Movie Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MOVIE RECOMMENDATION SYSTEM (CONTENT BASED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual. These results are based on their profile, search/browsing history, what other people with similar traits/demographics are watching, and how likely are you to watch those movies.Recommender systems produce a list of recommendations in any of the two ways"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CONTENT-BASED FILTERING : A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COLLABORATIVE FILTERING : To address some of the limitations of content-based filtering, collaborative filtering uses similarities between users and items simultaneously to provide recommendations. This allows for serendipitous recommendations; that is, collaborative filtering models can recommend an item to user A based on the interests of a similar user B. Furthermore, the embeddings can be learned automatically, without relying on hand-engineering of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we are recommending movies based on content filtering. I have used dataset of HOLLYWOOD FILMS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('movie_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>index</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>...</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>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>director</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>237000000</td>\n",
       "      <td>Action Adventure Fantasy Science Fiction</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>culture clash future space war space colony so...</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>...</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>Sam Worthington Zoe Saldana Sigourney Weaver S...</td>\n",
       "      <td>[{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...</td>\n",
       "      <td>James Cameron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>300000000</td>\n",
       "      <td>Adventure Fantasy Action</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>ocean drug abuse exotic island east india trad...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "      <td>Johnny Depp Orlando Bloom Keira Knightley Stel...</td>\n",
       "      <td>[{'name': 'Dariusz Wolski', 'gender': 2, 'depa...</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>245000000</td>\n",
       "      <td>Action Adventure Crime</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>spy based on novel secret agent sequel mi6</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>...</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "      <td>Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...</td>\n",
       "      <td>[{'name': 'Thomas Newman', 'gender': 2, 'depar...</td>\n",
       "      <td>Sam Mendes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>250000000</td>\n",
       "      <td>Action Crime Drama Thriller</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>dc comics crime fighter terrorist secret ident...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "      <td>Christian Bale Michael Caine Gary Oldman Anne ...</td>\n",
       "      <td>[{'name': 'Hans Zimmer', 'gender': 2, 'departm...</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>260000000</td>\n",
       "      <td>Action Adventure Science Fiction</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>based on novel mars medallion space travel pri...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "      <td>Taylor Kitsch Lynn Collins Samantha Morton Wil...</td>\n",
       "      <td>[{'name': 'Andrew Stanton', 'gender': 2, 'depa...</td>\n",
       "      <td>Andrew Stanton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     budget                                    genres  \\\n",
       "0      0  237000000  Action Adventure Fantasy Science Fiction   \n",
       "1      1  300000000                  Adventure Fantasy Action   \n",
       "2      2  245000000                    Action Adventure Crime   \n",
       "3      3  250000000               Action Crime Drama Thriller   \n",
       "4      4  260000000          Action Adventure Science Fiction   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  culture clash future space war space colony so...                en   \n",
       "1  ocean drug abuse exotic island east india trad...                en   \n",
       "2         spy based on novel secret agent sequel mi6                en   \n",
       "3  dc comics crime fighter terrorist secret ident...                en   \n",
       "4  based on novel mars medallion space travel pri...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  ... runtime  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577  ...   162.0   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615  ...   169.0   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788  ...   148.0   \n",
       "3  Following the death of District Attorney Harve...  112.312950  ...   165.0   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995  ...   132.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title vote_average vote_count  \\\n",
       "0                                    Avatar          7.2      11800   \n",
       "1  Pirates of the Caribbean: At World's End          6.9       4500   \n",
       "2                                   Spectre          6.3       4466   \n",
       "3                     The Dark Knight Rises          7.6       9106   \n",
       "4                               John Carter          6.1       2124   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Sam Worthington Zoe Saldana Sigourney Weaver S...   \n",
       "1  Johnny Depp Orlando Bloom Keira Knightley Stel...   \n",
       "2  Daniel Craig Christoph Waltz L\\u00e9a Seydoux ...   \n",
       "3  Christian Bale Michael Caine Gary Oldman Anne ...   \n",
       "4  Taylor Kitsch Lynn Collins Samantha Morton Wil...   \n",
       "\n",
       "                                                crew           director  \n",
       "0  [{'name': 'Stephen E. Rivkin', 'gender': 0, 'd...      James Cameron  \n",
       "1  [{'name': 'Dariusz Wolski', 'gender': 2, 'depa...     Gore Verbinski  \n",
       "2  [{'name': 'Thomas Newman', 'gender': 2, 'depar...         Sam Mendes  \n",
       "3  [{'name': 'Hans Zimmer', 'gender': 2, 'departm...  Christopher Nolan  \n",
       "4  [{'name': 'Andrew Stanton', 'gender': 2, 'depa...     Andrew Stanton  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa3ed00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Distribution of Ratings for a Movie')\n",
    "\n",
    "plt.ylabel('Movies')\n",
    "sns.distplot(df[\"vote_average\"],bins=10,kde=False,color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the movies are rated between 5 and 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2416a8c8>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Runtime of movies')\n",
    "plt.ylabel('Movies')\n",
    "sns.distplot(df['runtime'],bins=10,kde=False,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the movies have the runtime between 80 minutes to 200 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x241a46a0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "g=sns.jointplot(x='vote_average',y='budget',data=df,kind='scatter',color='blue')\n",
    "g.fig.suptitle('Budget vs Vote average',y=1.08)\n",
    "g.set_axis_labels('Budget','Vote average')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the features such as keywords cast genres and director a new column of combined features is created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=['keywords','genres','cast','director']#as this is content based recommendations\n",
    "#we select columns that provide info about movie content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def combined_features(row):\n",
    "    return row['keywords']+\" \"+row['genres']+\" \"+row['cast']+\" \"+row['director']\n",
    "#we combine all the unique features to form a column vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'budget', 'genres', 'homepage', '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', 'cast', 'crew', 'director'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "for feature in features:\n",
    "    df[feature]=df[feature].fillna(\" \") #all the null values are filled by \" \"(space)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'culture clash future space war space colony society Action Adventure Fantasy Science Fiction Sam Worthington Zoe Saldana Sigourney Weaver Stephen Lang Michelle Rodriguez James Cameron'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"combined_features\"]=df.apply(combined_features,axis=1)#we use apply to combine the keywords,geners,cast and director \n",
    "#column of every row to form a single column vector\n",
    "df.combined_features.iloc[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv=CountVectorizer()# feeding the combined_features string into CountVectorizer \n",
    "count_matrix=cv.fit_transform(df[\"combined_features\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.10540926 0.12038585 ... 0.         0.         0.        ]\n",
      " [0.10540926 1.         0.0761387  ... 0.03651484 0.         0.        ]\n",
      " [0.12038585 0.0761387  1.         ... 0.         0.11145564 0.        ]\n",
      " ...\n",
      " [0.         0.03651484 0.         ... 1.         0.         0.04264014]\n",
      " [0.         0.         0.11145564 ... 0.         1.         0.        ]\n",
      " [0.         0.         0.         ... 0.04264014 0.         1.        ]]\n"
     ]
    }
   ],
   "source": [
    "cosine_sim=cosine_similarity(count_matrix)\n",
    "print(cosine_sim)#automatically produces corelation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining helping functions to get the movie index from its title and vise-versa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_title_from_index(index):\n",
    "    return df[df.index==index]['title'].values[0]\n",
    "def get_index_from_title(title):\n",
    "    return df[df.title==title][\"index\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.0), (4793, 0.0), (4794, 0.0), (4795, 0.0), (4796, 0.07027283689263066), (4797, 0.0), (4798, 0.07548513560963972), (4799, 0.0), (4800, 0.0), (4801, 0.0), (4802, 0.0)]\n"
     ]
    }
   ],
   "source": [
    "movie_user_likes=\"Avatar\"# this is the movie liked by the user. Recommendations are made based on user liked movie.\n",
    "movie_index=get_index_from_title(movie_user_likes)\n",
    "similar_movies=list(enumerate(cosine_sim[movie_index]))# enumerate function is used to obtain the tuple (index,value)\n",
    "print(list(enumerate(cosine_sim[movie_index])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(sorted(similar_movies,key=lambda x:x[1],reverse=True)[1:]) # the values are sorted according to the cosine_sim values \n",
    "#which is the second datapoint of the tuple. Hence we use lambda x:x[1],where 1 is the second index of tuple.\n",
    "\n",
    "sorted_similar_movies=sorted(similar_movies,key=lambda x:x[1],reverse=True)[1:] #Since the movie liked by the user is 100% \n",
    "# correlated to the 1st value,we have to sort without that value. Also, we need to drop that value while showing recommendations\n",
    "# Hence,we use [1:]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 similar movies to Avatar are :\n",
      "Guardians of the Galaxy\n",
      "Aliens\n",
      "Star Wars: Clone Wars: Volume 1\n",
      "Star Trek Into Darkness\n",
      "Star Trek Beyond\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "print(\"Top 5 similar movies to\"+\" \"+movie_user_likes+\" are :\")\n",
    "for elem in sorted_similar_movies:\n",
    "    print(get_title_from_index(elem[0]))\n",
    "    i=i+1\n",
    "    if i>5 :\n",
    "        break"
   ]
  },
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