Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('updated_df2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th>review_summary</th>\n",
       "      <th>category</th>\n",
       "      <th>height</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "      <th>cup_size</th>\n",
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       "      <td>10.0</td>\n",
       "      <td>vacation</td>\n",
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       "      <td>hourglass</td>\n",
       "      <td>So many compliments!</td>\n",
       "      <td>jumpsuits</td>\n",
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       "      <td>Was in love with this dress !!!</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>work</td>\n",
       "      <td>Fit like a glove!</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>LOVE IT!!! First Item Im thinking of buying!</td>\n",
       "      <td>jumpsuits</td>\n",
       "      <td>69.0</td>\n",
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       "      <td>petite</td>\n",
       "      <td>LOVE it!</td>\n",
       "      <td>dresses</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>everyday</td>\n",
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       "      <td>straight &amp; narrow</td>\n",
       "      <td>Loud patterning, flattering fit</td>\n",
       "      <td>dresses</td>\n",
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       "      <td>pear</td>\n",
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       "        Unnamed: 0  fit  user_id  bust_size  item_id  weight  rating  \\\n",
       "0                0  fit   420272         34  2260466     137    10.0   \n",
       "1                1  fit   273551         34   153475     132    10.0   \n",
       "2                3  fit   909926         34   126335     135     8.0   \n",
       "3                4  fit   151944         34   616682     145    10.0   \n",
       "4                5  fit   734848         32   364092     138     8.0   \n",
       "...            ...  ...      ...        ...      ...     ...     ...   \n",
       "146131      192539  fit    66386         34  2252812     140    10.0   \n",
       "146132      192540  fit   118398         32   682043     100    10.0   \n",
       "146133      192541  fit    47002         36   683251     135     6.0   \n",
       "146134      192542  fit   961120         36   126335     165    10.0   \n",
       "146135      192543  fit   123612         36   127865     155    10.0   \n",
       "\n",
       "           rented_for                                        review_text  \\\n",
       "0            vacation  An adorable romper! Belt and zipper were a lit...   \n",
       "1               other  I rented this dress for a photo shoot. The the...   \n",
       "2       formal affair  I rented this for my company's black tie award...   \n",
       "3             wedding  I have always been petite in my upper body and...   \n",
       "4                date  Didn't actually wear it. It fit perfectly. The...   \n",
       "...               ...                                                ...   \n",
       "146131           work                                  Fit like a glove!   \n",
       "146132           work  The pattern contrast on this dress is really s...   \n",
       "146133       everyday  Like the other DVF wraps, the fit on this is f...   \n",
       "146134        wedding  This dress was PERFECTION.  it looked incredib...   \n",
       "146135        wedding  This dress was wonderful! I had originally pla...   \n",
       "\n",
       "                body_type                                     review_summary  \\\n",
       "0               hourglass                               So many compliments!   \n",
       "1       straight & narrow                            I felt so glamourous!!!   \n",
       "2                    pear   Dress arrived on time and in perfect condition.    \n",
       "3                athletic                    Was in love with this dress !!!   \n",
       "4                athletic                    Traditional with a touch a sass   \n",
       "...                   ...                                                ...   \n",
       "146131          hourglass       LOVE IT!!! First Item Im thinking of buying!   \n",
       "146132             petite                                           LOVE it!   \n",
       "146133  straight & narrow                    Loud patterning, flattering fit   \n",
       "146134               pear  loved this dress it was comfortable and photog...   \n",
       "146135           athletic  I wore this to a beautiful black tie optional ...   \n",
       "\n",
       "         category  height  size   age cup_size  \n",
       "0       jumpsuits    68.0    14  28.0        d  \n",
       "1         dresses    66.0    12  36.0        b  \n",
       "2         dresses    65.0     8  34.0        c  \n",
       "3         dresses    69.0    12  27.0        b  \n",
       "4         dresses    68.0     8  45.0        b  \n",
       "...           ...     ...   ...   ...      ...  \n",
       "146131  jumpsuits    69.0     8  42.0       dd  \n",
       "146132    dresses    61.0     4  29.0        c  \n",
       "146133    dresses    68.0     8  31.0        a  \n",
       "146134    dresses    66.0    16  31.0        c  \n",
       "146135    dresses    66.0    16  30.0        b  \n",
       "\n",
       "[146136 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit               0\n",
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       "dtype: int64"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['item_count'] = df.groupby(['item_id'])['item_id'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>dd</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146132</td>\n",
       "      <td>fit</td>\n",
       "      <td>118398</td>\n",
       "      <td>32</td>\n",
       "      <td>682043</td>\n",
       "      <td>100</td>\n",
       "      <td>10.0</td>\n",
       "      <td>work</td>\n",
       "      <td>The pattern contrast on this dress is really s...</td>\n",
       "      <td>petite</td>\n",
       "      <td>LOVE it!</td>\n",
       "      <td>dresses</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4</td>\n",
       "      <td>29.0</td>\n",
       "      <td>c</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146133</td>\n",
       "      <td>fit</td>\n",
       "      <td>47002</td>\n",
       "      <td>36</td>\n",
       "      <td>683251</td>\n",
       "      <td>135</td>\n",
       "      <td>6.0</td>\n",
       "      <td>everyday</td>\n",
       "      <td>Like the other DVF wraps, the fit on this is f...</td>\n",
       "      <td>straight &amp; narrow</td>\n",
       "      <td>Loud patterning, flattering fit</td>\n",
       "      <td>dresses</td>\n",
       "      <td>68.0</td>\n",
       "      <td>8</td>\n",
       "      <td>31.0</td>\n",
       "      <td>a</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146134</td>\n",
       "      <td>fit</td>\n",
       "      <td>961120</td>\n",
       "      <td>36</td>\n",
       "      <td>126335</td>\n",
       "      <td>165</td>\n",
       "      <td>10.0</td>\n",
       "      <td>wedding</td>\n",
       "      <td>This dress was PERFECTION.  it looked incredib...</td>\n",
       "      <td>pear</td>\n",
       "      <td>loved this dress it was comfortable and photog...</td>\n",
       "      <td>dresses</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16</td>\n",
       "      <td>31.0</td>\n",
       "      <td>c</td>\n",
       "      <td>1526</td>\n",
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       "    <tr>\n",
       "      <td>146135</td>\n",
       "      <td>fit</td>\n",
       "      <td>123612</td>\n",
       "      <td>36</td>\n",
       "      <td>127865</td>\n",
       "      <td>155</td>\n",
       "      <td>10.0</td>\n",
       "      <td>wedding</td>\n",
       "      <td>This dress was wonderful! I had originally pla...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>I wore this to a beautiful black tie optional ...</td>\n",
       "      <td>dresses</td>\n",
       "      <td>66.0</td>\n",
       "      <td>16</td>\n",
       "      <td>30.0</td>\n",
       "      <td>b</td>\n",
       "      <td>1116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146136 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        fit  user_id  bust_size  item_id  weight  rating     rented_for  \\\n",
       "0       fit   420272         34  2260466     137    10.0       vacation   \n",
       "1       fit   273551         34   153475     132    10.0          other   \n",
       "2       fit   909926         34   126335     135     8.0  formal affair   \n",
       "3       fit   151944         34   616682     145    10.0        wedding   \n",
       "4       fit   734848         32   364092     138     8.0           date   \n",
       "...     ...      ...        ...      ...     ...     ...            ...   \n",
       "146131  fit    66386         34  2252812     140    10.0           work   \n",
       "146132  fit   118398         32   682043     100    10.0           work   \n",
       "146133  fit    47002         36   683251     135     6.0       everyday   \n",
       "146134  fit   961120         36   126335     165    10.0        wedding   \n",
       "146135  fit   123612         36   127865     155    10.0        wedding   \n",
       "\n",
       "                                              review_text          body_type  \\\n",
       "0       An adorable romper! Belt and zipper were a lit...          hourglass   \n",
       "1       I rented this dress for a photo shoot. The the...  straight & narrow   \n",
       "2       I rented this for my company's black tie award...               pear   \n",
       "3       I have always been petite in my upper body and...           athletic   \n",
       "4       Didn't actually wear it. It fit perfectly. The...           athletic   \n",
       "...                                                   ...                ...   \n",
       "146131                                  Fit like a glove!          hourglass   \n",
       "146132  The pattern contrast on this dress is really s...             petite   \n",
       "146133  Like the other DVF wraps, the fit on this is f...  straight & narrow   \n",
       "146134  This dress was PERFECTION.  it looked incredib...               pear   \n",
       "146135  This dress was wonderful! I had originally pla...           athletic   \n",
       "\n",
       "                                           review_summary   category  height  \\\n",
       "0                                    So many compliments!  jumpsuits    68.0   \n",
       "1                                 I felt so glamourous!!!    dresses    66.0   \n",
       "2        Dress arrived on time and in perfect condition.     dresses    65.0   \n",
       "3                         Was in love with this dress !!!    dresses    69.0   \n",
       "4                         Traditional with a touch a sass    dresses    68.0   \n",
       "...                                                   ...        ...     ...   \n",
       "146131       LOVE IT!!! First Item Im thinking of buying!  jumpsuits    69.0   \n",
       "146132                                           LOVE it!    dresses    61.0   \n",
       "146133                    Loud patterning, flattering fit    dresses    68.0   \n",
       "146134  loved this dress it was comfortable and photog...    dresses    66.0   \n",
       "146135  I wore this to a beautiful black tie optional ...    dresses    66.0   \n",
       "\n",
       "        size   age cup_size  item_count  \n",
       "0         14  28.0        d          19  \n",
       "1         12  36.0        b         381  \n",
       "2          8  34.0        c        1526  \n",
       "3         12  27.0        b          89  \n",
       "4          8  45.0        b          21  \n",
       "...      ...   ...      ...         ...  \n",
       "146131     8  42.0       dd          30  \n",
       "146132     4  29.0        c          53  \n",
       "146133     8  31.0        a         109  \n",
       "146134    16  31.0        c        1526  \n",
       "146135    16  30.0        b        1116  \n",
       "\n",
       "[146136 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "condition3 = df['item_count'] < 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          True\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4          True\n",
       "          ...  \n",
       "146131     True\n",
       "146132     True\n",
       "146133    False\n",
       "146134    False\n",
       "146135    False\n",
       "Name: item_count, Length: 146136, dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "small_df = df[condition3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>8.0</td>\n",
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       "      <td>Traditional with a touch a sass</td>\n",
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       "      <td>45.0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>336066</td>\n",
       "      <td>34</td>\n",
       "      <td>568429</td>\n",
       "      <td>112</td>\n",
       "      <td>10.0</td>\n",
       "      <td>everyday</td>\n",
       "      <td>This dress is so sweet. I loved the print. The...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>LITERALLY THE CUTEST DRESS EVER</td>\n",
       "      <td>dresses</td>\n",
       "      <td>63.0</td>\n",
       "      <td>4</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>170</td>\n",
       "      <td>10.0</td>\n",
       "      <td>vacation</td>\n",
       "      <td>This dress is just darling. I would absolutely...</td>\n",
       "      <td>pear</td>\n",
       "      <td>Really cute and comfortable.</td>\n",
       "      <td>dresses</td>\n",
       "      <td>68.0</td>\n",
       "      <td>20</td>\n",
       "      <td>35.0</td>\n",
       "      <td>c</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>fit</td>\n",
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       "      <td>I rented this dress for a spring wedding. I ha...</td>\n",
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       "      <td>Perfect for a spring wedding!</td>\n",
       "      <td>dresses</td>\n",
       "      <td>65.0</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>small</td>\n",
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       "      <td>300980</td>\n",
       "      <td>220</td>\n",
       "      <td>10.0</td>\n",
       "      <td>wedding</td>\n",
       "      <td>First time using RTR. I wasn't sure what to ex...</td>\n",
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       "      <td>I felt fierce in this dress!</td>\n",
       "      <td>dresses</td>\n",
       "      <td>64.0</td>\n",
       "      <td>35</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>10.0</td>\n",
       "      <td>work</td>\n",
       "      <td>Everything about this Alexis top screams fashi...</td>\n",
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       "      <td>RTR Team Review</td>\n",
       "      <td>tops</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>16</td>\n",
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       "      <td>146125</td>\n",
       "      <td>small</td>\n",
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       "      <td>Absolutely stunning dress. details and tulle m...</td>\n",
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       "      <td>60.0</td>\n",
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       "      <td>146127</td>\n",
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       "      <td>431280</td>\n",
       "      <td>36</td>\n",
       "      <td>2464025</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>vacation</td>\n",
       "      <td>Very hard to wear because the shoulders are su...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>Needed a cute warm sweater.</td>\n",
       "      <td>sweaters</td>\n",
       "      <td>67.0</td>\n",
       "      <td>20</td>\n",
       "      <td>28.0</td>\n",
       "      <td>c</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146129</td>\n",
       "      <td>fit</td>\n",
       "      <td>537417</td>\n",
       "      <td>32</td>\n",
       "      <td>1400094</td>\n",
       "      <td>125</td>\n",
       "      <td>10.0</td>\n",
       "      <td>party</td>\n",
       "      <td>The materials is thick and very high quality. ...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>I wore this dress to a bachelorette party with...</td>\n",
       "      <td>dresses</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8</td>\n",
       "      <td>36.0</td>\n",
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       "      <td>10</td>\n",
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       "<p>36784 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "          fit  user_id  bust_size  item_id  weight  rating rented_for  \\\n",
       "0         fit   420272         34  2260466     137    10.0   vacation   \n",
       "4         fit   734848         32   364092     138     8.0       date   \n",
       "5         fit   336066         34   568429     112    10.0   everyday   \n",
       "14        fit   499943         34   909221     170    10.0   vacation   \n",
       "18        fit   661150         36   900878     145    10.0    wedding   \n",
       "...       ...      ...        ...      ...     ...     ...        ...   \n",
       "146121  small   597756         38   300980     220    10.0    wedding   \n",
       "146122    fit   257847         34  2747774     120    10.0       work   \n",
       "146125  small   838239         36   863680     150    10.0    wedding   \n",
       "146127    fit   431280         36  2464025     150     4.0   vacation   \n",
       "146129    fit   537417         32  1400094     125    10.0      party   \n",
       "\n",
       "                                              review_text          body_type  \\\n",
       "0       An adorable romper! Belt and zipper were a lit...          hourglass   \n",
       "4       Didn't actually wear it. It fit perfectly. The...           athletic   \n",
       "5       This dress is so sweet. I loved the print. The...          hourglass   \n",
       "14      This dress is just darling. I would absolutely...               pear   \n",
       "18      I rented this dress for a spring wedding. I ha...          hourglass   \n",
       "...                                                   ...                ...   \n",
       "146121  First time using RTR. I wasn't sure what to ex...          full bust   \n",
       "146122  Everything about this Alexis top screams fashi...  straight & narrow   \n",
       "146125  Absolutely stunning dress. details and tulle m...          hourglass   \n",
       "146127  Very hard to wear because the shoulders are su...          hourglass   \n",
       "146129  The materials is thick and very high quality. ...          hourglass   \n",
       "\n",
       "                                           review_summary   category  height  \\\n",
       "0                                    So many compliments!  jumpsuits    68.0   \n",
       "4                         Traditional with a touch a sass    dresses    68.0   \n",
       "5                         LITERALLY THE CUTEST DRESS EVER    dresses    63.0   \n",
       "14                           Really cute and comfortable.    dresses    68.0   \n",
       "18                          Perfect for a spring wedding!    dresses    65.0   \n",
       "...                                                   ...        ...     ...   \n",
       "146121                       I felt fierce in this dress!    dresses    64.0   \n",
       "146122                                    RTR Team Review       tops    65.0   \n",
       "146125  Made for a princess. Stunning, elegant, great ...    dresses    60.0   \n",
       "146127                       Needed a cute warm sweater.    sweaters    67.0   \n",
       "146129  I wore this dress to a bachelorette party with...    dresses    66.0   \n",
       "\n",
       "        size   age cup_size  item_count  \n",
       "0         14  28.0        d          19  \n",
       "4          8  45.0        b          21  \n",
       "5          4  27.0        c           3  \n",
       "14        20  35.0        c          23  \n",
       "18        12  33.0        d          16  \n",
       "...      ...   ...      ...         ...  \n",
       "146121    35  32.0       dd          15  \n",
       "146122     8  22.0        c          16  \n",
       "146125    24  55.0        c          17  \n",
       "146127    20  28.0        c          14  \n",
       "146129     8  36.0        d          10  \n",
       "\n",
       "[36784 rows x 16 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['user_id', 'item_id', 'rating']\n",
    "rec_df = small_df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>420272</td>\n",
       "      <td>2260466</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>734848</td>\n",
       "      <td>364092</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>336066</td>\n",
       "      <td>568429</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>499943</td>\n",
       "      <td>909221</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>661150</td>\n",
       "      <td>900878</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146121</td>\n",
       "      <td>597756</td>\n",
       "      <td>300980</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146122</td>\n",
       "      <td>257847</td>\n",
       "      <td>2747774</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146125</td>\n",
       "      <td>838239</td>\n",
       "      <td>863680</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146127</td>\n",
       "      <td>431280</td>\n",
       "      <td>2464025</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146129</td>\n",
       "      <td>537417</td>\n",
       "      <td>1400094</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36784 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  item_id  rating\n",
       "0        420272  2260466    10.0\n",
       "4        734848   364092     8.0\n",
       "5        336066   568429    10.0\n",
       "14       499943   909221    10.0\n",
       "18       661150   900878    10.0\n",
       "...         ...      ...     ...\n",
       "146121   597756   300980    10.0\n",
       "146122   257847  2747774    10.0\n",
       "146125   838239   863680    10.0\n",
       "146127   431280  2464025     4.0\n",
       "146129   537417  1400094    10.0\n",
       "\n",
       "[36784 rows x 3 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    0\n",
       "item_id    0\n",
       "rating     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from surprise import Dataset, Reader\n",
    "from surprise import SVD\n",
    "from surprise import accuracy\n",
    "from surprise.model_selection import cross_validate, train_test_split\n",
    "\n",
    "# importing relevant libraries\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise.prediction_algorithms import SVD\n",
    "from surprise.prediction_algorithms import KNNWithMeans, KNNBasic, KNNBaseline\n",
    "from surprise.model_selection import GridSearchCV\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_df = rec_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>336066</td>\n",
       "      <td>568429</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>610914</td>\n",
       "      <td>1739627</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>606693</td>\n",
       "      <td>1542652</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>126879</td>\n",
       "      <td>1406963</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>214108</td>\n",
       "      <td>2872079</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146101</td>\n",
       "      <td>301753</td>\n",
       "      <td>2941958</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146113</td>\n",
       "      <td>589122</td>\n",
       "      <td>598696</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146119</td>\n",
       "      <td>240016</td>\n",
       "      <td>1568818</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146127</td>\n",
       "      <td>431280</td>\n",
       "      <td>2464025</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146129</td>\n",
       "      <td>537417</td>\n",
       "      <td>1400094</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19902 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  item_id  rating\n",
       "5        336066   568429    10.0\n",
       "23       610914  1739627    10.0\n",
       "31       606693  1542652     8.0\n",
       "32       126879  1406963     4.0\n",
       "34       214108  2872079    10.0\n",
       "...         ...      ...     ...\n",
       "146101   301753  2941958    10.0\n",
       "146113   589122   598696     6.0\n",
       "146119   240016  1568818     8.0\n",
       "146127   431280  2464025     4.0\n",
       "146129   537417  1400094    10.0\n",
       "\n",
       "[19902 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import Reader, Dataset\n",
    "reader = Reader(rating_scale=(2, 10))\n",
    "data = Dataset.load_from_df(rec_df[['user_id', 'item_id', 'rating']],reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users:  18838 \n",
      "\n",
      "Number of items:  4247\n"
     ]
    }
   ],
   "source": [
    "dataset = data.build_full_trainset()\n",
    "print('Number of users: ', dataset.n_users, '\\n')\n",
    "print('Number of items: ', dataset.n_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train-test split\n",
    "train, test = train_test_split(data, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.dataset.DatasetAutoFolds at 0x1380c2990>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Perform a gridsearch with SVD\n",
    "# ⏰ This cell may take several minutes to run\n",
    "params = {'n_factors': [20, 50, 100],\n",
    "         'reg_all': [0.02, 0.05, 0.1]}\n",
    "g_s_svd = GridSearchCV(SVD,param_grid=params,n_jobs=-1)\n",
    "g_s_svd.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmse': 1.4896363860510966, 'mae': 1.175291859082889}\n",
      "{'rmse': {'n_factors': 20, 'reg_all': 0.1}, 'mae': {'n_factors': 20, 'reg_all': 0.02}}\n"
     ]
    }
   ],
   "source": [
    "print(g_s_svd.best_score)\n",
    "print(g_s_svd.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# skipping\n",
    "# cross validating with KNNBasic\n",
    "knn_basic = KNNBasic(sim_options={'name':'pearson', 'user_based':True})\n",
    "cv_knn_basic = cross_validate(knn_basic, data, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('test_rmse', array([1.53736769, 1.5449967 , 1.53438891, 1.54350497, 1.50956868]))\n",
      "('test_mae', array([1.2644745 , 1.27276017, 1.26721941, 1.2742289 , 1.2508619 ]))\n",
      "('fit_time', (28.641125917434692, 29.862709999084473, 29.911602020263672, 29.576086044311523, 28.87796711921692))\n",
      "('test_time', (0.32915210723876953, 0.18282222747802734, 0.19797587394714355, 0.1883382797241211, 0.17985820770263672))\n",
      "-----------------------\n",
      "1.5339653897769037\n"
     ]
    }
   ],
   "source": [
    "for i in cv_knn_basic.items():\n",
    "    print(i)\n",
    "print('-----------------------')\n",
    "print(np.mean(cv_knn_basic['test_rmse']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    }
   ],
   "source": [
    "# cross validating with KNNBaseline\n",
    "knn_baseline = KNNBaseline(sim_options={'name':'pearson', 'user_based':True})\n",
    "cv_knn_baseline = cross_validate(knn_baseline,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('test_rmse', array([1.47050375, 1.51505837, 1.48016857, 1.49454611, 1.49809003]))\n",
      "('test_mae', array([1.18279293, 1.20229953, 1.19068238, 1.19007169, 1.19388174]))\n",
      "('fit_time', (8.529433012008667, 7.716390132904053, 9.171010732650757, 8.537019968032837, 8.467432022094727))\n",
      "('test_time', (0.11099410057067871, 0.10410189628601074, 0.09749293327331543, 0.09763097763061523, 0.10077810287475586))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.4916733653857357"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in cv_knn_baseline.items():\n",
    "    print(i)\n",
    "\n",
    "np.mean(cv_knn_baseline['test_rmse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x138316e10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd = SVD(n_factors= 50, reg_all=0.05)\n",
    "svd.fit(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid=2, iid=4, r_ui=None, est=8.973901696389735, details={'was_impossible': False})"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.predict(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>499943</td>\n",
       "      <td>909221</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>661150</td>\n",
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       "      <td>...</td>\n",
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       "      <td>597756</td>\n",
       "      <td>300980</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <td>146122</td>\n",
       "      <td>257847</td>\n",
       "      <td>2747774</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <td>146125</td>\n",
       "      <td>838239</td>\n",
       "      <td>863680</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <td>146127</td>\n",
       "      <td>431280</td>\n",
       "      <td>2464025</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <td>146129</td>\n",
       "      <td>537417</td>\n",
       "      <td>1400094</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36784 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  item_id  rating\n",
       "0        420272  2260466    10.0\n",
       "4        734848   364092     8.0\n",
       "5        336066   568429    10.0\n",
       "14       499943   909221    10.0\n",
       "18       661150   900878    10.0\n",
       "...         ...      ...     ...\n",
       "146121   597756   300980    10.0\n",
       "146122   257847  2747774    10.0\n",
       "146125   838239   863680    10.0\n",
       "146127   431280  2464025     4.0\n",
       "146129   537417  1400094    10.0\n",
       "\n",
       "[36784 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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