movie-recommender-system.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b2d7ec5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b5f98c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "bedc9a97",
   "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": 105,
   "id": "9069572c",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.merge(credits, on = 'title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "65fc8752",
   "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": 107,
   "id": "3f6549ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Genres\n",
    "# id\n",
    "# keywords\n",
    "# title\n",
    "# Overview\n",
    "# ast\n",
    "# crew\n",
    "\n",
    "movies = movies[['movie_id', 'title', 'overview', 'genres', 'keywords', 'cast', 'crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "536875f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "250eafae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.iloc[0].genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a72db76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 28, 'name': 'Action'},\n",
       " {'id': 12, 'name': 'Adventure'},\n",
       " {'id': 14, 'name': 'Fantasy'},\n",
       " {'id': 878, 'name': 'Science Fiction'}]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ast\n",
    "ast.literal_eval(movies.iloc[0].genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "82e4eea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 112,
   "id": "dc98b72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dd4164f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['keywords'] = movies['keywords'].apply(convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "aa123a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 115,
   "id": "d8e9600c",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'] = movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "10eaad71",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_director(obj):\n",
    "    l = []\n",
    "    counter =0\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": 117,
   "id": "0af9bb3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['crew'] = movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f4b14dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['overview'] = movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e5aa263e",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 120,
   "id": "0d11df05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>genres</th>\n",
       "      <th>keywords</th>\n",
       "      <th>cast</th>\n",
       "      <th>crew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, 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": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "82f5fd69",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b3a849fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = movies[['movie_id', 'title', 'tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ad785dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(lambda x:\" \".join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "de46928b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "109f9d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=5000, stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "42a0cc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = cv.fit_transform(new_df['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0483c0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4d4e434f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "9115ed98",
   "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": 130,
   "id": "6bae1203",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a1e20055",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f37f7e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a5f2abcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(539, 0.26089696604360174),\n",
       " (1194, 0.2581988897471611),\n",
       " (507, 0.25302403842552984),\n",
       " (260, 0.25110592822973776),\n",
       " (1216, 0.24944382578492943)]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(enumerate(similarity[0])),reverse=True, key = lambda x:x[1])[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "de9503dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(movie):\n",
    "    movie_index = new_df[new_df['title'] == movie].index[0]\n",
    "    distances = similarity[movie_index]\n",
    "    movies_list = sorted(list(enumerate(distances)), reverse = True, key = lambda x:x[1])[1:6]\n",
    "    \n",
    "    for i in movies_list:\n",
    "        print(new_df.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "650e6031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplex\n",
      "The Helix... Loaded\n",
      "Star Trek II: The Wrath of Khan\n",
      "Timecop\n",
      "Chicago Overcoat\n"
     ]
    }
   ],
   "source": [
    "recommend('Inception')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "6d5c4c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "df15e9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(new_df, open('movies.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "ce6e07db",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(similarity, open('similarity.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f785c4a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_pickle('movies.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b87b162",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}