Demographic Filtering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e52895fa",
   "metadata": {},
   "source": [
    "In this notebook I am gonna apply Demographic Filtering to recommend top movies which got more ratings to a user's feed \n",
    "\n",
    "irrespective of content present in the movie(like genes,caste etc) \n",
    "\n",
    "Raw ratings by user should not be used directly because there may be few users rating a particular movie and many users rating \n",
    "\n",
    "another movie.So, we need to use weighted ratings instead of raw ratings for our dataset and then recommend the top few. \n",
    "\n",
    "Weighted rating for a movie according to IMDB stadards is given by \n",
    "    \n",
    "    W = (R*v+C*m/(v+m))\n",
    "\n",
    "where R is avg rating obtained for that movie\n",
    "\n",
    "v = number of ratings for that movie\n",
    "\n",
    "C = avg rating obtained over entire dataset\n",
    "\n",
    "m = minimum number of ratings to be considered for a particular movie "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2774827",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets start by loading required libraries\n",
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1eb04eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('rating.csv')\n",
    "names = pd.read_csv('movie.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff0017d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a3651739",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5255285642993797\n"
     ]
    }
   ],
   "source": [
    "#Lets calculate the avg rating obtained for the entire dataset\n",
    "C = df['rating'].mean()\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2180efa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating vote_count column to record number of votes received by each movie\n",
    "name = names.copy()\n",
    "l = [0]*len(name)\n",
    "name['vote_count'] = l\n",
    "p = list(name['movieId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa56cacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def votecount(train):\n",
    "    mode = dict(train['movieId'].value_counts())\n",
    "    #print(len(mode))\n",
    "    q = list(mode.keys())\n",
    "    #print(q)\n",
    "    for i in p:\n",
    "        if(i in q):\n",
    "            name.loc[name['movieId'] ==i,'vote_count'] = mode[i]\n",
    "    \n",
    "    return name\n",
    "name = votecount(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b3e2563",
   "metadata": {},
   "outputs": [
    {
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       "      <th>movieId</th>\n",
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       "      <th>vote_count</th>\n",
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       "  </thead>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 4 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  vote_count  \n",
       "0      Adventure|Animation|Children|Comedy|Fantasy       49695  \n",
       "1                       Adventure|Children|Fantasy       22243  \n",
       "2                                   Comedy|Romance       12735  \n",
       "3                             Comedy|Drama|Romance        2756  \n",
       "4                                           Comedy       12161  \n",
       "...                                            ...         ...  \n",
       "27273                                       Comedy           1  \n",
       "27274                                       Comedy           1  \n",
       "27275                                    Adventure           1  \n",
       "27276                           (no genres listed)           1  \n",
       "27277                     Adventure|Fantasy|Horror           1  \n",
       "\n",
       "[27278 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
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   "source": [
    "name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c8da9b",
   "metadata": {},
   "source": [
    "Determine appropriate value of m which is minimum number of ratings the movie should have in order to be listed in chart We will use 85% as our  cutoff.In other words, the movie should have atleast 85% more votes than other movies present in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd65d63a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "609.0\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_count</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",
       "      <td>49695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>22243</td>\n",
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       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>2756</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Comedy</td>\n",
       "      <td>12161</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>23665</th>\n",
       "      <td>112556</td>\n",
       "      <td>Gone Girl (2014)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>1479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23680</th>\n",
       "      <td>112623</td>\n",
       "      <td>Dawn of the Planet of the Apes (2014)</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23736</th>\n",
       "      <td>112852</td>\n",
       "      <td>Guardians of the Galaxy (2014)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>2049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24387</th>\n",
       "      <td>115569</td>\n",
       "      <td>Nightcrawler (2014)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24693</th>\n",
       "      <td>116797</td>\n",
       "      <td>The Imitation Game (2014)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4094 rows × 4 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",
       "23665   112556                       Gone Girl (2014)   \n",
       "23680   112623  Dawn of the Planet of the Apes (2014)   \n",
       "23736   112852         Guardians of the Galaxy (2014)   \n",
       "24387   115569                    Nightcrawler (2014)   \n",
       "24693   116797              The Imitation Game (2014)   \n",
       "\n",
       "                                            genres  vote_count  \n",
       "0      Adventure|Animation|Children|Comedy|Fantasy       49695  \n",
       "1                       Adventure|Children|Fantasy       22243  \n",
       "2                                   Comedy|Romance       12735  \n",
       "3                             Comedy|Drama|Romance        2756  \n",
       "4                                           Comedy       12161  \n",
       "...                                            ...         ...  \n",
       "23665                               Drama|Thriller        1479  \n",
       "23680                                       Sci-Fi         759  \n",
       "23736                      Action|Adventure|Sci-Fi        2049  \n",
       "24387                         Crime|Drama|Thriller         609  \n",
       "24693                               Drama|Thriller         759  \n",
       "\n",
       "[4094 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m= name['vote_count'].quantile(0.85)\n",
    "print(m)\n",
    "movies = name.copy().loc[name['vote_count'] >= m]\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ce811243",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = list(movies['movieId'])\n",
    "l = [0]*len(movies)\n",
    "movies['avg_rating'] = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2956fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To calculate average ratings obtained for each movie\n",
    "def avgcount(df):\n",
    "    for i in p:\n",
    "        movies.loc[movies['movieId'] ==i , 'avg_rating'] = np.nanmean(np.array(df.loc[df['movieId'] ==i, 'rating' ])) \n",
    "    return movies\n",
    "    \n",
    "movies = avgcount(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3355a274",
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_rating(movies, m=m, C=C):\n",
    "    v = movies['vote_count']\n",
    "    R = movies['avg_rating']\n",
    "    # Calculation based on the IMDB formula\n",
    "    return (v/(v+m) * R) + (m/(m+v) * C)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "82a0a291",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a new feature 'score' and calculate its value with `weighted_rating()`\n",
    "movies['score'] = movies.apply(weighted_rating, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d93bbe1e",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>63366</td>\n",
       "      <td>4.446990</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.438219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>Godfather, The (1972)</td>\n",
       "      <td>41355</td>\n",
       "      <td>4.364732</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.352553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Usual Suspects, The (1995)</td>\n",
       "      <td>47006</td>\n",
       "      <td>4.334372</td>\n",
       "      <td>Crime|Mystery|Thriller</td>\n",
       "      <td>4.324027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>50054</td>\n",
       "      <td>4.310175</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>4.300743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>Godfather: Part II, The (1974)</td>\n",
       "      <td>27398</td>\n",
       "      <td>4.275641</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>4.259330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Rear Window (1954)</td>\n",
       "      <td>17449</td>\n",
       "      <td>4.271334</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "      <td>4.246182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>24349</td>\n",
       "      <td>4.258327</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>4.240446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>Seven Samurai (Shichinin no samurai) (1954)</td>\n",
       "      <td>11611</td>\n",
       "      <td>4.274180</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>4.236870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>29932</td>\n",
       "      <td>4.248079</td>\n",
       "      <td>Drama</td>\n",
       "      <td>4.233671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
       "      <td>23220</td>\n",
       "      <td>4.247287</td>\n",
       "      <td>Comedy|War</td>\n",
       "      <td>4.228841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  vote_count  \\\n",
       "315                    Shawshank Redemption, The (1994)       63366   \n",
       "843                               Godfather, The (1972)       41355   \n",
       "49                           Usual Suspects, The (1995)       47006   \n",
       "523                             Schindler's List (1993)       50054   \n",
       "1195                     Godfather: Part II, The (1974)       27398   \n",
       "887                                  Rear Window (1954)       17449   \n",
       "895                                   Casablanca (1942)       24349   \n",
       "1935        Seven Samurai (Shichinin no samurai) (1954)       11611   \n",
       "1169             One Flew Over the Cuckoo's Nest (1975)       29932   \n",
       "737   Dr. Strangelove or: How I Learned to Stop Worr...       23220   \n",
       "\n",
       "      avg_rating                  genres     score  \n",
       "315     4.446990             Crime|Drama  4.438219  \n",
       "843     4.364732             Crime|Drama  4.352553  \n",
       "49      4.334372  Crime|Mystery|Thriller  4.324027  \n",
       "523     4.310175               Drama|War  4.300743  \n",
       "1195    4.275641             Crime|Drama  4.259330  \n",
       "887     4.271334        Mystery|Thriller  4.246182  \n",
       "895     4.258327           Drama|Romance  4.240446  \n",
       "1935    4.274180  Action|Adventure|Drama  4.236870  \n",
       "1169    4.248079                   Drama  4.233671  \n",
       "737     4.247287              Comedy|War  4.228841  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sort movies based on score calculated above\n",
    "movies = movies.sort_values('score', ascending=False)\n",
    "\n",
    "#Print the top 10 movies\n",
    "movies[['title', 'vote_count', 'avg_rating', 'genres', 'score']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d788b7d4",
   "metadata": {},
   "source": [
    "So, these movies appear in trending tab of these systems sorted by their score "
   ]
  }
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