bwyy+/mK+EMhgM7Nu3j+Dg4CdutyCSmt2/f5/BgwdTtmxZ1q5dS4YMGRg+fDh79+6lbt261i5PREQeSvJVUV988QWHDx/miy++wMHBlItatWpFeHg4FStWpFu3bhYvUsSafv31V3x8fMw9lW+//TaBgYG4ublZuTIREXlckoONk5MTs2bNYsuWLfz555/cuHGDLFmyULlyZWrXro0hod2IRVKh8+fP0717d5YsWQLASy+9hJ+fH++//75+zkVEbFSSg02nTp349NNPqV69OtWrV0+OmkSsKioqiuDgYL766itu376Ng4MDvr6+DB06VJdvi4jYuCTPsdmxY4eu+hC7tX37dipXrky3bt24ffs2VapUYefOnUyePFmhRkQkFUhysKlevTrff/89ERERyVGPiFXcuHGDzp078/rrr7N7926yZ8/OtGnT+OOPP/D09LR2eSIikkhJHorKkCEDq1atYvXq1RQsWDDe5d0Gg4F58+ZZrECR5GQ0Gvn222/p0aMHly5dAkyT4cePH0++fPmsXJ2IiCRVkoPNxYsX4/wFazQa49z/+G0RW3Xw4EF8fHz4/fffAShZsiTBwcG88cYbVq5MRESeV5KDzYIFC5KjDpEUc+/ePUaNGsXYsWOJjIwkY8aMDBw4kJ49e2oDVxGRVC5JwWbv3r2cO3eOIkWKULp06eSqSeTFJXQ5ttHIqlWr6NKlC8ePHwdMC04GBARQvHjxFC5QRESSQ6KCza1bt+jYsSN///03RqMRg8FA+fLlmTRpEvnz50/uGkWSJoFQcw74wmAg5OHtAgUKMGXKFJo2bao1aURE7Eiirory8/Nj//79dO3alRkzZtCnTx9OnDjBwIEDk7s+kaR5LKREAX5ASSAEcAR69OjBgQMHaNasmUKNiIidSVSPzbp16+jRowetW7cGoFatWuTLl4+ePXsSHh6Os7NzshYpkiiPhZQ/AW/g74e3XwemAR6TJsHEiSlamoiIpIxE9dhcvnyZMmXKxDlWpUoVoqOjuXDhQrIUJvK8rgOdgGqYQk0OYAawBfCwXlkiIpICEtVjExUVFe9qkWzZsgFooT6xGUZgAdATuPzwWGtgHJDXWkWJiEiKSvLl3o/TujViCw4cOEBnYP3D26WAqUBtq1UkIiLWkOQtFR6nyZdiTeHh4fTv3x8PDw/WA5mA0ZiGoBRqRETSnkT32AwZMoTMmTObb8f21AwcOBAXFxfzcW2pICll5cqVdOnShZMnTwLQsGFDAlasoOizHqheRhERu5WoYFOpUiUg/rBTQsc1NCXJ7ezZs3Tr1o3Q0FAAChYsSEBAAE2aNDH1ID6tF1E/nyIidi1RwUbbKIgtiIqKYsqUKQwaNIi7d+/i6OhI9+7dGTx4cJzeRIzGJ648LCIi9u2FJw+LpIStW7fSqVMn9u7dC0C1atWYOnUq5cqVS/gBCjEiImnSC08eFklO165d4/PPP6datWrs3buXnDlzMmvWLDZt2vTkUCMiImmWemzEJhmNRubPn0/Pnj25cuUKAG3atGHcuHHkzp3bytWJiIitUrARm/Pvv//SuXNnNm7cCECZMmWYOnUqNWvWtHJlIiJi6zQUJTYjPDycfv36Ub58eTZu3IizszNjx45l9+7dCjUiIpIo6rERm7BixQq6dOnCqVOnAGjcuDFTpkyhSJEiVq5MRERSE/XYiFWdPn2apk2b0qhRI06dOkXhwoVZvnw5y5cvV6gREZEkU7ARq4iMjGTChAmUKlWKZcuWkS5dOnr37s3+/ftp3LixtcsTEZFUSkNRkuK2bNlCp06d2LdvHwA1atRg6tSpvPrqq1auTEREUjv12EiKuXr1Ku3bt6dGjRrs27ePXLlyMWfOHDZs2KBQIyIiFqEeG0l2MTExzJ07l969e3P16lUA2rVrx9ixY8mVK5eVqxMREXuiYCPJat++fXh7e7N582YAXn31VaZNm0b16tWtXJmIiNgjDUVJsrh79y59+vTB09OTzZs34+zszPjx4/nrr78UakREJNmox0Ysbvny5fj6+nL69GkA3n33Xfz9/SlcuLCVKxMREXunYCMWc+rUKXx9ffnxxx8BKFKkCAEBATRq1MjKlYmISFqhoSh5YZGRkYwdO5bSpUvz448/ki5dOvr27cu///6rUCMiIilKPTbyQjZt2oS3tzf//vsvALVq1SI4OJgyZcpYuTIREUmL1GMjz+XKlSu0bduWWrVq8e+//5I7d27mzp3L+vXrFWpERMRqFGwkSWJiYpg1axbu7u58/fXXAHTo0IFDhw7RunVrDAaDlSsUEZG0TENRkmj//PMPnTp14o8//gCgXLlyTJ06lWrVqlm5MhEREZNU0WNz48YNBg0aRK1atahQoQItWrRg586d1i7L/hgM8T+AO3fu0LNnTzw9Pfnjjz9wcXFh4sSJ7Nq1S6FGRERsSqrosenRowdXr15l0qRJ5MyZk2+//ZZ27doRGhqKm5ubtcuzDwkMIRmBZQYDvgULcvbsWQCaNWuGn58fhQoVSuECRUREns3me2xOnTrFli1bGDx4MBUrVqR48eIMGDCAfPnysWLFCmuXZx8SCDUngEZAM+Ds2bMUK1aMlStXsnTpUoUaERGxWTYfbHLkyMGMGTPi7P5sMBgwGo3cvHnTipXZicdCzQNgNFAGWAmkB/oD+06coH79+ilenoiISFLY/FBU1qxZqV27dpxjq1at4vTp09SoUSPBx9StW/eJz3fhwgXy589v0RrtxQbAGzjw8HYdIBgoZa2CREREksjme2wet2vXLvr370/dunXx8vKydjl24RLQGlOQOQDkAeYDv6NQIyIiqYvN99g8as2aNfTs2RMPDw8mTZr0xHZr16594n1P681Ja2JiYpgF9AWuAwbgc0xDUTmsWZiIiMhzSjU9NgsXLqRr167UqlWLmTNnkjFjRmuXlKrt2bOHGjVq0BFTqCkP/AFMQ6FGRERSr1QRbL799luGDx9Oy5Yt8fPzw8nJydolpVq3b9+mR48evPbaa2zdupXMmTMzGdgBvP60BxqNKVOgiIjIC7D5YHPixAlGjRrFW2+9RceOHbl69SqXL1/m8uXL3L5929rlpRpGo5GlS5dSqlQpJk+eTHR0NO+//z4HDx7kC6Px6WOSCjUiIpJK2Pwcm19//ZXIyEhWr17N6tWr49zXtGlTxowZY6XKUo/jx4/TpUsXVq1aBUDx4sUJCgri7bff/q+R0ZjgejYKNSIikprYfLDp1KkTnTp1snYZqVJERAQTJkxgxIgR3L9/n/Tp09OnTx/69+9PpkyZ4j9AIUZERFI5mw828nzWrVtH586dOXjwIABvvPEGwcHBlCxZ0sqViYiIJB+bn2MjSRMWFkarVq3w8vLi4MGD5M2bl4ULF7J27VqFGhERsXsKNnYiJiaGadOmUbJkSRYuXIjBYMDb25uDBw/SsmVLDAnNnxEREbEzGoqyA7t376ZTp05s374dAE9PT6ZNm0blypWtXJmIiEjKUo9NKnbr1i2++OILKlasyPbt28mSJQv+/v5s375doUZERNIk9dikQkajkZCQEL744gvOnz8PwIcffsikSZN46aWXrFydiIiI9SjYpDLHjh2jS5cu/PLLLwC4ubkRHBxMvXr1rFyZiIiI9WkoKpWIiIhg+PDhvPrqq/zyyy84OTkxaNAg/vnnH4UaERGRh9Rjkwr8/vvveHt7c/jwYQDefPNNgoKCKFGihJUrExERsS3qsbFhFy9epGXLltStW5fDhw/j6urKt99+y2+//aZQIyIikgAFGxsUHR1tXiX422+/xWAw0KVLFw4ePEiLFi20Jo2IiMgTaCjKxuzatQtvb2927NgBwGuvvca0adOoWLGilSsTERGxfeqxsRE3b97E19eXypUrs2PHDrJmzUpgYCDbtm1TqBEREUkk9dhYmdFoZMmSJXTv3p0LFy4A8NFHHzFp0iTy589v5epERERSFwUbKzpy5AhdunTht99+A+CVV14hKCiIt956y8qViYiIpE4airKC+/fvM3ToUMqWLctvv/1GhgwZGDJkCHv37lWoEREReQHqsUlhq1evxsfHhyNHjgBQr149AgMDeeWVV6xcmYiISOqnHpsUcuHCBVq0aEG9evU4cuQI+fPnZ9GiRfzyyy8KNSIiIhaiYJPMoqOjCQwMpGTJkixatAgHBwe6du3KgQMH+PDDD7UmjYiIiAVpKMoSEgonRiM7d+6kU6dO7Nq1C4BKlSoxdepUXnvttRQuUEREJG1QsHlRCYSam8AAg4FggwGj0Ui2bNkYNWoUHTt2xNHRMeVrFBERSSMUbF7EY6HGCCwCegAXAYxGWrZsyYQJE3B1dU35+kRERNIYzbF5Xo+FmvPAW8DHmEJNCWANsPCbbxRqREREUoiCjYWMBdYCGYBhwF6grlUrEhERSXs0FGUhnYDMQFvAzcq1iIiIpFUKNhZSChhp7SJERETSOA1FiYiIiN1QsHleRqNl24mIiMgLU7B5Ec8KLQo1IiIiKUrB5kU9Kbwo1IiIiKQ4TR62BIUYERERm6AeGxEREbEbCjYiIiJiNxRsRERExG4o2IiIiIjdULARERERu6FgIyIiInZDwUZERETshoKNiIiI2A0FGxEREbEbCjYiIiJiNxRsRERExG4o2IiIiIjdULARERERu6FgIyIiInZDwUZERETshoKNiIiI2A0FGxEREbEbCjYiIiJiNxRsRERExG6kimATExPDlClTqFmzJh4eHrRt25ZTp05ZuywRERGxMaki2AQHB7No0SJGjBjB4sWLMRgMdOjQgQcPHli7NBEREbEhNh9sHjx4wJw5c+jatSu1a9emZMmSTJ48mbCwMFavXm3t8kRERMSGpLN2Ac9y8OBB7t69y+uvv24+ljVrVkqXLs2OHTto0KBBvMfUrVv3ic934cIF8ufPnyy1ioiIiHXZfI/NxYsXAeKFkbx583LhwgVrlCQiIiI2yuZ7bO7duweAk5NTnOMZMmTg5s2bCT5m7dq1T3y+p/XmiIiISOpm8z02GTNmBIg3UTgiIoJMmTJZoyQRERGxUTYfbGKHoC5duhTn+KVLl3B1dbVGSSIiImKjbD7YlCxZksyZM7Nt2zbzsVu3brF//34qVqxoxcpERETE1tj8HBsnJyc++eQTJkyYQM6cOSlQoADjx4/H1dWVt956y9rliYiIiA2x+WAD4OvrS1RUFF999RX379+nUqVKzJ49O96EYhEREUnbUkWwcXR0pFevXvTq1cvapYiIiIgNs/k5NiIiIiKJpWAjIiIidkPBRkREROyGgo2IiIjYDQUbERERsRsKNiIiImI3FGxERETEbijYiIiIiN1QsBERERG7oWAjIiIidkPBRkREROyGgo2IiIjYDQUbERERsRsKNiIiImI3FGxERETEbijYiIiIiN1QsBERERG7oWAjIiIidkPBRkREROyGgo2IiIjYDQUbERERsRsKNiIiImI30lm7gJR26dIloqOjqVu3rrVLERERkUS6cOECjo6Oz2yX5npsMmTIQLp0aS7PJdqFCxe4cOGCtcuQR+h7Ylv0/bAt+n7YluT8fqRLl44MGTI8s53BaDQak6UCSZVie7LWrl1r5Uoklr4ntkXfD9ui74dtsYXvR5rrsRERERH7pWAjIiIidkPBRkREROyGgo2IiIjYDQUbERERsRsKNiIiImI3dLm3iIiI2A312IiIiIjdULARERERu6FgIyIiInZDwUZERETshoKNiIiI2A0FGzG7ceMGgwYNolatWlSoUIEWLVqwc+dOa5clwIkTJ/D09CQ0NNTapaRpy5Yto379+pQtW5YGDRqwatUqa5eUZkVGRjJ58mTq1KmDp6cnH3/8MX/99Ze1y0qTgoODadWqVZxjBw4c4JNPPqF8+fLUqVOH2bNnp1g9CjZi1qNHD/bs2cOkSZMICQmhTJkytGvXjmPHjlm7tDQtMjKSnj17Eh4ebu1S0rTly5fTv39/PvzwQ1asWEH9+vXp0aMHu3fvtnZpadLUqVNZunQpI0aMYNmyZRQvXpwOHToQFhZm7dLSlLlz5zJlypQ4x65fv06bNm0oWrQoS5cupWvXrvj7+7N06dIUqUnBRgA4deoUW7ZsYfDgwVSsWJHixYszYMAA8uXLx4oVK6xdXpoWEBCAi4uLtctI04xGI/7+/rRu3ZrWrVtTpEgRfHx8qFatGtu3b7d2eWnS2rVradiwITVq1KBIkSL07duXO3fu8Pfff1u7tDQhLCyM9u3b4+/vT7FixeLct2TJEpycnBgyZAhubm689957fPbZZ8ycOTNFalOwEQBy5MjBjBkzePXVV83HDAYDRqORmzdvWrGytG3Hjh0sXryYsWPHWruUNO348eOcO3eORo0axTk+e/ZsOnbsaKWq0rbs2bOzbt06zp49S3R0NIsXL8bJyYlSpUpZu7Q04d9//yVbtmz8+OOPeHh4xLlv586dVKpUiXTp0pmPvf7665w4cYKrV68me23pnt1E0oKsWbNSu3btOMdWrVrF6dOnqVGjhpWqSttu3bpF7969+eqrr8ifP7+1y0nTTp48CUB4eDjt2rVj//79FCxYEG9vb7y8vKxbXBo1YMAAunfvTt26dXF0dMTBwQF/f38KFy5s7dLSBC8vryf+7F+8eJESJUrEOZY3b14Azp8/T65cuZK1NvXYSIJ27dpF//79qVu3rn5xW8mQIUMoX758vF4CSXl37twBoE+fPjRs2JA5c+ZQvXp1OnfuzNatW61cXdp07NgxsmbNSlBQEIsXL6ZZs2b06dOHgwcPWru0NO/+/fs4OTnFOZYhQwYAIiIikv386rGReNasWUPPnj3x8PBg0qRJ1i4nTVq2bBk7d+7kp59+snYpAqRPnx6Adu3a0bRpUwBKlSrF/v37+frrr6latao1y0tzzp07R69evZg7dy4VK1YEoGzZshw9epSAgACCgoKsXGHaljFjRh48eBDnWGygcXZ2Tvbzq8dG4li4cCFdu3alVq1azJw5k4wZM1q7pDRp6dKlXL161Xwpq6enJwCDBw+mQYMGVq4u7XF1dQWI173+8ssvc/bsWWuUlKbt3buXyMhIypYtG+e4h4eHedhQrMfV1ZVLly7FORZ7O1++fMl+fvXYiNm3337L8OHDadWqFf3798fBQbnXWiZMmMD9+/fjHKtXrx6+vr7Ur1/fSlWlXaVLl8bFxYU9e/aYewgADh8+rDkdVhA75+zQoUOUK1fOfPzw4cMUKVLEWmXJQ5UqVWLRokVER0fj6OgIwNatWylWrFiyz68BBRt56MSJE4waNYq33nqLjh07xpm5njFjRrJkyWLF6tKeJ/1VkytXLgoUKJDC1UjGjBlp3749QUFB5MuXj3LlyrFy5Uq2bNnC3LlzrV1emlOuXDkqVqxInz59GDx4MK6urixbtoytW7fy7bffWru8NO+9995j1qxZDBgwgPbt27N3717mzZvH0KFDU+T8CjYCwK+//kpkZCSrV69m9erVce5r2rQpY8aMsVJlIrahc+fOZMqUicmTJxMWFoabmxsBAQFUqVLF2qWlOQ4ODgQHB+Pn50e/fv24efMmJUqUYO7cuZQvX97a5aV5uXLlYtasWYwcOZKmTZuSJ08eevfubZ6fltwMRqPRmCJnEhEREUlmmkQhIiIidkPBRkREROyGgo2IiIjYDQUbERERsRsKNiIiImI3FGxERETEbijYiIiIiN1QsBERi7K3pbHs7fWI2DsFGxEx6927N+7u7syYMSPJj7148SIdO3bk3LlzFq9r27ZtuLu7s23btie2adWqFe7u7nE+SpYsyWuvvUbz5s1ZuXJlks979OhRWrRoEeeYu7s7AQEBSX4uEUkZ2lJBRAC4c+cOv/32GyVKlGDJkiV06NABg8GQ6Mf/8ccfrF+/noEDByZjlU9XunRpBg8ebL4dHR3NxYsXmTt3Lj169CBLlizUqlUr0c+3atUqdu/eHefY4sWLzbt9i4jtUbAREQBWrlxJdHQ0X331FZ9++imbN2+mZs2a1i4rSTJnzpzgXkG1a9ematWqLF26NEnBJiHai0jEtmkoSkQAWLp0KVWqVKFKlSoUK1aMRYsWxWuzcuVKmjVrhoeHB3Xq1GH8+PE8ePCA0NBQ+vXrB0DdunXp27cvAF5eXubPY4WGhuLu7s7Zs2fNx9asWcPHH3+Mp6cnr776Km+//TYLFy602GtzcnIiffr0cY7dv3+fiRMnUq9ePV599VUqVKhAmzZtOHDgAAABAQEEBgYCcYefHv08dohs69attG3bFg8PD6pVq8bYsWOJiooyn+vOnTsMGjSIqlWr4unpSffu3Zk7dy7u7u7mNmfOnMHb25sqVarg4eHBhx9+yIYNGyz2NRBJKxRsRIRjx46xZ88e8+67zZo1Y926dYSFhZnbLFq0iB49elCqVCkCAwPp2LEj3377LUOGDKFOnTp4e3sDEBgYSOfOnRN97vXr1+Pj40OZMmUIDg4mICCAAgUKMHz4cP76668kvQ6j0UhUVJT5IyIiglOnTvHVV19x9+5dmjRpYm7bu3dvQkJC+Pzzz5kzZw59+/bl8OHDdO/eHaPRSPPmzXn//fcB0/BT8+bNn3jenj178tprrzFt2jQaNWrEnDlzCAkJMd/v4+PDqlWr6Nq1K5MnT+bu3btMnDjRfH9MTAwdO3YkPDyccePGERwcTPbs2encuTOnTp1K0tdAJK3TUJSIEBISQtasWXnzzTcBePfdd/Hz8+P777+nS5cuxMTEEBAQwFtvvcXIkSPNj4uIiOCHH34gc+bMFC5cGIBSpUpRsGDBRJ/76NGjvPvuuwwYMMB8zNPTkypVqrBjxw4qVKiQ6OfasWMHZcqUiXPMYDBQokQJ/P398fLyAuDBgwfcvXuXgQMHUr9+fQAqV67M3bt3GTNmDJcvX8bV1dU8l+ZZw0/NmzfHx8cHgKpVq7JmzRrWr1/PRx99xNatW/nzzz8JCAigXr16ANSqVYtGjRpx9OhRAK5evcqxY8fo1KkTtWvXBqBcuXIEBgYSERGR6NcvIgo2ImleVFQUP/74I2+++SYRERFERESQMWNGqlSpwvfff4+3tzcnT57kypUr5uAT67PPPuOzzz57ofO3b98egPDwcE6fPs2JEyf4559/AIiMjEzSc5UpU4ahQ4cCEBYWhr+/P5GRkUyePBk3NzdzOycnJ2bPng3ApUuXOHXqFMePH2fdunXPdV5PT884t11dXQkPDwfgzz//JH369HG+dg4ODrzzzjvmIa3cuXPz8ssvM3DgQP744w9q1apFjRo1zMN7IpJ4CjYiadz69eu5cuUKoaGhhIaGxrt/3bp15MiRA4BcuXJZ/PzXrl1j8ODBrFmzBoPBQJEiRXjttdeApK8h4+LiQtmyZQEoW7Ysnp6eNGnShLZt2/LDDz+QM2dOc9tNmzYxatQojh8/jouLC+7u7ri4uDzXeTNmzBjntoODg/k5rl+/Tvbs2XFwiDvynzt3bvPnBoOBOXPmMHXqVFavXs0PP/xgDkNDhgwhe/bsSapHJC3THBuRNC4kJIQCBQowf/78eB/Zs2dn0aJFZM2aFTCFkEfduHGDLVu2cPfu3Sc+f3R0dJzbsT0ZsXr27MnevXv5+uuv+fvvv1m1alWcYakXkStXLgYNGsTFixfjDKGdPn0aHx8fSpYsyerVq/nrr7/47rvveOONNyxy3kfly5eP69evExMTE+f41atX47UbMmQImzdvZtmyZbRr147ffvuNyZMnW7wmEXumYCOShl25coVNmzbRoEED8xVRj37Ur1+fLVu2kCFDBnLkyMHatWvjPP6nn36iQ4cORERExOuRANPl1xcvXoxz7PEJwbt27eJ///sfr7/+Ok5OTgBs3LgRIF4YeB716tWjZs2arFixwrzA3759+4iIiKBjx47muUFg6sWB/3psEnpNSVW5cmWioqL4/fff4xxfs2aN+fPdu3dTrVo19u7di8FgoFSpUnTv3p0SJUrE+/qJyNNpKEokDfvhhx+IioqiQYMGCd7ftGlTvv32W77//nu6du3KsGHDGDJkCG+99RYnT57Ez8+PFi1akDNnTnOvzurVq6lVqxZubm688cYbTJ8+nWnTplG+fHnWr1/P1q1b45yjXLly/PTTT5QpUwZXV1d2797N9OnTMRgM3Lt3zyKvs3///jRu3JgRI0bwww8/UKZMGdKlS8f48eNp27at+ZL19evXA//1KsW+phUrVuDh4UGhQoWSfO5KlSpRvXp1BgwYwJUrV3jppZcICQnh4MGD5gUQS5cuTcaMGenduzddu3Yld+7c/PHHHxw4cIBPP/3UIl8DkbRCPTYiadgPP/zAK6+8QsmSJRO8v1y5chQvXpylS5fSvHlzxowZw86dO+nYsSNz5syhbdu25nVqqlSpQrVq1Zg4cSJjx44FoGPHjjRv3pw5c+bg7e1NWFhYnCEhgDFjxuDh4cHw4cPx8fFhzZo1DB06lBo1arBz506LvM7ixYvTqlUrDh8+zMKFCylSpAgTJ04kLCwMb29vBg0aBMCCBQswGAzm89arV4+yZcvSt29f82Tj5zF58mS8vLyYOHEi3bp1w8nJiRYtWuDs7AxAhgwZmDNnDq+88gojR46kXbt2rF27lmHDhtGsWbMX/wKIpCEGo3Z4ExFJNufOnePvv/+mbt26cSYZ+/r6cubMGX744QcrVidifzQUJSKSjBwcHOjbty9169bl/fffx9HRkY0bN/Lbb78xevRoa5cnYnfUYyMiksz+/PNPgoKCOHDgAFFRUbi5udGmTRsaNmxo7dJE7I6CjYiIiNgNTR4WERERu6FgIyIiInZDwUZERETshoKNiIiI2A0FGxEREbEbCjYiIiJiNxRsRERExG4o2IiIiIjd+D8a0EOOkzvcgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, color='blue', label='Predicted')\n",
    "plt.scatter(y_test, y_test, color='red', label='Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='black', label='Perfect Predictions')\n",
    "plt.ylim(-1, 11)\n",
    "plt.xlabel('Actual Ratings')\n",
    "plt.ylabel('Predicted Ratings')\n",
    "plt.title('Random Forest Results')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving forward, it would be a good idea to incorporate more data, from the other available IMDb data sets, and test out other variables that might be better predictors of a title's average rating than runtime. Obtaining the data directly from the IMDb API is also recommended as this will result in a more comprehensive and customizable list of attributes per title. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Part 2__ of this exercise will focus on obtaining the data directly from the IMDb database utilizing their API, as well as creating a recommender system for TV shows/movies using their data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}