Movie-Recommender-System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea071af9",
   "metadata": {},
   "source": [
    "# Content - Based Movie Recommender System\n",
    "## Author: Ammish Tandon\n",
    "### Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74cd7c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np   # Importing the libraries\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cca16cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('tmdb_5000_movies.csv')  # Loading in the datasets\n",
    "credits = pd.read_csv('tmdb_5000_credits.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "795ad889",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000000</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>245000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>260000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
       "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
       "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "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",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
       "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
       "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
       "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
       "2  [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   880674609   \n",
       "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16  1084939099   \n",
       "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   284139100   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title  vote_average  vote_count  \n",
       "0                                    Avatar           7.2       11800  \n",
       "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
       "2                                   Spectre           6.3        4466  \n",
       "3                     The Dark Knight Rises           7.6        9106  \n",
       "4                               John Carter           6.1        2124  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "903d8e33",
   "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_id</th>\n",
       "      <th>title</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
       "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd92fd8",
   "metadata": {},
   "source": [
    "We have the data about the same movies in both the DataFrames. Hence, we'll merge them into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "739b7f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4809, 23)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(movies, credits, on = 'title').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0fc49b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 20)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68d51bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "359e5863",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.merge(movies, credits, on = 'title')  # Merging both the DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78ddb1f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4809 entries, 0 to 4808\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   budget                4809 non-null   int64  \n",
      " 1   genres                4809 non-null   object \n",
      " 2   homepage              1713 non-null   object \n",
      " 3   id                    4809 non-null   int64  \n",
      " 4   keywords              4809 non-null   object \n",
      " 5   original_language     4809 non-null   object \n",
      " 6   original_title        4809 non-null   object \n",
      " 7   overview              4806 non-null   object \n",
      " 8   popularity            4809 non-null   float64\n",
      " 9   production_companies  4809 non-null   object \n",
      " 10  production_countries  4809 non-null   object \n",
      " 11  release_date          4808 non-null   object \n",
      " 12  revenue               4809 non-null   int64  \n",
      " 13  runtime               4807 non-null   float64\n",
      " 14  spoken_languages      4809 non-null   object \n",
      " 15  status                4809 non-null   object \n",
      " 16  tagline               3965 non-null   object \n",
      " 17  title                 4809 non-null   object \n",
      " 18  vote_average          4809 non-null   float64\n",
      " 19  vote_count            4809 non-null   int64  \n",
      " 20  movie_id              4809 non-null   int64  \n",
      " 21  cast                  4809 non-null   object \n",
      " 22  crew                  4809 non-null   object \n",
      "dtypes: float64(3), int64(5), object(15)\n",
      "memory usage: 901.7+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6680ff93",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    4510\n",
       "fr      70\n",
       "es      32\n",
       "zh      27\n",
       "de      27\n",
       "hi      19\n",
       "ja      16\n",
       "it      14\n",
       "ko      12\n",
       "cn      12\n",
       "ru      11\n",
       "pt       9\n",
       "da       7\n",
       "sv       5\n",
       "nl       4\n",
       "fa       4\n",
       "th       3\n",
       "he       3\n",
       "ta       2\n",
       "cs       2\n",
       "ro       2\n",
       "id       2\n",
       "ar       2\n",
       "vi       1\n",
       "sl       1\n",
       "ps       1\n",
       "no       1\n",
       "ky       1\n",
       "hu       1\n",
       "pl       1\n",
       "af       1\n",
       "nb       1\n",
       "tr       1\n",
       "is       1\n",
       "xx       1\n",
       "te       1\n",
       "el       1\n",
       "Name: original_language, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['original_language'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a571b9",
   "metadata": {},
   "source": [
    "Majority of the movies are in English, so it isn't a useful column (highly imbalanced) hence it can be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8eb5fcaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>...</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>...</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                      homepage     id  \\\n",
       "0  http://www.avatarmovie.com/  19995   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "\n",
       "  original_title                                           overview  \\\n",
       "0         Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "   popularity                               production_companies  ... runtime  \\\n",
       "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   162.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "\n",
       "                       tagline   title vote_average vote_count movie_id  \\\n",
       "0  Enter the World of Pandora.  Avatar          7.2      11800    19995   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94712b3b",
   "metadata": {},
   "source": [
    "Now, we'll remove those columns that won't help us in our analysis. We need to create a content - based recommender system which implies that we need to create a tag for each movie. Will the given column help me in creating tags or not?  \n",
    "In the approach that I'm taking, I won't consider numeric columns. I'll only consider the string columns to create tags. I'll filter out the numeric columns and the other ones which aren't useful in giving recommendations based on content using tags such as homepage, tagline(very vague), production_countries etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c3f82c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The columns that we'll keep:\n",
    "# genre\n",
    "# id / movie_id - will be useful to fetch posters of movies from TMDB during model deployment\n",
    "# keywords - are basically tags\n",
    "# title - original_title might've names in different languages so that might cause trouble\n",
    "# overview - very important if we want to recommend movies based on the similarity in contents. If the summaries of 2 movies are\n",
    "# similar then the movies are also similar\n",
    "# cast\n",
    "# crew\n",
    "movies = movies[['movie_id', 'title', 'overview', 'genres', 'keywords', 'cast', 'crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d88aefe8",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "3  Following the death of District Attorney Harve...   \n",
       "4  John Carter is a war-weary, former military ca...   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...   \n",
       "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...   \n",
       "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
       "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10fdc74a",
   "metadata": {},
   "source": [
    "We need a dataframe that contains only 3 columns: movie_id, title and tags. We'll create the tags by merging overiview, genres, keywords, cast(top 3 actors) and crew(director only). After joining all these strings, we'll get a paragraph for every movie.\n",
    "To do this, we need to preprocess the data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "34fa3d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "overview    3\n",
       "genres      0\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "47f9c5af",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da141381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124ef1aa",
   "metadata": {},
   "source": [
    "No duplicate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21d43fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['genres'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a89d1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# '[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'\n",
    "# We need to bring it into this format => ['Action', 'Adventure', 'Fantasy', 'SciFi']\n",
    "import ast\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "def convert(genre):\n",
    "    genre_list = []\n",
    "    for dictionary in ast.literal_eval(genre):   # Evaluating the string genre, and getting the list back from it\n",
    "        genre_list.append(dictionary[\"name\"])\n",
    "    return genre_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4166de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3702520e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
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       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>[Action, Adventure, Science Fiction]</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "3  Following the death of District Attorney Harve...   \n",
       "4  John Carter is a war-weary, former military ca...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "3               [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...   \n",
       "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...   \n",
       "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
       "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
       "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
       "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cd9b178",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['keywords'] = movies['keywords'].apply(convert) # Same function for keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b50a35fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"credit_id\": \"5602a8a7c3a3685532001c9a\", \"gender\": 2, \"id\": 65731, \"name\": \"Sam Worthington\", \"order\": 0}, {\"cast_id\": 3, \"character\": \"Neytiri\", \"credit_id\": \"52fe48009251416c750ac9cb\", \"gender\": 1, \"id\": 8691, \"name\": \"Zoe Saldana\", \"order\": 1}, {\"cast_id\": 25, \"character\": \"Dr. Grace Augustine\", \"credit_id\": \"52fe48009251416c750aca39\", \"gender\": 1, \"id\": 10205, \"name\": \"Sigourney Weaver\", \"order\": 2}, {\"cast_id\": 4, \"character\": \"Col. Quaritch\", \"credit_id\": \"52fe48009251416c750ac9cf\", \"gender\": 2, \"id\": 32747, \"name\": \"Stephen Lang\", \"order\": 3}, {\"cast_id\": 5, \"character\": \"Trudy Chacon\", \"credit_id\": \"52fe48009251416c750ac9d3\", \"gender\": 1, \"id\": 17647, \"name\": \"Michelle Rodriguez\", \"order\": 4}, {\"cast_id\": 8, \"character\": \"Selfridge\", \"credit_id\": \"52fe48009251416c750ac9e1\", \"gender\": 2, \"id\": 1771, \"name\": \"Giovanni Ribisi\", \"order\": 5}, {\"cast_id\": 7, \"character\": \"Norm Spellman\", \"credit_id\": \"52fe48009251416c750ac9dd\", \"gender\": 2, \"id\": 59231, \"name\": \"Joel David Moore\", \"order\": 6}, {\"cast_id\": 9, \"character\": \"Moat\", \"credit_id\": \"52fe48009251416c750ac9e5\", \"gender\": 1, \"id\": 30485, \"name\": \"CCH Pounder\", \"order\": 7}, {\"cast_id\": 11, \"character\": \"Eytukan\", \"credit_id\": \"52fe48009251416c750ac9ed\", \"gender\": 2, \"id\": 15853, \"name\": \"Wes Studi\", \"order\": 8}, {\"cast_id\": 10, \"character\": \"Tsu\\'Tey\", \"credit_id\": \"52fe48009251416c750ac9e9\", \"gender\": 2, \"id\": 10964, \"name\": \"Laz Alonso\", \"order\": 9}, {\"cast_id\": 12, \"character\": \"Dr. Max Patel\", \"credit_id\": \"52fe48009251416c750ac9f1\", \"gender\": 2, \"id\": 95697, \"name\": \"Dileep Rao\", \"order\": 10}, {\"cast_id\": 13, \"character\": \"Lyle Wainfleet\", \"credit_id\": \"52fe48009251416c750ac9f5\", \"gender\": 2, \"id\": 98215, \"name\": \"Matt Gerald\", \"order\": 11}, {\"cast_id\": 32, \"character\": \"Private Fike\", \"credit_id\": \"52fe48009251416c750aca5b\", \"gender\": 2, \"id\": 154153, \"name\": \"Sean Anthony Moran\", \"order\": 12}, {\"cast_id\": 33, \"character\": \"Cryo Vault Med Tech\", \"credit_id\": \"52fe48009251416c750aca5f\", \"gender\": 2, \"id\": 397312, \"name\": \"Jason Whyte\", \"order\": 13}, {\"cast_id\": 34, \"character\": \"Venture Star Crew Chief\", \"credit_id\": \"52fe48009251416c750aca63\", \"gender\": 2, \"id\": 42317, \"name\": \"Scott Lawrence\", \"order\": 14}, {\"cast_id\": 35, \"character\": \"Lock Up Trooper\", \"credit_id\": \"52fe48009251416c750aca67\", \"gender\": 2, \"id\": 986734, \"name\": \"Kelly Kilgour\", \"order\": 15}, {\"cast_id\": 36, \"character\": \"Shuttle Pilot\", \"credit_id\": \"52fe48009251416c750aca6b\", \"gender\": 0, \"id\": 1207227, \"name\": \"James Patrick Pitt\", \"order\": 16}, {\"cast_id\": 37, \"character\": \"Shuttle Co-Pilot\", \"credit_id\": \"52fe48009251416c750aca6f\", \"gender\": 0, \"id\": 1180936, \"name\": \"Sean Patrick Murphy\", \"order\": 17}, {\"cast_id\": 38, \"character\": \"Shuttle Crew Chief\", \"credit_id\": \"52fe48009251416c750aca73\", \"gender\": 2, \"id\": 1019578, \"name\": \"Peter Dillon\", \"order\": 18}, {\"cast_id\": 39, \"character\": \"Tractor Operator / Troupe\", \"credit_id\": \"52fe48009251416c750aca77\", \"gender\": 0, \"id\": 91443, \"name\": \"Kevin Dorman\", \"order\": 19}, {\"cast_id\": 40, \"character\": \"Dragon Gunship Pilot\", \"credit_id\": \"52fe48009251416c750aca7b\", \"gender\": 2, \"id\": 173391, \"name\": \"Kelson Henderson\", \"order\": 20}, {\"cast_id\": 41, \"character\": \"Dragon Gunship Gunner\", \"credit_id\": \"52fe48009251416c750aca7f\", \"gender\": 0, \"id\": 1207236, \"name\": \"David Van Horn\", \"order\": 21}, {\"cast_id\": 42, \"character\": \"Dragon Gunship Navigator\", \"credit_id\": \"52fe48009251416c750aca83\", \"gender\": 0, \"id\": 215913, \"name\": \"Jacob Tomuri\", \"order\": 22}, {\"cast_id\": 43, \"character\": \"Suit #1\", \"credit_id\": \"52fe48009251416c750aca87\", \"gender\": 0, \"id\": 143206, \"name\": \"Michael Blain-Rozgay\", \"order\": 23}, {\"cast_id\": 44, \"character\": \"Suit #2\", \"credit_id\": \"52fe48009251416c750aca8b\", \"gender\": 2, \"id\": 169676, \"name\": \"Jon Curry\", \"order\": 24}, {\"cast_id\": 46, \"character\": \"Ambient Room Tech\", \"credit_id\": \"52fe48009251416c750aca8f\", \"gender\": 0, \"id\": 1048610, \"name\": \"Luke Hawker\", \"order\": 25}, {\"cast_id\": 47, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48009251416c750aca93\", \"gender\": 0, \"id\": 42288, \"name\": \"Woody Schultz\", \"order\": 26}, {\"cast_id\": 48, \"character\": \"Horse Clan Leader\", \"credit_id\": \"52fe48009251416c750aca97\", \"gender\": 2, \"id\": 68278, \"name\": \"Peter Mensah\", \"order\": 27}, {\"cast_id\": 49, \"character\": \"Link Room Tech\", \"credit_id\": \"52fe48009251416c750aca9b\", \"gender\": 0, \"id\": 1207247, \"name\": \"Sonia Yee\", \"order\": 28}, {\"cast_id\": 50, \"character\": \"Basketball Avatar / Troupe\", \"credit_id\": \"52fe48009251416c750aca9f\", \"gender\": 1, \"id\": 1207248, \"name\": \"Jahnel Curfman\", \"order\": 29}, {\"cast_id\": 51, \"character\": \"Basketball Avatar\", \"credit_id\": \"52fe48009251416c750acaa3\", \"gender\": 0, \"id\": 89714, \"name\": \"Ilram Choi\", \"order\": 30}, {\"cast_id\": 52, \"character\": \"Na\\'vi Child\", \"credit_id\": \"52fe48009251416c750acaa7\", \"gender\": 0, \"id\": 1207249, \"name\": \"Kyla Warren\", \"order\": 31}, {\"cast_id\": 53, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaab\", \"gender\": 0, \"id\": 1207250, \"name\": \"Lisa Roumain\", \"order\": 32}, {\"cast_id\": 54, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaaf\", \"gender\": 1, \"id\": 83105, \"name\": \"Debra Wilson\", \"order\": 33}, {\"cast_id\": 57, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabb\", \"gender\": 0, \"id\": 1207253, \"name\": \"Chris Mala\", \"order\": 34}, {\"cast_id\": 55, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab3\", \"gender\": 0, \"id\": 1207251, \"name\": \"Taylor Kibby\", \"order\": 35}, {\"cast_id\": 56, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab7\", \"gender\": 0, \"id\": 1207252, \"name\": \"Jodie Landau\", \"order\": 36}, {\"cast_id\": 58, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabf\", \"gender\": 0, \"id\": 1207254, \"name\": \"Julie Lamm\", \"order\": 37}, {\"cast_id\": 59, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac3\", \"gender\": 0, \"id\": 1207257, \"name\": \"Cullen B. Madden\", \"order\": 38}, {\"cast_id\": 60, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac7\", \"gender\": 0, \"id\": 1207259, \"name\": \"Joseph Brady Madden\", \"order\": 39}, {\"cast_id\": 61, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacb\", \"gender\": 0, \"id\": 1207262, \"name\": \"Frankie Torres\", \"order\": 40}, {\"cast_id\": 62, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacf\", \"gender\": 1, \"id\": 1158600, \"name\": \"Austin Wilson\", \"order\": 41}, {\"cast_id\": 63, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad3\", \"gender\": 1, \"id\": 983705, \"name\": \"Sara Wilson\", \"order\": 42}, {\"cast_id\": 64, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad7\", \"gender\": 0, \"id\": 1207263, \"name\": \"Tamica Washington-Miller\", \"order\": 43}, {\"cast_id\": 65, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadb\", \"gender\": 1, \"id\": 1145098, \"name\": \"Lucy Briant\", \"order\": 44}, {\"cast_id\": 66, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadf\", \"gender\": 2, \"id\": 33305, \"name\": \"Nathan Meister\", \"order\": 45}, {\"cast_id\": 67, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae3\", \"gender\": 0, \"id\": 1207264, \"name\": \"Gerry Blair\", \"order\": 46}, {\"cast_id\": 68, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae7\", \"gender\": 2, \"id\": 33311, \"name\": \"Matthew Chamberlain\", \"order\": 47}, {\"cast_id\": 69, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaeb\", \"gender\": 0, \"id\": 1207265, \"name\": \"Paul Yates\", \"order\": 48}, {\"cast_id\": 70, \"character\": \"Op Center Duty Officer\", \"credit_id\": \"52fe48019251416c750acaef\", \"gender\": 0, \"id\": 1207266, \"name\": \"Wray Wilson\", \"order\": 49}, {\"cast_id\": 71, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaf3\", \"gender\": 2, \"id\": 54492, \"name\": \"James Gaylyn\", \"order\": 50}, {\"cast_id\": 72, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaf7\", \"gender\": 0, \"id\": 1207267, \"name\": \"Melvin Leno Clark III\", \"order\": 51}, {\"cast_id\": 73, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acafb\", \"gender\": 0, \"id\": 1207268, \"name\": \"Carvon Futrell\", \"order\": 52}, {\"cast_id\": 74, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaff\", \"gender\": 0, \"id\": 1207269, \"name\": \"Brandon Jelkes\", \"order\": 53}, {\"cast_id\": 75, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb03\", \"gender\": 0, \"id\": 1207270, \"name\": \"Micah Moch\", \"order\": 54}, {\"cast_id\": 76, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb07\", \"gender\": 0, \"id\": 1207271, \"name\": \"Hanniyah Muhammad\", \"order\": 55}, {\"cast_id\": 77, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0b\", \"gender\": 0, \"id\": 1207272, \"name\": \"Christopher Nolen\", \"order\": 56}, {\"cast_id\": 78, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0f\", \"gender\": 0, \"id\": 1207273, \"name\": \"Christa Oliver\", \"order\": 57}, {\"cast_id\": 79, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb13\", \"gender\": 0, \"id\": 1207274, \"name\": \"April Marie Thomas\", \"order\": 58}, {\"cast_id\": 80, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb17\", \"gender\": 0, \"id\": 1207275, \"name\": \"Bravita A. Threatt\", \"order\": 59}, {\"cast_id\": 81, \"character\": \"Mining Chief (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1b\", \"gender\": 0, \"id\": 1207276, \"name\": \"Colin Bleasdale\", \"order\": 60}, {\"cast_id\": 82, \"character\": \"Veteran Miner (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1f\", \"gender\": 0, \"id\": 107969, \"name\": \"Mike Bodnar\", \"order\": 61}, {\"cast_id\": 83, \"character\": \"Richard (uncredited)\", \"credit_id\": \"52fe48019251416c750acb23\", \"gender\": 0, \"id\": 1207278, \"name\": \"Matt Clayton\", \"order\": 62}, {\"cast_id\": 84, \"character\": \"Nav\\'i (uncredited)\", \"credit_id\": \"52fe48019251416c750acb27\", \"gender\": 1, \"id\": 147898, \"name\": \"Nicole Dionne\", \"order\": 63}, {\"cast_id\": 85, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2b\", \"gender\": 0, \"id\": 1207280, \"name\": \"Jamie Harrison\", \"order\": 64}, {\"cast_id\": 86, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2f\", \"gender\": 0, \"id\": 1207281, \"name\": \"Allan Henry\", \"order\": 65}, {\"cast_id\": 87, \"character\": \"Ground Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb33\", \"gender\": 2, \"id\": 1207282, \"name\": \"Anthony Ingruber\", \"order\": 66}, {\"cast_id\": 88, \"character\": \"Flight Crew Mechanic (uncredited)\", \"credit_id\": \"52fe48019251416c750acb37\", \"gender\": 0, \"id\": 1207283, \"name\": \"Ashley Jeffery\", \"order\": 67}, {\"cast_id\": 14, \"character\": \"Samson Pilot\", \"credit_id\": \"52fe48009251416c750ac9f9\", \"gender\": 0, \"id\": 98216, \"name\": \"Dean Knowsley\", \"order\": 68}, {\"cast_id\": 89, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3b\", \"gender\": 0, \"id\": 1201399, \"name\": \"Joseph Mika-Hunt\", \"order\": 69}, {\"cast_id\": 90, \"character\": \"Banshee (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3f\", \"gender\": 0, \"id\": 236696, \"name\": \"Terry Notary\", \"order\": 70}, {\"cast_id\": 91, \"character\": \"Soldier (uncredited)\", \"credit_id\": \"52fe48019251416c750acb43\", \"gender\": 0, \"id\": 1207287, \"name\": \"Kai Pantano\", \"order\": 71}, {\"cast_id\": 92, \"character\": \"Blast Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb47\", \"gender\": 0, \"id\": 1207288, \"name\": \"Logan Pithyou\", \"order\": 72}, {\"cast_id\": 93, \"character\": \"Vindum Raah (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4b\", \"gender\": 0, \"id\": 1207289, \"name\": \"Stuart Pollock\", \"order\": 73}, {\"cast_id\": 94, \"character\": \"Hero (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4f\", \"gender\": 0, \"id\": 584868, \"name\": \"Raja\", \"order\": 74}, {\"cast_id\": 95, \"character\": \"Ops Centreworker (uncredited)\", \"credit_id\": \"52fe48019251416c750acb53\", \"gender\": 0, \"id\": 1207290, \"name\": \"Gareth Ruck\", \"order\": 75}, {\"cast_id\": 96, \"character\": \"Engineer (uncredited)\", \"credit_id\": \"52fe48019251416c750acb57\", \"gender\": 0, \"id\": 1062463, \"name\": \"Rhian Sheehan\", \"order\": 76}, {\"cast_id\": 97, \"character\": \"Col. Quaritch\\'s Mech Suit (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5b\", \"gender\": 0, \"id\": 60656, \"name\": \"T. J. Storm\", \"order\": 77}, {\"cast_id\": 98, \"character\": \"Female Marine (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5f\", \"gender\": 0, \"id\": 1207291, \"name\": \"Jodie Taylor\", \"order\": 78}, {\"cast_id\": 99, \"character\": \"Ikran Clan Leader (uncredited)\", \"credit_id\": \"52fe48019251416c750acb63\", \"gender\": 1, \"id\": 1186027, \"name\": \"Alicia Vela-Bailey\", \"order\": 79}, {\"cast_id\": 100, \"character\": \"Geologist (uncredited)\", \"credit_id\": \"52fe48019251416c750acb67\", \"gender\": 0, \"id\": 1207292, \"name\": \"Richard Whiteside\", \"order\": 80}, {\"cast_id\": 101, \"character\": \"Na\\'vi (uncredited)\", \"credit_id\": \"52fe48019251416c750acb6b\", \"gender\": 0, \"id\": 103259, \"name\": \"Nikie Zambo\", \"order\": 81}, {\"cast_id\": 102, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48019251416c750acb6f\", \"gender\": 1, \"id\": 42286, \"name\": \"Julene Renee\", \"order\": 82}]'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['cast'][0]   # A list of dictionaries in the form of a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f8da7ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert3(cast):\n",
    "    cast_list = []\n",
    "    count = 0\n",
    "    for dictionary in ast.literal_eval(cast):   \n",
    "        if count != 3:       # We want only the top 3 cast names. \n",
    "            cast_list.append(dictionary[\"name\"])\n",
    "            count += 1\n",
    "        else:\n",
    "            break\n",
    "    return cast_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfc91d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'] = movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0af2683c",
   "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_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[dc comics, crime fighter, terrorist, secret i...</td>\n",
       "      <td>[Christian Bale, Michael Caine, Gary Oldman]</td>\n",
       "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>[Action, Adventure, Science Fiction]</td>\n",
       "      <td>[based on novel, mars, medallion, space travel...</td>\n",
       "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
       "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "3  Following the death of District Attorney Harve...   \n",
       "4  John Carter is a war-weary, former military ca...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "3               [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "3  [dc comics, crime fighter, terrorist, secret i...   \n",
       "4  [based on novel, mars, medallion, space travel...   \n",
       "\n",
       "                                               cast  \\\n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]   \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]   \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]   \n",
       "3      [Christian Bale, Michael Caine, Gary Oldman]   \n",
       "4    [Taylor Kitsch, Lynn Collins, Samantha Morton]   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
       "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
       "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
       "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03596ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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\"job\": \"Set Costumer\", \"name\": \"Lilia Mishel Acevedo\"}, {\"credit_id\": \"54959ff9c3a3686ae300440c\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1401814, \"job\": \"Set Costumer\", \"name\": \"Alejandro M. Hernandez\"}, {\"credit_id\": \"5495a0ddc3a3686ae10046fe\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1401815, \"job\": \"Digital Intermediate\", \"name\": \"Marvin Hall\"}, {\"credit_id\": \"5495a1f7c3a3686ae3004443\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401816, \"job\": \"Publicist\", \"name\": \"Judy Alley\"}, {\"credit_id\": \"5592b29fc3a36869d100002f\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1418381, \"job\": \"CG Supervisor\", \"name\": \"Mike Perry\"}, {\"credit_id\": \"5592b23a9251415df8001081\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1426854, \"job\": \"CG Supervisor\", \"name\": \"Andrew Morley\"}, {\"credit_id\": \"55491e1192514104c40002d8\", \"department\": \"Art\", \"gender\": 0, \"id\": 1438901, \"job\": \"Conceptual Design\", \"name\": \"Seth Engstrom\"}, {\"credit_id\": \"5525d5809251417276002b06\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1447362, \"job\": \"Visual Effects Art Director\", \"name\": \"Eric Oliver\"}, {\"credit_id\": \"554427ca925141586500312a\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1447503, \"job\": \"Modeling\", \"name\": \"Matsune Suzuki\"}, {\"credit_id\": \"551906889251415aab001c88\", \"department\": \"Art\", \"gender\": 0, \"id\": 1447524, \"job\": \"Art Department Manager\", \"name\": \"Paul Tobin\"}, {\"credit_id\": \"5592af8492514152cc0010de\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1452643, \"job\": \"Hairstylist\", \"name\": \"Roxane Griffin\"}, {\"credit_id\": \"553d3c109251415852001318\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1453938, \"job\": \"Lighting Artist\", \"name\": \"Arun Ram-Mohan\"}, {\"credit_id\": \"5592af4692514152d5001355\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1457305, \"job\": \"Makeup Artist\", \"name\": \"Georgia Lockhart-Adams\"}, {\"credit_id\": \"5592b2eac3a36877470012a5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1466035, \"job\": \"CG Supervisor\", \"name\": \"Thrain Shadbolt\"}, {\"credit_id\": \"5592b032c3a36877450015f1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483220, \"job\": \"CG Supervisor\", \"name\": \"Brad Alexander\"}, {\"credit_id\": \"5592b05592514152d80012f6\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483221, \"job\": \"CG Supervisor\", \"name\": \"Shadi Almassizadeh\"}, {\"credit_id\": \"5592b090c3a36877570010b5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483222, \"job\": \"CG Supervisor\", \"name\": \"Simon Clutterbuck\"}, {\"credit_id\": \"5592b0dbc3a368774b00112c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483223, \"job\": \"CG Supervisor\", \"name\": \"Graeme Demmocks\"}, {\"credit_id\": \"5592b0fe92514152db0010c1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483224, \"job\": \"CG Supervisor\", \"name\": \"Adrian Fernandes\"}, {\"credit_id\": \"5592b11f9251415df8001059\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483225, \"job\": \"CG Supervisor\", \"name\": \"Mitch Gates\"}, {\"credit_id\": \"5592b15dc3a3687745001645\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483226, \"job\": \"CG Supervisor\", \"name\": \"Jerry Kung\"}, {\"credit_id\": \"5592b18e925141645a0004ae\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483227, \"job\": \"CG Supervisor\", \"name\": \"Andy Lomas\"}, {\"credit_id\": \"5592b1bfc3a368775d0010e7\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483228, \"job\": \"CG Supervisor\", \"name\": \"Sebastian Marino\"}, {\"credit_id\": \"5592b2049251415df8001078\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483229, \"job\": \"CG Supervisor\", \"name\": \"Matthias Menz\"}, {\"credit_id\": \"5592b27b92514152d800136a\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483230, \"job\": \"CG Supervisor\", \"name\": \"Sergei Nevshupov\"}, {\"credit_id\": \"5592b2c3c3a36869e800003c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483231, \"job\": \"CG Supervisor\", \"name\": \"Philippe Rebours\"}, {\"credit_id\": \"5592b317c3a36877470012af\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483232, \"job\": \"CG Supervisor\", \"name\": \"Michael Takarangi\"}, {\"credit_id\": \"5592b345c3a36877470012bb\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483233, \"job\": \"CG Supervisor\", \"name\": \"David Weitzberg\"}, {\"credit_id\": \"5592b37cc3a368775100113b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483234, \"job\": \"CG Supervisor\", \"name\": \"Ben White\"}, {\"credit_id\": \"573c8e2f9251413f5d000094\", \"department\": \"Crew\", \"gender\": 1, \"id\": 1621932, \"job\": \"Stunts\", \"name\": \"Min Windle\"}]'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['crew'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0cdbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_director(crew):\n",
    "    director = []\n",
    "    for dictionary in ast.literal_eval(crew):  \n",
    "        if dictionary['job'] == 'Director':          # Want only the Director from the crew\n",
    "            director.append(dictionary['name'])\n",
    "            break\n",
    "    return director"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ea73d10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['crew'] = movies['crew'].apply(get_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6207448a",
   "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_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[James Cameron]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[Gore Verbinski]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[Sam Mendes]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[dc comics, crime fighter, terrorist, secret i...</td>\n",
       "      <td>[Christian Bale, Michael Caine, Gary Oldman]</td>\n",
       "      <td>[Christopher Nolan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>[Action, Adventure, Science Fiction]</td>\n",
       "      <td>[based on novel, mars, medallion, space travel...</td>\n",
       "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
       "      <td>[Andrew Stanton]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...   \n",
       "1  Captain Barbossa, long believed to be dead, ha...   \n",
       "2  A cryptic message from Bond’s past sends him o...   \n",
       "3  Following the death of District Attorney Harve...   \n",
       "4  John Carter is a war-weary, former military ca...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "3               [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "3  [dc comics, crime fighter, terrorist, secret i...   \n",
       "4  [based on novel, mars, medallion, space travel...   \n",
       "\n",
       "                                               cast                 crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]      [James Cameron]  \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]     [Gore Verbinski]  \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]         [Sam Mendes]  \n",
       "3      [Christian Bale, Michael Caine, Gary Oldman]  [Christopher Nolan]  \n",
       "4    [Taylor Kitsch, Lynn Collins, Samantha Morton]     [Andrew Stanton]  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7d8071",
   "metadata": {},
   "source": [
    "We'll also convert overview column to a list so that we can conactenate it with the other columns which also have list values.\n",
    "Their datatype will still be object though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38037179",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['overview'] = movies['overview'].apply(lambda x: x.split())  # Converting overview to a list of words so that we can \n",
    "# concatenate it with other columns to create tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ba690453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[James Cameron]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
       "      <td>[Gore Verbinski]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
       "      <td>[Sam Mendes]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[dc comics, crime fighter, terrorist, secret i...</td>\n",
       "      <td>[Christian Bale, Michael Caine, Gary Oldman]</td>\n",
       "      <td>[Christopher Nolan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "      <td>[Action, Adventure, Science Fiction]</td>\n",
       "      <td>[based on novel, mars, medallion, space travel...</td>\n",
       "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
       "      <td>[Andrew Stanton]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "3  [Following, the, death, of, District, Attorney...   \n",
       "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "1                   [Adventure, Fantasy, Action]   \n",
       "2                     [Action, Adventure, Crime]   \n",
       "3               [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "1  [ocean, drug abuse, exotic island, east india ...   \n",
       "2  [spy, based on novel, secret agent, sequel, mi...   \n",
       "3  [dc comics, crime fighter, terrorist, secret i...   \n",
       "4  [based on novel, mars, medallion, space travel...   \n",
       "\n",
       "                                               cast                 crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]      [James Cameron]  \n",
       "1     [Johnny Depp, Orlando Bloom, Keira Knightley]     [Gore Verbinski]  \n",
       "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]         [Sam Mendes]  \n",
       "3      [Christian Bale, Michael Caine, Gary Oldman]  [Christopher Nolan]  \n",
       "4    [Taylor Kitsch, Lynn Collins, Samantha Morton]     [Andrew Stanton]  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d81383",
   "metadata": {},
   "source": [
    "We need to perform a transformation to the last 4 columns. We need to remove the spaces between the elements of the list. Take the first row for instance. We have the actor Sam Worthington => Sam + Worthington (two separate tags will be created). In the 3rd row, we have the Director as Sam Mendes => Sam + Mendes. Suppose we want to watch Sam Worthington movies but the recommender might get confused and might recommend us Sam Mendes movies due to similar tag 'Sam'. Hence, we'll remove the space in between to make 'SamWorthington' (a single entity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69f2ab75",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(lambda x: [\"\".join(word.split()) for word in x])\n",
    "movies['keywords'] = movies['keywords'].apply(lambda x: [\"\".join(word.split()) for word in x])\n",
    "movies['cast'] = movies['cast'].apply(lambda x: [\"\".join(word.split()) for word in x])\n",
    "movies['crew'] = movies['crew'].apply(lambda x: [\"\".join(word.split()) for word in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51ac71c2",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "      <td>[JamesCameron]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[ocean, drugabuse, exoticisland, eastindiatrad...</td>\n",
       "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley]</td>\n",
       "      <td>[GoreVerbinski]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, basedonnovel, secretagent, sequel, mi6, ...</td>\n",
       "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux]</td>\n",
       "      <td>[SamMendes]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[dccomics, crimefighter, terrorist, secretiden...</td>\n",
       "      <td>[ChristianBale, MichaelCaine, GaryOldman]</td>\n",
       "      <td>[ChristopherNolan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "      <td>[Action, Adventure, ScienceFiction]</td>\n",
       "      <td>[basedonnovel, mars, medallion, spacetravel, p...</td>\n",
       "      <td>[TaylorKitsch, LynnCollins, SamanthaMorton]</td>\n",
       "      <td>[AndrewStanton]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "3  [Following, the, death, of, District, Attorney...   \n",
       "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
       "\n",
       "                                         genres  \\\n",
       "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "1                  [Adventure, Fantasy, Action]   \n",
       "2                    [Action, Adventure, Crime]   \n",
       "3              [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, ScienceFiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
       "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
       "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
       "3  [dccomics, crimefighter, terrorist, secretiden...   \n",
       "4  [basedonnovel, mars, medallion, spacetravel, p...   \n",
       "\n",
       "                                            cast                crew  \n",
       "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]      [JamesCameron]  \n",
       "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]     [GoreVerbinski]  \n",
       "2      [DanielCraig, ChristophWaltz, LéaSeydoux]         [SamMendes]  \n",
       "3      [ChristianBale, MichaelCaine, GaryOldman]  [ChristopherNolan]  \n",
       "4    [TaylorKitsch, LynnCollins, SamanthaMorton]     [AndrewStanton]  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2a3c02a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']\n",
    "# Concatenating the columns to create a list of tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ee8df989",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "      <td>[Action, Adventure, Fantasy, ScienceFiction]</td>\n",
       "      <td>[cultureclash, future, spacewar, spacecolony, ...</td>\n",
       "      <td>[SamWorthington, ZoeSaldana, SigourneyWeaver]</td>\n",
       "      <td>[JamesCameron]</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
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       "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley]</td>\n",
       "      <td>[GoreVerbinski]</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[spy, basedonnovel, secretagent, sequel, mi6, ...</td>\n",
       "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux]</td>\n",
       "      <td>[SamMendes]</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[dccomics, crimefighter, terrorist, secretiden...</td>\n",
       "      <td>[ChristianBale, MichaelCaine, GaryOldman]</td>\n",
       "      <td>[ChristopherNolan]</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "      <td>[Action, Adventure, ScienceFiction]</td>\n",
       "      <td>[basedonnovel, mars, medallion, spacetravel, p...</td>\n",
       "      <td>[TaylorKitsch, LynnCollins, SamanthaMorton]</td>\n",
       "      <td>[AndrewStanton]</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                            overview  \\\n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
       "3  [Following, the, death, of, District, Attorney...   \n",
       "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
       "\n",
       "                                         genres  \\\n",
       "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "1                  [Adventure, Fantasy, Action]   \n",
       "2                    [Action, Adventure, Crime]   \n",
       "3              [Action, Crime, Drama, Thriller]   \n",
       "4           [Action, Adventure, ScienceFiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
       "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
       "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
       "3  [dccomics, crimefighter, terrorist, secretiden...   \n",
       "4  [basedonnovel, mars, medallion, spacetravel, p...   \n",
       "\n",
       "                                            cast                crew  \\\n",
       "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]      [JamesCameron]   \n",
       "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]     [GoreVerbinski]   \n",
       "2      [DanielCraig, ChristophWaltz, LéaSeydoux]         [SamMendes]   \n",
       "3      [ChristianBale, MichaelCaine, GaryOldman]  [ChristopherNolan]   \n",
       "4    [TaylorKitsch, LynnCollins, SamanthaMorton]     [AndrewStanton]   \n",
       "\n",
       "                                                tags  \n",
       "0  [In, the, 22nd, century,, a, paraplegic, Marin...  \n",
       "1  [Captain, Barbossa,, long, believed, to, be, d...  \n",
       "2  [A, cryptic, message, from, Bond’s, past, send...  \n",
       "3  [Following, the, death, of, District, Attorney...  \n",
       "4  [John, Carter, is, a, war-weary,, former, mili...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4b9f0c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = movies[['movie_id', 'title', 'tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32c95dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Avatar</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>9367</td>\n",
       "      <td>El Mariachi</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
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       "      <th>4807</th>\n",
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       "      <td>Shanghai Calling</td>\n",
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       "      <th>4808</th>\n",
       "      <td>25975</td>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>[Ever, since, the, second, grade, when, he, fi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>4806 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "      movie_id                                     title  \\\n",
       "0        19995                                    Avatar   \n",
       "1          285  Pirates of the Caribbean: At World's End   \n",
       "2       206647                                   Spectre   \n",
       "3        49026                     The Dark Knight Rises   \n",
       "4        49529                               John Carter   \n",
       "...        ...                                       ...   \n",
       "4804      9367                               El Mariachi   \n",
       "4805     72766                                 Newlyweds   \n",
       "4806    231617                 Signed, Sealed, Delivered   \n",
       "4807    126186                          Shanghai Calling   \n",
       "4808     25975                         My Date with Drew   \n",
       "\n",
       "                                                   tags  \n",
       "0     [In, the, 22nd, century,, a, paraplegic, Marin...  \n",
       "1     [Captain, Barbossa,, long, believed, to, be, d...  \n",
       "2     [A, cryptic, message, from, Bond’s, past, send...  \n",
       "3     [Following, the, death, of, District, Attorney...  \n",
       "4     [John, Carter, is, a, war-weary,, former, mili...  \n",
       "...                                                 ...  \n",
       "4804  [El, Mariachi, just, wants, to, play, his, gui...  \n",
       "4805  [A, newlywed, couple's, honeymoon, is, upended...  \n",
       "4806  [\"Signed,, Sealed,, Delivered\", introduces, a,...  \n",
       "4807  [When, ambitious, New, York, attorney, Sam, is...  \n",
       "4808  [Ever, since, the, second, grade, when, he, fi...  \n",
       "\n",
       "[4806 rows x 3 columns]"
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     },
     "execution_count": 37,
     "metadata": {},
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   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "557aa07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(lambda x: \" \".join(x))  # Converting list of tags to string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ff500176",
   "metadata": {},
   "outputs": [
    {
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       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                                tags  \n",
       "0  In the 22nd century, a paraplegic Marine is di...  \n",
       "1  Captain Barbossa, long believed to be dead, ha...  \n",
       "2  A cryptic message from Bond’s past sends him o...  \n",
       "3  Following the death of District Attorney Harve...  \n",
       "4  John Carter is a war-weary, former military ca...  "
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a81b5fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting all the tags to lowercase\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a509584b",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>285</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>captain barbossa, long believed to be dead, ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>a cryptic message from bond’s past sends him o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>following the death of district attorney harve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>john carter is a war-weary, former military ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                                     title  \\\n",
       "0     19995                                    Avatar   \n",
       "1       285  Pirates of the Caribbean: At World's End   \n",
       "2    206647                                   Spectre   \n",
       "3     49026                     The Dark Knight Rises   \n",
       "4     49529                               John Carter   \n",
       "\n",
       "                                                tags  \n",
       "0  in the 22nd century, a paraplegic marine is di...  \n",
       "1  captain barbossa, long believed to be dead, ha...  \n",
       "2  a cryptic message from bond’s past sends him o...  \n",
       "3  following the death of district attorney harve...  \n",
       "4  john carter is a war-weary, former military ca...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939d30c9",
   "metadata": {},
   "source": [
    "We now have the tags for each movie. We need to create a website where user will tell the name of a movie and the system needs to recommend 5 most related movies with it. How to judge out of almost 5000 movies which movies are similar? We have to measure similarity on the basis of tags. We need to calculate the similarity score between these texts/tags. How to do this? We'll use text vectorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c0f9092",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.snowball import SnowballStemmer   # Stemming the tags\n",
    "ss = SnowballStemmer('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "016f7625",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(text):\n",
    "    y = []\n",
    "    for i in text.split():\n",
    "        y.append(ss.stem(i))\n",
    "    \n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ce52ea71",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d3252432",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0af17ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features = 5000, stop_words = 'english')  # Choosing top 5000 words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "716d470b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = cv.fit_transform(new_df['tags']).toarray()  # Converting Sparse Matrix to a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3ea55e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7f96760e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '007',\n",
       " '10',\n",
       " '100',\n",
       " '11',\n",
       " '12',\n",
       " '13',\n",
       " '14',\n",
       " '15',\n",
       " '16',\n",
       " '17',\n",
       " '17th',\n",
       " '18',\n",
       " '18th',\n",
       " '18thcenturi',\n",
       " '19',\n",
       " '1930s',\n",
       " '1940s',\n",
       " '1944',\n",
       " '1950',\n",
       " '1950s',\n",
       " '1960',\n",
       " '1960s',\n",
       " '1970s',\n",
       " '1971',\n",
       " '1974',\n",
       " '1976',\n",
       " '1980',\n",
       " '1980s',\n",
       " '1985',\n",
       " '1990s',\n",
       " '1999',\n",
       " '19th',\n",
       " '19thcenturi',\n",
       " '20',\n",
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       " '2003',\n",
       " '2009',\n",
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       " '24',\n",
       " '25',\n",
       " '30',\n",
       " '300',\n",
       " '3d',\n",
       " '40',\n",
       " '50',\n",
       " '500',\n",
       " '60',\n",
       " '60s',\n",
       " '70',\n",
       " '70s',\n",
       " 'aaron',\n",
       " 'aaroneckhart',\n",
       " 'abandon',\n",
       " 'abduct',\n",
       " 'abigailbreslin',\n",
       " 'abil',\n",
       " 'abl',\n",
       " 'aboard',\n",
       " 'abov',\n",
       " 'abus',\n",
       " 'academ',\n",
       " 'academi',\n",
       " 'accept',\n",
       " 'access',\n",
       " 'accid',\n",
       " 'accident',\n",
       " 'acclaim',\n",
       " 'accompani',\n",
       " 'accomplish',\n",
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       " 'accus',\n",
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       " 'angelabassett',\n",
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       " 'anger',\n",
       " 'angle',\n",
       " 'angri',\n",
       " 'ani',\n",
       " 'anim',\n",
       " 'animalattack',\n",
       " 'animalhorror',\n",
       " 'animals',\n",
       " 'anjelicahuston',\n",
       " 'ann',\n",
       " 'anna',\n",
       " 'annafari',\n",
       " 'annakendrick',\n",
       " 'annehathaway',\n",
       " 'annemoss',\n",
       " 'annetteben',\n",
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       " 'brother',\n",
       " 'brotherbrotherrelationship',\n",
       " 'brothers',\n",
       " 'brothersisterrelationship',\n",
       " 'brought',\n",
       " 'brown',\n",
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       " 'brucegreenwood',\n",
       " 'brucewilli',\n",
       " 'brutal',\n",
       " 'bryansing',\n",
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       " 'buddi',\n",
       " 'buddycomedi',\n",
       " 'buddycop',\n",
       " 'budget',\n",
       " 'build',\n",
       " 'building',\n",
       " 'built',\n",
       " 'bullet',\n",
       " 'bulli',\n",
       " 'bumbl',\n",
       " 'bunch',\n",
       " 'bunker',\n",
       " 'bunni',\n",
       " 'burglar',\n",
       " 'buri',\n",
       " 'burn',\n",
       " 'bus',\n",
       " 'bush',\n",
       " 'busi',\n",
       " 'business',\n",
       " 'businessman',\n",
       " 'bust',\n",
       " 'butcher',\n",
       " 'butler',\n",
       " 'butt',\n",
       " 'button',\n",
       " 'buy',\n",
       " 'buzz',\n",
       " 'cabin',\n",
       " 'caesar',\n",
       " 'cage',\n",
       " 'cain',\n",
       " 'cairo',\n",
       " 'cal',\n",
       " 'california',\n",
       " 'calvin',\n",
       " 'camcord',\n",
       " 'came',\n",
       " 'camera',\n",
       " 'cameraman',\n",
       " 'camerondiaz',\n",
       " 'camp',\n",
       " 'campaign',\n",
       " 'campbel',\n",
       " 'campbell',\n",
       " 'campus',\n",
       " 'canada',\n",
       " 'canadian',\n",
       " 'cancer',\n",
       " 'candi',\n",
       " 'candid',\n",
       " 'canin',\n",
       " 'cannib',\n",
       " 'canuxploit',\n",
       " 'capabl',\n",
       " 'caper',\n",
       " 'capit',\n",
       " 'capt',\n",
       " 'captain',\n",
       " 'captiv',\n",
       " 'captur',\n",
       " 'capture',\n",
       " 'car',\n",
       " 'caraccid',\n",
       " 'carchas',\n",
       " 'carcrash',\n",
       " 'card',\n",
       " 'care',\n",
       " 'career',\n",
       " 'carefre',\n",
       " 'caretak',\n",
       " 'careymulligan',\n",
       " 'caribbean',\n",
       " 'carjourney',\n",
       " 'carl',\n",
       " 'carlagugino',\n",
       " 'carmen',\n",
       " 'carol',\n",
       " 'carolina',\n",
       " 'carrac',\n",
       " 'carri',\n",
       " 'carrie',\n",
       " 'cartel',\n",
       " 'carter',\n",
       " 'cartoon',\n",
       " 'caryelw',\n",
       " 'case',\n",
       " 'caseyaffleck',\n",
       " 'cash',\n",
       " 'casino',\n",
       " 'cast',\n",
       " 'castl',\n",
       " 'cat',\n",
       " 'cataclysm',\n",
       " 'catastroph',\n",
       " 'catch',\n",
       " 'cateblanchett',\n",
       " 'catherinedeneuv',\n",
       " 'catherinekeen',\n",
       " 'catherinezeta',\n",
       " 'cathol',\n",
       " 'catholic',\n",
       " 'cattl',\n",
       " 'caught',\n",
       " 'caus',\n",
       " 'cavalri',\n",
       " 'cave',\n",
       " 'cavemen',\n",
       " 'celebr',\n",
       " 'celebration',\n",
       " 'cell',\n",
       " 'cellphon',\n",
       " 'cemeteri',\n",
       " 'center',\n",
       " 'centr',\n",
       " 'central',\n",
       " 'centuri',\n",
       " 'centuries',\n",
       " 'century',\n",
       " 'ceo',\n",
       " 'certain',\n",
       " 'chad',\n",
       " 'chain',\n",
       " 'chainsaw',\n",
       " 'challeng',\n",
       " 'chamber',\n",
       " 'champion',\n",
       " 'championship',\n",
       " 'chanc',\n",
       " 'chance',\n",
       " 'chang',\n",
       " 'change',\n",
       " 'changed',\n",
       " 'changes',\n",
       " 'channingtatum',\n",
       " 'chao',\n",
       " 'chaos',\n",
       " 'chaotic',\n",
       " 'chapter',\n",
       " 'charact',\n",
       " 'character',\n",
       " 'characters',\n",
       " 'charg',\n",
       " 'charismat',\n",
       " 'charl',\n",
       " 'charli',\n",
       " 'charlie',\n",
       " 'charliesheen',\n",
       " 'charlizetheron',\n",
       " 'charm',\n",
       " 'chart',\n",
       " 'chase',\n",
       " 'chauffeur',\n",
       " 'chazzpalminteri',\n",
       " 'cheat',\n",
       " 'check',\n",
       " 'cheerlead',\n",
       " 'chef',\n",
       " 'chemic',\n",
       " 'cher',\n",
       " 'chevychas',\n",
       " 'chicago',\n",
       " 'chicken',\n",
       " 'chief',\n",
       " 'child',\n",
       " 'childabus',\n",
       " 'childhero',\n",
       " 'childhood',\n",
       " 'childprodigi',\n",
       " 'children',\n",
       " 'chill',\n",
       " 'chimp',\n",
       " 'china',\n",
       " 'chines',\n",
       " 'chip',\n",
       " 'chipmunk',\n",
       " 'chiwetelejiofor',\n",
       " 'chloe',\n",
       " 'chloëgracemoretz',\n",
       " 'chloësevigni',\n",
       " 'chocol',\n",
       " 'choic',\n",
       " 'choice',\n",
       " 'choos',\n",
       " 'chosen',\n",
       " 'chowyun',\n",
       " 'chris',\n",
       " 'chriscolumbus',\n",
       " 'chriscoop',\n",
       " 'chrisevan',\n",
       " 'chrishemsworth',\n",
       " 'chrisklein',\n",
       " 'chrispin',\n",
       " 'chrisrock',\n",
       " 'christ',\n",
       " 'christian',\n",
       " 'christianbal',\n",
       " 'christianslat',\n",
       " 'christin',\n",
       " 'christinaappleg',\n",
       " 'christinaricci',\n",
       " 'christma',\n",
       " 'christmas',\n",
       " 'christmasparti',\n",
       " 'christmastre',\n",
       " 'christoph',\n",
       " 'christopherlambert',\n",
       " 'christopherlloyd',\n",
       " 'christophernolan',\n",
       " 'christopherplumm',\n",
       " 'christopherwalken',\n",
       " 'christophwaltz',\n",
       " 'chrisweitz',\n",
       " 'chronicl',\n",
       " 'chuck',\n",
       " 'church',\n",
       " 'cia',\n",
       " 'ciaránhind',\n",
       " 'cigarettesmok',\n",
       " 'cillianmurphi',\n",
       " 'cinema',\n",
       " 'circl',\n",
       " 'circuit',\n",
       " 'circumst',\n",
       " 'circus',\n",
       " 'citi',\n",
       " 'citizen',\n",
       " 'city',\n",
       " 'civil',\n",
       " 'civilian',\n",
       " 'civilwar',\n",
       " 'claim',\n",
       " 'clair',\n",
       " 'clairedan',\n",
       " 'claireforlani',\n",
       " 'clan',\n",
       " 'clark',\n",
       " 'clash',\n",
       " 'class',\n",
       " 'classdiffer',\n",
       " 'classic',\n",
       " 'classmat',\n",
       " 'classroom',\n",
       " 'claudevandamm',\n",
       " 'claus',\n",
       " 'clay',\n",
       " 'clean',\n",
       " 'clear',\n",
       " 'clerk',\n",
       " 'clever',\n",
       " 'client',\n",
       " 'cliff',\n",
       " 'climat',\n",
       " 'climb',\n",
       " 'clinteastwood',\n",
       " 'cliqu',\n",
       " 'cliveowen',\n",
       " 'clock',\n",
       " 'clone',\n",
       " 'close',\n",
       " 'closer',\n",
       " 'cloud',\n",
       " 'clown',\n",
       " 'club',\n",
       " 'clue',\n",
       " 'clueless',\n",
       " 'clutch',\n",
       " 'coach',\n",
       " 'coast',\n",
       " 'cocain',\n",
       " 'code',\n",
       " 'codi',\n",
       " 'coffin',\n",
       " 'cohen',\n",
       " 'col',\n",
       " 'cold',\n",
       " 'coldwar',\n",
       " 'cole',\n",
       " 'colehaus',\n",
       " 'colin',\n",
       " 'colinfarrel',\n",
       " 'colinfirth',\n",
       " 'collaps',\n",
       " 'colleagu',\n",
       " 'colleague',\n",
       " 'collect',\n",
       " 'collector',\n",
       " 'colleg',\n",
       " 'college',\n",
       " 'collid',\n",
       " 'collis',\n",
       " 'colombia',\n",
       " 'colonel',\n",
       " 'coloni',\n",
       " 'color',\n",
       " 'colorado',\n",
       " 'coma',\n",
       " 'combat',\n",
       " 'combin',\n",
       " 'come',\n",
       " 'comeback',\n",
       " 'comed',\n",
       " 'comedi',\n",
       " 'comedian',\n",
       " 'comedy',\n",
       " 'comet',\n",
       " 'comfort',\n",
       " 'comic',\n",
       " 'coming',\n",
       " 'comingofag',\n",
       " 'comingout',\n",
       " 'command',\n",
       " 'commando',\n",
       " 'commerci',\n",
       " 'commiss',\n",
       " 'commit',\n",
       " 'common',\n",
       " 'communic',\n",
       " 'communist',\n",
       " 'communiti',\n",
       " 'community',\n",
       " 'compani',\n",
       " 'companion',\n",
       " 'company',\n",
       " 'compet',\n",
       " 'competit',\n",
       " 'competition',\n",
       " 'complet',\n",
       " 'complex',\n",
       " 'complic',\n",
       " 'compos',\n",
       " 'compuls',\n",
       " 'comput',\n",
       " 'computervirus',\n",
       " 'conan',\n",
       " 'concern',\n",
       " 'concert',\n",
       " 'concoct',\n",
       " 'condit',\n",
       " 'condition',\n",
       " 'conduct',\n",
       " 'confeder',\n",
       " 'confess',\n",
       " 'confid',\n",
       " 'confin',\n",
       " 'conflict',\n",
       " 'confront',\n",
       " 'confus',\n",
       " 'congress',\n",
       " 'conman',\n",
       " 'connect',\n",
       " 'connecticut',\n",
       " 'connel',\n",
       " 'connor',\n",
       " 'conquer',\n",
       " 'consequ',\n",
       " 'consequences',\n",
       " 'conserv',\n",
       " 'consid',\n",
       " 'conspir',\n",
       " 'conspiraci',\n",
       " 'conspiracy',\n",
       " 'constant',\n",
       " 'construct',\n",
       " 'consum',\n",
       " 'contact',\n",
       " 'contain',\n",
       " 'contemporari',\n",
       " 'contend',\n",
       " 'content',\n",
       " 'contest',\n",
       " 'contin',\n",
       " 'continu',\n",
       " 'contract',\n",
       " 'contractor',\n",
       " 'control',\n",
       " 'controversi',\n",
       " 'convent',\n",
       " ...]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names()  # These are the top 5000 words that are in our corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab15e28d",
   "metadata": {},
   "source": [
    "Now, we need to calculate the distance of each movie (vector - 4806 in total) with every other movie (vector). Higher the distance, less the similarity. The Euclidean distance is not a good measure in higher dimensions datasets. It is not a reliable measure. We'll use the cosine distance in which we measure the angle between 2 vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce395628",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity   # Inversely proportional to distance. Always between 0 and 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393add62",
   "metadata": {},
   "source": [
    "Calculating similarity of each vector with itself and 4805 other vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4ac60dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a8fe1eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.08471737, 0.0860309 , ..., 0.04543109, 0.        ,\n",
       "        0.        ],\n",
       "       [0.08471737, 1.        , 0.06154575, ..., 0.02437575, 0.        ,\n",
       "        0.        ],\n",
       "       [0.0860309 , 0.06154575, 1.        , ..., 0.02475369, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.04543109, 0.02437575, 0.02475369, ..., 1.        , 0.04174829,\n",
       "        0.0437374 ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.04174829, 1.        ,\n",
       "        0.04656202],\n",
       "       [0.        , 0.        , 0.        , ..., 0.0437374 , 0.04656202,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "41ee6ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 4806)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "977136c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.08471737, 0.0860309 , ..., 0.04543109, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58bdda45",
   "metadata": {},
   "source": [
    "Similarity of first movie with itself and other movies. The similarity of first movie with itself is 1, with the 2nd movie is 0.084 and so on.   \n",
    "If we sort this to get the top 5 similar movies to the first movie then we'll lose the index of the movies. Therefore, we need to keep a hold of the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e4d68fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(movie):\n",
    "    movie_index = new_df[new_df['title'] == movie].index[0]\n",
    "    distances = similarity[movie_index]\n",
    "    # Extracting the top 5 movies, alongwith keeping a hold of their index and sorting based off the similarity score and not \n",
    "    # the index.\n",
    "    top_5 = sorted(list(enumerate(distances)), reverse = True, key = lambda x: x[1])[1:6]\n",
    "\n",
    "    for i in top_5:\n",
    "        print(new_df.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "743abe7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aliens vs Predator: Requiem\n",
      "Aliens\n",
      "Falcon Rising\n",
      "Independence Day\n",
      "Titan A.E.\n"
     ]
    }
   ],
   "source": [
    "recommend('Avatar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "573a624d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum of Solace\n",
      "Never Say Never Again\n",
      "Skyfall\n",
      "From Russia with Love\n",
      "Octopussy\n"
     ]
    }
   ],
   "source": [
    "recommend('Spectre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "db495149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mission: Impossible II\n",
      "Mission: Impossible III\n",
      "The November Man\n",
      "Bad Company\n",
      "Mission: Impossible - Rogue Nation\n"
     ]
    }
   ],
   "source": [
    "recommend('Mission: Impossible')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "15256b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle   # This for Model Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9813c283",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(new_df, open('movies.pkl', 'wb'))  # Dumping new_df in a file named 'movies.pkl' in write binary (wb) mode."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b006c81a",
   "metadata": {},
   "source": [
    "We need the full movie list on our website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae72b66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(similarity, open('similarity.pkl', 'wb'))"
   ]
  }
 ],
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