Collaborative_Filtering_Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Collaborative Filtering Recommendation System </h2>\n",
    "\n",
    "Dataset source: https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%205/data/moviedataset.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((34208, 3), (22884377, 4))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesds = pd.read_csv('movies.csv')\n",
    "ratingsds = pd.read_csv('ratings.csv')\n",
    "moviesds.shape, ratingsds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "  </tbody>\n",
       "</table>\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",
       "                                        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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
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       "      <td>1995</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995</td>\n",
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      "text/plain": [
       "   movieId                        title  year\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# Formatting the data types \n",
    "moviesds['year'] = moviesds['title'].str.extract('(\\(\\d\\d\\d\\d\\))', expand=False)\n",
    "moviesds['year'] = moviesds['year'].str.extract('(\\d\\d\\d\\d)', expand=False)\n",
    "moviesds['title'] = moviesds['title'].str.extract(r'(.*)\\s\\(\\d{4}\\)')\n",
    "moviesds.drop('genres', axis=1, inplace=True)\n",
    "\n",
    "moviesds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "   userId  movieId  rating   timestamp\n",
       "0       1      169     2.5  1204927694\n",
       "1       1     2471     3.0  1204927438\n",
       "2       1    48516     5.0  1204927435\n",
       "3       2     2571     3.5  1436165433\n",
       "4       2   109487     4.0  1436165496"
      ]
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "ratingsds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>movieId</th>\n",
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       "   userId  movieId  rating\n",
       "0       1      169     2.5\n",
       "1       1     2471     3.0\n",
       "2       1    48516     5.0\n",
       "3       2     2571     3.5\n",
       "4       2   109487     4.0"
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     "execution_count": 6,
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   ],
   "source": [
    "# Dropping timestamp as we don't need it\n",
    "ratingsds.drop('timestamp', axis=1, inplace=True)\n",
    "ratingsds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Breakfast Club, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akira</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 title  rating\n",
       "0  Breakfast Club, The     5.0\n",
       "1            Toy Story     3.5\n",
       "2              Jumanji     2.0\n",
       "3         Pulp Fiction     5.0\n",
       "4                Akira     4.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a hypothetical user to recommend movies to. \n",
    "# The movies already viewed and rated by the user are as follows\n",
    "userinput = [\n",
    "    {'title': 'Breakfast Club, The', 'rating': 5},\n",
    "    {'title': 'Toy Story', 'rating': 3.5},\n",
    "    {'title': 'Jumanji', 'rating': 2},\n",
    "    {'title': 'Pulp Fiction', 'rating': 5},\n",
    "    {'title': 'Akira', 'rating': 4.5},\n",
    "]\n",
    "userinput = pd.DataFrame(userinput)\n",
    "userinput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharmeen\\AppData\\Local\\Temp\\ipykernel_8796\\1520513191.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  inputid.drop('year', axis=1, inplace=True)\n"
     ]
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       "      <th>movieId</th>\n",
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       "      <td>Akira</td>\n",
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       "      <th>1885</th>\n",
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       "      <td>Breakfast Club, The</td>\n",
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      "text/plain": [
       "      movieId                title\n",
       "0           1            Toy Story\n",
       "1           2              Jumanji\n",
       "293       296         Pulp Fiction\n",
       "1246     1274                Akira\n",
       "1885     1968  Breakfast Club, The"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the IDs of the rated movies from the movies dataset\n",
    "inputid = moviesds[moviesds['title'].isin(userinput['title'].tolist())]\n",
    "inputid.drop('year', axis=1, inplace=True)\n",
    "\n",
    "inputid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   movieId                title  rating\n",
       "0        1            Toy Story     3.5\n",
       "1        2              Jumanji     2.0\n",
       "2      296         Pulp Fiction     5.0\n",
       "3     1274                Akira     4.5\n",
       "4     1968  Breakfast Club, The     5.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging to add movie IDs to the ratings dataset\n",
    "userinput = pd.merge(inputid, userinput)\n",
    "userinput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>296</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>12</td>\n",
       "      <td>1968</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>13</td>\n",
       "      <td>1274</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>14</td>\n",
       "      <td>296</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating\n",
       "19        4      296     4.0\n",
       "441      12     1968     3.0\n",
       "479      13        2     2.0\n",
       "531      13     1274     5.0\n",
       "681      14      296     2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting the ratings of the users who rated the same movies\n",
    "subset = ratingsds[ratingsds['movieId'].isin(userinput['movieId'].tolist())]\n",
    "subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104167</th>\n",
       "      <td>1130</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104168</th>\n",
       "      <td>1130</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104214</th>\n",
       "      <td>1130</td>\n",
       "      <td>296</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104363</th>\n",
       "      <td>1130</td>\n",
       "      <td>1274</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104443</th>\n",
       "      <td>1130</td>\n",
       "      <td>1968</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating\n",
       "104167    1130        1     0.5\n",
       "104168    1130        2     4.0\n",
       "104214    1130      296     4.0\n",
       "104363    1130     1274     4.5\n",
       "104443    1130     1968     4.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the ratings subset by user IDs\n",
    "groupedsubset = subset.groupby('userId')\n",
    "groupedsubset.get_group(1130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(75,\n",
       "        userId  movieId  rating\n",
       "  7507      75        1     5.0\n",
       "  7508      75        2     3.5\n",
       "  7540      75      296     5.0\n",
       "  7633      75     1274     4.5\n",
       "  7673      75     1968     5.0),\n",
       " (106,\n",
       "        userId  movieId  rating\n",
       "  9083     106        1     2.5\n",
       "  9084     106        2     3.0\n",
       "  9115     106      296     3.5\n",
       "  9198     106     1274     3.0\n",
       "  9238     106     1968     3.5),\n",
       " (686,\n",
       "         userId  movieId  rating\n",
       "  61336     686        1     4.0\n",
       "  61337     686        2     3.0\n",
       "  61377     686      296     4.0\n",
       "  61478     686     1274     4.0\n",
       "  61569     686     1968     5.0)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the grouped subset so the users that share the most movies \n",
    "# in common with the input user have higher priority.\n",
    "\n",
    "groupedsubset = sorted(groupedsubset, key=lambda x: len(x[1]), reverse=True)\n",
    "groupedsubset = groupedsubset[:100]\n",
    "groupedsubset[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([(75, 0.8272781516947562), (106, 0.5860090386731182), (686, 0.8320502943378437), (815, 0.5765566601970551), (1040, 0.9434563530497265), (1130, 0.2891574659831201), (1502, 0.8770580193070299), (1599, 0.4385290096535153), (1625, 0.716114874039432), (1950, 0.179028718509858), (2065, 0.4385290096535153), (2128, 0.5860090386731196), (2432, 0.1386750490563073), (2791, 0.8770580193070299), (2839, 0.8204126541423674), (2948, -0.11720180773462392), (3025, 0.45124262819713973), (3040, 0.89514359254929), (3186, 0.6784622064861935), (3271, 0.26989594817970664), (3429, 0.0), (3734, -0.15041420939904673), (4099, 0.05860090386731196), (4208, 0.29417420270727607), (4282, -0.4385290096535115), (4292, 0.6564386345361464), (4415, -0.11183835382312353), (4586, -0.9024852563942795), (4725, -0.08006407690254357), (4818, 0.4885967564883424), (5104, 0.7674257668936507), (5165, -0.4385290096535153), (5547, 0.17200522903844556), (6082, -0.04728779924109591), (6207, 0.9615384615384616), (6366, 0.6577935144802716), (6482, 0.0), (6530, -0.3516054232038709), (7235, 0.6981407669689391), (7403, 0.11720180773462363), (7641, 0.7161148740394331), (7996, 0.626600514784504), (8008, -0.22562131409856986), (8086, 0.6933752452815365), (8245, 0.0), (8572, 0.8600261451922278), (8675, 0.5370861555295773), (9101, -0.08600261451922278), (9358, 0.692178738358485), (9663, 0.193972725041952), (9994, 0.5030272728659587), (10248, -0.24806946917841693), (10315, 0.537086155529574), (10368, 0.4688072309384945), (10607, 0.41602514716892186), (10707, 0.9615384615384616), (10863, 0.6020183016345595), (11314, 0.8204126541423654), (11399, 0.517260600111872), (11769, 0.9376144618769914), (11827, 0.4902903378454601), (12069, 0.0), (12120, 0.9292940047327363), (12211, 0.8600261451922278), (12325, 0.9616783115081544), (12916, 0.5860090386731196), (12921, 0.6611073566849309), (13053, 0.9607689228305227), (13142, 0.6016568375961863), (13260, 0.7844645405527362), (13366, 0.8951435925492911), (13768, 0.8770580193070289), (13888, 0.2508726030021272), (13923, 0.3516054232038718), (13934, 0.17200522903844556), (14529, 0.7417901772340937), (14551, 0.537086155529574), (14588, 0.21926450482675766), (14984, 0.716114874039432), (15137, 0.5860090386731196), (15157, 0.9035841064985974), (15466, 0.7205766921228921), (15670, 0.516015687115336), (15834, 0.22562131409856986), (16292, 0.6577935144802716), (16456, 0.7161148740394331), (16506, 0.5481612620668942), (17246, 0.48038446141526137), (17438, 0.7093169886164387), (17501, 0.8168748513121271), (17502, 0.8272781516947562), (17666, 0.7689238340176859), (17735, 0.7042381820123422), (17742, 0.3922322702763681), (17757, 0.64657575013984), (17854, 0.537086155529574), (17897, 0.8770580193070289), (17944, 0.2713848825944774), (18301, 0.29838119751643016), (18509, 0.1322214713369862)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating Pearson's correlation values to find users with similar preferences\n",
    "\n",
    "from math import sqrt\n",
    "pearson = {}\n",
    "\n",
    "for name, group in groupedsubset:\n",
    "    group = group.sort_values(by='movieId')\n",
    "    userinput = userinput.sort_values(by='movieId')\n",
    "\n",
    "    n = len(group)\n",
    "\n",
    "    temp_df = userinput[userinput['movieId'].isin(group['movieId'].tolist())]\n",
    "    temp_rating = temp_df['rating'].tolist()\n",
    "    temp_grouprating = group['rating'].tolist()\n",
    "\n",
    "    Sxx = sum([i**2 for i in temp_rating]) - pow(sum(temp_rating), 2)/float(n)\n",
    "    Syy = sum([i**2 for i in temp_grouprating]) - pow(sum(temp_grouprating), 2)/float(n)\n",
    "    Sxy = sum( i*j for i, j in zip(temp_rating, temp_grouprating)) - sum(temp_rating)*sum(temp_grouprating)/float(n)\n",
    "    \n",
    "    pearson[name] = (Sxy / sqrt(Sxx * Syy)) if Sxx != 0 and Syy != 0 else 0\n",
    "\n",
    "pearson.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>userId</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.827278</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.586009</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.832050</td>\n",
       "      <td>686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0.576557</td>\n",
       "      <td>815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>0.943456</td>\n",
       "      <td>1040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17854</th>\n",
       "      <td>0.537086</td>\n",
       "      <td>17854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17897</th>\n",
       "      <td>0.877058</td>\n",
       "      <td>17897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17944</th>\n",
       "      <td>0.271385</td>\n",
       "      <td>17944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18301</th>\n",
       "      <td>0.298381</td>\n",
       "      <td>18301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18509</th>\n",
       "      <td>0.132221</td>\n",
       "      <td>18509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           coef  userId\n",
       "75     0.827278      75\n",
       "106    0.586009     106\n",
       "686    0.832050     686\n",
       "815    0.576557     815\n",
       "1040   0.943456    1040\n",
       "...         ...     ...\n",
       "17854  0.537086   17854\n",
       "17897  0.877058   17897\n",
       "17944  0.271385   17944\n",
       "18301  0.298381   18301\n",
       "18509  0.132221   18509\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Formatting the correlation data\n",
    "pearson = pd.DataFrame.from_dict(pearson, orient='index')\n",
    "pearson.columns = ['coef']\n",
    "pearson['userId'] = pearson.index\n",
    "pearson.reset_index()\n",
    "pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>coef</th>\n",
       "      <th>userId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12325</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
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       "    <tr>\n",
       "      <th>6207</th>\n",
       "      <td>0.961538</td>\n",
       "      <td>6207</td>\n",
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       "    <tr>\n",
       "      <th>10707</th>\n",
       "      <td>0.961538</td>\n",
       "      <td>10707</td>\n",
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       "    <tr>\n",
       "      <th>13053</th>\n",
       "      <td>0.960769</td>\n",
       "      <td>13053</td>\n",
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       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>0.943456</td>\n",
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      ],
      "text/plain": [
       "           coef  userId\n",
       "12325  0.961678   12325\n",
       "6207   0.961538    6207\n",
       "10707  0.961538   10707\n",
       "13053  0.960769   13053\n",
       "1040   0.943456    1040"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting users with the top 20 correlation values\n",
    "topusers = pearson.sort_values(by='coef', ascending=False)[:20]\n",
    "topusers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>12325</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>11</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>17</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       coef  userId  movieId  rating\n",
       "0  0.961678   12325        1     3.5\n",
       "1  0.961678   12325        2     1.5\n",
       "2  0.961678   12325        3     3.0\n",
       "3  0.961678   12325        5     0.5\n",
       "4  0.961678   12325        6     2.5\n",
       "5  0.961678   12325        7     3.0\n",
       "6  0.961678   12325       10     3.0\n",
       "7  0.961678   12325       11     2.5\n",
       "8  0.961678   12325       17     4.0\n",
       "9  0.961678   12325       19     1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out what other movies the users with similar taste profiles liked by\n",
    "# adding those ratings to the user correlation data\n",
    "topuserrating = topusers.merge(ratingsds, left_on='userId', right_on='userId', how='inner')\n",
    "topuserrating[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>weighted_rating</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.365874</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.442517</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.961678</td>\n",
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       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.885035</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.480839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.404196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.885035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.885035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>11</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.404196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>17</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.846713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.961678</td>\n",
       "      <td>12325</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.961678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       coef  userId  movieId  rating  weighted_rating\n",
       "0  0.961678   12325        1     3.5         3.365874\n",
       "1  0.961678   12325        2     1.5         1.442517\n",
       "2  0.961678   12325        3     3.0         2.885035\n",
       "3  0.961678   12325        5     0.5         0.480839\n",
       "4  0.961678   12325        6     2.5         2.404196\n",
       "5  0.961678   12325        7     3.0         2.885035\n",
       "6  0.961678   12325       10     3.0         2.885035\n",
       "7  0.961678   12325       11     2.5         2.404196\n",
       "8  0.961678   12325       17     4.0         3.846713\n",
       "9  0.961678   12325       19     1.0         0.961678"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the weighted rating by multiplying with similarity coefficient\n",
    "topuserrating['weighted_rating'] = topuserrating['coef'] * topuserrating['rating']\n",
    "topuserrating[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sum_coef</th>\n",
       "      <th>sum_weights</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.885064</td>\n",
       "      <td>67.129990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.885064</td>\n",
       "      <td>45.099094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.434590</td>\n",
       "      <td>14.616637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.929294</td>\n",
       "      <td>2.787882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.498128</td>\n",
       "      <td>10.346701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sum_coef  sum_weights\n",
       "movieId                        \n",
       "1        17.885064    67.129990\n",
       "2        17.885064    45.099094\n",
       "3         5.434590    14.616637\n",
       "4         0.929294     2.787882\n",
       "5         5.498128    10.346701"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the weighted ratings for each movie\n",
    "temp_toprating = topuserrating.groupby('movieId').sum()\n",
    "temp_toprating = temp_toprating[['coef', 'weighted_rating']]\n",
    "temp_toprating.columns = ['sum_coef', 'sum_weights']\n",
    "temp_toprating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>weighted_rec_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.753411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.521607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.689556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.881859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted_rec_score\n",
       "movieId                    \n",
       "1                  3.753411\n",
       "2                  2.521607\n",
       "3                  2.689556\n",
       "4                  3.000000\n",
       "5                  1.881859"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the weighted recommendation score based on the weighted ratings\n",
    "# and sum of similarity coefficients\n",
    "recs = pd.DataFrame()\n",
    "recs['weighted_rec_score'] = temp_toprating['sum_weights'] / temp_toprating['sum_coef']\n",
    "recs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>weighted_rec_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4381</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73587</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71264</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3730</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3759</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3769</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3775</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3776</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65685</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65514</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3964</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60487</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56788</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55067</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53956</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4160</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50912</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3729</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted_rec_score\n",
       "movieId                    \n",
       "4381                    5.0\n",
       "73587                   5.0\n",
       "71264                   5.0\n",
       "3730                    5.0\n",
       "3759                    5.0\n",
       "3769                    5.0\n",
       "3775                    5.0\n",
       "3776                    5.0\n",
       "65685                   5.0\n",
       "65514                   5.0\n",
       "3964                    5.0\n",
       "60487                   5.0\n",
       "56788                   5.0\n",
       "2041                    5.0\n",
       "55067                   5.0\n",
       "53956                   5.0\n",
       "4144                    5.0\n",
       "4160                    5.0\n",
       "50912                   5.0\n",
       "3729                    5.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting to extract the top 20 recommendations\n",
    "recs = recs.sort_values(by='weighted_rec_score', ascending=False).head(20)\n",
    "recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Closet, The (Placard, Le)', '2001'],\n",
       " ['Soul Kitchen', '2009'],\n",
       " ['Cloudy with a Chance of Meatballs', '2009'],\n",
       " ['Conversation, The', '1974'],\n",
       " ['Fun and Fancy Free', '1947'],\n",
       " ['Thunderbolt and Lightfoot', '1974'],\n",
       " ['Make Mine Music', '1946'],\n",
       " ['Melody Time', '1948'],\n",
       " ['Inkheart', '2008'],\n",
       " ['Ip Man', '2008'],\n",
       " ['Adventures of Ichabod and Mr. Toad, The', '1949'],\n",
       " [\"It's the Great Pumpkin, Charlie Brown\", '1966'],\n",
       " [\"Charlie Wilson's War\", '2007'],\n",
       " ['Condorman', '1981'],\n",
       " ['Requiem', '2006'],\n",
       " ['Death at a Funeral', '2007'],\n",
       " ['In the Mood For Love (Fa yeung nin wa)', '2000'],\n",
       " ['Widow of St. Pierre, The (Veuve de Saint-Pierre, La)', '2000'],\n",
       " [\"Paris, I Love You (Paris, je t'aime)\", '2006'],\n",
       " ['Shaft', '1971']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the movie information from recommended IDs\n",
    "movierecs = []\n",
    "\n",
    "for rec_id in recs.index:\n",
    "    name = moviesds[moviesds['movieId'] == rec_id]\n",
    "    name = [name['title'].tolist()[0], name['year'].tolist()[0]]\n",
    "    movierecs.append(name)\n",
    "\n",
    "movierecs"
   ]
  }
 ],
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