Item-Similarity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8e682ae9",
   "metadata": {},
   "source": [
    "### Movie Recommendation System (Item Similarity)\n",
    "\n",
    "We pick a movie first and find what other movies are correlated with it using the user ratings and filtering the data using number of ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b886661",
   "metadata": {},
   "source": [
    "Lets import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34c28b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92faea00",
   "metadata": {},
   "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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      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ratings = pd.read_csv(\"ratings.csv\",sep=',')\n",
    "movie_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7468556b",
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
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      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_id = pd.read_csv('movies.csv')\n",
    "movie_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64efd471",
   "metadata": {},
   "outputs": [
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       "   userId  movieId  rating   timestamp             title  \\\n",
       "0       1        1     4.0   964982703  Toy Story (1995)   \n",
       "1       5        1     4.0   847434962  Toy Story (1995)   \n",
       "2       7        1     4.5  1106635946  Toy Story (1995)   \n",
       "3      15        1     2.5  1510577970  Toy Story (1995)   \n",
       "4      17        1     4.5  1305696483  Toy Story (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "2  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "3  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "4  Adventure|Animation|Children|Comedy|Fantasy  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "movies_data  = pd.merge(movie_ratings,movie_id,on='movieId')\n",
    "movies_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a7ec8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_data.drop(['genres'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44a267f1",
   "metadata": {},
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       "   userId  movieId  rating   timestamp             title\n",
       "0       1        1     4.0   964982703  Toy Story (1995)\n",
       "1       5        1     4.0   847434962  Toy Story (1995)\n",
       "2       7        1     4.5  1106635946  Toy Story (1995)\n",
       "3      15        1     2.5  1510577970  Toy Story (1995)\n",
       "4      17        1     4.5  1305696483  Toy Story (1995)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e77d5fbb",
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       "      <th>'71 (2014)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'Tis the Season for Love (2015)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>[REC]² (2009)</th>\n",
       "      <th>[REC]³ 3 Génesis (2012)</th>\n",
       "      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
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       "title   '71 (2014)  'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                                        \n",
       "1              NaN                                      NaN   \n",
       "2              NaN                                      NaN   \n",
       "3              NaN                                      NaN   \n",
       "4              NaN                                      NaN   \n",
       "5              NaN                                      NaN   \n",
       "\n",
       "title   'Round Midnight (1986)  'Salem's Lot (2004)  \\\n",
       "userId                                                \n",
       "1                          NaN                  NaN   \n",
       "2                          NaN                  NaN   \n",
       "3                          NaN                  NaN   \n",
       "4                          NaN                  NaN   \n",
       "5                          NaN                  NaN   \n",
       "\n",
       "title   'Til There Was You (1997)  'Tis the Season for Love (2015)  \\\n",
       "userId                                                               \n",
       "1                             NaN                              NaN   \n",
       "2                             NaN                              NaN   \n",
       "3                             NaN                              NaN   \n",
       "4                             NaN                              NaN   \n",
       "5                             NaN                              NaN   \n",
       "\n",
       "title   'burbs, The (1989)  'night Mother (1986)  (500) Days of Summer (2009)  \\\n",
       "userId                                                                          \n",
       "1                      NaN                   NaN                          NaN   \n",
       "2                      NaN                   NaN                          NaN   \n",
       "3                      NaN                   NaN                          NaN   \n",
       "4                      NaN                   NaN                          NaN   \n",
       "5                      NaN                   NaN                          NaN   \n",
       "\n",
       "title   *batteries not included (1987)  ...  Zulu (2013)  [REC] (2007)  \\\n",
       "userId                                  ...                              \n",
       "1                                  NaN  ...          NaN           NaN   \n",
       "2                                  NaN  ...          NaN           NaN   \n",
       "3                                  NaN  ...          NaN           NaN   \n",
       "4                                  NaN  ...          NaN           NaN   \n",
       "5                                  NaN  ...          NaN           NaN   \n",
       "\n",
       "title   [REC]² (2009)  [REC]³ 3 Génesis (2012)  \\\n",
       "userId                                           \n",
       "1                 NaN                      NaN   \n",
       "2                 NaN                      NaN   \n",
       "3                 NaN                      NaN   \n",
       "4                 NaN                      NaN   \n",
       "5                 NaN                      NaN   \n",
       "\n",
       "title   anohana: The Flower We Saw That Day - The Movie (2013)  \\\n",
       "userId                                                           \n",
       "1                                                     NaN        \n",
       "2                                                     NaN        \n",
       "3                                                     NaN        \n",
       "4                                                     NaN        \n",
       "5                                                     NaN        \n",
       "\n",
       "title   eXistenZ (1999)  xXx (2002)  xXx: State of the Union (2005)  \\\n",
       "userId                                                                \n",
       "1                   NaN         NaN                             NaN   \n",
       "2                   NaN         NaN                             NaN   \n",
       "3                   NaN         NaN                             NaN   \n",
       "4                   NaN         NaN                             NaN   \n",
       "5                   NaN         NaN                             NaN   \n",
       "\n",
       "title   ¡Three Amigos! (1986)  À nous la liberté (Freedom for Us) (1931)  \n",
       "userId                                                                    \n",
       "1                         4.0                                        NaN  \n",
       "2                         NaN                                        NaN  \n",
       "3                         NaN                                        NaN  \n",
       "4                         NaN                                        NaN  \n",
       "5                         NaN                                        NaN  \n",
       "\n",
       "[5 rows x 9719 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_user_grid = movies_data.pivot_table(index='userId',columns='title',values='rating')\n",
    "movie_user_grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a5ac741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean ratings</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'71 (2014)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         mean ratings  count\n",
       "title                                                       \n",
       "'71 (2014)                                        4.0      1\n",
       "'Hellboy': The Seeds of Creation (2004)           4.0      1\n",
       "'Round Midnight (1986)                            3.5      2\n",
       "'Salem's Lot (2004)                               5.0      1\n",
       "'Til There Was You (1997)                         4.0      2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = movies_data.groupby('title')['rating']\n",
    "recommender_data = pd.DataFrame(temp.mean())\n",
    "recommender_data['count'] = pd.DataFrame(temp.count())\n",
    "recommender_data.rename(columns = {'rating':'mean ratings'},inplace=True)\n",
    "recommender_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddab7741",
   "metadata": {},
   "source": [
    "Most rated movies by count:(Impressive as expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8f8599e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean ratings</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>4.164134</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.429022</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulp Fiction (1994)</th>\n",
       "      <td>4.197068</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silence of the Lambs, The (1991)</th>\n",
       "      <td>4.161290</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matrix, The (1999)</th>\n",
       "      <td>4.192446</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  mean ratings  count\n",
       "title                                                \n",
       "Forrest Gump (1994)                   4.164134    329\n",
       "Shawshank Redemption, The (1994)      4.429022    317\n",
       "Pulp Fiction (1994)                   4.197068    307\n",
       "Silence of the Lambs, The (1991)      4.161290    279\n",
       "Matrix, The (1999)                    4.192446    278"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommender_data.sort_values('count',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6551679c",
   "metadata": {},
   "source": [
    "Most highly rated movies:(Quite a disappointing list) {Not a good parameter for recommendation I guess}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "982d5639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean ratings</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gena the Crocodile (1969)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Stories (1986)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cosmic Scrat-tastrophe (2015)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Love and Pigeons (1985)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Red Sorghum (Hong gao liang) (1987)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     mean ratings  count\n",
       "title                                                   \n",
       "Gena the Crocodile (1969)                     5.0      1\n",
       "True Stories (1986)                           5.0      1\n",
       "Cosmic Scrat-tastrophe (2015)                 5.0      1\n",
       "Love and Pigeons (1985)                       5.0      1\n",
       "Red Sorghum (Hong gao liang) (1987)           5.0      1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommender_data.sort_values('mean ratings',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578eee36",
   "metadata": {},
   "source": [
    "Lets recommend some movies to Forrest Gump watchers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc182701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId\n",
       "1    4.0\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "Name: Forrest Gump (1994), dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forrest_gump_ratings = movie_user_grid['Forrest Gump (1994)']\n",
    "forrest_gump_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acde4675",
   "metadata": {},
   "source": [
    "Lets find some similar movies and clean the output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e57725f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2821: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  c = cov(x, y, rowvar, dtype=dtype)\n",
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2680: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  c *= np.true_divide(1, fact)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "title\n",
       "'burbs, The (1989)                0.197712\n",
       "(500) Days of Summer (2009)       0.234095\n",
       "*batteries not included (1987)    0.892710\n",
       "...And Justice for All (1979)     0.928571\n",
       "10 Cent Pistol (2015)            -1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_to_forrest = movie_user_grid.corrwith(forrest_gump_ratings)\n",
    "similar_to_forrest.dropna(inplace=True)\n",
    "similar_to_forrest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "704e8c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Similarity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>0.197712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>0.234095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <td>0.892710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Cent Pistol (2015)</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Similarity\n",
       "title                                     \n",
       "'burbs, The (1989)                0.197712\n",
       "(500) Days of Summer (2009)       0.234095\n",
       "*batteries not included (1987)    0.892710\n",
       "...And Justice for All (1979)     0.928571\n",
       "10 Cent Pistol (2015)            -1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_forrest = pd.DataFrame(similar_to_forrest,columns=['Similarity'])\n",
    "rel_forrest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c10204b",
   "metadata": {},
   "source": [
    "Let's sort the bad boys by similarity (descending ofc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfb88c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similarity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lost &amp; Found (1999)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Century of the Self, The (2002)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The 5th Wave (2016)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Play Time (a.k.a. Playtime) (1967)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Memories (Memorîzu) (1995)</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Similarity\n",
       "title                                         \n",
       "Lost & Found (1999)                        1.0\n",
       "Century of the Self, The (2002)            1.0\n",
       "The 5th Wave (2016)                        1.0\n",
       "Play Time (a.k.a. Playtime) (1967)         1.0\n",
       "Memories (Memorîzu) (1995)                 1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_forrest.sort_values('Similarity',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8895c7b",
   "metadata": {},
   "source": [
    "Never heard about any of the films tbh lets filter out using the count "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56793ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similarity</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>0.197712</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <td>0.234095</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*batteries not included (1987)</th>\n",
       "      <td>0.892710</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>0.928571</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Cent Pistol (2015)</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Similarity  count\n",
       "title                                            \n",
       "'burbs, The (1989)                0.197712     17\n",
       "(500) Days of Summer (2009)       0.234095     42\n",
       "*batteries not included (1987)    0.892710      7\n",
       "...And Justice for All (1979)     0.928571      3\n",
       "10 Cent Pistol (2015)            -1.000000      2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_forrest = rel_forrest.join(recommender_data['count'])\n",
    "rel_forrest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "720bf689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Similarity</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Forrest Gump (1994)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Good Will Hunting (1997)</th>\n",
       "      <td>0.484042</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aladdin (1992)</th>\n",
       "      <td>0.464268</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American History X (1998)</th>\n",
       "      <td>0.457287</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Truman Show, The (1998)</th>\n",
       "      <td>0.432556</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Similarity  count\n",
       "title                                       \n",
       "Forrest Gump (1994)          1.000000    329\n",
       "Good Will Hunting (1997)     0.484042    141\n",
       "Aladdin (1992)               0.464268    183\n",
       "American History X (1998)    0.457287    129\n",
       "Truman Show, The (1998)      0.432556    125"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_forrest[rel_forrest['count']>100].sort_values('Similarity',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5842b7b2",
   "metadata": {},
   "source": [
    "I guess its good :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0497d5c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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