movie-recommender-system.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfa33dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing liberay that we need in this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efb9bdb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#assign the varible to each data set to its easy to handle\n",
    "movies = pd.read_csv('tmdb_5000_movies.csv')\n",
    "credits = pd.read_csv('tmdb_5000_credits.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8af64e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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  \\\n",
       "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "\n",
       "                       tagline   title  vote_average  vote_count  \n",
       "0  Enter the World of Pandora.  Avatar           7.2       11800  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking in the movies heading\n",
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a7046c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   movie_id   title                                               cast  \\\n",
       "0     19995  Avatar  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#it show us the heading of credits dataset\n",
    "credits.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19365ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging both dataset on bases of titles\n",
    "#reassign the dataset name by movies and adding data in movies varible that we created already on start\n",
    "movies = movies.merge(credits,on='title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "04ea689f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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      ],
      "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seeing the merge dataset head\n",
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc6aada0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Thing that we are only taking in data set and except we are deleting so over data will short and valuable as per need\n",
    "#we use same movies varible that we assign earlier\n",
    "#genres\n",
    "#id\n",
    "#keywords\n",
    "#title\n",
    "#overview\n",
    "#cast\n",
    "#crew\n",
    "movies = movies[['movie_id', 'title', 'overview', 'genres', 'keywords', 'cast', 'crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d4e5f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_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",
       "    </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>[{\"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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seeing the dataset after take usefull things only \n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f84ef2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from this onward we are creating new dataset with 3 table only\n",
    "#movie_id/title/tags\n",
    "#movies_id are same // title are same // but for tags we are merging (overview + geners + keywords + cast + Crew)\n",
    "#the tags data are in weird format so we first simplyfy the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99f6cde3",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are finding any missing data from the set\n",
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec387a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have 3 missing data in overview \n",
    "#we are droping the missing data that we found inshort deleting the 3 missing data movies\n",
    "movies.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ab6e566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "overview    0\n",
       "genres      0\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now review that the missing data is avilable or not now afte \n",
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eac38a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we are finding any duplicate data from this set\n",
    "movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ed09dfa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seeing inside the genres colum so we can know how to filter that \n",
    "movies.iloc[0].genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6be43ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorting the genres so we can simply read that by doing helper function by only read genres name \n",
    "#now the genres are string so we have to use we have to convert to integer \n",
    "#so in python we have ast module that we can use\n",
    "def convert(obj):\n",
    "    L = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        L.append(i['name'])\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34073b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "4804                        [Action, Crime, Thriller]\n",
       "4805                                [Comedy, Romance]\n",
       "4806               [Comedy, Drama, Romance, TV Movie]\n",
       "4807                                               []\n",
       "4808                                    [Documentary]\n",
       "Name: genres, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bde03241",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c73b3796",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>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>[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",
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       "      <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",
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       "      <td>206647</td>\n",
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       "      <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",
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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",
       "                                            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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0659ffad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#same filtering system that we use in genres we add in keywords\n",
    "#we are using same def function that we created on genres so we dont have to writ all that again\n",
    "movies['keywords'] = movies['keywords'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "22f868f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>19995</td>\n",
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       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
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       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <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>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
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       "      <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>[{\"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",
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       "      <td>[Action, Adventure, Science Fiction]</td>\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",
       "                                            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  [{\"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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae848eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we are sorting the cast so we have to pick only top 3 actror and rest we have to delete \n",
    "#by the same function that we use on top both genres or keyword but adding some modification using counter we use that to shown top 3 actor on movies\n",
    "def convert3(obj):\n",
    "    L = []\n",
    "    counter = 0\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if counter != 3:\n",
    "            L.append(i['name'])\n",
    "            counter+=1\n",
    "        else:\n",
    "            break\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "219f88df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "4804    [Carlos Gallardo, Jaime de Hoyos, Peter Marqua...\n",
       "4805         [Edward Burns, Kerry Bishé, Marsha Dietlein]\n",
       "4806           [Eric Mabius, Kristin Booth, Crystal Lowe]\n",
       "4807            [Daniel Henney, Eliza Coupe, Bill Paxton]\n",
       "4808    [Drew Barrymore, Brian Herzlinger, Corey Feldman]\n",
       "Name: cast, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d246b060",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are adding the new filter cast in the same movie function that we created\n",
    "movies['cast'] = movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2860f63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19995</td>\n",
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       "      <td>Pirates of the Caribbean: At World's End</td>\n",
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       "      <td>[Adventure, Fantasy, Action]</td>\n",
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       "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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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",
       "                                            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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking the new filter cast are showing in data \n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "85c19dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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{\"credit_id\": \"54959f07c3a3680fc60027f9\", \"department\": \"Camera\", \"gender\": 2, \"id\": 1401806, \"job\": \"Still Photographer\", \"name\": \"Mark Fellman\"}, {\"credit_id\": \"54959f47c3a3681153002774\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1401807, \"job\": \"Lighting Technician\", \"name\": \"Scott Sprague\"}, {\"credit_id\": \"54959f8cc3a36831b8001df2\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401808, \"job\": \"Animation Director\", \"name\": \"Jeremy Hollobon\"}, {\"credit_id\": \"54959fa0c3a36831b8001dfb\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401809, \"job\": \"Animation Director\", \"name\": \"Orlando Meunier\"}, {\"credit_id\": \"54959fb6c3a3686af3003f54\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1401810, \"job\": \"Animation Director\", \"name\": \"Taisuke Tanimura\"}, {\"credit_id\": \"54959fd2c3a36831b8001e02\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1401812, \"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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#chekcing the crew column to know how to filter\n",
    "movies['crew'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "44622540",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we are only adding director in crew\n",
    "#because most of fans watch movie by whom is directed \n",
    "def fetch_director(obj):\n",
    "    L = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if i['job'] == 'Director':\n",
    "            L.append(i['name'])\n",
    "            break\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d5d20b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           [James Cameron]\n",
       "1          [Gore Verbinski]\n",
       "2              [Sam Mendes]\n",
       "3       [Christopher Nolan]\n",
       "4          [Andrew Stanton]\n",
       "               ...         \n",
       "4804     [Robert Rodriguez]\n",
       "4805         [Edward Burns]\n",
       "4806          [Scott Smith]\n",
       "4807          [Daniel Hsia]\n",
       "4808     [Brian Herzlinger]\n",
       "Name: crew, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ae01c655",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are adding the new filter crew in the same movie function that we created\n",
    "movies['crew'] = movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8c000abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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",
       "    </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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking the new filter crew are appearing in data \n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4deb84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we are cheking overview column so we can filter it by according to over need\n",
    "movies['overview'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "281fd54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: overview, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#overview colum  is string so we are converting in list because we can concatenate with other list\n",
    "#now we are using lambda function to create a string into list \n",
    "#if we find x then it will split x\n",
    "movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7863d958",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we are adding the new filter crew in the same movie function that we created\n",
    "movies['overview'] = movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1a7d4818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking the new filter overview are appearing in data \n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e2cd773c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we are removing the spaces between the two names becasue the suppse there is two person in the same staring name but ending name is differnet so\n",
    "#if we remove the space the two same name sam is become indival because of its surname \n",
    "#and it will not confused or model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d604c8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           [JamesCameron]\n",
       "1          [GoreVerbinski]\n",
       "2              [SamMendes]\n",
       "3       [ChristopherNolan]\n",
       "4          [AndrewStanton]\n",
       "               ...        \n",
       "4804     [RobertRodriguez]\n",
       "4805         [EdwardBurns]\n",
       "4806          [ScottSmith]\n",
       "4807          [DanielHsia]\n",
       "4808     [BrianHerzlinger]\n",
       "Name: crew, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we first applying on genres \n",
    "# now we are using lambda function\n",
    "#we are use list comprehension i dot replace any places with nothing for i in x\n",
    "#simmilarly the same we adding for other colum that we need to remove spaces \n",
    "movies['genres'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['keywords'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['cast'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['crew'].apply(lambda x:[i.replace(\" \",\"\") for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "212f1ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we remove space in genres,cast,crew,keywords so we can return update this on data with same function movie\n",
    "movies['genres'] = movies['genres'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['keywords'] = movies['keywords'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['cast'] = movies['cast'].apply(lambda x:[i.replace(\" \",\"\") for i in x])\n",
    "movies['crew'] = movies['crew'].apply(lambda x:[i.replace(\" \",\"\") for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ea2d481b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_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, ScienceFiction]</td>\n",
       "      <td>[cultureclash, future, spacewar, spacecolony, ...</td>\n",
       "      <td>[SamWorthington, ZoeSaldana, SigourneyWeaver]</td>\n",
       "      <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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking the updates genres in dataset \n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bb3b3ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now the main task is we are creating the new tags and this colum we add this 4-5 pre existed colum\n",
    "#we are concatenate the column and create the new column knwo as \"TAG\"\n",
    "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "66577730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
       "      <th>tags</th>\n",
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       "  <tbody>\n",
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       "      <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, 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",
       "    </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",
       "      <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",
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       "      <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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we check the new column that we created meriging 5 columns\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c111f4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we get the tag column but we dont need the other column that we add in tags so we remove that\n",
    "#for that we create a new data frame \n",
    "new_df = movies[['movie_id','title','tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dfc7715b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>The Dark Knight Rises</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>9367</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>[El, Mariachi, just, wants, to, play, his, gui...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>72766</td>\n",
       "      <td>Newlyweds</td>\n",
       "      <td>[A, newlywed, couple's, honeymoon, is, upended...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>231617</td>\n",
       "      <td>Signed, Sealed, Delivered</td>\n",
       "      <td>[\"Signed,, Sealed,, Delivered\", introduces, a,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4807</th>\n",
       "      <td>126186</td>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>[When, ambitious, New, York, attorney, Sam, is...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "</table>\n",
       "<p>4806 rows × 3 columns</p>\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",
       "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]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the new dataframe that we created new_df\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "70ab6d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "4804    El Mariachi just wants to play his guitar and ...\n",
       "4805    A newlywed couple's honeymoon is upended by th...\n",
       "4806    \"Signed, Sealed, Delivered\" introduces a dedic...\n",
       "4807    When ambitious New York attorney Sam is sent t...\n",
       "4808    Ever since the second grade when he first saw ...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now the tag column are list because we have to merge we created it list already but now we dont need list so we convert into string\n",
    "#now we are doing to remove space is use lambda function\n",
    "#x we have to join x in each space \n",
    "new_df['tags'].apply(lambda x:\" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "50122590",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meenakshi patil\\AppData\\Local\\Temp\\ipykernel_1856\\1170533445.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(lambda x:\" \".join(x))\n"
     ]
    }
   ],
   "source": [
    "#now we created a new tags so we have to add this tag in old tags function so it can appear in dataset\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x:\" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b91f2f6f",
   "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>tags</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",
       "    </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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking the new tag funtion that appear as string or not\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dae27344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization. Action Adventure Fantasy ScienceFiction cultureclash future spacewar spacecolony society spacetravel futuristic romance space alien tribe alienplanet cgi marine soldier battle loveaffair antiwar powerrelations mindandsoul 3d SamWorthington ZoeSaldana SigourneyWeaver JamesCameron'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now lets see the tags function deeply so what we get for now\n",
    "new_df['tags'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "400e7160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "4804    el mariachi just wants to play his guitar and ...\n",
       "4805    a newlywed couple's honeymoon is upended by th...\n",
       "4806    \"signed, sealed, delivered\" introduces a dedic...\n",
       "4807    when ambitious new york attorney sam is sent t...\n",
       "4808    ever since the second grade when he first saw ...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now the tags are in some part is uppar case so are converting into lower case as per onlin suggestion\n",
    "#now we are using lamda to lower the case \n",
    "#if we find x then x will lower case\n",
    "new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2b5fccf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meenakshi patil\\AppData\\Local\\Temp\\ipykernel_1856\\3631261567.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(lambda x:x.lower())\n"
     ]
    }
   ],
   "source": [
    "#now return we update this tags function to preexisted tags so it will appear on the data set\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c785c9ea",
   "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>tags</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",
       "    </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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now check the return data that the new tags are appear or not\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cf483f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-------------------------------------------------------------------------------------------#\n",
    "# we are doing this for because of multiple text with same meaning we have to gave them one name so it didnt confuse model after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "daf6e915",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b116a8ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "12f30ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(text):\n",
    "    y = []\n",
    "    for i in text.split():\n",
    "        y.append(ps.stem(i))\n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ed6d7d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       in the 22nd century, a parapleg marin is dispa...\n",
       "1       captain barbossa, long believ to be dead, ha c...\n",
       "2       a cryptic messag from bond’ past send him on a...\n",
       "3       follow the death of district attorney harvey d...\n",
       "4       john carter is a war-weary, former militari ca...\n",
       "                              ...                        \n",
       "4804    el mariachi just want to play hi guitar and ca...\n",
       "4805    a newlyw couple' honeymoon is upend by the arr...\n",
       "4806    \"signed, sealed, delivered\" introduc a dedic q...\n",
       "4807    when ambiti new york attorney sam is sent to s...\n",
       "4808    ever sinc the second grade when he first saw h...\n",
       "Name: tags, Length: 4806, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fe0a8094",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meenakshi patil\\AppData\\Local\\Temp\\ipykernel_1856\\3213734980.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['tags'] = new_df['tags'].apply(stem)\n"
     ]
    }
   ],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "da344f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#-------------------------------------------------------------------------------------------#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "77b9c99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we are doing text vectorization\n",
    "#now we recommend the movie on bases of tags "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "539a2b56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'in the 22nd century, a parapleg marin is dispatch to the moon pandora on a uniqu mission, but becom torn between follow order and protect an alien civilization. action adventur fantasi sciencefict cultureclash futur spacewar spacecoloni societi spacetravel futurist romanc space alien tribe alienplanet cgi marin soldier battl loveaffair antiwar powerrel mindandsoul 3d samworthington zoesaldana sigourneyweav jamescameron'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#eg1:\n",
    "new_df['tags'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b24a7093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"captain barbossa, long believ to be dead, ha come back to life and is head to the edg of the earth with will turner and elizabeth swann. but noth is quit as it seems. adventur fantasi action ocean drugabus exoticisland eastindiatradingcompani loveofone'slif traitor shipwreck strongwoman ship allianc calypso afterlif fighter pirat swashbuckl aftercreditssting johnnydepp orlandobloom keiraknightley goreverbinski\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#eg2:\n",
    "new_df['tags'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c196ca63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#so according to the eg1 and eg2 we have to find a way to comapare the text similarity of both the example and recommend it to user\n",
    "#so our strategy is to convert the text into vector t\n",
    "#and while doing text vectorization we didnt conside stop words for do this we use scikit-learn liberay we use countvectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cd91d493",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we are extracting scikit learn liberay \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=5000,stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8975fe80",
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting tags into array\n",
    "cv.fit_transform(new_df['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d0d6d7cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 5000)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#after converting array we saw the size of array\n",
    "cv.fit_transform(new_df['tags']).toarray().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "63361bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#and after that we are assign the varible to this function so we easily call and save the data \n",
    "vectors = cv.fit_transform(new_df['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1c2d45fb",
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we are checking the varible vector so it can show proper data or not \n",
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d70f639a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meenakshi patil\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '007',\n",
       " '10',\n",
       " '100',\n",
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       " '14',\n",
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       " '1940',\n",
       " '1944',\n",
       " '1950',\n",
       " '1950s',\n",
       " '1960',\n",
       " '1960s',\n",
       " '1970',\n",
       " '1970s',\n",
       " '1971',\n",
       " '1974',\n",
       " '1976',\n",
       " '1980',\n",
       " '1985',\n",
       " '1990',\n",
       " '1999',\n",
       " '19th',\n",
       " '19thcenturi',\n",
       " '20',\n",
       " '200',\n",
       " '2003',\n",
       " '2009',\n",
       " '20th',\n",
       " '21st',\n",
       " '23',\n",
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       " 'ciaránhind',\n",
       " 'cigarettesmok',\n",
       " 'cillianmurphi',\n",
       " 'cinema',\n",
       " 'circl',\n",
       " 'circu',\n",
       " 'circuit',\n",
       " 'circumst',\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",
       " 'clay',\n",
       " 'clean',\n",
       " 'clear',\n",
       " 'clerk',\n",
       " 'clever',\n",
       " 'client',\n",
       " 'clients',\n",
       " 'cliff',\n",
       " 'climat',\n",
       " 'climb',\n",
       " 'clinteastwood',\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",
       " 'coffin',\n",
       " 'cohen',\n",
       " 'col',\n",
       " 'cold',\n",
       " 'coldwar',\n",
       " 'cole',\n",
       " 'colin',\n",
       " 'colinfarrel',\n",
       " 'colinfirth',\n",
       " 'collaps',\n",
       " 'colleagu',\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",
       " 'commun',\n",
       " 'communist',\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",
       " 'computerviru',\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",
       " 'constantli',\n",
       " 'construct',\n",
       " 'consum',\n",
       " 'contact',\n",
       " 'contain',\n",
       " 'contemporari',\n",
       " 'contend',\n",
       " 'content',\n",
       " 'contest',\n",
       " 'continu',\n",
       " 'contract',\n",
       " 'contractor',\n",
       " 'control',\n",
       " 'controversi',\n",
       " 'convent',\n",
       " 'converg',\n",
       " 'convers',\n",
       " 'convict',\n",
       " 'convinc',\n",
       " 'cook',\n",
       " ...]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now checking the most frequent 5000 words \n",
    "cv.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d9a077e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#so now main problem with array is there are so many word with same meaning so we have to merge that and fix that issue \n",
    "#but that issue we fix in upar code using nltk libery and using stem method\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f1c14375",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we are measuring the thita between vectore angle to recommendation\n",
    "#we have libery for that in sikitlearn we are using that\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "81f90e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.08346223, 0.0860309 , ..., 0.04499213, 0.        ,\n",
       "        0.        ],\n",
       "       [0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
       "        0.02615329],\n",
       "       [0.0860309 , 0.06063391, 1.        , ..., 0.02451452, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.04499213, 0.02378257, 0.02451452, ..., 1.        , 0.03962144,\n",
       "        0.04229549],\n",
       "       [0.        , 0.        , 0.        , ..., 0.03962144, 1.        ,\n",
       "        0.08714204],\n",
       "       [0.        , 0.02615329, 0.        , ..., 0.04229549, 0.08714204,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ce183ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 4806)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarity(vectors).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bb8d5173",
   "metadata": {},
   "outputs": [],
   "source": [
    "#assign the varible to function so its easy to call\n",
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "681188f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
       "       0.02615329])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dab62a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we creating a function to recommend the 5 movies from index\n",
    "def recommend(movie):\n",
    "    movie_index = new_df[new_df['title'] == movie].index[0] #its function that find index value from table\n",
    "    distances = similarity[movie_index] #measuring distance of array\n",
    "    # For next step we sort the list with emurate function so it convert with tuple \n",
    "    # Then it will reverse the list and then its will give 5 simmilar cordinate suggestion using lambda function\n",
    "    movies_list = sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[1:6] \n",
    "    for i in movies_list:\n",
    "        print(new_df.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "faa03067",
   "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": 93,
   "id": "49e48d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to send movies list from here to frontend\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dc706a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(new_df,open('movies.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e24fb5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#but because of silt problem streamlit doesnt supports pandas file so we have to convert our file into dictory to import to frontemd \n",
    "pickle.dump(new_df.to_dict(),open('movie_dict.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e8f3a20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we dont have simialriy so get that we use simialrity pickle\n",
    "pickle.dump(similarity,open('similarity.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d1ef30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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