movie_content_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-03 09:05:15--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 160301210 (153M) [application/zip]\n",
      "Saving to: ‘moviedataset.zip’\n",
      "\n",
      "moviedataset.zip    100%[===================>] 152.88M  32.7MB/s    in 4.6s    \n",
      "\n",
      "2019-06-03 09:05:20 (33.6 MB/s) - ‘moviedataset.zip’ saved [160301210/160301210]\n",
      "\n",
      "unziping ...\n",
      "Archive:  moviedataset.zip\n",
      "  inflating: links.csv               \n",
      "  inflating: movies.csv              \n",
      "  inflating: ratings.csv             \n",
      "  inflating: README.txt              \n",
      "  inflating: tags.csv                \n"
     ]
    }
   ],
   "source": [
    "!wget -O moviedataset.zip https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip\n",
    "print('unziping ...')\n",
    "!unzip -o -j moviedataset.zip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df = pd.read_csv('movies.csv')\n",
    "rating_df = pd.read_csv('ratings.csv')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                        genres year  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  NaN  \n",
       "1                   Adventure|Children|Fantasy  NaN  \n",
       "2                               Comedy|Romance  NaN  \n",
       "3                         Comedy|Drama|Romance  NaN  \n",
       "4                                       Comedy  NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
    "#Removing the parentheses\n",
    "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
    "#Removing the years from the 'title' column\n",
    "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
    "#Applying the strip function to get rid of any ending whitespace characters that may have appeared\n",
    "movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
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       "      <th>genres</th>\n",
       "      <th>year</th>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres year  \n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  NaN  \n",
       "1                     [Adventure, Children, Fantasy]  NaN  \n",
       "2                                  [Comedy, Romance]  NaN  \n",
       "3                           [Comedy, Drama, Romance]  NaN  \n",
       "4                                           [Comedy]  NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['genres'] = movies_df.genres.str.split('|')\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
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       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Jumanji</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]     0        1.0   \n",
       "1                     [Adventure, Children, Fantasy]     0        1.0   \n",
       "2                                  [Comedy, Romance]     0        0.0   \n",
       "3                           [Comedy, Drama, Romance]     0        0.0   \n",
       "4                                           [Comedy]     0        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "2        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "3        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "2     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "3     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "4     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movieswithgenres_df = movies_df.copy()\n",
    "for index, row in movies_df.iterrows():\n",
    "    for genre in row['genres']:\n",
    "        movieswithgenres_df.at[index, genre] = 1\n",
    "        \n",
    "movieswithgenres_df = movieswithgenres_df.fillna(0)     \n",
    "movieswithgenres_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1436165496</td>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      169     2.5  1204927694\n",
       "1       1     2471     3.0  1204927438\n",
       "2       1    48516     5.0  1204927435\n",
       "3       2     2571     3.5  1436165433\n",
       "4       2   109487     4.0  1436165496"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0       1      169     2.5\n",
       "1       1     2471     3.0\n",
       "2       1    48516     5.0\n",
       "3       2     2571     3.5\n",
       "4       2   109487     4.0"
      ]
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     "execution_count": 48,
     "metadata": {},
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   "source": [
    "rating_df = rating_df.drop('timestamp', 1)\n",
    "rating_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Toy Story</td>\n",
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      ],
      "text/plain": [
       "   rating                title\n",
       "0     5.0  Breakfast Club, The\n",
       "1     3.5            Toy Story\n",
       "2     2.0              Jumanji\n",
       "3     5.0         Pulp Fiction\n",
       "4     4.5                Akira"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "userinput  = [\n",
    "                {'title': 'Breakfast Club, The', 'rating': 5},\n",
    "                {'title':'Toy Story', 'rating':3.5},\n",
    "                {'title':'Jumanji', 'rating':2},\n",
    "                {'title':\"Pulp Fiction\", 'rating':5},\n",
    "                {'title':'Akira', 'rating':4.5}\n",
    "]\n",
    "inputmovies = pd.DataFrame(userinput)\n",
    "inputmovies"
   ]
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   "execution_count": 61,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      movieId                title  \\\n",
       "0           1            Toy Story   \n",
       "1           2              Jumanji   \n",
       "293       296         Pulp Fiction   \n",
       "1246     1274                Akira   \n",
       "1885     1968  Breakfast Club, The   \n",
       "\n",
       "                                                 genres year  \n",
       "0     [Adventure, Animation, Children, Comedy, Fantasy]  NaN  \n",
       "1                        [Adventure, Children, Fantasy]  NaN  \n",
       "293                    [Comedy, Crime, Drama, Thriller]  NaN  \n",
       "1246             [Action, Adventure, Animation, Sci-Fi]  NaN  \n",
       "1885                                    [Comedy, Drama]  NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputid = movies_df[movies_df['title'].isin(inputmovies['title'].tolist())]\n",
    "inputid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   movieId                title  \\\n",
       "0        1            Toy Story   \n",
       "1        2              Jumanji   \n",
       "2      296         Pulp Fiction   \n",
       "3     1274                Akira   \n",
       "4     1968  Breakfast Club, The   \n",
       "\n",
       "                                              genres year  rating  \n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  NaN     3.5  \n",
       "1                     [Adventure, Children, Fantasy]  NaN     2.0  \n",
       "2                   [Comedy, Crime, Drama, Thriller]  NaN     5.0  \n",
       "3             [Action, Adventure, Animation, Sci-Fi]  NaN     4.5  \n",
       "4                                    [Comedy, Drama]  NaN     5.0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
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   "source": [
    "inputmovies = pd.merge(inputid, inputmovies)\n",
    "inputmovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   movieId                title  rating\n",
       "0        1            Toy Story     3.5\n",
       "1        2              Jumanji     2.0\n",
       "2      296         Pulp Fiction     5.0\n",
       "3     1274                Akira     4.5\n",
       "4     1968  Breakfast Club, The     5.0"
      ]
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   "source": [
    "inputmovies = inputmovies.drop('year', 1).drop('genres', 1)\n",
    "inputmovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1246</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
       "      <td>[Action, Adventure, Animation, Sci-Fi]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>1968</td>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "      movieId                title  \\\n",
       "0           1            Toy Story   \n",
       "1           2              Jumanji   \n",
       "293       296         Pulp Fiction   \n",
       "1246     1274                Akira   \n",
       "1885     1968  Breakfast Club, The   \n",
       "\n",
       "                                                 genres  year  Adventure  \\\n",
       "0     [Adventure, Animation, Children, Comedy, Fantasy]     0        1.0   \n",
       "1                        [Adventure, Children, Fantasy]     0        1.0   \n",
       "293                    [Comedy, Crime, Drama, Thriller]     0        0.0   \n",
       "1246             [Action, Adventure, Animation, Sci-Fi]     0        1.0   \n",
       "1885                                    [Comedy, Drama]     0        0.0   \n",
       "\n",
       "      Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0           1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1           0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "293         0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "1246        1.0       0.0     0.0      0.0      0.0  ...     0.0      0.0   \n",
       "1885        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "      Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0        0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1        0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "293      0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1246     1.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1885     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "      (no genres listed)  \n",
       "0                    0.0  \n",
       "1                    0.0  \n",
       "293                  0.0  \n",
       "1246                 0.0  \n",
       "1885                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usermovies = movieswithgenres_df[movieswithgenres_df['movieId'].isin(inputmovies['movieId'].tolist())]\n",
    "usermovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>Pulp Fiction</td>\n",
       "      <td>[Comedy, Crime, Drama, Thriller]</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1274</td>\n",
       "      <td>Akira</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "   movieId                title  \\\n",
       "0        1            Toy Story   \n",
       "1        2              Jumanji   \n",
       "2      296         Pulp Fiction   \n",
       "3     1274                Akira   \n",
       "4     1968  Breakfast Club, The   \n",
       "\n",
       "                                              genres  year  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]     0        1.0   \n",
       "1                     [Adventure, Children, Fantasy]     0        1.0   \n",
       "2                   [Comedy, Crime, Drama, Thriller]     0        0.0   \n",
       "3             [Action, Adventure, Animation, Sci-Fi]     0        1.0   \n",
       "4                                    [Comedy, Drama]     0        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
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       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "2     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
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       "4     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usermovies = usermovies.reset_index(drop=True)\n",
    "usermovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  Action  \\\n",
       "0        1.0        1.0       1.0     1.0      1.0      0.0    0.0     0.0   \n",
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       "   Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  War  Musical  \\\n",
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       "4    0.0       0.0     0.0      0.0     0.0   0.0          0.0  0.0      0.0   \n",
       "\n",
       "   Western  Film-Noir  (no genres listed)  \n",
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       "3      0.0        0.0                 0.0  \n",
       "4      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usergenretable = usermovies.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
    "usergenretable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.5\n",
       "1    2.0\n",
       "2    5.0\n",
       "3    4.5\n",
       "4    5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputmovies['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adventure             10.0\n",
       "Animation              8.0\n",
       "Children               5.5\n",
       "Comedy                13.5\n",
       "Fantasy                5.5\n",
       "Romance                0.0\n",
       "Drama                 10.0\n",
       "Action                 4.5\n",
       "Crime                  5.0\n",
       "Thriller               5.0\n",
       "Horror                 0.0\n",
       "Mystery                0.0\n",
       "Sci-Fi                 4.5\n",
       "IMAX                   0.0\n",
       "Documentary            0.0\n",
       "War                    0.0\n",
       "Musical                0.0\n",
       "Western                0.0\n",
       "Film-Noir              0.0\n",
       "(no genres listed)     0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userprofile = usergenretable.transpose().dot(inputmovies['rating'])\n",
    "userprofile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>Adventure</th>\n",
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       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
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       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>(no genres listed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                        title  \\\n",
       "0        1                    Toy Story   \n",
       "1        2                      Jumanji   \n",
       "2        3             Grumpier Old Men   \n",
       "3        4            Waiting to Exhale   \n",
       "4        5  Father of the Bride Part II   \n",
       "\n",
       "                                              genres  year  Adventure  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]     0        1.0   \n",
       "1                     [Adventure, Children, Fantasy]     0        1.0   \n",
       "2                                  [Comedy, Romance]     0        0.0   \n",
       "3                           [Comedy, Drama, Romance]     0        0.0   \n",
       "4                                           [Comedy]     0        0.0   \n",
       "\n",
       "   Animation  Children  Comedy  Fantasy  Romance  ...  Horror  Mystery  \\\n",
       "0        1.0       1.0     1.0      1.0      0.0  ...     0.0      0.0   \n",
       "1        0.0       1.0     0.0      1.0      0.0  ...     0.0      0.0   \n",
       "2        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "3        0.0       0.0     1.0      0.0      1.0  ...     0.0      0.0   \n",
       "4        0.0       0.0     1.0      0.0      0.0  ...     0.0      0.0   \n",
       "\n",
       "   Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "1     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "2     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "3     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "4     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "   (no genres listed)  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movieswithgenres_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         Adventure  Animation  Children  Comedy  Fantasy  Romance  Drama  \\\n",
       "movieId                                                                    \n",
       "1              1.0        1.0       1.0     1.0      1.0      0.0    0.0   \n",
       "2              1.0        0.0       1.0     0.0      1.0      0.0    0.0   \n",
       "3              0.0        0.0       0.0     1.0      0.0      1.0    0.0   \n",
       "4              0.0        0.0       0.0     1.0      0.0      1.0    1.0   \n",
       "5              0.0        0.0       0.0     1.0      0.0      0.0    0.0   \n",
       "\n",
       "         Action  Crime  Thriller  Horror  Mystery  Sci-Fi  IMAX  Documentary  \\\n",
       "movieId                                                                        \n",
       "1           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "2           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "3           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "4           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "5           0.0    0.0       0.0     0.0      0.0     0.0   0.0          0.0   \n",
       "\n",
       "         War  Musical  Western  Film-Noir  (no genres listed)  \n",
       "movieId                                                        \n",
       "1        0.0      0.0      0.0        0.0                 0.0  \n",
       "2        0.0      0.0      0.0        0.0                 0.0  \n",
       "3        0.0      0.0      0.0        0.0                 0.0  \n",
       "4        0.0      0.0      0.0        0.0                 0.0  \n",
       "5        0.0      0.0      0.0        0.0                 0.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genretable = movieswithgenres_df.set_index(movieswithgenres_df['movieId'])\n",
    "genretable = genretable.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
    "genretable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    0.594406\n",
       "2    0.293706\n",
       "3    0.188811\n",
       "4    0.328671\n",
       "5    0.188811\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_table = ((genretable*userprofile).sum(axis=1))/userprofile.sum()\n",
    "recommendation_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "5018      0.748252\n",
       "26093     0.734266\n",
       "27344     0.720280\n",
       "148775    0.685315\n",
       "6902      0.678322\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_table = recommendation_table.sort_values(ascending = False)\n",
    "recommendation_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "      <th>664</th>\n",
       "      <td>673</td>\n",
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       "      <td>[Adventure, Animation, Children, Comedy, Fanta...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Twelve Tasks of Asterix, The (Les douze travau...</td>\n",
       "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
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       "      <th>9296</th>\n",
       "      <td>27344</td>\n",
       "      <td>Revolutionary Girl Utena: Adolescence of Utena...</td>\n",
       "      <td>[Action, Adventure, Animation, Comedy, Drama, ...</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>9825</th>\n",
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       "      <td>Atlantis: Milo's Return</td>\n",
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       "      <td>51939</td>\n",
       "      <td>TMNT (Teenage Mutant Ninja Turtles)</td>\n",
       "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13250</th>\n",
       "      <td>64645</td>\n",
       "      <td>The Wrecking Crew</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16055</th>\n",
       "      <td>81132</td>\n",
       "      <td>Rubber</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Film...</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>18312</th>\n",
       "      <td>91335</td>\n",
       "      <td>Gruffalo, The</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>22778</th>\n",
       "      <td>108540</td>\n",
       "      <td>Ernest &amp; Célestine (Ernest et Célestine)</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Drama...</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>22881</th>\n",
       "      <td>108932</td>\n",
       "      <td>The Lego Movie</td>\n",
       "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>25218</th>\n",
       "      <td>117646</td>\n",
       "      <td>Dragonheart 2: A New Beginning</td>\n",
       "      <td>[Action, Adventure, Comedy, Drama, Fantasy, Th...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>26442</th>\n",
       "      <td>122787</td>\n",
       "      <td>The 39 Steps</td>\n",
       "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>32854</th>\n",
       "      <td>146305</td>\n",
       "      <td>Princes and Princesses</td>\n",
       "      <td>[Animation, Children, Comedy, Drama, Fantasy, ...</td>\n",
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       "      <th>33509</th>\n",
       "      <td>148775</td>\n",
       "      <td>Wizards of Waverly Place: The Movie</td>\n",
       "      <td>[Adventure, Children, Comedy, Drama, Fantasy, ...</td>\n",
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      "text/plain": [
       "       movieId                                              title  \\\n",
       "664        673                                          Space Jam   \n",
       "1824      1907                                              Mulan   \n",
       "2902      2987                           Who Framed Roger Rabbit?   \n",
       "4923      5018                                           Motorama   \n",
       "6793      6902                                      Interstate 60   \n",
       "8605     26093         Wonderful World of the Brothers Grimm, The   \n",
       "8783     26340  Twelve Tasks of Asterix, The (Les douze travau...   \n",
       "9296     27344  Revolutionary Girl Utena: Adolescence of Utena...   \n",
       "9825     32031                                             Robots   \n",
       "11716    51632                            Atlantis: Milo's Return   \n",
       "11751    51939                TMNT (Teenage Mutant Ninja Turtles)   \n",
       "13250    64645                                  The Wrecking Crew   \n",
       "16055    81132                                             Rubber   \n",
       "18312    91335                                      Gruffalo, The   \n",
       "22778   108540           Ernest & Célestine (Ernest et Célestine)   \n",
       "22881   108932                                     The Lego Movie   \n",
       "25218   117646                     Dragonheart 2: A New Beginning   \n",
       "26442   122787                                       The 39 Steps   \n",
       "32854   146305                             Princes and Princesses   \n",
       "33509   148775                Wizards of Waverly Place: The Movie   \n",
       "\n",
       "                                                  genres year  \n",
       "664    [Adventure, Animation, Children, Comedy, Fanta...  NaN  \n",
       "1824   [Adventure, Animation, Children, Comedy, Drama...  NaN  \n",
       "2902   [Adventure, Animation, Children, Comedy, Crime...  NaN  \n",
       "4923   [Adventure, Comedy, Crime, Drama, Fantasy, Mys...  NaN  \n",
       "6793   [Adventure, Comedy, Drama, Fantasy, Mystery, S...  NaN  \n",
       "8605   [Adventure, Animation, Children, Comedy, Drama...  NaN  \n",
       "8783   [Action, Adventure, Animation, Children, Comed...  NaN  \n",
       "9296   [Action, Adventure, Animation, Comedy, Drama, ...  NaN  \n",
       "9825   [Adventure, Animation, Children, Comedy, Fanta...  NaN  \n",
       "11716  [Action, Adventure, Animation, Children, Comed...  NaN  \n",
       "11751  [Action, Adventure, Animation, Children, Comed...  NaN  \n",
       "13250  [Action, Adventure, Comedy, Crime, Drama, Thri...  NaN  \n",
       "16055  [Action, Adventure, Comedy, Crime, Drama, Film...  NaN  \n",
       "18312    [Adventure, Animation, Children, Comedy, Drama]  NaN  \n",
       "22778  [Adventure, Animation, Children, Comedy, Drama...  NaN  \n",
       "22881  [Action, Adventure, Animation, Children, Comed...  NaN  \n",
       "25218  [Action, Adventure, Comedy, Drama, Fantasy, Th...  NaN  \n",
       "26442  [Action, Adventure, Comedy, Crime, Drama, Thri...  NaN  \n",
       "32854  [Animation, Children, Comedy, Drama, Fantasy, ...  NaN  \n",
       "33509  [Adventure, Children, Comedy, Drama, Fantasy, ...  NaN  "
      ]
     },
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   ],
   "source": [
    "movies_df.loc[movies_df['movieId'].isin(recommendation_table.head(20).keys())]"
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