Movie_recommendation.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fefa5f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29ff97dd",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "      <th>4</th>\n",
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       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
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       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
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       "      <td>Flint (2017)</td>\n",
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       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
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       "<p>9742 rows × 3 columns</p>\n",
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      ],
      "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",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \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  \n",
       "...                                           ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie = pd.read_csv(\"movies.csv\")\n",
    "# Opening the movies.csv dataset\n",
    "movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0799d68e",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
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       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "rating = pd.read_csv(\"ratings.csv\")\n",
    "# Opening the ratings.csv dataset\n",
    "rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b68cc182",
   "metadata": {},
   "outputs": [
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       "      userId  movieId               tag   timestamp\n",
       "0          2    60756             funny  1445714994\n",
       "1          2    60756   Highly quotable  1445714996\n",
       "2          2    60756      will ferrell  1445714992\n",
       "3          2    89774      Boxing story  1445715207\n",
       "4          2    89774               MMA  1445715200\n",
       "...      ...      ...               ...         ...\n",
       "3678     606     7382         for katie  1171234019\n",
       "3679     606     7936           austere  1173392334\n",
       "3680     610     3265            gun fu  1493843984\n",
       "3681     610     3265  heroic bloodshed  1493843978\n",
       "3682     610   168248  Heroic Bloodshed  1493844270\n",
       "\n",
       "[3683 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "tags = pd.read_csv(\"tags.csv\")\n",
    "# Opening the tags.csv dataset\n",
    "tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b88ca5fa",
   "metadata": {},
   "outputs": [
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      ],
      "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\n",
       "...       ...      ...       ...\n",
       "9737   193581  5476944  432131.0\n",
       "9738   193583  5914996  445030.0\n",
       "9739   193585  6397426  479308.0\n",
       "9740   193587  8391976  483455.0\n",
       "9741   193609   101726   37891.0\n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links = pd.read_csv(\"links.csv\")\n",
    "# opening the links.csv dataset\n",
    "links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bd542f2",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1106635946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1510577970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>17</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1305696483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>184</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1537109082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>184</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1537109545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
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       "      <td>3.5</td>\n",
       "      <td>1537109805</td>\n",
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       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>184</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1537110021</td>\n",
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       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>331</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1537157606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId                                      title  \\\n",
       "0             1                           Toy Story (1995)   \n",
       "1             1                           Toy Story (1995)   \n",
       "2             1                           Toy Story (1995)   \n",
       "3             1                           Toy Story (1995)   \n",
       "4             1                           Toy Story (1995)   \n",
       "...         ...                                        ...   \n",
       "100831   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "100832   193583               No Game No Life: Zero (2017)   \n",
       "100833   193585                               Flint (2017)   \n",
       "100834   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "100835   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                             genres  userId  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy       1     4.0   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy       5     4.0   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy       7     4.5   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy      15     2.5   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      17     4.5   \n",
       "...                                             ...     ...     ...   \n",
       "100831              Action|Animation|Comedy|Fantasy     184     4.0   \n",
       "100832                     Animation|Comedy|Fantasy     184     3.5   \n",
       "100833                                        Drama     184     3.5   \n",
       "100834                             Action|Animation     184     3.5   \n",
       "100835                                       Comedy     331     4.0   \n",
       "\n",
       "         timestamp  \n",
       "0        964982703  \n",
       "1        847434962  \n",
       "2       1106635946  \n",
       "3       1510577970  \n",
       "4       1305696483  \n",
       "...            ...  \n",
       "100831  1537109082  \n",
       "100832  1537109545  \n",
       "100833  1537109805  \n",
       "100834  1537110021  \n",
       "100835  1537157606  \n",
       "\n",
       "[100836 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(movie,rating)\n",
    "# Merge movie and rating dataset\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8cecd87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.5</td>\n",
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       "      <th>100833</th>\n",
       "      <td>193585</td>\n",
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       "      <td>3.5</td>\n",
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       "      <th>100834</th>\n",
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       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
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       "      <th>100835</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
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       "      <td>4.0</td>\n",
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       "  </tbody>\n",
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       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId                                      title  userId  rating\n",
       "0             1                           Toy Story (1995)       1     4.0\n",
       "1             1                           Toy Story (1995)       5     4.0\n",
       "2             1                           Toy Story (1995)       7     4.5\n",
       "3             1                           Toy Story (1995)      15     2.5\n",
       "4             1                           Toy Story (1995)      17     4.5\n",
       "...         ...                                        ...     ...     ...\n",
       "100831   193581  Black Butler: Book of the Atlantic (2017)     184     4.0\n",
       "100832   193583               No Game No Life: Zero (2017)     184     3.5\n",
       "100833   193585                               Flint (2017)     184     3.5\n",
       "100834   193587        Bungo Stray Dogs: Dead Apple (2018)     184     3.5\n",
       "100835   193609        Andrew Dice Clay: Dice Rules (1991)     331     4.0\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"genres\",\"timestamp\"], axis = 1, inplace = True)\n",
    "# Remove genres and timestamp columns\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "998683b6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>title</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 9719 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              NaN                                      NaN   \n",
       "2              NaN                                      NaN   \n",
       "3              NaN                                      NaN   \n",
       "4              NaN                                      NaN   \n",
       "5              NaN                                      NaN   \n",
       "...            ...                                      ...   \n",
       "606            NaN                                      NaN   \n",
       "607            NaN                                      NaN   \n",
       "608            NaN                                      NaN   \n",
       "609            NaN                                      NaN   \n",
       "610            4.0                                      NaN   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                          NaN                  NaN   \n",
       "2                          NaN                  NaN   \n",
       "3                          NaN                  NaN   \n",
       "4                          NaN                  NaN   \n",
       "5                          NaN                  NaN   \n",
       "...                        ...                  ...   \n",
       "606                        NaN                  NaN   \n",
       "607                        NaN                  NaN   \n",
       "608                        NaN                  NaN   \n",
       "609                        NaN                  NaN   \n",
       "610                        NaN                  NaN   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                             NaN                              NaN   \n",
       "2                             NaN                              NaN   \n",
       "3                             NaN                              NaN   \n",
       "4                             NaN                              NaN   \n",
       "5                             NaN                              NaN   \n",
       "...                           ...                              ...   \n",
       "606                           NaN                              NaN   \n",
       "607                           NaN                              NaN   \n",
       "608                           NaN                              NaN   \n",
       "609                           NaN                              NaN   \n",
       "610                           NaN                              NaN   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                      NaN                   NaN                          NaN   \n",
       "2                      NaN                   NaN                          NaN   \n",
       "3                      NaN                   NaN                          NaN   \n",
       "4                      NaN                   NaN                          NaN   \n",
       "5                      NaN                   NaN                          NaN   \n",
       "...                    ...                   ...                          ...   \n",
       "606                    NaN                   NaN                          NaN   \n",
       "607                    NaN                   NaN                          NaN   \n",
       "608                    NaN                   NaN                          NaN   \n",
       "609                    NaN                   NaN                          NaN   \n",
       "610                    NaN                   NaN                          3.5   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                                  NaN  ...          NaN           NaN   \n",
       "2                                  NaN  ...          NaN           NaN   \n",
       "3                                  NaN  ...          NaN           NaN   \n",
       "4                                  NaN  ...          NaN           NaN   \n",
       "5                                  NaN  ...          NaN           NaN   \n",
       "...                                ...  ...          ...           ...   \n",
       "606                                NaN  ...          NaN           NaN   \n",
       "607                                NaN  ...          NaN           NaN   \n",
       "608                                NaN  ...          NaN           NaN   \n",
       "609                                NaN  ...          NaN           NaN   \n",
       "610                                NaN  ...          NaN           4.0   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1                 NaN                      NaN   \n",
       "2                 NaN                      NaN   \n",
       "3                 NaN                      NaN   \n",
       "4                 NaN                      NaN   \n",
       "5                 NaN                      NaN   \n",
       "...               ...                      ...   \n",
       "606               NaN                      NaN   \n",
       "607               NaN                      NaN   \n",
       "608               NaN                      NaN   \n",
       "609               NaN                      NaN   \n",
       "610               3.5                      3.0   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                                     NaN        \n",
       "2                                                     NaN        \n",
       "3                                                     NaN        \n",
       "4                                                     NaN        \n",
       "5                                                     NaN        \n",
       "...                                                   ...        \n",
       "606                                                   NaN        \n",
       "607                                                   NaN        \n",
       "608                                                   NaN        \n",
       "609                                                   NaN        \n",
       "610                                                   NaN        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1                   NaN         NaN                             NaN   \n",
       "2                   NaN         NaN                             NaN   \n",
       "3                   NaN         NaN                             NaN   \n",
       "4                   NaN         NaN                             NaN   \n",
       "5                   NaN         NaN                             NaN   \n",
       "...                 ...         ...                             ...   \n",
       "606                 NaN         NaN                             NaN   \n",
       "607                 NaN         NaN                             NaN   \n",
       "608                 4.5         3.5                             NaN   \n",
       "609                 NaN         NaN                             NaN   \n",
       "610                 NaN         2.0                             1.5   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                         4.0                                        NaN  \n",
       "2                         NaN                                        NaN  \n",
       "3                         NaN                                        NaN  \n",
       "4                         NaN                                        NaN  \n",
       "5                         NaN                                        NaN  \n",
       "...                       ...                                        ...  \n",
       "606                       NaN                                        NaN  \n",
       "607                       NaN                                        NaN  \n",
       "608                       NaN                                        NaN  \n",
       "609                       NaN                                        NaN  \n",
       "610                       NaN                                        NaN  \n",
       "\n",
       "[610 rows x 9719 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pivot = data.pivot_table(index='userId',columns='title',values='rating')\n",
    "# Create a spreadsheet-style PivotTable as a DataFrame\n",
    "data_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10fe5f69",
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193581</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193583</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193585</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193587</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193609</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  NaN  NaN  NaN  4.0  NaN  4.5  NaN  NaN  NaN  ...  4.0  NaN  4.0   \n",
       "2        NaN  NaN  NaN  NaN  NaN  4.0  NaN  4.0  NaN  NaN  ...  NaN  4.0  NaN   \n",
       "3        4.0  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "4        NaN  NaN  NaN  NaN  NaN  3.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "5        NaN  NaN  NaN  NaN  NaN  5.0  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "193583   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "193585   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "193587   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "193609   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  NaN  NaN  2.0  NaN  NaN  \n",
       "3        NaN  NaN  NaN  NaN  2.0  NaN  NaN  \n",
       "4        NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "5        3.0  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "193581   NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "193583   NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "193585   NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "193587   NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "193609   NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pivot2 = pd.pivot(index = \"movieId\", columns = \"userId\", data = rating, values = \"rating\")\n",
    "# Returns reshaped DataFrame organized by given index/column values.\n",
    "data_pivot2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "276178bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>9724 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "      movieId  rating\n",
       "0           1     215\n",
       "1           2     110\n",
       "2           3      52\n",
       "3           4       7\n",
       "4           5      49\n",
       "...       ...     ...\n",
       "9719   193581       1\n",
       "9720   193583       1\n",
       "9721   193585       1\n",
       "9722   193587       1\n",
       "9723   193609       1\n",
       "\n",
       "[9724 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Movie_voted = pd.DataFrame(rating.groupby(\"movieId\")[\"rating\"].agg(\"count\"))\n",
    "Movie_voted.reset_index(level = 0, inplace = True)\n",
    "Movie_voted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6185c6b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>607</th>\n",
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       "      <td>831</td>\n",
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       "</table>\n",
       "<p>610 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  rating\n",
       "0         1     232\n",
       "1         2      29\n",
       "2         3      39\n",
       "3         4     216\n",
       "4         5      44\n",
       "..      ...     ...\n",
       "605     606    1115\n",
       "606     607     187\n",
       "607     608     831\n",
       "608     609      37\n",
       "609     610    1302\n",
       "\n",
       "[610 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "User_Voted = pd.DataFrame(rating.groupby(\"userId\")[\"rating\"].agg(\"count\"))\n",
    "User_Voted.reset_index(level = 0, inplace = True)\n",
    "User_Voted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8af58f5",
   "metadata": {},
   "outputs": [
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       "<p>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  ...  4.0  0.0  4.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  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  3.0  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  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  0.0  0.0  2.0  0.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  2.0  0.0  0.0  \n",
       "4        0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pivot2.fillna(0, inplace = True)\n",
    "data_pivot2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34311297",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>4</th>\n",
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       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>176371</th>\n",
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       "      <td>...</td>\n",
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       "      <td>4.5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>179819</th>\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>...</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>187593</th>\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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 334 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    4    6    7    10   11   15   16   17   18   ...  600  601  602  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  4.5  0.0  0.0  2.5  0.0  4.5  3.5  ...  2.5  4.0  0.0   \n",
       "2        0.0  0.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  ...  4.0  0.0  4.0   \n",
       "3        4.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  2.5  0.0  0.0   \n",
       "6        4.0  0.0  4.0  0.0  0.0  5.0  0.0  0.0  0.0  4.0  ...  0.0  0.0  3.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.5  0.0   \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   603  604  605  606  607  608  610  \n",
       "movieId                                     \n",
       "1        4.0  3.0  4.0  2.5  4.0  2.5  5.0  \n",
       "2        0.0  5.0  3.5  0.0  0.0  2.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  0.0  2.0  0.0  \n",
       "5        0.0  3.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6        4.0  3.0  0.0  0.0  0.0  0.0  5.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2121 rows x 334 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataLast = data_pivot2.loc[Movie_voted[Movie_voted[\"rating\"] > 10][\"movieId\"],:]\n",
    "dataLast = dataLast.loc[:, User_Voted[User_Voted[\"rating\"] > 60][\"userId\"]]\n",
    "# The final version of the dataset\n",
    "dataLast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aaeb990b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "csr_data = csr_matrix(dataLast.values)\n",
    "# create a matrix with the dataLast dataset\n",
    "dataLast.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83e71684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2121x334 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 70666 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "290b1662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_jobs=-1, n_neighbors=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_jobs=-1, n_neighbors=20)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_jobs=-1, n_neighbors=20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "# Import KNN\n",
    "knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n",
    "knn.fit(csr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "605967ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def movie_recommendation(movie_name):\n",
    "    \n",
    "    n_movies_to_reccomend = 10\n",
    "    movie_list = movie[movie['title'].str.contains(movie_name)] # Find movie name\n",
    "    \n",
    "    if len(movie_list):\n",
    "        \n",
    "        movie_idx= movie_list.iloc[0]['movieId']\n",
    "        movie_idx = dataLast[dataLast['movieId'] == movie_idx].index[0]\n",
    "        \n",
    "        distances , indices = knn.kneighbors(csr_data[movie_idx],n_neighbors=n_movies_to_reccomend+1)    \n",
    "        rec_movie_indices = sorted(list(zip(indices.squeeze().tolist(),distances.squeeze().tolist())),key=lambda x: x[1])[:0:-1]\n",
    "        recommend_frame = []\n",
    "        \n",
    "        for val in rec_movie_indices:\n",
    "            movie_idx = dataLast.iloc[val[0]]['movieId']\n",
    "            idx = movie[movie['movieId'] == movie_idx].index\n",
    "            recommend_frame.append({'Title':movie.iloc[idx]['title'].values[0],'Distance':val[1]})\n",
    "        df = pd.DataFrame(recommend_frame,index=range(1,n_movies_to_reccomend+1))\n",
    "        return df\n",
    "    \n",
    "    else:\n",
    "        return \"No movies found. Please check your input\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99ec01da",
   "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>Title</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: The Curse of the Bla...</td>\n",
       "      <td>0.374269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minority Report (2002)</td>\n",
       "      <td>0.370963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ocean's Eleven (2001)</td>\n",
       "      <td>0.348840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bourne Identity, The (2002)</td>\n",
       "      <td>0.346696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lord of the Rings: The Return of the King, The...</td>\n",
       "      <td>0.345529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lord of the Rings: The Two Towers, The (2002)</td>\n",
       "      <td>0.337262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>0.332940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fight Club (1999)</td>\n",
       "      <td>0.325253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>0.297221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "      <td>0.296231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  Distance\n",
       "1   Pirates of the Caribbean: The Curse of the Bla...  0.374269\n",
       "2                              Minority Report (2002)  0.370963\n",
       "3                               Ocean's Eleven (2001)  0.348840\n",
       "4                         Bourne Identity, The (2002)  0.346696\n",
       "5   Lord of the Rings: The Return of the King, The...  0.345529\n",
       "6       Lord of the Rings: The Two Towers, The (2002)  0.337262\n",
       "7                          Saving Private Ryan (1998)  0.332940\n",
       "8                                   Fight Club (1999)  0.325253\n",
       "9                                  Matrix, The (1999)  0.297221\n",
       "10  Lord of the Rings: The Fellowship of the Ring,...  0.296231"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_recommendation('Gladiator')\n",
    "# result of Recommendation System "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fdef68f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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