movie_re.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7ec26ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import zipfile\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8942bf",
   "metadata": {},
   "source": [
    "<font color=red size=5>Load Data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a780ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path = \"C:\\\\Users\\\\Pro\\\\Desktop\\\\ML_project\"\n",
    "files = os.listdir(folder_path)\n",
    "datasets = {}\n",
    "for file in files:\n",
    "    if file.endswith(\".csv\"):\n",
    "        file_path = os.path.join(folder_path, file)\n",
    "        df = pd.read_csv(file_path)\n",
    "        dataset_name = os.path.splitext(file)[0]\n",
    "        datasets[dataset_name] = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4c216426",
   "metadata": {},
   "outputs": [],
   "source": [
    "genome_scores = datasets['genome_scores']\n",
    "genome_tags= datasets['genome_tags']\n",
    "link=datasets['link']\n",
    "movie = datasets['movie']\n",
    "rating = datasets['rating']\n",
    "tag=datasets['tag']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23cab476",
   "metadata": {},
   "source": [
    "<font color=red size=5>Data Exploration</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dab0b2b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 3 columns</p>\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",
       "27273   131254        Kein Bund für's Leben (2007)   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)   \n",
       "27275   131258                  The Pirates (2014)   \n",
       "27276   131260                 Rentun Ruusu (2001)   \n",
       "27277   131262                    Innocence (2014)   \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",
       "27273                                       Comedy  \n",
       "27274                                       Comedy  \n",
       "27275                                    Adventure  \n",
       "27276                           (no genres listed)  \n",
       "27277                     Adventure|Fantasy|Horror  \n",
       "\n",
       "[27278 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "83cb6493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate = movie.duplicated(subset=['movieId', 'title']).sum()# check for duplicate rows\n",
    "duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "14a08ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataexp(data):\n",
    "    duplicate = data.duplicated().sum()# check for duplicate rows\n",
    "    missing_values_total = data.isnull().sum()#missing values\n",
    "    print('missig values :',missing_values_total)\n",
    "    print('\\n')\n",
    "    print('duplicte: ', duplicate)\n",
    "    print('\\n')\n",
    "    print('types :',data.dtypes)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "85358002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    0\n",
       "title      0\n",
       "genres     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_total = movie.isnull().sum()#missing values\n",
    "missing_values_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b50d6ca6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <td>2.5</td>\n",
       "      <td>2009-10-17 20:25:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating            timestamp\n",
       "0              1        2     3.5  2005-04-02 23:53:47\n",
       "1              1       29     3.5  2005-04-02 23:31:16\n",
       "2              1       32     3.5  2005-04-02 23:33:39\n",
       "3              1       47     3.5  2005-04-02 23:32:07\n",
       "4              1       50     3.5  2005-04-02 23:29:40\n",
       "...          ...      ...     ...                  ...\n",
       "20000258  138493    68954     4.5  2009-11-13 15:42:00\n",
       "20000259  138493    69526     4.5  2009-12-03 18:31:48\n",
       "20000260  138493    69644     3.0  2009-12-07 18:10:57\n",
       "20000261  138493    70286     5.0  2009-11-13 15:42:24\n",
       "20000262  138493    71619     2.5  2009-10-17 20:25:36\n",
       "\n",
       "[20000263 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4879121a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missig values : userId       0\n",
      "movieId      0\n",
      "rating       0\n",
      "timestamp    0\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "duplicte:  0\n",
      "\n",
      "\n",
      "types : userId         int64\n",
      "movieId        int64\n",
      "rating       float64\n",
      "timestamp     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "dataexp(rating)     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac98479e",
   "metadata": {},
   "source": [
    "<font color= blue size =2>Recommendations of movies based on high average ratings and the number of rating by users </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "77a62a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_ppl= rating.groupby('movieId').agg({'rating': ['mean', 'count']})#groupe by movieid and aggregte rating by mean and count\n",
    "movie_ppl.columns = ['average_rating', 'num_ratings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4f9f32f5",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
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       "      <th>movieId</th>\n",
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       "      <td>3.921240</td>\n",
       "      <td>49695</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>26744 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         average_rating  num_ratings\n",
       "movieId                             \n",
       "1              3.921240        49695\n",
       "2              3.211977        22243\n",
       "3              3.151040        12735\n",
       "4              2.861393         2756\n",
       "5              3.064592        12161\n",
       "...                 ...          ...\n",
       "131254         4.000000            1\n",
       "131256         4.000000            1\n",
       "131258         2.500000            1\n",
       "131260         3.000000            1\n",
       "131262         4.000000            1\n",
       "\n",
       "[26744 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ppl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a8b993bb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_rating</th>\n",
       "      <th>num_ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
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       "    <tr>\n",
       "      <th>131062</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131152</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26744 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         average_rating  num_ratings\n",
       "movieId                             \n",
       "296            4.174231        67310\n",
       "356            4.029000        66172\n",
       "318            4.446990        63366\n",
       "593            4.177057        63299\n",
       "480            3.664741        59715\n",
       "...                 ...          ...\n",
       "130672         0.500000            1\n",
       "130682         0.500000            1\n",
       "130900         0.500000            1\n",
       "131062         0.500000            1\n",
       "131152         0.500000            1\n",
       "\n",
       "[26744 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_movie_ppl = movie_ppl.sort_values(by=['num_ratings', 'average_rating'], ascending=False)#sort by number of rating and avr rating\n",
    "sorted_movie_ppl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bf6d5b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([296, 356, 318, 593, 480, 260, 110, 589, 2571, 527], dtype='int64', name='movieId')\n"
     ]
    }
   ],
   "source": [
    "top_10= sorted_movie_ppl.head(10).index.get_level_values('movieId')\n",
    "print(top_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e7cbf4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top 10 movies of all time :\n",
      "\t Priest (1994)\n",
      "\t I Love Trouble (1994)\n",
      "\t Strawberry and Chocolate (Fresa y chocolate) (1993)\n",
      "\t Wild Bunch, The (1969)\n",
      "\t Lassie (1994)\n",
      "\t Ladybird Ladybird (1994)\n",
      "\t Rumble in the Bronx (Hont faan kui) (1995)\n",
      "\t Beauty and the Beast (1991)\n",
      "\t Rocky Horror Picture Show, The (1975)\n",
      "\t Secret Garden, The (1993)\n"
     ]
    }
   ],
   "source": [
    "top_10_names=movie.loc[top_10,'title'].tolist()# obtain the names of top movies\n",
    "print('top 10 movies of all time :')\n",
    "for movie_name in top_10_names:\n",
    "    print('\\t',movie_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9093329",
   "metadata": {},
   "source": [
    "## Content-based filtering using movies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4282c31f",
   "metadata": {},
   "outputs": [],
   "source": [
    "g= movie['genres']#find unique genrs\n",
    "listt=[]\n",
    "for i in g :\n",
    "    l=i.split('|')\n",
    "    for m in l :\n",
    "        if m not in listt:\n",
    "            listt.append(m)\n",
    "#make to every unique genre column and assign 1 if the movie is of that genre\n",
    "for i, j in zip(g, range(len(g))):\n",
    "    l=i.split('|')\n",
    "    for m in l : \n",
    "        movie.loc[j, m]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "52958c0b",
   "metadata": {
    "scrolled": true
   },
   "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>movieId</th>\n",
       "      <th>title</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>Drama</th>\n",
       "      <th>Action</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</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>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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</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>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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</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>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>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</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>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>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</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",
       "      <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>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                               title  Adventure  Animation  \\\n",
       "0            1                    Toy Story (1995)        1.0        1.0   \n",
       "1            2                      Jumanji (1995)        1.0        0.0   \n",
       "2            3             Grumpier Old Men (1995)        0.0        0.0   \n",
       "3            4            Waiting to Exhale (1995)        0.0        0.0   \n",
       "4            5  Father of the Bride Part II (1995)        0.0        0.0   \n",
       "...        ...                                 ...        ...        ...   \n",
       "27273   131254        Kein Bund für's Leben (2007)        0.0        0.0   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)        0.0        0.0   \n",
       "27275   131258                  The Pirates (2014)        1.0        0.0   \n",
       "27276   131260                 Rentun Ruusu (2001)        0.0        0.0   \n",
       "27277   131262                    Innocence (2014)        1.0        0.0   \n",
       "\n",
       "       Children  Comedy  Fantasy  Romance  Drama  Action  ...  Horror  \\\n",
       "0           1.0     1.0      1.0      0.0    0.0     0.0  ...     0.0   \n",
       "1           1.0     0.0      1.0      0.0    0.0     0.0  ...     0.0   \n",
       "2           0.0     1.0      0.0      1.0    0.0     0.0  ...     0.0   \n",
       "3           0.0     1.0      0.0      1.0    1.0     0.0  ...     0.0   \n",
       "4           0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "...         ...     ...      ...      ...    ...     ...  ...     ...   \n",
       "27273       0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27274       0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27275       0.0     0.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27276       0.0     0.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27277       0.0     0.0      1.0      0.0    0.0     0.0  ...     1.0   \n",
       "\n",
       "       Mystery  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        0.0   \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",
       "...        ...     ...   ...          ...  ...      ...      ...        ...   \n",
       "27273      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27274      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27275      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27276      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27277      0.0     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",
       "27273                 0.0  \n",
       "27274                 0.0  \n",
       "27275                 0.0  \n",
       "27276                 1.0  \n",
       "27277                 0.0  \n",
       "\n",
       "[27278 rows x 22 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df=movie.fillna(0)#fill nan by 0\n",
    "movie_df=movie_df.drop(columns='genres')\n",
    "movie_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d52ffb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_counts = movie_df.iloc[:, 2:].sum() # count how many movies of each genre\n",
    "genre_counts = pd.to_numeric(genre_counts, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "80b33f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "genre_counts.sort_values(ascending=False).plot(kind='bar', color='skyblue')\n",
    "plt.title('Counts of Each Genre')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fcb962a",
   "metadata": {},
   "source": [
    "#### recommend movies based on a spf genre of movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3ed86f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rec_genres(data, genre):\n",
    "    genre_movies = data[data[genre] == 1]\n",
    "    for title in genre_movies['title'].head(10):\n",
    "        print(title)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "484fe66b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toy Story (1995)\n",
      "Jumanji (1995)\n",
      "Tom and Huck (1995)\n",
      "Balto (1995)\n",
      "Now and Then (1995)\n",
      "Babe (1995)\n",
      "It Takes Two (1995)\n",
      "Pocahontas (1995)\n",
      "Big Green, The (1995)\n",
      "Kids of the Round Table (1995)\n"
     ]
    }
   ],
   "source": [
    "rec_genres(movie_df, 'Children') # movies that are for childrens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dfa43be5",
   "metadata": {
    "scrolled": true
   },
   "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>movieId</th>\n",
       "      <th>title</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>Drama</th>\n",
       "      <th>Action</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <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>...</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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</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>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>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</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>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>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</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>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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</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>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>...</th>\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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</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>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>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</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>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>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</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",
       "      <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>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                               title  Adventure  Animation  \\\n",
       "0            1                    Toy Story (1995)        1.0        1.0   \n",
       "1            2                      Jumanji (1995)        1.0        0.0   \n",
       "2            3             Grumpier Old Men (1995)        0.0        0.0   \n",
       "3            4            Waiting to Exhale (1995)        0.0        0.0   \n",
       "4            5  Father of the Bride Part II (1995)        0.0        0.0   \n",
       "...        ...                                 ...        ...        ...   \n",
       "27273   131254        Kein Bund für's Leben (2007)        0.0        0.0   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)        0.0        0.0   \n",
       "27275   131258                  The Pirates (2014)        1.0        0.0   \n",
       "27276   131260                 Rentun Ruusu (2001)        0.0        0.0   \n",
       "27277   131262                    Innocence (2014)        1.0        0.0   \n",
       "\n",
       "       Children  Comedy  Fantasy  Romance  Drama  Action  ...  Horror  \\\n",
       "0           1.0     1.0      1.0      0.0    0.0     0.0  ...     0.0   \n",
       "1           1.0     0.0      1.0      0.0    0.0     0.0  ...     0.0   \n",
       "2           0.0     1.0      0.0      1.0    0.0     0.0  ...     0.0   \n",
       "3           0.0     1.0      0.0      1.0    1.0     0.0  ...     0.0   \n",
       "4           0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "...         ...     ...      ...      ...    ...     ...  ...     ...   \n",
       "27273       0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27274       0.0     1.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27275       0.0     0.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27276       0.0     0.0      0.0      0.0    0.0     0.0  ...     0.0   \n",
       "27277       0.0     0.0      1.0      0.0    0.0     0.0  ...     1.0   \n",
       "\n",
       "       Mystery  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        0.0   \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",
       "...        ...     ...   ...          ...  ...      ...      ...        ...   \n",
       "27273      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27274      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27275      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27276      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "27277      0.0     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",
       "27273                 0.0  \n",
       "27274                 0.0  \n",
       "27275                 0.0  \n",
       "27276                 1.0  \n",
       "27277                 0.0  \n",
       "\n",
       "[27278 rows x 22 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7616d94",
   "metadata": {},
   "source": [
    "## using knn to recommend movies with the same geners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5281ceac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 20)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors \n",
    "import random\n",
    "\n",
    "X = movie_df.iloc[:, 2:] # X is data with only geners\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "00ef9bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(metric=&#x27;euclidean&#x27;)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(metric=&#x27;euclidean&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(metric='euclidean')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model = NearestNeighbors(n_neighbors=5, metric='euclidean')\n",
    "knn_model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9f870c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20218, 99502)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ids = list(movie_df.movieId)# list of movies ids \n",
    "movie_id = random.choice(movie_ids)# choose a rondam id of movie\n",
    "movie_index = movie_df.index[movie_df['movieId'] == movie_id].tolist()[0]# searsh index of the chosen movieid in data\n",
    "movie_index,movie_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f543a134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_features = X.loc[movie_index, :].values.reshape(1, -1)\n",
    "movie_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ae67788b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0., 0., 0., 0., 0.]]), array([[81, 74, 71, 94, 83]], dtype=int64))"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances, indices = knn_model.kneighbors(movie_features)\n",
    "distances,indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e792a71a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended Movies for \"Remember the Daze (Beautiful Ordinary, The) (2007)\" are :\n",
      "  ['Once Upon a Time... When We Were Colored (1995)', 'Screamers (1995)', 'Misérables, Les (1995)', 'Shopping (1994)', 'Angels and Insects (1995)']\n"
     ]
    }
   ],
   "source": [
    "recommended_movie_ids = movie_df.iloc[indices[0], 0]\n",
    "list(recommended_movie_ids)\n",
    "recomm=movie_df.loc[list(recommended_movie_ids),'title']\n",
    "print(f'Recommended Movies for \"{movie_df.loc[movie_index, \"title\"]}\" are :\\n  {list(recomm)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0f5b537e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended Movies for \"Chronicle of a Disappearance (1996)\" are : \n",
      "Twelve Monkeys (a.k.a. 12 Monkeys) (1995)\n",
      "Now and Then (1995)\n",
      "Cutthroat Island (1995)\n",
      "Mortal Kombat (1995)\n",
      "Richard III (1995)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors \n",
    "import random\n",
    "\n",
    "X = movie_df.iloc[:, 2:] # X is data with only geners\n",
    "\n",
    "knn_model = NearestNeighbors(n_neighbors=5, metric='euclidean')\n",
    "knn_model.fit(X)\n",
    "\n",
    "movie_ids = list(movie_df.movieId)# list of movies ids \n",
    "movie_id = random.choice(movie_ids)# choose a rondam id of movie\n",
    "\n",
    "movie_index = movie_df.index[movie_df['movieId'] == movie_id].tolist()[0]# searsh index of the chosen movie in data\n",
    "\n",
    "movie_features = X.loc[movie_index, :].values.reshape(1, -1)\n",
    "\n",
    "distances, indices = knn_model.kneighbors(movie_features)\n",
    "recommended_movie_ids = movie_df.iloc[indices[0], 0]\n",
    "list(recommended_movie_ids)\n",
    "\n",
    "recomm=movie_df.loc[list(recommended_movie_ids),'title']\n",
    "print(f'Recommended Movies for \"{movie_df.loc[movie_index, \"title\"]}\" are : ')\n",
    "for i in  list(recomm):\n",
    "         print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bec60c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30, 25, 13, 42, 39]], dtype=int64)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2a741ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbor_indices = indices[0][1:].reshape(1, -1)\n",
    "neighbor_distances = distances[0][1:].reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ad07a2c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25, 13, 42, 39]], dtype=int64)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbor_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "37ccb5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbor_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afa126e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b87018ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "406a6df5",
   "metadata": {},
   "source": [
    "## Collaborative Filtering using knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4400135f",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie__df=movie.iloc[:,0:2]\n",
    "rating_df=rating.iloc[:,0:3]\n",
    "data = pd.merge(movie__df,rating_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2b31ff8a",
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       "          movieId                          title  userId  rating\n",
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       "4               1               Toy Story (1995)      11     4.5\n",
       "...           ...                            ...     ...     ...\n",
       "20000258   131254   Kein Bund für's Leben (2007)   79570     4.0\n",
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       "\n",
       "[20000263 rows x 4 columns]"
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     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
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    "data"
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  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d761b0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.iloc[:1000000,:]\n",
    "pivot_table = pd.pivot_table(data,index=\"userId\", columns=\"title\", values=\"rating\")\n",
    "pivot_table=pivot_table.fillna(0)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "844daf14",
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      "text/plain": [
       "title   Ace Ventura: When Nature Calls (1995)  Across the Sea of Time (1995)  \\\n",
       "userId                                                                         \n",
       "1                                         0.0                            0.0   \n",
       "2                                         0.0                            0.0   \n",
       "3                                         0.0                            0.0   \n",
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       "...                                       ...                            ...   \n",
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       "138492                                    0.0                            0.0   \n",
       "138493                                    4.0                            0.0   \n",
       "\n",
       "title   Amazing Panda Adventure, The (1995)  American President, The (1995)  \\\n",
       "userId                                                                        \n",
       "1                                       0.0                             0.0   \n",
       "2                                       0.0                             0.0   \n",
       "3                                       0.0                             0.0   \n",
       "4                                       0.0                             0.0   \n",
       "5                                       0.0                             5.0   \n",
       "...                                     ...                             ...   \n",
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       "138492                                  0.0                             0.0   \n",
       "138493                                  0.0                             0.0   \n",
       "\n",
       "title   Angela (1995)  Angels and Insects (1995)  \\\n",
       "userId                                             \n",
       "1                 0.0                        0.0   \n",
       "2                 0.0                        0.0   \n",
       "3                 0.0                        0.0   \n",
       "4                 0.0                        0.0   \n",
       "5                 0.0                        0.0   \n",
       "...               ...                        ...   \n",
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       "138491            0.0                        0.0   \n",
       "138492            0.0                        0.0   \n",
       "138493            0.0                        0.0   \n",
       "\n",
       "title   Anne Frank Remembered (1995)  Antonia's Line (Antonia) (1995)  \\\n",
       "userId                                                                  \n",
       "1                                0.0                              0.0   \n",
       "2                                0.0                              0.0   \n",
       "3                                0.0                              0.0   \n",
       "4                                0.0                              0.0   \n",
       "5                                0.0                              0.0   \n",
       "...                              ...                              ...   \n",
       "138489                           0.0                              0.0   \n",
       "138490                           0.0                              3.0   \n",
       "138491                           0.0                              0.0   \n",
       "138492                           0.0                              0.0   \n",
       "138493                           0.0                              0.0   \n",
       "\n",
       "title   Assassins (1995)  Babe (1995)  ...  Unforgettable (1996)  \\\n",
       "userId                                 ...                         \n",
       "1                    0.0          0.0  ...                   0.0   \n",
       "2                    0.0          0.0  ...                   0.0   \n",
       "3                    0.0          0.0  ...                   0.0   \n",
       "4                    0.0          0.0  ...                   0.0   \n",
       "5                    0.0          0.0  ...                   0.0   \n",
       "...                  ...          ...  ...                   ...   \n",
       "138489               0.0          0.0  ...                   0.0   \n",
       "138490               0.0          5.0  ...                   0.0   \n",
       "138491               0.0          0.0  ...                   0.0   \n",
       "138492               0.0          0.0  ...                   0.0   \n",
       "138493               0.0          3.0  ...                   0.0   \n",
       "\n",
       "title   Up Close and Personal (1996)  Usual Suspects, The (1995)  \\\n",
       "userId                                                             \n",
       "1                                0.0                         3.5   \n",
       "2                                0.0                         0.0   \n",
       "3                                0.0                         5.0   \n",
       "4                                0.0                         0.0   \n",
       "5                                2.0                         0.0   \n",
       "...                              ...                         ...   \n",
       "138489                           0.0                         4.5   \n",
       "138490                           0.0                         0.0   \n",
       "138491                           0.0                         0.0   \n",
       "138492                           0.0                         0.0   \n",
       "138493                           0.0                         0.0   \n",
       "\n",
       "title   Vampire in Brooklyn (1995)  Waiting to Exhale (1995)  \\\n",
       "userId                                                         \n",
       "1                              0.0                       0.0   \n",
       "2                              0.0                       0.0   \n",
       "3                              0.0                       0.0   \n",
       "4                              0.0                       0.0   \n",
       "5                              0.0                       0.0   \n",
       "...                            ...                       ...   \n",
       "138489                         0.0                       0.0   \n",
       "138490                         0.0                       0.0   \n",
       "138491                         0.0                       0.0   \n",
       "138492                         0.0                       0.0   \n",
       "138493                         0.0                       0.0   \n",
       "\n",
       "title   When Night Is Falling (1995)  \\\n",
       "userId                                 \n",
       "1                                0.0   \n",
       "2                                0.0   \n",
       "3                                0.0   \n",
       "4                                0.0   \n",
       "5                                0.0   \n",
       "...                              ...   \n",
       "138489                           0.0   \n",
       "138490                           0.0   \n",
       "138491                           0.0   \n",
       "138492                           0.0   \n",
       "138493                           0.0   \n",
       "\n",
       "title   White Balloon, The (Badkonake sefid) (1995)  White Squall (1996)  \\\n",
       "userId                                                                     \n",
       "1                                               0.0                  0.0   \n",
       "2                                               0.0                  0.0   \n",
       "3                                               0.0                  0.0   \n",
       "4                                               0.0                  0.0   \n",
       "5                                               0.0                  0.0   \n",
       "...                                             ...                  ...   \n",
       "138489                                          0.0                  0.0   \n",
       "138490                                          0.0                  0.0   \n",
       "138491                                          0.0                  0.0   \n",
       "138492                                          0.0                  0.0   \n",
       "138493                                          0.0                  0.0   \n",
       "\n",
       "title   Wings of Courage (1995)  Young Poisoner's Handbook, The (1995)  \n",
       "userId                                                                  \n",
       "1                           0.0                                    0.0  \n",
       "2                           0.0                                    0.0  \n",
       "3                           0.0                                    0.0  \n",
       "4                           0.0                                    0.0  \n",
       "5                           0.0                                    0.0  \n",
       "...                         ...                                    ...  \n",
       "138489                      0.0                                    0.0  \n",
       "138490                      0.0                                    0.0  \n",
       "138491                      0.0                                    0.0  \n",
       "138492                      0.0                                    0.0  \n",
       "138493                      0.0                                    0.0  \n",
       "\n",
       "[123161 rows x 146 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2da31004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [4., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "pivot_table = pd.pivot_table(data,index=\"userId\", columns=\"title\", values=\"rating\")\n",
    "pivot_table=pivot_table.fillna(0)\n",
    "\n",
    "ratings_matrix_array = pivot_table.values\n",
    "target_user_id = 6\n",
    "ratings_matrix_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d65812d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 4., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 2., 0., 0., 0., 2., 0., 0., 0., 3., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 3., 0.,\n",
       "        0., 0., 0., 3., 0., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "        0., 0.]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_user_ratings = ratings_matrix_array[target_user_id, :].reshape(1, -1)# user target rating \n",
    "target_user_ratings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "325cf687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = NearestNeighbors(n_neighbors=5, metric='cosine', algorithm='brute') # knn model\n",
    "knn.fit(ratings_matrix_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4d125529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user 6 has a neighbor : 16039 far by a distance of: 0.25946836882264535\n",
      "user 6 has a neighbor : 92570 far by a distance of: 0.32352941176470584\n",
      "user 6 has a neighbor : 28668 far by a distance of: 0.32740846071498997\n",
      "user 6 has a neighbor : 83289 far by a distance of: 0.33041051999302473\n"
     ]
    }
   ],
   "source": [
    "distances, indices = knn.kneighbors(target_user_ratings)\n",
    "neighbor_indices = indices[0][1:].reshape(1, -1)\n",
    "neighbor_distances = distances[0][1:].reshape(1, -1)\n",
    "for i in range(neighbor_indices.shape[1]):\n",
    "    print(f'user {target_user_id} has a neighbor : {neighbor_indices[0][i]} far by a distance of: {neighbor_distances[0][i]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2e317b4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted rating of movies for our user \n",
      ": [[0.    0.    0.    3.625 0.    1.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.625 0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.75  0.\n",
      "  0.    3.5   0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    2.75  0.    3.125 0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "  0.    0.   ]]\n"
     ]
    }
   ],
   "source": [
    "predicted_ratings = np.mean(ratings_matrix_array[neighbor_indices], axis=1)# mean of rating of neighbors by axis = 1 ( columns)\n",
    "print('predicted rating of movies for our user \\n:',predicted_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d9d48c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.  , 0.  , 0.  , 0.  , 1.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.75, 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ,\n",
       "       0.  , 0.  , 0.  ])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "watched_movie_indices = np.where(ratings_matrix_array[target_user_id] > 0)[0]# this code bach nhydo movies already watched by our user\n",
    "p=predicted_ratings.flatten()\n",
    "p[watched_movie_indices] = 0\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "4fafbec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5,  58, 145,  45,  51,  50,  49,  48,  47,  46,  44,  53,  43,\n",
       "        42,  41,  40,  39,  38,  52,  55,  54,  64,  70,  69,  68,  67,\n",
       "        66,  65,  63,  36,  62,  61,  60,  59,  57,  56,  37,  35, 144,\n",
       "         9,  15,  14,  13,  12,  11,  10,   8,  17,   7,   6,   4,   3,\n",
       "         2,   1,  16,  18,  34,  27,  33,  32,  31,  30,  29,  28,  26,\n",
       "        19,  25,  24,  23,  22,  21,  20,  71,  72,  73,  74, 125, 124,\n",
       "       123, 122, 121, 120, 119, 118, 117, 116, 115, 114, 113, 112, 111,\n",
       "       126, 127, 128, 137, 143, 142, 141, 140, 139, 138, 136, 129, 135,\n",
       "       134, 133, 132, 131, 130, 110, 109, 108,  82,  88,  87,  86,  85,\n",
       "        84,  83,  81,  90,  80,  79,  78,  77,  76,  75,  89,  91, 107,\n",
       "       100, 106, 105, 104, 103, 102, 101,  99,  92,  98,  97,  96,  95,\n",
       "        94,  93,   0], dtype=int64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_movie_indices = np.argsort(p)[::-1]\n",
    "sorted_movie_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cad299ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top recommended movies for the target user 6 are:\n",
      "Angels and Insects (1995)\n",
      "Get Shorty (1995)\n",
      "Young Poisoner's Handbook, The (1995)\n",
      "Dracula: Dead and Loving It (1995)\n",
      "Four Rooms (1995)\n"
     ]
    }
   ],
   "source": [
    "top_recommendations = pivot_table.columns[sorted_movie_indices]\n",
    "print(f'Top recommended movies for the target user {target_user_id} are:')\n",
    "for i in range(5):\n",
    "    print(top_recommendations[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e6c1fd42",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def recom_userneighbors(pivot_table,target_user_id,n,movies_n): # fonction to do all\n",
    "    ratings_matrix_array = pivot_table.values\n",
    "    \n",
    "    target_user_ratings = ratings_matrix_array[target_user_id, :].reshape(1, -1)\n",
    "    knn = NearestNeighbors(n_neighbors=n+1, metric='cosine', algorithm='brute')\n",
    "    knn.fit(ratings_matrix_array)\n",
    "\n",
    "    distances, indices = knn.kneighbors(target_user_ratings)\n",
    "    neighbor_indices = indices[0][1:].reshape(1, -1)\n",
    "    neighbor_distances = distances[0][1:].reshape(1, -1)\n",
    "\n",
    "    predicted_ratings = np.mean(ratings_matrix_array[neighbor_indices], axis=1)\n",
    "\n",
    "    watched_movie_indices = np.where(ratings_matrix_array[target_user_id] > 0)[0]\n",
    "\n",
    "    p=predicted_ratings.flatten()\n",
    "    p[watched_movie_indices] = 0\n",
    "\n",
    "    sorted_movie_indices = np.argsort(p)[::-1]\n",
    "    top_recommendations = pivot_table.columns[sorted_movie_indices]\n",
    "    for i in range(neighbor_indices.shape[1]):\n",
    "          print(f'user {target_user_id} has a neighbor : {neighbor_indices[0][i]} far by a distance of: {neighbor_distances[0][i]}')\n",
    "    print(f'Top {movies_n} recommended movies for the target user {target_user_id} are:')\n",
    "    for i in range(movies_n):\n",
    "         print(top_recommendations[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "84de0bc3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user 22902 has a neighbor : 56231 far by a distance of: 0.2671858526299895\n",
      "user 22902 has a neighbor : 83375 far by a distance of: 0.26729081819726097\n",
      "user 22902 has a neighbor : 61350 far by a distance of: 0.26843647904971\n",
      "user 22902 has a neighbor : 51853 far by a distance of: 0.26843647904971\n",
      "user 22902 has a neighbor : 118801 far by a distance of: 0.26843647904971024\n",
      "Top 10 recommended movies for the target user 22902 are:\n",
      "Young Poisoner's Handbook, The (1995)\n",
      "Copycat (1995)\n",
      "Frankie Starlight (1995)\n",
      "Four Rooms (1995)\n",
      "Flirting With Disaster (1996)\n",
      "Father of the Bride Part II (1995)\n",
      "Fair Game (1995)\n",
      "Eye for an Eye (1996)\n",
      "Dunston Checks In (1996)\n",
      "Dracula: Dead and Loving It (1995)\n"
     ]
    }
   ],
   "source": [
    "user_id = list(pivot_table.index)# list of  ids \n",
    "target_user_id = random.choice(user_id)# choose a rondam id of user\n",
    "\n",
    "recom_userneighbors(pivot_table,target_user_id,n=5,movies_n=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fceb092",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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