tmdb-movies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project:TMDb movie data\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> What can we say about the success of a movie before it is released? Are there certain companies (Pixar?) that have found a consistent formula? Given that major films costing over $100 million to produce can still flop, this question is more important than ever to the industry. Film aficionados might have different interests. Can we predict which films will be highly rated, whether or not they are a commercial success?\n",
    "\n",
    "> This data set contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue.\n",
    "\n",
    "> The dataset contains the following features:-\n",
    "- id - A unique identifier for each movie.\n",
    "- cast - The name of lead and supporting actors.\n",
    "- crew - The name of Director, Editor, Composer, Writer etc.\n",
    "- budget - The budget in which the movie was made.\n",
    "- genre - The genre of the movie, Action, Comedy ,Thriller etc.\n",
    "- homepage - A link to the homepage of the movie.\n",
    "- original_title - The title of the movie before translation or adaptation.\n",
    "- overview - A brief description of the movie.\n",
    "- popularity - A numeric quantity specifying the movie popularity.\n",
    "- production_companies - The production house of the movie.\n",
    "- release_date - The date on which it was released.\n",
    "- revenue - The worldwide revenue generated by the movie.\n",
    "- runtime - The running time of the movie in minutes.\n",
    "- tagline - Movie's tagline.\n",
    "- title - Title of the movie.\n",
    "- vote_count - the count of votes recieved.\n",
    "- release_year = the year the movie was released\n",
    "- vote_average - average ratings the movie recieved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline  ...  \\\n",
       "0              The park is open.  ...   \n",
       "1             What a Lovely Day.  ...   \n",
       "2     One Choice Can Destroy You  ...   \n",
       "3  Every generation has a story.  ...   \n",
       "4            Vengeance Hits Home  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot     12/15/15       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...       4/1/15       2947   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "3           7.5          2015  1.839999e+08  1.902723e+09  \n",
       "4           7.3          2015  1.747999e+08  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('tmdb-movies.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for the number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>42194</td>\n",
       "      <td>tt0411951</td>\n",
       "      <td>0.59643</td>\n",
       "      <td>30000000</td>\n",
       "      <td>967000</td>\n",
       "      <td>TEKKEN</td>\n",
       "      <td>Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dwight H. Little</td>\n",
       "      <td>Survival is no game</td>\n",
       "      <td>...</td>\n",
       "      <td>In the year of 2039, after World Wars destroy ...</td>\n",
       "      <td>92</td>\n",
       "      <td>Crime|Drama|Action|Thriller|Science Fiction</td>\n",
       "      <td>Namco|Light Song Films</td>\n",
       "      <td>3/20/10</td>\n",
       "      <td>110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>967000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id    imdb_id  popularity    budget  revenue original_title  \\\n",
       "2090  42194  tt0411951     0.59643  30000000   967000         TEKKEN   \n",
       "\n",
       "                                                   cast homepage  \\\n",
       "2090  Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...      NaN   \n",
       "\n",
       "              director              tagline  ...  \\\n",
       "2090  Dwight H. Little  Survival is no game  ...   \n",
       "\n",
       "                                               overview runtime  \\\n",
       "2090  In the year of 2039, after World Wars destroy ...      92   \n",
       "\n",
       "                                           genres    production_companies  \\\n",
       "2090  Crime|Drama|Action|Thriller|Science Fiction  Namco|Light Song Films   \n",
       "\n",
       "     release_date vote_count  vote_average  release_year  budget_adj  \\\n",
       "2090      3/20/10        110           5.0          2010  30000000.0   \n",
       "\n",
       "      revenue_adj  \n",
       "2090     967000.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.duplicated() == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13434"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7930\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick insight using `.info()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick insight using `.describe()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66064.177434</td>\n",
       "      <td>0.646441</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>102.070863</td>\n",
       "      <td>217.389748</td>\n",
       "      <td>5.974922</td>\n",
       "      <td>2001.322658</td>\n",
       "      <td>1.755104e+07</td>\n",
       "      <td>5.136436e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92130.136561</td>\n",
       "      <td>1.000185</td>\n",
       "      <td>3.091321e+07</td>\n",
       "      <td>1.170035e+08</td>\n",
       "      <td>31.381405</td>\n",
       "      <td>575.619058</td>\n",
       "      <td>0.935142</td>\n",
       "      <td>12.812941</td>\n",
       "      <td>3.430616e+07</td>\n",
       "      <td>1.446325e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.250000</td>\n",
       "      <td>0.207583</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20669.000000</td>\n",
       "      <td>0.383856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75610.000000</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.369710e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10866.000000  10866.000000  1.086600e+04  1.086600e+04  10866.000000   \n",
       "mean    66064.177434      0.646441  1.462570e+07  3.982332e+07    102.070863   \n",
       "std     92130.136561      1.000185  3.091321e+07  1.170035e+08     31.381405   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.250000      0.207583  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20669.000000      0.383856  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75610.000000      0.713817  1.500000e+07  2.400000e+07    111.000000   \n",
       "max    417859.000000     32.985763  4.250000e+08  2.781506e+09    900.000000   \n",
       "\n",
       "         vote_count  vote_average  release_year    budget_adj   revenue_adj  \n",
       "count  10866.000000  10866.000000  10866.000000  1.086600e+04  1.086600e+04  \n",
       "mean     217.389748      5.974922   2001.322658  1.755104e+07  5.136436e+07  \n",
       "std      575.619058      0.935142     12.812941  3.430616e+07  1.446325e+08  \n",
       "min       10.000000      1.500000   1960.000000  0.000000e+00  0.000000e+00  \n",
       "25%       17.000000      5.400000   1995.000000  0.000000e+00  0.000000e+00  \n",
       "50%       38.000000      6.000000   2006.000000  0.000000e+00  0.000000e+00  \n",
       "75%      145.750000      6.600000   2011.000000  2.085325e+07  3.369710e+07  \n",
       "max     9767.000000      9.200000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## **Data Wrangling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove extranous columns.\n",
    "**These include:**\n",
    "- id\n",
    "- imdb_id\n",
    "- homepage\n",
    "- tagline\n",
    "- budget_adj\n",
    "- revenue_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['id', 'imdb_id', 'tagline', 'homepage', 'budget_adj', 'revenue_adj']\n",
    "df.drop(columns=drop_cols, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "director                  44\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let check for the total number of rows with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_null = df[df.isnull().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2199, 15)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of row in the dataset: 10865\n",
      "Total number of rows with missing values: 2199\n",
      "Percentage of missing rows: 20.24%\n"
     ]
    }
   ],
   "source": [
    "print(f'''Total number of row in the dataset: {df.shape[0]}\n",
    "Total number of rows with missing values: {df_null.shape[0]}\n",
    "Percentage of missing rows: {round((df_null.shape[0]/df.shape[0] * 100), 2)}%''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total row: 8666\n",
      "Total null: 0\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace = True)\n",
    "\n",
    "## Confirm the new number of rows\n",
    "print(f'''Total row: {df.shape[0]}\n",
    "Total null: {df.isnull().sum().sum()}''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix incorrect datatypes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8666 entries, 0 to 10865\n",
      "Data columns (total 15 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   popularity            8666 non-null   float64\n",
      " 1   budget                8666 non-null   int64  \n",
      " 2   revenue               8666 non-null   int64  \n",
      " 3   original_title        8666 non-null   object \n",
      " 4   cast                  8666 non-null   object \n",
      " 5   director              8666 non-null   object \n",
      " 6   keywords              8666 non-null   object \n",
      " 7   overview              8666 non-null   object \n",
      " 8   runtime               8666 non-null   int64  \n",
      " 9   genres                8666 non-null   object \n",
      " 10  production_companies  8666 non-null   object \n",
      " 11  release_date          8666 non-null   object \n",
      " 12  vote_count            8666 non-null   int64  \n",
      " 13  vote_average          8666 non-null   float64\n",
      " 14  release_year          8666 non-null   int64  \n",
      "dtypes: float64(2), int64(5), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename `original_title` as `title`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns= {'original_title': 'title'}, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change release_date to datetime and create the month and day columns for easy analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>release_month</th>\n",
       "      <th>release_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster|dna|tyrannosaurus rex|velociraptor|island</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>June</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>future|chase|post-apocalyptic|dystopia|australia</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>May</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity     budget     revenue               title  \\\n",
       "0   32.985763  150000000  1513528810      Jurassic World   \n",
       "1   28.419936  150000000   378436354  Mad Max: Fury Road   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...    George Miller   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  monster|dna|tyrannosaurus rex|velociraptor|island   \n",
       "1   future|chase|post-apocalyptic|dystopia|australia   \n",
       "\n",
       "                                            overview  runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...      124   \n",
       "1  An apocalyptic story set in the furthest reach...      120   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...        5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...        6185   \n",
       "\n",
       "   vote_average  release_year release_month release_day  \n",
       "0           6.5          2015          June     Tuesday  \n",
       "1           7.1          2015           May   Wednesday  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Convert to datetime\n",
    "df['release_date'] = pd.to_datetime(df.release_date)\n",
    "\n",
    "## Create release_month and release date name\n",
    "df['release_month'] = df['release_date'].dt.month_name()\n",
    "df['release_day'] = df['release_date'].dt.day_name()\n",
    "\n",
    "## Drop the release_date column\n",
    "df.drop(columns=['release_date'], inplace = True)\n",
    "\n",
    "## Confirm changes\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the `profit` column from `revenue` and `budget` and drop them\n",
    "> Since profit is the sole of business"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>release_month</th>\n",
       "      <th>release_day</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster|dna|tyrannosaurus rex|velociraptor|island</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>June</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1363528810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>future|chase|post-apocalyptic|dystopia|australia</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>May</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>228436354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>based on novel|revolution|dystopia|sequel|dyst...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>March</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>185238201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity               title  \\\n",
       "0   32.985763      Jurassic World   \n",
       "1   28.419936  Mad Max: Fury Road   \n",
       "2   13.112507           Insurgent   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   Colin Trevorrow   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...     George Miller   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...  Robert Schwentke   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  monster|dna|tyrannosaurus rex|velociraptor|island   \n",
       "1   future|chase|post-apocalyptic|dystopia|australia   \n",
       "2  based on novel|revolution|dystopia|sequel|dyst...   \n",
       "\n",
       "                                            overview  runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...      124   \n",
       "1  An apocalyptic story set in the furthest reach...      120   \n",
       "2  Beatrice Prior must confront her inner demons ...      119   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...        5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...        6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...        2480   \n",
       "\n",
       "   vote_average  release_year release_month release_day      profit  \n",
       "0           6.5          2015          June     Tuesday  1363528810  \n",
       "1           7.1          2015           May   Wednesday   228436354  \n",
       "2           6.3          2015         March   Wednesday   185238201  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['profit'] = df.revenue - df.budget\n",
    "df.drop(columns= ['revenue', 'budget'], inplace = True)\n",
    "\n",
    "## Confirm changes\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-write the keywords and genres columns\n",
    "> This will made the word in the columns readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>release_month</th>\n",
       "      <th>release_day</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster dna tyrannosaurus rex velociraptor island</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action, Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Universal Studios, Amblin Entertainment, Legen...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>June</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1363528810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy, Charlize Theron, Hugh Keays-Byrne, ...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>future chase post-apocalyptic dystopia australia</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action, Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Village Roadshow Pictures, Kennedy Miller Prod...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>May</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>228436354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley, Theo James, Kate Winslet, An...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>based on novel revolution dystopia sequel dyst...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Summit Entertainment, Mandeville Films, Red Wa...</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>March</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>185238201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity               title  \\\n",
       "0   32.985763      Jurassic World   \n",
       "1   28.419936  Mad Max: Fury Road   \n",
       "2   13.112507           Insurgent   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...   Colin Trevorrow   \n",
       "1  Tom Hardy, Charlize Theron, Hugh Keays-Byrne, ...     George Miller   \n",
       "2  Shailene Woodley, Theo James, Kate Winslet, An...  Robert Schwentke   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  monster dna tyrannosaurus rex velociraptor island   \n",
       "1   future chase post-apocalyptic dystopia australia   \n",
       "2  based on novel revolution dystopia sequel dyst...   \n",
       "\n",
       "                                            overview  runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...      124   \n",
       "1  An apocalyptic story set in the furthest reach...      120   \n",
       "2  Beatrice Prior must confront her inner demons ...      119   \n",
       "\n",
       "                                         genres  \\\n",
       "0  Action, Adventure, Science Fiction, Thriller   \n",
       "1  Action, Adventure, Science Fiction, Thriller   \n",
       "2          Adventure, Science Fiction, Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios, Amblin Entertainment, Legen...        5562   \n",
       "1  Village Roadshow Pictures, Kennedy Miller Prod...        6185   \n",
       "2  Summit Entertainment, Mandeville Films, Red Wa...        2480   \n",
       "\n",
       "   vote_average  release_year release_month release_day      profit  \n",
       "0           6.5          2015          June     Tuesday  1363528810  \n",
       "1           7.1          2015           May   Wednesday   228436354  \n",
       "2           6.3          2015         March   Wednesday   185238201  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['keywords'] = df['keywords'].apply(lambda x: x.replace(\"|\", \" \"))\n",
    "df['cast'] = df['cast'].apply(lambda x: x.replace(\"|\", \", \"))\n",
    "df['genres'] = df['genres'].apply(lambda x: x.replace(\"|\", \", \"))\n",
    "df['production_companies'] = df['production_companies'].apply(lambda x: x.replace(\"|\", \", \"))\n",
    "\n",
    "## Confirm changes\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorizing the runtime using `pandas cut` into\n",
    "- Short\n",
    "- Moderate\n",
    "- Long\n",
    "- Very long\n",
    "\n",
    "**Why:**<br>\n",
    "This values are so many that it cannot be used for analysis (no meaniful inference can bo gotten) unlesss categorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.runtime.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8666.000000\n",
       "mean      103.815140\n",
       "std        26.207186\n",
       "min         0.000000\n",
       "25%        91.000000\n",
       "50%       100.000000\n",
       "75%       113.000000\n",
       "max       705.000000\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.runtime.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>release_month</th>\n",
       "      <th>release_day</th>\n",
       "      <th>profit</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster dna tyrannosaurus rex velociraptor island</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action, Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Universal Studios, Amblin Entertainment, Legen...</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>June</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1363528810</td>\n",
       "      <td>Very long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.419936</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy, Charlize Theron, Hugh Keays-Byrne, ...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>future chase post-apocalyptic dystopia australia</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action, Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Village Roadshow Pictures, Kennedy Miller Prod...</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>May</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>228436354</td>\n",
       "      <td>Long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.112507</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley, Theo James, Kate Winslet, An...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>based on novel revolution dystopia sequel dyst...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure, Science Fiction, Thriller</td>\n",
       "      <td>Summit Entertainment, Mandeville Films, Red Wa...</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>March</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>185238201</td>\n",
       "      <td>Long</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   popularity               title  \\\n",
       "0   32.985763      Jurassic World   \n",
       "1   28.419936  Mad Max: Fury Road   \n",
       "2   13.112507           Insurgent   \n",
       "\n",
       "                                                cast          director  \\\n",
       "0  Chris Pratt, Bryce Dallas Howard, Irrfan Khan,...   Colin Trevorrow   \n",
       "1  Tom Hardy, Charlize Theron, Hugh Keays-Byrne, ...     George Miller   \n",
       "2  Shailene Woodley, Theo James, Kate Winslet, An...  Robert Schwentke   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  monster dna tyrannosaurus rex velociraptor island   \n",
       "1   future chase post-apocalyptic dystopia australia   \n",
       "2  based on novel revolution dystopia sequel dyst...   \n",
       "\n",
       "                                            overview  runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...      124   \n",
       "1  An apocalyptic story set in the furthest reach...      120   \n",
       "2  Beatrice Prior must confront her inner demons ...      119   \n",
       "\n",
       "                                         genres  \\\n",
       "0  Action, Adventure, Science Fiction, Thriller   \n",
       "1  Action, Adventure, Science Fiction, Thriller   \n",
       "2          Adventure, Science Fiction, Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios, Amblin Entertainment, Legen...        5562   \n",
       "1  Village Roadshow Pictures, Kennedy Miller Prod...        6185   \n",
       "2  Summit Entertainment, Mandeville Films, Red Wa...        2480   \n",
       "\n",
       "   vote_average  release_year release_month release_day      profit   duration  \n",
       "0           6.5          2015          June     Tuesday  1363528810  Very long  \n",
       "1           7.1          2015           May   Wednesday   228436354       Long  \n",
       "2           6.3          2015         March   Wednesday   185238201       Long  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_edges = [0, 30, 60, 120, 705]\n",
    "bin_names = ['Short', 'Moderate', 'Long', 'Very long']\n",
    "df['duration'] = pd.cut(df.runtime, bin_edges, labels = bin_names)\n",
    "\n",
    "## Confirm chages\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define function for resuability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barhplot(x, y, xlabel, title):\n",
    "    plt.figure(figsize=  (14, 8))\n",
    "    plt.barh(x, y, align = 'center')\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.xlabel(xlabel, fontsize = 18)\n",
    "    plt.title(title, fontsize = 18)\n",
    "    plt.show();\n",
    "\n",
    "def barplot(x, y, xlabel, title):\n",
    "    plt.figure(figsize=  (10, 7))\n",
    "    plt.bar(x, y, align = 'center')\n",
    "    plt.xlabel(xlabel, fontsize = 18)\n",
    "    plt.title(title, fontsize = 18)\n",
    "    plt.show();\n",
    "\n",
    "def lineplot(value, xlabel, ylabel, title):\n",
    "    plt.figure(figsize=  (17, 9))\n",
    "    plt.plot(value)\n",
    "    plt.xlabel(xlabel, fontsize = 18)\n",
    "    plt.ylabel(ylabel, fontsize = 18)\n",
    "    plt.title(title, fontsize = 23)\n",
    "    plt.show();\n",
    "\n",
    "def sortby(column):\n",
    "    df1 = df.sort_values(by = column, ascending = False)\n",
    "    return df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1. First 10 most popular movie\n",
    "> This is to check for the tiltes of the first ten most popular movies among the people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.drop of                                       title  popularity\n",
       "0                            Jurassic World   32.985763\n",
       "1                        Mad Max: Fury Road   28.419936\n",
       "629                            Interstellar   24.949134\n",
       "630                 Guardians of the Galaxy   14.311205\n",
       "2                                 Insurgent   13.112507\n",
       "631     Captain America: The Winter Soldier   12.971027\n",
       "1329                              Star Wars   12.037933\n",
       "632                               John Wick   11.422751\n",
       "3              Star Wars: The Force Awakens   11.173104\n",
       "633   The Hunger Games: Mockingjay - Part 1   10.739009>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top = df.sort_values(by = 'popularity', ascending = False)\n",
    "top_10 = top[['title', 'popularity']].head(10)\n",
    "top_10.drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barhplot(top_10.title, top_10.popularity, 'popularity', 'First 10 Most Popular Movies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first ten most popular movies include Jurassic World, Mad Max: Fury Roaad, etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2. What are the attributes of most popular movies\n",
    "> This is to check for numerical parameter that most be fufilled for a movie to become popular and the benefi to get as a producer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>popularity</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8666.000000</td>\n",
       "      <td>8666.000000</td>\n",
       "      <td>8666.000000</td>\n",
       "      <td>8666.000000</td>\n",
       "      <td>8666.000000</td>\n",
       "      <td>8.666000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.741937</td>\n",
       "      <td>103.815140</td>\n",
       "      <td>264.204823</td>\n",
       "      <td>6.003612</td>\n",
       "      <td>2000.129818</td>\n",
       "      <td>3.153926e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.087657</td>\n",
       "      <td>26.207186</td>\n",
       "      <td>635.195808</td>\n",
       "      <td>0.893539</td>\n",
       "      <td>13.221278</td>\n",
       "      <td>1.069616e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.249990</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.449377</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.842984</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>1.966175e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.985763</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.544506e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        popularity      runtime   vote_count  vote_average  release_year  \\\n",
       "count  8666.000000  8666.000000  8666.000000   8666.000000   8666.000000   \n",
       "mean      0.741937   103.815140   264.204823      6.003612   2000.129818   \n",
       "std       1.087657    26.207186   635.195808      0.893539     13.221278   \n",
       "min       0.000188     0.000000    10.000000      1.500000   1960.000000   \n",
       "25%       0.249990    91.000000    20.000000      5.500000   1993.000000   \n",
       "50%       0.449377   100.000000    54.000000      6.100000   2004.000000   \n",
       "75%       0.842984   113.000000   200.000000      6.600000   2011.000000   \n",
       "max      32.985763   705.000000  9767.000000      8.700000   2015.000000   \n",
       "\n",
       "             profit  \n",
       "count  8.666000e+03  \n",
       "mean   3.153926e+07  \n",
       "std    1.069616e+08  \n",
       "min   -4.139124e+08  \n",
       "25%    0.000000e+00  \n",
       "50%    0.000000e+00  \n",
       "75%    1.966175e+07  \n",
       "max    2.544506e+09  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- For a movie to be popuplar, it should have a run time of approximately 104 minutes and vote_count of about 264\n",
    "- This will increase the profit to about 32.5 million"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q3. How does keywords affect the popularity of a movie\n",
    "> To have an insight on the desccriptive words that are normally used for these movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>keywords</th>\n",
       "      <th>popularity</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>monster dna tyrannosaurus rex velociraptor island</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>Jurassic World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>future chase post-apocalyptic dystopia australia</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>saving the world artificial intelligence fathe...</td>\n",
       "      <td>24.949134</td>\n",
       "      <td>Interstellar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>marvel comic spaceship space scene during end ...</td>\n",
       "      <td>14.311205</td>\n",
       "      <td>Guardians of the Galaxy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>based on novel revolution dystopia sequel dyst...</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>Insurgent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              keywords  popularity  \\\n",
       "0    monster dna tyrannosaurus rex velociraptor island   32.985763   \n",
       "1     future chase post-apocalyptic dystopia australia   28.419936   \n",
       "629  saving the world artificial intelligence fathe...   24.949134   \n",
       "630  marvel comic spaceship space scene during end ...   14.311205   \n",
       "2    based on novel revolution dystopia sequel dyst...   13.112507   \n",
       "\n",
       "                       title  \n",
       "0             Jurassic World  \n",
       "1         Mad Max: Fury Road  \n",
       "629             Interstellar  \n",
       "630  Guardians of the Galaxy  \n",
       "2                  Insurgent  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_key = sortby('popularity')\n",
    "top_key[['keywords', 'popularity', 'title']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barhplot(top_key.keywords[0:10], top_key.popularity[0:10], \"Popularity\", \"First 10 most Popular Movies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be deduced here the most of the movies on the list are scientific in nature from their keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q4. Highly rated \n",
    "> To check of popularity of a movie is a factor of hoe high it's rated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>title</th>\n",
       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6911</th>\n",
       "      <td>Pink Floyd: Pulse</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>Queen - Rock Montreal</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3690</th>\n",
       "      <td>The Art of Flight</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4178</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5986</th>\n",
       "      <td>Guten Tag, RamÃ³n</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>The Jinx: The Life and Deaths of Robert Durst</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7948</th>\n",
       "      <td>Stop Making Sense</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>Dear Zachary: A Letter to a Son About His Father</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2313</th>\n",
       "      <td>Tick Tock Tale</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>Iron Maiden: Flight 666</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title  vote_average\n",
       "6911                                 Pink Floyd: Pulse           8.7\n",
       "8411                             Queen - Rock Montreal           8.5\n",
       "3690                                 The Art of Flight           8.5\n",
       "4178                          The Shawshank Redemption           8.4\n",
       "5986                                 Guten Tag, RamÃ³n           8.4\n",
       "609      The Jinx: The Life and Deaths of Robert Durst           8.4\n",
       "7948                                 Stop Making Sense           8.4\n",
       "3310  Dear Zachary: A Letter to a Son About His Father           8.3\n",
       "2313                                    Tick Tock Tale           8.3\n",
       "1864                           Iron Maiden: Flight 666           8.3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vote = sortby('vote_average')\n",
    "top_vote = vote[['title', 'vote_average']].head(10)\n",
    "top_vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barhplot(top_vote.title, top_vote.vote_average, \"Popularity\", \"First 10 most Popular Movies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows the two parameters, popularity and vote, are not directly proportional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q5. Checking the influence of duration on popularity and profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration\n",
       "Very long    1.248461\n",
       "Long         0.651793\n",
       "Short        0.459411\n",
       "Moderate     0.274689\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_pop = df.groupby(['duration']).mean()['popularity']\n",
    "duration_pop.sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(duration_pop.index, duration_pop, \"Popularity\", \"Popularity of movies based on the duration\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "supprisingly, on average, the longer the movie, the more popular it is.\n",
    "Then, we need to check if profit also follow thesame trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration\n",
       "Very long    1.248461\n",
       "Long         0.651793\n",
       "Short        0.459411\n",
       "Moderate     0.274689\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_prof = df.groupby(['duration']).mean()['profit']\n",
    "duration_pop.sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(duration_prof.index, duration_prof, \"Profit\", \"Profit from movies based on the duration\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That awesom, the profit also increases as the duration increasea and perphaps, with increase in popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q6. Checking the influence of the day a movie was on popularity and profit\n",
    "> To check on which days are the popular moviese rerleased so that they can be target for future release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_day\n",
       "Wednesday    1.046971\n",
       "Thursday     0.907663\n",
       "Tuesday      0.729910\n",
       "Monday       0.702053\n",
       "Friday       0.636682\n",
       "Sunday       0.558103\n",
       "Saturday     0.542088\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_pop = df.groupby(['release_day']).mean()['popularity']\n",
    "day_pop.sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(day_pop.index, day_pop, \"Popularity\", \"Popularity of movies based on the day it was released\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, movies are better released between Mondays to Thursdays. Early days of the week. Then, we need to check if profit also follow thesame trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_day\n",
       "Wednesday    5.744613e+07\n",
       "Thursday     4.358354e+07\n",
       "Tuesday      3.704923e+07\n",
       "Monday       3.582095e+07\n",
       "Friday       2.081881e+07\n",
       "Sunday       1.721086e+07\n",
       "Saturday     1.252642e+07\n",
       "Name: profit, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_prof = df.groupby(['release_day']).mean()['profit']\n",
    "day_prof.sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot(day_prof.index, day_prof, \"Profit\", \"Profit from movies based on the day it was released\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the first graph, movies are better released between Mondays to Thursdays. Weekends are out of it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q7. Checking the influence of the month a movie was released on popularity and profit\n",
    "> To check on which months are the popular moviese rerleased so that they can be target for future release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_month\n",
       "June         0.908816\n",
       "November     0.866646\n",
       "July         0.857632\n",
       "May          0.855181\n",
       "December     0.854900\n",
       "March        0.746987\n",
       "October      0.702567\n",
       "February     0.668109\n",
       "August       0.653187\n",
       "April        0.645327\n",
       "September    0.630807\n",
       "January      0.554892\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_pop = df.groupby(['release_month']).mean()['popularity'].sort_values(ascending= False)\n",
    "month_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barhplot(month_pop.index, month_pop, \"popularity\", \"Popularity of a movies based on the month it was released\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, movies are better released between May to July, or November and December. Early days of the week. Then, we need to check if profit also follow the same trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_month\n",
       "June         43806655927\n",
       "December     38440122065\n",
       "May          34346594407\n",
       "November     30493854871\n",
       "July         29518226018\n",
       "March        19223428454\n",
       "October      16300150008\n",
       "April        15885471649\n",
       "August       14356556697\n",
       "September    13465619027\n",
       "February     10627155845\n",
       "January       6855390876\n",
       "Name: profit, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "month_prof = df.groupby(['release_month']).sum()['profit'].sort_values(ascending= False)\n",
    "month_prof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z4pqXibozfT72abLzo6/zfiYzgc7qnFjf7Q1+2z/J9td3n/s6hjN5rm4M3wAO6Xpj92HdBCffAN6cZFFXflpVTSSqJPlH2vV0J9B6DX9FG5a8Pa3Xf/A59N0kd6Fdp7V39/cU4DVJHtgltdMZ9R4xcZyeQUuKR5nqeqWJ9d/O5NdD/n7g9idpPdbLaK/lF9OGUT6CNtR1cJ8/mOS/gP1oPYFPAF6Y5NNV9eSB7Rfwt0x+Ppw9UBdGv25szPNF84xJljR7P6e9Yd0DOHNo2cQ3/8Pf5I7qEZgoG3wjuph2HcuwmfbgPIXWa/C3Xc8S8Kdhe7PprZnMdB9OJ66NOTjJWbRhbW8Z/sZ/EhMTV+zIDSexGHWsZuOxtETq0YPDxOBPkz5srCmZJ+LfccSyu9M+KAzv40doQ1WfmOQntIkEDp3BtiYm8rh8psNBq01OcRbw1iS3BL5Pm8zg/YPDtKZwD9oQteGytbTJNGCW52j3gX1590fabIsraRf070dL1op2vdN0+3khbRKEe4xYNpteu3OBuyVZOHhud0Ma70r/F8P3lRTO+pzYQC6k9Sjf4HmQ5Fa0IbKnr2fb40ig13e736BNgPIIYE/Wzd56Im3o4KNpPTJvG1rv6bShsH9bVddNFCZ5+MjAqi6jfen1ua7eC2hDmw9mYCKIWZo4ly5az3NpYv21063fvRY9EvhYVT1vaNm+o9bpegqPBI5Mm7X1Y8CBSd7eDTP9GfBw4H9HDIUcNvF+dA9u+Nwe9VoiAQ4XlNbHsd3/VybrpiRPshPtTfGU7jqdQQ9NsttA3dBmghpsD9qwjFsk2XOg7k1YN4PedCa+kRv+du1V9PN8n5il69ajFnbXCRxNS0Jf2xV/aFTdEY6jfVD5p4HhhiRZQpuB8Re0oWPrY+RxSZv+98/Xs81Zq6oLabPSPao7XybiCG1WNWgTMAw6njY05hnd33W0XpvpfI32YfbQJDd4vJLcLN308mlTHl/v/Oiu7zmPNoHCljPYHsArhp4Tu9GG3Z7YfdCDWZyj3fURw35M65m6dRfn72jXEz0ubSbL60mzXVd3Le26yT2S7D1Yh3XPx5k4ljbkbfgnC57blQ8/hjfWlM+7WZjxObEhdYnBF4F7j0gMDqWdB+t7DPs6Vuuz3dlu8yTaa94/067d/Ab86cuOC2kTfoSB67E6a7v1Bp9rCxnx5cskz6GJ64NvzDH6DO3LqdcludmI7S7ueuImcxrtC53nJbnBl4jdtWYT8U32+r2EoedgkptnaBr77nk/8YXoRJsf6/6/KQM/nTHQzuBPOky8N/1jrv8zGxOvb9JI9mRJs1RVX0/yGeDJwK2663wmpnC/ijb97LAzgG8keT9taMX+tBfnj3VDniYsow3T+kKSd9OGgDyBmT9Xv0BLyL6cZFm3/kNpFxhfNNWKM/QD2of8V3ffOF8OnDd4ATxtAox/Ag4EvlkznBq9qn7SDVd7BW3a70+zbgr3rYGnTjEMaTpfoZu5K8n7aMNQHkD7BvnnbNzXwn+gXV9xcnc+/B/tW9q/oc3UdeJg5ar6Y5JjaNOh7w78dzdpwpSq6vIkz6AlBD9JchSt1+eWtF6zx9F6+JbTkreXJvlCV+ePtGE2fwN8ptZNMT2dOwJfS3IcrTfihbSEaHAq59mco/+R5Pa0YVG/oH27fwDtvPjoQL3n0y6W/1aSj9I+wN2E1gO8f1f3sK7ua2hDhL6U5L3A+bTJMIYnq5nKW2nTR7+/+6B1Gm1SloNpQ1FnM4nGTPyQ1vPzgiRX0K5ju7Cqhj98T2mW58SG9ira435skiO6OB5Ee3y/xfTXHI7UDTE9B3hykp/Tprq/vKq+2E/Yk/oerQf/DbRrE68Dvjg4YcMksZ5Jm7Z89WDPLi0BO4D2XDx5aNX/pE0Q8pUkn6dN/PAURk/H/qMk36P1Sv+KdT/DcQ1teO56qarzkzyf1lv0oyQfoz1Ht6Nd5/oYWu/w6knWryRPpyWQZ3bn4tm0L3X+knYuvpI22+cfkpwAPC3td7p+QHut+TvaF0GD19HdFfhm91p2Fu21/h6014jz6I5lVf0gyWtp14SdnuSzA8dnd9p7w027uj/uXqtfSHsf/xzt+s4X0t7b13tCJm3iag5Mceiff3Phj3VTIb98BnUXAv+P9mY6ceHxscDOQ/WWdm0eRks6Ji5U/iXtt0tGTcP7CNpQmatpL/pvoV28f71piZl8CvfH0IZTXU770Pop4C8YPe1wMcmU1UwyXTvtN3B+SHuTHrk+bbhLAU9fj8fhubQPrVfRrqH6OvDXI+otZ3ZTuD+I9kH8D7QPqcfThuLMqJ3JjvdUsUxxDO/VnS8Xd4/zj2jJ5YJJtr0766bbf+okdW7w+HblO9F6vi7oHrPf0HrT/hm4dVdnV9qH2nO68+ZS2oeHlwGLZnBsju5i2472DfHvaEntN4DdR9Sf0TlK+6B1HC0Rupp2Ufo3gcePaHNb2tCnn3bnziW039d6Nzf8WYGdaYnb5d1j8Anah6ZJnw8jtrcdbUKO82kfbs+nDcHadibnwGzPYdrrwqndvtXEcWLq6d2Ppps8b7bnxDSxTPa8n2x7I2OkTSDyMVqvzTW0oVhvAm4+VO8wJpkme/ic6cr2pP1UwsR1l6tn8ByedBsz3OZtaMPxLmYW03rTrkkq4END5c/tyk8Zsc4CWgJyDu158QtaYn+P4f2j9W59qzvGE+89n2XgJ0WmiG3S4zVQ5wG0L04mHsNf0RLElwFbTneu05KlD3bH9Braa8dKWhJ5h6Hn95Fd+1fRntvPZej5RUu43kl7D72E9iXPObRrb5eM2P5+tB7eidfiX9K+lHv+UL2b0H5T6xddvbNoMxTO+Lzxb/P7S9W4hi9Lm760KWTPA15XVYeNN5qNJ8mXaTMz3q5m3gsiSZK0SfCaLEm9SvKXtGFmHzPBkiRJmyOvyZLUiyT3pQ1XeTFt2Mc7xhuRJEnSeNiTJakvz6f9yOk2tOuGVo83HEmSpPHwmixJkiRJ6pE9WZIkSZLUI6/JGmHbbbetpUuXjjsMSZIkSXPUypUrL6qqkb+zaJI1wtKlS1mxYsW4w5AkSZI0RyX5xWTLHC4oSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHi0cdwBz0aoL1rD00OPHHYYkSZIkYPXh+407hFmxJ0uSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPVo3iRZSS4bdwySJEmSNJ15k2RJkiRJ0nwwr5KsJHsl+dLA/fclOai7vTrJ65KcmmRVkrt35VslOSrJD5KclmT/MYUvSZIkaTMwr5KsGbioqnYDPgC8vCt7NfCNqroPsDfwb0m2GleAkiRJkjZtm1qS9fnu/0pgaXf7YcChSU4HlgNbAn8xvGKSQ5KsSLJi7RVrNnykkiRJkjZJC8cdwCxdy/UTwy2Hll/d/V/Lun0L8Piq+slUDVfVMmAZwKIlO9SND1WSJEnS5mi+9WT9ArhnkkVJFgMPmcE6XwNelCQASe69IQOUJEmStHmbFz1ZSRYCV1fVL5N8BjgT+Blw2gxWfwPwLuDMLtFaDTxyA4UqSZIkaTM3L5IsYEfg5wBV9QrgFcMVqmrpwO0VwF7d7SuBv9sYQUqSJEnSnB8umOR5wDHAa8YdiyRJkiRNZ873ZFXVB4EPjjsOSZIkSZqJOd+TJUmSJEnziUmWJEmSJPXIJEuSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj+b8FO7jsPP2i1lx+H7jDkOSJEnSPGRPliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpR07hPsKqC9aw9NDjxx2GJEmS5rDV/uSPJmFPliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6pFJliRJkiT1aFZJVpK1SU5PcnaSM5L8Y5KxJGpJLhvHdiVJkiRpKgtnWf/KqtoVIMltgE8Ci4HX9hzXBpVkYVVdO+44JEmSJG161rsXqqouBA4BXphmQZJ/S/KDJGcm+buJuklekWRV1/t1eFd2lyRfTbIyyclJ7t6VH53kA0lOSnJukgcnOSrJj5IcPRhDkrcnOTXJiUm2m0G770hyEvCW9d1vSZIkSZrKbHuyrqeqzu2GC94G2B9YU1X3SbII+HaSE4C7A48B7ltVVyS5dbf6MuB5VfWzJPcFjgD26Zbdqrv9aOCLwAOA5wA/SLJrVZ0ObAWcWlUvS/IvtN60F07T7l2Bfatq7Y3Zb0mSJEmazI1Ksjrp/j8M2CXJE7r7i4EdgH2BD1fVFQBVdXGSrYH7A59NJlZn0UCbX6yqSrIK+E1VrQJIcjawFDgduA74dFf/48DnZ9DuZydLsJIcQuuZY8E2281m/yVJkiTpT25UkpXkzsBa4EJasvWiqvraUJ2HAzW06k2ASyau7xrh6u7/dQO3J+5PFnPNoN3LJymnqpbResFYtGSH4XglSZIkaUbW+5qs7hqoDwLvq6oCvgY8P8kW3fK7JtkKOAF4dpKbd+W3rqpLgfOSPLErS5J7rUfsE71mTwFO6aldSZIkSVpvs+3JulmS04EtgGuBjwHv6JYdSRvKd2raWL3fAo+pqq8m2RVYkeQa4MvAq4CnAh9I8pquvU8BZ8wilsuBHZOsBNYAB3TlN7ZdSZIkSVpvaZ1QGrRoyQ615JnvGncYkiRJmsNWH77fuEPQGCVZWVV7jFo2lh8SliRJkqRNlUmWJEmSJPXIJEuSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1KOF4w5gLtp5+8Ws8MflJEmSJK0He7IkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST1yCvcRVl2whqWHHj/uMCRJkuaE1f60jTQr9mRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQezZskK0kl+djA/YVJfpvkS+OMS5IkSZIGzZskC7gc2CnJzbr7DwUuGGM8kiRJknQD8ynJAvgKsF93+0DgmIkFSfZM8p0kp3X/79aVn5xk14F6306yy8YMWpIkSdLmY74lWZ8CnpxkS2AX4PsDy34MPKiq7g38C/CmrvxI4CCAJHcFFlXVmRstYkmSJEmblXmVZHXJ0VJaL9aXhxYvBj6b5CzgncCOXflngUcm2QJ4NnD0qLaTHJJkRZIVa69YswGilyRJkrQ5mFdJVuc44G0MDBXsvAE4qap2Ah4FbAlQVVcAXwf2B54EfHJUo1W1rKr2qKo9Ftx88YaKXZIkSdImbuG4A1gPRwFrqmpVkr0GyhezbiKMg4bWORL4InByVV28oQOUJEmStPmadz1ZVXV+Vb17xKK3Am9O8m1gwdA6K4FLgQ9vhBAlSZIkbcbmTU9WVW09omw5sLy7/V3grgOL/3niRpLb0RLKEzZokJIkSZI2e/OuJ2u2kjyDNgvhq6vqunHHI0mSJGnTNm96stZXVX0U+Oi445AkSZK0edjke7IkSZIkaWMyyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSerRJj+F+/rYefvFrDh8v3GHIUmSJGkesidLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjp3AfYdUFa1h66PHjDkOSJM0Bq/1ZF0mzZE+WJEmSJPXIJEuSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPVoVklWkkry9oH7L09yWO9RzSyWy8axXUmSJEmaymx7sq4GHpdk2w0RzMaSZOG4Y5AkSZK0aZptknUtsAx46fCCJHdMcmKSM7v/f5FkcZLVSW7S1bl5kl8m2SLJXZJ8NcnKJCcnuXtX5+gkH0hyUpJzkzw4yVFJfpTk6KFtvj3Jqd32tuvKpmr3HUlOAt4y6yMlSZIkSTOwPtdkvR94apLFQ+XvAz5aVbsAnwDeU1VrgDOAB3d1HgV8rar+SEvWXlRVuwMvB44YaOtWwD60ZO6LwDuBHYGdk+za1dkKOLWqdgO+Cby2K5+q3bsC+1bVy9ZjvyVJkiRpWrMeNldVlyb5KPBi4MqBRfcDHtfd/hjw1u72p4EDgJOAJwNHJNkauD/w2SQT6y8aaOuLVVVJVgG/qapVAEnOBpYCpwPXdW0DfBz4/Aza/WxVrR21X0kOAQ4BWLDNdtMeB0mSJEkaZX2vTXoXcCrw4SnqVPf/OODNSW4N7A58g9YLdUlV7TrJuld3/68buD1xf7KYi9YzN1W7l08abNUyWi8Yi5bsUJPVkyRJkqSprNcU7lV1MfAZ4OCB4u/QeqoAngqc0tW9DPgf4N3Al6pqbVVdCpyX5IkAae61HrE/obv9FOCUntqVJEmSpPV2Y34n6+3A4CyDLwaeleRM4OnAPwws+zTwNNYN74OWiB2c5AzgbGD/WW7/cmDHJCtp12+9vqd2JUmSJGm9pcqRccMWLdmhljzzXeMOQ5IkzQGrD99v3CFImoOSrKyqPUYtuzE9WZIkSZKkISZZkiRJktQjkyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo8WjjuAuWjn7Rezwh8elCRJkrQe7MmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIKdxHWHXBGpYeevy4w5AkSTfSan+SRdIY2JMlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZIk9cgkS5IkSZJ6NC+TrCSXTbN8eZI9NlY8kiRJkjRhXiZZkiRJkjRXzdskK8leSb40cP99SQ4aqnNwkncO3H9ukndsxDAlSZIkbWbmbZI1Q58CHp1ki+7+s4APjzEeSZIkSZu4TTrJqqrLgW8Aj0xyd2CLqlo1qm6SQ5KsSLJi7RVrNmqckiRJkjYd8znJupbrx7/lJPWOBA5iml6sqlpWVXtU1R4Lbr64tyAlSZIkbV4WjjuAG+EXwD2TLKIlWA8BThmuVFXfT3IHYDdgl40boiRJkqTNzbxLspIsBK6uql8m+QxwJvAz4LQpVvsMsGtV/X5jxChJkiRp8zXvkixgR+DnAFX1CuAVwxWqaq+hogcC7xyuJ0mSJEl9m1fXZCV5HnAM8JoZ1r9lkp8CV1bViRs0OEmSJElinvVkVdUHgQ/Oov4lwF03WECSJEmSNGRe9WRJkiRJ0lxnkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6tG8+p2sjWXn7Rez4vD9xh2GJEmSpHnInixJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQeObvgCKsuWMPSQ48fdxiSNGesdsZVSZJmzJ4sSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSerR2JOsJJXkYwP3Fyb5bZIv9dT+ZX20I0mSJEkzMfYkC7gc2CnJzbr7DwUumE0DSRb2HpUkSZIkrYe5kGQBfAXYr7t9IHDMxIIkeyb5TpLTuv9368oPSvLZJF8ETkiydZIPJ1mV5Mwkjx9o441JzkjyvSS33Zg7JkmSJGnzMleSrE8BT06yJbAL8P2BZT8GHlRV9wb+BXjTwLL7Ac+sqn2AfwbWVNXOVbUL8I2uzlbA96rqXsC3gOdu2F2RJEmStDmbE8PsqurMJEtpvVhfHlq8GPhIkh2AArYYWPb1qrq4u70v8OSBNn/f3bwGmLi+ayVtOOINJDkEOARgwTbbrfe+SJIkSdq8zZWeLIDjgLcxMFSw8wbgpKraCXgUsOXAsssHboeWhA37Y1VNlK9lksSyqpZV1R5VtceCmy9en/glSZIkaU4lWUcBr6+qVUPli1k3EcZBU6x/AvDCiTtJbtVrdJIkSZI0A3Mmyaqq86vq3SMWvRV4c5JvAwumaOJfgVslOSvJGcDeGyJOSZIkSZpK1o2k04RFS3aoJc9817jDkKQ5Y/Xh+01fSZKkzUiSlVW1x6hlc6YnS5IkSZI2BSZZkiRJktQjkyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo8WjjuAuWjn7Rezwh/elCRJkrQe7MmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIKdxHWHXBGpYeevy4w5C0iVjtT0JIkrRZsSdLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6pFJliRJkiT1aIMnWUlun+S/kvwsyc+TvDvJTaeo/5IkN59Bu5f1G6kkSZIk3XgbNMlKEuDzwLFVtQNwV2Br4I1TrPYSYNok60bGtXBDti9JkiRp87Whe7L2Aa6qqg8DVNVa4KXAs5NsleRtSVYlOTPJi5K8GLgdcFKSkwCSHNjVOSvJWwYbT/L2JKcmOTHJdl3ZXZJ8NcnKJCcnuXtXfnSSd3TtXq8dSZIkSerLhk6ydgRWDhZU1aXA/wLPAe4E3LuqdgE+UVXvAX4F7F1Veye5HS0h2gfYFbhPksd0TW0FnFpVuwHfBF7blS8DXlRVuwMvB44Y2PxdgX2r6mV976gkSZIkAWzoYXMBapLyBwEfrKprAarq4hH17gMsr6rfAiT5RLfescB1wKe7eh8HPp9ka+D+wGfbSEUAFg2099muN+2GASWHAIcALNhmuxnuniRJkiRd34ZOss4GHj9YkGQb4A7AuYxOwK5XfRbbKlrP3CVVteskdS6fdOWqZbReMBYt2WG6uCRJkiRppA09XPBE4OZJngGQZAHwduBo4ATgeROTUCS5dbfOH4BbdLe/Dzw4ybbdugfShgZOxP6E7vZTgFO6oYjnJXli12aS3GsD7p8kSZIkXc8GTbKqqoDHAk9M8jPgp8BVwKuAI2nXZp2Z5AxaogStN+krSU6qql8DrwROAs6gXYP1X129y4Edk6ykXbP1+q78qcDBXZtnA/tvyH2UJEmSpEFpeZAGLVqyQy155rvGHYakTcTqw/cbdwiSJKlnSVZW1R6jlm3wHyOWJEmSpM2JSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSerRwnEHMBftvP1iVhy+37jDkCRJkjQP2ZMlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI2cXHGHVBWtYeujx4w5D0jyy2hlJJUlSx54sSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSerRvEiykjw2SSW5+3qse2SSe3a3VyfZtv8IJUmSJKmZF0kWcCBwCvDk2ayUZEFVPaeqfrhhwpIkSZKk65vzSVaSrYEHAAfTJVlJ9kryrSRfSPLDJB9McpNu2WVJXp/k+8D9kixPssf49kCSJEnS5mTOJ1nAY4CvVtVPgYuT7NaV7wm8DNgZuAvwuK58K+CsqrpvVZ2ysYOVJEmStHmbD0nWgcCnutuf6u4D/E9VnVtVa4FjgAd25WuBz812I0kOSbIiyYq1V6y5sTFLkiRJ2kwtHHcAU0nyZ8A+wE5JClgAFPDl7v+giftXdYnXrFTVMmAZwKIlOwy3LUmSJEkzMtd7sp4AfLSq7lhVS6vqDsB5tF6rPZPcqbsW6wDaxBiSJEmSNFZzPck6EPjCUNnngKcA3wUOB86iJV7D9SRJkiRpo5vTwwWraq8RZe9Jcibw8qo6YMTyrSdro6qW9h+lJEmSJK0z13uyJEmSJGlemdM9WZOpquXA8jGHIUmSJEk3YE+WJEmSJPXIJEuSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlH83IK9w1t5+0Xs+Lw/cYdhiRJkqR5yJ4sSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPnMJ9hFUXrGHpocePOwxJPVjtzzFIkqSNzJ4sSZIkSeqRSZYkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUo7EkWUkem6SS3H0jbvMlSW6+sbYnSZIkafM0rp6sA4FTgCdvxG2+BDDJkiRJkrRBbfQkK8nWwAOAg+mSrCR7JfnSQJ33JTmou/2IJD9OckqS90zUS3JYkpcPrHNWkqVJtkpyfJIzurIDkrwYuB1wUpKTNt7eSpIkSdrcLBzDNh8DfLWqfprk4iS7TVYxyZbAvwMPqqrzkhwzg/YfDvyqqvbr2lhcVWuS/COwd1Vd1MM+SJIkSdJI4xgueCDwqe72p7r7k7k7cG5Vndfdn0mStQrYN8lbkvx1Va2ZSVBJDkmyIsmKtVfMaBVJkiRJuoGN2pOV5M+AfYCdkhSwACjgOK6f8G05scoUzV07ap2uh2x34BHAm5OcUFWvny62qloGLANYtGSHmtkeSZIkSdL1beyerCcAH62qO1bV0qq6AzDRS3XPJIuSLAYe0pX9GLhzkqXd/QMG2loN7AbQDTm8U3f7dsAVVfVx4G0TdYA/ALfYIHslSZIkSZ2NfU3WgcDhQ2WfA54CfAY4E/gZcBpAVV2Z5AXAV5NcBPzP0HrPSHI68APgp135zsC/JbkO+CPw/K58GfCVJL+uqr373jFJkiRJAkjV3B4Zl2TrqrosSYD3Az+rqnduyG0uWrJDLXnmuzbkJiRtJKsP32/cIUiSpE1QkpVVtceoZeP6nazZeG7XW3U2sJg226AkSZIkzUnjmMJ9Vrpeqw3acyVJkiRJfZkPPVmSJEmSNG+YZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIJEuSJEmSejTnfydrHHbefjErDt9v3GFIkiRJmofsyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRswuOsOqCNSw99PhxhyGps9rZPiVJ0jxiT5YkSZIk9cgkS5IkSZJ6ZJIlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZIk9WhGSVaSVyc5O8mZSU5Pct/ZbijJXknuP/sQJ21vdZJt+2pPkiRJkvqwcLoKSe4HPBLYraqu7hKbm67HtvYCLgO+sx7r9irJwqq6dtxxSJIkSdr0TJtkAUuAi6rqaoCqugggye7AO4CtgYuAg6rq10mWA6cDewLbAM8GLgSeB6xN8jTgRcCPgQ8Cf9Ft5yVV9e0khwF36rZ7V+Afgb8C/ha4AHhUVf2xW+efkuzd3X5KVZ2TZLsp2r0dsLSL9ykzO0SSJEmSNHMzGS54AnCHJD9NckSSByfZAngv8ISq2h04CnjjwDpbVdX9gRcAR1XValri886q2rWqTgbe3d2/D/B44MiB9e8C7AfsD3wcOKmqdgau7MonXFpVewLvA97VlU3V7u7A/lVlgiVJkiRpg5i2J6uqLut6rf4a2Bv4NPCvwE7A15MALAB+PbDaMd2630qyTZJbjmh6X+Ce3foA2yS5RXf7K1X1xySrura/2pWvovVEXW873f93zqDd46rqylH7meQQ4BCABdtsN6qKJEmSJE1rJsMFqaq1wHJgeZf4/D1wdlXdb7JVprkPrRftfsNJT5ccTQxNvC7JH6tqYv3rhmKuEbenavfySeKlqpYBywAWLdlhVLySJEmSNK1phwsmuVuSHQaKdgV+BGzXTYpBki2S7DhQ54Cu/IHAmqpaA/wBuMVAnROAFw5sZ9f1iP+Agf/f7bFdSZIkSVovM+nJ2hp4bzfk71rgHNqwumXAe5Is7tp5F3B2t87vk3yHdRNfAHwR+M8k+9Mmvngx8P4kZ3brf4s2OcZsLEryfVqyeGBX1ke7kiRJkrResm4kXk8NttkFX15VK3pteCNatGSHWvLMd407DEmd1YfvN30lSZKkjSjJyqraY9SyGf0YsSRJkiRpZmY08cVsVNVefbcpSZIkSfOFPVmSJEmS1COTLEmSJEnqkUmWJEmSJPXIJEuSJEmSemSSJUmSJEk9MsmSJEmSpB71PoX7pmDn7Rezwh8/lSRJkrQe7MmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIKdxHWHXBGpYeevy4w5B6sdqfI5AkSdqo7MmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIJEuSJEmSemSSJUmSJEk96jXJSrI2yekDf0unqLs8yR59bl+SJEmSxm1hz+1dWVW79tVYkoVVde2NbGNBVa3tKyZJkiRJmsoGHy6YZPck30yyMsnXkiwZWPy0JN9JclaSPbv6hyVZluQE4KNJDkryvoH2vpRkr+72B5KsSHJ2ktcN1Fmd5F+SnAIcmuTUgWU7JFm5gXdbkiRJ0maq756smyU5vbt9HvAk4L3A/lX12yQHAG8Ent3V2aqq7p/kQcBRwE5d+e7AA6vqyiQHTbG9V1fVxUkWACcm2aWqzuyWXVVVDwRIsm+SXavqdOBZwNF97KwkSZIkDdugwwWT7ERLnL6eBGAB8OuB+scAVNW3kmyT5JZd+XFVdeUMtvekJIfQ9mMJcE9gIsn69EC9I4FnJflH4ABgz+GGunYOAViwzXYz2LQkSZIk3VDfSdawAGdX1f0mWV6T3L98oOxarj+scUuAJHcCXg7cp6p+n+ToiWUj2vgc8FrgG8DKqvrdDQKpWgYsA1i0ZIfhuCRJkiRpRjb0NVk/AbZLcj+AJFsk2XFg+QFd+QOBNVW1ZkQbq4Fdk9wkyR1Y1wu1DS2RWpPktsDfThZEVV0FfA34APDhG7dLkiRJkjS5DdqTVVXXJHkC8J4ki7vtvQs4u6vy+yTfoSVMzx7dCt+mXd+1CjgLOLVr+4wkp3VtndvVm8ongMcBJ6z3DkmSJEnSNHpNsqpq6xFlpwMPGlG+1yRtHDZ0v4CnTlL3oEnKl44ofiBwlNO5S5IkSdqQNvQ1WXNCki8AdwH2GXcskiRJkjZtm0WSVVWPHXcMkiRJkjYPG/zHiCVJkiRpc2KSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnq0WbxO1mztfP2i1lx+H7jDkOSJEnSPGRPliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo+cXXCEVResYemhx487DPVktTNFSpIkaSOyJ0uSJEmSemSSJUmSJEk9MsmSJEmSpB6ZZEmSJElSj0yyJEmSJKlHJlmSJEmS1COTLEmSJEnqkUmWJEmSJPXIJEuSJEmSerTRkqwkl22sbUmSJEnSuGzSPVlJFow7BkmSJEmbl42aZCXZOsmJSU5NsirJ/l350iQ/SvIfSc5OckKSm3XLlifZo7u9bZLVA+uc3LV1apL7d+V7JTkpySeBVUnekOQfBmJ4Y5IXb8z9liRJkrT5WLiRt3cV8NiqujTJtsD3khzXLdsBOLCqnpvkM8DjgY9P0daFwEOr6qokOwDHAHt0y/YEdqqq85IsBT4PvDvJTYAnd8slSZIkqXcbO8kK8KYkDwKuA7YHbtstO6+qTu9urwSWTtPWFsD7kuwKrAXuOrDsf6rqPICqWp3kd0nu3W3rtKr63Q0CSw4BDgFYsM12s98zSZIkSWLjJ1lPBbYDdq+qP3ZD/7bsll09UG8tcLPu9rWsG9a45UCdlwK/Ae7VLb9qYNnlQ9s9EjgI+HPgqFGBVdUyYBnAoiU71Ex3SJIkSZIGbeyJLxYDF3YJ1t7AHWewzmpg9+72E4ba+nVVXQc8HZhqkosvAA8H7gN8bbZBS5IkSdJMbZQkK8lCWk/VJ4A9kqyg9Wr9eAarvw14fpLvANsOlB8BPDPJ92hDBYd7r/6kqq4BTgI+U1Vr128vJEmSJGl6G2u44I7Az6vqIuB+k9TZaeJGVb1t4PaPgV0G6r2mK//ZUPkru/LlwPLBhrsJL/4KeOL67oAkSZIkzcQG78lK8jzazH+v2dDbmmT79wTOAU7sEjNJkiRJ2mA2eE9WVX0Q+OCG3s4U2/8hcOdxbV+SJEnS5mVjT3whSZIkSZs0kyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQebawfI55Xdt5+MSsO32/cYUiSJEmah+zJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpRyZZkiRJktQjkyxJkiRJ6pFJliRJkiT1yCRLkiRJknpkkiVJkiRJPTLJkiRJkqQemWRJkiRJUo9MsiRJkiSpR6mqcccw5yT5A/CTccchTWNb4KJxByFNw/NUc53nqOYDz9O56Y5Vtd2oBQs3diTzxE+qao9xByFNJckKz1PNdZ6nmus8RzUfeJ7OPw4XlCRJkqQemWRJkiRJUo9MskZbNu4ApBnwPNV84Hmquc5zVPOB5+k848QXkiRJktQje7IkSZIkqUcmWQOSPDzJT5Kck+TQcccjjZLkqCQXJjlr3LFIoyS5Q5KTkvwoydlJ/mHcMUnDkmyZ5H+SnNGdp68bd0zSZJIsSHJaki+NOxbNjElWJ8kC4P3A3wL3BA5Mcs/xRiWNdDTw8HEHIU3hWuBlVXUP4K+Av/f1VHPQ1cA+VXUvYFfg4Un+arwhSZP6B+BH4w5CM2eStc6ewDlVdW5VXQN8Cth/zDFJN1BV3wIuHncc0mSq6tdVdWp3+w+0Dwbbjzcq6fqquay7u0X354XqmnOS3B7YDzhy3LFo5kyy1tke+OXA/fPxQ4Ek3ShJlgL3Br4/5lCkG+iGYJ0OXAh8vao8TzUXvQt4BXDdmOPQLJhkrZMRZX6jJUnrKcnWwOeAl1TVpeOORxpWVWuralfg9sCeSXYac0jS9SR5JHBhVa0cdyyaHZOsdc4H7jBw//bAr8YUiyTNa0m2oCVYn6iqz487HmkqVXUJsByvd9Xc8wDg0UlW0y5l2SfJx8cbkmbCJGudHwA7JLlTkpsCTwaOG3NMkjTvJAnwIeBHVfWOcccjjZJkuyS37G7fDNgX+PFYg5KGVNUrq+r2VbWU9tn0G1X1tDGHpRkwyepU1bXAC4Gv0S7S/kxVnT3eqKQbSnIM8F3gbknOT3LwuGOShjwAeDrtG9fTu79HjDsoacgS4KQkZ9K+aP16VTk9tqRepMrLjiRJkiSpL/ZkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSeqRSZYkSZKkTU6So5JcmOSsGdR9UJJTk1yb5AlDy56Z5Gfd3zNnsm2TLEmSZiDJ6iTLN1DbS5NUksM2RPuStJk6mpn/yPj/AgcBnxwsTHJr4LXAfYE9gdcmudV0jZlkSZI0ByW5ZZLDkuw17lgkaT6qqm8BFw+WJblLkq8mWZnk5CR37+qurqozgeuGmvkb2u/oXVxVvwe+zgwSt4X97IIkSboRfgHcDLh2oOyWtG9PAZZv5HgkaVO1DHheVf0syX2BI4B9pqi/PfDLgfvnd2VTMsmSJGlMktyiqv5QVQVcNe54JGlTlmRr4P7AZ5NMFC+abrURZTXdthwuKEna6JIc1F2DtG83JO4XSa5OcmaSJ4+o/5gk305yWff37ST7j6i3OsnyJLsl+UZX9+IkH0lym6G6h3UxLJ2snRnsx8OSfDrJuUmuTHJJkhOSPHhE3eVdu3dO8p9JLgYu7ZZd75qsbojged2qr+2WVbf+bZNck+Tjk8R0RJLrktxxuvglaTNzE+CSqtp14O8e06xzPnCHgfu3B341kw1JkjQubwGeDHwA+BfgpsAxSQ6aqJDkBcAXgFsD/wq8obt9bJJDRrR5e+BE4FzgFcDngacDJyW5ec/xH9TF8lHgRcA7gXsAJyb56xH1twa+SRsW+GrgsEna/RHw0u72F2jxPx14SVX9BjgOeHySWw6ulGRL4EDgv6vqF+u7U5K0KaqqS4HzkjwRIM29plnta8DDktyqm/DiYV3ZlBwuKEkap22BXapqDUCSDwJnAu9I8mlgS+CtwM+B+3ZvkCT5AHAa8PYkn6mqSwbavAvw0qp610RBkrOBdwAvBg7vMf7nVtXlgwXdPpwNvBI4eaj+nwFvrKrXTNVoVf0mybG0pO3MqhrutVoGPB54Cu16ggmPp13LdeTsdkOSNj1JjgH2ArZNcj7tOtenAh9I8hpgC+BTwBlJ7kP7UutWwKOSvK6qdqyqi5O8AfhB1+zrq+ri4W0NM8mSJI3TByYSLICqWtMlKW+ivTHeAtgKeM9EgtXVuzTJe2lJyL7Afw60eSmtZ2zQEbReo8fSY5I1mGB1Y/0XAWuB7wN/Nclqb+th01+nDSc8mOsnWQcDvwOO7WEbkjSvVdWBkyy6weyAVfUD2kiIUe0cBRw1m207XFCSNE4/GlH2w+7/nYE7dbfPHlHvrIF6g86tqqsHC7r7546oe6N0UwF/KsnvgT8AFwG/BR5B+zZ02G+Het3WSzdRxpHAbkl27WK5My0x/VhVXXNjtyFJWn8mWZKkcRo1Q1MmuX1j2hzV1lSzQ0070qPrufoW7RvRdwNPoP2eykOBb4zYHsAV07U7C0fRru06uLv/7G6bDhWUpDEzyZIkjdM9R5RNzPR0Lu1aLIAdp1j33KHyuyS56WBBkkW0XrHBuhNj6m89VHdLYMnUYQPwEOB2tOu/Dquqz1XVCVX137QhjjfWlFMEV9X/AV8EnppkK+CZwPeralSvnyRpIzLJkiSN0/OTLJ64091+HnAJbRa+rwOXAy9KcouBeregzeZ3WVdn0DbAC4bKXtCVHztQ9tPu/75DdV/KzN4f106EM1iY5GHAfWew/nQu6/7feoo6/0EblvhB2rUE9mJJ0hzgxBeSpHG6CPh+kqNoycqzgL8AnlNVVwBXJHkF8P6u3tHdegcBfwn83eDEGZ2f035baidgJbA7bSjdj4H3DNT7767s9Un+jDaRxANpE1ZcNIPYTwH+jzbD4VLab6nsSptqfRWw84yOwCSq6ndJzgGenOTnwG+Ay6vqiwPVvgb8AngaLRn91I3ZpiSpH/ZkSZLG6f8BnwZeCLyedo3RU6vqQxMVquoI4HG03q3Xdn+XAI+tqmUj2jyfNpTvzrSZ/B4PfALYa3A2wKpaC+wPLKf1ih1O+52uB9MSlil1E1j8DW0mwRcBb6cNYXwEcOpMdn4Gngr8jDbb4jHAe4diuA6YOFafrqrLkCSNXdoERZIkbTzdjw1/GNi7qpb32O5qYHVV7dVXm3Nd19P3FuD+VfXdcccjSbInS5KkeSvJQuDvgFUmWJI0d3hNliRJ80ySOwH3ow13vDMw2Q9uSpLGwCRLkqT558G04ZYXAa+vKie8kKQ5xGuyJEmSJKlHXpMlSZIkST0yyZIkSZKkHplkSZIkSVKPTLIkSZIkqUcmWZIkSZLUI5MsSZIkSerR/wcFzuvTdDnCPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barhplot(month_prof.index, month_prof, \"popularity\", \"Popularity of a movies based on the month it was released\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this graph, it'll be a good advice to that a movie is released in June, December or at least May."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q8. Changed in the profit made from movies over the years\n",
    "> To check for the future of the industry. Where it was in the past, where it is and perhaps, where it is going"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_year\n",
       "2015    19188039506\n",
       "2012    16386295850\n",
       "2014    16295928255\n",
       "2013    15319942280\n",
       "2011    14643137595\n",
       "Name: profit, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_year = df.groupby('release_year').sum()['profit'].sort_values(ascending= False)\n",
    "top_year[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lineplot(top_year, \"Year\", \"Profit\", \"Changed in the profit made from movies over the years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Greate! The future is bright for the film industry.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Findings:\n",
    "> It has been gathered that movies like Jurassic World\t32.985763, Mad Max: Fury Road, Interstellar, etc are well know by the people. However, this does not relate to higher rating nor increase in profit. Instead, movies like Pink Floyd: Pulse, Queen - Rock Montreal, The Art of Flight, The Shawshank Redemption\t are not popularly known but are highly rated.\n",
    "> Also, it has been established that the duration of a movie, day, monthe and year of release of a movie affect its popularity and most importantly, profit.<br>\n",
    "> Lastly, It is a good thing to note the upward trend in the film industry over the year.And, based on this, the following conclusions were reached:\n",
    "- The best day of the week to release a movie is Wednesday\n",
    "- The best month of the year to release a movie is Wednesday\n",
    "- That the future is bright for the film industry\n",
    "\n",
    "### Limitations:\n",
    "- The number of rows with misssing values are to many (2199 after dropping extraneous columns). This reduced the total number of available data\n",
    "- Get the time of release (perhaps in the release_date column)would have been helpful in determining when people normally get time for movies\n",
    "- Having a single (most appropriate class) for columns like genres would have made it easy to determine which genre of movies are highly rated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<li><a href=\"https://stackoverflow.com\"> Stack over flow</a></li>\n",
    "<li><a href=\"https://www.geeksforgeeks.org\">Geeks for geeks</a></li>\n",
    "<li><a href=\"https://pandas.pydata.org\">Pandas documentation</a></li>\n",
    "<li><a href=\"https://towardsdatascience.com\">Towards data science</a></li>\n",
    "<li><a href=\"https://matplotlib.org\">Matplotlib documentation</a></li>\n",
    "<li><a href=\"https://realpython.com\">real pthon</a></li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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