movie-recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "1c257742",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import math\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "98ac0ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies  = pd.read_csv('movies.csv')\n",
    "df_ratings = pd.read_csv('ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "12354dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.merge(df_movies,df_ratings,on=\"movieId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "2bedbbf0",
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "      <th>userId</th>\n",
       "      <th>rating</th>\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",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>A Very Murray Christmas (2015)</td>\n",
       "      <td>Comedy</td>\n",
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       "      <td>The Big Short (2015)</td>\n",
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       "      <td>458</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1452014749</td>\n",
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       "      <td>The Big Short (2015)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>576</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1451687664</td>\n",
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       "      <th>105337</th>\n",
       "      <td>148626</td>\n",
       "      <td>The Big Short (2015)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>668</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1451148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105338</th>\n",
       "      <td>149532</td>\n",
       "      <td>Marco Polo: One Hundred Eyes (2015)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>475</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1451223429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105339 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",
       "105334   148238       A Very Murray Christmas (2015)   \n",
       "105335   148626                 The Big Short (2015)   \n",
       "105336   148626                 The Big Short (2015)   \n",
       "105337   148626                 The Big Short (2015)   \n",
       "105338   149532  Marco Polo: One Hundred Eyes (2015)   \n",
       "\n",
       "                                             genres  userId  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy       2     5.0   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy       5     4.0   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy       8     5.0   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy      11     4.0   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      14     4.0   \n",
       "...                                             ...     ...     ...   \n",
       "105334                                       Comedy     475     3.0   \n",
       "105335                                        Drama     458     4.0   \n",
       "105336                                        Drama     576     4.5   \n",
       "105337                                        Drama     668     4.5   \n",
       "105338                           (no genres listed)     475     4.0   \n",
       "\n",
       "         timestamp  \n",
       "0        859046895  \n",
       "1       1303501039  \n",
       "2        858610933  \n",
       "3        850815810  \n",
       "4        851766286  \n",
       "...            ...  \n",
       "105334  1451213043  \n",
       "105335  1452014749  \n",
       "105336  1451687664  \n",
       "105337  1451148148  \n",
       "105338  1451223429  \n",
       "\n",
       "[105339 rows x 6 columns]"
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     },
     "execution_count": 297,
     "metadata": {},
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   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "a9d8da54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dataset['movieId'].ne(dataset['movieId'].shift()).cumsum() \n",
    "df = dataset.groupby(df).first()\n",
    "df = df.iloc[:,:3]\n",
    "df.index.name='index'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "758b62d4",
   "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",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</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>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
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       "      <td>Father of the Bride Part II (1995)</td>\n",
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       "      <td>146684</td>\n",
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       "      <th>10322</th>\n",
       "      <td>146878</td>\n",
       "      <td>Le Grand Restaurant (1966)</td>\n",
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       "      <th>10323</th>\n",
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       "      <th>10324</th>\n",
       "      <td>148626</td>\n",
       "      <td>The Big Short (2015)</td>\n",
       "      <td>Drama</td>\n",
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       "    <tr>\n",
       "      <th>10325</th>\n",
       "      <td>149532</td>\n",
       "      <td>Marco Polo: One Hundred Eyes (2015)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10325 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "       movieId                                title  \\\n",
       "index                                                 \n",
       "1            1                     Toy Story (1995)   \n",
       "2            2                       Jumanji (1995)   \n",
       "3            3              Grumpier Old Men (1995)   \n",
       "4            4             Waiting to Exhale (1995)   \n",
       "5            5   Father of the Bride Part II (1995)   \n",
       "...        ...                                  ...   \n",
       "10321   146684        Cosmic Scrat-tastrophe (2015)   \n",
       "10322   146878           Le Grand Restaurant (1966)   \n",
       "10323   148238       A Very Murray Christmas (2015)   \n",
       "10324   148626                 The Big Short (2015)   \n",
       "10325   149532  Marco Polo: One Hundred Eyes (2015)   \n",
       "\n",
       "                                            genres  \n",
       "index                                               \n",
       "1      Adventure|Animation|Children|Comedy|Fantasy  \n",
       "2                       Adventure|Children|Fantasy  \n",
       "3                                   Comedy|Romance  \n",
       "4                             Comedy|Drama|Romance  \n",
       "5                                           Comedy  \n",
       "...                                            ...  \n",
       "10321                    Animation|Children|Comedy  \n",
       "10322                                       Comedy  \n",
       "10323                                       Comedy  \n",
       "10324                                        Drama  \n",
       "10325                           (no genres listed)  \n",
       "\n",
       "[10325 rows x 3 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "ad0417ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_mean = dataset.groupby('movieId')['rating'].mean()\n",
    "ratings_count = dataset.groupby('movieId')['rating'].count()\n",
    "rating = pd.DataFrame({'Mean_Ratings':ratings_mean,\n",
    "                'Ratings_count':ratings_count})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "8880da61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df,rating,on=\"movieId\")\n",
    "df['genres'] = [re.sub(\"\\|\",\" \",str(genre)) for genre in df['genres']]\n",
    "df['movie_year'] = df['title'].str.split(r\" \\(\",expand=True).iloc[:,1]\n",
    "df['title'] = df['title'].str.split(r\" \\(\",expand=True).iloc[:,0]\n",
    "df['movie_year'] = [re.sub(\"\\)\",\"\",str(year)) for year in df['movie_year']]\n",
    "df['vectors'] = df['title'] +\" \" + df['genres']\n",
    "df.insert(0,'Index',[0 for i in range(len(df.index))])\n",
    "orig_rows = len(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "db91d061",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Mean_Ratings</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "      <td>3.907328</td>\n",
       "      <td>232</td>\n",
       "      <td>1995</td>\n",
       "      <td>Toy Story Adventure Animation Children Comedy ...</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Jumanji</td>\n",
       "      <td>Adventure Children Fantasy</td>\n",
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       "      <td>Grumpier Old Men</td>\n",
       "      <td>Comedy Romance</td>\n",
       "      <td>3.189655</td>\n",
       "      <td>58</td>\n",
       "      <td>1995</td>\n",
       "      <td>Grumpier Old Men Comedy Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Comedy Drama Romance</td>\n",
       "      <td>2.818182</td>\n",
       "      <td>11</td>\n",
       "      <td>1995</td>\n",
       "      <td>Waiting to Exhale Comedy Drama Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>62</td>\n",
       "      <td>1995</td>\n",
       "      <td>Father of the Bride Part II Comedy</td>\n",
       "    </tr>\n",
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       "      <td>Cosmic Scrat-tastrophe</td>\n",
       "      <td>Animation Children Comedy</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Cosmic Scrat-tastrophe Animation Children Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10321</th>\n",
       "      <td>0</td>\n",
       "      <td>146878</td>\n",
       "      <td>Le Grand Restaurant</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1966</td>\n",
       "      <td>Le Grand Restaurant Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10322</th>\n",
       "      <td>0</td>\n",
       "      <td>148238</td>\n",
       "      <td>A Very Murray Christmas</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>A Very Murray Christmas Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10323</th>\n",
       "      <td>0</td>\n",
       "      <td>148626</td>\n",
       "      <td>The Big Short</td>\n",
       "      <td>Drama</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>The Big Short Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10324</th>\n",
       "      <td>0</td>\n",
       "      <td>149532</td>\n",
       "      <td>Marco Polo: One Hundred Eyes</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Marco Polo: One Hundred Eyes (no genres listed)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10325 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Index  movieId                         title  \\\n",
       "0          0        1                     Toy Story   \n",
       "1          0        2                       Jumanji   \n",
       "2          0        3              Grumpier Old Men   \n",
       "3          0        4             Waiting to Exhale   \n",
       "4          0        5   Father of the Bride Part II   \n",
       "...      ...      ...                           ...   \n",
       "10320      0   146684        Cosmic Scrat-tastrophe   \n",
       "10321      0   146878           Le Grand Restaurant   \n",
       "10322      0   148238       A Very Murray Christmas   \n",
       "10323      0   148626                 The Big Short   \n",
       "10324      0   149532  Marco Polo: One Hundred Eyes   \n",
       "\n",
       "                                            genres  Mean_Ratings  \\\n",
       "0      Adventure Animation Children Comedy Fantasy      3.907328   \n",
       "1                       Adventure Children Fantasy      3.353261   \n",
       "2                                   Comedy Romance      3.189655   \n",
       "3                             Comedy Drama Romance      2.818182   \n",
       "4                                           Comedy      3.250000   \n",
       "...                                            ...           ...   \n",
       "10320                    Animation Children Comedy      4.000000   \n",
       "10321                                       Comedy      2.500000   \n",
       "10322                                       Comedy      3.000000   \n",
       "10323                                        Drama      4.333333   \n",
       "10324                           (no genres listed)      4.000000   \n",
       "\n",
       "       Ratings_count movie_year  \\\n",
       "0                232       1995   \n",
       "1                 92       1995   \n",
       "2                 58       1995   \n",
       "3                 11       1995   \n",
       "4                 62       1995   \n",
       "...              ...        ...   \n",
       "10320              1       2015   \n",
       "10321              1       1966   \n",
       "10322              1       2015   \n",
       "10323              3       2015   \n",
       "10324              1       2015   \n",
       "\n",
       "                                                 vectors  \n",
       "0      Toy Story Adventure Animation Children Comedy ...  \n",
       "1                     Jumanji Adventure Children Fantasy  \n",
       "2                        Grumpier Old Men Comedy Romance  \n",
       "3                 Waiting to Exhale Comedy Drama Romance  \n",
       "4                     Father of the Bride Part II Comedy  \n",
       "...                                                  ...  \n",
       "10320   Cosmic Scrat-tastrophe Animation Children Comedy  \n",
       "10321                         Le Grand Restaurant Comedy  \n",
       "10322                     A Very Murray Christmas Comedy  \n",
       "10323                                The Big Short Drama  \n",
       "10324    Marco Polo: One Hundred Eyes (no genres listed)  \n",
       "\n",
       "[10325 rows x 8 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "a4f39963",
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = TfidfVectorizer(stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "f918c891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.sort_values(by = 'Ratings_count', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "3aaf8d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Mean_Ratings</th>\n",
       "      <th>Ratings_count</th>\n",
       "      <th>movie_year</th>\n",
       "      <th>vectors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0</td>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>Comedy Crime Drama Thriller</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>325</td>\n",
       "      <td>1994</td>\n",
       "      <td>Pulp Fiction Comedy Crime Drama Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>0</td>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>Comedy Drama Romance War</td>\n",
       "      <td>4.138264</td>\n",
       "      <td>311</td>\n",
       "      <td>1994</td>\n",
       "      <td>Forrest Gump Comedy Drama Romance War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>Shawshank Redemption, The</td>\n",
       "      <td>Crime Drama</td>\n",
       "      <td>4.454545</td>\n",
       "      <td>308</td>\n",
       "      <td>1994</td>\n",
       "      <td>Shawshank Redemption, The Crime Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>Jurassic Park</td>\n",
       "      <td>Action Adventure Sci-Fi Thriller</td>\n",
       "      <td>3.659864</td>\n",
       "      <td>294</td>\n",
       "      <td>1993</td>\n",
       "      <td>Jurassic Park Action Adventure Sci-Fi Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>Silence of the Lambs, The</td>\n",
       "      <td>Crime Horror Thriller</td>\n",
       "      <td>4.194828</td>\n",
       "      <td>290</td>\n",
       "      <td>1991</td>\n",
       "      <td>Silence of the Lambs, The Crime Horror Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6224</th>\n",
       "      <td>0</td>\n",
       "      <td>26726</td>\n",
       "      <td>Dutch</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1991</td>\n",
       "      <td>Dutch Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6223</th>\n",
       "      <td>0</td>\n",
       "      <td>26717</td>\n",
       "      <td>Begotten</td>\n",
       "      <td>Drama Horror</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1990</td>\n",
       "      <td>Begotten Drama Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6221</th>\n",
       "      <td>0</td>\n",
       "      <td>26712</td>\n",
       "      <td>35 Up</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1991</td>\n",
       "      <td>35 Up Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6219</th>\n",
       "      <td>0</td>\n",
       "      <td>26703</td>\n",
       "      <td>Riff-Raff</td>\n",
       "      <td>Comedy Drama</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1991</td>\n",
       "      <td>Riff-Raff Comedy Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10324</th>\n",
       "      <td>0</td>\n",
       "      <td>149532</td>\n",
       "      <td>Marco Polo: One Hundred Eyes</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>Marco Polo: One Hundred Eyes (no genres listed)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10325 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Index  movieId                         title  \\\n",
       "260        0      296                  Pulp Fiction   \n",
       "316        0      356                  Forrest Gump   \n",
       "279        0      318     Shawshank Redemption, The   \n",
       "426        0      480                 Jurassic Park   \n",
       "525        0      593     Silence of the Lambs, The   \n",
       "...      ...      ...                           ...   \n",
       "6224       0    26726                         Dutch   \n",
       "6223       0    26717                      Begotten   \n",
       "6221       0    26712                         35 Up   \n",
       "6219       0    26703                     Riff-Raff   \n",
       "10324      0   149532  Marco Polo: One Hundred Eyes   \n",
       "\n",
       "                                 genres  Mean_Ratings  Ratings_count  \\\n",
       "260         Comedy Crime Drama Thriller      4.160000            325   \n",
       "316            Comedy Drama Romance War      4.138264            311   \n",
       "279                         Crime Drama      4.454545            308   \n",
       "426    Action Adventure Sci-Fi Thriller      3.659864            294   \n",
       "525               Crime Horror Thriller      4.194828            290   \n",
       "...                                 ...           ...            ...   \n",
       "6224                             Comedy      2.500000              1   \n",
       "6223                       Drama Horror      4.000000              1   \n",
       "6221                        Documentary      4.500000              1   \n",
       "6219                       Comedy Drama      2.500000              1   \n",
       "10324                (no genres listed)      4.000000              1   \n",
       "\n",
       "      movie_year                                          vectors  \n",
       "260         1994         Pulp Fiction Comedy Crime Drama Thriller  \n",
       "316         1994            Forrest Gump Comedy Drama Romance War  \n",
       "279         1994            Shawshank Redemption, The Crime Drama  \n",
       "426         1993   Jurassic Park Action Adventure Sci-Fi Thriller  \n",
       "525         1991  Silence of the Lambs, The Crime Horror Thriller  \n",
       "...          ...                                              ...  \n",
       "6224        1991                                     Dutch Comedy  \n",
       "6223        1990                            Begotten Drama Horror  \n",
       "6221        1991                                35 Up Documentary  \n",
       "6219        1991                           Riff-Raff Comedy Drama  \n",
       "10324       2015  Marco Polo: One Hundred Eyes (no genres listed)  \n",
       "\n",
       "[10325 rows x 8 columns]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "4fe8048f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[10325] = [1, [], '', '', [], np.nan, [], ''] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "9a309330",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_recommendation():\n",
    "    count = 0\n",
    "    movies = []\n",
    "    mov_ids = []\n",
    "    sim_score = []\n",
    "\n",
    "    for ind in df1.index:\n",
    "        if count<10:\n",
    "            if df1['Mean_Ratings'][ind]>3.5:\n",
    "                mov_ids.append(df1['movieId'][ind])\n",
    "                movies.append(df[df.movieId==df1['movieId'][ind]]['title'].values[0] + ' (' + df[df.movieId==df1['movieId'][ind]]['movie_year'].values[0] + ')')\n",
    "                sim_score.append(0)\n",
    "                count+=1\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    recommendation = pd.DataFrame({'Movie_Id':mov_ids, 'Movie':movies, 'Cosine_Similarity_Score':sim_score})\n",
    "    return(recommendation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "18b6fd30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_movie(movie,rating):\n",
    "    if rating > 2.5:\n",
    "        title = df['title'][df.index[-1]] + ' ' + movie\n",
    "        movie_id = df['movieId'][df.index[-1]]\n",
    "        movie_id.append(df[df.title==movie]['movieId'].values[0])\n",
    "        movie_year = df['movie_year'][df.index[-1]]\n",
    "        movie_year.append(df[df.title==movie]['movie_year'].values[0])\n",
    "        genre = df['genres'][df.index[-1]] + ' ' + df[df.title==movie]['genres'].values[0]\n",
    "        rate = df['Mean_Ratings'][df.index[-1]]\n",
    "        rate.append(rating)\n",
    "        #vectors = df['vectors'][df.index[-1]] + \" \" + movie + \" \" + genre\n",
    "        vectors = movie + \" \" + genre\n",
    "        df.loc[df.index[-1]] = [1, movie_id, title, genre, rate, np.nan, movie_year, vectors] \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "5ae62743",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Index</th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
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       "      <th>Mean_Ratings</th>\n",
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       "      <th>vectors</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "      <td>3.907328</td>\n",
       "      <td>232.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Toy Story Adventure Animation Children Comedy ...</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>Jumanji</td>\n",
       "      <td>Adventure Children Fantasy</td>\n",
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       "      <td>92.0</td>\n",
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       "      <td>Jumanji Adventure Children Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>Comedy Romance</td>\n",
       "      <td>3.189655</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Grumpier Old Men Comedy Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Comedy Drama Romance</td>\n",
       "      <td>2.818182</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Waiting to Exhale Comedy Drama Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.25</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>Father of the Bride Part II Comedy</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",
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       "    <tr>\n",
       "      <th>10321</th>\n",
       "      <td>0</td>\n",
       "      <td>146878</td>\n",
       "      <td>Le Grand Restaurant</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1966</td>\n",
       "      <td>Le Grand Restaurant Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10322</th>\n",
       "      <td>0</td>\n",
       "      <td>148238</td>\n",
       "      <td>A Very Murray Christmas</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>A Very Murray Christmas Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10323</th>\n",
       "      <td>0</td>\n",
       "      <td>148626</td>\n",
       "      <td>The Big Short</td>\n",
       "      <td>Drama</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>The Big Short Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10324</th>\n",
       "      <td>0</td>\n",
       "      <td>149532</td>\n",
       "      <td>Marco Polo: One Hundred Eyes</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>Marco Polo: One Hundred Eyes (no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10325</th>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10326 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Index movieId                         title  \\\n",
       "0          0       1                     Toy Story   \n",
       "1          0       2                       Jumanji   \n",
       "2          0       3              Grumpier Old Men   \n",
       "3          0       4             Waiting to Exhale   \n",
       "4          0       5   Father of the Bride Part II   \n",
       "...      ...     ...                           ...   \n",
       "10321      0  146878           Le Grand Restaurant   \n",
       "10322      0  148238       A Very Murray Christmas   \n",
       "10323      0  148626                 The Big Short   \n",
       "10324      0  149532  Marco Polo: One Hundred Eyes   \n",
       "10325      1      []                                 \n",
       "\n",
       "                                            genres Mean_Ratings  \\\n",
       "0      Adventure Animation Children Comedy Fantasy     3.907328   \n",
       "1                       Adventure Children Fantasy     3.353261   \n",
       "2                                   Comedy Romance     3.189655   \n",
       "3                             Comedy Drama Romance     2.818182   \n",
       "4                                           Comedy         3.25   \n",
       "...                                            ...          ...   \n",
       "10321                                       Comedy          2.5   \n",
       "10322                                       Comedy          3.0   \n",
       "10323                                        Drama     4.333333   \n",
       "10324                           (no genres listed)          4.0   \n",
       "10325                                                        []   \n",
       "\n",
       "       Ratings_count movie_year  \\\n",
       "0              232.0       1995   \n",
       "1               92.0       1995   \n",
       "2               58.0       1995   \n",
       "3               11.0       1995   \n",
       "4               62.0       1995   \n",
       "...              ...        ...   \n",
       "10321            1.0       1966   \n",
       "10322            1.0       2015   \n",
       "10323            3.0       2015   \n",
       "10324            1.0       2015   \n",
       "10325            NaN         []   \n",
       "\n",
       "                                                 vectors  \n",
       "0      Toy Story Adventure Animation Children Comedy ...  \n",
       "1                     Jumanji Adventure Children Fantasy  \n",
       "2                        Grumpier Old Men Comedy Romance  \n",
       "3                 Waiting to Exhale Comedy Drama Romance  \n",
       "4                     Father of the Bride Part II Comedy  \n",
       "...                                                  ...  \n",
       "10321                         Le Grand Restaurant Comedy  \n",
       "10322                     A Very Murray Christmas Comedy  \n",
       "10323                                The Big Short Drama  \n",
       "10324    Marco Polo: One Hundred Eyes (no genres listed)  \n",
       "10325                                                     \n",
       "\n",
       "[10326 rows x 8 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "b65aea82",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(movie,rating):\n",
    "    \n",
    "    if rating<2.5:\n",
    "        return\n",
    "    \n",
    "    vecs = vec.fit_transform(df['vectors'].apply(lambda x: np.str_(x)))\n",
    "    cos_similarity = cosine_similarity(vecs)\n",
    "    \n",
    "    scores = {}\n",
    "    movie_id = df[df.title==movie]['movieId'].values[0]\n",
    "    \n",
    "    for i in range(df.index[-1]):\n",
    "        if (movie_id != df['movieId'][i]):\n",
    "            scores[df['movieId'][i]] = cos_similarity[df.index[-1]][i]\n",
    "            \n",
    "    scores=(sorted(scores.items(), key=lambda elem:(elem[1], elem[0]),reverse=True))\n",
    "    \n",
    "    movies = []\n",
    "    sim_score = []\n",
    "    mov_ids = []\n",
    "    count=0\n",
    "    \n",
    "    for s in scores:\n",
    "        if count<10:\n",
    "            if s[0] not in df['movieId'][df.index[-1]]:\n",
    "                movie_name = df[df.movieId==s[0]]['title'].values[0] + ' (' + df[df.movieId==s[0]]['movie_year'].values[0] + ')'\n",
    "                if len(dataset[dataset.title==movie_name]['movieId'].values)!=0:\n",
    "                    movies.append(movie_name)\n",
    "                    sim_score.append(s[1])\n",
    "                    mov_ids.append(dataset[dataset.title==movie_name]['movieId'].values[0])\n",
    "                    count+=1\n",
    "    recommendation = pd.DataFrame({'Movie Id':mov_ids, 'Movie':movies, 'Cosine Similarity Score':sim_score})\n",
    "    return(recommendation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "d08afeb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommendation = initial_recommendation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "8be2f223",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Movie_Id                                      Movie  \\\n",
       "0       296                        Pulp Fiction (1994)   \n",
       "1       356                        Forrest Gump (1994)   \n",
       "2       318           Shawshank Redemption, The (1994)   \n",
       "3       480                       Jurassic Park (1993)   \n",
       "4       593           Silence of the Lambs, The (1991)   \n",
       "5       260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "6      2571                         Matrix, The (1999)   \n",
       "7       589          Terminator 2: Judgment Day (1991)   \n",
       "8       527                    Schindler's List (1993)   \n",
       "9       110                          Braveheart (1995)   \n",
       "\n",
       "   Cosine_Similarity_Score  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "5                        0  \n",
       "6                        0  \n",
       "7                        0  \n",
       "8                        0  \n",
       "9                        0  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "bd6162a6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the movie: Spider-Man\n",
      "Enter the rating: 4\n"
     ]
    }
   ],
   "source": [
    "movie_name = input('Enter the movie: ')\n",
    "rating = float(input('Enter the rating: '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "38486b51",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shreyas\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3162: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return asarray(a).ndim\n"
     ]
    }
   ],
   "source": [
    "input_movie(movie_name,rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "fa24f05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "recommendation = recommend(movie_name,rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "c3a38716",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.490687</td>\n",
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       "   Movie Id                            Movie  Cosine Similarity Score\n",
       "0     52722              Spider-Man 3 (2007)                 0.775019\n",
       "1      8636              Spider-Man 2 (2004)                 0.755724\n",
       "2     95510   Amazing Spider-Man, The (2012)                 0.638183\n",
       "3     71057                         9 (2009)                 0.584952\n",
       "4      9004                D.A.R.Y.L. (1985)                 0.574398\n",
       "5    130520                      Home (2015)                 0.567379\n",
       "6    138036  Man from U.N.C.L.E., The (2015)                 0.562446\n",
       "7    110553  The Amazing Spider-Man 2 (2014)                 0.558876\n",
       "8     68954                        Up (2009)                 0.532973\n",
       "9     48774           Children of Men (2006)                 0.490687"
      ]
     },
     "execution_count": 292,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "a071abf7",
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   "outputs": [
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       "      <td>62.0</td>\n",
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       "      <td>A Very Murray Christmas Comedy</td>\n",
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       "      <th>10323</th>\n",
       "      <td>0</td>\n",
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       "      <td>4.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>The Big Short Drama</td>\n",
       "    </tr>\n",
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       "      <th>10324</th>\n",
       "      <td>0</td>\n",
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       "      <td>Marco Polo: One Hundred Eyes</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Marco Polo: One Hundred Eyes (no genres listed)</td>\n",
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       "      <td>Spider-Man  Adventure Animation Children Comed...</td>\n",
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       "<p>10326 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Index    movieId                         title  \\\n",
       "0          0          1                     Toy Story   \n",
       "1          0          2                       Jumanji   \n",
       "2          0          3              Grumpier Old Men   \n",
       "3          0          4             Waiting to Exhale   \n",
       "4          0          5   Father of the Bride Part II   \n",
       "...      ...        ...                           ...   \n",
       "10321      0     146878           Le Grand Restaurant   \n",
       "10322      0     148238       A Very Murray Christmas   \n",
       "10323      0     148626                 The Big Short   \n",
       "10324      0     149532  Marco Polo: One Hundred Eyes   \n",
       "10325      1  [1, 5349]          Toy Story Spider-Man   \n",
       "\n",
       "                                                  genres Mean_Ratings  \\\n",
       "0            Adventure Animation Children Comedy Fantasy     3.907328   \n",
       "1                             Adventure Children Fantasy     3.353261   \n",
       "2                                         Comedy Romance     3.189655   \n",
       "3                                   Comedy Drama Romance     2.818182   \n",
       "4                                                 Comedy         3.25   \n",
       "...                                                  ...          ...   \n",
       "10321                                             Comedy          2.5   \n",
       "10322                                             Comedy          3.0   \n",
       "10323                                              Drama     4.333333   \n",
       "10324                                 (no genres listed)          4.0   \n",
       "10325   Adventure Animation Children Comedy Fantasy A...   [4.0, 4.0]   \n",
       "\n",
       "       Ratings_count    movie_year  \\\n",
       "0              232.0          1995   \n",
       "1               92.0          1995   \n",
       "2               58.0          1995   \n",
       "3               11.0          1995   \n",
       "4               62.0          1995   \n",
       "...              ...           ...   \n",
       "10321            1.0          1966   \n",
       "10322            1.0          2015   \n",
       "10323            3.0          2015   \n",
       "10324            1.0          2015   \n",
       "10325            NaN  [1995, 2002]   \n",
       "\n",
       "                                                 vectors  \n",
       "0      Toy Story Adventure Animation Children Comedy ...  \n",
       "1                     Jumanji Adventure Children Fantasy  \n",
       "2                        Grumpier Old Men Comedy Romance  \n",
       "3                 Waiting to Exhale Comedy Drama Romance  \n",
       "4                     Father of the Bride Part II Comedy  \n",
       "...                                                  ...  \n",
       "10321                         Le Grand Restaurant Comedy  \n",
       "10322                     A Very Murray Christmas Comedy  \n",
       "10323                                The Big Short Drama  \n",
       "10324    Marco Polo: One Hundred Eyes (no genres listed)  \n",
       "10325  Spider-Man  Adventure Animation Children Comed...  \n",
       "\n",
       "[10326 rows x 8 columns]"
      ]
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