Movie_Recommendation_System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   <h1><b>MOVIE RECOMMENDER SYSTEMS</b></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"netflix.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Importing libraries</b></h2>\n",
    "\n",
    "We have imported the dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from ast import literal_eval\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from surprise import Reader, Dataset, SVD,BaselineOnly,NormalPredictor,accuracy\n",
    "from surprise.model_selection import cross_validate,train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Loading Dataset</b></h2>\n",
    "\n",
    "We have used Movie lens dataset for the building recommender systems\n",
    "\n",
    "Full dataset: This dataset consists of 26,000,000 ratings by 270,000 users for 45,000 movies.\n",
    "\n",
    "Sample dataset: we have taken The subset of 100,000 ratings from 700 users on 9,000 movies for building recommender system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "credits = pd.read_csv('./data/credits.csv')\n",
    "keywords = pd.read_csv('./data/keywords.csv')\n",
    "links_small = pd.read_csv('./data/links_small.csv')\n",
    "meta_data = pd.read_csv('./data/movies_metadata.csv')\n",
    "small_ratings = pd.read_csv('./data/ratings_small.csv')\n",
    "full_ratings = pd.read_csv('./data/full_ratings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Understanding the Dataset</b></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1425941529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1425942435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1221</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      110     1.0  1425941529\n",
       "1       1      147     4.5  1425942435\n",
       "2       1      858     5.0  1425941523\n",
       "3       1     1221     5.0  1425941546\n",
       "4       1     1246     5.0  1425941556"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>userId:</b> It is id for User\n",
    "\n",
    "<b>movieId:</b> It is TMDb movie id.\n",
    "\n",
    "<b>rating:</b> Rating given for the particular movie by specific user\n",
    "\n",
    "<b>timestamp:</b> Time stamp when rating has been given by user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0    6998802\n",
       "3.0    5256722\n",
       "5.0    3812499\n",
       "3.5    3116213\n",
       "4.5    2170441\n",
       "2.0    1762440\n",
       "2.5    1255358\n",
       "1.0     843310\n",
       "0.5     404897\n",
       "1.5     403607\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_ratings['rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "users count: 270896\n",
      "movies count: 45115\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>rating</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.888157</td>\n",
       "      <td>66008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.236953</td>\n",
       "      <td>26060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.175550</td>\n",
       "      <td>15497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2.875713</td>\n",
       "      <td>2981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.079565</td>\n",
       "      <td>15258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rating  num_ratings\n",
       "movieId                       \n",
       "1        3.888157        66008\n",
       "2        3.236953        26060\n",
       "3        3.175550        15497\n",
       "4        2.875713         2981\n",
       "5        3.079565        15258"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('users count: {}\\nmovies count: {}'.format(full_ratings['userId'].nunique(), full_ratings['movieId'].nunique()))\n",
    "movie_mean_rating = pd.DataFrame(full_ratings.groupby('movieId')['rating'].mean())\n",
    "movie_mean_rating['num_ratings'] = full_ratings.groupby('movieId')['rating'].count()\n",
    "movie_mean_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "movie_mean_rating[movie_mean_rating['num_ratings'] < 100]['num_ratings'].hist(bins=25)\n",
    "plt.title('Histogram of movies with less than 100 ratings')\n",
    "plt.xlabel(\"Num_ratings\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "movie_mean_rating['rating'].hist(bins=70)\n",
    "plt.title('Ratings value')\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>cast</th>\n",
       "      <th>crew</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[{'cast_id': 14, 'character': 'Woody (voice)',...</td>\n",
       "      <td>[{'credit_id': '52fe4284c3a36847f8024f49', 'de...</td>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'Alan Parrish', '...</td>\n",
       "      <td>[{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...</td>\n",
       "      <td>8844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[{'cast_id': 2, 'character': 'Max Goldman', 'c...</td>\n",
       "      <td>[{'credit_id': '52fe466a9251416c75077a89', 'de...</td>\n",
       "      <td>15602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[{'cast_id': 1, 'character': \"Savannah 'Vannah...</td>\n",
       "      <td>[{'credit_id': '52fe44779251416c91011acb', 'de...</td>\n",
       "      <td>31357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[{'cast_id': 1, 'character': 'George Banks', '...</td>\n",
       "      <td>[{'credit_id': '52fe44959251416c75039ed7', 'de...</td>\n",
       "      <td>11862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                cast  \\\n",
       "0  [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
       "1  [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
       "2  [{'cast_id': 2, 'character': 'Max Goldman', 'c...   \n",
       "3  [{'cast_id': 1, 'character': \"Savannah 'Vannah...   \n",
       "4  [{'cast_id': 1, 'character': 'George Banks', '...   \n",
       "\n",
       "                                                crew     id  \n",
       "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...    862  \n",
       "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   8844  \n",
       "2  [{'credit_id': '52fe466a9251416c75077a89', 'de...  15602  \n",
       "3  [{'credit_id': '52fe44779251416c91011acb', 'de...  31357  \n",
       "4  [{'credit_id': '52fe44959251416c75039ed7', 'de...  11862  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credits.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>cast:</b> Information about casting. Name of actor, gender and it's character name in movie\n",
    "\n",
    "<b>crew:</b> Information about crew members. About director, editor and writer etc.,\n",
    "\n",
    "<b>id:</b> It's movie ID given by TMDb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45476 entries, 0 to 45475\n",
      "Data columns (total 3 columns):\n",
      "cast    45476 non-null object\n",
      "crew    45476 non-null object\n",
      "id      45476 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "credits.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>id</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>862</td>\n",
       "      <td>[{'id': 931, 'name': 'jealousy'}, {'id': 4290,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8844</td>\n",
       "      <td>[{'id': 10090, 'name': 'board game'}, {'id': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>15602</td>\n",
       "      <td>[{'id': 1495, 'name': 'fishing'}, {'id': 12392...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>31357</td>\n",
       "      <td>[{'id': 818, 'name': 'based on novel'}, {'id':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11862</td>\n",
       "      <td>[{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                           keywords\n",
       "0    862  [{'id': 931, 'name': 'jealousy'}, {'id': 4290,...\n",
       "1   8844  [{'id': 10090, 'name': 'board game'}, {'id': 1...\n",
       "2  15602  [{'id': 1495, 'name': 'fishing'}, {'id': 12392...\n",
       "3  31357  [{'id': 818, 'name': 'based on novel'}, {'id':...\n",
       "4  11862  [{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n..."
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>id:</b> It's movie ID given by TMDb\n",
    "\n",
    "<b>Keywords:</b> Tags/keywords for the movie. It list of tags/keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 46419 entries, 0 to 46418\n",
      "Data columns (total 2 columns):\n",
      "id          46419 non-null int64\n",
      "keywords    46419 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 725.4+ KB\n"
     ]
    }
   ],
   "source": [
    "keywords.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "      <td>8844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_small.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>movieId:</b> It's serial number for movie\n",
    "\n",
    "<b>imdbId:</b> Movie id given on IMDb platform\n",
    "\n",
    "<b>tmdbId:</b> Movie id given on TMDb platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9125 entries, 0 to 9124\n",
      "Data columns (total 3 columns):\n",
      "movieId    9125 non-null int64\n",
      "imdbId     9125 non-null int64\n",
      "tmdbId     9112 non-null float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 214.0 KB\n"
     ]
    }
   ],
   "source": [
    "links_small.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
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       "      <th>tagline</th>\n",
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       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>This dataset contains columns</b> - adult, belongs_to_collection, budget, genres, homepage, id, imdb_id, original_language, original_title, overview, release_date, revenue, runtime, spoken_languages, status, tagline, title, video, vote_average, vote_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45466 entries, 0 to 45465\n",
      "Data columns (total 24 columns):\n",
      "adult                    45466 non-null object\n",
      "belongs_to_collection    4494 non-null object\n",
      "budget                   45466 non-null object\n",
      "genres                   45466 non-null object\n",
      "homepage                 7782 non-null object\n",
      "id                       45466 non-null object\n",
      "imdb_id                  45449 non-null object\n",
      "original_language        45455 non-null object\n",
      "original_title           45466 non-null object\n",
      "overview                 44512 non-null object\n",
      "popularity               45461 non-null object\n",
      "poster_path              45080 non-null object\n",
      "production_companies     45463 non-null object\n",
      "production_countries     45463 non-null object\n",
      "release_date             45379 non-null object\n",
      "revenue                  45460 non-null float64\n",
      "runtime                  45203 non-null float64\n",
      "spoken_languages         45460 non-null object\n",
      "status                   45379 non-null object\n",
      "tagline                  20412 non-null object\n",
      "title                    45460 non-null object\n",
      "video                    45460 non-null object\n",
      "vote_average             45460 non-null float64\n",
      "vote_count               45460 non-null float64\n",
      "dtypes: float64(4), object(20)\n",
      "memory usage: 8.3+ MB\n"
     ]
    }
   ],
   "source": [
    "meta_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26024289 entries, 0 to 26024288\n",
      "Data columns (total 4 columns):\n",
      "userId       int64\n",
      "movieId      int64\n",
      "rating       float64\n",
      "timestamp    int64\n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 794.2 MB\n"
     ]
    }
   ],
   "source": [
    "full_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "links_small = links_small[links_small['tmdbId'].notna()]['tmdbId'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          862\n",
       "1         8844\n",
       "2        15602\n",
       "3        31357\n",
       "4        11862\n",
       "         ...  \n",
       "9120    402672\n",
       "9121    315011\n",
       "9122    391698\n",
       "9123    137608\n",
       "9124    410803\n",
       "Name: tmdbId, Length: 9112, dtype: int32"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_int(x):\n",
    "    try:\n",
    "        return int(x)\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_data['id'] = meta_data['id'].apply(to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining NaN values count 0\n"
     ]
    }
   ],
   "source": [
    "meta_data=meta_data.drop(meta_data[meta_data['id'].isnull()].index)\n",
    "print(f'Remaining NaN values count { meta_data.id.isna().sum()}')\n",
    "meta_data['id'] = meta_data['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9099, 24)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_meta_data = meta_data[meta_data['id'].isin(links_small)]\n",
    "filtered_meta_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Pre-processing</b></h2>\n",
    "    \n",
    "We have taken the sample dataset form the original full dataset and performed the pre processing activity as and when required for each model implementation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Building recommender systems</b></h2>\n",
    "\n",
    "The Simple Recommender system offers generalized recommendations to every user based on movie popularity and genre.\n",
    "\n",
    "We have implemented the recommender systems based on the following types:\n",
    "\n",
    "1. Content based recommendation model \n",
    "2. Colloborative recommendation model \n",
    "3. Hybrid recommendation model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Content based recommendation model by Using movie description & taglines</b></h2>\n",
    "\n",
    "The Basic idea behind the <b>Content based model</b> filtering relies upon similarities between the items themselves. That is between the two movies or two songs or two purchased items. \n",
    "\n",
    "Here, the content based model first we need to figure out the similarities between the movies. These similarities can be features or qualities of each movies. \n",
    "\n",
    "<h4> Steps perfomed to carry out the Content based filtering </h4>\n",
    "\n",
    "1. First, We have combined the \"tagline\" and \"overview\" from the meta_data.csv and created a new filed called \"description\"\n",
    "\n",
    "2. we have foumd the <b>TF-IDF vectorizer</b> for the \"description\" and picked the most important words from the column.\n",
    "\n",
    "3. Finally we have found the <b>cosine similarity</b> for the tf-idf matrix. \n",
    "\n",
    "4. Based on the similarity strength we have filtered and sorted the best movies to recommend to the user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data['tagline'] = filtered_meta_data['tagline'].fillna('')\n",
    "filtered_meta_data['description'] = filtered_meta_data['overview'] + filtered_meta_data['tagline']\n",
    "filtered_meta_data['description'] = filtered_meta_data['description'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"When siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.Roll the dice and unleash the excitement!\""
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_meta_data['description'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "tfidf_matrix = tf.fit_transform(filtered_meta_data['description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.00680476, 0.        , ..., 0.        , 0.00344913,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data = filtered_meta_data.reset_index()\n",
    "titles = filtered_meta_data['title']\n",
    "indices = pd.Series(filtered_meta_data.index, index=filtered_meta_data['title'])\n",
    "#indices.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations(title):\n",
    "    if type(indices[title])==np.int64:\n",
    "        idx = indices[title]\n",
    "    else:\n",
    "        idx=indices[title][:1][0]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:31]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    return titles.iloc[movie_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5390        Harry Potter and the Prisoner of Azkaban\n",
       "6280             Harry Potter and the Goblet of Fire\n",
       "7649    Harry Potter and the Deathly Hallows: Part 1\n",
       "7821    Harry Potter and the Deathly Hallows: Part 2\n",
       "7257          Harry Potter and the Half-Blood Prince\n",
       "6720       Harry Potter and the Order of the Phoenix\n",
       "3806        Harry Potter and the Philosopher's Stone\n",
       "3840             Porn Star: The Legend of Ron Jeremy\n",
       "3542                                   The Dead Pool\n",
       "2903                            The Fighting Seabees\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Harry Potter and the Chamber of Secrets').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7931                      The Dark Knight Rises\n",
       "132                              Batman Forever\n",
       "1113                             Batman Returns\n",
       "8227    Batman: The Dark Knight Returns, Part 2\n",
       "7565                 Batman: Under the Red Hood\n",
       "524                                      Batman\n",
       "7901                           Batman: Year One\n",
       "2579               Batman: Mask of the Phantasm\n",
       "2696                                        JFK\n",
       "8165    Batman: The Dark Knight Returns, Part 1\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Dark Knight').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Content based by Using movie description,taglines,keywords, cast, director and genres</b></h2>\n",
    "\n",
    "Here, as a continuation for the content based model we have added more features for improving the recommendation model. \n",
    "\n",
    "1. movie description,taglines,keywords, cast, director and genres are the additional features which are added to the list in implementing the model. \n",
    "2. Performed the same steps as above and based on the similarity strength we have filtered and sorted the best movies to recommend to the user. \n",
    "3. We could see some improvements in the results of the model after adding more features into the list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords['id'] = keywords['id'].astype('int')\n",
    "credits['id'] = credits['id'].astype('int')\n",
    "meta_data['id'] = meta_data['id'].apply(to_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_data = meta_data.merge(credits, on='id')\n",
    "meta_data = meta_data.merge(keywords, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9219, 27)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_meta_data = meta_data[meta_data['id'].isin(links_small)]\n",
    "filtered_meta_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data['cast'] = filtered_meta_data['cast'].apply(literal_eval)\n",
    "filtered_meta_data['crew'] = filtered_meta_data['crew'].apply(literal_eval)\n",
    "filtered_meta_data['keywords'] = filtered_meta_data['keywords'].apply(literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_director(x):\n",
    "    for i in x:\n",
    "        if i['job'] == 'Director':\n",
    "            return i['name']\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data['director'] = filtered_meta_data['crew'].apply(get_director)\n",
    "filtered_meta_data['director'] = filtered_meta_data['director'].astype('str').apply(lambda x: str.lower(x.replace(\" \", \"\")))\n",
    "filtered_meta_data['director'] = filtered_meta_data['director'].apply(lambda x: [x,x, x])\n",
    "filtered_meta_data['cast'] = filtered_meta_data['cast'].apply(lambda x: [i['name'] for i in x])\n",
    "filtered_meta_data['cast'] = filtered_meta_data['cast'].apply(lambda x: x[:3] if len(x) >=3 else x)\n",
    "filtered_meta_data['cast'] = filtered_meta_data['cast'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])\n",
    "filtered_meta_data['keywords'] = filtered_meta_data['keywords'].apply(lambda x: [i['name'] for i in x])\n",
    "filtered_meta_data['genres'] = filtered_meta_data['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "independent film        610\n",
       "woman director          550\n",
       "murder                  399\n",
       "duringcreditsstinger    327\n",
       "based on novel          318\n",
       "violence                264\n",
       "love                    222\n",
       "sex                     219\n",
       "musical                 219\n",
       "suspense                212\n",
       "Name: keyword, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemming = filtered_meta_data.apply(lambda x: pd.Series(x['keywords']),axis=1).stack().reset_index(level=1, drop=True)\n",
    "stemming.name = 'keyword'\n",
    "stemming = stemming.value_counts()\n",
    "stemming = stemming[stemming > 1]\n",
    "stemming[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "def useful_keywords(x):\n",
    "    words = []\n",
    "    for i in x:\n",
    "        if i in stemming:\n",
    "            words.append(i)\n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data['keywords'] = filtered_meta_data['keywords'].apply(useful_keywords)\n",
    "filtered_meta_data['keywords'] = filtered_meta_data['keywords'].apply(lambda x: [SnowballStemmer('english').stem(i) for i in x])\n",
    "filtered_meta_data['keywords'] = filtered_meta_data['keywords'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])\n",
    "filtered_meta_data['soup'] = filtered_meta_data['keywords'] + filtered_meta_data['cast']+ filtered_meta_data['director'] + filtered_meta_data['genres']\n",
    "filtered_meta_data['soup'] = filtered_meta_data['soup'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "count_matrix = count.fit_transform(filtered_meta_data['soup'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim = cosine_similarity(count_matrix, count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_meta_data = filtered_meta_data.reset_index()\n",
    "titles = filtered_meta_data['title']\n",
    "indices = pd.Series(filtered_meta_data.index, index=filtered_meta_data['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8031         The Dark Knight Rises\n",
       "6218                 Batman Begins\n",
       "6623                  The Prestige\n",
       "2085                     Following\n",
       "7648                     Inception\n",
       "4145                      Insomnia\n",
       "3381                       Memento\n",
       "8613                  Interstellar\n",
       "7659    Batman: Under the Red Hood\n",
       "1134                Batman Returns\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Dark Knight').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Collaborative recommender system</b></h2>\n",
    "\n",
    "The basic idea behind the <b>collabarative filtering model</b> is, Suppose we have a user X to whom we want to make the recommendations. Here, we are going to find the other users/items whose likes and dislikes matches with X.\n",
    "\n",
    "We call these group of users the neighbourhood of X. We find the other movies that are liked by the group of users and recommend those movies to the user X. \n",
    "\n",
    "1. User-based collaborative filtering \n",
    "2. Item based collaborative filtering \n",
    "\n",
    "We will discuss about the Item based collaborative filtering. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Item-Based Collaborate filtering.</b></h2>\n",
    "\n",
    "Steps performed to implement the item based collaborative filtering:\n",
    "\n",
    "1. Find every pair of movies that were watched by the same person.\n",
    "2. Measure the similarity of their ratings across all users who watched both.\n",
    "3. Sort by movie, then by similarity strength and reommend it to the user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_cols = ['user_id', 'movie_id', 'rating']\n",
    "ratings = pd.read_csv('./data/ratings.dat', sep = '::', names = ratings_cols, usecols = range(3), encoding = 'latin-1')\n",
    "\n",
    "movie_cols = ['movie_id', 'title']\n",
    "movies = pd.read_csv('./data/movies.dat', sep = '::', names = movie_cols, usecols = range(2), encoding = 'latin-1')\n",
    "\n",
    "# Combine the movie and ratings DataFrames into a single DataFrame\n",
    "ratings = pd.merge(movies, ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "title    $1,000,000 Duck (1971)  'Night Mother (1986)  \\\n",
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       "user_id                                                             \n",
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       "title    Young Poisoner's Handbook, The (1995)  Young Sherlock Holmes (1985)  \\\n",
       "user_id                                                                        \n",
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       "user_id                                                                 \n",
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     "execution_count": 48,
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   ],
   "source": [
    "user_rating_pivot = ratings.pivot_table(index=['user_id'],columns=['title'],values='rating')\n",
    "user_rating_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
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       "title                          $1,000,000 Duck (1971)  'Night Mother (1986)  \\\n",
       "title                                                                         \n",
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       "title                                                                          \n",
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       "title                                                                        \n",
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       "title                          10 Things I Hate About You (1999)  \\\n",
       "title                                                              \n",
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       "\n",
       "title                          101 Dalmatians (1961)  101 Dalmatians (1996)  \\\n",
       "title                                                                         \n",
       "$1,000,000 Duck (1971)                           NaN                    NaN   \n",
       "'Night Mother (1986)                             NaN                    NaN   \n",
       "'Til There Was You (1997)                        NaN                    NaN   \n",
       "'burbs, The (1989)                          0.088541               0.101064   \n",
       "...And Justice for All (1979)               0.166965                    NaN   \n",
       "\n",
       "title                          12 Angry Men (1957)  ...  \\\n",
       "title                                               ...   \n",
       "$1,000,000 Duck (1971)                         NaN  ...   \n",
       "'Night Mother (1986)                           NaN  ...   \n",
       "'Til There Was You (1997)                      NaN  ...   \n",
       "'burbs, The (1989)                        0.047590  ...   \n",
       "...And Justice for All (1979)             0.303093  ...   \n",
       "\n",
       "title                          Young Poisoner's Handbook, The (1995)  \\\n",
       "title                                                                  \n",
       "$1,000,000 Duck (1971)                                           NaN   \n",
       "'Night Mother (1986)                                             NaN   \n",
       "'Til There Was You (1997)                                        NaN   \n",
       "'burbs, The (1989)                                               NaN   \n",
       "...And Justice for All (1979)                                    NaN   \n",
       "\n",
       "title                          Young Sherlock Holmes (1985)  \\\n",
       "title                                                         \n",
       "$1,000,000 Duck (1971)                                  NaN   \n",
       "'Night Mother (1986)                                    NaN   \n",
       "'Til There Was You (1997)                               NaN   \n",
       "'burbs, The (1989)                                 0.278425   \n",
       "...And Justice for All (1979)                           NaN   \n",
       "\n",
       "title                          Young and Innocent (1937)  \\\n",
       "title                                                      \n",
       "$1,000,000 Duck (1971)                               NaN   \n",
       "'Night Mother (1986)                                 NaN   \n",
       "'Til There Was You (1997)                            NaN   \n",
       "'burbs, The (1989)                                   NaN   \n",
       "...And Justice for All (1979)                        NaN   \n",
       "\n",
       "title                          Your Friends and Neighbors (1998)  \\\n",
       "title                                                              \n",
       "$1,000,000 Duck (1971)                                       NaN   \n",
       "'Night Mother (1986)                                         NaN   \n",
       "'Til There Was You (1997)                                    NaN   \n",
       "'burbs, The (1989)                                           NaN   \n",
       "...And Justice for All (1979)                                NaN   \n",
       "\n",
       "title                          Zachariah (1971)  Zed & Two Noughts, A (1985)  \\\n",
       "title                                                                          \n",
       "$1,000,000 Duck (1971)                      NaN                          NaN   \n",
       "'Night Mother (1986)                        NaN                          NaN   \n",
       "'Til There Was You (1997)                   NaN                          NaN   \n",
       "'burbs, The (1989)                          NaN                          NaN   \n",
       "...And Justice for All (1979)               NaN                          NaN   \n",
       "\n",
       "title                          Zero Effect (1998)  \\\n",
       "title                                               \n",
       "$1,000,000 Duck (1971)                        NaN   \n",
       "'Night Mother (1986)                          NaN   \n",
       "'Til There Was You (1997)                     NaN   \n",
       "'burbs, The (1989)                       0.039503   \n",
       "...And Justice for All (1979)                 NaN   \n",
       "\n",
       "title                          Zero Kelvin (Kjærlighetens kjøtere) (1995)  \\\n",
       "title                                                                       \n",
       "$1,000,000 Duck (1971)                                                NaN   \n",
       "'Night Mother (1986)                                                  NaN   \n",
       "'Til There Was You (1997)                                             NaN   \n",
       "'burbs, The (1989)                                                    NaN   \n",
       "...And Justice for All (1979)                                         NaN   \n",
       "\n",
       "title                          Zeus and Roxanne (1997)  eXistenZ (1999)  \n",
       "title                                                                    \n",
       "$1,000,000 Duck (1971)                             NaN              NaN  \n",
       "'Night Mother (1986)                               NaN              NaN  \n",
       "'Til There Was You (1997)                          NaN              NaN  \n",
       "'burbs, The (1989)                                 NaN              NaN  \n",
       "...And Justice for All (1979)                      NaN              NaN  \n",
       "\n",
       "[5 rows x 3706 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = user_rating_pivot.corr(method='pearson', min_periods=50)\n",
    "correlation_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Airplane! (1980)                                          4.0\n",
       "Aladdin (1992)                                            4.0\n",
       "Antz (1998)                                               4.0\n",
       "Apollo 13 (1995)                                          5.0\n",
       "Awakenings (1990)                                         5.0\n",
       "Back to the Future (1985)                                 5.0\n",
       "Bambi (1942)                                              4.0\n",
       "Beauty and the Beast (1991)                               5.0\n",
       "Ben-Hur (1959)                                            5.0\n",
       "Big (1988)                                                4.0\n",
       "Bug's Life, A (1998)                                      5.0\n",
       "Christmas Story, A (1983)                                 5.0\n",
       "Cinderella (1950)                                         5.0\n",
       "Close Shave, A (1995)                                     3.0\n",
       "Dead Poets Society (1989)                                 4.0\n",
       "Driving Miss Daisy (1989)                                 4.0\n",
       "Dumbo (1941)                                              5.0\n",
       "E.T. the Extra-Terrestrial (1982)                         4.0\n",
       "Erin Brockovich (2000)                                    4.0\n",
       "Fargo (1996)                                              4.0\n",
       "Ferris Bueller's Day Off (1986)                           4.0\n",
       "Gigi (1958)                                               4.0\n",
       "Girl, Interrupted (1999)                                  4.0\n",
       "Hercules (1997)                                           4.0\n",
       "Hunchback of Notre Dame, The (1996)                       4.0\n",
       "James and the Giant Peach (1996)                          3.0\n",
       "Last Days of Disco, The (1998)                            5.0\n",
       "Mary Poppins (1964)                                       5.0\n",
       "Meet Joe Black (1998)                                     3.0\n",
       "Miracle on 34th Street (1947)                             4.0\n",
       "Mulan (1998)                                              4.0\n",
       "My Fair Lady (1964)                                       3.0\n",
       "One Flew Over the Cuckoo's Nest (1975)                    5.0\n",
       "Pleasantville (1998)                                      3.0\n",
       "Pocahontas (1995)                                         5.0\n",
       "Ponette (1996)                                            4.0\n",
       "Princess Bride, The (1987)                                3.0\n",
       "Rain Man (1988)                                           5.0\n",
       "Run Lola Run (Lola rennt) (1998)                          4.0\n",
       "Saving Private Ryan (1998)                                5.0\n",
       "Schindler's List (1993)                                   5.0\n",
       "Secret Garden, The (1993)                                 4.0\n",
       "Sixth Sense, The (1999)                                   4.0\n",
       "Snow White and the Seven Dwarfs (1937)                    4.0\n",
       "Sound of Music, The (1965)                                5.0\n",
       "Star Wars: Episode IV - A New Hope (1977)                 4.0\n",
       "Tarzan (1999)                                             3.0\n",
       "Titanic (1997)                                            4.0\n",
       "To Kill a Mockingbird (1962)                              4.0\n",
       "Toy Story (1995)                                          5.0\n",
       "Toy Story 2 (1999)                                        4.0\n",
       "Wallace & Gromit: The Best of Aardman Animation (1996)    3.0\n",
       "Wizard of Oz, The (1939)                                  4.0\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_given_ratings = user_rating_pivot.loc[1].dropna()\n",
    "user_given_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_items = pd.Series()\n",
    "for i in range(0, len(user_given_ratings.index)):\n",
    "    # Finding similar movies to the already rated movies.\n",
    "    sims = correlation_matrix[user_given_ratings.index[i]].dropna()\n",
    "    # Based on how the user rated the movie scale the similarity values. \n",
    "    sims = sims.map(lambda x: x * user_given_ratings[i])\n",
    "    similar_items = similar_items.append(sims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One Flew Over the Cuckoo's Nest (1975)    5.0\n",
       "Saving Private Ryan (1998)                5.0\n",
       "Beauty and the Beast (1991)               5.0\n",
       "Schindler's List (1993)                   5.0\n",
       "Christmas Story, A (1983)                 5.0\n",
       "Dumbo (1941)                              5.0\n",
       "Ben-Hur (1959)                            5.0\n",
       "Sound of Music, The (1965)                5.0\n",
       "Mary Poppins (1964)                       5.0\n",
       "Bug's Life, A (1998)                      5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_items.sort_values(inplace = True, ascending = False)\n",
    "similar_items.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "watched_list=[]\n",
    "for i in similar_items.index:\n",
    "    if i in user_given_ratings.index:\n",
    "        watched_list.append(i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Big One, The (1997)                                 3.023370\n",
       "Raisin in the Sun, A (1961)                         3.014906\n",
       "Robin Hood (1973)                                   2.946287\n",
       "Flying Tigers (1942)                                2.940856\n",
       "Balto (1995)                                        2.904670\n",
       "Year of Living Dangerously (1982)                   2.802704\n",
       "Killing, The (1956)                                 2.796749\n",
       "Amistad (1997)                                      2.789153\n",
       "Women on the Verge of a Nervous Breakdown (1988)    2.787036\n",
       "Fanny and Alexander (1982)                          2.745285\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_similar_items =similar_items.drop(watched_list)\n",
    "filtered_similar_items.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Collaborate filtering using Suprise Library.</b></h2>\n",
    "\n",
    "By using the <b>Surprise Library</b> we built the train and test data to measure the performance of the recommender systems. \n",
    "\n",
    "We fed the train data and predicted the RMSE error on the test dataset. The implementation steps are shown:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_ratings = full_ratings[['userId', 'movieId', 'rating', 'timestamp']]\n",
    "full_ratings = full_ratings.iloc[:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader()\n",
    "full_ratings = Dataset.load_from_df(full_ratings[['userId', 'movieId', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset, testset = train_test_split(full_ratings, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x22771abf188>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVD()\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7997\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7996775761219451"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = algo.test(testset)\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 196        item: 302        r_ui = 4.00   est = 3.53   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "uid = str(196)  # raw user id (as in the ratings file)\n",
    "iid = str(302)  # raw item id (as in the ratings file)\n",
    "# get a prediction for specific users and items.\n",
    "pred = algo.predict(uid, iid, r_ui=4, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Hybrid Recommendation System.</b></h2>\n",
    "\n",
    "A system that combines content-based filtering and collaborative filtering could take advantage from both the representation of the content as well as the similarities among users also overcomes the problems within both the models.\n",
    "\n",
    "The implementation steps are shown below:\n",
    "\n",
    "By using both content based features and Collaborative model features we have implemented the hybrid model and the best results are displayed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "link_id = pd.read_csv('./data1/links_small.csv')[['movieId', 'tmdbId']]\n",
    "link_id['tmdbId'] = link_id['tmdbId'].apply(to_int)\n",
    "link_id.columns = ['movieId', 'id']\n",
    "link_id = link_id.merge(filtered_meta_data[['title', 'id']], on='id').set_index('title')\n",
    "id_index_link = link_id.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hybrid(userId, title):\n",
    "    idx = indices[title]\n",
    "    tmdbId = link_id.loc[title]['id']\n",
    "    movie_id = link_id.loc[title]['movieId']\n",
    "    sim_scores = list(enumerate(cosine_sim[int(idx)]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:26]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    movies = filtered_meta_data.iloc[movie_indices][['title', 'vote_count', 'vote_average', 'id']]\n",
    "    movies['est'] = movies['id'].apply(lambda x: algo.predict(userId, id_index_link.loc[x]['movieId']).est)\n",
    "    movies = movies.sort_values('est', ascending=False)\n",
    "    return movies.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>id</th>\n",
       "      <th>est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7345</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "      <td>5435.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>767</td>\n",
       "      <td>4.731634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7742</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>5708.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12444</td>\n",
       "      <td>4.721997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5452</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban</td>\n",
       "      <td>6037.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>673</td>\n",
       "      <td>4.703019</td>\n",
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       "    <tr>\n",
       "      <td>7921</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
       "      <td>6141.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>12445</td>\n",
       "      <td>4.645835</td>\n",
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       "    <tr>\n",
       "      <td>6354</td>\n",
       "      <td>Harry Potter and the Goblet of Fire</td>\n",
       "      <td>5758.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>674</td>\n",
       "      <td>4.637411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3840</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "      <td>7188.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>671</td>\n",
       "      <td>4.631440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6801</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
       "      <td>5633.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>675</td>\n",
       "      <td>4.616970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4509</td>\n",
       "      <td>The Last Unicorn</td>\n",
       "      <td>117.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10150</td>\n",
       "      <td>4.242911</td>\n",
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       "    <tr>\n",
       "      <td>521</td>\n",
       "      <td>Aladdin</td>\n",
       "      <td>3495.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>812</td>\n",
       "      <td>4.192355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6357</td>\n",
       "      <td>Rent</td>\n",
       "      <td>159.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1833</td>\n",
       "      <td>4.165380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             title  vote_count  vote_average  \\\n",
       "7345        Harry Potter and the Half-Blood Prince      5435.0           7.4   \n",
       "7742  Harry Potter and the Deathly Hallows: Part 1      5708.0           7.5   \n",
       "5452      Harry Potter and the Prisoner of Azkaban      6037.0           7.7   \n",
       "7921  Harry Potter and the Deathly Hallows: Part 2      6141.0           7.9   \n",
       "6354           Harry Potter and the Goblet of Fire      5758.0           7.5   \n",
       "3840      Harry Potter and the Philosopher's Stone      7188.0           7.5   \n",
       "6801     Harry Potter and the Order of the Phoenix      5633.0           7.4   \n",
       "4509                              The Last Unicorn       117.0           7.0   \n",
       "521                                        Aladdin      3495.0           7.4   \n",
       "6357                                          Rent       159.0           6.7   \n",
       "\n",
       "         id       est  \n",
       "7345    767  4.731634  \n",
       "7742  12444  4.721997  \n",
       "5452    673  4.703019  \n",
       "7921  12445  4.645835  \n",
       "6354    674  4.637411  \n",
       "3840    671  4.631440  \n",
       "6801    675  4.616970  \n",
       "4509  10150  4.242911  \n",
       "521     812  4.192355  \n",
       "6357   1833  4.165380  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hybrid(1, 'Harry Potter and the Chamber of Secrets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7969</td>\n",
       "      <td>The Avengers</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>24428</td>\n",
       "      <td>4.511596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8626</td>\n",
       "      <td>Captain America: The Winter Soldier</td>\n",
       "      <td>5881.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>100402</td>\n",
       "      <td>4.504146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6252</td>\n",
       "      <td>Serenity</td>\n",
       "      <td>1287.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>16320</td>\n",
       "      <td>4.474061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7009</td>\n",
       "      <td>Iron Man</td>\n",
       "      <td>8951.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1726</td>\n",
       "      <td>4.431473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8872</td>\n",
       "      <td>Captain America: Civil War</td>\n",
       "      <td>7462.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>271110</td>\n",
       "      <td>4.416808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7923</td>\n",
       "      <td>Captain America: The First Avenger</td>\n",
       "      <td>7174.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1771</td>\n",
       "      <td>4.356813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3049</td>\n",
       "      <td>X-Men</td>\n",
       "      <td>4172.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>36657</td>\n",
       "      <td>4.346595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8401</td>\n",
       "      <td>Star Trek Into Darkness</td>\n",
       "      <td>4479.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>54138</td>\n",
       "      <td>4.336882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9006</td>\n",
       "      <td>Star Trek Beyond</td>\n",
       "      <td>2636.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>188927</td>\n",
       "      <td>4.335702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8392</td>\n",
       "      <td>Iron Man 3</td>\n",
       "      <td>8951.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>68721</td>\n",
       "      <td>4.330455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    title  vote_count  vote_average      id  \\\n",
       "7969                         The Avengers     12000.0           7.4   24428   \n",
       "8626  Captain America: The Winter Soldier      5881.0           7.6  100402   \n",
       "6252                             Serenity      1287.0           7.4   16320   \n",
       "7009                             Iron Man      8951.0           7.4    1726   \n",
       "8872           Captain America: Civil War      7462.0           7.1  271110   \n",
       "7923   Captain America: The First Avenger      7174.0           6.6    1771   \n",
       "3049                                X-Men      4172.0           6.8   36657   \n",
       "8401              Star Trek Into Darkness      4479.0           7.4   54138   \n",
       "9006                     Star Trek Beyond      2636.0           6.6  188927   \n",
       "8392                           Iron Man 3      8951.0           6.8   68721   \n",
       "\n",
       "           est  \n",
       "7969  4.511596  \n",
       "8626  4.504146  \n",
       "6252  4.474061  \n",
       "7009  4.431473  \n",
       "8872  4.416808  \n",
       "7923  4.356813  \n",
       "3049  4.346595  \n",
       "8401  4.336882  \n",
       "9006  4.335702  \n",
       "8392  4.330455  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hybrid(89, \"Avengers: Age of Ultron\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>5074</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>8226.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>122</td>\n",
       "      <td>4.462505</td>\n",
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       "      <td>4436</td>\n",
       "      <td>The Lord of the Rings: The Two Towers</td>\n",
       "      <td>7641.0</td>\n",
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       "      <td>121</td>\n",
       "      <td>4.408538</td>\n",
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       "    <tr>\n",
       "      <td>1693</td>\n",
       "      <td>The Lord of the Rings</td>\n",
       "      <td>187.0</td>\n",
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       "      <td>123</td>\n",
       "      <td>3.564093</td>\n",
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       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>Heavenly Creatures</td>\n",
       "      <td>299.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1024</td>\n",
       "      <td>3.525093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8295</td>\n",
       "      <td>The Hobbit: An Unexpected Journey</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49051</td>\n",
       "      <td>3.502951</td>\n",
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       "    <tr>\n",
       "      <td>8537</td>\n",
       "      <td>The Hobbit: The Desolation of Smaug</td>\n",
       "      <td>4633.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>57158</td>\n",
       "      <td>3.360785</td>\n",
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       "    <tr>\n",
       "      <td>5852</td>\n",
       "      <td>The Hobbit</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1362</td>\n",
       "      <td>3.244664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8833</td>\n",
       "      <td>The Hobbit: The Battle of the Five Armies</td>\n",
       "      <td>4884.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>122917</td>\n",
       "      <td>3.165536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6368</td>\n",
       "      <td>King Kong</td>\n",
       "      <td>2403.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>254</td>\n",
       "      <td>3.125367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7543</td>\n",
       "      <td>Tom Thumb</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>56931</td>\n",
       "      <td>3.104650</td>\n",
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       "                                              title  vote_count  vote_average  \\\n",
       "5074  The Lord of the Rings: The Return of the King      8226.0           8.1   \n",
       "4436          The Lord of the Rings: The Two Towers      7641.0           8.0   \n",
       "1693                          The Lord of the Rings       187.0           6.1   \n",
       "219                              Heavenly Creatures       299.0           6.9   \n",
       "8295              The Hobbit: An Unexpected Journey      8427.0           7.0   \n",
       "8537            The Hobbit: The Desolation of Smaug      4633.0           7.6   \n",
       "5852                                     The Hobbit        76.0           6.0   \n",
       "8833      The Hobbit: The Battle of the Five Armies      4884.0           7.1   \n",
       "6368                                      King Kong      2403.0           6.6   \n",
       "7543                                      Tom Thumb         8.0           6.2   \n",
       "\n",
       "          id       est  \n",
       "5074     122  4.462505  \n",
       "4436     121  4.408538  \n",
       "1693     123  3.564093  \n",
       "219     1024  3.525093  \n",
       "8295   49051  3.502951  \n",
       "8537   57158  3.360785  \n",
       "5852    1362  3.244664  \n",
       "8833  122917  3.165536  \n",
       "6368     254  3.125367  \n",
       "7543   56931  3.104650  "
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     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "hybrid(999, \"The Lord of the Rings: The Fellowship of the Ring\")"
   ]
  }
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