Movie_Recommendor_System.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abf0e297",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast \n",
    "import nltk\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba11daab",
   "metadata": {},
   "source": [
    "### Reading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8aedc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_forward_slash(file_path):\n",
    "    return file_path.replace('\\\\', '/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c6c0690f",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path_movies = r\"C:\\Users\\Priyaranjan\\Desktop\\Kaggle datasets\\TMDB Movies dataser\\tmdb_5000_movies.csv\"\n",
    "file_path_credits = r\"C:\\Users\\Priyaranjan\\Desktop\\Kaggle datasets\\TMDB Movies dataser\\tmdb_5000_credits.csv\"\n",
    "\n",
    "file_path_fslash_movies = convert_to_forward_slash(file_path_movies)\n",
    "file_path_fslash_credits = convert_to_forward_slash(file_path_credits)\n",
    "\n",
    "file_path_movies = file_path_fslash_movies\n",
    "file_path_credits = file_path_fslash_credits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68524e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies = pd.read_csv(file_path_movies)\n",
    "df_credits = pd.read_csv(file_path_credits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c13cd7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['budget', 'genres', 'homepage', 'id', 'keywords', 'original_language',\n",
       "       'original_title', 'overview', 'popularity', 'production_companies',\n",
       "       'production_countries', 'release_date', 'revenue', 'runtime',\n",
       "       'spoken_languages', 'status', 'tagline', 'title', 'vote_average',\n",
       "       'vote_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6418cb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'title', 'cast', 'crew'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credits.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370e992d",
   "metadata": {},
   "source": [
    "Merging both the columns based on 'movie_id'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a95f818",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping title column from here as its present in the first table\n",
    "df_credits = df_credits.drop(columns='title',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a145fda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['budget', 'genres', 'homepage', 'movie_id', 'keywords',\n",
       "       'original_language', 'original_title', 'overview', 'popularity',\n",
       "       'production_companies', 'production_countries', 'release_date',\n",
       "       'revenue', 'runtime', 'spoken_languages', 'status', 'tagline', 'title',\n",
       "       'vote_average', 'vote_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.rename(columns={'id': 'movie_id'}, inplace=True)\n",
    "df_movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1dbd0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4803, 22)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies = df_movies.merge(df_credits, on='movie_id')\n",
    "df_movies.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6325ebb",
   "metadata": {},
   "source": [
    "Now, lets pick the variables which we require for the recommendor system afterwards.\n",
    "\n",
    "- genres\n",
    "- movie_id\n",
    "- keywords\n",
    "- title\n",
    "- overview\n",
    "- cast\n",
    "- crew\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a6d41dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies = df_movies[['movie_id','title', 'genres', 'overview', 'keywords', 'cast', 'crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "366f7f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>genres</th>\n",
       "      <th>overview</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</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>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>Following the death of District Attorney Harve...</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>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</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",
       "                                              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",
       "                                            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",
       "                                            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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f911764",
   "metadata": {},
   "source": [
    "### Data Preprocessing stage:\n",
    "\n",
    "- The basic idea here is to get a cleaner df for our recommendation systems.\n",
    "- We will first check the missing or null values in the columns.\n",
    "- Then, we are going to merge the genres, overview, keywords, cast & crew details together into a single corpus.\n",
    "    - Currently the info on keywords, cast & crew is in a list of dictionaries format where dictionary contains different information about a specific entity or person.\n",
    "    - Hence, we will be extracting only the names of entities / persons here.\n",
    "        - We will go with first 3 entities in 'cast' column\n",
    "        - Also, for column 'crew' , we will go with the director name as director of the movie is important in recommending a movie to any user based on content based filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da417f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "genres      0\n",
       "overview    3\n",
       "keywords    0\n",
       "cast        0\n",
       "crew        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ec18609",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the 3 rows which has null values in overview as its an important variable in recommendor system.\n",
    "df_movies.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d9f5374",
   "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": 15,
   "id": "dd4f5ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert3(obj):\n",
    "    count = 0\n",
    "    L = []\n",
    "    for i in ast.literal_eval(obj):\n",
    "        if count < 4:\n",
    "            L.append(i['name'])\n",
    "            count+=1\n",
    "        else:\n",
    "            break\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74aedaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 17,
   "id": "8b083b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "\n",
    "def stem(text):\n",
    "    L = []\n",
    "    for i in text.split():\n",
    "        L.append(ps.stem(i))\n",
    "    return \" \".join(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "101f7e48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>genres</th>\n",
       "      <th>overview</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>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
       "      <td>[culture clash, future, space war, space colon...</td>\n",
       "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weave...</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>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</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>[Action, Adventure, Crime]</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
       "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux, R...</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>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "      <td>[dc comics, crime fighter, terrorist, secret i...</td>\n",
       "      <td>[Christian Bale, Michael Caine, Gary Oldman, A...</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>[Action, Adventure, Science Fiction]</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</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",
       "                                          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",
       "                                            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",
       "                                            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 Weave...      [James Cameron]  \n",
       "1  [Johnny Depp, Orlando Bloom, Keira Knightley, ...     [Gore Verbinski]  \n",
       "2  [Daniel Craig, Christoph Waltz, Léa Seydoux, R...         [Sam Mendes]  \n",
       "3  [Christian Bale, Michael Caine, Gary Oldman, A...  [Christopher Nolan]  \n",
       "4  [Taylor Kitsch, Lynn Collins, Samantha Morton,...     [Andrew Stanton]  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies['genres'] = df_movies['genres'].apply(convert)\n",
    "df_movies['keywords'] = df_movies['keywords'].apply(convert)\n",
    "df_movies['cast'] = df_movies['cast'].apply(convert3)\n",
    "df_movies['crew'] = df_movies['crew'].apply(fetch_director)\n",
    "df_movies['overview'] = df_movies['overview'].apply(lambda x:x.split())\n",
    "\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833e52d9",
   "metadata": {},
   "source": [
    "Transformations we would need to apply now:\n",
    "- We will be merging the last 4 columns into a single feature which will store tags for the respective movies.\n",
    "- However, before that we need to apply some transformation on last 4 columns too.\n",
    "    - We need to merge the words found in a single element which will be stored as comma-separated tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "05c1543f",
   "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>genres</th>\n",
       "      <th>overview</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>[Action, Adventure, Fantasy, ScienceFiction]</td>\n",
       "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</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>[Adventure, Fantasy, Action]</td>\n",
       "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
       "      <td>[ocean, drugabuse, exoticisland, eastindiatrad...</td>\n",
       "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley, Ste...</td>\n",
       "      <td>[GoreVerbinski]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>[Action, Adventure, Crime]</td>\n",
       "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
       "      <td>[spy, basedonnovel, secretagent, sequel, mi6, ...</td>\n",
       "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux, Ralp...</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>[Action, Crime, Drama, Thriller]</td>\n",
       "      <td>[Following, the, death, of, District, Attorney...</td>\n",
       "      <td>[dccomics, crimefighter, terrorist, secretiden...</td>\n",
       "      <td>[ChristianBale, MichaelCaine, GaryOldman, Anne...</td>\n",
       "      <td>[ChristopherNolan]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[Action, Adventure, ScienceFiction]</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "      <td>[basedonnovel, mars, medallion, spacetravel, p...</td>\n",
       "      <td>[TaylorKitsch, LynnCollins, SamanthaMorton, Wi...</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",
       "                                         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",
       "                                            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",
       "                                            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, Ste...     [GoreVerbinski]  \n",
       "2  [DanielCraig, ChristophWaltz, LéaSeydoux, Ralp...         [SamMendes]  \n",
       "3  [ChristianBale, MichaelCaine, GaryOldman, Anne...  [ChristopherNolan]  \n",
       "4  [TaylorKitsch, LynnCollins, SamanthaMorton, Wi...     [AndrewStanton]  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies['genres'] = df_movies['genres'].apply(lambda x:[i.replace(' ','') for i in x])\n",
    "df_movies['keywords'] = df_movies['keywords'].apply(lambda x:[i.replace(' ','') for i in x])\n",
    "df_movies['cast'] = df_movies['cast'].apply(lambda x:[i.replace(' ','') for i in x])\n",
    "df_movies['crew'] = df_movies['crew'].apply(lambda x:[i.replace(' ','') for i in x])\n",
    "\n",
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "23039e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies['tags'] = df_movies['overview'] + df_movies['genres'] + df_movies['keywords'] + df_movies['cast'] + df_movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "afd380ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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, 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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4798</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>4799</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>4800</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>4801</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>4802</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>4800 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",
       "4798      9367                               El Mariachi   \n",
       "4799     72766                                 Newlyweds   \n",
       "4800    231617                 Signed, Sealed, Delivered   \n",
       "4801    126186                          Shanghai Calling   \n",
       "4802     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",
       "4798  [El, Mariachi, just, wants, to, play, his, gui...  \n",
       "4799  [A, newlywed, couple's, honeymoon, is, upended...  \n",
       "4800  [\"Signed,, Sealed,, Delivered\", introduces, a,...  \n",
       "4801  [When, ambitious, New, York, attorney, Sam, is...  \n",
       "4802  [Ever, since, the, second, grade, when, he, fi...  \n",
       "\n",
       "[4800 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies_new = df_movies[['movie_id', 'title', 'tags']]\n",
    "df_movies_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef442b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>a cryptic message from bond’s past sends him o...</td>\n",
       "    </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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49529</td>\n",
       "      <td>John Carter</td>\n",
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       "  </tbody>\n",
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       "</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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting the list into a string format\n",
    "# converting to lowercase\n",
    "df_movies_new['tags'] = df_movies_new['tags'].apply(lambda x: ' '.join(x))\n",
    "df_movies_new['tags'] = df_movies_new['tags'].apply(lambda x: x.lower())\n",
    "df_movies_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c7875b5",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "Now that we have our desired data, our next steps woudl be as follows.\n",
    "\n",
    "- text cleaning: stemming & lemmatizing\n",
    "- remove stopwords from tags\n",
    "- apply word vectorization ( using CountVectorizer Class in scikit learn library )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ac7b363",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies_new['tags'] = df_movies_new['tags'].apply(stem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31aa5fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features=5000, stop_words='english')\n",
    "vectors = cv.fit_transform(df_movies_new['tags']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "07c9e1b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000' '007' '10' ... 'zone' 'zoo' 'zooeydeschanel']\n"
     ]
    }
   ],
   "source": [
    "print(cv.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5f3faa",
   "metadata": {},
   "source": [
    "Post the vectorization of the movies, calculating the distances between 2 vectors (movies)\n",
    "- We cannot use 'euclidean distance' as it doesnt perform well in higher dimensions ( Curse of dimensionality )\n",
    "- Instead we can calculate the cosine distance ( angle between the vectors in that dimensional space)\n",
    "\n",
    "Then , we can calculate similarity based on cosine distance (cosine similarity) as distance is inversely proportional to similarity.\n",
    "- using cosine_similarity function from sklearn.metrics.pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1daaa679",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ce3a3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.08226127, 0.0860309 , ..., 0.04499213, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392817af",
   "metadata": {},
   "source": [
    "### Recommendor Function\n",
    "\n",
    "- Creating a recommendor function which will recommend 5 movies out when provided with a movie as input\n",
    "    - when provided with a movie title as input, find the index position in the data.\n",
    "    - using index of the input movie, fetch the cosine similarity vector for that index position.\n",
    "        - fetch the top 5 movies ( first 5 similar movies from the descending sorted cosine simialrity vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2cc7c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommendor(movie):\n",
    "    movie_index = df_movies_new[df_movies_new['title'] == movie].index[0]\n",
    "    distances = sorted(list(enumerate(similarity[movie_index])),reverse=True,key = lambda x: x[1])\n",
    "  \n",
    "    for i in distances[1:6]:\n",
    "        print(df_movies_new.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5be21218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avengers: Age of Ultron\n",
      "Iron Man 3\n",
      "Captain America: Civil War\n",
      "Captain America: The First Avenger\n",
      "Iron Man\n"
     ]
    }
   ],
   "source": [
    "recommendor('The Avengers')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37599669",
   "metadata": {},
   "source": [
    "Now that we have our recommendor function ready, dumping the data file used for this into a pickle file to be used by our app file further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "55dd81c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19995</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>in the 22nd century, a parapleg marin is dispa...</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 believ to be dead, ha c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206647</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>a cryptic messag from bond’ past send him on a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49026</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>follow the death of district attorney harvey d...</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 militari 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 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...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84c0a7b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file saved to path: C:/Users/Priyaranjan/Desktop/My Projects/movies-recommendor-system\n"
     ]
    }
   ],
   "source": [
    "# Dumping the df to be used by app\n",
    "directory = 'C:/Users/Priyaranjan/Desktop/My Projects/movies-recommendor-system'\n",
    "filename = 'movies_list.pkl'\n",
    "\n",
    "full_file_path = f'{directory}/{filename}'\n",
    "\n",
    "with open(full_file_path,'wb') as file:\n",
    "    pkl.dump(df_movies_new,file)\n",
    "    \n",
    "print('Pickle file saved to path:',directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23edae1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file saved to path: C:/Users/Priyaranjan/Desktop/My Projects/movies-recommendor-system\n"
     ]
    }
   ],
   "source": [
    "# Dumping the similarity pickle file to be used by app\n",
    "directory = 'C:/Users/Priyaranjan/Desktop/My Projects/movies-recommendor-system'\n",
    "filename = 'similarity.pkl'\n",
    "\n",
    "full_file_path = f'{directory}/{filename}'\n",
    "\n",
    "with open(full_file_path,'wb') as file:\n",
    "    pkl.dump(similarity,file)\n",
    "    \n",
    "print('Pickle file saved to path:',directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1719c5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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