movie_recommender_sys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01bb3a07-c36a-4a07-8976-9e71f574733d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ead139e3-2934-4641-b73f-7dfd7217aa37",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('tmdb_5000_movies.csv')\n",
    "credits = pd.read_csv('tmdb_5000_credits.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdcf6b00-0849-4eeb-8a78-9baafa2ae1a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.merge(credits, on='title')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53583c60",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "badd5a18-5555-470a-9663-ffd33bb00676",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>...</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>...</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
       "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                      homepage     id  \\\n",
       "0  http://www.avatarmovie.com/  19995   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "\n",
       "  original_title                                           overview  \\\n",
       "0         Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "   popularity                               production_companies  ... runtime  \\\n",
       "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   162.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "\n",
       "                       tagline   title vote_average vote_count movie_id  \\\n",
       "0  Enter the World of Pandora.  Avatar          7.2      11800    19995   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2d3101a-53c6-4b3f-99b8-609cd0adddbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4809 entries, 0 to 4808\n",
      "Data columns (total 23 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   budget                4809 non-null   int64  \n",
      " 1   genres                4809 non-null   object \n",
      " 2   homepage              1713 non-null   object \n",
      " 3   id                    4809 non-null   int64  \n",
      " 4   keywords              4809 non-null   object \n",
      " 5   original_language     4809 non-null   object \n",
      " 6   original_title        4809 non-null   object \n",
      " 7   overview              4806 non-null   object \n",
      " 8   popularity            4809 non-null   float64\n",
      " 9   production_companies  4809 non-null   object \n",
      " 10  production_countries  4809 non-null   object \n",
      " 11  release_date          4808 non-null   object \n",
      " 12  revenue               4809 non-null   int64  \n",
      " 13  runtime               4807 non-null   float64\n",
      " 14  spoken_languages      4809 non-null   object \n",
      " 15  status                4809 non-null   object \n",
      " 16  tagline               3965 non-null   object \n",
      " 17  title                 4809 non-null   object \n",
      " 18  vote_average          4809 non-null   float64\n",
      " 19  vote_count            4809 non-null   int64  \n",
      " 20  movie_id              4809 non-null   int64  \n",
      " 21  cast                  4809 non-null   object \n",
      " 22  crew                  4809 non-null   object \n",
      "dtypes: float64(3), int64(5), object(15)\n",
      "memory usage: 864.2+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ca64925-46cf-4666-a123-4b6110700301",
   "metadata": {},
   "outputs": [],
   "source": [
    "#task is now to remove unnecessary col and keep what we want\n",
    "#generes\n",
    "#id\n",
    "#keywords\n",
    "#title\n",
    "#cast\n",
    "#crew\n",
    "\n",
    "movies = movies[['movie_id', 'title', 'overview', 'genres', 'keywords', 'cast', 'crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fdaa5be-d6b8-42ef-ab35-c13888b21bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
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       "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
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      ],
      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
       "\n",
       "                                                cast  \\\n",
       "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "df5799a7-63ec-4246-874e-42346db9c985",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00597f8d-b99c-4733-9b44-3fc882e2ade5",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26354145-7861-4e55-9ec3-4328fcb8a514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "967bedab-e0c8-4860-a503-5952f6de0756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[0].genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc240e22-5896-48be-be52-5c900be97a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we want to change the format of genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4866a525-d601-4bff-ac83-a6b4ff66a261",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "# Function to convert genres string to a list of genre names\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": 14,
   "id": "b97198ef-5b8b-4942-a424-b83276c75a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "303b3706-9b19-433c-983b-1b003abad6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['keywords'] = movies['keywords'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "950f6aa6-5c39-4606-8cfe-a5a151da0828",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for top 3 cast\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": 17,
   "id": "605c6985-5055-4259-b5ad-a9853a124d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'] = movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e2f9d8f-e3b7-4db2-a8bc-dbbddc5bddcb",
   "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",
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       "      <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",
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      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "\n",
       "                                               cast  \\\n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]   \n",
       "\n",
       "                                                crew  \n",
       "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "671f42d4-6fc7-4042-89ca-3287f0adb110",
   "metadata": {},
   "outputs": [
    {
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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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['crew'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "62abbb2b-9f34-4775-95b4-ec61279f3d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to extract the director's name from the crew data\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'])  # Append the director's name to the list\n",
    "            break  \n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f165b3af-8adf-4bed-a772-3d92625e75c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['crew'] = movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df920bf2-7a73-42ef-a44c-a9d7f873166c",
   "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",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
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       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
       "      <td>[James Cameron]</td>\n",
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      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "\n",
       "                                               cast             crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]  [James Cameron]  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "31b49ccc-dd3c-48a9-b89a-23ce966668d0",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['overview'][0]\n",
    "#this gives us string now we'll convert it to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b64f91f9-3c62-4d9c-ba1f-d5afaea34544",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['overview']=movies['overview'].apply(lambda x : x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "31cb2331-b392-48fa-8755-1e2fff4248e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
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       "      <th>genres</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, 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",
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       "</table>\n",
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      ],
      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "\n",
       "                                          genres  \\\n",
       "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [culture clash, future, space war, space colon...   \n",
       "\n",
       "                                               cast             crew  \n",
       "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]  [James Cameron]  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "213e1530-bb22-4eff-afe6-abf7ae87de92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove spaces between words in the names\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": 27,
   "id": "6069b1de-ba32-40c3-be96-1d9208f17140",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
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       "      <td>[Action, Adventure, Fantasy, ScienceFiction]</td>\n",
       "      <td>[cultureclash, future, spacewar, spacecolony, ...</td>\n",
       "      <td>[SamWorthington, ZoeSaldana, SigourneyWeaver]</td>\n",
       "      <td>[JamesCameron]</td>\n",
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      ],
      "text/plain": [
       "   movie_id   title                                           overview  \\\n",
       "0     19995  Avatar  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
       "\n",
       "                                         genres  \\\n",
       "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
       "\n",
       "                                            cast            crew  \n",
       "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]  [JamesCameron]  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "movies.head(1)"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "989f9f18-704d-449d-9fdc-0910b508d730",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['tags'] = movies['overview']+movies['genres']+movies['keywords']+movies['cast']+movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ab698c87-df66-4c50-8955-22e375a80ffa",
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <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",
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       "      <td>Spectre</td>\n",
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       "      <td>[Action, Crime, Drama, Thriller]</td>\n",
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       "      <td>[ChristianBale, MichaelCaine, GaryOldman]</td>\n",
       "      <td>[ChristopherNolan]</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
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       "    <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",
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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, 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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7dff7079-0790-4707-b7a5-f6b9419b8542",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = movies[['movie_id','title','tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "11fa8006-1758-4779-a692-81c32b7ab68b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4eae4c48-a4c4-4477-8f45-fbf10d17b918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New dataset size: (3500, 3)\n"
     ]
    }
   ],
   "source": [
    "num_to_drop = len(new_df) - 3500\n",
    "new_df = new_df.iloc[:-num_to_drop]\n",
    "\n",
    "# Print the size of the new dataset\n",
    "print(f\"New dataset size: {new_df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "889d6280-d4bd-4cdc-bc14-f9c5b3eb2032",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert list to string in 'tags' column\n",
    "new_df['tags'] = new_df['tags'].apply(lambda x: \" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9db968fa-6202-4e18-8906-9744e929101e",
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d9cba203-cca3-465f-8961-87101e25a51d",
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tags'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ada868a2-61ce-408f-8540-3cf62a137fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a4e40b5-beae-4ad6-942d-a7f0f67ec9e7",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf82fce5-c3e0-409c-91f7-b268d8d60a66",
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tags'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3ac4a505-98eb-40a7-a826-5eaf98d97572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"captain barbossa, long believed to be dead, has come back to life and is headed to the edge of the earth with will turner and elizabeth swann. but nothing is quite as it seems. adventure fantasy action ocean drugabuse exoticisland eastindiatradingcompany loveofone'slife traitor shipwreck strongwoman ship alliance calypso afterlife fighter pirate swashbuckler aftercreditsstinger johnnydepp orlandobloom keiraknightley goreverbinski\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['tags'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2f305a64-8f24-4e33-a06a-7bfd6decda76",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the data similarity will tell how much they are similar to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "28d446db-7e82-47af-8a82-71acf173c93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features = 5000, stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b547cf1d-74f9-44cc-85f0-07fd32ab0080",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = cv.fit_transform(new_df['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "89412e8f-e459-42cb-9819-6f1479106d6e",
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "86e9d4c9-3aa7-480a-be2c-a6c049a5c9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc7e917c-83ae-42d0-858b-4c11d635f925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000' '007' '10' ... 'zone' 'zoo' 'zooeydeschanel']\n"
     ]
    }
   ],
   "source": [
    "feature_names = cv.get_feature_names_out()\n",
    "\n",
    "# Check the feature names\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d26a6d9c-ce05-477f-8308-5d53ff07753a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "60632b77-1c27-472f-a0ec-7552dee79f20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e70f6b7e-f4dc-49a6-be21-1890ce46633d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(text):\n",
    "    y = []\n",
    "\n",
    "    for i in text.split():\n",
    "        y.append(ps.stem(i))\n",
    "\n",
    "    return \" \".join(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "590935a6-265f-4cef-a69e-3e5c4cc75100",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f5890808-1f95-495b-9540-aaa29ac68a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4295474a-ed96-42c8-8548-5ee68decc294",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9ad05552-6e1c-47b1-855a-8454988d0067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3500, 3500)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9c9e0cb7-1d0b-4864-83ce-720e7fa5c114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.08492078, 0.05574947, ..., 0.        , 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c681cb50-7e60-4b01-9f96-1a5a3a48df92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(539, 0.24715576637149034),\n",
       " (1192, 0.24459979523511427),\n",
       " (507, 0.241948228618021),\n",
       " (582, 0.23112508176051216),\n",
       " (260, 0.23007892341722033)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the list of tuples (index, similarity score) based on the similarity score in descending order\n",
    "sorted(list(enumerate(similarity[0])), reverse=True, key=lambda x: x[1])[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7d7be1ef-4d5f-4a57-a4b4-3970316c72a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(movie):\n",
    "    # Get the index of the movie in the DataFrame\n",
    "    movie_index = new_df[new_df['title'] == movie].index[0]\n",
    "    \n",
    "    # Get similarity scores for the given movie\n",
    "    distances = similarity[movie_index]\n",
    "    \n",
    "    # Sort movies based on similarity scores in descending order and exclude the first one (itself)\n",
    "    movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6]\n",
    "\n",
    "    # Print the recommended movie titles based on the indices\n",
    "    for i in movies_list:\n",
    "        print(new_df.iloc[i[0]].title)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb7752f5-bfd3-4ae3-809c-632ff764409d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Titan A.E.\n",
      "Small Soldiers\n",
      "Independence Day\n",
      "Battle: Los Angeles\n",
      "Ender's Game\n"
     ]
    }
   ],
   "source": [
    "recommend('Avatar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b9adaea0-0bb8-483d-8428-8b7067a7b1fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Dark Knight\n",
      "The Dark Knight Rises\n",
      "Batman\n",
      "Batman & Robin\n",
      "Batman\n"
     ]
    }
   ],
   "source": [
    "recommend('Batman Begins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f4f1ce54-bc4b-4d05-9235-7ec72472e8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Convert the DataFrame to a dictionary and save it to a pickle file\n",
    "pickle.dump(new_df.to_dict(), open('movies_dict.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "457d7572-d0cb-43ed-b841-6b36657c49fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the similarity matrix to a pickle file\n",
    "pickle.dump(similarity, open('similarity.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cef4e3bf-1d09-4786-8d96-fa60a608967f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 },
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