Movie_Recommender_System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing Libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"]=(12,8)\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\minak\\Desktop\\AIML\\Recommender System\\Untitled Folder 1\\movie_metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <td>Color</td>\n",
       "      <td>Color</td>\n",
       "      <td>Color</td>\n",
       "      <td>Color</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director_name</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Doug Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <td>723.00</td>\n",
       "      <td>302.00</td>\n",
       "      <td>602.00</td>\n",
       "      <td>813.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>178.00</td>\n",
       "      <td>169.00</td>\n",
       "      <td>148.00</td>\n",
       "      <td>164.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <td>0.00</td>\n",
       "      <td>563.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>131.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <td>855.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>161.00</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_2_name</th>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Rob Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>131.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross</th>\n",
       "      <td>760505847.00</td>\n",
       "      <td>309404152.00</td>\n",
       "      <td>200074175.00</td>\n",
       "      <td>448130642.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_1_name</th>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Doug Walker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_voted_users</th>\n",
       "      <td>886204</td>\n",
       "      <td>471220</td>\n",
       "      <td>275868</td>\n",
       "      <td>1144337</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <td>4834</td>\n",
       "      <td>48350</td>\n",
       "      <td>11700</td>\n",
       "      <td>106759</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_3_name</th>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plot_keywords</th>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
       "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
       "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
       "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
       "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
       "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
       "      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <td>3054.00</td>\n",
       "      <td>1238.00</td>\n",
       "      <td>994.00</td>\n",
       "      <td>2701.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language</th>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>UK</td>\n",
       "      <td>USA</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>content_rating</th>\n",
       "      <td>PG-13</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>237000000.00</td>\n",
       "      <td>300000000.00</td>\n",
       "      <td>245000000.00</td>\n",
       "      <td>250000000.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_year</th>\n",
       "      <td>2009.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <td>936.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>393.00</td>\n",
       "      <td>23000.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imdb_score</th>\n",
       "      <td>7.90</td>\n",
       "      <td>7.10</td>\n",
       "      <td>6.80</td>\n",
       "      <td>8.50</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aspect_ratio</th>\n",
       "      <td>1.78</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2.35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "      <td>85000</td>\n",
       "      <td>164000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           0  \\\n",
       "color                                                                  Color   \n",
       "director_name                                                  James Cameron   \n",
       "num_critic_for_reviews                                                723.00   \n",
       "duration                                                              178.00   \n",
       "director_facebook_likes                                                 0.00   \n",
       "actor_3_facebook_likes                                                855.00   \n",
       "actor_2_name                                                Joel David Moore   \n",
       "actor_1_facebook_likes                                               1000.00   \n",
       "gross                                                           760505847.00   \n",
       "genres                                       Action|Adventure|Fantasy|Sci-Fi   \n",
       "actor_1_name                                                     CCH Pounder   \n",
       "movie_title                                                          Avatar    \n",
       "num_voted_users                                                       886204   \n",
       "cast_total_facebook_likes                                               4834   \n",
       "actor_3_name                                                       Wes Studi   \n",
       "facenumber_in_poster                                                    0.00   \n",
       "plot_keywords                         avatar|future|marine|native|paraplegic   \n",
       "movie_imdb_link            http://www.imdb.com/title/tt0499549/?ref_=fn_t...   \n",
       "num_user_for_reviews                                                 3054.00   \n",
       "language                                                             English   \n",
       "country                                                                  USA   \n",
       "content_rating                                                         PG-13   \n",
       "budget                                                          237000000.00   \n",
       "title_year                                                           2009.00   \n",
       "actor_2_facebook_likes                                                936.00   \n",
       "imdb_score                                                              7.90   \n",
       "aspect_ratio                                                            1.78   \n",
       "movie_facebook_likes                                                   33000   \n",
       "\n",
       "                                                                           1  \\\n",
       "color                                                                  Color   \n",
       "director_name                                                 Gore Verbinski   \n",
       "num_critic_for_reviews                                                302.00   \n",
       "duration                                                              169.00   \n",
       "director_facebook_likes                                               563.00   \n",
       "actor_3_facebook_likes                                               1000.00   \n",
       "actor_2_name                                                   Orlando Bloom   \n",
       "actor_1_facebook_likes                                              40000.00   \n",
       "gross                                                           309404152.00   \n",
       "genres                                              Action|Adventure|Fantasy   \n",
       "actor_1_name                                                     Johnny Depp   \n",
       "movie_title                        Pirates of the Caribbean: At World's End    \n",
       "num_voted_users                                                       471220   \n",
       "cast_total_facebook_likes                                              48350   \n",
       "actor_3_name                                                  Jack Davenport   \n",
       "facenumber_in_poster                                                    0.00   \n",
       "plot_keywords              goddess|marriage ceremony|marriage proposal|pi...   \n",
       "movie_imdb_link            http://www.imdb.com/title/tt0449088/?ref_=fn_t...   \n",
       "num_user_for_reviews                                                 1238.00   \n",
       "language                                                             English   \n",
       "country                                                                  USA   \n",
       "content_rating                                                         PG-13   \n",
       "budget                                                          300000000.00   \n",
       "title_year                                                           2007.00   \n",
       "actor_2_facebook_likes                                               5000.00   \n",
       "imdb_score                                                              7.10   \n",
       "aspect_ratio                                                            2.35   \n",
       "movie_facebook_likes                                                       0   \n",
       "\n",
       "                                                                           2  \\\n",
       "color                                                                  Color   \n",
       "director_name                                                     Sam Mendes   \n",
       "num_critic_for_reviews                                                602.00   \n",
       "duration                                                              148.00   \n",
       "director_facebook_likes                                                 0.00   \n",
       "actor_3_facebook_likes                                                161.00   \n",
       "actor_2_name                                                    Rory Kinnear   \n",
       "actor_1_facebook_likes                                              11000.00   \n",
       "gross                                                           200074175.00   \n",
       "genres                                             Action|Adventure|Thriller   \n",
       "actor_1_name                                                 Christoph Waltz   \n",
       "movie_title                                                         Spectre    \n",
       "num_voted_users                                                       275868   \n",
       "cast_total_facebook_likes                                              11700   \n",
       "actor_3_name                                                Stephanie Sigman   \n",
       "facenumber_in_poster                                                    1.00   \n",
       "plot_keywords                            bomb|espionage|sequel|spy|terrorist   \n",
       "movie_imdb_link            http://www.imdb.com/title/tt2379713/?ref_=fn_t...   \n",
       "num_user_for_reviews                                                  994.00   \n",
       "language                                                             English   \n",
       "country                                                                   UK   \n",
       "content_rating                                                         PG-13   \n",
       "budget                                                          245000000.00   \n",
       "title_year                                                           2015.00   \n",
       "actor_2_facebook_likes                                                393.00   \n",
       "imdb_score                                                              6.80   \n",
       "aspect_ratio                                                            2.35   \n",
       "movie_facebook_likes                                                   85000   \n",
       "\n",
       "                                                                           3  \\\n",
       "color                                                                  Color   \n",
       "director_name                                              Christopher Nolan   \n",
       "num_critic_for_reviews                                                813.00   \n",
       "duration                                                              164.00   \n",
       "director_facebook_likes                                             22000.00   \n",
       "actor_3_facebook_likes                                              23000.00   \n",
       "actor_2_name                                                  Christian Bale   \n",
       "actor_1_facebook_likes                                              27000.00   \n",
       "gross                                                           448130642.00   \n",
       "genres                                                       Action|Thriller   \n",
       "actor_1_name                                                       Tom Hardy   \n",
       "movie_title                                           The Dark Knight Rises    \n",
       "num_voted_users                                                      1144337   \n",
       "cast_total_facebook_likes                                             106759   \n",
       "actor_3_name                                            Joseph Gordon-Levitt   \n",
       "facenumber_in_poster                                                    0.00   \n",
       "plot_keywords              deception|imprisonment|lawlessness|police offi...   \n",
       "movie_imdb_link            http://www.imdb.com/title/tt1345836/?ref_=fn_t...   \n",
       "num_user_for_reviews                                                 2701.00   \n",
       "language                                                             English   \n",
       "country                                                                  USA   \n",
       "content_rating                                                         PG-13   \n",
       "budget                                                          250000000.00   \n",
       "title_year                                                           2012.00   \n",
       "actor_2_facebook_likes                                              23000.00   \n",
       "imdb_score                                                              8.50   \n",
       "aspect_ratio                                                            2.35   \n",
       "movie_facebook_likes                                                  164000   \n",
       "\n",
       "                                                                           4  \n",
       "color                                                                    NaN  \n",
       "director_name                                                    Doug Walker  \n",
       "num_critic_for_reviews                                                   NaN  \n",
       "duration                                                                 NaN  \n",
       "director_facebook_likes                                               131.00  \n",
       "actor_3_facebook_likes                                                   NaN  \n",
       "actor_2_name                                                      Rob Walker  \n",
       "actor_1_facebook_likes                                                131.00  \n",
       "gross                                                                    NaN  \n",
       "genres                                                           Documentary  \n",
       "actor_1_name                                                     Doug Walker  \n",
       "movie_title                Star Wars: Episode VII - The Force Awakens    ...  \n",
       "num_voted_users                                                            8  \n",
       "cast_total_facebook_likes                                                143  \n",
       "actor_3_name                                                             NaN  \n",
       "facenumber_in_poster                                                    0.00  \n",
       "plot_keywords                                                            NaN  \n",
       "movie_imdb_link            http://www.imdb.com/title/tt5289954/?ref_=fn_t...  \n",
       "num_user_for_reviews                                                     NaN  \n",
       "language                                                                 NaN  \n",
       "country                                                                  NaN  \n",
       "content_rating                                                           NaN  \n",
       "budget                                                                   NaN  \n",
       "title_year                                                               NaN  \n",
       "actor_2_facebook_likes                                                 12.00  \n",
       "imdb_score                                                              7.10  \n",
       "aspect_ratio                                                             NaN  \n",
       "movie_facebook_likes                                                       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5043 entries, 0 to 5042\n",
      "Data columns (total 28 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   color                      5024 non-null   object \n",
      " 1   director_name              4939 non-null   object \n",
      " 2   num_critic_for_reviews     4993 non-null   float64\n",
      " 3   duration                   5028 non-null   float64\n",
      " 4   director_facebook_likes    4939 non-null   float64\n",
      " 5   actor_3_facebook_likes     5020 non-null   float64\n",
      " 6   actor_2_name               5030 non-null   object \n",
      " 7   actor_1_facebook_likes     5036 non-null   float64\n",
      " 8   gross                      4159 non-null   float64\n",
      " 9   genres                     5043 non-null   object \n",
      " 10  actor_1_name               5036 non-null   object \n",
      " 11  movie_title                5043 non-null   object \n",
      " 12  num_voted_users            5043 non-null   int64  \n",
      " 13  cast_total_facebook_likes  5043 non-null   int64  \n",
      " 14  actor_3_name               5020 non-null   object \n",
      " 15  facenumber_in_poster       5030 non-null   float64\n",
      " 16  plot_keywords              4890 non-null   object \n",
      " 17  movie_imdb_link            5043 non-null   object \n",
      " 18  num_user_for_reviews       5022 non-null   float64\n",
      " 19  language                   5031 non-null   object \n",
      " 20  country                    5038 non-null   object \n",
      " 21  content_rating             4740 non-null   object \n",
      " 22  budget                     4551 non-null   float64\n",
      " 23  title_year                 4935 non-null   float64\n",
      " 24  actor_2_facebook_likes     5030 non-null   float64\n",
      " 25  imdb_score                 5043 non-null   float64\n",
      " 26  aspect_ratio               4714 non-null   float64\n",
      " 27  movie_facebook_likes       5043 non-null   int64  \n",
      "dtypes: float64(13), int64(3), object(12)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets remove unnecessary column from our dataset\n",
    "column_to_drop=[\"color\",\"director_facebook_likes\",\"actor_3_facebook_likes\",\"actor_1_facebook_likes\",\n",
    "                \"cast_total_facebook_likes\",\"facenumber_in_poster\",\"actor_2_facebook_likes\",\"country\",\n",
    "                \"movie_imdb_link\",\"plot_keywords\",\"aspect_ratio\"]\n",
    "data.drop(column_to_drop,axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 17)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the final shape\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director_name             104\n",
       "num_critic_for_reviews     50\n",
       "duration                   15\n",
       "actor_2_name               13\n",
       "gross                     884\n",
       "genres                      0\n",
       "actor_1_name                7\n",
       "movie_title                 0\n",
       "num_voted_users             0\n",
       "actor_3_name               23\n",
       "num_user_for_reviews       21\n",
       "language                   12\n",
       "content_rating            303\n",
       "budget                    492\n",
       "title_year                108\n",
       "imdb_score                  0\n",
       "movie_facebook_likes        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for NaN\n",
    "#round(100*(data.isna().sum()/len(data)),3)\n",
    "data.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('num_user_for_reviews',\n",
       " 'language',\n",
       " 'country',\n",
       " 'actor_2_facebook_likes',\n",
       " 'plot_keywords')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'director_name', 'num_critic_for_reviews','duration','director_facebook_likes',\n",
    "'actor_3_facebook_likes','actor_2_name','actor_1_facebook_likes','actor_1_name','actor_3_name','facenumber_in_poster',\n",
    "'num_user_for_reviews','language','country','actor_2_facebook_likes','plot_keywords'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleting Nan values from gross and budget\n",
    "data.dropna(subset=[\"director_name\",\"num_critic_for_reviews\",\"duration\",\"actor_2_name\",\"actor_1_name\",\"actor_3_name\",\"num_user_for_reviews\",\"language\"],how=\"any\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director_name               0\n",
       "num_critic_for_reviews      0\n",
       "duration                    0\n",
       "actor_2_name                0\n",
       "gross                     725\n",
       "genres                      0\n",
       "actor_1_name                0\n",
       "movie_title                 0\n",
       "num_voted_users             0\n",
       "actor_3_name                0\n",
       "num_user_for_reviews        0\n",
       "language                    0\n",
       "content_rating            221\n",
       "budget                    372\n",
       "title_year                  0\n",
       "imdb_score                  0\n",
       "movie_facebook_likes        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the three columns are skewed that why we will replace the NaNs with median values\n",
    "data[\"gross\"].fillna(data[\"gross\"].median(),inplace=True)\n",
    "data[\"content_rating\"].fillna(data[\"content_rating\"].mode()[0],inplace=True)\n",
    "data[\"budget\"].fillna(data[\"budget\"].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "director_name             0\n",
       "num_critic_for_reviews    0\n",
       "duration                  0\n",
       "actor_2_name              0\n",
       "gross                     0\n",
       "genres                    0\n",
       "actor_1_name              0\n",
       "movie_title               0\n",
       "num_voted_users           0\n",
       "actor_3_name              0\n",
       "num_user_for_reviews      0\n",
       "language                  0\n",
       "content_rating            0\n",
       "budget                    0\n",
       "title_year                0\n",
       "imdb_score                0\n",
       "movie_facebook_likes      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(keep=\"first\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>profit</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>523505847.00</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>502177271.00</td>\n",
       "      <td>Jurassic World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>458672302.00</td>\n",
       "      <td>Titanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>449935665.00</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>424449459.00</td>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>403279547.00</td>\n",
       "      <td>The Avengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>377783777.00</td>\n",
       "      <td>The Lion King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>359544677.00</td>\n",
       "      <td>Star Wars: Episode I - The Phantom Menace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>348316061.00</td>\n",
       "      <td>The Dark Knight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>329999255.00</td>\n",
       "      <td>The Hunger Games</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           profit                                 movie_title\n",
       "0    523505847.00                                     Avatar \n",
       "29   502177271.00                             Jurassic World \n",
       "26   458672302.00                                    Titanic \n",
       "3024 449935665.00         Star Wars: Episode IV - A New Hope \n",
       "3080 424449459.00                 E.T. the Extra-Terrestrial \n",
       "17   403279547.00                               The Avengers \n",
       "509  377783777.00                              The Lion King \n",
       "240  359544677.00  Star Wars: Episode I - The Phantom Menace \n",
       "66   348316061.00                            The Dark Knight \n",
       "439  329999255.00                           The Hunger Games "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"profit\"]=data[\"gross\"]-data[\"budget\"]\n",
    "\n",
    "# lets check top 10 movies with maximum profit\n",
    "data.loc[:,[\"profit\",\"movie_title\"]].sort_values(by=\"profit\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "English       4504\n",
       "French          72\n",
       "Spanish         40\n",
       "Hindi           27\n",
       "Mandarin        24\n",
       "German          19\n",
       "Japanese        16\n",
       "Cantonese       11\n",
       "Russian         11\n",
       "Italian         10\n",
       "Korean           8\n",
       "Portuguese       8\n",
       "Danish           5\n",
       "Dutch            4\n",
       "Persian          4\n",
       "Swedish          4\n",
       "Norwegian        4\n",
       "Arabic           4\n",
       "Hebrew           4\n",
       "Chinese          3\n",
       "Thai             3\n",
       "Dari             2\n",
       "Aboriginal       2\n",
       "Zulu             2\n",
       "None             2\n",
       "Indonesian       2\n",
       "Romanian         2\n",
       "Slovenian        1\n",
       "Tamil            1\n",
       "Kazakh           1\n",
       "Vietnamese       1\n",
       "Dzongkha         1\n",
       "Panjabi          1\n",
       "Mongolian        1\n",
       "Telugu           1\n",
       "Icelandic        1\n",
       "Polish           1\n",
       "Filipino         1\n",
       "Swahili          1\n",
       "Czech            1\n",
       "Maya             1\n",
       "Hungarian        1\n",
       "Bosnian          1\n",
       "Greek            1\n",
       "Aramaic          1\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"language\"].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the language with foregin if no of movies are less than 10\n",
    "def language_bins():\n",
    "    cols=[\"English\",\"French\",\"Spanish\",\"Hindi\",\"Mandarin\",\"German\",\"Japanese\",\"Russian\",\"Cantonese\",\"Italian\"]\n",
    "    data[\"language\"]= data[\"language\"].apply(lambda x: x if (x in cols) else \"Foregin\")\n",
    "\n",
    "language_bins()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>language</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>English</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3466</th>\n",
       "      <td>English</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>English</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2837</th>\n",
       "      <td>English</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>English</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4822</th>\n",
       "      <td>English</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>English</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>English</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>Italian</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>English</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     language                                     movie_title  imdb_score\n",
       "1937  English                       The Shawshank Redemption         9.30\n",
       "3466  English                                  The Godfather         9.20\n",
       "66    English                                The Dark Knight         9.00\n",
       "2837  English                         The Godfather: Part II         9.00\n",
       "1874  English                               Schindler's List         8.90\n",
       "4822  English                                   12 Angry Men         8.90\n",
       "3355  English                                   Pulp Fiction         8.90\n",
       "339   English  The Lord of the Rings: The Return of the King         8.90\n",
       "4498  Italian                 The Good, the Bad and the Ugly         8.90\n",
       "836   English                                   Forrest Gump         8.80"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top ten movies based on imdb score\n",
    "data[[\"language\",\"movie_title\",\"imdb_score\"]].sort_values(by=\"imdb_score\",ascending=False).head(10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>language</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>Italian</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4747</th>\n",
       "      <td>Japanese</td>\n",
       "      <td>Seven Samurai</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4029</th>\n",
       "      <td>Foregin</td>\n",
       "      <td>City of God</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2373</th>\n",
       "      <td>Japanese</td>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4921</th>\n",
       "      <td>Foregin</td>\n",
       "      <td>Children of Heaven</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4259</th>\n",
       "      <td>German</td>\n",
       "      <td>The Lives of Others</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3870</th>\n",
       "      <td>Hindi</td>\n",
       "      <td>Airlift</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3931</th>\n",
       "      <td>Foregin</td>\n",
       "      <td>Samsara</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>Foregin</td>\n",
       "      <td>Baahubali: The Beginning</td>\n",
       "      <td>8.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4659</th>\n",
       "      <td>Foregin</td>\n",
       "      <td>A Separation</td>\n",
       "      <td>8.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      language                      movie_title  imdb_score\n",
       "4498   Italian  The Good, the Bad and the Ugly         8.90\n",
       "4747  Japanese                   Seven Samurai         8.70\n",
       "4029   Foregin                     City of God         8.70\n",
       "2373  Japanese                   Spirited Away         8.60\n",
       "4921   Foregin              Children of Heaven         8.50\n",
       "4259    German             The Lives of Others         8.50\n",
       "3870     Hindi                         Airlift         8.50\n",
       "3931   Foregin                         Samsara         8.50\n",
       "1329   Foregin        Baahubali: The Beginning         8.40\n",
       "4659   Foregin                    A Separation         8.40"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top ten movies other than english\n",
    "data.loc[~(data[\"language\"]==\"English\")][[\"language\",\"movie_title\",\"imdb_score\"]].sort_values(by=\"imdb_score\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Duration of Movies is not varying a lot but we know that most of the users either like watching long movies or short \n",
    "# duration movies. we can categorize the movies in two part i.e., short and long.\n",
    "\n",
    "# lets define a function for categorizing Duration of Movies\n",
    "data[\"duration\"]=data[\"duration\"].apply(lambda x: \"short\" if x <120 else \"long\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "short    3692\n",
       "long     1123\n",
       "Name: duration, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"moviegenres\"]=data[\"genres\"].str.split(\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Action, Adventure, Fantasy, Sci-Fi]\n",
       "1               [Action, Adventure, Fantasy]\n",
       "2              [Action, Adventure, Thriller]\n",
       "3                         [Action, Thriller]\n",
       "5                [Action, Adventure, Sci-Fi]\n",
       "                        ...                 \n",
       "5037                         [Comedy, Drama]\n",
       "5038                         [Comedy, Drama]\n",
       "5040               [Drama, Horror, Thriller]\n",
       "5041                [Comedy, Drama, Romance]\n",
       "5042                           [Documentary]\n",
       "Name: moviegenres, Length: 4815, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"moviegenres\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see from the above output that most of the movies are having a lot of genres\n",
    "#  so lets keep five genres\n",
    "#segregatig the genres of each movie\n",
    "data[\"genre1\"]=data[\"moviegenres\"].apply(lambda x: x[0] )\n",
    "data[\"genre2\"]= data[\"moviegenres\"].apply(lambda x: x[1] if len(x)>1 else x[0])\n",
    "data[\"genre3\"]= data[\"moviegenres\"].apply(lambda x: x[2] if len(x)>2 else x[0])\n",
    "data['genre4'] = data['moviegenres'].apply(lambda x: x[3] if len(x) > 3 else x[0])\n",
    "data['genre5'] =data[\"moviegenres\"].apply(lambda x: x[4] if len(x)>4 else x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>moviegenres</th>\n",
       "      <th>genres</th>\n",
       "      <th>genre1</th>\n",
       "      <th>genre2</th>\n",
       "      <th>genre3</th>\n",
       "      <th>genre4</th>\n",
       "      <th>genre5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>[Action, Adventure, Fantasy, Sci-Fi]</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Action, Adventure, Fantasy]</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>[Action, Adventure, Thriller]</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Action, Thriller]</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John Carter</td>\n",
       "      <td>[Action, Adventure, Sci-Fi]</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Action</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>Signed Sealed Delivered</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>[Drama, Horror, Thriller]</td>\n",
       "      <td>Drama|Horror|Thriller</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>[Documentary]</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4815 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    movie_title  \\\n",
       "0                                       Avatar    \n",
       "1     Pirates of the Caribbean: At World's End    \n",
       "2                                      Spectre    \n",
       "3                        The Dark Knight Rises    \n",
       "5                                  John Carter    \n",
       "...                                         ...   \n",
       "5037                                 Newlyweds    \n",
       "5038                   Signed Sealed Delivered    \n",
       "5040                      A Plague So Pleasant    \n",
       "5041                          Shanghai Calling    \n",
       "5042                         My Date with Drew    \n",
       "\n",
       "                               moviegenres                           genres  \\\n",
       "0     [Action, Adventure, Fantasy, Sci-Fi]  Action|Adventure|Fantasy|Sci-Fi   \n",
       "1             [Action, Adventure, Fantasy]         Action|Adventure|Fantasy   \n",
       "2            [Action, Adventure, Thriller]        Action|Adventure|Thriller   \n",
       "3                       [Action, Thriller]                  Action|Thriller   \n",
       "5              [Action, Adventure, Sci-Fi]          Action|Adventure|Sci-Fi   \n",
       "...                                    ...                              ...   \n",
       "5037                       [Comedy, Drama]                     Comedy|Drama   \n",
       "5038                       [Comedy, Drama]                     Comedy|Drama   \n",
       "5040             [Drama, Horror, Thriller]            Drama|Horror|Thriller   \n",
       "5041              [Comedy, Drama, Romance]             Comedy|Drama|Romance   \n",
       "5042                         [Documentary]                      Documentary   \n",
       "\n",
       "           genre1       genre2       genre3       genre4       genre5  \n",
       "0          Action    Adventure      Fantasy       Sci-Fi       Action  \n",
       "1          Action    Adventure      Fantasy       Action       Action  \n",
       "2          Action    Adventure     Thriller       Action       Action  \n",
       "3          Action     Thriller       Action       Action       Action  \n",
       "5          Action    Adventure       Sci-Fi       Action       Action  \n",
       "...           ...          ...          ...          ...          ...  \n",
       "5037       Comedy        Drama       Comedy       Comedy       Comedy  \n",
       "5038       Comedy        Drama       Comedy       Comedy       Comedy  \n",
       "5040        Drama       Horror     Thriller        Drama        Drama  \n",
       "5041       Comedy        Drama      Romance       Comedy       Comedy  \n",
       "5042  Documentary  Documentary  Documentary  Documentary  Documentary  \n",
       "\n",
       "[4815 rows x 8 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,[\"movie_title\",\"moviegenres\",\"genres\",\"genre1\",\"genre2\",\"genre3\",\"genre4\",\"genre5\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['director_name', 'num_critic_for_reviews', 'duration', 'actor_2_name',\n",
       "       'gross', 'genres', 'actor_1_name', 'movie_title', 'num_voted_users',\n",
       "       'actor_3_name', 'num_user_for_reviews', 'language', 'content_rating',\n",
       "       'budget', 'title_year', 'imdb_score', 'movie_facebook_likes', 'profit',\n",
       "       'moviegenres', 'genre1', 'genre2', 'genre3', 'genre4', 'genre5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Social_Media_Popularity')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Social media popularity of movies\n",
    "data['Social_Media_Popularity'] = (data['num_user_for_reviews']/\n",
    "                                   data['num_voted_users'])*data[\"movie_facebook_likes\"]\n",
    "\n",
    "# lets also check the Top 10 Most Popular Movies on Social Media\n",
    "plt.figure()\n",
    "sns.barplot(x=\"movie_title\",y=\"Social_Media_Popularity\",data=data.loc[:,[\"movie_title\",\"Social_Media_Popularity\"]].sort_values(by=\"Social_Media_Popularity\",ascending=False).head(10))\n",
    "plt.title(\"Top 10 Movies Based on popularity Score\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Movies\")\n",
    "plt.ylabel(\"Social_Media_Popularity\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1820c39d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot genre against gross to check which genre is most bankable\n",
    "data.loc[:,[\"gross\",\"genre1\"]].groupby(\"genre1\").agg([\"min\",\"max\",\"mean\"]).plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'movies')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most profitable movie from each genre\n",
    "data.loc[data.groupby(\"genre1\")[\"profit\"].idxmax(),[\"movie_title\",\"genre1\",\"gross\",\"profit\"]]\n",
    "sns.barplot(x=\"movie_title\",y=\"profit\",data=data.loc[data.groupby(\"genre1\")[\"profit\"].idxmax(),[\"movie_title\",\"genre1\",\"gross\",\"profit\"]]);\n",
    "plt.title(\"Top 10 profitable movies\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"movies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 10 Most Profitable Years in Box Office\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title_year</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.00</td>\n",
       "      <td>3829868954.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015.00</td>\n",
       "      <td>3318040899.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012.00</td>\n",
       "      <td>3169394181.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.00</td>\n",
       "      <td>2626084590.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002.00</td>\n",
       "      <td>2215370582.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009.00</td>\n",
       "      <td>2147899214.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007.00</td>\n",
       "      <td>2016195658.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003.00</td>\n",
       "      <td>1939920161.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1994.00</td>\n",
       "      <td>1615916072.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011.00</td>\n",
       "      <td>1456642485.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   title_year        profit\n",
       "0     2014.00 3829868954.00\n",
       "1     2015.00 3318040899.00\n",
       "2     2012.00 3169394181.00\n",
       "3     2013.00 2626084590.00\n",
       "4     2002.00 2215370582.00\n",
       "5     2009.00 2147899214.00\n",
       "6     2007.00 2016195658.00\n",
       "7     2003.00 1939920161.00\n",
       "8     1994.00 1615916072.00\n",
       "9     2011.00 1456642485.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('top 10 Most Profitable Years in Box Office')\n",
    "data.groupby(\"title_year\")[\"profit\"].sum().sort_values(ascending=False).reset_index().head(10)\n",
    "#sns.barplot(x=\"title_year\",y=\"profit\",.head(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d182264b20>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting most profitable years \n",
    "sns.lineplot(x=\"title_year\",y=\"profit\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>-44925825.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John Carter</td>\n",
       "      <td>-190641321.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tangled</td>\n",
       "      <td>-59192738.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Superman Returns</td>\n",
       "      <td>-8930592.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Quantum of Solace</td>\n",
       "      <td>-31631573.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>The Lone Ranger</td>\n",
       "      <td>-125710090.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>The Chronicles of Narnia: Prince Caspian</td>\n",
       "      <td>-83385977.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>-8936125.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Men in Black 3</td>\n",
       "      <td>-45979146.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Robin Hood</td>\n",
       "      <td>-94780265.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     movie_title        profit\n",
       "2                                       Spectre   -44925825.00\n",
       "5                                   John Carter  -190641321.00\n",
       "7                                       Tangled   -59192738.00\n",
       "11                             Superman Returns    -8930592.00\n",
       "12                            Quantum of Solace   -31631573.00\n",
       "14                              The Lone Ranger  -125710090.00\n",
       "16     The Chronicles of Narnia: Prince Caspian   -83385977.00\n",
       "18  Pirates of the Caribbean: On Stranger Tides    -8936125.00\n",
       "19                               Men in Black 3   -45979146.00\n",
       "22                                   Robin Hood   -94780265.00"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top ten movies that made that maximun Losses\n",
    "data.loc[data[\"profit\"]<-2000,[\"movie_title\",\"profit\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>duration</th>\n",
       "      <th>gross</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>long</td>\n",
       "      <td>760505847.00</td>\n",
       "      <td>523505847.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>long</td>\n",
       "      <td>652177271.00</td>\n",
       "      <td>502177271.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>long</td>\n",
       "      <td>658672302.00</td>\n",
       "      <td>458672302.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3024</th>\n",
       "      <td>Star Wars: Episode IV - A New Hope</td>\n",
       "      <td>long</td>\n",
       "      <td>460935665.00</td>\n",
       "      <td>449935665.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "      <td>long</td>\n",
       "      <td>434949459.00</td>\n",
       "      <td>424449459.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>long</td>\n",
       "      <td>623279547.00</td>\n",
       "      <td>403279547.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Star Wars: Episode I - The Phantom Menace</td>\n",
       "      <td>long</td>\n",
       "      <td>474544677.00</td>\n",
       "      <td>359544677.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>long</td>\n",
       "      <td>533316061.00</td>\n",
       "      <td>348316061.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>The Hunger Games</td>\n",
       "      <td>long</td>\n",
       "      <td>407999255.00</td>\n",
       "      <td>329999255.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>The Hunger Games: Catching Fire</td>\n",
       "      <td>long</td>\n",
       "      <td>424645577.00</td>\n",
       "      <td>294645577.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     movie_title duration        gross  \\\n",
       "0                                        Avatar      long 760505847.00   \n",
       "29                               Jurassic World      long 652177271.00   \n",
       "26                                      Titanic      long 658672302.00   \n",
       "3024         Star Wars: Episode IV - A New Hope      long 460935665.00   \n",
       "3080                 E.T. the Extra-Terrestrial      long 434949459.00   \n",
       "17                                 The Avengers      long 623279547.00   \n",
       "240   Star Wars: Episode I - The Phantom Menace      long 474544677.00   \n",
       "66                              The Dark Knight      long 533316061.00   \n",
       "439                            The Hunger Games      long 407999255.00   \n",
       "186             The Hunger Games: Catching Fire      long 424645577.00   \n",
       "\n",
       "           profit  \n",
       "0    523505847.00  \n",
       "29   502177271.00  \n",
       "26   458672302.00  \n",
       "3024 449935665.00  \n",
       "3080 424449459.00  \n",
       "17   403279547.00  \n",
       "240  359544677.00  \n",
       "66   348316061.00  \n",
       "439  329999255.00  \n",
       "186  294645577.00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 10 long duration movies with maximum profit\n",
    "data.loc[data[\"duration\"]==\"long\",[\"movie_title\",\"duration\",\"gross\",\"profit\"]].sort_values(by=\"profit\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>duration</th>\n",
       "      <th>gross</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>The Lion King</td>\n",
       "      <td>short</td>\n",
       "      <td>422783777.00</td>\n",
       "      <td>377783777.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>short</td>\n",
       "      <td>363024263.00</td>\n",
       "      <td>305024263.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>Despicable Me 2</td>\n",
       "      <td>short</td>\n",
       "      <td>368049635.00</td>\n",
       "      <td>292049635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Finding Nemo</td>\n",
       "      <td>short</td>\n",
       "      <td>380838870.00</td>\n",
       "      <td>286838870.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Shrek 2</td>\n",
       "      <td>short</td>\n",
       "      <td>436471036.00</td>\n",
       "      <td>286471036.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>Home Alone</td>\n",
       "      <td>short</td>\n",
       "      <td>285761243.00</td>\n",
       "      <td>267761243.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Minions</td>\n",
       "      <td>short</td>\n",
       "      <td>336029560.00</td>\n",
       "      <td>262029560.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>The Sixth Sense</td>\n",
       "      <td>short</td>\n",
       "      <td>293501675.00</td>\n",
       "      <td>253501675.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Frozen</td>\n",
       "      <td>short</td>\n",
       "      <td>400736600.00</td>\n",
       "      <td>250736600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>The Secret Life of Pets</td>\n",
       "      <td>short</td>\n",
       "      <td>323505540.00</td>\n",
       "      <td>248505540.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   movie_title duration        gross       profit\n",
       "509             The Lion King     short 422783777.00 377783777.00\n",
       "812                  Deadpool     short 363024263.00 305024263.00\n",
       "521           Despicable Me 2     short 368049635.00 292049635.00\n",
       "338              Finding Nemo     short 380838870.00 286838870.00\n",
       "582                   Shrek 2     short 436471036.00 286471036.00\n",
       "2601               Home Alone     short 285761243.00 267761243.00\n",
       "563                   Minions     short 336029560.00 262029560.00\n",
       "1181          The Sixth Sense     short 293501675.00 253501675.00\n",
       "125                    Frozen     short 400736600.00 250736600.00\n",
       "519   The Secret Life of Pets     short 323505540.00 248505540.00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data[\"duration\"]==\"short\",[\"movie_title\",\"duration\",\"gross\",\"profit\"]].sort_values(by=\"profit\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d182ba4340>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PHgSgZcuW2Gw2AMLCwujRowfGGGw2G3l5eQDk5+czfPhw9uzZgzGGM2fOVOyF+ZjekCciIiJSw3z99df4+/vTrFkzAOrWres5lpKSwuHDh8nOzsblctG8eXOKiooACAwM9LTz8/PzbPv5+VFcXAzA5MmT6d69Ozk5Ofztb3/z9K0pFI5FREREapDDhw8zZswYHnvsMYwxFxzPz8+nWbNm1KpVi8zMTPbt23dF4+fn53PjjTcCkJyc7I2SqxStORYRERGp5goLC3E4HJw5c4aAgACGDRvGf/3Xf5XZdsiQIfTr1w+n04nD4aBdu3ZXdK6nn36a4cOHM3PmTO68805vlF+lmJ+uR6lsTqfTysrKquwyROQnli85ctV9BwwK9mIlIiJVU25uLqGhoZVdhlxEWb+PMSbbsixnWe21rEJERERExE3hWERERETETeFYRERERMRN4VhERERExE3hWERERETETeFYRERERMRN4VhERESkhli2bBnGGHbt2nXZtklJSezcufOaz5mXl8c777zj2c7KyuLxxx+/5nEri14CIiIiIuJF//jLPV4dr0fS++Vum5qaSpcuXUhLS2Pq1KmXbPuXv/zlGisrdS4cP/jggwA4nU6czjIfIVwtaOZYREREpAYoKChg/fr1vPHGG6SlpQGwevVq4uLiuO+++2jXrh1Dhgzh3Avg4uLiOPfytXr16vHMM88QHR3NXXfdxebNm4mLi6NVq1asXLkSKA3Bd9xxB1FRUURFRbFhwwYAnn32WdauXYvD4eDll19m9erV9O3bF4Bjx44xYMAA7HY7nTp1YseOHQBMnTqVUaNGec4xe/bsCv2uLkXhWERERKQGWL58Ob1796Zt27Y0btyYrVu3ArBt2zZmzZrFzp07+frrr1m/fv0FfU+dOkVcXBzZ2dnUr1+f3/3ud3zyyScsW7aMKVOmANCsWTM++eQTtm7dyuLFiz1LJ6ZPn84dd9yBy+Vi/Pjx5437/PPPExkZyY4dO/jjH//IQw895Dm2a9cuPvroIzZv3swLL7zAmTNnfPXVXBGFYxEREZEaIDU1lcTERAASExNJTU0FoEOHDtx00034+fnhcDjIy8u7oG/t2rXp3bs3ADabjW7dulGrVi1sNpun/ZkzZxg9ejQ2m41BgwaVa73yunXrGDZsGAB33nknR48eJT8/H4B77rmHwMBAgoODadasGQcPHrzWr8ArtOZYREREpJo7evQoq1atIicnB2MMJSUlGGPo06cPgYGBnnb+/v4UFxdf0L9WrVoYYwDw8/Pz9PHz8/O0f/nll2nevDnbt2/n7Nmz1KlT57J1nVvC8VPnzlOeuiqDZo5FREREqrn09HQeeugh9u3bR15eHvv376dly5asW7fOa+fIz8+nRYsW+Pn58dZbb1FSUgJA/fr1OXnyZJl9unbtSkpKClC6/jk4OJjrr7/eazX5gsKxiIiISDWXmppKQkLCefsGDhx43iPWrtXYsWNZtGgRnTp1Yvfu3dStWxcAu91OQEAAERERvPzyy+f1mTp1KllZWdjtdp599lkWLVrktXp8xZQ13V1ZnE6nde6uSRGpGpYvOXLVfQcMCvZiJSIiVVNubi6hoaGVXYZcRFm/jzEm27KsMp83p5ljERERERE3hWMRERERETeFYxERERERN4VjERERERE3hWMRERERETeFYxERERERN4VjERERkRrg+++/JzExkdatW9O+fXv69OnD7t27K+TcISEhHDly9Y/+rEr0+mgRERERL0pb2Mur4yWO/OiybSzLIiEhgeHDh5OWlgaAy+Xi4MGDtG3b1qv11HSaORYRERGp5jIzM6lVqxZjxozx7HM4HHTp0oWJEycSHh6OzWZj8eLFQOmrnLt168b9999P27ZtefbZZ0lJSaFDhw7YbDb27t0LwOHDhxk4cCAxMTHExMSwfv16AI4ePUp8fDyRkZH85je/4dxL5SZPnswrr7ziqWHSpEnMnj27or4Gr1A4FhEREanmcnJyiI6OvmD/u+++i8vlYvv27WRkZDBx4kQOHDgAwPbt23nllVf4/PPPeeutt9i9ezebN28mKSmJOXPmAPDEE08wfvx4tmzZwtKlS0lKSgLghRdeoEuXLmzbto3+/fvzzTffAPDrX//a84ros2fPkpaWxpAhQyriK/AaLasQERERqaHWrVvHAw88gL+/P82bN6dbt25s2bKF66+/npiYGFq0aAFA69atiY+PB8Bms5GZmQlARkYGO3fu9Ix34sQJTp48yZo1a3j33XcBuOeee2jUqBFQuva4SZMmbNu2jYMHDxIZGUmTJk0q8pKvmcKxiIiISDUXFhZGenr6BfvPLXcoS2BgoOezn5+fZ9vPz4/i4mKgdPZ348aNBAUFXdDfGFPmuElJSSQnJ/P9998zatSoK7qOqkDLKkRERESquTvvvJMffviB119/3bNvy5YtNGrUiMWLF1NSUsLhw4dZs2YNHTp0KPe48fHxvPrqq55tl8sFQNeuXUlJSQHg73//O//+9789bRISEvjwww/ZsmULvXp59+bEiqCZYxEREZFqzhjDsmXLePLJJ5k+fTp16tQhJCSEWbNmUVBQQEREBMYYXnrpJX7xi1+wa9euco07e/ZsHn30Uex2O8XFxXTt2pV58+bx/PPP88ADDxAVFUW3bt24+eabPX1q165N9+7dadiwIf7+/r66ZJ8xl5pur2hOp9PKysqq7DJE5CeWL7n651YOGBTsxUpERKqm3NxcQkNDK7uMKuPs2bNERUWxZMkS2rRpU9nllPn7GGOyLctyltVeyypERERExCt27tzJrbfeSo8ePapEML4aWlYhIiIiIl7Rvn17vv7668ou45po5lhERERExE3hWERERETETeFYRERERMRN4VhERERExE3hWERERKSaM8YwbNgwz3ZxcTFNmzalb9++VzyWy+Xigw8+8GZ51YqeViEiIiLiRXNSvPtWuHFDPrpsm7p165KTk0NhYSFBQUF88skn3HjjjVd1PpfLRVZWFn369Cl3n+LiYgICakas1MyxiIiISA1w99138/777wOQmprKAw88AJS+lKNNmzYcPnzYs33rrbdy5MgRlixZQnh4OBEREXTt2pUff/yRKVOmsHjxYhwOB4sXL+bUqVOMGjWKmJgYIiMjWbFiBQDJyckMGjSIfv36ER8fz7BhwzzHAIYMGcLKlSsr+Fu4dgrHIiIiIjVAYmIiaWlpFBUVsWPHDjp27AiAn58fQ4cOJSUlBYCMjAwiIiIIDg5m2rRpfPTRR2zfvp2VK1dSu3Ztpk2bxuDBg3G5XAwePJg//OEP3HnnnWzZsoXMzEwmTpzIqVOnANi4cSOLFi1i1apVJCUlsXDhQgDy8/PZsGHDFc0+VxUKxyIiIiI1gN1uJy8vj9TU1AtC6ahRo3jzzTcBWLBgASNHjgSgc+fOjBgxgtdff52SkpIyx/3444+ZPn06DoeDuLg4ioqK+OabbwDo2bMnjRs3BqBbt2589dVXHDp0iNTUVAYOHFgtl1pUv4pFREREpEz9+/dnwoQJrF69mqNHj3r2//KXv6R58+asWrWKTZs2eWaR582bx6ZNm3j//fdxOBy4XK4LxrQsi6VLl3Lbbbedt3/Tpk3UrVv3vH3Dhg0jJSWFtLQ0FixY4IMr9D3NHIuIiIjUEKNGjWLKlCnYbLYLjiUlJTF06FDuv/9+/P39Adi7dy8dO3Zk2rRpBAcHs3//furXr8/Jkyc9/Xr16sWcOXOwLAuAbdu2XfT8I0aMYNasWQCEhYV589IqjMKxiIiISA1x00038cQTT5R5rH///hQUFHiWVABMnDgRm81GeHg4Xbt2JSIigu7du7Nz507PDXmTJ0/mzJkz2O12wsPDmTx58kXP37x5c0JDQ887R3Vjzv1XQFXgdDqtrKysyi5DRH5i+ZIjV913wKBgL1YiIlI15ebmEhoaWtllXFZWVhbjx49n7dq1PjvH6dOnsdlsbN26lQYNGvjsPFeirN/HGJNtWZazrPaaORYRERGp4aZPn87AgQP505/+5LNzZGRk0K5dO8aNG1dlgvHV0A15IiIiIjXcs88+y7PPPuvTc9x1112ep1hUZ5o5FhERERFxUzgWEREREXFTOBYRERERcVM4FhERERFxUzgWERERqQG+//57EhMTad26Ne3bt6dPnz7s3r37gnaxsbGVUF31oadViIiIiHjRhPTeXh1vxn0fXraNZVkkJCQwfPhw0tLSAHC5XBw8eJC2bdsCUFJSgr+/Pxs2bPBqfTWNZo5FREREqrnMzExq1arFmDFjPPscDgclJSV0796dBx980PNK6Xr16gGwevVqunXrxv3330/btm159tlnSUlJoUOHDthsNvbu3QvA4cOHGThwIDExMcTExLB+/fqKv8AKpJljERERkWouJyeH6OjoMo9t3ryZnJwcWrZsecGx7du3k5ubS+PGjWnVqhVJSUls3ryZV155hTlz5jBr1iyeeOIJxo8fT5cuXfjmm2/o1asXubm5vr6kSqNwLCIiIlKDdejQocxgDBATE0OLFi0AaN26NfHx8QDYbDYyMzOB0jff7dy509PnxIkTnDx5kvr16/u48sqhcCwiIiJSzYWFhZGenl7msbp16160X2BgoOezn5+fZ9vPz4/i4mIAzp49y8aNGwkKCvJixVWX1hyLiIiIVHN33nknP/zwA6+//rpn35YtW/j000+veez4+HheffVVz7bL5brmMasyhWMRERGRas4Yw7Jly/jkk09o3bo1YWFhTJ06lRtuuOGax549ezZZWVnY7Xbat2/PvHnzvFBx1WUsy6rsGjycTqeVlZVV2WWIyE8sX3LkqvsOGBTsxUpERKqm3NxcQkNDK7sMuYiyfh9jTLZlWc6y2mvmWERERETETeFYRERERMRN4VhERERExE3hWERERETETeFYRERERMRN4VhERERExE3hWERERKSa8/f3x+FwEBERQVRUFBs2bADgu+++47777qu0uqZOncqMGTMq7fxXQ6+PFhEREfGiu5eP8+p4fx8w57JtgoKCPG+u++ijj/jtb3/Lp59+yg033HDR10pfqZKSEvz9/b0yVlWmmWMRERGRGuTEiRM0atQIgLy8PMLDwwEoKipi5MiR2Gw2IiMjyczMBOD06dPcf//92O12Bg8eTMeOHTn3UrZ69eoxZcoUOnbsyMaNG5k2bRoxMTGEh4fz8MMPc+5lcnFxcTz55JPExsYSHh7O5s2bPfXs3LmTuLg4WrVqxezZswGYPHkyr7zyiqfNpEmTPMcqm2aORURERKq5wsJCHA4HRUVFHDhwgFWrVl3Q5rXXXgPg888/Z9euXcTHx7N7927+93//l0aNGrFjxw5ycnJwOByePqdOnSI8PJxp06YB0L59e6ZMmQLAsGHDeO+99+jXr5+n7YYNG1izZg2jRo0iJycHgF27dpGZmcnJkye57bbbeOSRR/j1r3/NvffeyxNPPMHZs2dJS0s7L1BXJs0ci4iIiFRz55ZV7Nq1iw8//JCHHnrIM6t7zrp16xg2bBgA7dq145ZbbmH37t2sW7eOxMREAMLDw7Hb7Z4+/v7+DBw40LOdmZlJx44dsdlsrFq1ii+++MJz7IEHHgCga9eunDhxguPHjwNwzz33EBgYSHBwMM2aNePgwYOEhITQpEkTtm3bxscff0xkZCRNmjTxzZdzhTRzLCIiIlKD3H777Rw5coTDhw+ft/8/w/Ll9gPUqVPHs864qKiIsWPHkpWVxS9/+UumTp1KUVGRp60x5ry+57YDAwM9+/z9/SkuLgYgKSmJ5ORkvv/+e0aNGnUFV+hbmjkWERERqUF27dpFSUnJBTOxXbt2JSUlBYDdu3fzzTffcNttt9GlSxf++te/AqXrgz///PMyxz0XhIODgykoKLjgRr/FixcDpTPUDRo0oEGDBpesMyEhgQ8//JAtW7bQq1evK79QH9HMsYiIiEg1d27NMZTOBC9atOiCJ0uMHTuWMWPGYLPZCAgIIDk5mcDAQMaOHcvw4cOx2+1ERkZit9vLDLYNGzZk9OjR2Gw2QkJCiImJOe94o0aNiI2N5cSJEyxYsOCyNdeuXZvu3bvTsGHDKvUUDHOpqdhUgdcAACAASURBVPSK5nQ6rXN3R4pI1bB8yZGr7jtgULAXKxERqZpyc3MJDQ2t7DKuWklJCWfOnKFOnTrs3buXHj16sHv3bmrXrl3uMeLi4pgxYwZOp7Pcfc6ePUtUVBRLliyhTZs2V1N6uZT1+xhjsi3LKrNYzRyLiIiI/IydPn2a7t27c+bMGSzLYu7cuVcUjK/Gzp076du3LwkJCT4NxldD4VhERETkZ6x+/fpc6/9zv3r16itq3759e77++utrOqev6IY8ERERERE3hWMRERERETeFYxERERERN4VjERERERE3hWMRERGRas7f3x+Hw+H5y8vLu+IxkpOT+e6777xfXDXjs6dVGGPqAGuAQPd50i3Let5X5xMRERGpCu5593+8Ot779z512TZBQUG4XK5rOk9ycjLh4eHccMMN1zROdefLmeMfgDsty4oAHEBvY0wnH55PRERERICCggJ69OhBVFQUNpuNFStWAJCXl0doaCijR48mLCyM+Ph4CgsLSU9PJysriyFDhuBwOCgsLGTatGnExMQQHh7Oww8/zLkXx82ePZv27dtjt9tJTEzk7NmztGnThsOHDwOlL/e49dZbOXLk6l8iVZl8Fo6tUgXuzVruv6rzOj4RERGRGuLc66MdDgcJCQnUqVOHZcuWsXXrVjIzM3nqqac84XbPnj08+uijfPHFFzRs2JClS5dy33334XQ6SUlJweVyERQUxGOPPcaWLVvIycmhsLCQ9957D4Dp06ezbds2duzYwbx58/Dz82Po0KGkpKQAkJGRQUREBMHB1fMtqT5dc2yM8TfGuIBDwCeWZW3y5flEREREfo7OLatwuVwsW7YMy7J47rnnsNvt3HXXXXz77bccPHgQgJYtW+JwOACIjo6+6PrkzMxMOnbsiM1mY9WqVXzxxRcA2O12hgwZwttvv01AQOkK3VGjRvHmm28CsGDBAkaOHOnjK/Ydn4Zjy7JKLMtyADcBHYwx4f/ZxhjzsDEmyxiTdW46XkRERESuXkpKCocPHyY7OxuXy0Xz5s0pKioCIDAw0NPO39+f4uLiC/oXFRUxduxY0tPT+fzzzxk9erSn//vvv8+jjz5KdnY20dHRFBcX88tf/pLmzZuzatUqNm3axN13310xF+oDFfK0CsuyjgOrgd5lHJtvWZbTsixn06ZNK6IcERERkRotPz+fZs2aUatWLTIzM9m3b99l+9SvX5+TJ08CeIJwcHAwBQUFpKenA6Xriffv30/37t156aWXOH78OAUFpatok5KSGDp0KPfffz/+/v4+ujLf8+XTKpoCZyzLOm6MCQLuAv7bV+cTERERkVJDhgyhX79+OJ1OHA4H7dq1u2yfESNGMGbMGIKCgti4cSOjR4/GZrMREhJCTEwMACUlJQwdOpT8/Hwsy2L8+PE0bNgQgP79+zNy5MhqvaQCwJxbnO31gY2xA4sAf0pnqP9qWda0S/VxOp1WVlaWT+oRkauzfMnV3208YFD1vBlDRORK5ObmEhoaWtllVLqsrCzGjx/P2rVrK7uU85T1+xhjsi3LcpbV3mczx5Zl7QAifTW+iIiIiFQN06dPZ+7cuZ4nVlRnekOeiIiIiFyTZ599ln379tGlS5fKLuWaKRyLiIiIiLgpHIuIiIiIuCkci4iIiIi4KRyLiIiIiLgpHIuIiIhUc/7+/jgcDsLCwoiIiGDmzJmcPXu2sssqF5fLxQcffFDZZXj47FFuIiIiIj9HfZcu8Op47w0cddk2QUFBuFwuAA4dOsSDDz5Ifn4+L7zwgldr8QWXy0VWVhZ9+vQpd5/i4mICAnwTYzVzLCIiIlKDNGvWjPnz5/Pqq69iWRZFRUWMHDkSm81GZGQkmZmZQOnb7iZMmIDNZsNutzNnzhwAQkJCOHKk9AVQWVlZxMXFATB16lSGDx9OfHw8ISEhvPvuuzz99NPYbDZ69+7NmTNnAMjOzqZbt25ER0fTq1cvDhw4AEBcXBzPPPMMHTp0oG3btqxdu5Yff/yRKVOmsHjxYhwOB4sXL2bz5s3ExsYSGRlJbGwsX375JQDJyckMGjSIfv36ER8fz7Bhw1ixYoXnuocMGcLKlSuv+fvTzLGIiIhIDdOqVSvOnj3LoUOHePvttwH4/PPP2bVrF/Hx8ezevZuFCxfyz3/+k23bthEQEMCxY8cuO+7evXvJzMxk586d3H777SxdupSXXnqJhIQE3n//fe655x7GjRvHihUraNq0KYsXL2bSpEksWFA6m15cXMzmzZv54IMPeOGFF8jIyGDatGlkZWXx6quvAnDixAnWrFlDQEAAGRkZPPfccyxduhSAjRs3smPHDho3bsynn37Kyy+/zK9+9Svy8/PZsGEDixYtuubvTuFYREREpAayLAuAdevWMW7cOADatWvHLbfcwu7du8nIyGDMmDGe5QmNGze+7Jh33303tWrVwmazUVJSQu/evQGw2Wzk5eXx5ZdfkpOTQ8+ePYHS2ekWLVp4+t97770AREdHk5eXV+Y58vPzGT58OHv27MEY45mRBujZs6enzm7duvHoo49y6NAh3n33XQYOHOiVpRYKxyIiIiI1zNdff42/vz/NmjXzhOT/ZFkWxpgL9gcEBHhu5isqKjrvWGBgIAB+fn7UqlXL09/Pz4/i4mIsyyIsLIyNGzeWec5z/f39/SkuLi6zzeTJk+nevTvLli0jLy/Ps6wDoG7duue1HTZsGCkpKaSlpXlmp6+V1hyLiIiI1CCHDx9mzJgxPPbYYxhj6Nq1KykpKQDs3r2bb775httuu434+HjmzZvnCannllWEhISQnZ0N4FnOUF633XYbhw8f9oTjM2fO8MUXX1yyT/369Tl58qRnOz8/nxtvvBEoXWd8KSNGjGDWrFkAhIWFXVGtF6NwLCIiIlLNFRYWeh7ldtdddxEfH8/zzz8PwNixYykpKcFmszF48GCSk5MJDAwkKSmJm2++GbvdTkREBO+88w4Azz//PE888QR33HEH/v7+V1RH7dq1SU9P55lnniEiIgKHw8GGDRsu2ad79+7s3LnTc0Pe008/zW9/+1s6d+5MSUnJJfs2b96c0NBQRo4ceUV1Xoq52FR7ZXA6nVZWVlZllyEiP7F8yZGr7jtgULAXKxERqZpyc3MJDQ2t7DJ+lk6fPo3NZmPr1q00aNCgzDZl/T7GmGzLspxltdfMsYiIiIhUOxkZGbRr145x48ZdNBhfDd2QJyIiIiLVzl133cU333zj9XE1cywiIiIi4qZwLCIiIiLipnAsIiIiIuKmcCwiIiIi4qZwLCIiIlID/OEPfyAsLAy73Y7D4WDTpk1ltsvKyuLxxx8v81hcXBy33XYbDocDh8NBeno6SUlJ7Ny505elVyl6WoWIiIiIF/Vdku7V8d4bdN9l22zcuJH33nuPrVu3EhgYyJEjR/jxxx/LbOt0OnE6y3zELwApKSnnHb/vvsufvybRzLGIiIhINXfgwAGCg4MJDAwEIDg4mBtuuIEtW7YQGxtLREQEHTp04OTJk6xevZq+ffuWe+y4uDh+Ti9pUzgWERERqebi4+PZv38/bdu2ZezYsXz66af8+OOPDB48mFdeeYXt27eTkZFBUFDQZccaMmSIZ1nF0aNHK6D6qkXLKkRERESquXr16pGdnc3atWvJzMxk8ODBTJo0iRYtWhATEwPA9ddfX66x/nNZxc+NwrGIiIhIDeDv709cXBxxcXHYbDZee+01jDGX7NOrVy8OHjyI0+nkL3/5SwVVWrUpHIuIiIhUc19++SV+fn60adMGAJfLRWhoKB9++CFbtmwhJiaGkydPXrCs4qOPPqqMcqs0hWMRERGRaq6goIBx48Zx/PhxAgICuPXWW5k/fz4jR45k3LhxFBYWEhQUREZGRmWXWuUZy7IquwYPp9Np/ZzuhhSpDpYvOXLVfQcMCvZiJSIiVVNubi6hoaGVXYZcRFm/jzEm27KsMhdW62kVIiIiIiJuCsciIiIiIm4KxyIiIiIibgrHIiIiIiJuCsciIiIiIm4KxyIiIiIibgrHIiIiItVcvXr1zttOTk7mscceq6Rqqje9BERERETEiwake/dFG8vvu8ur45WluLiYgICAi26Xt19NULOuRkRERETOs2/fPkaNGsXhw4dp2rQpCxcu5Oabb2bEiBE0btyYbdu2ERUVxdGjR8/bHjZsGGPGjOH06dO0bt2aBQsW0KhRI+Li4oiNjWX9+vX079+fp556qrIv0asUjkVERESqucLCQhwOh2f72LFj9O/fH4DHHnuMhx56iOHDh7NgwQIef/xxli9fDsDu3bvJyMjA39+fESNGnLdtt9uZM2cO3bp1Y8qUKbzwwgvMmjULgOPHj/Ppp59W/IVWAIVjERERkWouKCgIl8vl2U5OTiYrKwuAjRs38u677wIwbNgwnn76aU+7QYMG4e/vf8F2fn4+x48fp1u3bgAMHz6cQYMGedoNHjzYp9dTmXRDnoiIiMjPiDHG87lu3brnHfvP7Yspb7vqSOFYREREpAaLjY0lLS0NgJSUFLp06XLZPg0aNKBRo0asXbsWgLfeesszi1zTaVmFiIiISA02e/ZsRo0axZ///GfPDXnlsWjRIs8Nea1atSp3v+rOWJZV2TV4OJ1O69z6GBGpGpYvOXLVfQcMCvZiJSIiVVNubi6hoaGVXYZcRFm/jzEm27IsZ1nttaxCRERERMRN4VhERERExE3hWERERETETeFYRERERMRN4VhERERExE3hWERERETETeFYREREpJrz9/fH4XAQHh5Ov379OH78eGWXVG3pJSAiIiIiXjRo6Q6vjrdkoP2ybYKCgnC5XAAMHz6c1157jUmTJnm1jp+LK545NsY0MsZc/lcSERERkQp3++238+233wLgcrno1KkTdrudhIQE/v3vfwMQFxfH+PHj6dq1K6GhoWzZsoV7772XNm3a8Lvf/c4z1oABA4iOjiYsLIz58+d79terV49JkyYRERFBp06dOHjwIAAHDx4kISGBiIgIIiIi2LBhAwBvv/02HTp0wOFw8Jvf/IaSkpKK+jquWLnCsTFmtTHmemNMY2A7sNAYM9O3pYmIiIjIlSgpKeEf//gH/fv3B+Chhx7iv//7v9mxYwc2m40XXnjB07Z27dqsWbOGMWPG8Ktf/YrXXnuNnJwckpOTOXr0KAALFiwgOzubrKwsZs+e7dl/6tQpOnXqxPbt2+natSuvv/46AI8//jjdunVj+/btbN26lbCwMHJzc1m8eDHr16/H5XLh7+9PSkpKBX8z5VfemeMGlmWdAO4FFlqWFQ3c5buyRERERKS8CgsLcTgcNGnShGPHjtGzZ0/y8/M5fvw43bp1A0qXW6xZs8bT51yAttlshIWF0aJFCwIDA2nVqhX79+8HYPbs2Z7Z4f3797Nnzx6gNFj37dsXgOjoaPLy8gBYtWoVjzzyCFC6DrpBgwb84x//IDs7m5iYGBwOB//4xz/4+uuvK+R7uRrlDccBxpgWwP3Aez6sR0RERESu0Lk1x/v27ePHH3/ktddeu2yfwMBAAPz8/Dyfz20XFxezevVqMjIy2LhxI9u3bycyMpKioiIAatWqhTEGKA3BxcXFFz2PZVkMHz4cl8uFy+Xiyy+/ZOrUqddwtb5V3nA8DfgI+MqyrC3GmFbAHt+VJSIiIiJXqkGDBsyePZsZM2Zw3XXX0ahRI9auXQvAW2+95ZlFLo/8/HwaNWrEddddx65du/jss88u26dHjx7MnTsXKF3iceLECXr06EF6ejqHDh0C4NixY+zbt+8qrq5ilCscW5a1xLIsu2VZY93bX1uWNdC3pYmIiIjIlYqMjCQiIoK0tDQWLVrExIkTsdvtuFwupkyZUu5xevfuTXFxMXa7ncmTJ9OpU6fL9nnllVfIzMzEZrMRHR3NF198Qfv27XnxxReJj4/HbrfTs2dPDhw4cC2X6FPGsqzLNzLmJeBFoBD4EIgAnrQs621vFuN0Oq2srCxvDiki12j5kiNX3XfAoGAvViIiUjXl5uYSGhpa2WXIRZT1+xhjsi3LcpbVvrzLKuLdN+T1Bf4FtAUmXkuhIiIiIiJVTXnDcS33v32AVMuyjvmoHhERERGRSlPeN+T9zRizi9JlFWONMU2BIt+VJSIiIiJS8cp7Q96zwO2A07KsM8Ap4Fe+LExEREREpKKVa+bYGFMLGAZ0dT/T7lNgng/rEhERERGpcOVdVjGX0nXH/+veHubel+SLokREREREKkN5b8iLsSxruGVZq9x/I4EYXxYmIiIiIpd39OhRHA4HDoeDX/ziF9x44404HA4aNmxI+/btyzXGvHnzePPNNwEYMWIE6enpAMTFxfFze8xueWeOS4wxrS3L2gvgfkNeie/KEhEREame/rDMuy+4mJTQ4pLHmzRpgsvlAmDq1KnUq1ePCRMmkJeXR9++fS87fnFxMWPGjPFKrSUlJfj7+3tlrMpS3nA8Acg0xnwNGOAWYKTPqhIRERGRa1ZSUsLo0aPZsGEDN954IytWrCAoKIi4uDhiY2NZv349/fv35+TJk55QfTEff/wxzz//PD/88AOtW7dm4cKF1KtXj5CQEEaNGsXHH3/MY489RmJiYgVeofdddlmFMcaf0jfitQEed//dZllWpo9rExEREZFrsGfPHh599FG++OILGjZsyNKlSz3Hjh8/zqeffspTTz112XGOHDnCiy++SEZGBlu3bsXpdDJz5kzP8Tp16rBu3bpqH4yhHDPHlmWVGGP6W5b1MrCjAmoSERERES9o2bIlDocDgOjoaPLy8jzHBg8eXO5xPvvsM3bu3Ennzp0B+PHHH7n99tuvaqyqrrzLKjYYY14FFlP6jGMALMva6pOqREREROSaBQYGej77+/tTWFjo2a5bt265x7Esi549e5Kamlrm8SsZq6or79MqYoEw4AVgBvA/7n9FREREpIbr1KkT69ev56uvvgLg9OnT7N69u5Kr8o3yzhy/B1iU3oyH+/MJY4zDsiyXTyoTERERkSqhadOmJCcn88ADD/DDDz8A8OKLL9K2bdtKrsz7jGVZl29kzDuAE1hJaUC+B9gCtAOWWJb1kjeKcTqd1s/tWXoiVd3yJUeuuu+AQcFerEREpGrKzc0lNDS0ssuQiyjr9zHGZFuW5SyrfXlnjpsAUZZlFbgHfB5IB7oC2YBXwrGIiIiISGUq75rjm4Eff7J9BrjFsqxC4AevVyUiIiIiUgnKO3P8DvCZMWaFe7sfkGqMqQvs9EllIiIiIiIVrFzh2LKs3xtjPgC6ULrmeIxlWecWBw/xVXEiIiIiIhWpvDPHWJaVTen6YhERERGRGqm8a45FRERERGo8hWMRERGRas7f3x+Hw+H5++lroq/GypUrmT59OgBTp05lxoyfz7vfyr2sQkREREQu71qeD1+W8jwzPigoCJfLe+9l69+/P/379/faeNWJZo5FREREapiCggJ69OhBVFQUNpuNFStKHziWl5dHu3btSEpKIjw8nCFDhpCRkUHnzp1p06YNmzdvBiA5OZnHHnvsvDH37t1LVFSUZ3vPnj1ER0dX3EVVEIVjERERkWqusLDQs6QiISGBOnXqsGzZMrZu3UpmZiZPPfUU596K/NVXX/HEE0+wY8cOdu3axTvvvMO6deuYMWMGf/zjHy96jtatW9OgQQPPDPXChQsZMWJERVxehdKyChEREZFq7j+XVZw5c4bnnnuONWvW4Ofnx7fffsvBgwcBaNmyJTabDYCwsDB69OiBMQabzXbZtcpJSUksXLiQmTNnsnjxYs9Mc02imWMRERGRGiYlJYXDhw+TnZ2Ny+WiefPmFBUVARAYGOhp5+fn59n28/OjuLj4kuMOHDiQv//977z33ntER0fTpEkT311EJVE4FhEREalh8vPzadasGbVq1SIzM5N9+/Z5Zdw6derQq1cvHnnkEUaOHOmVMasan4VjY8wvjTGZxphcY8wXxpgnfHUuEREREfn/hgwZQlZWFk6nk5SUFNq1a+fVsY0xxMfHe23MqsScW5zt9YGNaQG0sCxrqzGmPqVv1xtgWdbOi/VxOp1WVlbWxQ6LSCW4lkcSlefxQyIi1V1ubi6hoaGVXUaFmTFjBvn5+fz+97+v7FLKpazfxxiTbVmWs6z2Prshz7KsA8AB9+eTxphc4EbgouFYRERERKquhIQE9u7dy6pVqyq7FJ+pkKdVGGNCgEhgUxnHHgYeBrj55psrohwRERERuQrLli2r7BJ8zuc35Blj6gFLgSctyzrxn8cty5pvWZbTsixn06ZNfV2OiIiIiMhF+TQcG2NqURqMUyzLeteX5xIRERERuVa+fFqFAd4Aci3Lmumr84iIiIiIeIsvZ447A8OAO40xLvdfHx+eT0RERETkmvgsHFuWtc6yLGNZlt2yLIf77wNfnU9ERETk58oYw7BhwzzbxcXFNG3alL59+17VeElJSezc+fN8wFiFPK1CRERE5Odi88JDXh2vw8hml21Tt25dcnJyKCwsJCgoiE8++YQbb7zxqs/5l7/85ar7Vnd6fbSIiIhIDXD33Xfz/vvvA5CamsoDDzzgOTZ16lRmzJjh2Q4PDycvL49Tp05xzz33EBERQXh4OIsXLwYgLi6Ocy9m+/DDD4mKiiIiIoIePXpU4BVVDoVjERERkRogMTGRtLQ0ioqK2LFjBx07drxsnw8//JAbbriB7du3k5OTQ+/evc87fvjwYUaPHs3SpUvZvn07S5Ys8VX5VYbCsYiIiEgNYLfbycvLIzU1lT59yvcMBJvNRkZGBs888wxr166lQYMG5x3/7LPP6Nq1Ky1btgSgcePGXq+7qlE4FhEREakh+vfvz4QJE85bUgEQEBDA2bNnPdtFRUUAtG3bluzsbGw2G7/97W+ZNm3aef0sy6L06bw/HwrHIiIiIjXEqFGjmDJlCjab7bz9ISEhbN26FYCtW7fyz3/+E4DvvvuO6667jqFDhzJhwgRPm3Nuv/12Pv30U0/7Y8eOVcBVVC49rUJERESkhrjpppt44oknLtg/cOBA3nzzTRwOBzExMbRt2xaAzz//nIkTJ+Ln50etWrWYO3fuef2aNm3K/Pnzuffeezl79izNmjXjk08+qZBrqSzGsqzKrsHD6XRa5+6MFJGqYfmSI1fdd8CgYC9WIiJSNeXm5hIaGlrZZchFlPX7GGOyLctyltVeyypERERERNwUjkVERERE3BSORURERETcFI5FRERERNwUjkVERERE3BSORURERETcFI5FREREqrnx48cza9Ysz3avXr1ISkrybD/11FPMnDmz3OOtXr2aDRs2eLXG6kIvARERERHxon/N+N6r49004ReXbRMbG8uSJUt48sknOXv2LEeOHOHEiROe4xs2bDgvPF/O6tWrqVevHrGxseXuU1xcTEBA9Y+W1f8KRERERH7mOnfuzPjx4wH44osvCA8P58CBA/z73//muuuuIzc3F4Bu3bpRUFBAcHAwycnJtGjRgtmzZzNv3jwCAgJo374906dPZ968efj7+/P22/+vvXuPrrK61z3+/AgxBAhhpAlNAU/h7L0RWhKCpq1cpCLbeuHSqLVoUezotg4saqXSuJHRsVO7zznuFLUFd2U7vA1bCg4QrZUOKra11B0cFDRyET2eIWlkZUcTkUAQhIR5/sh0bQJvuCTrzbsu388YjrnWXPN912+BLp/MzHe+v9KyZcs0evRozZs3T/X19ZKkn/3sZ5o0aZKqqqrU0NCguro6FRYWatSoUaqvr9e7776r+vp63Xnnnbrjjjsi+3PpDsIxAABAihs6dKj69u2r+vp61dTUaMKECYrFYtq0aZPy8/M1ZswYLViwQL/5zW9UVFSkp59+WosXL9bjjz+u++67T7t371ZOTo727dunwYMHa968eRo4cKAWLlwoSfrWt76lBQsWaPLkyaqvr9dll10WD9xbt27VK6+8otzcXFVVVemtt97Sn/70Jx04cEDnnXeebr31VmVnZ0f5x3NWCMcAAABpYNKkSaqpqVFNTY1+8IMfKBaLqaamRvn5+Ro2bJhefPFFXXrppZKk9vZ2fe5zn5MklZaWas6cOaqoqFBFRUXguV966SW9+eab8ef79+/XgQMHJEmzZs1Sbm5u/LXp06crJydHOTk5GjJkiN5//30NHz48rI+dcIRjAACANDBx4kTV1NRo+/btGjt2rM4991zdf//9GjRokC655JL4TPKJ1q1bp40bN+r555/XT37yE+3cufOkMceOHdOmTZs6heBPDRgwoNPznJyc+OOsrCy1tbUl4NP1HnarAAAASAOTJk3SCy+8oIKCAmVlZamgoED79u3Tpk2bNHv2bDU1NcXD8dGjR7Vz504dO3ZM7733nqZOnarq6mrt27dPra2tysvLi88MS9LXvvY1PfTQQ/HntbW1vf75egvhGAAAIA2UlJSoublZF154Yae+/Px8DRkyRGvWrNHdd9+tcePGqaysTDU1NWpvb9cNN9ygkpISjR8/XgsWLNDgwYM1c+ZMPfvssyorK9Nf/vIXLV26VFu2bFFpaam+8IUvaPny5RF+0nCZcy7qGuLKy8vdli1boi4DwHGeW93c7WMrri1MYCUAkJx27dqlMWPGRF0GuhD092NmW51z5UHjmTkGAAAAPMIxAAAA4BGOAQAAAI9wDAAAAHiEYwAAAMAjHAMAAAAe4RgAACDFmZluvPHG+PO2tjYVFRVpxowZ3TrfzTff3Ol20Wfq5Zdf7vZ7JgtuHw0AAJBA7z+Y2LvHfXZB2WnHDBgwQDt27NChQ4eUm5urDRs2aNiwYd1+z0cffbTbx6Y6Zo4BAADSwBVXXKF169ZJklauXKnrr78+/lpVVZWWLFkSfz527FjV1dXp4MGDmj59usaNG6exY8fq6aefliRdfPHF+vTGbOvXr9f555+vcePGadq0aZKkzZs3a+LEiRo/frwmTpyot99+xEgxCwAAE4FJREFUu7c+ZugIxwAAAGnguuuu06pVq3T48GFt27ZNX/nKV057zPr16zV06FC98cYb2rFjhy6//PJOrzc1Nem73/2unnnmGb3xxhtavXq1JGn06NHauHGjXn/9dd1777265557QvlMUWBZBQAAQBooLS1VXV2dVq5cqSuvvPKMjikpKdHChQt19913a8aMGbrooos6vf7qq69qypQpGjlypCSpoKBAktTS0qKbbrpJ77zzjsxMR48eTeyHiRAzxwAAAGli1qxZWrhwYaclFZLUt29fHTt2LP788OHDkqRRo0Zp69atKikp0aJFi3Tvvfd2Os45JzM76X1+9KMfaerUqdqxY4d++9vfxs+XDpg5Ro9VVlaqsbFRxcXFqq6ujrocAAAy1ne+8x3l5+erpKREL7/8crx/xIgReuGFFyRJr732mnbv3i1JamhoUEFBgW644QYNHDhQTz75ZKfzTZgwQfPnz9fu3bs1cuRI7d27VwUFBWppaYlf8HfiMamOcIwea2xsVCwWi7oMAAAy3vDhw/X973//pP5rrrlGTz31lMrKyvSlL31Jo0aNkiRt375dP/zhD9WnTx9lZ2fr4Ycf7nRcUVGRHnnkEV199dU6duyYhgwZog0bNqiyslI33XSTHnjgAV1yySW98tl6iznnoq4hrry83H16ZSRSx9y5cxWLxTRs2DA99dRTUZeDBHtudXO3j624tjCBlQBActq1a5fGjBkTdRnoQtDfj5ltdc6VB41nzTEAAADgEY4BAAAAj3AMAAAAeIRjAAAAwCMcAwAAAB7hGAAAAPAIxwAAACkuKytLZWVl8X/q6uo0ceJESVJdXZ3Gjh3bo/Obme6666748yVLlqiqquqUxyxfvjwlt3jlJiAAAAAJ9MGyDQk935DbLz3tmNzcXNXW1nbqq6mpSVgNOTk5Wrt2rRYtWqTCwjPbw37evHmB/W1tberbN3kjKDPHAAAAaWjgwIEn9T355JOqqKjQzJkzNXLkSD300EN64IEHNH78eF144YXau3dv4Ln69u2rW265RQ8++OBJr/3tb3/TtGnTVFpaqmnTpqm+vl6SVFVVpSVLlkiSLr74Yt1zzz366le/qp///OcJ/JSJRzgGAABIcYcOHYovqbjqqqtOOXbHjh369a9/rc2bN2vx4sXq37+/Xn/9dU2YMOGUyyDmz5+vFStWqKWlpVP/bbfdprlz52rbtm2aM2eO7rjjjsDj9+3bpz//+c+dlmcko+Sd0wYAAMAZCVpW0ZWpU6cqLy9PeXl5ys/P18yZMyVJJSUl2rZtW5fHDRo0SHPnztXSpUuVm5sb79+0aZPWrl0rSbrxxhtVWVkZePzs2bPP9ONEipljAACADJKTkxN/3KdPn/jzPn36qK2tTe+99158Fnr58uWdjr3zzjv12GOP6eDBg12e38wC+wcMGJCA6sNHOAYAAEDcueeeq9raWtXW1p50UV1BQYG++c1v6rHHHov3TZw4UatWrZIkrVixQpMnT+7VehONcAwAAIAzdtddd6m5uTn+fOnSpXriiSdUWlqqX/7yl0l/wd3pmHMu6hriysvL3ZYtW6IuA2dp7ty5isViGjZsWEruZ4hTe2518+kHdaHi2jPb7gcAUtmuXbs0ZsyYqMtAF4L+fsxsq3OuPGg8M8cAAACAx24VAAAAOMmePXt09OhRZWdna/jw4VGX02sIxwAAADjJ0aNHdeTIkajL6HWE4zNUWVmpxsZGFRcXq7q6OupyAABAEnHOdbmFGaLTnWvrCMdnqLGxUbFYLOoyAABAkunXr58+/PBDfeYznyEgJxHnnD788EP169fvrI4jHAMAAPTA8OHDtWfPHjU1NUVdSkI1Nzervb1dWVlZOnz4cNTldEu/fv3Oer004RgAAKAHsrOzNXLkyKjLSLhM3aqVrdwAAAAAj3AMAAAAeIRjAAAAwGPNccjYAg4AACB1EI5DxhZwAAAAqYNlFQAAAIBHOAYAAAA8wjEAAADgEY4BAAAAj3AMAAAAeOxWcYKmh38V2N/eciDeBo0puvWGUOsCAABA+Jg5BgAAADzCMQAAAOARjgEAAACPcAwAAAB4hGMAAADAIxwDAAAAHuEYAAAA8AjHAAAAgEc4BgAAADzCMQAAAOARjgEAAACPcAwAAAB4hGMAAADA6xt1AamiqP/ATu2JGh/+cWB/e8veeBs0pvjWf0lQhQAAAOgpwvEZWjzlsqhLAAAAQMhCW1ZhZo+b2QdmtiOs9wAAAAASKcyZ4yclPSTpqRDfAwCAHqusrFRjY6OKi4tVXV0ddTkAIhRaOHbObTSzEWGdHwCARGlsbFQsFou6DABJgN0qAAAAAC/ycGxmt5jZFjPb0tTUFHU5AAAAyGCRh2Pn3CPOuXLnXHlRUVHU5QAAACCDRR6OAQAAgGQR5lZuKyVtknSeme0xs38K670AAACARAhzt4rrwzo3AAAAEAaWVQAAAAAe4RgAAADwCMcAAACAF+bto5Fmlq24LLB/34E238YCx9w+5/eh1gUAAJAozBwDAAAAHuEYAAAA8FhWgYxRWVmpxsZGFRcXq7q6OupyupQqdQIAkI4Ix8gYjY2NisViUZdxWqlSJwAA6YhlFQAAAIBHOAYAAAA8wjEAAADgseY4ZIX9czq1AAAASF6E45AtmlIadQkAAAA4Q4RjAEDSYUtDAFEhHAMAkg5bGgKIChfkAQAAAB4zx8Bp8OtdIH384dHpgf2H9n/i24bAMdNuXhdqXQCSB+EYOA1+vQsAQOZgWQUAAADgEY4BAAAAj3AMAAAAeIRjAAAAwCMcAwAAAB67VQAAItP48I8D+9tb9sbboDHFt/5LqHUByFzMHAMAAAAe4RgAAADwCMcAAACARzgGAAAAPMIxAAAA4LFbBQAAyFiVlZVqbGxUcXGxqquroy4HSYBwDAAAMlZjY6NisVjUZSCJEI4hKb1+cp6+9v7A/k9aP5IkNbR+FDhm3dV3hVoXAABIfoRjSOInZwDJpbB/TqcWAHoL4RiRSafZagCJtWhKadQlAMhQhGNEhtlqAACQbAjHQEQq1rwU2N/a+rEkqaH148Axz33jH0OtC8hE+QOsUwsgcxGOkXJYjgEg0a6fek7UJQBIEoRjpByWYwAAgLBwhzwAAADAY+YYABAalkEBSDWEYwBAaFgGBSS/9x+sDexv3/dJvA0a89kFZaHWFRXCcYZZ9cRlgf0H9rf5NtblGHG9CgAASHOsOQYAAAA8Zo6TVDqt01u45vLA/ubWo76NBY5Z8o31odYFAABwIsJxkmKdHoBU0vTwrwL721sOxNuuxgBAMmFZBQAAAOAxc4ykdcVztwf2HznYJEmKHWwKHNNHI8IsCwAApDHCMQAAvSydrisB0g3hGAAQmqL+Azu16MB1JUDyIhwDAEKzeEoX+6YDXWBWHVEjHEOSlDfQJDnfAgAQDWbVETXCccR2/mJWYP+Rlo992xA45ovfez6hdVw5LSuh5wMApAdmcpFpCMeIzDl5HbPVHS0AIBkxk4tMQzhGZP5+Jv/6AQCA5EI6QcqxvGw53wIAACQS4RgpJ7vi81GXAAAA0hThGACAbuJiNSD9EI6TVEF/69Qms/5+G7j+bAMHIMNwsVry4AcVJArhOEnNvyg36hLO2KTL02MbuBnPPB7Yf7h1vySpoXV/4JgXrvlOqHUBAE6PH1SQKIRjIMn0ycvXMd8CSA6rngi+09+B/W2+jQWOaTon+Hz7DrT5NqZlK04+7vY5v+9mpd03fe39gf2ftH4kSWpo/ShwzLqr7wq1LqC3EY6BJNN/5vVRlwAAaefaZ7YF9u9rPSJJ+q/WI4FjVl9TGmpdSD6EY2QMG9S/UwsASD0Va14K7G9t7bizbEPrx4Fjsm1IqHUlK9Zinz3CMTLGObO+HHUJANJMnr8gOY8LkpGkWIt99gjHAAB005XT0uOC5CjMWL0msP9wa6skqaG1NXBMXxscal0A4RjIcKn0K7dUqhVA70qVi5n5Hkt+hGMgxXR1UcnpdHVRSSr9yi2VagXC0JNgle6hLFUuZk6n77EPlm3o1nFDbr80wZUkFuEYAJA20j0A9iRYpVMoA8JEOAYApI1UCYCpdGdRdvoJlu4/iGUywjEAAL0sle4syk4/wVLlBzGcPcIxAABJZuGaywP7m1uP+jbW5Zgl31gfWl04c8+tbg7sP9h6LN4Gjam4trBb77dnSWNgf9tH7fG2qzHZqfOzWq8gHAMAUs7OX8wK7D/S8rFvG4LHdHE7ZwD4FOEYAIA0csVztwf2HznYJEmKHWwKHNNHIxJaB2tykaoIxwAAIOHSZU3u/3r2vwL797a2x9ugMV9Udqh1ITyEYyBD8AWPTFDQ3zq16eacvI5dLjpaAGEgHAMA0sb8i3KjLiFUfz+T/20DYeO/MgBJp7tXXQ9fWBxqXQDCZ3l5nVqgtxGOAQDIAJaXLefbZJYzY2bUJYQqL6+oU4vkQzgGACADZFd8PpTzznjm8cD+w637JUkNrfu7GDMolHqSXcWMxVGXgNMgHAMAAKSpwtzCTi1Oj3AMAABwlvoNKuzUJqvKLy+KuoSUQzgGAAA4S+O//s9Rl4CQEI4BAEDCWd6ATi2QKgjHAAAg4XJmTYu6BKBb+kRdAAAAAJAsCMcAAACAx7IK4DRYN5c82JIIABC2UMOxmV0u6eeSsiQ96py7L8z3A8LAurnkwZZEAICwhRaOzSxL0r9LulTSHkl/NbPnnXNvhvWeAAAAqWzzEx9067ihCa4jk4W55vjLkv6fc+5d59wRSaskfT3E9wMAAAB6JMxlFcMkvXfc8z2SvhLi+wEAACBBinILOrWZwpxz4ZzY7FpJlznnbvbPb5T0Zefc7SeMu0XSLf7peZLeDqUgoGcKJTVHXQQApBi+O5GsPu+cKwp6IcyZ4z2Szj3u+XBJDScOcs49IumREOsAeszMtjjnyqOuAwBSCd+dSEVhrjn+q6R/MLORZnaOpOskPR/i+wEAAAA9EtrMsXOuzcxuk/R7dWzl9rhzbmdY7wcAAAD0VKj7HDvnfifpd2G+B9BLWPoDAGeP706knNAuyAMAAABSTZhrjgEAAICUQjhGRjOz1qhrAIBUZmZ1ZlbYg+PLzOzKRNYE9AThGAAARMLM+koqk0Q4RtIgHAOSrMNPzWyHmW03s9m+/2Ize9nM1pjZW2a2wszMv3al73vFzJaa2QvRfgoACJeZDTCzdWb2hv++nO1fut3MXvPfn6P92AIze87MtpnZq2ZW6vurzOwRM3tR0lOS7pU028xqjzsfEJlQd6sAUsjV6pi9GKeOOzr91cw2+tfGS/qiOm5i85+SJpnZFkn/IWmKc263ma2MoGYA6G2XS2pwzk2XJDPLl/Rvkpqdc+eb2fckLZR0s6QfS3rdOVdhZpeoIwiX+fNcIGmyc+6QmX1bUrlz7rZe/ixAIGaOgQ6TJa10zrU7596X9GdJX/KvbXbO7XHOHZNUK2mEpNGS3nXO7fZjCMcAMsF2Sf9oZv9mZhc551p8/1rfblXHd6TU8b36S0lyzv1R0md8mJak551zh3qpZuCsMHMMdLBTvPbJcY/b1fHfzanGA0Bacs79XzO7QB1rhP+PXxoh/ff35KffkVLw9+Sn+8ceDK9KoGeYOQY6bFTHmrcsMyuSNEXS5lOMf0vS/zSzEf456+QApD0zGyrpY+fcryQtkXT+KYZvlDTHH3exOpZe7A8Yd0BSXoJLBbqNcAx0eFbSNklvSPqjpErnXGNXg/2vA78nab2ZvSLpfUktXY0HgDRRImmzmdVKWizpX08xtkpSuZltk3SfpJu6GPcnSV/ggjwkC+6QB3STmQ10zrX63Sv+XdI7zrkHo64LAAB0HzPHQPd918+e7JSUr47dKwAAQApj5hgAAADwmDkGAAAAPMIxAAAA4BGOAQAAAI9wDAARMrMqM1uYgPMM9rfu/fT5UDNb09PzAkCmIRwDQIows1Pd1XSwOvbeliQ55xqcc98IvyoASC+EYwDoZWa22MzeNrOXJJ3n+142s3L/uNDM6vzjb5vZajP7raQXzWygmf3BzF4zs+1m9nV/2vsk/Z2/kcJPzWyEme3w5+hnZk/48a+b2dTjzr3WzNab2TtmVt3LfxQAkHRONQsBAEgwM7tA0nWSxqvjO/g1SVtPc9gESaXOub1+9vgq59x+MyuU9KqZPS/pnyWNdc6V+fcZcdzx8yXJOVdiZqPVEbJH+dfKfC2fSHrbzJY5595LwEcFgJREOAaA3nWRpGedcx9Lkg+2p7PBObfXPzZJ/9vMpkg6JmmYpM+e5vjJkpZJknPuLTP7m6RPw/EfnHMtvpY3JX1eEuEYQMYiHANA7wu6+1Kb/nupW78TXjt43OM5kookXeCcO+qXX5w4/kR2itc+Oe5xu/j/AoAMx5pjAOhdGyVdZWa5ZpYnaabvr5N0gX98qgvp8iV94IPxVHXM9ErSAUl5p3jPOZLkl1P8D0lvd/sTAEAaIxwDQC9yzr0m6WlJtZKekfQX/9ISSbeaWY2kwlOcYoWkcjPboo7A+5Y/74eS/tPMdpjZT0845heSssxsu3/vbzvnPhEA4CTmXNBv9wAAAIDMw8wxAAAA4BGOAQAAAI9wDAAAAHiEYwAAAMAjHAMAAAAe4RgAAADwCMcAAACARzgGAAAAvP8PnfXbLMEpLbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gross comparisio on the basis of duration\n",
    "sns.barplot(x=\"duration\",y=\"gross\",data=data,hue=\"genre1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average IMDB score for long Duration movie is:7.05\n",
      "Average IMDB score for short Duration movie is:6.22\n"
     ]
    }
   ],
   "source": [
    "print(\"Average IMDB score for long Duration movie is:{0:.2f}\".format(data[data[\"duration\"]==\"long\"][\"imdb_score\"].mean()))\n",
    "print(\"Average IMDB score for short Duration movie is:{0:.2f}\".format(data[data[\"duration\"]==\"short\"][\"imdb_score\"].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Highest Rated Long Duration Movie\n",
      "                     movie_title  imdb_score\n",
      "1937  The Shawshank Redemption         9.30\n",
      "\n",
      "Highest Rated Short Duration Movie\n",
      "         movie_title  imdb_score\n",
      "4822  12 Angry Men         8.90\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nHighest Rated Long Duration Movie\\n\",data[data[\"duration\"]==\"long\"][[\"movie_title\",\"imdb_score\"]].sort_values(by=\"imdb_score\",ascending=False).head(1))\n",
    "print(\"\\nHighest Rated Short Duration Movie\\n\",data[data[\"duration\"]==\"short\"][[\"movie_title\",\"imdb_score\"]].sort_values(by=\"imdb_score\",ascending=False).head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data[\"duration\"],y=data[\"imdb_score\"],data=data,palette = 'copper')\n",
    "plt.title(\"IMDB ratings vs Duration\",fontsize=20)\n",
    "plt.xlabel(\"DURATION\",fontsize=20)\n",
    "plt.ylabel(\"IMDB SCORE\",fontsize=20)\n",
    "plt.xticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to get the movies of an actor in decreasing order of their imdb score\n",
    "def query_actor(actor):\n",
    "    df1=data[data[\"actor_1_name\"]==actor]\n",
    "    df2=data[data[\"actor_2_name\"]==actor]\n",
    "    df3=data[data[\"actor_3_name\"]==actor]\n",
    "    \n",
    "    df=pd.concat([df1,df2,df3])\n",
    "    df=df[[\"movie_title\",\"budget\",\"gross\",\"title_year\",\"imdb_score\"]].sort_values(by=\"imdb_score\",ascending=False).reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Deer Hunter</td>\n",
       "      <td>15000000.00</td>\n",
       "      <td>25675765.00</td>\n",
       "      <td>1978.00</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantastic Mr. Fox</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>20999103.00</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>7.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adaptation.</td>\n",
       "      <td>19000000.00</td>\n",
       "      <td>22245861.00</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Hours</td>\n",
       "      <td>25000000.00</td>\n",
       "      <td>41597830.00</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doubt</td>\n",
       "      <td>20000000.00</td>\n",
       "      <td>33422556.00</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>The Bridges of Madison County</td>\n",
       "      <td>35000000.00</td>\n",
       "      <td>70960517.00</td>\n",
       "      <td>1995.00</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Julia</td>\n",
       "      <td>7840000.00</td>\n",
       "      <td>25675765.00</td>\n",
       "      <td>1977.00</td>\n",
       "      <td>7.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>August: Osage County</td>\n",
       "      <td>25000000.00</td>\n",
       "      <td>37738400.00</td>\n",
       "      <td>2013.00</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Out of Africa</td>\n",
       "      <td>31000000.00</td>\n",
       "      <td>87100000.00</td>\n",
       "      <td>1985.00</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florence Foster Jenkins</td>\n",
       "      <td>29000000.00</td>\n",
       "      <td>25675765.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Julie &amp; Julia</td>\n",
       "      <td>40000000.00</td>\n",
       "      <td>94125426.00</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>One True Thing</td>\n",
       "      <td>30000000.00</td>\n",
       "      <td>23209440.00</td>\n",
       "      <td>1998.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>The Devil Wears Prada</td>\n",
       "      <td>35000000.00</td>\n",
       "      <td>124732962.00</td>\n",
       "      <td>2006.00</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Rendition</td>\n",
       "      <td>27500000.00</td>\n",
       "      <td>9664316.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A Prairie Home Companion</td>\n",
       "      <td>10000000.00</td>\n",
       "      <td>20338609.00</td>\n",
       "      <td>2006.00</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Marvin's Room</td>\n",
       "      <td>23000000.00</td>\n",
       "      <td>12782508.00</td>\n",
       "      <td>1996.00</td>\n",
       "      <td>6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>It's Complicated</td>\n",
       "      <td>85000000.00</td>\n",
       "      <td>112703470.00</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The Giver</td>\n",
       "      <td>25000000.00</td>\n",
       "      <td>45089048.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Iron Lady</td>\n",
       "      <td>13000000.00</td>\n",
       "      <td>29959436.00</td>\n",
       "      <td>2011.00</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Death Becomes Her</td>\n",
       "      <td>55000000.00</td>\n",
       "      <td>58422650.00</td>\n",
       "      <td>1992.00</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mamma Mia!</td>\n",
       "      <td>52000000.00</td>\n",
       "      <td>143704210.00</td>\n",
       "      <td>2008.00</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The River Wild</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>46815748.00</td>\n",
       "      <td>1994.00</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hope Springs</td>\n",
       "      <td>30000000.00</td>\n",
       "      <td>63536011.00</td>\n",
       "      <td>2012.00</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lions for Lambs</td>\n",
       "      <td>35000000.00</td>\n",
       "      <td>14998070.00</td>\n",
       "      <td>2007.00</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Into the Woods</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>127997349.00</td>\n",
       "      <td>2014.00</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The Ant Bully</td>\n",
       "      <td>50000000.00</td>\n",
       "      <td>28133159.00</td>\n",
       "      <td>2006.00</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       movie_title      budget        gross  title_year  \\\n",
       "0                 The Deer Hunter  15000000.00  25675765.00     1978.00   \n",
       "1               Fantastic Mr. Fox  40000000.00  20999103.00     2009.00   \n",
       "2                     Adaptation.  19000000.00  22245861.00     2002.00   \n",
       "3                       The Hours  25000000.00  41597830.00     2002.00   \n",
       "4                           Doubt  20000000.00  33422556.00     2008.00   \n",
       "5   The Bridges of Madison County  35000000.00  70960517.00     1995.00   \n",
       "6                           Julia   7840000.00  25675765.00     1977.00   \n",
       "7            August: Osage County  25000000.00  37738400.00     2013.00   \n",
       "8                   Out of Africa  31000000.00  87100000.00     1985.00   \n",
       "9         Florence Foster Jenkins  29000000.00  25675765.00     2016.00   \n",
       "10                  Julie & Julia  40000000.00  94125426.00     2009.00   \n",
       "11                 One True Thing  30000000.00  23209440.00     1998.00   \n",
       "12          The Devil Wears Prada  35000000.00 124732962.00     2006.00   \n",
       "13                      Rendition  27500000.00   9664316.00     2007.00   \n",
       "14       A Prairie Home Companion  10000000.00  20338609.00     2006.00   \n",
       "15                  Marvin's Room  23000000.00  12782508.00     1996.00   \n",
       "16               It's Complicated  85000000.00 112703470.00     2009.00   \n",
       "17                      The Giver  25000000.00  45089048.00     2014.00   \n",
       "18                  The Iron Lady  13000000.00  29959436.00     2011.00   \n",
       "19              Death Becomes Her  55000000.00  58422650.00     1992.00   \n",
       "20                     Mamma Mia!  52000000.00 143704210.00     2008.00   \n",
       "21                 The River Wild  45000000.00  46815748.00     1994.00   \n",
       "22                   Hope Springs  30000000.00  63536011.00     2012.00   \n",
       "23                Lions for Lambs  35000000.00  14998070.00     2007.00   \n",
       "24                 Into the Woods  50000000.00 127997349.00     2014.00   \n",
       "25                  The Ant Bully  50000000.00  28133159.00     2006.00   \n",
       "\n",
       "    imdb_score  \n",
       "0         8.20  \n",
       "1         7.80  \n",
       "2         7.70  \n",
       "3         7.60  \n",
       "4         7.50  \n",
       "5         7.50  \n",
       "6         7.40  \n",
       "7         7.30  \n",
       "8         7.20  \n",
       "9         7.10  \n",
       "10        7.00  \n",
       "11        7.00  \n",
       "12        6.80  \n",
       "13        6.80  \n",
       "14        6.80  \n",
       "15        6.70  \n",
       "16        6.60  \n",
       "17        6.50  \n",
       "18        6.40  \n",
       "19        6.40  \n",
       "20        6.30  \n",
       "21        6.30  \n",
       "22        6.30  \n",
       "23        6.20  \n",
       "24        6.00  \n",
       "25        5.90  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_actor(\"Meryl Streep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "def actors_report(actor):\n",
    "    df1=data[data[\"actor_1_name\"]==actor]\n",
    "    df2=data[data[\"actor_2_name\"]==actor]\n",
    "    df3=data[data[\"actor_3_name\"]==actor]\n",
    "    \n",
    "    df=pd.concat([df1,df2,df3])\n",
    "    hitmovies=df[(df[\"imdb_score\"]>5) & (df[\"profit\"]>0)].shape[0]\n",
    "    totalmovies=df.shape[0]\n",
    "    hitratio=hitmovies/totalmovies\n",
    "    print(\"Hit ratio {0:.2f}\".format(hitratio*100))\n",
    "    print(\"First Movie released in year {}\".format(int(df.loc[:,\"title_year\"].sort_values().iloc[0])))\n",
    "    print(\"Last Movie released in year {}\".format(int(df.loc[:,\"title_year\"].sort_values().iloc[len(df)-1])))\n",
    "    print(\"Max Gross {0:.2f}\".format(df[\"gross\"].max()))\n",
    "    print(\"Min Gross {0:.2f}\".format(df[\"gross\"].min()))\n",
    "    print(\"Number of 100 Million Movies :{} \".format(df[df[\"gross\"]>100000000].shape[0]))\n",
    "    print(\"Avg IMDB Score : {0:.2f}\".format(df[\"imdb_score\"].mean()))\n",
    "    print(\"Most Common Genres:\\n\",df['genre1'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hit ratio 72.41\n",
      "First Movie released in year 1983\n",
      "Last Movie released in year 2015\n",
      "Max Gross 234277056.00\n",
      "Min Gross 14998070.00\n",
      "Number of 100 Million Movies :17 \n",
      "Avg IMDB Score : 7.01\n",
      "Most Common Genres:\n",
      " Action       14\n",
      "Drama         8\n",
      "Crime         2\n",
      "Comedy        2\n",
      "Adventure     1\n",
      "Biography     1\n",
      "Fantasy       1\n",
      "Name: genre1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "actors_report(\"Tom Cruise\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommend Movies Based on Actors Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recom_on_actor(actor):\n",
    "    df1=data.loc[(data[\"actor_1_name\"]==actor),[\"movie_title\",\"imdb_score\"]]\n",
    "    df2=data.loc[data[\"actor_2_name\"]==actor,[\"movie_title\",\"imdb_score\"]]\n",
    "    df3=data.loc[data[\"actor_3_name\"]==actor,[\"movie_title\",\"imdb_score\"]]\n",
    "    \n",
    "    df=pd.concat([df1,df2,df3])\n",
    "    return df.sort_values(by=\"imdb_score\",ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rain Man</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Edge of Tomorrow</td>\n",
       "      <td>7.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minority Report</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Last Samurai</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Collateral</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A Few Good Men</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Interview with the Vampire: The Vampire Chroni...</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mission: Impossible - Ghost Protocol</td>\n",
       "      <td>7.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mission: Impossible - Rogue Nation</td>\n",
       "      <td>7.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Eyes Wide Shut</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jerry Maguire</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>The Outsiders</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Born on the Fourth of July</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Valkyrie</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mission: Impossible</td>\n",
       "      <td>7.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jack Reacher</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Oblivion</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The Color of Money</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jack Reacher</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Top Gun</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Vanilla Sky</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mission: Impossible III</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The Firm</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>War of the Worlds</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Knight and Day</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Lions for Lambs</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mission: Impossible II</td>\n",
       "      <td>6.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Days of Thunder</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Losin' It</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          movie_title  imdb_score\n",
       "0                                           Rain Man         8.00\n",
       "1                                   Edge of Tomorrow         7.90\n",
       "2                                    Minority Report         7.70\n",
       "3                                   The Last Samurai         7.70\n",
       "4                                         Collateral         7.60\n",
       "5                                     A Few Good Men         7.60\n",
       "6   Interview with the Vampire: The Vampire Chroni...        7.60\n",
       "7               Mission: Impossible - Ghost Protocol         7.40\n",
       "8                 Mission: Impossible - Rogue Nation         7.40\n",
       "9                                     Eyes Wide Shut         7.30\n",
       "10                                     Jerry Maguire         7.30\n",
       "11                                     The Outsiders         7.20\n",
       "12                        Born on the Fourth of July         7.20\n",
       "13                                          Valkyrie         7.10\n",
       "14                               Mission: Impossible         7.10\n",
       "15                                      Jack Reacher         7.00\n",
       "16                                          Oblivion         7.00\n",
       "17                                The Color of Money         7.00\n",
       "18                                      Jack Reacher         7.00\n",
       "19                                           Top Gun         6.90\n",
       "20                                       Vanilla Sky         6.90\n",
       "21                           Mission: Impossible III         6.90\n",
       "22                                          The Firm         6.80\n",
       "23                                 War of the Worlds         6.50\n",
       "24                                    Knight and Day         6.30\n",
       "25                                   Lions for Lambs         6.20\n",
       "26                            Mission: Impossible II         6.10\n",
       "27                                   Days of Thunder         5.90\n",
       "28                                         Losin' It         4.80"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recom_on_actor(\"Tom Cruise\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset=data[[\"genres\",\"movie_title\",\"director_name\",\"actor_1_name\",\"actor_2_name\",\"actor_3_name\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset=data_subset.set_index(\"movie_title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_subset[\"genres\"]=data_subset[\"genres\"].apply(lambda x: x.replace(\"|\",\" \").lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset[\"director_name\"]=data_subset[\"director_name\"].apply(lambda x: x.lower().replace(\" \",\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title\n",
       "Avatar                                           jamescameron\n",
       "Pirates of the Caribbean: At World's End        goreverbinski\n",
       "Spectre                                             sammendes\n",
       "The Dark Knight Rises                        christophernolan\n",
       "John Carter                                     andrewstanton\n",
       "                                                   ...       \n",
       "Newlyweds                                         edwardburns\n",
       "Signed Sealed Delivered                            scottsmith\n",
       "A Plague So Pleasant                          benjaminroberds\n",
       "Shanghai Calling                                   danielhsia\n",
       "My Date with Drew                                     jongunn\n",
       "Name: director_name, Length: 4815, dtype: object"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subset[\"director_name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset[\"actor_1_name\"]=data_subset[\"actor_1_name\"].apply(lambda x: x.lower().replace(\" \",\"\"))\n",
    "data_subset[\"actor_2_name\"]=data_subset[\"actor_2_name\"].apply(lambda x: x.lower().replace(\" \",\"\"))\n",
    "data_subset[\"actor_3_name\"]=data_subset[\"actor_3_name\"].apply(lambda x: x.lower().replace(\" \",\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genres</th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Avatar</th>\n",
       "      <td>action adventure fantasy sci-fi</td>\n",
       "      <td>jamescameron</td>\n",
       "      <td>cchpounder</td>\n",
       "      <td>joeldavidmoore</td>\n",
       "      <td>wesstudi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pirates of the Caribbean: At World's End</th>\n",
       "      <td>action adventure fantasy</td>\n",
       "      <td>goreverbinski</td>\n",
       "      <td>johnnydepp</td>\n",
       "      <td>orlandobloom</td>\n",
       "      <td>jackdavenport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>action adventure thriller</td>\n",
       "      <td>sammendes</td>\n",
       "      <td>christophwaltz</td>\n",
       "      <td>rorykinnear</td>\n",
       "      <td>stephaniesigman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Dark Knight Rises</th>\n",
       "      <td>action thriller</td>\n",
       "      <td>christophernolan</td>\n",
       "      <td>tomhardy</td>\n",
       "      <td>christianbale</td>\n",
       "      <td>josephgordon-levitt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Carter</th>\n",
       "      <td>action adventure sci-fi</td>\n",
       "      <td>andrewstanton</td>\n",
       "      <td>darylsabara</td>\n",
       "      <td>samanthamorton</td>\n",
       "      <td>pollywalker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newlyweds</th>\n",
       "      <td>comedy drama</td>\n",
       "      <td>edwardburns</td>\n",
       "      <td>kerrybishé</td>\n",
       "      <td>caitlinfitzgerald</td>\n",
       "      <td>daniellapineda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Signed Sealed Delivered</th>\n",
       "      <td>comedy drama</td>\n",
       "      <td>scottsmith</td>\n",
       "      <td>ericmabius</td>\n",
       "      <td>daphnezuniga</td>\n",
       "      <td>crystallowe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Plague So Pleasant</th>\n",
       "      <td>drama horror thriller</td>\n",
       "      <td>benjaminroberds</td>\n",
       "      <td>evaboehnke</td>\n",
       "      <td>maxwellmoody</td>\n",
       "      <td>davidchandler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai Calling</th>\n",
       "      <td>comedy drama romance</td>\n",
       "      <td>danielhsia</td>\n",
       "      <td>alanruck</td>\n",
       "      <td>danielhenney</td>\n",
       "      <td>elizacoupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>My Date with Drew</th>\n",
       "      <td>documentary</td>\n",
       "      <td>jongunn</td>\n",
       "      <td>johnaugust</td>\n",
       "      <td>brianherzlinger</td>\n",
       "      <td>jongunn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4815 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    genres  \\\n",
       "movie_title                                                                  \n",
       "Avatar                                     action adventure fantasy sci-fi   \n",
       "Pirates of the Caribbean: At World's End          action adventure fantasy   \n",
       "Spectre                                          action adventure thriller   \n",
       "The Dark Knight Rises                                      action thriller   \n",
       "John Carter                                        action adventure sci-fi   \n",
       "...                                                                    ...   \n",
       "Newlyweds                                                     comedy drama   \n",
       "Signed Sealed Delivered                                       comedy drama   \n",
       "A Plague So Pleasant                                 drama horror thriller   \n",
       "Shanghai Calling                                      comedy drama romance   \n",
       "My Date with Drew                                              documentary   \n",
       "\n",
       "                                              director_name    actor_1_name  \\\n",
       "movie_title                                                                   \n",
       "Avatar                                         jamescameron      cchpounder   \n",
       "Pirates of the Caribbean: At World's End      goreverbinski      johnnydepp   \n",
       "Spectre                                           sammendes  christophwaltz   \n",
       "The Dark Knight Rises                      christophernolan        tomhardy   \n",
       "John Carter                                   andrewstanton     darylsabara   \n",
       "...                                                     ...             ...   \n",
       "Newlyweds                                       edwardburns      kerrybishé   \n",
       "Signed Sealed Delivered                          scottsmith      ericmabius   \n",
       "A Plague So Pleasant                        benjaminroberds      evaboehnke   \n",
       "Shanghai Calling                                 danielhsia        alanruck   \n",
       "My Date with Drew                                   jongunn      johnaugust   \n",
       "\n",
       "                                                actor_2_name  \\\n",
       "movie_title                                                    \n",
       "Avatar                                        joeldavidmoore   \n",
       "Pirates of the Caribbean: At World's End        orlandobloom   \n",
       "Spectre                                          rorykinnear   \n",
       "The Dark Knight Rises                          christianbale   \n",
       "John Carter                                   samanthamorton   \n",
       "...                                                      ...   \n",
       "Newlyweds                                  caitlinfitzgerald   \n",
       "Signed Sealed Delivered                         daphnezuniga   \n",
       "A Plague So Pleasant                            maxwellmoody   \n",
       "Shanghai Calling                                danielhenney   \n",
       "My Date with Drew                            brianherzlinger   \n",
       "\n",
       "                                                  actor_3_name  \n",
       "movie_title                                                     \n",
       "Avatar                                                wesstudi  \n",
       "Pirates of the Caribbean: At World's End         jackdavenport  \n",
       "Spectre                                        stephaniesigman  \n",
       "The Dark Knight Rises                      josephgordon-levitt  \n",
       "John Carter                                        pollywalker  \n",
       "...                                                        ...  \n",
       "Newlyweds                                       daniellapineda  \n",
       "Signed Sealed Delivered                            crystallowe  \n",
       "A Plague So Pleasant                             davidchandler  \n",
       "Shanghai Calling                                    elizacoupe  \n",
       "My Date with Drew                                      jongunn  \n",
       "\n",
       "[4815 rows x 5 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_subset['bag_of_words'] = data_subset[data_subset.columns[0:]].apply(lambda x:\" \".join(x.astype(str)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Signed Sealed Delivered</th>\n",
       "      <td>comedy drama</td>\n",
       "      <td>scottsmith</td>\n",
       "      <td>ericmabius</td>\n",
       "      <td>daphnezuniga</td>\n",
       "      <td>crystallowe</td>\n",
       "      <td>comedy drama scottsmith ericmabius daphnezunig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Plague So Pleasant</th>\n",
       "      <td>drama horror thriller</td>\n",
       "      <td>benjaminroberds</td>\n",
       "      <td>evaboehnke</td>\n",
       "      <td>maxwellmoody</td>\n",
       "      <td>davidchandler</td>\n",
       "      <td>drama horror thriller benjaminroberds evaboehn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai Calling</th>\n",
       "      <td>comedy drama romance</td>\n",
       "      <td>danielhsia</td>\n",
       "      <td>alanruck</td>\n",
       "      <td>danielhenney</td>\n",
       "      <td>elizacoupe</td>\n",
       "      <td>comedy drama romance danielhsia alanruck danie...</td>\n",
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       "      <th>My Date with Drew</th>\n",
       "      <td>documentary</td>\n",
       "      <td>jongunn</td>\n",
       "      <td>johnaugust</td>\n",
       "      <td>brianherzlinger</td>\n",
       "      <td>jongunn</td>\n",
       "      <td>documentary jongunn johnaugust brianherzlinger...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4815 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                    genres  \\\n",
       "movie_title                                                                  \n",
       "Avatar                                     action adventure fantasy sci-fi   \n",
       "Pirates of the Caribbean: At World's End          action adventure fantasy   \n",
       "Spectre                                          action adventure thriller   \n",
       "The Dark Knight Rises                                      action thriller   \n",
       "John Carter                                        action adventure sci-fi   \n",
       "...                                                                    ...   \n",
       "Newlyweds                                                     comedy drama   \n",
       "Signed Sealed Delivered                                       comedy drama   \n",
       "A Plague So Pleasant                                 drama horror thriller   \n",
       "Shanghai Calling                                      comedy drama romance   \n",
       "My Date with Drew                                              documentary   \n",
       "\n",
       "                                              director_name    actor_1_name  \\\n",
       "movie_title                                                                   \n",
       "Avatar                                         jamescameron      cchpounder   \n",
       "Pirates of the Caribbean: At World's End      goreverbinski      johnnydepp   \n",
       "Spectre                                           sammendes  christophwaltz   \n",
       "The Dark Knight Rises                      christophernolan        tomhardy   \n",
       "John Carter                                   andrewstanton     darylsabara   \n",
       "...                                                     ...             ...   \n",
       "Newlyweds                                       edwardburns      kerrybishé   \n",
       "Signed Sealed Delivered                          scottsmith      ericmabius   \n",
       "A Plague So Pleasant                        benjaminroberds      evaboehnke   \n",
       "Shanghai Calling                                 danielhsia        alanruck   \n",
       "My Date with Drew                                   jongunn      johnaugust   \n",
       "\n",
       "                                                actor_2_name  \\\n",
       "movie_title                                                    \n",
       "Avatar                                        joeldavidmoore   \n",
       "Pirates of the Caribbean: At World's End        orlandobloom   \n",
       "Spectre                                          rorykinnear   \n",
       "The Dark Knight Rises                          christianbale   \n",
       "John Carter                                   samanthamorton   \n",
       "...                                                      ...   \n",
       "Newlyweds                                  caitlinfitzgerald   \n",
       "Signed Sealed Delivered                         daphnezuniga   \n",
       "A Plague So Pleasant                            maxwellmoody   \n",
       "Shanghai Calling                                danielhenney   \n",
       "My Date with Drew                            brianherzlinger   \n",
       "\n",
       "                                                  actor_3_name  \\\n",
       "movie_title                                                      \n",
       "Avatar                                                wesstudi   \n",
       "Pirates of the Caribbean: At World's End         jackdavenport   \n",
       "Spectre                                        stephaniesigman   \n",
       "The Dark Knight Rises                      josephgordon-levitt   \n",
       "John Carter                                        pollywalker   \n",
       "...                                                        ...   \n",
       "Newlyweds                                       daniellapineda   \n",
       "Signed Sealed Delivered                            crystallowe   \n",
       "A Plague So Pleasant                             davidchandler   \n",
       "Shanghai Calling                                    elizacoupe   \n",
       "My Date with Drew                                      jongunn   \n",
       "\n",
       "                                                                                bag_of_words  \n",
       "movie_title                                                                                   \n",
       "Avatar                                     action adventure fantasy sci-fi jamescameron c...  \n",
       "Pirates of the Caribbean: At World's End   action adventure fantasy goreverbinski johnnyd...  \n",
       "Spectre                                    action adventure thriller sammendes christophw...  \n",
       "The Dark Knight Rises                      action thriller christophernolan tomhardy chri...  \n",
       "John Carter                                action adventure sci-fi andrewstanton darylsab...  \n",
       "...                                                                                      ...  \n",
       "Newlyweds                                  comedy drama edwardburns kerrybishé caitlinfit...  \n",
       "Signed Sealed Delivered                    comedy drama scottsmith ericmabius daphnezunig...  \n",
       "A Plague So Pleasant                       drama horror thriller benjaminroberds evaboehn...  \n",
       "Shanghai Calling                           comedy drama romance danielhsia alanruck danie...  \n",
       "My Date with Drew                          documentary jongunn johnaugust brianherzlinger...  \n",
       "\n",
       "[4815 rows x 6 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "count=CountVectorizer()\n",
    "count_matrix = count.fit_transform(data_subset['bag_of_words'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                      Avatar \n",
       "1    Pirates of the Caribbean: At World's End \n",
       "2                                     Spectre \n",
       "3                       The Dark Knight Rises \n",
       "4                                 John Carter \n",
       "Name: movie_title, dtype: object"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a Series for the movie titles so they are associated to an ordered numerical\n",
    "# list I will use later to match the indexes\n",
    "indices = pd.Series(data_subset.index)\n",
    "indices[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'action': 22,\n",
       " 'adventure': 75,\n",
       " 'fantasy': 2534,\n",
       " 'sci': 7200,\n",
       " 'fi': 2566,\n",
       " 'jamescameron': 3365,\n",
       " 'cchpounder': 1218,\n",
       " 'joeldavidmoore': 3849,\n",
       " 'wesstudi': 8291,\n",
       " 'goreverbinski': 2913,\n",
       " 'johnnydepp': 3998,\n",
       " 'orlandobloom': 6123,\n",
       " 'jackdavenport': 3275,\n",
       " 'thriller': 7847,\n",
       " 'sammendes': 7110,\n",
       " 'christophwaltz': 1456,\n",
       " 'rorykinnear': 6967,\n",
       " 'stephaniesigman': 7512,\n",
       " 'christophernolan': 1444,\n",
       " 'tomhardy': 7964,\n",
       " 'christianbale': 1387,\n",
       " 'josephgordon': 4143,\n",
       " 'levitt': 4837,\n",
       " 'andrewstanton': 390,\n",
       " 'darylsabara': 1811,\n",
       " 'samanthamorton': 7095,\n",
       " 'pollywalker': 6473,\n",
       " 'romance': 6932,\n",
       " 'samraimi': 7118,\n",
       " 'simmons': 7412,\n",
       " 'jamesfranco': 3386,\n",
       " 'kirstendunst': 4614,\n",
       " 'animation': 448,\n",
       " 'comedy': 1564,\n",
       " 'family': 2530,\n",
       " 'musical': 5825,\n",
       " 'nathangreno': 5887,\n",
       " 'bradgarrett': 903,\n",
       " 'donnamurphy': 2148,\n",
       " 'gainey': 2710,\n",
       " 'josswhedon': 4189,\n",
       " 'chrishemsworth': 1353,\n",
       " 'robertdowneyjr': 6802,\n",
       " 'scarlettjohansson': 7194,\n",
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       " 'davidyates': 1964,\n",
       " 'alanrickman': 139,\n",
       " 'danielradcliffe': 1733,\n",
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       " 'franklangella': 2630,\n",
       " 'marcforster': 5129,\n",
       " 'giancarlogiannini': 2847,\n",
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       " 'western': 8293,\n",
       " 'ruthwilson': 7032,\n",
       " 'tomwilkinson': 8005,\n",
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       " 'nicolescherzinger': 5990,\n",
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       " 'aidanturner': 99,\n",
       " 'adambrown': 28,\n",
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       " 'marcwebb': 5153,\n",
       " 'emmastone': 2400,\n",
       " 'andrewgarfield': 377,\n",
       " 'chriszylka': 1460,\n",
       " 'drama': 2192,\n",
       " 'history': 3130,\n",
       " 'ridleyscott': 6751,\n",
       " 'markaddy': 5212,\n",
       " 'williamhurt': 8338,\n",
       " 'scottgrimes': 7216,\n",
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       " 'christopherlee': 1436,\n",
       " 'evagreen': 2502,\n",
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       " 'naomiwatts': 5858,\n",
       " 'thomaskretschmann': 7832,\n",
       " 'evanparke': 2509,\n",
       " 'leonardodicaprio': 4804,\n",
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       " 'liamneeson': 4850,\n",
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       " 'colintrevorrow': 1552,\n",
       " 'brycedallashoward': 1063,\n",
       " 'judygreer': 4220,\n",
       " 'omarsy': 6111,\n",
       " 'albertfinney': 153,\n",
       " 'helenmccrory': 3090,\n",
       " 'shaneblack': 7307,\n",
       " 'jonfavreau': 4087,\n",
       " 'doncheadle': 2136,\n",
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       " 'annehathaway': 480,\n",
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       " 'glennmorshower': 2893,\n",
       " 'kevindunn': 4523,\n",
       " 'ramonrodriguez': 6552,\n",
       " 'bingbingli': 821,\n",
       " 'sophiamyles': 7462,\n",
       " 'timholmes': 7873,\n",
       " 'milakunis': 5737,\n",
       " 'novak': 6052,\n",
       " 'josephkosinski': 4148,\n",
       " 'jeffbridges': 3586,\n",
       " 'oliviawilde': 6095,\n",
       " 'jamesfrain': 3385,\n",
       " 'sport': 7476,\n",
       " 'johnlasseter': 3970,\n",
       " 'joemantegna': 3861,\n",
       " 'eddieizzard': 2250,\n",
       " 'martincampbell': 5282,\n",
       " 'ryanreynolds': 7048,\n",
       " 'temueramorrison': 7773,\n",
       " 'taikawaititi': 7696,\n",
       " 'leeunkrich': 4777,\n",
       " 'tomhanks': 7963,\n",
       " 'johnratzenberger': 4017,\n",
       " 'donrickles': 2159,\n",
       " 'mcg': 5443,\n",
       " 'common': 1565,\n",
       " 'crime': 1634,\n",
       " 'jameswan': 3436,\n",
       " 'jasonstatham': 3526,\n",
       " 'paulwalker': 6295,\n",
       " 'vindiesel': 8219,\n",
       " 'horror': 3148,\n",
       " 'petercapaldi': 6338,\n",
       " 'bradpitt': 914,\n",
       " 'mireilleenos': 5761,\n",
       " 'jenniferlawrence': 3648,\n",
       " 'abrams': 20,\n",
       " 'benedictcumberbatch': 703,\n",
       " 'brucegreenwood': 1039,\n",
       " 'noelclarke': 6032,\n",
       " 'eddiemarsan': 2254,\n",
       " 'ewenbremner': 2520,\n",
       " 'ralphbrown': 6544,\n",
       " 'bazluhrmann': 679,\n",
       " 'elizabethdebicki': 2333,\n",
       " 'stevebisley': 7560,\n",
       " 'mikenewell': 5726,\n",
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       " 'guillermodeltoro': 2972,\n",
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       " 'larryjoecampbell': 4706,\n",
       " 'lesterspeight': 4831,\n",
       " 'stevenspielberg': 7601,\n",
       " 'harrisonford': 3041,\n",
       " 'raywinstone': 6599,\n",
       " 'jimbroadbent': 3757,\n",
       " 'petersohn': 6396,\n",
       " 'buckley': 1067,\n",
       " 'jackmcgraw': 3292,\n",
       " 'markandrews': 5213,\n",
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       " 'justinlin': 4287,\n",
       " 'sofiaboutella': 7447,\n",
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       " 'lydiawilson': 5043,\n",
       " 'fredwillard': 2675,\n",
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       " 'tzima': 8104,\n",
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       " 'tyesheridan': 8089,\n",
       " 'heathledger': 3078,\n",
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       " 'stevecarell': 7566,\n",
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       " 'mcphee': 5445,\n",
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       " 'carlrinsch': 1146,\n",
       " 'keanureeves': 4421,\n",
       " 'cary': 1181,\n",
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       " 'jasonflemyng': 3504,\n",
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       " 'wolfgangpetersen': 8382,\n",
       " 'mikevogel': 5733,\n",
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       " 'brucespence': 1051,\n",
       " 'laurabrent': 4720,\n",
       " 'war': 8257,\n",
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       " 'biography': 824,\n",
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       " 'zachcallison': 8451,\n",
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       " 'sebastianroché': 7266,\n",
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       " 'elodieyung': 2357,\n",
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       " 'samshepard': 7121,\n",
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       " 'mattfrewer': 5354,\n",
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       " 'richarddonner': 6680,\n",
       " 'renerusso': 6641,\n",
       " 'darlenelove': 1792,\n",
       " 'anglee': 438,\n",
       " 'kevinrankin': 4548,\n",
       " 'regidavis': 6622,\n",
       " 'celiaweston': 1227,\n",
       " 'jonm': 4101,\n",
       " 'chu': 1462,\n",
       " 'dwaynejohnson': 2220,\n",
       " 'breckeisner': 933,\n",
       " 'hironobusakaguchi': 3126,\n",
       " 'ming': 5748,\n",
       " 'nawen': 5897,\n",
       " 'jeansimmons': 3577,\n",
       " 'mariagraziacucinotta': 5167,\n",
       " 'desmondllewelyn': 2060,\n",
       " 'peterweir': 6404,\n",
       " 'jamesd': 3371,\n",
       " 'arcy': 541,\n",
       " 'leeingleby': 4766,\n",
       " 'davidthrelfall': 1952,\n",
       " 'billcondon': 780,\n",
       " 'taylorlautner': 7752,\n",
       " 'louisleterrier': 4979,\n",
       " 'petermensah': 6384,\n",
       " 'markrylance': 5247,\n",
       " 'penelopewilton': 6314,\n",
       " 'rafespall': 6532,\n",
       " 'alejandrog': 165,\n",
       " 'iñárritu': 3270,\n",
       " 'lukashaas': 5025,\n",
       " 'davidsoren': 1946,\n",
       " 'snoopdogg': 7445,\n",
       " 'benschwartz': 728,\n",
       " 'stephenroot': 7546,\n",
       " 'annetmahendru': 486,\n",
       " 'andyrichter': 415,\n",
       " 'paulgreengrass': 6258,\n",
       " 'mattdamon': 5351,\n",
       " 'markosborne': 5238,\n",
       " 'peytonreed': 6412,\n",
       " 'sandraellislafferty': 7136,\n",
       " 'timjohnson': 7875,\n",
       " 'jimparsons': 3786,\n",
       " 'mattjones': 5389,\n",
       " 'aprilwinchell': 533,\n",
       " 'lisaannwalter': 4900,\n",
       " 'rickgonzalez': 6737,\n",
       " 'henryrollins': 3104,\n",
       " 'jordimollà': 4123,\n",
       " 'constancemarie': 1584,\n",
       " 'amysedaris': 334,\n",
       " 'phillipnoyce': 6437,\n",
       " 'augustdiehl': 607,\n",
       " 'darrenaronofsky': 1795,\n",
       " 'emmawatson': 2401,\n",
       " 'loganlerman': 4938,\n",
       " 'tobyjones': 7915,\n",
       " 'mackenziecrook': 5062,\n",
       " 'tonycurran': 8018,\n",
       " 'alfonsocuarón': 230,\n",
       " 'eddiebaroo': 2245,\n",
       " 'ericleighton': 2436,\n",
       " 'alfrewoodard': 233,\n",
       " 'sweeney': 7675,\n",
       " 'dellareese': 2006,\n",
       " 'tommcgrath': 7974,\n",
       " 'willferrell': 8311,\n",
       " 'chriscolumbus': 1342,\n",
       " 'vernetroyer': 8158,\n",
       " 'robertschwentke': 6845,\n",
       " 'stephanieszostak': 7513,\n",
       " 'sethgabel': 7290,\n",
       " 'jürgenprochnow': 4300,\n",
       " 'carlossaldanha': 1144,\n",
       " 'miguelferrer': 5683,\n",
       " 'rachelcrow': 6513,\n",
       " 'jeffreygarcia': 3609,\n",
       " 'brucedavison': 1033,\n",
       " 'aaronstanford': 8,\n",
       " 'guyritchie': 2986,\n",
       " 'paulanderson': 6230,\n",
       " 'alexadavalos': 177,\n",
       " 'paulverhoeven': 6293,\n",
       " 'ronnycox': 6956,\n",
       " 'rachelticotin': 6524,\n",
       " 'marshallbell': 5272,\n",
       " 'johnmctiernan': 3986,\n",
       " 'vladimirkulich': 8231,\n",
       " 'cliverussell': 1526,\n",
       " 'tonygilroy': 8024,\n",
       " 'scottglenn': 7215,\n",
       " 'stacykeach': 7480,\n",
       " 'joelschumacher': 3858,\n",
       " 'michaelgough': 5564,\n",
       " 'johnglover': 3948,\n",
       " 'clinthoward': 1521,\n",
       " 'thyne': 7849,\n",
       " 'mollyshannon': 5791,\n",
       " 'johnwoo': 4044,\n",
       " 'dougrayscott': 2185,\n",
       " 'karenallen': 4323,\n",
       " 'maryelizabethmastrantonio': 5310,\n",
       " 'bobgunton': 868,\n",
       " 'timstory': 7896,\n",
       " 'ioangruffudd': 3230,\n",
       " 'surajsharma': 7650,\n",
       " 'tabu': 7692,\n",
       " 'markstevenjohnson': 5249,\n",
       " 'mattlong': 5393,\n",
       " 'peterfonda': 6353,\n",
       " 'rizahmed': 6773,\n",
       " 'atoessandoh': 599,\n",
       " 'demimoore': 2016,\n",
       " 'justintheroux': 4292,\n",
       " 'nathanlane': 5891,\n",
       " 'melaniegriffith': 5465,\n",
       " 'neillblomkamp': 5915,\n",
       " 'davidtwohy': 1953,\n",
       " 'christinacox': 1406,\n",
       " 'josépadilha': 4194,\n",
       " 'abbiecornish': 12,\n",
       " 'jenniferehle': 3634,\n",
       " 'scottporter': 7227,\n",
       " 'kickgurry': 4569,\n",
       " 'nicholaselia': 5941,\n",
       " 'jamesl': 3400,\n",
       " 'brooks': 1027,\n",
       " 'shelleyconn': 7358,\n",
       " 'domenicklombardozzi': 2110,\n",
       " 'teyonahparris': 7808,\n",
       " 'jamesmangold': 3408,\n",
       " 'marcblucas': 5120,\n",
       " 'zoëbell': 8492,\n",
       " 'georgelucas': 2805,\n",
       " 'haydenchristensen': 3062,\n",
       " 'jamescoburn': 3367,\n",
       " 'taookamoto': 7731,\n",
       " 'rilafukushima': 6756,\n",
       " 'ianmcdiarmid': 3201,\n",
       " 'kirkdemicco': 4606,\n",
       " 'frédéricforestier': 2686,\n",
       " 'alaindelon': 119,\n",
       " 'santiagosegura': 7149,\n",
       " 'vanessahessler': 8135,\n",
       " 'rogerwillie': 6920,\n",
       " 'noahemmerich': 6023,\n",
       " 'cedricnicolas': 1223,\n",
       " 'troyan': 8074,\n",
       " 'noelfisher': 6033,\n",
       " 'dannywoodburn': 1772,\n",
       " 'jeremyhoward': 3672,\n",
       " 'phaldutsharma': 6414,\n",
       " 'bashersavage': 674,\n",
       " 'amywarren': 337,\n",
       " 'rogerdonaldson': 6909,\n",
       " 'jamierenéesmith': 3452,\n",
       " 'grantheslov': 2925,\n",
       " 'davegreen': 1823,\n",
       " 'stephenamell': 7515,\n",
       " 'joshtrank': 4172,\n",
       " 'timblakenelson': 7859,\n",
       " 'rege': 6620,\n",
       " 'cathey': 1212,\n",
       " 'timheidecker': 7871,\n",
       " 'bradpeyton': 913,\n",
       " 'archiepanjabi': 540,\n",
       " 'rogerspottiswoode': 6918,\n",
       " 'vincentschiavelli': 8214,\n",
       " 'joedonbaker': 3838,\n",
       " 'adambaldwin': 25,\n",
       " 'stevensoderbergh': 7600,\n",
       " 'juliaroberts': 4245,\n",
       " 'minianden': 5750,\n",
       " 'stephaniemarch': 7511,\n",
       " 'theojames': 7819,\n",
       " 'mekhiphifer': 5463,\n",
       " 'adamscott': 47,\n",
       " 'francesconroy': 2603,\n",
       " 'jamescorden': 3368,\n",
       " 'catherinetate': 1211,\n",
       " 'ollyalexander': 6102,\n",
       " 'michelgondry': 5659,\n",
       " 'chadl': 1236,\n",
       " 'coleman': 1540,\n",
       " 'noammurro': 6031,\n",
       " 'sullivanstapleton': 7637,\n",
       " 'rajagosnell': 6535,\n",
       " 'mahadeoshivraj': 5089,\n",
       " 'timgunn': 7870,\n",
       " 'madisonmckinley': 5076,\n",
       " 'music': 5824,\n",
       " 'willfinn': 8312,\n",
       " 'roseannebarr': 6977,\n",
       " 'bailey': 632,\n",
       " 'toreymichaeladkins': 8041,\n",
       " 'olgafonda': 6073,\n",
       " 'jacobtremblay': 3312,\n",
       " 'nancyo': 5852,\n",
       " 'dell': 2005,\n",
       " 'vanessamatsui': 8140,\n",
       " 'jandebont': 3460,\n",
       " 'jasonpatric': 3519,\n",
       " 'loischiles': 4939,\n",
       " 'moisesarias': 5783,\n",
       " 'aramisknight': 535,\n",
       " 'lenwiseman': 4795,\n",
       " 'jonathansadowski': 4076,\n",
       " 'cyrilraffaelli': 1656,\n",
       " 'billyboyd': 807,\n",
       " 'frankcoraci': 2619,\n",
       " 'céciledefrance': 1658,\n",
       " 'michaelmann': 5594,\n",
       " 'bowelch': 894,\n",
       " 'kellypreston': 4455,\n",
       " 'spencerbreslin': 7469,\n",
       " 'chimcbride': 1323,\n",
       " 'philipglenister': 6424,\n",
       " 'chazzpalminteri': 1297,\n",
       " 'jeffreyjones': 3610,\n",
       " 'ronclements': 6945,\n",
       " 'jeniferlewis': 3627,\n",
       " 'anikanonirose': 445,\n",
       " 'donaldglover': 2126,\n",
       " 'benedictwong': 704,\n",
       " 'peterchelsom': 6341,\n",
       " 'delzamora': 2011,\n",
       " 'warrenbeatty': 8262,\n",
       " 'dominicsena': 2117,\n",
       " 'robertduvall': 6803,\n",
       " 'connienielsen': 1570,\n",
       " 'oliverreed': 6082,\n",
       " 'frankgrillo': 2625,\n",
       " 'jessicacapshaw': 3718,\n",
       " 'tobiasmenzies': 7912,\n",
       " 'ivanamilicevic': 3259,\n",
       " 'estellawarren': 2483,\n",
       " 'erickavari': 2431,\n",
       " 'jenettegoldstein': 3626,\n",
       " 'epathamerkerson': 2411,\n",
       " 'denzelwashington': 2040,\n",
       " 'rubydee': 6998,\n",
       " 'rza': 7055,\n",
       " 'jamieleecurtis': 3451,\n",
       " 'tiacarrere': 7850,\n",
       " 'tomarnold': 7937,\n",
       " 'tonyscott': 8037,\n",
       " 'michaelrispoli': 5629,\n",
       " 'paulweitz': 6297,\n",
       " 'robertdeniro': 6801,\n",
       " 'blythedanner': 844,\n",
       " 'teripolo': 7786,\n",
       " 'adammckay': 41,\n",
       " 'derekjeter': 2049,\n",
       " 'chuckrussell': 1466,\n",
       " 'vanessawilliams': 8143,\n",
       " 'romamaffia': 6931,\n",
       " 'quentintarantino': 6499,\n",
       " 'garytrousdale': 2753,\n",
       " 'jasonalexander': 3491,\n",
       " 'billfagerbakke': 785,\n",
       " 'markdindal': 5222,\n",
       " 'earthakitt': 2238,\n",
       " 'wendiemalick': 8282,\n",
       " 'johnfiedler': 3935,\n",
       " 'simonwest': 7423,\n",
       " 'sylvesterstallone': 7684,\n",
       " 'armandoriesco': 565,\n",
       " 'annieparisse': 490,\n",
       " 'stefenfangmeier': 7502,\n",
       " 'edspeleers': 2277,\n",
       " 'garylewis': 2740,\n",
       " 'spikejonze': 7474,\n",
       " 'catherineo': 1207,\n",
       " 'hara': 3023,\n",
       " 'ryancorr': 7037,\n",
       " 'maxrecords': 5435,\n",
       " 'chriswedge': 1457,\n",
       " 'emmakenney': 2392,\n",
       " 'troyevans': 8076,\n",
       " 'florianhenckelvondonnersmarck': 2589,\n",
       " 'rufussewell': 7004,\n",
       " 'peterhyams': 6365,\n",
       " 'markmargolis': 5234,\n",
       " 'udokier': 8108,\n",
       " 'stephencollins': 7524,\n",
       " 'reneauberjonois': 6636,\n",
       " 'debimazar': 1985,\n",
       " 'jakebusey': 3331,\n",
       " 'patrickmuldoon': 6206,\n",
       " 'sethgilliam': 7291,\n",
       " 'tomtykwer': 7999,\n",
       " 'jimsturgess': 3791,\n",
       " 'anthonylapaglia': 508,\n",
       " 'pitof': 6467,\n",
       " 'christopherheyerdahl': 1430,\n",
       " 'alexborstein': 195,\n",
       " 'ingridbolsøberdal': 3226,\n",
       " 'michaelwincott': 5648,\n",
       " 'bradsilberling': 917,\n",
       " 'annafriel': 460,\n",
       " 'bobb': 846,\n",
       " 'ej': 2297,\n",
       " 'thompson': 7844,\n",
       " 'patrickhughes': 6199,\n",
       " 'ericsoncore': 2448,\n",
       " 'lawrenceguterman': 4751,\n",
       " 'jamiekennedy': 3450,\n",
       " 'traylorhoward': 8060,\n",
       " 'benstein': 731,\n",
       " 'benjaminwalker': 715,\n",
       " 'frankdillane': 2621,\n",
       " 'ronunderwood': 6963,\n",
       " 'randyquaid': 6567,\n",
       " 'burtyoung': 1079,\n",
       " 'seanhuze': 7251,\n",
       " 'igalnaor': 3214,\n",
       " 'stevemartino': 7582,\n",
       " 'francescacapaldi': 2600,\n",
       " 'venusschultheis': 8153,\n",
       " 'billmelendez': 796,\n",
       " 'davidmamet': 1908,\n",
       " 'bengazzara': 708,\n",
       " 'felicityhuffman': 2545,\n",
       " 'campbellscott': 1118,\n",
       " 'patriciavelasquez': 6186,\n",
       " 'yimouzhang': 8421,\n",
       " 'nini': 6012,\n",
       " 'shigeokobayashi': 7374,\n",
       " 'ashbrannon': 581,\n",
       " 'zooeydeschanel': 8490,\n",
       " 'jonheder': 4093,\n",
       " 'frankoz': 2636,\n",
       " 'jonlovitz': 4099,\n",
       " 'rogerbart': 6906,\n",
       " 'jayroach': 3558,\n",
       " 'thomasmiddleditch': 7838,\n",
       " 'katherinelanasa': 4375,\n",
       " 'lucbesson': 4995,\n",
       " 'millajovovich': 5739,\n",
       " 'michaelpatrickking': 5619,\n",
       " 'chrisnoth': 1367,\n",
       " 'lizaminnelli': 4922,\n",
       " 'kristindavis': 4640,\n",
       " 'bibobergeron': 771,\n",
       " 'frankwelker': 2642,\n",
       " 'rosieperez': 6982,\n",
       " 'eltonjohn': 2360,\n",
       " 'drake': 2189,\n",
       " 'derekjacobi': 2048,\n",
       " 'michaelimperioli': 5575,\n",
       " 'ajmichalka': 107,\n",
       " 'alexandergould': 179,\n",
       " 'bernardhill': 741,\n",
       " 'sergeybodrov': 7281,\n",
       " 'oliviawilliams': 6096,\n",
       " 'chrisbarrie': 1334,\n",
       " 'wallypfister': 8243,\n",
       " 'michaeljeter': 5579,\n",
       " 'trevormorgan': 8067,\n",
       " 'alessandronivola': 175,\n",
       " 'rupertwyatt': 7015,\n",
       " 'davidoyelowo': 1926,\n",
       " 'markwaters': 5255,\n",
       " 'todfennell': 7932,\n",
       " 'joanplowright': 3811,\n",
       " 'johnmoore': 3991,\n",
       " 'colehauser': 1539,\n",
       " 'megalynechikunwoke': 5447,\n",
       " 'johnleehancock': 3971,\n",
       " 'hollyhunter': 3137,\n",
       " 'craigt': 1630,\n",
       " 'nelson': 5923,\n",
       " 'louromano': 4987,\n",
       " 'rennyharlin': 6642,\n",
       " 'christophermasterson': 1439,\n",
       " 'matthewmodine': 5373,\n",
       " 'lindafiorentino': 4876,\n",
       " 'ethansuplee': 2491,\n",
       " 'meimelançon': 5461,\n",
       " 'johnlone': 3973,\n",
       " 'harrisyulin': 3044,\n",
       " 'ambervalletta': 307,\n",
       " 'mirandaotto': 5757,\n",
       " 'phillord': 6440,\n",
       " 'willforte': 8313,\n",
       " 'alroker': 283,\n",
       " 'denisleary': 2026,\n",
       " 'maileflanagan': 5093,\n",
       " 'kellykeaton': 4448,\n",
       " 'benstiller': 732,\n",
       " 'adrianmartinez': 65,\n",
       " 'joeyslotnick': 3880,\n",
       " 'llcoolj': 4928,\n",
       " 'kellylynch': 4451,\n",
       " 'tonybancroft': 8011,\n",
       " 'harveyfierstein': 3055,\n",
       " 'juneforay': 4267,\n",
       " 'brandont': 929,\n",
       " 'jackson': 3301,\n",
       " 'goranvisnjic': 2907,\n",
       " 'joelyrichardson': 3859,\n",
       " 'aldishodge': 159,\n",
       " 'kevinchamberlin': 4516,\n",
       " 'robertmaillet': 6823,\n",
       " 'timurbekmambetov': 7898,\n",
       " 'jimvarney': 3792,\n",
       " 'creesummer': 1633,\n",
       " 'waltbecker': 8245,\n",
       " 'bellathorne': 692,\n",
       " 'joshuamikel': 4177,\n",
       " 'jessemccartney': 3708,\n",
       " 'dennisdugan': 2031,\n",
       " 'adamsandler': 46,\n",
       " 'sayedbadreya': 7191,\n",
       " 'kevinnealon': 4543,\n",
       " 'haleyjoelosment': 2995,\n",
       " 'kevinsussman': 4554,\n",
       " 'marshathomason': 5273,\n",
       " 'rachaelharris': 6507,\n",
       " 'marcjohnjefferies': 5133,\n",
       " 'tomskerritt': 7996,\n",
       " 'larryking': 4707,\n",
       " 'greggrunberg': 2938,\n",
       " 'kimdickens': 4587,\n",
       " 'sydneypollack': 7681,\n",
       " 'curtisscook': 1644,\n",
       " 'georgeharris': 2798,\n",
       " 'michaelwright': 5654,\n",
       " 'thorfreudenthal': 7846,\n",
       " 'levenrambin': 4835,\n",
       " 'sanaalathan': 7130,\n",
       " 'alunarmstrong': 286,\n",
       " 'michaelbyrne': 5533,\n",
       " 'velibortopic': 8150,\n",
       " 'stephendillane': 7526,\n",
       " 'catherinemccormack': 1206,\n",
       " 'briandepalma': 971,\n",
       " 'kimdelaney': 4586,\n",
       " 'wandasykes': 8256,\n",
       " 'will': 8306,\n",
       " 'am': 290,\n",
       " 'mickjackson': 5677,\n",
       " 'anneheche': 481,\n",
       " 'gabyhoffmann': 2706,\n",
       " 'alanj': 131,\n",
       " 'pakula': 6144,\n",
       " 'nataschamcelhone': 5875,\n",
       " 'kathrynbigelow': 4387,\n",
       " 'christiancamargo': 1389,\n",
       " 'lexshrapnel': 4846,\n",
       " 'johnmilius': 3990,\n",
       " 'williamsmith': 8360,\n",
       " 'sandahlbergman': 7132,\n",
       " 'paddyconsidine': 6140,\n",
       " 'brucemcgill': 1047,\n",
       " 'rosemariedewitt': 6979,\n",
       " 'andreykonchalovskiy': 394,\n",
       " 'shirleyhenderson': 7384,\n",
       " 'richarde': 6683,\n",
       " 'grant': 2923,\n",
       " 'garyross': 2747,\n",
       " 'michaelangarano': 5519,\n",
       " 'alanruck': 140,\n",
       " 'jamigertz': 3455,\n",
       " 'paulsanchez': 6282,\n",
       " 'nicksearcy': 5973,\n",
       " 'elizabethdaily': 2332,\n",
       " 'joanallen': 3800,\n",
       " 'oksanaakinshina': 6065,\n",
       " 'deanstockwell': 1980,\n",
       " 'elliottgould': 2354,\n",
       " 'paulw': 6294,\n",
       " 'anderson': 350,\n",
       " 'genndytartakovsky': 2775,\n",
       " 'kevinlima': 4534,\n",
       " 'jeffbennett': 3585,\n",
       " 'tealadunn': 7757,\n",
       " 'fredtatasciore': 2672,\n",
       " 'danielespinosa': 1714,\n",
       " 'jimcarter': 3759,\n",
       " 'gaboureysidibe': 2691,\n",
       " 'nancymeyers': 5851,\n",
       " 'sarahparish': 7165,\n",
       " 'lisabonet': 4903,\n",
       " 'merylstreep': 5505,\n",
       " 'hunterparrish': 3187,\n",
       " 'zoekazan': 8484,\n",
       " 'alpacino': 282,\n",
       " 'rogerallers': 6903,\n",
       " 'debramessing': 1995,\n",
       " 'janekrakowski': 3469,\n",
       " 'neilburger': 5908,\n",
       " 'jean': 3562,\n",
       " 'jacquesannaud': 3320,\n",
       " 'bobhoskins': 869,\n",
       " 'gabrielthomson': 2705,\n",
       " 'clemensschick': 1514,\n",
       " 'murrayabraham': 5820,\n",
       " 'tomnoonan': 7986,\n",
       " 'ligong': 4855,\n",
       " 'karlyune': 4342,\n",
       " 'amberstevenswest': 306,\n",
       " 'zacherytybryan': 8453,\n",
       " 'nikkigriffin': 6002,\n",
       " 'sarahsmith': 7170,\n",
       " 'imeldastaunton': 3222,\n",
       " 'michaelpalin': 5613,\n",
       " 'martinbrest': 5281,\n",
       " 'andrewdavis': 370,\n",
       " 'raymondcruz': 6593,\n",
       " 'rickworthy': 6743,\n",
       " 'jsugarcia': 4200,\n",
       " 'bobfosse': 865,\n",
       " 'royscheider': 6994,\n",
       " 'benvereen': 733,\n",
       " 'maxwright': 5439,\n",
       " 'tarsemsingh': 7738,\n",
       " 'edgarwright': 2266,\n",
       " 'annakendrick': 466,\n",
       " 'kieranculkin': 4571,\n",
       " 'ellenwong': 2350,\n",
       " 'jonamiel': 4049,\n",
       " 'tchékykaryo': 7756,\n",
       " 'rekhasharma': 6634,\n",
       " 'petersegal': 6394,\n",
       " 'larrymiller': 4708,\n",
       " 'janetjackson': 3472,\n",
       " 'chriselliott': 1346,\n",
       " 'sarahmichellegellar': 7163,\n",
       " ...}"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count.vocabulary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['50cent',\n",
       " 'aaliyah',\n",
       " 'aaronashmore',\n",
       " 'aaronhann',\n",
       " 'aaronhughes',\n",
       " 'aaronkwok',\n",
       " 'aaronschneider',\n",
       " 'aaronseltzer',\n",
       " 'aaronstanford',\n",
       " 'aaronstaton',\n",
       " 'aaronyoo',\n",
       " 'aasifmandvi',\n",
       " 'abbiecornish',\n",
       " 'abbyelliott',\n",
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       " 'beaugarrett',\n",
       " 'beauknapp',\n",
       " 'beaumirchoff',\n",
       " 'bebeneuwirth',\n",
       " 'beckysmith',\n",
       " 'beebankidron',\n",
       " 'beech',\n",
       " 'behrouzvossoughi',\n",
       " 'bell',\n",
       " 'bellaheathcote',\n",
       " 'bellathorne',\n",
       " 'belénrueda',\n",
       " 'benaffleck',\n",
       " 'benbrowder',\n",
       " 'benchaplin',\n",
       " 'bencrompton',\n",
       " 'bencross',\n",
       " 'bendaniels',\n",
       " 'bendavies',\n",
       " 'bendiskin',\n",
       " 'benedekfliegauf',\n",
       " 'benedictcumberbatch',\n",
       " 'benedictwong',\n",
       " 'benedikterlingsson',\n",
       " 'benfalcone',\n",
       " 'benfeldman',\n",
       " 'bengazzara',\n",
       " 'benhzeitlin',\n",
       " 'benjamina',\n",
       " 'benjamindickinson',\n",
       " 'benjaminfletcher',\n",
       " 'benjaminj',\n",
       " 'benjaminroberds',\n",
       " 'benjaminwalker',\n",
       " 'benjohnson',\n",
       " 'benlewin',\n",
       " 'benmendelsohn',\n",
       " 'benmiles',\n",
       " 'benmiller',\n",
       " 'bennettmiller',\n",
       " 'bennidiez',\n",
       " 'bennofürmann',\n",
       " 'bennyboom',\n",
       " 'benoîtmagimel',\n",
       " 'benoîtpoelvoorde',\n",
       " 'benplatt',\n",
       " 'benschwartz',\n",
       " 'bensonlee',\n",
       " 'benstassen',\n",
       " 'benstein',\n",
       " 'benstiller',\n",
       " 'benvereen',\n",
       " 'benwheatley',\n",
       " 'benyoucef',\n",
       " 'benyounger',\n",
       " 'bergüzarkorel',\n",
       " 'bernadettepeters',\n",
       " 'bernardcribbins',\n",
       " 'bernardfox',\n",
       " 'bernardhill',\n",
       " 'bernardobertolucci',\n",
       " 'bernardosaracino',\n",
       " 'berncohen',\n",
       " 'berniecasey',\n",
       " 'berniemac',\n",
       " 'berniesanders',\n",
       " 'bertrandcantat',\n",
       " 'bessarmstrong',\n",
       " 'bessette',\n",
       " 'bessielove',\n",
       " 'bethanyblakey',\n",
       " 'bethanyjoylenz',\n",
       " 'bethbroderick',\n",
       " 'bethgrant',\n",
       " 'bethriesgraf',\n",
       " 'betogómez',\n",
       " 'betsybaker',\n",
       " 'betsypalmer',\n",
       " 'betsyrussell',\n",
       " 'bettyhutton',\n",
       " 'bettythomas',\n",
       " 'beverlyd',\n",
       " 'beverlyrandolph',\n",
       " 'bhavkhandansinghrakhra',\n",
       " 'bholebaba',\n",
       " 'bhoomikachawla',\n",
       " 'biancabrigittevandamme',\n",
       " 'biancacollins',\n",
       " 'biancakajlich',\n",
       " 'bibobergeron',\n",
       " 'bigasluna',\n",
       " 'bigbudah',\n",
       " 'billallen',\n",
       " 'billbailey',\n",
       " 'billbaird',\n",
       " 'billbenenson',\n",
       " 'billclinton',\n",
       " 'billcobbs',\n",
       " 'billcondon',\n",
       " 'billduke',\n",
       " 'billeaugust',\n",
       " 'billerwin',\n",
       " 'billewoodruff',\n",
       " 'billfagerbakke',\n",
       " 'billfarmer',\n",
       " 'billhickman',\n",
       " 'billhunter',\n",
       " 'billieburke',\n",
       " 'billiewhitelaw',\n",
       " 'billirwin',\n",
       " 'billjohnson',\n",
       " 'billlake',\n",
       " 'billmaher',\n",
       " 'billmartone',\n",
       " 'billmelendez',\n",
       " 'billmuir',\n",
       " 'billmurray',\n",
       " 'billnunn',\n",
       " 'billpaterson',\n",
       " 'billpaxton',\n",
       " 'billplympton',\n",
       " 'billyaaronbrown',\n",
       " 'billybarty',\n",
       " 'billyblair',\n",
       " 'billybobthornton',\n",
       " 'billyboyd',\n",
       " 'billybrown',\n",
       " 'billyburke',\n",
       " 'billycampbell',\n",
       " 'billycrudup',\n",
       " 'billydrago',\n",
       " 'billygardell',\n",
       " 'billygilbert',\n",
       " 'billykent',\n",
       " 'billyray',\n",
       " 'billywest',\n",
       " 'billywilder',\n",
       " 'bindler',\n",
       " 'bingbingfan',\n",
       " 'bingbingli',\n",
       " 'binsu',\n",
       " 'binwon',\n",
       " 'biography',\n",
       " 'bipashabasu',\n",
       " 'birgittehjortsørensen',\n",
       " 'birolünel',\n",
       " 'björk',\n",
       " 'bjørnsundquist',\n",
       " 'blackthomas',\n",
       " 'blaineanderson',\n",
       " 'blairbrown',\n",
       " 'blairerickson',\n",
       " 'blairhayes',\n",
       " 'blairunderwood',\n",
       " 'blakeedwards',\n",
       " 'blakelindsley',\n",
       " 'blanchardryan',\n",
       " 'blaustein',\n",
       " 'blazzavrsnik',\n",
       " 'blige',\n",
       " 'blonkadurden',\n",
       " 'blytheauffarth',\n",
       " 'blythedanner',\n",
       " 'boazyakin',\n",
       " 'bobb',\n",
       " 'bobbalaban',\n",
       " 'bobbiephillips',\n",
       " 'bobbybatson',\n",
       " 'bobbyblock',\n",
       " 'bobbybrown',\n",
       " 'bobbycoleman',\n",
       " 'bobbyfarrelly',\n",
       " 'bobbylee',\n",
       " 'bobbymoynihan',\n",
       " 'bobbyroth',\n",
       " 'bobcatgoldthwait',\n",
       " 'bobclark',\n",
       " 'bobclendenin',\n",
       " 'bobcostas',\n",
       " 'bobdole',\n",
       " 'bobdolman',\n",
       " 'bobdylan',\n",
       " 'bobeubanks',\n",
       " 'bobfosse',\n",
       " 'bobgiraldi',\n",
       " 'bobgosse',\n",
       " 'bobgunton',\n",
       " 'bobhoskins',\n",
       " 'bobmarley',\n",
       " 'bobneill',\n",
       " 'bobnewhart',\n",
       " 'bobodenkirk',\n",
       " 'bobpeck',\n",
       " 'bobrafelson',\n",
       " 'bobross',\n",
       " 'bobsaget',\n",
       " 'bobspiers',\n",
       " 'bohuang',\n",
       " 'bohung',\n",
       " 'bojananovakovic',\n",
       " 'bokeemwoodbine',\n",
       " 'bolajibadejo',\n",
       " 'boloyeung',\n",
       " 'bomanirani',\n",
       " 'bonniehenna',\n",
       " 'bonniehunt',\n",
       " 'bonnieleebouman',\n",
       " 'bonniesomerville',\n",
       " 'bono',\n",
       " 'bonzmalone',\n",
       " 'boriskodjoe',\n",
       " 'borisrodriguez',\n",
       " 'bowelch',\n",
       " 'boydbanks',\n",
       " 'boydholbrook',\n",
       " 'boysiimen',\n",
       " 'bozenga',\n",
       " 'bradanderson',\n",
       " 'bradbird',\n",
       " 'bradcopeland',\n",
       " 'bradfurman',\n",
       " 'bradgarrett',\n",
       " 'bradj',\n",
       " 'bradjames',\n",
       " 'bradleewind',\n",
       " 'bradleland',\n",
       " 'bradleycooper',\n",
       " 'bradleyparker',\n",
       " 'bradleypierce',\n",
       " 'bradleyrustgray',\n",
       " 'bradleywhitford',\n",
       " 'bradpeyton',\n",
       " 'bradpitt',\n",
       " 'bradrenfro',\n",
       " 'bradschmidt',\n",
       " 'bradsilberling',\n",
       " 'bradwilliamhenke',\n",
       " 'bradycorbet',\n",
       " 'braedenlemasters',\n",
       " 'brahimachabbakhe',\n",
       " 'brahmanandam',\n",
       " 'brandoncamp',\n",
       " 'brandoncronenberg',\n",
       " 'brandonjohnson',\n",
       " 'brandonmiddleton',\n",
       " 'brandonmolale',\n",
       " 'brandonsmith',\n",
       " 'brandont',\n",
       " 'brandontrost',\n",
       " 'brandynorwood',\n",
       " 'brankodjuric',\n",
       " 'breckeisner',\n",
       " 'breeturner',\n",
       " 'brendabakke',\n",
       " 'brendablethyn',\n",
       " 'brendachapman',\n",
       " 'brendafricker',\n",
       " 'brendancoyle',\n",
       " 'brendandoogiemilewski',\n",
       " 'brendanfehr',\n",
       " 'brendanfraser',\n",
       " 'brendanmackey',\n",
       " 'brendanmalloy',\n",
       " 'brendansextoniii',\n",
       " 'brendasong',\n",
       " 'brendastrong',\n",
       " 'brendavaccaro',\n",
       " 'brendoneggertsen',\n",
       " 'brenner',\n",
       " 'brentanderson',\n",
       " 'brentdavidfraser',\n",
       " 'brentneale',\n",
       " 'brentonspencer',\n",
       " 'brettcullen',\n",
       " 'brettleonard',\n",
       " 'brettpiper',\n",
       " 'brettratner',\n",
       " 'brettwagner',\n",
       " 'brian',\n",
       " 'brianamiller',\n",
       " 'briananthonywilson',\n",
       " 'brianaustingreen',\n",
       " 'brianbaugh',\n",
       " 'brianblessed',\n",
       " 'brianbosworth',\n",
       " 'briancaunter',\n",
       " 'briand',\n",
       " 'briandannelly',\n",
       " 'briandennehy',\n",
       " 'briandepalma',\n",
       " 'briandietzen',\n",
       " 'briandorton',\n",
       " 'briandoyle',\n",
       " 'brianeno',\n",
       " 'briangeraghty',\n",
       " 'briangibson',\n",
       " 'brianhelgeland',\n",
       " 'brianhenson',\n",
       " 'brianherzlinger',\n",
       " 'brianhowe',\n",
       " 'brianjohnson',\n",
       " 'briankeith',\n",
       " 'brianklugman',\n",
       " 'briankoppelman',\n",
       " 'brianleefranklin',\n",
       " 'brianlevant',\n",
       " 'brianmcnamara',\n",
       " 'briannabrown',\n",
       " 'briannebrozey',\n",
       " 'briano',\n",
       " 'brianpercival',\n",
       " 'brianrobbins',\n",
       " 'briansteele',\n",
       " 'brianstepanek',\n",
       " 'brianthompson',\n",
       " 'briantrenchard',\n",
       " 'brianvanholt',\n",
       " 'brianwhitaker',\n",
       " ...]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.37796447, 0.25197632, ..., 0.        , 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generating the cosine similarity matrix\n",
    "cosine_sim = cosine_similarity(count_matrix, count_matrix)\n",
    "cosine_sim[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that takes in movie title as input and returns the top 10 recommended movies\n",
    "def recommendations(title, cosine_sim = cosine_sim):\n",
    "    \n",
    "    recommended_movies = []\n",
    "    \n",
    "    # gettin the index of the movie that matches the title\n",
    "    idx = indices[indices == title].index[0]\n",
    "\n",
    "    # creating a Series with the similarity scores in descending order\n",
    "    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)\n",
    "\n",
    "    # getting the indexes of the 10 most similar movies\n",
    "    top_10_indexes = list(score_series.iloc[1:11].index)\n",
    "    print(top_10_indexes)\n",
    "    \n",
    "    # populating the list with the titles of the best 10 matching movies\n",
    "    for i in top_10_indexes:\n",
    "        recommended_movies.append(list(data_subset.index)[i])\n",
    "        \n",
    "    return recommended_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[153, 213, 425, 139, 209, 717, 4279, 3820, 574, 29]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Mission: Impossible - Ghost Protocol\\xa0',\n",
       " 'Mission: Impossible II\\xa0',\n",
       " 'Mission: Impossible\\xa0',\n",
       " 'Mission: Impossible III\\xa0',\n",
       " 'The Bourne Legacy\\xa0',\n",
       " 'Jack Reacher\\xa0',\n",
       " 'Jack Reacher\\xa0',\n",
       " 'A Lonely Place to Die\\xa0',\n",
       " 'S.W.A.T.\\xa0',\n",
       " 'Skyfall\\xa0']"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie=\"\"\n",
    "recommendations(\"Mission: Impossible - Rogue Nation\" +'\\xa0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Pirates of the Caribbean: At World's End\\xa0\""
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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