FinalProject_group064.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Star and Director Effects on Box Office Successes\n",
    "\n",
    "#### Group 64: Granny Smith Geckos\n",
    "- Amal Alhaidari (XXXXXXXXX)\n",
    "- Brandon Tong (XXXXXXXXX)\n",
    "- Weijin Xu (XXXXXXXXX)\n",
    "- Quyen-Vi Nguyen (XXXXXXXXX)\n",
    "\n",
    "### INTRO AND BACKGROUND\n",
    "\n",
    "#### Overview\n",
    "Discussed known information on movie success based on certain variables and isolated social media presence to be left out in many predictions. Located dataset of movies with social media measurement and cleaned extraneous information outside the scope of interest. Performed comparison of distributions and normality tests followed by regression models. Afterwards, concluded that social media presence is a poor indicator for a movie’s gross income\n",
    "\n",
    "#### Research Question\n",
    "Which is a better predictor of a movie’s gross income, the actors’ or the director’s FB likes?\n",
    "*Note: We generalize popularity to FB likes due to the nature of social media being used to promote certain individuals\n",
    "\n",
    "#### Background and Prior Work \n",
    "Since Barry Litman’s initial study of financial film success in 1983, multiple newer studies have tried to make more multiple regression models to find new or confirm old determinants of box office success. In their models, they compared revenue, as a dependent variable, to possible determinants of profitability as the independent variables. The overall evidence between multiple studies’ variables that have consistently shown to be significantly and positively correlated to revenue have been production cost, the assistance of a major distributor, academy award nominations, and sequels with the strongest relationship being production budget. Other possible factors such as critic reviews, time of release, advertising and specific genres are less certain since the correlational findings across different studies show contradictory results. Additionally, there have been other studies that have analyzed stock markets and simulations to look at the specific relationship between star power and box office performance, however these studies’ findings contradict each other, leading us to conclude that there are no conclusive findings yet. \n",
    "\n",
    "Our interest in this topic stems from the possibility of there being another possible measurement of star power and the effects it may have on the financial success of movies: social media. Social media has played a growing part in how society keeps up to date on what’s happening in pop culture. This project will use Facebook likes as a measurement of the attention a star or director garners from the public and correlate these values to see if these parts of the creative sphere affect the box office.\n",
    "\n",
    "References: \n",
    "* [Determinants of Motion Picture Box Office and Profitability: An Interrelationship Approach](https://www.marketingcenter.de/sites/mcm/files/downloads/research/lmm/literature/hennig-thurau_et_al._2007_rms_determinants_of_motion_picture_box_office_and_profitability_an_interrelationship_approach.pdf?fbclid=IwAR1ggtw2b3jv_UDPUuA69VFWVKoGRI4bx1QQtQtSNFbXuqr8ykiz3hStico)\n",
    "* [The Determinants Of Domestic Box Office Performance In The Motion Picture Industry](https://pdfs.semanticscholar.org/0d55/44616785fdd71f11dd83abbe189219c7299c.pdf?fbclid=IwAR3fPQGLCIow9cIwWbaOlp_AgFL_a-5JSkSNpYI6blPhzouiDlHp-nHh9Wg)\n",
    "* [The determinants of box office performance in the film industry revisited](https://pdfs.semanticscholar.org/c960/7aaa7746ec9735a19d0ab2e524e53d20ab7f.pdf)\n",
    "* [Power of Stars: So Star Actors Drive the Success of Movies?](http://www.people.hbs.edu/aelberse/publications/Elberse_2007.pdf)\n",
    "\n",
    "#### Hypothesis\n",
    "We believe that an actor’s popularity will be a stronger predictor of a movie’s gross income while the popularity of a director will be a stronger predictor of a movie’s rating. From this, a model can be made to predict a movie’s income based on the popularity of a director and actors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA ANALYSIS\n",
    "#### Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import normaltest\n",
    "\n",
    "pd.options.display.max_rows = 12\n",
    "pd.options.display.max_columns = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the data\n",
    "movies_df = pd.read_csv( \"movie_metadata.csv\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning and Pre-Processing\n",
    "When parsing through the IMDB 5000 Movie Dataset, we were only concerned with columns that were useful in identifying the movies, gross, cast, directors, and the Facebook likes connected to each entry. While cleaning our dataset, we wanted to focus our scope on US movies, so we dropped entries of movies that weren’t from the US and weren’t in English. Afterwards, we dropped columns that were unnecessary to our analysis of movie revenue and actor/director likes. We wanted to keep the genre column to consider the potential effect it may have along with the Facebook likes on the gross. We also dropped duplicates so they don't give us an illusion of how many distinct movie titles we actually have. Following that we discovered that there were duplicate titles that didn't match on every entry in the cell. Further investigation lead us to find that these duplicates differ on actor likes by around 1. We therefore decide to manually drop the 8 rows that have duplicate titles due to similar discrepencies in entered data. We then add a column for each genre that exists in the dataframe to assist us in our prediction of genre's effect with Facebook likes on the movie gross. Finally, as we decide to log transform the data to get a graph closer to a normal curve, we realize that some actor and director Facebook likes are 0, so we shift all the Facebook likes by 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop labels we aren't interested in\n",
    "movies_df.drop( labels = [ 'color', 'duration', 'facenumber_in_poster',\n",
    "                          'plot_keywords', 'movie_imdb_link', 'imdb_score',\n",
    "                          'movie_facebook_likes', 'content_rating',\n",
    "                          'aspect_ratio', 'num_critic_for_reviews',\n",
    "                          'num_user_for_reviews', 'budget', 'num_voted_users',\n",
    "                          'cast_total_facebook_likes' ],\n",
    "               axis = 1, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rearrange column labels\n",
    "movies_df = movies_df [ [ 'movie_title', 'title_year', 'country', 'language',\n",
    "                         'gross', 'director_name', 'director_facebook_likes', \n",
    "                         'actor_1_name', 'actor_1_facebook_likes', 'actor_2_name',\n",
    "                         'actor_2_facebook_likes', 'actor_3_name',\n",
    "                         'actor_3_facebook_likes', 'genres' ] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop potential duplicates\n",
    "movies_df.drop_duplicates(keep='first',inplace=True)\n",
    "#drop movies that aren't in english\n",
    "movies_df = movies_df.drop(movies_df[movies_df.language != \"English\"].index)\n",
    "#drop movies that aren't USA movies\n",
    "movies_df = movies_df.drop( movies_df[movies_df.country != 'USA'].index )\n",
    "#drop country and language\n",
    "movies_df.drop(labels = ['country', 'language'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                  0\n",
       "title_year                  72\n",
       "gross                      552\n",
       "director_name               72\n",
       "director_facebook_likes     72\n",
       "actor_1_name                 4\n",
       "actor_1_facebook_likes       4\n",
       "actor_2_name                 7\n",
       "actor_2_facebook_likes       7\n",
       "actor_3_name                13\n",
       "actor_3_facebook_likes      13\n",
       "genres                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check null values\n",
    "movies_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop null values\n",
    "movies_df.dropna(inplace = True)\n",
    "#Reindexing the dataframe for duplicate dropping\n",
    "movies_df.index = range(len(movies_df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>Disturbia</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>...</td>\n",
       "      <td>501.0</td>\n",
       "      <td>Drama|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2172</th>\n",
       "      <td>Brothers</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2296</th>\n",
       "      <td>Precious</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>736.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>Murder by Numbers</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>...</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2685</th>\n",
       "      <td>Pan</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>...</td>\n",
       "      <td>394.0</td>\n",
       "      <td>Adventure|Family|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2739</th>\n",
       "      <td>The Watch</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>...</td>\n",
       "      <td>536.0</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>Mercury Rising</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>652.0</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>The Lovely Bones</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>...</td>\n",
       "      <td>310.0</td>\n",
       "      <td>Drama|Fantasy|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             movie_title  title_year             ...               \\\n",
       "1704          Disturbia       2007.0             ...                \n",
       "2172           Brothers       2009.0             ...                \n",
       "2296           Precious       2009.0             ...                \n",
       "2586  Murder by Numbers       2002.0             ...                \n",
       "2685                Pan       2015.0             ...                \n",
       "2739          The Watch       2012.0             ...                \n",
       "2908     Mercury Rising       1998.0             ...                \n",
       "2960   The Lovely Bones       2009.0             ...                \n",
       "\n",
       "      actor_3_facebook_likes                       genres  \n",
       "1704                   501.0       Drama|Mystery|Thriller  \n",
       "2172                  1000.0               Drama|Thriller  \n",
       "2296                   736.0                        Drama  \n",
       "2586                   400.0       Crime|Mystery|Thriller  \n",
       "2685                   394.0     Adventure|Family|Fantasy  \n",
       "2739                   536.0         Action|Comedy|Sci-Fi  \n",
       "2908                   652.0  Action|Crime|Drama|Thriller  \n",
       "2960                   310.0       Drama|Fantasy|Thriller  \n",
       "\n",
       "[8 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df[movies_df.duplicated('movie_title')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Disturbia</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>...</td>\n",
       "      <td>502.0</td>\n",
       "      <td>Drama|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>Disturbia</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>...</td>\n",
       "      <td>501.0</td>\n",
       "      <td>Drama|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_title  title_year           ...            actor_3_facebook_likes  \\\n",
       "1082  Disturbia       2007.0           ...                             502.0   \n",
       "1704  Disturbia       2007.0           ...                             501.0   \n",
       "\n",
       "                      genres  \n",
       "1082  Drama|Mystery|Thriller  \n",
       "1704  Drama|Mystery|Thriller  \n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#investigate an example of any of the remaining duplicates on titles\n",
    "movies_df[movies_df['movie_title'].str.contains('Disturbia')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We include this as an example of the duplicates we encountered that have the same movies title. These one differ on\n",
    "actor_3_facebook_likes by 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = movies_df.drop(movies_df.index[[1082,1704,1479,2172,1107,2296,\n",
    "                                            835,2586,125,2685,728,2739,664,\n",
    "                                            2908,284,2960]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this variable will store all genres in the movie dataset\n",
    "genre_dict = {}\n",
    "for genres in movies_df.genres:\n",
    "    #remove spaces from string\n",
    "    genres_no_space = genres.strip()\n",
    "    #need to extract genre by delimiter |\n",
    "    genres_list = genres_no_space.split(\"|\")\n",
    "    \n",
    "    #adds the genre as a key in dictionary and updates the count\n",
    "    for genre in genres_list:     \n",
    "        #key exists: increment the count for movies with this genre\n",
    "        if genre in genre_dict:\n",
    "            genre_dict[genre] = genre_dict.get(genre) + 1   \n",
    "        else: #doesn't exist: add it\n",
    "            key_value = {genre : 1}\n",
    "            genre_dict.update(key_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list to store the genres\n",
    "genre_list = []\n",
    "#add dictionary keys (genres) into our genre_list\n",
    "for key in genre_dict:\n",
    "    #statistical insignificance if >= 20\n",
    "    if genre_dict[key] >= 20:\n",
    "        genre_list.append(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Action',\n",
       " 'Adventure',\n",
       " 'Fantasy',\n",
       " 'Sci-Fi',\n",
       " 'Thriller',\n",
       " 'Romance',\n",
       " 'Animation',\n",
       " 'Comedy',\n",
       " 'Family',\n",
       " 'Musical',\n",
       " 'Western',\n",
       " 'Drama',\n",
       " 'History',\n",
       " 'Sport',\n",
       " 'Crime',\n",
       " 'Horror',\n",
       " 'Mystery',\n",
       " 'War',\n",
       " 'Biography',\n",
       " 'Music',\n",
       " 'Documentary']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check to see which genres are statistically significant for later use\n",
    "genre_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add specific genre columns to dataframe\n",
    "# loop through dataframe and add into lists a 1 if it exists in the genre and a 0 otherwise\n",
    "#after for loop ends insert the columns with the respective lists\n",
    "action = []\n",
    "adventure = []\n",
    "fantasy = []\n",
    "scifi = []\n",
    "thriller = []\n",
    "romance = []\n",
    "animation = []\n",
    "comedy = []\n",
    "family = []\n",
    "musical = []\n",
    "western = []\n",
    "drama = []\n",
    "history = []\n",
    "sport = []\n",
    "crime = []\n",
    "horror = []\n",
    "biography = []\n",
    "mystery = []\n",
    "war = []\n",
    "music = []\n",
    "documentary = []\n",
    "\n",
    "#create the genre lists, 1 if a movie is that genre, 0 if not\n",
    "for i, rows in movies_df.iterrows():\n",
    "    if (rows['genres'].find('Action') >= 0):\n",
    "        action.append(1)\n",
    "    else:\n",
    "        action.append(0)\n",
    "        \n",
    "    if (rows['genres'].find('Adventure') >= 0):\n",
    "        adventure.append(1)\n",
    "    else:\n",
    "        adventure.append(0)\n",
    "        \n",
    "    if (rows['genres'].find('Fantasy') >= 0):\n",
    "        fantasy.append(1)\n",
    "    else:\n",
    "        fantasy.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Sci-Fi') >= 0):\n",
    "        scifi.append(1)\n",
    "    else:\n",
    "        scifi.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Thriller') >= 0):\n",
    "        thriller.append(1)\n",
    "    else:\n",
    "        thriller.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Romance') >= 0):\n",
    "        romance.append(1)\n",
    "    else:\n",
    "        romance.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Animation') >= 0):\n",
    "        animation.append(1)\n",
    "    else:\n",
    "        animation.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Comedy') >= 0):\n",
    "        comedy.append(1)\n",
    "    else:\n",
    "        comedy.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Family') >= 0):\n",
    "        family.append(1)\n",
    "    else:\n",
    "        family.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Musical') >= 0):\n",
    "        musical.append(1)\n",
    "    else:\n",
    "        musical.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Western') >= 0):\n",
    "        western.append(1)\n",
    "    else:\n",
    "        western.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Drama') >= 0):\n",
    "        drama.append(1)\n",
    "    else:\n",
    "        drama.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('History') >= 0):\n",
    "        history.append(1)\n",
    "    else:\n",
    "        history.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Sport') >= 0):\n",
    "        sport.append(1)\n",
    "    else:\n",
    "        sport.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Crime') >= 0):\n",
    "        crime.append(1)\n",
    "    else:\n",
    "        crime.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Horror') >= 0):\n",
    "        horror.append(1)\n",
    "    else:\n",
    "        horror.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Biography') >= 0):\n",
    "        biography.append(1)\n",
    "    else:\n",
    "        biography.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Mystery') >= 0):\n",
    "        mystery.append(1)\n",
    "    else:\n",
    "        mystery.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('War') >= 0):\n",
    "        war.append(1)\n",
    "    else:\n",
    "        war.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Music') >= 0):\n",
    "        music.append(1)\n",
    "    else:\n",
    "        music.append(0)\n",
    "    \n",
    "    if (rows['genres'].find('Documentary') >= 0):\n",
    "        documentary.append(1)\n",
    "    else:\n",
    "        documentary.append(0)\n",
    "\n",
    "#add columns to data frame\n",
    "movies_df['Action'] = action\n",
    "movies_df['Adventure'] = adventure\n",
    "movies_df['Fantasy'] = fantasy\n",
    "movies_df['Scifi'] = scifi\n",
    "movies_df['Thriller'] = thriller\n",
    "movies_df['Romance'] = romance\n",
    "movies_df['Animation'] = animation\n",
    "movies_df['Comedy'] = comedy\n",
    "movies_df['Family'] = family\n",
    "movies_df['Musical'] = musical\n",
    "movies_df['Western'] = western\n",
    "movies_df['Drama'] = drama\n",
    "movies_df['History'] = history\n",
    "movies_df['Sport'] = sport\n",
    "movies_df['Crime'] = crime\n",
    "movies_df['Horror'] = horror\n",
    "movies_df['Biography'] = biography\n",
    "movies_df['Mystery'] = mystery\n",
    "movies_df['War'] = war\n",
    "movies_df['Music'] = music\n",
    "movies_df['Documentary'] = documentary "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Visualization and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 1 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot for actor1_facebook_likes and movie gross with regression line\n",
    "#max actor_1 likes\n",
    "max_actor1_likes = int(movies_df.actor_1_facebook_likes.max())\n",
    "\n",
    "a1, b1 = np.polyfit(movies_df.actor_1_facebook_likes, movies_df.gross, 1)\n",
    "\n",
    "pred_likes1_list = list()\n",
    "for i in range(0, max_actor1_likes):\n",
    "    pred_likes1_list.append((a1 * i) + b1)\n",
    "\n",
    "pred_likes1 = np.array(pred_likes1_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_1_facebook_likes, movies_df.gross)\n",
    "\n",
    "plt.plot(range(0, max_actor1_likes), pred_likes1, color='red')\n",
    "\n",
    "plt.xlabel('Actor 1 Facebook Likes', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 1 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 2 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot for actor2_facebook_likes and movie gross with regression line\n",
    "#max actor_2 likes\n",
    "max_actor2_likes = int(movies_df.actor_2_facebook_likes.max())\n",
    "\n",
    "a2, b2 = np.polyfit(movies_df.actor_2_facebook_likes, movies_df.gross, 1)\n",
    "\n",
    "pred_likes2_list = list()\n",
    "for i in range(0, max_actor2_likes):\n",
    "    pred_likes2_list.append((a2 * i) + b2)\n",
    "\n",
    "pred_likes2 = np.array(pred_likes2_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_2_facebook_likes, movies_df.gross)\n",
    "\n",
    "plt.plot(range(0, max_actor2_likes), pred_likes2, color='red')\n",
    "\n",
    "plt.xlabel('Actor 2 Facebook Likes', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 2 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 3 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot for actor3_facebook_likes and movie gross with regression line\n",
    "#max actor_3 likes\n",
    "max_actor3_likes = int(movies_df.actor_3_facebook_likes.max())\n",
    "\n",
    "a3, b3 = np.polyfit(movies_df.actor_3_facebook_likes, movies_df.gross, 1)\n",
    "\n",
    "pred_likes3_list = list()\n",
    "for i in range(0, max_actor3_likes):\n",
    "    pred_likes3_list.append((a3 * i) + b3)\n",
    "\n",
    "pred_likes3 = np.array(pred_likes3_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_3_facebook_likes, movies_df.gross)\n",
    "\n",
    "plt.plot(range(0, max_actor3_likes), pred_likes3, color='red')\n",
    "\n",
    "plt.xlabel('Actor 3 Facebook Likes', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 3 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Director Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot for director_facebook_likes and movie gross with regression line\n",
    "#max director likes\n",
    "max_director_likes = int(movies_df.director_facebook_likes.max())\n",
    "\n",
    "a, b = np.polyfit(movies_df.director_facebook_likes, movies_df.gross, 1)\n",
    "\n",
    "pred_likes_list = list()\n",
    "for i in range(0, max_director_likes):\n",
    "    pred_likes_list.append((a * i) + b)\n",
    "\n",
    "pred_likes = np.array(pred_likes_list)\n",
    "\n",
    "plt.scatter(movies_df.director_facebook_likes, movies_df.gross)\n",
    "\n",
    "plt.plot(range(0, max_director_likes), pred_likes, color='red')\n",
    "\n",
    "plt.xlabel('Director Facebook Likes', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Director Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Initial Scatter Plot summary\n",
    "The scatterplots demonstrate a positive correlation between facebook likes and movie gross income with the strongest corelation being between actor 2 likes and movie gross income. However, the distributions are  too variance and therefore need to be transformed for a desired normal distribution. From this, we decided to use a log transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title                550\n",
       "title_year                 550\n",
       "gross                      550\n",
       "director_name              550\n",
       "director_facebook_likes    550\n",
       "actor_1_name               550\n",
       "                          ... \n",
       "Horror                     550\n",
       "Biography                  550\n",
       "Mystery                    550\n",
       "War                        550\n",
       "Music                      550\n",
       "Documentary                550\n",
       "Length: 33, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#having any 0 likes affects log transformation\n",
    "movies_df[(movies_df.actor_1_facebook_likes == 0) |\n",
    "          (movies_df.actor_2_facebook_likes == 0) |\n",
    "          (movies_df.actor_3_facebook_likes == 0) |\n",
    "          (movies_df.director_facebook_likes == 0)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add .1 weight to all likes\n",
    "movies_df.actor_1_facebook_likes = (\n",
    "    movies_df.actor_1_facebook_likes.apply(lambda x: x+.1))\n",
    "movies_df.actor_2_facebook_likes = (\n",
    "    movies_df.actor_2_facebook_likes.apply(lambda x: x+.1))\n",
    "movies_df.actor_3_facebook_likes = (\n",
    "    movies_df.actor_3_facebook_likes.apply(lambda x: x+.1))\n",
    "movies_df.director_facebook_likes = (\n",
    "    movies_df.director_facebook_likes.apply(lambda x: x+.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 1 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing like values of actor 1 using log10\n",
    "movies_df['actor_1_facebook_likes_log'] = (\n",
    "    np.log10(movies_df['actor_1_facebook_likes']))\n",
    "\n",
    "#Scatterplot for actor1_facebook_likes and movie gross with regression line\n",
    "#max actor_1 likes\n",
    "max_logactor1_likes = movies_df.actor_1_facebook_likes_log.max()\n",
    "\n",
    "a1, b1 = (\n",
    "    np.polyfit(movies_df.actor_1_facebook_likes_log, movies_df.gross, 1))\n",
    "\n",
    "pred_loglikes1_list = list()\n",
    "for i in np.arange(0, max_logactor1_likes):\n",
    "    pred_loglikes1_list.append((a1 * i) + b1)\n",
    "\n",
    "pred_loglikes1 = np.array(pred_loglikes1_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_1_facebook_likes_log, movies_df.gross)\n",
    "\n",
    "plt.plot(np.arange(0, max_logactor1_likes), pred_loglikes1, color='red')\n",
    "\n",
    "plt.xlabel('Actor 1 Facebook Likes by Hundred Thousands', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 1 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log transformation has an effect on increasing the r-value. There is a slight increase in the\n",
    "correlation between likes to gross income. However, the correlation is still substantially weak. It is important to note that the points near the negative values shown on the scatterplots are actors/directors who had 0 FB likes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 1 Scatter Plot Analysis\n",
    "After log transformation, we can now see that the scatterpoints for Actor 1 Facebook Likes are also spread more evenly. We can see now that there are two distinct groups of actors, one between the 0-3 hundred thousand likes and abother between 3-5 hundred thousand likes creating a bimodal distribution. Though we can see the data shown by our scatterplot more clearly, our positive correlation is weakened due to the many outliers between 3-5 hundred thousand FB likes as well as those outliers that are in the >5 hundred thousand FB like group. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1312265665513149\n",
      "0.2489085388174903\n"
     ]
    }
   ],
   "source": [
    "#stats for actor 1 likes with gross\n",
    "slope1, intercept1, r1_value, p1_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_1_facebook_likes'],\n",
    "                     movies_df['gross']))\n",
    "print(r1_value)\n",
    "slope1, intercept1, r1_value, p1_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_1_facebook_likes_log'],\n",
    "                     movies_df['gross']))\n",
    "print(r1_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 2 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing like values of actor 2 using log10\n",
    "movies_df['actor_2_facebook_likes_log'] = (\n",
    "    np.log10(movies_df['actor_2_facebook_likes']))\n",
    "\n",
    "#Scatterplot for actor1_facebook_likes and movie gross with regression line\n",
    "#max actor_1 likes\n",
    "max_logactor2_likes = movies_df.actor_2_facebook_likes_log.max()\n",
    "\n",
    "a2, b2 = (\n",
    "    np.polyfit(movies_df.actor_2_facebook_likes_log, movies_df.gross, 1))\n",
    "\n",
    "pred_loglikes2_list = list()\n",
    "for i in np.arange(0, max_logactor2_likes):\n",
    "    pred_loglikes2_list.append((a2 * i) + b2)\n",
    "\n",
    "pred_loglikes2 = np.array(pred_loglikes2_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_2_facebook_likes_log, movies_df.gross)\n",
    "\n",
    "plt.plot(np.arange(0, max_logactor2_likes), pred_loglikes2, color='red')\n",
    "\n",
    "plt.xlabel('Actor 2 Facebook Likes by Hundred Thousands', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 2 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 2 Scatter Plot Analysis\n",
    "After log transformation, we can now see that the scatterpoints for Actor 2 Facebook Likes are also spread more evenly through out the scatterplot similar to Actor 1. The shape of the scatterplot appears to be the same as actor 1 and that could be the result of the actor 2 group having the same actors from the actor 1 group. Here we see a similar line as from the actor 1 scatterplot with the exception that this line extends more through out the scatterplot and this is because we have less outliers in actor 2 than actor 1. Though with less outliers shown here, we can more comfortably assume that the positive correlation shown here is slightly more accurate than the one in actor 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23465147465274078\n",
      "0.28477527226635774\n"
     ]
    }
   ],
   "source": [
    "#stats for actor 2 likes with gross\n",
    "slope2, intercept2, r2_value, p2_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_2_facebook_likes'],\n",
    "                     movies_df['gross']))\n",
    "print(r2_value)\n",
    "slope2, intercept2, r2_value, p2_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_2_facebook_likes_log'],\n",
    "                     movies_df['gross']))\n",
    "print(r2_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Actor 3 Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing like values of actor 3 using log10\n",
    "movies_df['actor_3_facebook_likes_log'] = (\n",
    "    np.log10(movies_df['actor_3_facebook_likes']))\n",
    "\n",
    "#Scatterplot for actor1_facebook_likes and movie gross with regression line\n",
    "#max actor_1 likes\n",
    "max_logactor3_likes = movies_df.actor_3_facebook_likes_log.max()\n",
    "\n",
    "a3, b3 = (\n",
    "    np.polyfit(movies_df.actor_3_facebook_likes_log, movies_df.gross, 1))\n",
    "\n",
    "pred_loglikes3_list = list()\n",
    "for i in np.arange(0, max_logactor3_likes):\n",
    "    pred_loglikes3_list.append((a3 * i) + b3)\n",
    "\n",
    "pred_loglikes3 = np.array(pred_loglikes3_list)\n",
    "\n",
    "plt.scatter(movies_df.actor_3_facebook_likes_log, movies_df.gross)\n",
    "\n",
    "plt.plot(np.arange(0, max_logactor3_likes), pred_loglikes3, color='red')\n",
    "\n",
    "#labeling\n",
    "plt.xlabel('Actor 3 Facebook Likes by Hundred Thousands', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Actor 3 Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 3 Scatter Plot Analysis\n",
    "After this log transformation, we see a drastic change in distribution.  The data is less spread out across the plot, clutters mostly around the 0-300,000 likes area, and more clearly shifts up as we move from left to right indicating a positive correlation. This has changed the regression line to have a shallower slope, indicating less of a positive correlation. It's also important to note the outliers that exist beyond the clumped areas in the 0-3 range along the x axis. Because of these outliers, the positive correlation here may be even weaker than depicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2770023668785392\n",
      "0.2630527011471118\n"
     ]
    }
   ],
   "source": [
    "#stats for actor 3 likes with gross\n",
    "slope3, intercept1, r3_value, p3_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_3_facebook_likes'],\n",
    "                     movies_df['gross']))\n",
    "print(r3_value)\n",
    "slope3, intercept1, r3_value, p3_value, std_err = (\n",
    "    stats.linregress(movies_df['actor_3_facebook_likes_log'],\n",
    "                     movies_df['gross']))\n",
    "print(r3_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.08, 'Director Facebook Likes and Movie Gross Scatterplot')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Changing like values of director likes using log10\n",
    "movies_df['director_facebook_likes_log'] = (\n",
    "    np.log10(movies_df['director_facebook_likes']))\n",
    "\n",
    "#Scatterplot for actor1_facebook_likes and movie gross with regression line\n",
    "#max actor_1 likes\n",
    "max_logdirector_likes = movies_df.director_facebook_likes_log.max()\n",
    "\n",
    "a, b = (\n",
    "    np.polyfit(movies_df.director_facebook_likes_log, movies_df.gross, 1))\n",
    "\n",
    "pred_loglikes_list = list()\n",
    "for i in np.arange(0, max_logdirector_likes):\n",
    "    pred_loglikes_list.append((a * i) + b)\n",
    "\n",
    "pred_loglikes = np.array(pred_loglikes_list)\n",
    "\n",
    "plt.scatter(movies_df.director_facebook_likes_log, movies_df.gross)\n",
    "\n",
    "plt.plot(np.arange(0, max_logdirector_likes), pred_loglikes, color='red')\n",
    "\n",
    "#labeling\n",
    "plt.xlabel('Director Facebook Likes by Hundred Thousands', fontsize = 16)\n",
    "plt.ylabel('Movie Gross Income by Millions', fontsize = 14)\n",
    "plt.title('Director Facebook Likes and Movie Gross Scatterplot', y=1.08)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Director Scatter Plot Analysis\n",
    "The distribution for Director Facebook likes had the most variance out of all the scatter plots. Even after the log transformation, the scatter plot still has four distinct groups above -1, 0-3, 3-4, and >4. It's also important to note that the bulk of the height for the biggest group (in range 0-3) peaks at around 2 million dollars while the bulk of the pillar above -1 peaks around 4 million dollars. This uneven distribution caused by the large amounts of directors who previously were observed to have 0 FB likes (more than any other category) is the likely reason that the regression line currently shows that there is little to no correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14059768143149814\n",
      "0.07842459804293193\n"
     ]
    }
   ],
   "source": [
    "#stats for director likes with gross\n",
    "slope, intercept, r_value, p_value, std_err = (\n",
    "    stats.linregress(movies_df['director_facebook_likes'],\n",
    "                     movies_df['gross']))\n",
    "print(r_value)\n",
    "slope, intercept, r_value, p_value, std_err = (\n",
    "    stats.linregress(movies_df['director_facebook_likes_log'],\n",
    "                     movies_df['gross']))\n",
    "print(r_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving into Models and Observing Actor and Director Likes by Genres\n",
    "\n",
    "From the scatter plots we see that there is a weak correlation between the observed likes and the gross income of a movie with r-values presenting at less than 0.3. For the sake of the project, we continued with the models. Before we do so however, we wanted to see if genres had a certain distribution that can be controlled for to create a better model for prediction. We believe that genres have differing popularities that influence an actor's social media presence as well as the movie's gross income. All boxplots were made using the log transformed data of each actor and director's Facebook likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Action'),\n",
       " Text(0, 0, 'Adventure'),\n",
       " Text(0, 0, 'Fantasy'),\n",
       " Text(0, 0, 'Sci-Fi'),\n",
       " Text(0, 0, 'Thriller'),\n",
       " Text(0, 0, 'Romance'),\n",
       " Text(0, 0, 'Animation'),\n",
       " Text(0, 0, 'Comedy'),\n",
       " Text(0, 0, 'Family'),\n",
       " Text(0, 0, 'Musical'),\n",
       " Text(0, 0, 'Western'),\n",
       " Text(0, 0, 'Drama'),\n",
       " Text(0, 0, 'History'),\n",
       " Text(0, 0, 'Sport'),\n",
       " Text(0, 0, 'Crime'),\n",
       " Text(0, 0, 'Horror'),\n",
       " Text(0, 0, 'Biography'),\n",
       " Text(0, 0, 'Mystery'),\n",
       " Text(0, 0, 'War'),\n",
       " Text(0, 0, 'Music'),\n",
       " Text(0, 0, 'Documentary')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add all actor1 likes into its respective list\n",
    "\n",
    "action1 = []\n",
    "adventure1 = []\n",
    "fantasy1 = []\n",
    "scifi1 = []\n",
    "thriller1 = []\n",
    "romance1 = []\n",
    "animation1 = []\n",
    "comedy1 = []\n",
    "family1 = []\n",
    "musical1 = []\n",
    "western1 = []\n",
    "drama1 = []\n",
    "history1 = []\n",
    "sport1 = []\n",
    "crime1 = []\n",
    "horror1 = []\n",
    "biography1 = []\n",
    "mystery1 = []\n",
    "war1 = []\n",
    "music1 = []\n",
    "documentary1 = []\n",
    "\n",
    "\n",
    "for i, rows in movies_df.iterrows():\n",
    "    currLikes = rows['actor_1_facebook_likes_log']\n",
    "    if (rows['genres'].find('Action') >= 0):\n",
    "        action1.append(currLikes)\n",
    "    if (rows['genres'].find('Adventure') >= 0):\n",
    "        adventure1.append(currLikes)\n",
    "    if (rows['genres'].find('Fantasy') >= 0):\n",
    "        fantasy1.append(currLikes)\n",
    "    if (rows['genres'].find('Sci-Fi') >= 0):\n",
    "        scifi1.append(currLikes)\n",
    "    if (rows['genres'].find('Thriller') >= 0):\n",
    "        thriller1.append(currLikes)\n",
    "    if (rows['genres'].find('Romance') >= 0):\n",
    "        romance1.append(currLikes)\n",
    "    if (rows['genres'].find('Animation') >= 0):\n",
    "        animation1.append(currLikes)\n",
    "    if (rows['genres'].find('Comedy') >= 0):\n",
    "        comedy1.append(currLikes)\n",
    "    if (rows['genres'].find('Family') >= 0):\n",
    "        family1.append(currLikes)\n",
    "    if (rows['genres'].find('Musical') >= 0):\n",
    "        musical1.append(currLikes)\n",
    "    if (rows['genres'].find('Western') >= 0):\n",
    "        western1.append(currLikes)\n",
    "    if (rows['genres'].find('Drama') >= 0):\n",
    "        drama1.append(currLikes)\n",
    "    if (rows['genres'].find('History') >= 0):\n",
    "        history1.append(currLikes)\n",
    "    if (rows['genres'].find('Sport') >= 0):\n",
    "        sport1.append(currLikes)\n",
    "    if (rows['genres'].find('Crime') >= 0):\n",
    "        crime1.append(currLikes)\n",
    "    if (rows['genres'].find('Horror') >= 0):\n",
    "        horror1.append(currLikes)\n",
    "    if (rows['genres'].find('Biography') >= 0):\n",
    "        biography1.append(currLikes)\n",
    "    if (rows['genres'].find('Mystery') >= 0):\n",
    "        mystery1.append(currLikes)\n",
    "    if (rows['genres'].find('War') >= 0):\n",
    "        war1.append(currLikes)\n",
    "    if (rows['genres'].find('Music') >= 0):\n",
    "        music1.append(currLikes)\n",
    "    if (rows['genres'].find('Documentary') >= 0):\n",
    "        documentary1.append(currLikes)\n",
    "        \n",
    "        \n",
    "actor1Likes = [action1, adventure1, fantasy1, scifi1, thriller1, romance1, animation1,\n",
    "               comedy1,family1 ,musical1 ,western1, drama1, history1 , sport1 , crime1 ,\n",
    "               horror1, biography1 , mystery1 , war1 , music1 ,documentary1]\n",
    "\n",
    "fig = plt.figure(1, figsize=(21, 12))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bp = plt.boxplot(actor1Likes)\n",
    "\n",
    "ax.set_title('Actor 1 Facebook Likes by Genre', fontsize=30)\n",
    "ax.set_xlabel('Genres', fontsize=25)\n",
    "ax.set_ylabel('Actor 1 Facebook Likes', fontsize=25)\n",
    "ax.set_xticklabels(\n",
    "           [\"Action\",\"Adventure\",\"Fantasy\", \"Sci-Fi\", \"Thriller\", \"Romance\",\n",
    "            \"Animation\", \"Comedy\", \"Family\", \"Musical\", \"Western\", \"Drama\", \"History\",\n",
    "            \"Sport\", \"Crime\", \"Horror\", \"Biography\", \"Mystery\", \"War\", \"Music\", \"Documentary\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 1 Facebook Likes Distribution by Genres\n",
    "\n",
    "Here we see the distribution of likes for actor 1 has some variability amongst certain genres. Music, Musical, and Documentary are genres that seem to display different distributions in comparison to other genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Action'),\n",
       " Text(0, 0, 'Adventure'),\n",
       " Text(0, 0, 'Fantasy'),\n",
       " Text(0, 0, 'Sci-Fi'),\n",
       " Text(0, 0, 'Thriller'),\n",
       " Text(0, 0, 'Romance'),\n",
       " Text(0, 0, 'Animation'),\n",
       " Text(0, 0, 'Comedy'),\n",
       " Text(0, 0, 'Family'),\n",
       " Text(0, 0, 'Musical'),\n",
       " Text(0, 0, 'Western'),\n",
       " Text(0, 0, 'Drama'),\n",
       " Text(0, 0, 'History'),\n",
       " Text(0, 0, 'Sport'),\n",
       " Text(0, 0, 'Crime'),\n",
       " Text(0, 0, 'Horror'),\n",
       " Text(0, 0, 'Biography'),\n",
       " Text(0, 0, 'Mystery'),\n",
       " Text(0, 0, 'War'),\n",
       " Text(0, 0, 'Music'),\n",
       " Text(0, 0, 'Documentary')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add all actor2 likes into its respective list\n",
    "\n",
    "action2 = []\n",
    "adventure2 = []\n",
    "fantasy2 = []\n",
    "scifi2 = []\n",
    "thriller2 = []\n",
    "romance2 = []\n",
    "animation2 = []\n",
    "comedy2 = []\n",
    "family2 = []\n",
    "musical2 = []\n",
    "western2 = []\n",
    "drama2 = []\n",
    "history2 = []\n",
    "sport2 = []\n",
    "crime2 = []\n",
    "horror2 = []\n",
    "biography2 = []\n",
    "mystery2 = []\n",
    "war2 = []\n",
    "music2 = []\n",
    "documentary2 = []\n",
    "\n",
    "\n",
    "for i, rows in movies_df.iterrows():\n",
    "    currLikes = rows['actor_2_facebook_likes_log']\n",
    "    if (rows['genres'].find('Action') >= 0):\n",
    "        action2.append(currLikes)\n",
    "    if (rows['genres'].find('Adventure') >= 0):\n",
    "        adventure2.append(currLikes)\n",
    "    if (rows['genres'].find('Fantasy') >= 0):\n",
    "        fantasy2.append(currLikes)\n",
    "    if (rows['genres'].find('Sci-Fi') >= 0):\n",
    "        scifi2.append(currLikes)\n",
    "    if (rows['genres'].find('Thriller') >= 0):\n",
    "        thriller2.append(currLikes)\n",
    "    if (rows['genres'].find('Romance') >= 0):\n",
    "        romance2.append(currLikes)\n",
    "    if (rows['genres'].find('Animation') >= 0):\n",
    "        animation2.append(currLikes)\n",
    "    if (rows['genres'].find('Comedy') >= 0):\n",
    "        comedy2.append(currLikes)\n",
    "    if (rows['genres'].find('Family') >= 0):\n",
    "        family2.append(currLikes)\n",
    "    if (rows['genres'].find('Musical') >= 0):\n",
    "        musical2.append(currLikes)\n",
    "    if (rows['genres'].find('Western') >= 0):\n",
    "        western2.append(currLikes)\n",
    "    if (rows['genres'].find('Drama') >= 0):\n",
    "        drama2.append(currLikes)\n",
    "    if (rows['genres'].find('History') >= 0):\n",
    "        history2.append(currLikes)\n",
    "    if (rows['genres'].find('Sport') >= 0):\n",
    "        sport2.append(currLikes)\n",
    "    if (rows['genres'].find('Crime') >= 0):\n",
    "        crime2.append(currLikes)\n",
    "    if (rows['genres'].find('Horror') >= 0):\n",
    "        horror2.append(currLikes)\n",
    "    if (rows['genres'].find('Biography') >= 0):\n",
    "        biography2.append(currLikes)\n",
    "    if (rows['genres'].find('Mystery') >= 0):\n",
    "        mystery2.append(currLikes)\n",
    "    if (rows['genres'].find('War') >= 0):\n",
    "        war2.append(currLikes)\n",
    "    if (rows['genres'].find('Music') >= 0):\n",
    "        music2.append(currLikes)\n",
    "    if (rows['genres'].find('Documentary') >= 0):\n",
    "        documentary2.append(currLikes)\n",
    "        \n",
    "        \n",
    "actor2Likes = [action2, adventure2, fantasy2, scifi2, thriller2, romance2, animation2,\n",
    "               comedy2,family2 ,musical2 ,western2, drama2, history2 , sport2 , crime2 ,\n",
    "               horror2, biography2 , mystery2 , war2 , music2 ,documentary2]\n",
    "\n",
    "fig = plt.figure(1, figsize=(21, 12))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bp = ax.boxplot(actor2Likes)\n",
    "\n",
    "ax.set_title('Actor 2 Facebook Likes by Genre', fontsize=30)\n",
    "ax.set_xlabel('Genres', fontsize=25)\n",
    "ax.set_ylabel('Actor 2 Facebook Likes', fontsize=25)\n",
    "ax.set_xticklabels(\n",
    "           [\"Action\",\"Adventure\",\"Fantasy\", \"Sci-Fi\", \"Thriller\", \"Romance\",\n",
    "            \"Animation\", \"Comedy\", \"Family\", \"Musical\", \"Western\", \"Drama\", \"History\",\n",
    "            \"Sport\", \"Crime\", \"Horror\", \"Biography\", \"Mystery\", \"War\", \"Music\", \"Documentary\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 2 Facebook Likes Distribution by Genres\n",
    "\n",
    "Here we see that the distributions for all genres seem relatively the same throughout. Documentary seems to fall into a different distribution. This is most in part due to the nature in which actors are not placed in importance as well as the lack of documentaries in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Action'),\n",
       " Text(0, 0, 'Adventure'),\n",
       " Text(0, 0, 'Fantasy'),\n",
       " Text(0, 0, 'Sci-Fi'),\n",
       " Text(0, 0, 'Thriller'),\n",
       " Text(0, 0, 'Romance'),\n",
       " Text(0, 0, 'Animation'),\n",
       " Text(0, 0, 'Comedy'),\n",
       " Text(0, 0, 'Family'),\n",
       " Text(0, 0, 'Musical'),\n",
       " Text(0, 0, 'Western'),\n",
       " Text(0, 0, 'Drama'),\n",
       " Text(0, 0, 'History'),\n",
       " Text(0, 0, 'Sport'),\n",
       " Text(0, 0, 'Crime'),\n",
       " Text(0, 0, 'Horror'),\n",
       " Text(0, 0, 'Biography'),\n",
       " Text(0, 0, 'Mystery'),\n",
       " Text(0, 0, 'War'),\n",
       " Text(0, 0, 'Music'),\n",
       " Text(0, 0, 'Documentary')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add all actor3 likes into its respective list\n",
    "\n",
    "action3 = []\n",
    "adventure3 = []\n",
    "fantasy3 = []\n",
    "scifi3 = []\n",
    "thriller3 = []\n",
    "romance3 = []\n",
    "animation3 = []\n",
    "comedy3 = []\n",
    "family3 = []\n",
    "musical3 = []\n",
    "western3 = []\n",
    "drama3 = []\n",
    "history3 = []\n",
    "sport3 = []\n",
    "crime3 = []\n",
    "horror3 = []\n",
    "biography3 = []\n",
    "mystery3 = []\n",
    "war3 = []\n",
    "music3 = []\n",
    "documentary3 = []\n",
    "\n",
    "\n",
    "for i, rows in movies_df.iterrows():\n",
    "    currLikes = rows['actor_3_facebook_likes_log']\n",
    "    if (rows['genres'].find('Action') >= 0):\n",
    "        action3.append(currLikes)\n",
    "    if (rows['genres'].find('Adventure') >= 0):\n",
    "        adventure3.append(currLikes)\n",
    "    if (rows['genres'].find('Fantasy') >= 0):\n",
    "        fantasy3.append(currLikes)\n",
    "    if (rows['genres'].find('Sci-Fi') >= 0):\n",
    "        scifi3.append(currLikes)\n",
    "    if (rows['genres'].find('Thriller') >= 0):\n",
    "        thriller3.append(currLikes)\n",
    "    if (rows['genres'].find('Romance') >= 0):\n",
    "        romance3.append(currLikes)\n",
    "    if (rows['genres'].find('Animation') >= 0):\n",
    "        animation3.append(currLikes)\n",
    "    if (rows['genres'].find('Comedy') >= 0):\n",
    "        comedy3.append(currLikes)\n",
    "    if (rows['genres'].find('Family') >= 0):\n",
    "        family3.append(currLikes)\n",
    "    if (rows['genres'].find('Musical') >= 0):\n",
    "        musical3.append(currLikes)\n",
    "    if (rows['genres'].find('Western') >= 0):\n",
    "        western3.append(currLikes)\n",
    "    if (rows['genres'].find('Drama') >= 0):\n",
    "        drama3.append(currLikes)\n",
    "    if (rows['genres'].find('History') >= 0):\n",
    "        history3.append(currLikes)\n",
    "    if (rows['genres'].find('Sport') >= 0):\n",
    "        sport3.append(currLikes)\n",
    "    if (rows['genres'].find('Crime') >= 0):\n",
    "        crime3.append(currLikes)\n",
    "    if (rows['genres'].find('Horror') >= 0):\n",
    "        horror3.append(currLikes)\n",
    "    if (rows['genres'].find('Biography') >= 0):\n",
    "        biography3.append(currLikes)\n",
    "    if (rows['genres'].find('Mystery') >= 0):\n",
    "        mystery3.append(currLikes)\n",
    "    if (rows['genres'].find('War') >= 0):\n",
    "        war3.append(currLikes)\n",
    "    if (rows['genres'].find('Music') >= 0):\n",
    "        music3.append(currLikes)\n",
    "    if (rows['genres'].find('Documentary') >= 0):\n",
    "        documentary3.append(currLikes)\n",
    "        \n",
    "        \n",
    "actor3Likes = [action3, adventure3, fantasy3, scifi3, thriller3, romance3, animation3,\n",
    "               comedy3,family3 ,musical3 ,western3, drama3, history3 , sport3 , crime3 ,\n",
    "               horror3, biography3 , mystery3 , war3 , music3 ,documentary3]\n",
    "\n",
    "fig = plt.figure(1, figsize=(21, 12))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bp = ax.boxplot(actor2Likes)\n",
    "\n",
    "ax.set_title('Actor 3 Facebook Likes by Genre', fontsize=30)\n",
    "ax.set_xlabel('Genres', fontsize=25)\n",
    "ax.set_ylabel('Actor 3 Facebook Likes', fontsize=25)\n",
    "ax.set_xticklabels(\n",
    "           [\"Action\",\"Adventure\",\"Fantasy\", \"Sci-Fi\", \"Thriller\", \"Romance\",\n",
    "            \"Animation\", \"Comedy\", \"Family\", \"Musical\", \"Western\", \"Drama\", \"History\",\n",
    "            \"Sport\", \"Crime\", \"Horror\", \"Biography\", \"Mystery\", \"War\", \"Music\", \"Documentary\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 3 Facebook Likes by Genre\n",
    "\n",
    "Once again we see that documentary has a distribution unlike the other entries. We can surmise that the total amount of documentaries  can attest to the distribution of likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Action'),\n",
       " Text(0, 0, 'Adventure'),\n",
       " Text(0, 0, 'Fantasy'),\n",
       " Text(0, 0, 'Sci-Fi'),\n",
       " Text(0, 0, 'Thriller'),\n",
       " Text(0, 0, 'Romance'),\n",
       " Text(0, 0, 'Animation'),\n",
       " Text(0, 0, 'Comedy'),\n",
       " Text(0, 0, 'Family'),\n",
       " Text(0, 0, 'Musical'),\n",
       " Text(0, 0, 'Western'),\n",
       " Text(0, 0, 'Drama'),\n",
       " Text(0, 0, 'History'),\n",
       " Text(0, 0, 'Sport'),\n",
       " Text(0, 0, 'Crime'),\n",
       " Text(0, 0, 'Horror'),\n",
       " Text(0, 0, 'Biography'),\n",
       " Text(0, 0, 'Mystery'),\n",
       " Text(0, 0, 'War'),\n",
       " Text(0, 0, 'Music'),\n",
       " Text(0, 0, 'Documentary')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Add all actor1 likes into its respective list\n",
    "\n",
    "actionD = []\n",
    "adventureD = []\n",
    "fantasyD = []\n",
    "scifiD = []\n",
    "thrillerD = []\n",
    "romanceD = []\n",
    "animationD = []\n",
    "comedyD = []\n",
    "familyD = []\n",
    "musicalD = []\n",
    "westernD = []\n",
    "dramaD = []\n",
    "historyD = []\n",
    "sportD = []\n",
    "crimeD = []\n",
    "horrorD = []\n",
    "biographyD = []\n",
    "mysteryD = []\n",
    "warD = []\n",
    "musicD = []\n",
    "documentaryD = []\n",
    "\n",
    "\n",
    "for i, rows in movies_df.iterrows():\n",
    "    currLikes = rows['director_facebook_likes_log']\n",
    "    if (rows['genres'].find('Action') >= 0):\n",
    "        actionD.append(currLikes)\n",
    "    if (rows['genres'].find('Adventure') >= 0):\n",
    "        adventureD.append(currLikes)\n",
    "    if (rows['genres'].find('Fantasy') >= 0):\n",
    "        fantasyD.append(currLikes)\n",
    "    if (rows['genres'].find('Sci-Fi') >= 0):\n",
    "        scifiD.append(currLikes)\n",
    "    if (rows['genres'].find('Thriller') >= 0):\n",
    "        thrillerD.append(currLikes)\n",
    "    if (rows['genres'].find('Romance') >= 0):\n",
    "        romanceD.append(currLikes)\n",
    "    if (rows['genres'].find('Animation') >= 0):\n",
    "        animationD.append(currLikes)\n",
    "    if (rows['genres'].find('Comedy') >= 0):\n",
    "        comedyD.append(currLikes)\n",
    "    if (rows['genres'].find('Family') >= 0):\n",
    "        familyD.append(currLikes)\n",
    "    if (rows['genres'].find('Musical') >= 0):\n",
    "        musicalD.append(currLikes)\n",
    "    if (rows['genres'].find('Western') >= 0):\n",
    "        westernD.append(currLikes)\n",
    "    if (rows['genres'].find('Drama') >= 0):\n",
    "        dramaD.append(currLikes)\n",
    "    if (rows['genres'].find('History') >= 0):\n",
    "        historyD.append(currLikes)\n",
    "    if (rows['genres'].find('Sport') >= 0):\n",
    "        sportD.append(currLikes)\n",
    "    if (rows['genres'].find('Crime') >= 0):\n",
    "        crimeD.append(currLikes)\n",
    "    if (rows['genres'].find('Horror') >= 0):\n",
    "        horrorD.append(currLikes)\n",
    "    if (rows['genres'].find('Biography') >= 0):\n",
    "        biographyD.append(currLikes)\n",
    "    if (rows['genres'].find('Mystery') >= 0):\n",
    "        mysteryD.append(currLikes)\n",
    "    if (rows['genres'].find('War') >= 0):\n",
    "        warD.append(currLikes)\n",
    "    if (rows['genres'].find('Music') >= 0):\n",
    "        musicD.append(currLikes)\n",
    "    if (rows['genres'].find('Documentary') >= 0):\n",
    "        documentaryD.append(currLikes)\n",
    "        \n",
    "        \n",
    "actorDLikes = [actionD, adventureD, fantasyD, scifiD, thrillerD, romanceD, animationD,\n",
    "               comedyD,familyD ,musicalD ,westernD, dramaD, historyD , sportD , crimeD ,\n",
    "               horrorD, biographyD , mysteryD , warD , musicD ,documentaryD]\n",
    "\n",
    "fig = plt.figure(1, figsize=(21, 12))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "bp = plt.boxplot(actorDLikes)\n",
    "\n",
    "ax.set_title('Director Facebook Likes by Genre', fontsize=30)\n",
    "ax.set_xlabel('Genres', fontsize=25)\n",
    "ax.set_ylabel('Director Facebook Likes', fontsize=25)\n",
    "ax.set_xticklabels(\n",
    "           [\"Action\",\"Adventure\",\"Fantasy\", \"Sci-Fi\", \"Thriller\", \"Romance\",\n",
    "            \"Animation\", \"Comedy\", \"Family\", \"Musical\", \"Western\", \"Drama\", \"History\",\n",
    "            \"Sport\", \"Crime\", \"Horror\", \"Biography\", \"Mystery\", \"War\", \"Music\", \"Documentary\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Director Facebook Likes by Genre\n",
    "\n",
    "The distributions for directors differs from actors and instead have a relatively spread out distribution that have less outliers. We believe this is due to the distribution we saw in the earlier scatterplot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Likes by Genres\n",
    "\n",
    "We see that actor likes have distributions that contain many outliers found in almost all genres by actor 1, actor 2, and actor 3. Directors however, have a distribution that do not contain as many outliers in comparison to actors. This is mostly due to the clustering found in actors whereas directors have a more spread out distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of Models\n",
    "\n",
    "We created models of each variable as well as a multi regression model to generate future predictions. In addition to this, we created both a non controlled as well as a controlled version that accounts for genre based like distributions. We created this using a binary method. The movies had tags associated with a genre. If it was listed with a genre it was given a 1 and if it was not listed with a genre it was given a 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.062\n",
      "Model:                            OLS   Adj. R-squared:                  0.062\n",
      "Method:                 Least Squares   F-statistic:                     205.8\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           3.03e-45\n",
      "Time:                        19:49:04   Log-Likelihood:                -60699.\n",
      "No. Observations:                3118   AIC:                         1.214e+05\n",
      "Df Residuals:                    3116   BIC:                         1.214e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -2.551e+07   5.74e+06     -4.441      0.000   -3.68e+07   -1.42e+07\n",
      "actor_1_facebook_likes_log  2.371e+07   1.65e+06     14.346      0.000    2.05e+07    2.69e+07\n",
      "==============================================================================\n",
      "Omnibus:                     2054.989   Durbin-Watson:                   0.863\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            30723.826\n",
      "Skew:                           2.945   Prob(JB):                         0.00\n",
      "Kurtosis:                      17.206   Cond. No.                         17.5\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 1 likes to predict gross income of movie\n",
    "outcome_1, predictors_1 = (\n",
    "    patsy.dmatrices('gross ~ actor_1_facebook_likes_log', movies_df))\n",
    "mod_1 = sm.OLS (outcome_1,predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.235\n",
      "Model:                            OLS   Adj. R-squared:                  0.230\n",
      "Method:                 Least Squares   F-statistic:                     43.30\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):          5.65e-162\n",
      "Time:                        19:49:04   Log-Likelihood:                -60381.\n",
      "No. Observations:                3118   AIC:                         1.208e+05\n",
      "Df Residuals:                    3095   BIC:                         1.209e+05\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                   -1.01e+07   6.49e+06     -1.557      0.120   -2.28e+07    2.62e+06\n",
      "actor_1_facebook_likes_log  1.938e+07   1.59e+06     12.217      0.000    1.63e+07    2.25e+07\n",
      "Action                      1.848e+07   3.33e+06      5.551      0.000     1.2e+07     2.5e+07\n",
      "Adventure                   3.092e+07   3.56e+06      8.693      0.000    2.39e+07    3.79e+07\n",
      "Fantasy                     1.336e+07   3.68e+06      3.634      0.000    6.15e+06    2.06e+07\n",
      "Scifi                        1.56e+07   3.82e+06      4.086      0.000    8.12e+06    2.31e+07\n",
      "Thriller                   -8.761e+06   3.29e+06     -2.662      0.008   -1.52e+07   -2.31e+06\n",
      "Romance                     2.482e+06    2.9e+06      0.855      0.393   -3.21e+06    8.17e+06\n",
      "Animation                   2.954e+07    6.3e+06      4.692      0.000    1.72e+07    4.19e+07\n",
      "Comedy                     -1.131e+07   2.91e+06     -3.889      0.000    -1.7e+07   -5.61e+06\n",
      "Family                      1.159e+07   4.52e+06      2.567      0.010    2.74e+06    2.04e+07\n",
      "Musical                     9.878e+04    8.7e+06      0.011      0.991    -1.7e+07    1.72e+07\n",
      "Western                    -1.191e+07   9.19e+06     -1.296      0.195   -2.99e+07     6.1e+06\n",
      "Drama                      -1.731e+07   2.83e+06     -6.115      0.000   -2.29e+07   -1.18e+07\n",
      "History                     6.205e+06   7.51e+06      0.826      0.409   -8.53e+06    2.09e+07\n",
      "Sport                      -2.274e+06   5.69e+06     -0.400      0.689   -1.34e+07    8.88e+06\n",
      "Crime                       -5.37e+06   3.37e+06     -1.594      0.111    -1.2e+07    1.24e+06\n",
      "Horror                     -1.751e+07   4.43e+06     -3.954      0.000   -2.62e+07   -8.83e+06\n",
      "Biography                   2.553e+06   5.46e+06      0.468      0.640   -8.16e+06    1.33e+07\n",
      "Mystery                     2.083e+06   4.18e+06      0.498      0.619   -6.12e+06    1.03e+07\n",
      "War                         9.476e+06   7.34e+06      1.291      0.197   -4.92e+06    2.39e+07\n",
      "Music                      -1.782e+06   5.66e+06     -0.315      0.753   -1.29e+07    9.31e+06\n",
      "Documentary                -1.665e+07   8.42e+06     -1.978      0.048   -3.32e+07   -1.41e+05\n",
      "==============================================================================\n",
      "Omnibus:                     1879.100   Durbin-Watson:                   1.190\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28870.984\n",
      "Skew:                           2.581   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.985   Cond. No.                         32.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 1 likes with genres to predict gross income of movie\n",
    "outcome_1, predictors_1 = (\n",
    "    patsy.dmatrices('gross ~ actor_1_facebook_likes_log + Action + Adventure + '\n",
    "                    'Fantasy + Scifi + Thriller + Romance + Animation + Comedy + '\n",
    "                    'Family + Musical + Western + Drama + History + Sport + Crime + '\n",
    "                    'Horror + Biography + Mystery + War + Music + Documentary', movies_df))\n",
    "mod_1 = sm.OLS (outcome_1,predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 1 Facebook Likes Regression Model for Gross Income\n",
    "\n",
    "The initial model held the low r-squared value of 0.062. When controlling for genres we see an increase of the r-squared to 0.235. In the genre controlled model, we see that Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, Music have a p-value greater than 0.05. We believe that the reason is due to the amount of movies that are listed in those categories have less than the others. From this, their weight in the models is not significant enough to evaluate a prediction based on those categories.\n",
    "\n",
    "The model Actor 1 Likes Controlled for Genres is\n",
    "\n",
    "Gross = 19,380,000 * Log(actor 1 likes) + 18,480,000 (action) + 30,920,000 (adventure) + 13,360,000 (fantasy) + 15,600,000 (scifi) - 8,761,000 (thriller) + 29,540,000 (animation) - 11,310,000 (comedy) + 11,590,000 (family) - 17,310,000 (drama) - 17,510,000 (horror) - 16,650,000 (documentary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.081\n",
      "Model:                            OLS   Adj. R-squared:                  0.081\n",
      "Method:                 Least Squares   F-statistic:                     275.0\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           2.97e-59\n",
      "Time:                        19:49:04   Log-Likelihood:                -60667.\n",
      "No. Observations:                3118   AIC:                         1.213e+05\n",
      "Df Residuals:                    3116   BIC:                         1.214e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -3.603e+07   5.62e+06     -6.410      0.000   -4.71e+07    -2.5e+07\n",
      "actor_2_facebook_likes_log   3.19e+07   1.92e+06     16.583      0.000    2.81e+07    3.57e+07\n",
      "==============================================================================\n",
      "Omnibus:                     1992.753   Durbin-Watson:                   0.886\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            27647.355\n",
      "Skew:                           2.848   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.430   Cond. No.                         15.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 2 likes to predict gross income of movie\n",
    "outcome_2, predictors_2 = (\n",
    "    patsy.dmatrices('gross ~ actor_2_facebook_likes_log', movies_df))\n",
    "mod_2 = sm.OLS (outcome_2,predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.250\n",
      "Model:                            OLS   Adj. R-squared:                  0.245\n",
      "Method:                 Least Squares   F-statistic:                     46.99\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):          4.58e-175\n",
      "Time:                        19:49:04   Log-Likelihood:                -60350.\n",
      "No. Observations:                3118   AIC:                         1.207e+05\n",
      "Df Residuals:                    3095   BIC:                         1.209e+05\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -2.198e+07   6.41e+06     -3.432      0.001   -3.45e+07   -9.42e+06\n",
      "actor_2_facebook_likes_log  2.724e+07   1.86e+06     14.642      0.000    2.36e+07    3.09e+07\n",
      "Action                      1.744e+07    3.3e+06      5.286      0.000     1.1e+07    2.39e+07\n",
      "Adventure                   3.095e+07   3.52e+06      8.794      0.000     2.4e+07    3.79e+07\n",
      "Fantasy                     1.342e+07   3.64e+06      3.689      0.000    6.29e+06    2.06e+07\n",
      "Scifi                        1.51e+07   3.78e+06      3.994      0.000    7.69e+06    2.25e+07\n",
      "Thriller                   -7.679e+06   3.26e+06     -2.359      0.018   -1.41e+07    -1.3e+06\n",
      "Romance                     2.411e+06   2.87e+06      0.839      0.402   -3.23e+06    8.05e+06\n",
      "Animation                   3.118e+07   6.23e+06      5.002      0.000     1.9e+07    4.34e+07\n",
      "Comedy                     -1.266e+07   2.88e+06     -4.396      0.000   -1.83e+07   -7.01e+06\n",
      "Family                      1.115e+07   4.47e+06      2.495      0.013    2.39e+06    1.99e+07\n",
      "Musical                     9.894e+05   8.61e+06      0.115      0.909   -1.59e+07    1.79e+07\n",
      "Western                    -1.208e+07   9.09e+06     -1.328      0.184   -2.99e+07    5.75e+06\n",
      "Drama                      -1.667e+07    2.8e+06     -5.951      0.000   -2.22e+07   -1.12e+07\n",
      "History                     4.491e+06   7.44e+06      0.604      0.546   -1.01e+07    1.91e+07\n",
      "Sport                      -2.398e+06   5.63e+06     -0.426      0.670   -1.34e+07    8.65e+06\n",
      "Crime                      -5.177e+06   3.34e+06     -1.552      0.121   -1.17e+07    1.36e+06\n",
      "Horror                     -1.888e+07   4.37e+06     -4.324      0.000   -2.74e+07   -1.03e+07\n",
      "Biography                    3.89e+06    5.4e+06      0.720      0.472   -6.71e+06    1.45e+07\n",
      "Mystery                     2.689e+06   4.14e+06      0.649      0.516   -5.43e+06    1.08e+07\n",
      "War                         1.118e+07   7.27e+06      1.538      0.124   -3.07e+06    2.54e+07\n",
      "Music                      -3.456e+06   5.59e+06     -0.618      0.537   -1.44e+07    7.51e+06\n",
      "Documentary                -4.118e+06   8.48e+06     -0.486      0.627   -2.07e+07    1.25e+07\n",
      "==============================================================================\n",
      "Omnibus:                     1822.896   Durbin-Watson:                   1.213\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            26224.564\n",
      "Skew:                           2.498   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.300   Cond. No.                         28.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 2 likes with genres to predict gross income of movie\n",
    "outcome_2, predictors_2 = (\n",
    "    patsy.dmatrices('gross ~ actor_2_facebook_likes_log + Action + Adventure + '\n",
    "                    'Fantasy + Scifi + Thriller + Romance + Animation + Comedy + '\n",
    "                    'Family + Musical + Western + Drama + History + Sport + Crime + '\n",
    "                    'Horror + Biography + Mystery + War + Music + Documentary', movies_df))\n",
    "mod_2 = sm.OLS (outcome_2,predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 2 Facebook Likes Regression Model for Gross Income\n",
    "\n",
    "The initial model held a low r-squared value of 0.081. When controlling for genres we see an increase of the r-squared to 0.250. In the genre controlled model, we see Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, Music, and Documentary having a p-value greater than 0.05. \n",
    "\n",
    "The model Actor 2 Likes Controlled for Genre is\n",
    "\n",
    "Gross = 27,240,000 * Log(actor 2 likes) + 17,440,000 (action) + 30,950,000 (adventure) + 13,420,000 (fantasy) + 15,100,000 (scifi) - 7,679,000 (thriller) + 31,180,000 (animation) - 12,660,000 (comedy) + 11,150,000 (family) - 16,670,000 (drama) - 18,880,000 (horror)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.069\n",
      "Model:                            OLS   Adj. R-squared:                  0.069\n",
      "Method:                 Least Squares   F-statistic:                     231.6\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           1.64e-50\n",
      "Time:                        19:49:04   Log-Likelihood:                -60687.\n",
      "No. Observations:                3118   AIC:                         1.214e+05\n",
      "Df Residuals:                    3116   BIC:                         1.214e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -2.248e+07   5.23e+06     -4.294      0.000   -3.27e+07   -1.22e+07\n",
      "actor_3_facebook_likes_log  3.044e+07      2e+06     15.220      0.000    2.65e+07    3.44e+07\n",
      "==============================================================================\n",
      "Omnibus:                     2000.138   Durbin-Watson:                   0.852\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            27603.899\n",
      "Skew:                           2.866   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.402   Cond. No.                         12.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 3 likes to predict gross income of movie\n",
    "outcome_3, predictors_3 = (\n",
    "    patsy.dmatrices('gross ~ actor_3_facebook_likes_log', movies_df))\n",
    "mod_3 = sm.OLS (outcome_3,predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.239\n",
      "Model:                            OLS   Adj. R-squared:                  0.234\n",
      "Method:                 Least Squares   F-statistic:                     44.24\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):          2.42e-165\n",
      "Time:                        19:49:04   Log-Likelihood:                -60373.\n",
      "No. Observations:                3118   AIC:                         1.208e+05\n",
      "Df Residuals:                    3095   BIC:                         1.209e+05\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -9.331e+06   6.21e+06     -1.503      0.133   -2.15e+07    2.84e+06\n",
      "actor_3_facebook_likes_log  2.535e+07   1.97e+06     12.880      0.000    2.15e+07    2.92e+07\n",
      "Action                      1.763e+07   3.32e+06      5.304      0.000    1.11e+07    2.41e+07\n",
      "Adventure                   3.202e+07   3.54e+06      9.038      0.000    2.51e+07     3.9e+07\n",
      "Fantasy                     1.311e+07   3.67e+06      3.574      0.000    5.92e+06    2.03e+07\n",
      "Scifi                       1.542e+07   3.81e+06      4.050      0.000    7.96e+06    2.29e+07\n",
      "Thriller                   -7.338e+06   3.28e+06     -2.237      0.025   -1.38e+07   -9.07e+05\n",
      "Romance                     2.606e+06    2.9e+06      0.900      0.368   -3.07e+06    8.28e+06\n",
      "Animation                   3.387e+07   6.29e+06      5.388      0.000    2.15e+07    4.62e+07\n",
      "Comedy                     -1.352e+07    2.9e+06     -4.657      0.000   -1.92e+07   -7.82e+06\n",
      "Family                      9.085e+06    4.5e+06      2.018      0.044    2.57e+05    1.79e+07\n",
      "Musical                     1.982e+06   8.68e+06      0.228      0.819    -1.5e+07     1.9e+07\n",
      "Western                    -1.044e+07   9.16e+06     -1.140      0.255   -2.84e+07    7.52e+06\n",
      "Drama                      -1.497e+07   2.82e+06     -5.301      0.000   -2.05e+07   -9.44e+06\n",
      "History                     3.798e+06    7.5e+06      0.507      0.612   -1.09e+07    1.85e+07\n",
      "Sport                      -3.771e+06   5.68e+06     -0.664      0.507   -1.49e+07    7.36e+06\n",
      "Crime                      -4.748e+06   3.36e+06     -1.413      0.158   -1.13e+07    1.84e+06\n",
      "Horror                      -2.01e+07    4.4e+06     -4.573      0.000   -2.87e+07   -1.15e+07\n",
      "Biography                    4.17e+06   5.44e+06      0.766      0.444   -6.51e+06    1.48e+07\n",
      "Mystery                     2.317e+06   4.17e+06      0.555      0.579   -5.87e+06    1.05e+07\n",
      "War                          1.17e+07   7.33e+06      1.597      0.110   -2.67e+06    2.61e+07\n",
      "Music                      -5.003e+06   5.63e+06     -0.888      0.374    -1.6e+07    6.04e+06\n",
      "Documentary                -2.453e+06   8.68e+06     -0.283      0.777   -1.95e+07    1.46e+07\n",
      "==============================================================================\n",
      "Omnibus:                     1819.279   Durbin-Watson:                   1.186\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            26000.569\n",
      "Skew:                           2.494   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.238   Cond. No.                         26.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for actor 3 likes with genres to predict gross income of movie\n",
    "outcome_3, predictors_3 = (\n",
    "    patsy.dmatrices('gross ~ actor_3_facebook_likes_log + Action + Adventure + '\n",
    "                    'Fantasy + Scifi + Thriller + Romance + Animation + Comedy + '\n",
    "                    'Family + Musical + Western + Drama + History + Sport + Crime + '\n",
    "                    'Horror + Biography + Mystery + War + Music + Documentary', movies_df))\n",
    "mod_3 = sm.OLS (outcome_3,predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Actor 3 Facebook Likes Regression Model for Gross Income\n",
    "\n",
    "The initial model held a low r-squared value of 0.069. When controlling for genres we see an increase of the r-squared to 0.239. In the genre controlled model, we see Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, Music, and Documentary having a p-value greater than 0.05. \n",
    "\n",
    "The model Actor 3 Likes Controlled for Genre is\n",
    "\n",
    "Gross = 25,350,000 * Log(actor 3 likes) + 17,630,000 (action) + 32,020,000 (adventure) + 13,110,000 (fantasy)+ 15,420,000 (scifi) - 7,338,000 (thriller) + 33,870,000 (animation) - 13,520,000 (comedy) + 9,085,000 (family) - 14,970,000 (drama) - 20,100,000 (horror) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.088\n",
      "Model:                            OLS   Adj. R-squared:                  0.087\n",
      "Method:                 Least Squares   F-statistic:                     100.6\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           3.77e-62\n",
      "Time:                        19:49:04   Log-Likelihood:                -60655.\n",
      "No. Observations:                3118   AIC:                         1.213e+05\n",
      "Df Residuals:                    3114   BIC:                         1.213e+05\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -4.813e+07   6.15e+06     -7.829      0.000   -6.02e+07   -3.61e+07\n",
      "actor_1_facebook_likes_log  8.717e+06   2.32e+06      3.764      0.000    4.18e+06    1.33e+07\n",
      "actor_2_facebook_likes_log  1.639e+07   3.74e+06      4.379      0.000    9.05e+06    2.37e+07\n",
      "actor_3_facebook_likes_log  1.051e+07   3.37e+06      3.122      0.002    3.91e+06    1.71e+07\n",
      "==============================================================================\n",
      "Omnibus:                     2007.867   Durbin-Watson:                   0.896\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28544.002\n",
      "Skew:                           2.869   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.667   Cond. No.                         27.3\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for all actor likes to predict gross income of movie\n",
    "outcome_ag, predictors_ag = (\n",
    "    patsy.dmatrices('gross ~ actor_1_facebook_likes_log + '\n",
    "                    'actor_2_facebook_likes_log + '\n",
    "                    'actor_3_facebook_likes_log', movies_df))\n",
    "mod_ag = sm.OLS (outcome_ag,predictors_ag)\n",
    "res_ag = mod_ag.fit()\n",
    "print(res_ag.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.254\n",
      "Model:                            OLS   Adj. R-squared:                  0.248\n",
      "Method:                 Least Squares   F-statistic:                     43.93\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):          9.52e-177\n",
      "Time:                        19:49:05   Log-Likelihood:                -60342.\n",
      "No. Observations:                3118   AIC:                         1.207e+05\n",
      "Df Residuals:                    3093   BIC:                         1.209e+05\n",
      "Df Model:                          24                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -3.243e+07   6.92e+06     -4.689      0.000    -4.6e+07   -1.89e+07\n",
      "actor_1_facebook_likes_log  6.764e+06   2.14e+06      3.162      0.002    2.57e+06     1.1e+07\n",
      "actor_2_facebook_likes_log  1.643e+07   3.41e+06      4.825      0.000    9.75e+06    2.31e+07\n",
      "actor_3_facebook_likes_log  7.178e+06   3.14e+06      2.283      0.022    1.01e+06    1.33e+07\n",
      "Action                       1.73e+07   3.29e+06      5.257      0.000    1.08e+07    2.38e+07\n",
      "Adventure                    3.06e+07   3.51e+06      8.708      0.000    2.37e+07    3.75e+07\n",
      "Fantasy                     1.263e+07   3.64e+06      3.475      0.001     5.5e+06    1.98e+07\n",
      "Scifi                       1.516e+07   3.77e+06      4.019      0.000    7.76e+06    2.26e+07\n",
      "Thriller                   -8.069e+06   3.25e+06     -2.482      0.013   -1.44e+07   -1.69e+06\n",
      "Romance                     2.421e+06   2.87e+06      0.844      0.399    -3.2e+06    8.04e+06\n",
      "Animation                   3.166e+07   6.23e+06      5.080      0.000    1.94e+07    4.39e+07\n",
      "Comedy                     -1.251e+07   2.88e+06     -4.347      0.000   -1.82e+07   -6.87e+06\n",
      "Family                      1.101e+07   4.47e+06      2.465      0.014    2.25e+06    1.98e+07\n",
      "Musical                     1.933e+06    8.6e+06      0.225      0.822   -1.49e+07    1.88e+07\n",
      "Western                    -1.168e+07   9.08e+06     -1.287      0.198   -2.95e+07    6.12e+06\n",
      "Drama                      -1.646e+07    2.8e+06     -5.870      0.000    -2.2e+07    -1.1e+07\n",
      "History                     4.431e+06   7.43e+06      0.597      0.551   -1.01e+07     1.9e+07\n",
      "Sport                      -2.702e+06   5.62e+06     -0.480      0.631   -1.37e+07    8.33e+06\n",
      "Crime                      -5.326e+06   3.33e+06     -1.600      0.110   -1.19e+07     1.2e+06\n",
      "Horror                     -1.761e+07   4.37e+06     -4.026      0.000   -2.62e+07   -9.03e+06\n",
      "Biography                   3.269e+06    5.4e+06      0.606      0.545   -7.31e+06    1.38e+07\n",
      "Mystery                     2.312e+06   4.13e+06      0.559      0.576   -5.79e+06    1.04e+07\n",
      "War                         1.118e+07   7.26e+06      1.541      0.124   -3.05e+06    2.54e+07\n",
      "Music                      -2.756e+06   5.59e+06     -0.493      0.622   -1.37e+07     8.2e+06\n",
      "Documentary                 2.879e+05    8.6e+06      0.033      0.973   -1.66e+07    1.72e+07\n",
      "==============================================================================\n",
      "Omnibus:                     1836.013   Durbin-Watson:                   1.215\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            27018.505\n",
      "Skew:                           2.515   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.516   Cond. No.                         48.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for all actor likes to predict gross income of movie\n",
    "outcome_ag, predictors_ag = (\n",
    "    patsy.dmatrices('gross ~ actor_1_facebook_likes_log + '\n",
    "                    'actor_2_facebook_likes_log + '\n",
    "                    'actor_3_facebook_likes_log + Action + Adventure + '\n",
    "                    'Fantasy + Scifi + Thriller + Romance + Animation + Comedy + '\n",
    "                    'Family + Musical + Western + Drama + History + Sport + Crime + '\n",
    "                    'Horror + Biography + Mystery + War + Music + Documentary', movies_df))\n",
    "mod_ag = sm.OLS (outcome_ag,predictors_ag)\n",
    "res_ag = mod_ag.fit()\n",
    "print(res_ag.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### All Actor Facebook Likes Regression Model for Gross Income\n",
    "\n",
    "The initial model held a low r-squared value of 0.088. When controlling for genres we see an increase of the r-squared to 0.254. In the genre controlled model, we see Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, Music, and Documentary having a p-value greater than 0.05.\n",
    "\n",
    "The model for all Actor Likes Controlled for Genre is\n",
    "\n",
    "Gross = 6,764,000 * Log(actor 1 likes) + 16,430,000 * Log(actor 2 likes) + 7,178,000 * Log(actor 3 likes) + 17,300,000 (action) + 30,600,000 (adventure) + 12,630,000 (fantasy) + 15,160,000 (scifi) - 8,069,000 (thriller) + 31,660,000 (animation) - 12,510,000 (comedy) + 11,010,000 (family) - 16,460,000 (drama) - 17,610,000 (horror)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.006\n",
      "Model:                            OLS   Adj. R-squared:                  0.006\n",
      "Method:                 Least Squares   F-statistic:                     19.28\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           1.16e-05\n",
      "Time:                        17:04:40   Log-Likelihood:                -60789.\n",
      "No. Observations:                3118   AIC:                         1.216e+05\n",
      "Df Residuals:                    3116   BIC:                         1.216e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                    4.878e+07    1.9e+06     25.730      0.000    4.51e+07    5.25e+07\n",
      "director_facebook_likes_log  4.122e+06   9.39e+05      4.391      0.000    2.28e+06    5.96e+06\n",
      "==============================================================================\n",
      "Omnibus:                     2091.215   Durbin-Watson:                   0.746\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            31103.911\n",
      "Skew:                           3.023   Prob(JB):                         0.00\n",
      "Kurtosis:                      17.243   Cond. No.                         3.46\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for director likes to predict gross income of movie\n",
    "outcome, predictors = (\n",
    "    patsy.dmatrices('gross ~ director_facebook_likes_log', movies_df))\n",
    "mod = sm.OLS (outcome,predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  gross   R-squared:                       0.203\n",
      "Model:                            OLS   Adj. R-squared:                  0.197\n",
      "Method:                 Least Squares   F-statistic:                     35.79\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):          1.24e-134\n",
      "Time:                        17:04:40   Log-Likelihood:                -60445.\n",
      "No. Observations:                3118   AIC:                         1.209e+05\n",
      "Df Residuals:                    3095   BIC:                         1.211e+05\n",
      "Df Model:                          22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                    5.022e+07   3.94e+06     12.736      0.000    4.25e+07    5.79e+07\n",
      "director_facebook_likes_log  3.511e+06   8.53e+05      4.115      0.000    1.84e+06    5.18e+06\n",
      "Action                       1.877e+07   3.41e+06      5.509      0.000    1.21e+07    2.54e+07\n",
      "Adventure                    3.386e+07   3.62e+06      9.341      0.000    2.67e+07     4.1e+07\n",
      "Fantasy                      1.633e+07   3.75e+06      4.360      0.000    8.99e+06    2.37e+07\n",
      "Scifi                        1.624e+07    3.9e+06      4.166      0.000     8.6e+06    2.39e+07\n",
      "Thriller                    -7.627e+06   3.36e+06     -2.271      0.023   -1.42e+07   -1.04e+06\n",
      "Romance                      2.658e+06   2.96e+06      0.897      0.370   -3.15e+06    8.47e+06\n",
      "Animation                    3.069e+07   6.43e+06      4.772      0.000    1.81e+07    4.33e+07\n",
      "Comedy                      -1.226e+07   2.97e+06     -4.130      0.000   -1.81e+07   -6.44e+06\n",
      "Family                        9.69e+06   4.61e+06      2.103      0.036    6.54e+05    1.87e+07\n",
      "Musical                     -2.809e+06   8.88e+06     -0.316      0.752   -2.02e+07    1.46e+07\n",
      "Western                     -1.129e+07   9.38e+06     -1.204      0.229   -2.97e+07     7.1e+06\n",
      "Drama                        -1.63e+07   2.89e+06     -5.641      0.000    -2.2e+07   -1.06e+07\n",
      "History                      7.564e+06   7.68e+06      0.985      0.325   -7.49e+06    2.26e+07\n",
      "Sport                       -3.095e+06   5.81e+06     -0.533      0.594   -1.45e+07     8.3e+06\n",
      "Crime                       -4.492e+06   3.44e+06     -1.306      0.192   -1.12e+07    2.25e+06\n",
      "Horror                      -2.287e+07    4.5e+06     -5.087      0.000   -3.17e+07   -1.41e+07\n",
      "Biography                    3.526e+06   5.58e+06      0.632      0.528   -7.42e+06    1.45e+07\n",
      "Mystery                      2.482e+06   4.27e+06      0.581      0.561    -5.9e+06    1.09e+07\n",
      "War                          8.444e+06    7.5e+06      1.126      0.260   -6.26e+06    2.31e+07\n",
      "Music                       -6.304e+06   5.77e+06     -1.093      0.274   -1.76e+07       5e+06\n",
      "Documentary                 -3.801e+07   8.39e+06     -4.531      0.000   -5.45e+07   -2.16e+07\n",
      "==============================================================================\n",
      "Omnibus:                     1889.620   Durbin-Watson:                   1.141\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            28561.617\n",
      "Skew:                           2.608   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.880   Cond. No.                         19.8\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Regression Model for director likes with genres to predict gross income of movie\n",
    "outcome, predictors = (\n",
    "    patsy.dmatrices('gross ~ director_facebook_likes_log + Action + Adventure + '\n",
    "                    'Fantasy + Scifi + Thriller + Romance + Animation + Comedy + '\n",
    "                    'Family + Musical + Western + Drama + History + Sport + Crime + '\n",
    "                    'Horror + Biography + Mystery + War + Music + Documentary', movies_df))\n",
    "mod = sm.OLS (outcome,predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Director Facebook Likes Regression Model for Gross Income\n",
    "\n",
    "The initial model held a low r-squared value of 0.006. When controlling for genres we see an increase of the r-squared to 0.203. In the genre controlled model, we see Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, and Music having a p-value greater than 0.05.\n",
    "\n",
    "The model for all Actor Likes Controlled for Genre is\n",
    "\n",
    "Gross = 3,511,000 * Log(director likes) + 18,770,000 (action) + 33,860,000 (adventure) + 16,330,000 (fantasy) + 16,240,000 (scifi) - 7,627,000 (thriller) + 30,690,000 (animation) - 12,260,000 (comedy) + 9,690,000 (family) - 16,300,000 (drama) - 22,870,000 (horror) - 38,010,000 (documentary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Models\n",
    "\n",
    "The models presented have a consistent disregard for Romance, Musical, Western, History, Sport, Crime, Biography, Mystery, War, Music and sometimes Documentary with Actor 1 and Director models being the exceptions. This is likely due to the ability for certain genres that saturate the film industry to encompass gross earnings. The aforemention genres that are discredited from the models seem to reflect their impact on the market with lesser monetary gains as well as the lack of films in those genres. However that may be, these models serve to highlight the inability for facebook likes to accurately predict a films success even after adjusting for genre variations. The bimodal distributions causes our models to conform to the data we used from our dataset and therefore strict. As a result, the low r-squared across all models determines the model's ability to account for variations due to random chance that is often found in the natural world. Due to the subpar variation accountability, these models would do poorly to gauge a film's success based on an actor or director's popularity on Facebook's platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ethics and Privacy Considerations\n",
    "The dataset has some issues of certain facebook likes where certain actors and directors have incorrect listings. For example, the director Spike Lee within the dataset had a listing of 0 facebook likes. However, future observation displayed an official facebook page with 765,000 likes. In addition, we observed duplicate indexed movies in the data set that have inconsistent entries in cast likes as well as number voted users. Although our observation of the data does not include this column, the fact that there are inconsistencies in the first place leads to some doubts about the dataset as a whole. As a result, this potentiates the issue of false data which would give way to incorrect predictions and assessments.\n",
    "\n",
    "In regards to the method in which the data was collected, we do not know for sure as the dataset does not explain their process in cataloging information. However, we have reason to believe that privacy is not infringed upon as most of the information that we will focus on is made public on either IMDb and Facebook. Since we are focusing on Facebook likes, we believe that the potential privacy issues is relatively low as they are metrics generated anonymously. The dataset is also made public on kaggle, a dataset sharing website. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion & Discussion\n",
    "From observing the regression models, each prediction instance has a low p-value meaning there is a significance to their inclusion in the model with the exception of a few genres. However, the model itself accounts for very little variation demonstrated by the low r-squared. This is mostly explained due to the bimodal distribution of actor and director likes which causes a strict model to form. From this, we conclude that models generated from actor and director Facebook likes serve as poor indicators for a movie’s gross income. Therefore, we reject our hypothesis and posit that there is no correlation of FB likes of actors and directors to a movie’s gross income which is further identified in the low r-values in our scatterplots. In addition, there are confounding variables that influence both likes and gross income such as advertisement or the movie’s release date. Advertisement can bring consumers to movies as well as interest in the actors in the movie. A movie’s release date changes the rate in which consumers are likely to watch movies such as during vacation breaks. These confounding variables could have contributed in the distribution of likes as well.\n",
    "\n",
    "If we had any further resources and time to continue this project, we would have liked other measurements to observe social media presence such as Instagram and Twitter. To coincide the need for better dataset information, we would like the dataset to be updated to relieve rounding as well as inconsistencies. For future use, updates to new releases since the original posting would allow for further observations.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}