{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retail Product Recommender Engine\n",
    "**By [Czarina Luna](https://czarinaluna.com)**\n",
    "\n",
    "### Contents\n",
    "* [I. Overview](#I.-Overview)\n",
    "* [II. Business Problem](#II.-Business-Problem)\n",
    "* [III. Data Understanding](#III.-Data-Understanding)\n",
    "* [IV. Recommendation Systems](#IV.-Recommendation-Systems)\n",
    "    * [Popularity Recommendations](#Popularity-Recommendations)\n",
    "    * [Content-Based Recommenders](#Content-Based-Recommenders)\n",
    "    * [Collaborative Filtering Systems](#Collaborative-Filtering-Systems)\n",
    "* [V. Results and Recommendations](#V.-Results-and-Recommendations)\n",
    "* [VI. Further Research](#VI.-Further-Research)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Overview\n",
    "\n",
    "A recommender engine is developed to increase revenue of clothing rental companies by predicting user preferences and recommending products for users to rent. I apply different algorithms to create personalized recommendations using content-based and collaborative filtering systems. The algorithm that attained the lowest mean absolute error of 0.5 is the Singular Value Decomposition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Business Problem\n",
    "\n",
    "The clothing rental industry grows as more companies follow suit of the retailer Rent the Runway, which pioneered online services and subscriptions for designer rentals. To help grow the revenue of clothing rental companies, I develop recommendation systems that predict a set of user preferences and recommend the top preferences for the user. Doing so will conveniently expose users to relevant products to rent that tailor to their preferences. Using data from  Rent the Runway, I conduct an analysis of the product reviews, model the data to predict user ratings, and provide recommendations accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Data Understanding\n",
    "\n",
    "The Rent the Runway reviews ([data source](https://cseweb.ucsd.edu/~jmcauley/datasets.html#clothing_fit)) contain 200,000 ratings of 6,000 unique items rented between 2010 and 2018 by over 100,000 unique users.\n",
    "\n",
    "A quick look at the data structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "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>fit</th>\n",
       "      <th>user_id</th>\n",
       "      <th>bust size</th>\n",
       "      <th>item_id</th>\n",
       "      <th>weight</th>\n",
       "      <th>rating</th>\n",
       "      <th>rented for</th>\n",
       "      <th>review_text</th>\n",
       "      <th>body type</th>\n",
       "      <th>review_summary</th>\n",
       "      <th>category</th>\n",
       "      <th>height</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "      <th>review_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fit</td>\n",
       "      <td>420272</td>\n",
       "      <td>34d</td>\n",
       "      <td>2260466</td>\n",
       "      <td>137lbs</td>\n",
       "      <td>10.0</td>\n",
       "      <td>vacation</td>\n",
       "      <td>An adorable romper! Belt and zipper were a lit...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>So many compliments!</td>\n",
       "      <td>romper</td>\n",
       "      <td>5' 8\"</td>\n",
       "      <td>14</td>\n",
       "      <td>28.0</td>\n",
       "      <td>April 20, 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fit</td>\n",
       "      <td>273551</td>\n",
       "      <td>34b</td>\n",
       "      <td>153475</td>\n",
       "      <td>132lbs</td>\n",
       "      <td>10.0</td>\n",
       "      <td>other</td>\n",
       "      <td>I rented this dress for a photo shoot. The the...</td>\n",
       "      <td>straight &amp; narrow</td>\n",
       "      <td>I felt so glamourous!!!</td>\n",
       "      <td>gown</td>\n",
       "      <td>5' 6\"</td>\n",
       "      <td>12</td>\n",
       "      <td>36.0</td>\n",
       "      <td>June 18, 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit  user_id bust size  item_id  weight  rating rented for  \\\n",
       "0  fit   420272       34d  2260466  137lbs    10.0   vacation   \n",
       "1  fit   273551       34b   153475  132lbs    10.0      other   \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",
       "\n",
       "            review_summary category height  size   age     review_date  \n",
       "0     So many compliments!   romper  5' 8\"    14  28.0  April 20, 2016  \n",
       "1  I felt so glamourous!!!     gown  5' 6\"    12  36.0   June 18, 2013  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "raw_data = pd.read_csv('data/data.csv')\n",
    "raw_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit                   0\n",
       "user_id               0\n",
       "bust size         18411\n",
       "item_id               0\n",
       "weight            29982\n",
       "rating               82\n",
       "rented for           10\n",
       "review_text          62\n",
       "body type         14637\n",
       "review_summary      345\n",
       "category              0\n",
       "height              677\n",
       "size                  0\n",
       "age                 960\n",
       "review_date           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0    124537\n",
       "8.0      53391\n",
       "6.0      10697\n",
       "4.0       2791\n",
       "2.0       1046\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning** \n",
    "\n",
    "To perform the pre-processing steps, I define the function `preprocess_data`:\n",
    "- Drop missing values for `rating`  and change the scale for from 2-10 to 1-5.\n",
    "- Remove the units of measurement for `weight` and `height` to only keep the numerical values.\n",
    "- Impute missing values with the median for `age` and a few other features and with mode for `rented_for`.\n",
    "- Impute missing value for `bust_size` with the median value by `body_type` and vice versa.\n",
    "- Count the number of words in `text_summary` and `text_review` together to create new feature `length`.\n",
    "- Create new features `review_month`, `review_season`, and `review_year` based on `review_date`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def convert_height(x):\n",
    "    '''\n",
    "    Converts height from string format as feet and inches to integer in inches.\n",
    "    '''\n",
    "    height = [int(i) for i in x.replace('\\'', '').replace('\"', '').split()]\n",
    "    return height[0]*12 + height[1]\n",
    "\n",
    "def preprocess_data(df):\n",
    "    '''\n",
    "    Cleans the dataframe using imputation and feature engineering.\n",
    "    '''\n",
    "    df.columns = df.columns.str.replace(' ', '_')\n",
    "    df = df.dropna(subset=['rating'])\n",
    "    \n",
    "    df['weight'] = df['weight'].str.replace('lbs', '')\n",
    "    df['rating'] = df['rating']/2\n",
    "    df['height'] = df['height'].apply(lambda x: convert_height(x) if pd.notnull(x) else x)\n",
    "    \n",
    "    to_num = ['rating', 'weight', 'age']\n",
    "    df[to_num] = df[to_num].apply(pd.to_numeric, errors='coerce')\n",
    "    \n",
    "    for col in ['height', 'age']:\n",
    "        df[col] = df[col].fillna(df[col].median())\n",
    "        \n",
    "    weight_map = dict(df.groupby('height')['weight'].median())\n",
    "    df['weight'] = df['weight'].fillna(df['height'].map(weight_map))\n",
    "    \n",
    "    for col in ['review_text', 'review_summary']:\n",
    "        df[col] = df[col].replace('-', np.nan)\n",
    "    df['review'] = df['review_summary'] + ' ' + df['review_text']\n",
    "    df['review'] = df['review'].fillna('')\n",
    "    df['review_length'] = df['review_text'].fillna('').apply(lambda x: len(x.split()))\n",
    "        \n",
    "    age_limit = (df['age'] > 60) | (df['age'] < 13)\n",
    "    df['age'] = np.where(age_limit==True, df['age'].median(), df['age'])\n",
    "    \n",
    "    for col in ['bust_size', 'body_type']:\n",
    "        to_map = dict(df.groupby('size')[col].last())\n",
    "        df[col] = df[col].fillna(df['size'].map(to_map))\n",
    "    \n",
    "    df['rented_for'] = df['rented_for'].fillna(df['rented_for'].value_counts().index[0])\n",
    "    \n",
    "    df['review_date'] = pd.to_datetime(df['review_date'])\n",
    "    df['review_month'] = pd.DatetimeIndex(df['review_date']).month\n",
    "    df['review_season'] = pd.cut(df['review_month'].replace(12, 0), [0, 3, 6, 9, 11], include_lowest=True, labels=['Winter', 'Spring', 'Summer', 'Fall'])\n",
    "    df['review_year'] = pd.DatetimeIndex(df['review_date']).year\n",
    "    return df\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new dataframe for processed data\n",
    "data = preprocess_data(raw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>136.92</td>\n",
       "      <td>20.43</td>\n",
       "      <td>50.00</td>\n",
       "      <td>125.00</td>\n",
       "      <td>135.00</td>\n",
       "      <td>145.00</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>65.31</td>\n",
       "      <td>2.66</td>\n",
       "      <td>54.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>12.25</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>58.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>33.60</td>\n",
       "      <td>7.39</td>\n",
       "      <td>14.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_length</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>58.40</td>\n",
       "      <td>43.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>398.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_month</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>6.85</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_year</th>\n",
       "      <td>192462.00</td>\n",
       "      <td>2015.69</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2018.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count    mean   std     min     25%     50%     75%     max\n",
       "weight        192462.00  136.92 20.43   50.00  125.00  135.00  145.00  300.00\n",
       "rating        192462.00    4.55  0.72    1.00    4.00    5.00    5.00    5.00\n",
       "height        192462.00   65.31  2.66   54.00   63.00   65.00   67.00   78.00\n",
       "size          192462.00   12.25  8.50    0.00    8.00   12.00   16.00   58.00\n",
       "age           192462.00   33.60  7.39   14.00   29.00   32.00   37.00   60.00\n",
       "review_length 192462.00   58.40 43.04    0.00   27.00   50.00   79.00  398.00\n",
       "review_month  192462.00    6.85  3.38    1.00    4.00    7.00   10.00   12.00\n",
       "review_year   192462.00 2015.69  1.33 2010.00 2015.00 2016.00 2017.00 2018.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n",
    "# Summary statistics for numerical features\n",
    "data.drop(columns=['user_id', 'item_id']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization\n",
    "\n",
    "Let's explore and visualize the processed data!\n",
    "***\n",
    "\n",
    "**User Data** I create a separate table for user information by grouping the data by `user_id` and adding new features:\n",
    "- `rating_count` is the total number of items the user rated and reviewed.\n",
    "- `rating_average` is the average rating of the items reviewed by the user.\n",
    "- `rented_for_top` is the user's most common reason for renting an item.\n",
    "- `category_top` is the most common clothing category among the items reviewed by the user.\n",
    "- `review_length_average` is the average length of text review posted by the user.\n",
    "- `review_month_top` and `review_season_top` are the most common month and season the user posted the review.\n",
    "- `rented_for_all` is a list of all the user's reasons for renting the items.\n",
    "- `category_for_all` is a list of all the clothing categories of the items reviewed by the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_user_data(df):\n",
    "    '''\n",
    "    Groups the data by user and returns dataframe containing user information. \n",
    "    '''\n",
    "    user_df = pd.DataFrame(df.groupby('user_id').count().reset_index()['user_id'])\n",
    "    \n",
    "    for col in df.columns:\n",
    "        if col in ['bust_size', 'weight']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('user_id')[col].first()\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col == 'item_id':\n",
    "            feature = df.groupby(df['user_id']).count()[col]\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col == 'rating':\n",
    "            feature = df.groupby(df['user_id']).mean()[col]\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col in ['body_type', 'height', 'size', 'age']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('user_id')[col].first()\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col == 'review_length':\n",
    "            feature = df.groupby(df['user_id']).mean()[col]\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col in ['review_month', 'review_season']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('user_id')[col].agg(lambda x: x.value_counts().index[0])\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        if col in ['rented_for', 'category']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('user_id')[col].agg(pd.Series.mode).apply(lambda x: x[0] if type(x)==np.ndarray else x)\n",
    "            user_df = user_df.merge(feature, on='user_id')\n",
    "        else:\n",
    "            continue\n",
    "        \n",
    "    for col in ['rented_for', 'category']:\n",
    "        feature = df.groupby('user_id')[col].apply(set).apply(lambda x: list(x))\n",
    "        user_df = user_df.merge(feature, on='user_id')\n",
    "    \n",
    "    user_df.columns = ['user_id', 'bust_size', 'rating_count', 'weight', 'rating_average', 'rented_for_top', \n",
    "                       'body_type', 'category_top', 'height', 'size', 'age', 'review_length_average', \n",
    "                       'review_month_top', 'review_season_top', 'rented_for_all', 'category_all']\n",
    "    \n",
    "    return user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "\n",
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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>bust_size</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>weight</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>body_type</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>category_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>32c</td>\n",
       "      <td>2</td>\n",
       "      <td>145.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>pear</td>\n",
       "      <td>dress</td>\n",
       "      <td>66.00</td>\n",
       "      <td>8</td>\n",
       "      <td>32.00</td>\n",
       "      <td>121.50</td>\n",
       "      <td>3</td>\n",
       "      <td>Winter</td>\n",
       "      <td>[formal affair, wedding]</td>\n",
       "      <td>[dress, gown]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>34ddd/e</td>\n",
       "      <td>1</td>\n",
       "      <td>130.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>party</td>\n",
       "      <td>full bust</td>\n",
       "      <td>legging</td>\n",
       "      <td>67.00</td>\n",
       "      <td>8</td>\n",
       "      <td>40.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
       "      <td>[party]</td>\n",
       "      <td>[legging]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id bust_size  rating_count  weight  rating_average rented_for_top  \\\n",
       "0        9       32c             2  145.00            5.00  formal affair   \n",
       "1       25   34ddd/e             1  130.00            5.00          party   \n",
       "\n",
       "   body_type category_top  height  size   age  review_length_average  \\\n",
       "0       pear        dress   66.00     8 32.00                 121.50   \n",
       "1  full bust      legging   67.00     8 40.00                  14.00   \n",
       "\n",
       "   review_month_top review_season_top            rented_for_all   category_all  \n",
       "0                 3            Winter  [formal affair, wedding]  [dress, gown]  \n",
       "1                12            Winter                   [party]      [legging]  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe for user data\n",
    "user_data = create_user_data(data)\n",
    "user_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sort data by bust size\n",
    "bust_size_sorted_data = user_data.loc[(user_data['bust_size']>='32a') & (user_data['bust_size']<='38ddd/e')].sort_values('bust_size')\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(nrows=2, figsize=(20, 18))\n",
    "\n",
    "# Plot the distribution of users by bust size\n",
    "sns.countplot(x='bust_size', data=bust_size_sorted_data, palette='twilight', ax=axes[0])\n",
    "axes[0].set_title('User Count by Bust Size', fontsize=16)\n",
    "axes[0].set_xlabel('Bust Size')\n",
    "axes[0].set_ylabel('User Count')\n",
    "axes[0].set_xticklabels(bust_size_sorted_data['bust_size'].unique(), rotation=90, fontsize=12)\n",
    "\n",
    "# Plot the distribution of users by size\n",
    "sns.countplot(x='size', data=user_data, palette='PuBu_r', ax=axes[1])\n",
    "axes[1].set_title('User Count by Size', fontsize=16)\n",
    "axes[1].set_xlabel('Size')\n",
    "axes[1].set_ylabel('User Count')\n",
    "plt.savefig('data/images/fig0.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight_data = user_data.loc[(user_data['weight']>=90) & (user_data['weight']<=210)]\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(ncols=3, figsize=(15, 5))\n",
    "\n",
    "# Plot the distribution of users by weight\n",
    "sns.histplot(x='weight', data=weight_data, bins=24, color='darksalmon', kde=True, ax=axes[0])\n",
    "axes[0].set_title('User Count by Weight', fontsize=16)\n",
    "axes[0].set_xlabel('Weight')\n",
    "axes[0].set_ylabel('User Count')\n",
    "axes[0].grid(axis='x')\n",
    "\n",
    "# Plot the distribution of users by height\n",
    "sns.histplot(x='height', data=user_data, bins=24, color='midnightblue', ax=axes[1])\n",
    "axes[1].set_title('User Count by Height', fontsize=16)\n",
    "axes[1].set_xlabel('Height')\n",
    "axes[1].set(ylabel=None)\n",
    "axes[1].grid(axis='x')\n",
    "\n",
    "# Plot the distribution of users by age\n",
    "sns.histplot(x='age', data=user_data, bins=24, color='rebeccapurple', kde=True, ax=axes[2])\n",
    "axes[2].set_title('User Count by Age', fontsize=16)\n",
    "axes[2].set_xlabel('Age')\n",
    "axes[2].set(ylabel=None)\n",
    "axes[2].grid(axis='x')\n",
    "\n",
    "plt.savefig('data/images/fig1.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally distributed and diverse ranges of weight, height, and age above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "body_type_values = user_data['body_type'].value_counts().values\n",
    "body_type_names = user_data['body_type'].value_counts().index\n",
    "\n",
    "body_type_circle = plt.Circle((0,0), 0.7, color='white')\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.figure(figsize=(8,8))\n",
    "colors = ['#DC7F8E', '#E5A1AA', '#F4BFBE', '#FFE0DA', '#F4C4B2', '#E8B08D', '#C68C73']\n",
    "\n",
    "# Plot a donut chart of user body type\n",
    "plt.pie(body_type_values, labels=body_type_names, colors=colors, autopct='%1.0f%%', startangle=40, pctdistance=0.85)\n",
    "p = plt.gcf()\n",
    "p.gca().add_artist(body_type_circle)\n",
    "\n",
    "plt.title('User Percentage by Body Type', fontsize=16)\n",
    "plt.savefig('data/images/fig2.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "\n",
    "axes[0] = plt.subplot2grid((1, 5), (0, 0))\n",
    "axes[1] = plt.subplot2grid((1, 5), (0, 1), colspan=4)\n",
    "\n",
    "# Categorize rating count into binary classes\n",
    "item_count_data = user_data.copy()\n",
    "item_count_data['rating_count'] = item_count_data['rating_count'].apply(lambda x: 'Only one' if x==1 else 'More than one')\n",
    "\n",
    "# Plot the distribution of binary classes\n",
    "sns.countplot(x='rating_count', data=item_count_data, palette=['#5d2349', '#861d23'], order=['Only one', 'More than one'], ax=axes[0])\n",
    "axes[0].set(xlabel=None)\n",
    "axes[0].set_ylabel('User Count')\n",
    "\n",
    "rating_count_data = user_data.loc[(user_data['rating_count']>=2) & (user_data['rating_count']<=10)]\n",
    "\n",
    "# Show the distribution of the second class\n",
    "sns.countplot(x='rating_count', data=rating_count_data, palette='Reds_r', ax=axes[1])\n",
    "axes[1].set_title('User Count by Number of Items Rated', fontsize=16)\n",
    "axes[1].set_xlabel('Item rating count', fontsize=12)\n",
    "axes[1].set(ylabel=None)\n",
    "\n",
    "plt.savefig('data/images/fig3.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, two thirds of users rented only one item and the remaining third rented more than one, on the left chart. Majority of those who rented more than once rented exactly two items, on the right chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "\n",
    "# Plot the distribution of users by average rating\n",
    "sns.histplot(x='rating_average', data=user_data, bins=10, color='thistle', ax=axes[0])\n",
    "axes[0].set_title('User Count by Average Rating', fontsize=16)\n",
    "axes[0].set_xlabel('Average Rating')\n",
    "axes[0].set_ylabel('User Count')\n",
    "axes[0].grid(axis='x')\n",
    "\n",
    "# Plot the distribution of users by average review length\n",
    "sns.histplot(x='review_length_average', data=user_data, bins=30, color='lightsteelblue', kde=True, ax=axes[1])\n",
    "axes[1].set_title('User Count by Average Review Length', fontsize=16)\n",
    "axes[1].set_xlabel('Average Review Length')\n",
    "axes[1].set(ylabel=None)\n",
    "axes[1].grid(axis='x')\n",
    "\n",
    "plt.savefig('data/images/fig4.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A left-skewed distribution for the average rating per user with most of them giving the highest rating, on the left chart. And a right-skewed distribution for the average length of text review per user, on the right chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "\n",
    "# Plot the distribution of users by month of review posted\n",
    "sns.countplot(x='review_month_top', data=user_data, palette='PuBuGn', ax=axes[0])\n",
    "axes[0].set_title('User Count by Top Month Rated', fontsize=16)\n",
    "axes[0].set_xlabel('Top Month Rated')\n",
    "axes[0].set_ylabel('User Count')\n",
    "\n",
    "# Plot the distribution of users by season of review posted\n",
    "sns.countplot(x='review_season_top', data=user_data, order=['Spring', 'Summer', 'Fall', 'Winter'], palette='PuBuGn', ax=axes[1])\n",
    "axes[1].set_title('User Count by Top Season Rated', fontsize=16)\n",
    "axes[1].set_xlabel('Top Season Rated')\n",
    "axes[1].set(ylabel=None)\n",
    "\n",
    "plt.savefig('data/images/fig5.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 8))\n",
    "\n",
    "# Show top five clothing categories and top five reasons for rent\n",
    "category_top_data = user_data.loc[user_data['category_top'].isin(user_data['category_top'].value_counts()[:5].index.tolist())]\n",
    "rented_for_top_data = user_data.loc[user_data['rented_for_top'].isin(user_data['rented_for_top'].value_counts()[:5].index.tolist())]\n",
    "\n",
    "category_top_values = (category_top_data['category_top'].value_counts(normalize=True).values*100).tolist()\n",
    "category_top_labels = category_top_data['category_top'].value_counts().index.tolist()\n",
    "category_colors = ['#E5E4F4', '#E8F1DE', '#FDF9F0', '#F2E6F0', '#D9E4FB']\n",
    "\n",
    "# Plot the distribution of users by clothing category\n",
    "axes[0].pie(category_top_values, labels=category_top_labels, colors=category_colors, autopct='%.0f%%')\n",
    "axes[0].set_title('User Percentage by Top 5 Clothing Category', fontsize=16)\n",
    "\n",
    "rented_for_top_values = (rented_for_top_data['rented_for_top'].value_counts(normalize=True).values*100).tolist()\n",
    "rented_for_top_labels = rented_for_top_data['rented_for_top'].value_counts().index.tolist()\n",
    "rented_for_colors = ['#FBFBFB', '#FAEDDA', '#D2C1CE', '#E1CEC9', '#F3C0A1']\n",
    "\n",
    "# Plot the distribution of users by reason for rent\n",
    "axes[1].pie(rented_for_top_values, labels=rented_for_top_labels, colors=rented_for_colors, autopct='%.0f%%')\n",
    "axes[1].set_title('User Percentage by Top 5 Reason for Rent', fontsize=16)\n",
    "\n",
    "plt.savefig('data/images/fig6.png', dpi=200, transparent=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common clothing categories are dresses and gowns that align with the most common reasons for renting which are for wedding, formal affair, and party. \n",
    "***\n",
    "**Item Data** I create a separate table for item information by grouping the data by `item_id` and adding new features:\n",
    "- `fit_small`, `fit_large`, and `fit` are the count of users who rated the item as too small, too large, or right fit.\n",
    "- `user_count` is the total numbers of users who rated and reviewed the item.\n",
    "- `bust_size_top` and `body_type_top` are the most common bust size and body type of the users who rented the item.\n",
    "- `mean` and `median` of the `weight`, `height`, `size`, and `age` of all users who rented the item.\n",
    "- `rating_average` is the average of all user ratings of the item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_item_data(df):\n",
    "    '''\n",
    "    Groups the data by item and returns dataframe containing item details. \n",
    "    '''\n",
    "    item_df = pd.DataFrame(df.groupby('item_id').count().reset_index()['item_id'])\n",
    "    \n",
    "    for col in df.columns:\n",
    "        if col == 'fit':\n",
    "            feature_small = df.loc[df[col]=='small'].groupby('item_id').count()[col]\n",
    "            feature_fit = df.loc[df[col]=='fit'].groupby('item_id').count()[col]\n",
    "            feature_large = df.loc[df[col]=='large'].groupby('item_id').count()[col]\n",
    "            for idx, feature in enumerate([feature_small, feature_fit, feature_large]):\n",
    "                item_df = item_df.join(feature, on='item_id', rsuffix=idx).fillna(0)\n",
    "        if col == 'user_id':\n",
    "            feature = df.groupby(df['item_id']).count()[col]\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        if col in ['bust_size', 'body_type']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('item_id')[col].agg(pd.Series.mode).apply(lambda x: x[0] if type(x)==np.ndarray else x)\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        if col in ['weight', 'height', 'size', 'age']:\n",
    "            feature_mean = df.groupby(df['item_id']).mean()[col]\n",
    "            feature_median = df.groupby(df['item_id']).median()[col]\n",
    "            for feature in [feature_mean, feature_median]:\n",
    "                item_df = item_df.merge(feature, on='item_id')\n",
    "        if col in ['rating', 'review_length']:\n",
    "            feature = df.groupby(df['item_id']).mean()[col]\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        if col in ['rented_for', 'category']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('item_id')[col].agg(pd.Series.mode).apply(lambda x: x[0] if type(x)==np.ndarray else x)\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        if col == 'rented_for':\n",
    "            feature = df.groupby('item_id')[col].apply(set).apply(lambda x: list(x))\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        if col in ['review_month', 'review_season']:\n",
    "            feature = df.sort_values('review_date', ascending=False).groupby('item_id')[col].agg(lambda x: x.value_counts().index[0])\n",
    "            item_df = item_df.merge(feature, on='item_id')\n",
    "        else:\n",
    "            continue\n",
    "            \n",
    "    item_df.columns = ['item_id', 'fit_small', 'fit', 'fit_large', 'user_count', 'bust_size_top', 'weight_mean', \n",
    "                       'weight_median', 'rating_average', 'rented_for_top', 'rented_for_all', 'body_type_top', \n",
    "                       'category_top', 'height_mean', 'height_median', 'size_mean', 'size_median', 'age_mean', \n",
    "                       'age_median', 'review_length_average', 'review_month_top', 'review_season_top']\n",
    "    \n",
    "    return item_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table to use for item to item recommendations later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>item_id</th>\n",
       "      <th>fit_small</th>\n",
       "      <th>fit</th>\n",
       "      <th>fit_large</th>\n",
       "      <th>user_count</th>\n",
       "      <th>bust_size_top</th>\n",
       "      <th>weight_mean</th>\n",
       "      <th>weight_median</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>...</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_median</th>\n",
       "      <th>size_mean</th>\n",
       "      <th>size_median</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_median</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123373</td>\n",
       "      <td>73.00</td>\n",
       "      <td>566.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>686</td>\n",
       "      <td>36d</td>\n",
       "      <td>140.67</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.40</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>...</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.39</td>\n",
       "      <td>65.00</td>\n",
       "      <td>15.12</td>\n",
       "      <td>13.00</td>\n",
       "      <td>34.36</td>\n",
       "      <td>33.00</td>\n",
       "      <td>66.08</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123793</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1497.00</td>\n",
       "      <td>152.00</td>\n",
       "      <td>1714</td>\n",
       "      <td>34b</td>\n",
       "      <td>132.98</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>...</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.06</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.72</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.31</td>\n",
       "      <td>31.00</td>\n",
       "      <td>74.41</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  fit_small     fit  fit_large  user_count bust_size_top  \\\n",
       "0   123373      73.00  566.00      47.00         686           36d   \n",
       "1   123793      65.00 1497.00     152.00        1714           34b   \n",
       "\n",
       "   weight_mean  weight_median  rating_average rented_for_top  ...  \\\n",
       "0       140.67         135.00            4.40  formal affair  ...   \n",
       "1       132.98         130.00            4.77  formal affair  ...   \n",
       "\n",
       "  category_top height_mean height_median  size_mean  size_median  age_mean  \\\n",
       "0         gown       65.39         65.00      15.12        13.00     34.36   \n",
       "1         gown       65.06         65.00       9.72         8.00     31.31   \n",
       "\n",
       "   age_median  review_length_average  review_month_top  review_season_top  \n",
       "0       33.00                  66.08                12             Winter  \n",
       "1       31.00                  74.41                 5             Winter  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe for item data\n",
    "item_data = create_item_data(data)\n",
    "item_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "time_series_data = data.sort_values('review_date').set_index('review_date', drop=True).drop('2010-11-03')\n",
    "\n",
    "# Resample data to yearly count of reviews\n",
    "yearly_data = time_series_data.resample('Y').count()\n",
    "yearly_data = yearly_data.drop(yearly_data.index[-1])\n",
    "\n",
    "# Plot the aggregated yearly count of reviews\n",
    "yearly_data['rating'].plot(figsize=(8,5), colormap='PRGn', xlabel='')\n",
    "\n",
    "plt.title('Total Count of Reviews By Year', fontsize=16)\n",
    "plt.savefig('data/images/fig7.png', dpi=200, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count of reviews increased over the years from 10,000 in 2013 to almost 70,000 by 2018. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample data to monthly count of reviews\n",
    "monthly_data = time_series_data[~(time_series_data['review_year']==2011)].resample('MS').count()\n",
    "monthly_data = monthly_data.drop(monthly_data.index[-1])\n",
    "\n",
    "# Plot the aggregated monthly count of reviews\n",
    "monthly_data['rating'].plot(figsize=(8,5), colormap='seismic', xlabel='')\n",
    "\n",
    "plt.title('Total Count of Reviews By Month', fontsize=16)\n",
    "plt.savefig('data/images/fig8.png', dpi=200, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count of reviews peak during months of spring and fall with the highest spike of over 8,000 reviews in October of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample data to yearly average rating of reviews\n",
    "yearly_data = time_series_data.resample('Y').mean()\n",
    "yearly_data = yearly_data.drop(yearly_data.index[-1])\n",
    "\n",
    "# Plot the aggregated to yearly average rating of reviews\n",
    "yearly_data['rating'].plot(figsize=(8,5), colormap='PRGn', xlabel='')\n",
    "\n",
    "plt.title('Average Rating of Reviews By Year', fontsize=16)\n",
    "plt.savefig('data/images/fig9.png', dpi=200, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average ratings steadily increased from over 4.45 in 2013 to 4.575 in 2016 but went down by less then 0.025 in 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample data to monthly average rating of reviews\n",
    "monthly_data = time_series_data[~(time_series_data['review_year']==2011)].resample('MS').mean()\n",
    "monthly_data = monthly_data.drop(monthly_data.index[-1])\n",
    "\n",
    "# Plot the aggregated to monthly average rating of reviews\n",
    "monthly_data['rating'].plot(figsize=(8,5), colormap='seismic', xlabel='')\n",
    "\n",
    "plt.title('Average Rating of Reviews By Month', fontsize=16)\n",
    "plt.savefig('data/images/fig10.png', dpi=200, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average ratings peak during the latter months of the year and aligned with the higher counts of rentals in the fall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IV. Recommendation Systems\n",
    "\n",
    "> \"Recommendation Systems are software agents that elicit the interests and preferences of individual consumers […] and make recommendations accordingly. They have the potential to support and improve the quality of the\n",
    "decisions consumers make while searching for and selecting products online.\" [(Bo Xiao and Izak Benbasat)](https://misq.org/e-commerce-product-recommendation-agents-use-characteristics-and-impact.html)\n",
    "\n",
    "To start, I create a set of generalized recommendations that are based on all the data. For all the items, I calculate a weighted rating and return the top 10 highest-rated items across the board. To **personalize the recommendations**, I apply the different algorithms for Content-Based Recommenders and Collaborative Filtering Systems, which I implement using the `surprise` library later.\n",
    "***\n",
    "\n",
    "### Popularity Recommendations\n",
    "\n",
    "##### Bayesian Average\n",
    "\n",
    "$$W = \\left(\\frac{v}{v + m} \\right)R + \\left(\\frac{m}{v + m} \\right)C$$\n",
    "where:\n",
    "\n",
    "$W$ = Weighted rating <br>\n",
    "$v$ = Number of ratings for the item <br>\n",
    "$m$ = Minimum number of ratings required to be listed on top chart <br>\n",
    "$R$ = Average rating of the item <br>\n",
    "$C$ = Mean rating across the entire data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = item_data['user_count'].quantile(0.9)\n",
    "C = item_data['rating_average'].mean()\n",
    "\n",
    "def weighted_rating(x, m=m, C=C):\n",
    "    '''\n",
    "    Calculates weighted rating based on Bayesian Average.\n",
    "    '''\n",
    "    v = x['user_count']\n",
    "    R = x['rating_average']\n",
    "    return (v/(v+m) * R) + (m/(m+v) * C)\n",
    "\n",
    "def popular_recommendation(df=data, n=10):\n",
    "    '''\n",
    "    Returns the most popular items according to the highest weighted ratings.\n",
    "    '''\n",
    "    item_df = create_item_data(df)\n",
    "    \n",
    "    top_item_ratings = item_df.loc[(item_df['user_count']>=m)]\n",
    "    top_item_ratings['score'] = top_item_ratings.apply(weighted_rating, axis=1)\n",
    "    top_item_ratings = top_item_ratings.sort_values('score', ascending=False)\n",
    "    \n",
    "    return top_item_ratings.head(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 Popularity-Based Recommendations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>item_id</th>\n",
       "      <th>fit_small</th>\n",
       "      <th>fit</th>\n",
       "      <th>fit_large</th>\n",
       "      <th>user_count</th>\n",
       "      <th>bust_size_top</th>\n",
       "      <th>weight_mean</th>\n",
       "      <th>weight_median</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>body_type_top</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_median</th>\n",
       "      <th>size_mean</th>\n",
       "      <th>size_median</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_median</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>1064397</td>\n",
       "      <td>12.00</td>\n",
       "      <td>257.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>284</td>\n",
       "      <td>34b</td>\n",
       "      <td>129.45</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.81</td>\n",
       "      <td>65.00</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.00</td>\n",
       "      <td>28.65</td>\n",
       "      <td>28.00</td>\n",
       "      <td>60.74</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>709832</td>\n",
       "      <td>30.00</td>\n",
       "      <td>151.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>190</td>\n",
       "      <td>34b</td>\n",
       "      <td>134.62</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.86</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.11</td>\n",
       "      <td>65.00</td>\n",
       "      <td>12.14</td>\n",
       "      <td>12.00</td>\n",
       "      <td>33.95</td>\n",
       "      <td>32.00</td>\n",
       "      <td>72.49</td>\n",
       "      <td>3</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>1213427</td>\n",
       "      <td>27.00</td>\n",
       "      <td>270.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>308</td>\n",
       "      <td>34b</td>\n",
       "      <td>132.22</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.82</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, everyday, other, party, weddin...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>66.23</td>\n",
       "      <td>66.00</td>\n",
       "      <td>9.27</td>\n",
       "      <td>8.00</td>\n",
       "      <td>29.32</td>\n",
       "      <td>29.00</td>\n",
       "      <td>61.10</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>903647</td>\n",
       "      <td>13.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>143</td>\n",
       "      <td>34b</td>\n",
       "      <td>140.04</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.88</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.94</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.67</td>\n",
       "      <td>12.00</td>\n",
       "      <td>34.43</td>\n",
       "      <td>32.00</td>\n",
       "      <td>60.87</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>1378631</td>\n",
       "      <td>20.00</td>\n",
       "      <td>302.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>330</td>\n",
       "      <td>34b</td>\n",
       "      <td>139.89</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.81</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>maxi</td>\n",
       "      <td>65.93</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.42</td>\n",
       "      <td>11.00</td>\n",
       "      <td>32.98</td>\n",
       "      <td>32.00</td>\n",
       "      <td>65.14</td>\n",
       "      <td>6</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123793</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1497.00</td>\n",
       "      <td>152.00</td>\n",
       "      <td>1714</td>\n",
       "      <td>34b</td>\n",
       "      <td>132.98</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.06</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.72</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.31</td>\n",
       "      <td>31.00</td>\n",
       "      <td>74.41</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812</th>\n",
       "      <td>1003076</td>\n",
       "      <td>5.00</td>\n",
       "      <td>122.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>132</td>\n",
       "      <td>34b</td>\n",
       "      <td>137.98</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.96</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.55</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.80</td>\n",
       "      <td>32.00</td>\n",
       "      <td>55.21</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>1186923</td>\n",
       "      <td>9.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>103</td>\n",
       "      <td>34c</td>\n",
       "      <td>137.48</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.92</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.33</td>\n",
       "      <td>65.00</td>\n",
       "      <td>13.53</td>\n",
       "      <td>14.00</td>\n",
       "      <td>30.33</td>\n",
       "      <td>30.00</td>\n",
       "      <td>65.70</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>1260666</td>\n",
       "      <td>25.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>154</td>\n",
       "      <td>34b</td>\n",
       "      <td>130.13</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.84</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>64.82</td>\n",
       "      <td>65.00</td>\n",
       "      <td>10.66</td>\n",
       "      <td>8.00</td>\n",
       "      <td>32.77</td>\n",
       "      <td>32.50</td>\n",
       "      <td>54.05</td>\n",
       "      <td>7</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>714374</td>\n",
       "      <td>23.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>137</td>\n",
       "      <td>34b</td>\n",
       "      <td>133.23</td>\n",
       "      <td>132.00</td>\n",
       "      <td>4.85</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.83</td>\n",
       "      <td>66.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.49</td>\n",
       "      <td>30.00</td>\n",
       "      <td>55.97</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id  fit_small     fit  fit_large  user_count bust_size_top  \\\n",
       "1948  1064397      12.00  257.00      15.00         284           34b   \n",
       "1223   709832      30.00  151.00       9.00         190           34b   \n",
       "2260  1213427      27.00  270.00      11.00         308           34b   \n",
       "1608   903647      13.00  126.00       4.00         143           34b   \n",
       "2599  1378631      20.00  302.00       8.00         330           34b   \n",
       "1      123793      65.00 1497.00     152.00        1714           34b   \n",
       "1812  1003076       5.00  122.00       5.00         132           34b   \n",
       "2201  1186923       9.00   91.00       3.00         103           34c   \n",
       "2351  1260666      25.00  126.00       3.00         154           34b   \n",
       "1236   714374      23.00  112.00       2.00         137           34b   \n",
       "\n",
       "      weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "1948       129.45         130.00            4.89        wedding   \n",
       "1223       134.62         135.00            4.86  formal affair   \n",
       "2260       132.22         130.00            4.82        wedding   \n",
       "1608       140.04         138.00            4.88  formal affair   \n",
       "2599       139.89         135.00            4.81        wedding   \n",
       "1          132.98         130.00            4.77  formal affair   \n",
       "1812       137.98         135.00            4.89        wedding   \n",
       "2201       137.48         135.00            4.92          party   \n",
       "2351       130.13         130.00            4.84          party   \n",
       "1236       133.23         132.00            4.85        wedding   \n",
       "\n",
       "                                         rented_for_all body_type_top  \\\n",
       "1948       [formal affair, other, party, wedding, date]      athletic   \n",
       "1223       [formal affair, other, party, wedding, date]     hourglass   \n",
       "2260  [formal affair, everyday, other, party, weddin...      athletic   \n",
       "1608       [formal affair, other, party, wedding, date]      athletic   \n",
       "2599  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "1     [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "1812  [formal affair, other, party, wedding, date, v...      athletic   \n",
       "2201  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "2351  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "1236  [formal affair, work, other, party, wedding, d...      athletic   \n",
       "\n",
       "     category_top  height_mean  height_median  size_mean  size_median  \\\n",
       "1948         gown        64.81          65.00       8.14         8.00   \n",
       "1223         gown        65.11          65.00      12.14        12.00   \n",
       "2260         gown        66.23          66.00       9.27         8.00   \n",
       "1608         gown        65.94          66.00      12.67        12.00   \n",
       "2599         maxi        65.93          66.00      12.42        11.00   \n",
       "1            gown        65.06          65.00       9.72         8.00   \n",
       "1812        dress        65.96          66.00      11.55        12.00   \n",
       "2201        dress        65.33          65.00      13.53        14.00   \n",
       "2351        dress        64.82          65.00      10.66         8.00   \n",
       "1236        dress        65.83          66.00      10.18         8.00   \n",
       "\n",
       "      age_mean  age_median  review_length_average  review_month_top  \\\n",
       "1948     28.65       28.00                  60.74                10   \n",
       "1223     33.95       32.00                  72.49                 3   \n",
       "2260     29.32       29.00                  61.10                 4   \n",
       "1608     34.43       32.00                  60.87                10   \n",
       "2599     32.98       32.00                  65.14                 6   \n",
       "1        31.31       31.00                  74.41                 5   \n",
       "1812     32.80       32.00                  55.21                10   \n",
       "2201     30.33       30.00                  65.70                11   \n",
       "2351     32.77       32.50                  54.05                 7   \n",
       "1236     31.49       30.00                  55.97                10   \n",
       "\n",
       "     review_season_top  score  \n",
       "1948            Winter   4.82  \n",
       "1223            Winter   4.77  \n",
       "2260            Spring   4.77  \n",
       "1608            Winter   4.76  \n",
       "2599            Spring   4.76  \n",
       "1               Winter   4.76  \n",
       "1812            Winter   4.76  \n",
       "2201            Winter   4.76  \n",
       "2351            Spring   4.74  \n",
       "1236              Fall   4.74  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 30)\n",
    "\n",
    "top10_overall = popular_recommendation()\n",
    "top10_overall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simulate the online shopping experience, I can also filter the popularity-based recommendations on the data features such as `dress` for clothing category and `wedding` for reason to rent using the function I define as `filter_popular_recommendation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_list = []\n",
    "operator_list = []\n",
    "condition_list = []\n",
    "\n",
    "def append_condition(column, operation, condition):\n",
    "    '''\n",
    "    Appends a filter to column, operator, and condition lists.\n",
    "    ''' \n",
    "    column_list.append(column)\n",
    "    operator_list.append(operation)\n",
    "    condition_list.append(condition)\n",
    "\n",
    "def filter_popular_recommendation(df=data, n=10, bust_size=None, weight=None, rating=None, rented_for=None, \n",
    "                                  body_type=None, category=None, height=None, size=None, age=None, \n",
    "                                  review_month=None, review_season=None, review_year=None):\n",
    "    '''\n",
    "    Returns the most popular recommendations filtered by the features passed as arguments.\n",
    "    '''\n",
    "    if bust_size:\n",
    "        append_condition('bust_size', '==', bust_size)\n",
    "    if weight:\n",
    "        append_condition('weight', '>=', weight-10)\n",
    "        append_condition('weight', '<=', weight+10)\n",
    "    if rented_for:\n",
    "        append_condition('rented_for', '==', rented_for)\n",
    "    if body_type:\n",
    "        append_condition('body_type', '==', body_type)\n",
    "    if category:\n",
    "        append_condition('category', '==', category)\n",
    "    if height:\n",
    "        append_condition('height', '>=', height-2)\n",
    "        append_condition('height', '>=', height+2)\n",
    "    if size:\n",
    "        append_condition('size', '==', size)\n",
    "    if age:\n",
    "        append_condition('age', '>=', age-4)\n",
    "        append_condition('age', '<=', age+4)\n",
    "    if review_month:\n",
    "        append_condition('review_month', '==', review_month)\n",
    "    if review_season:\n",
    "        append_condition('review_season', '==', review_season)\n",
    "    if review_year:\n",
    "        append_condition('review_year', '==', review_year)\n",
    "    \n",
    "    condition = ' & '.join(f'{col} {op} {repr(cond)}' for col, op, cond in zip(column_list, operator_list, condition_list))\n",
    "    filtered_df = df.query(condition)\n",
    "    \n",
    "    return popular_recommendation(filtered_df, n)\n",
    "\n",
    "def reset_condition():\n",
    "    '''\n",
    "    Reinitializes lists for query for filtered popularity recommender.\n",
    "    '''\n",
    "    column_list = []\n",
    "    operator_list = []\n",
    "    condition_list = []\n",
    "    return column_list, operator_list, condition_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 Popular Recommendations for Dress:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>item_id</th>\n",
       "      <th>fit_small</th>\n",
       "      <th>fit</th>\n",
       "      <th>fit_large</th>\n",
       "      <th>user_count</th>\n",
       "      <th>bust_size_top</th>\n",
       "      <th>weight_mean</th>\n",
       "      <th>weight_median</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>body_type_top</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_median</th>\n",
       "      <th>size_mean</th>\n",
       "      <th>size_median</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_median</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>1003076</td>\n",
       "      <td>5.00</td>\n",
       "      <td>122.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>132</td>\n",
       "      <td>34b</td>\n",
       "      <td>137.98</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.96</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.55</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.80</td>\n",
       "      <td>32.00</td>\n",
       "      <td>55.21</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>1186923</td>\n",
       "      <td>9.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>103</td>\n",
       "      <td>34c</td>\n",
       "      <td>137.48</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.92</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.33</td>\n",
       "      <td>65.00</td>\n",
       "      <td>13.53</td>\n",
       "      <td>14.00</td>\n",
       "      <td>30.33</td>\n",
       "      <td>30.00</td>\n",
       "      <td>65.70</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>1260666</td>\n",
       "      <td>25.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>154</td>\n",
       "      <td>34b</td>\n",
       "      <td>130.13</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.84</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>64.82</td>\n",
       "      <td>65.00</td>\n",
       "      <td>10.66</td>\n",
       "      <td>8.00</td>\n",
       "      <td>32.77</td>\n",
       "      <td>32.50</td>\n",
       "      <td>54.05</td>\n",
       "      <td>7</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>714374</td>\n",
       "      <td>23.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>137</td>\n",
       "      <td>34b</td>\n",
       "      <td>133.23</td>\n",
       "      <td>132.00</td>\n",
       "      <td>4.85</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.83</td>\n",
       "      <td>66.00</td>\n",
       "      <td>10.18</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.49</td>\n",
       "      <td>30.00</td>\n",
       "      <td>55.97</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>724319</td>\n",
       "      <td>5.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>118</td>\n",
       "      <td>32d</td>\n",
       "      <td>134.67</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.86</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.27</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.83</td>\n",
       "      <td>8.00</td>\n",
       "      <td>33.51</td>\n",
       "      <td>33.00</td>\n",
       "      <td>51.08</td>\n",
       "      <td>10</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>1106101</td>\n",
       "      <td>2.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>140</td>\n",
       "      <td>36b</td>\n",
       "      <td>142.86</td>\n",
       "      <td>140.00</td>\n",
       "      <td>4.84</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, everyday, other, party, weddin...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.81</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.57</td>\n",
       "      <td>14.00</td>\n",
       "      <td>31.01</td>\n",
       "      <td>30.00</td>\n",
       "      <td>48.90</td>\n",
       "      <td>11</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>241461</td>\n",
       "      <td>10.00</td>\n",
       "      <td>227.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>241</td>\n",
       "      <td>34d</td>\n",
       "      <td>141.32</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.78</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, everyday, other, party, weddin...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>66.10</td>\n",
       "      <td>66.00</td>\n",
       "      <td>14.15</td>\n",
       "      <td>12.00</td>\n",
       "      <td>31.70</td>\n",
       "      <td>30.00</td>\n",
       "      <td>67.82</td>\n",
       "      <td>6</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2540</th>\n",
       "      <td>1940985</td>\n",
       "      <td>14.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>114</td>\n",
       "      <td>34d</td>\n",
       "      <td>139.75</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.83</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.60</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.93</td>\n",
       "      <td>14.00</td>\n",
       "      <td>34.98</td>\n",
       "      <td>34.00</td>\n",
       "      <td>49.37</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>1031440</td>\n",
       "      <td>71.00</td>\n",
       "      <td>160.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>232</td>\n",
       "      <td>36c</td>\n",
       "      <td>138.88</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.31</td>\n",
       "      <td>65.00</td>\n",
       "      <td>15.09</td>\n",
       "      <td>14.00</td>\n",
       "      <td>31.23</td>\n",
       "      <td>31.00</td>\n",
       "      <td>60.11</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>263699</td>\n",
       "      <td>1.00</td>\n",
       "      <td>105.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>144</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.67</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.81</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.45</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.37</td>\n",
       "      <td>8.00</td>\n",
       "      <td>34.01</td>\n",
       "      <td>32.00</td>\n",
       "      <td>66.34</td>\n",
       "      <td>6</td>\n",
       "      <td>Winter</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id  fit_small    fit  fit_large  user_count bust_size_top  \\\n",
       "1223  1003076       5.00 122.00       5.00         132           34b   \n",
       "1483  1186923       9.00  91.00       3.00         103           34c   \n",
       "1584  1260666      25.00 126.00       3.00         154           34b   \n",
       "832    714374      23.00 112.00       2.00         137           34b   \n",
       "847    724319       5.00  99.00      14.00         118           32d   \n",
       "1378  1106101       2.00 111.00      27.00         140           36b   \n",
       "154    241461      10.00 227.00       4.00         241           34d   \n",
       "2540  1940985      14.00  99.00       1.00         114           34d   \n",
       "1262  1031440      71.00 160.00       1.00         232           36c   \n",
       "181    263699       1.00 105.00      38.00         144           34b   \n",
       "\n",
       "      weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "1223       137.98         135.00            4.89        wedding   \n",
       "1483       137.48         135.00            4.92          party   \n",
       "1584       130.13         130.00            4.84          party   \n",
       "832        133.23         132.00            4.85        wedding   \n",
       "847        134.67         135.00            4.86        wedding   \n",
       "1378       142.86         140.00            4.84        wedding   \n",
       "154        141.32         135.00            4.78        wedding   \n",
       "2540       139.75         138.00            4.83        wedding   \n",
       "1262       138.88         135.00            4.77          party   \n",
       "181        136.67         135.00            4.81  formal affair   \n",
       "\n",
       "                                         rented_for_all body_type_top  \\\n",
       "1223  [formal affair, other, party, wedding, date, v...      athletic   \n",
       "1483  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "1584  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "832   [formal affair, work, other, party, wedding, d...      athletic   \n",
       "847   [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "1378  [formal affair, everyday, other, party, weddin...     hourglass   \n",
       "154   [formal affair, everyday, other, party, weddin...     hourglass   \n",
       "2540  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "1262  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "181        [formal affair, other, party, wedding, date]     hourglass   \n",
       "\n",
       "     category_top  height_mean  height_median  size_mean  size_median  \\\n",
       "1223        dress        65.96          66.00      11.55        12.00   \n",
       "1483        dress        65.33          65.00      13.53        14.00   \n",
       "1584        dress        64.82          65.00      10.66         8.00   \n",
       "832         dress        65.83          66.00      10.18         8.00   \n",
       "847         dress        65.27          65.00       9.83         8.00   \n",
       "1378        dress        65.81          66.00      11.57        14.00   \n",
       "154         dress        66.10          66.00      14.15        12.00   \n",
       "2540        dress        65.60          66.00      12.93        14.00   \n",
       "1262        dress        65.31          65.00      15.09        14.00   \n",
       "181         dress        65.45          65.00       9.37         8.00   \n",
       "\n",
       "      age_mean  age_median  review_length_average  review_month_top  \\\n",
       "1223     32.80       32.00                  55.21                10   \n",
       "1483     30.33       30.00                  65.70                11   \n",
       "1584     32.77       32.50                  54.05                 7   \n",
       "832      31.49       30.00                  55.97                10   \n",
       "847      33.51       33.00                  51.08                10   \n",
       "1378     31.01       30.00                  48.90                11   \n",
       "154      31.70       30.00                  67.82                 6   \n",
       "2540     34.98       34.00                  49.37                11   \n",
       "1262     31.23       31.00                  60.11                12   \n",
       "181      34.01       32.00                  66.34                 6   \n",
       "\n",
       "     review_season_top  score  \n",
       "1223            Winter   4.76  \n",
       "1483            Winter   4.76  \n",
       "1584            Spring   4.74  \n",
       "832               Fall   4.74  \n",
       "847             Summer   4.73  \n",
       "1378            Summer   4.73  \n",
       "154             Spring   4.72  \n",
       "2540            Winter   4.71  \n",
       "1262            Winter   4.71  \n",
       "181             Winter   4.71  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_dress = filter_popular_recommendation(category='dress')\n",
    "column_list, operator_list, condition_list = reset_condition()\n",
    "top10_dress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 Popular Recommendations for Wedding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\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>item_id</th>\n",
       "      <th>fit_small</th>\n",
       "      <th>fit</th>\n",
       "      <th>fit_large</th>\n",
       "      <th>user_count</th>\n",
       "      <th>bust_size_top</th>\n",
       "      <th>weight_mean</th>\n",
       "      <th>weight_median</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>body_type_top</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_median</th>\n",
       "      <th>size_mean</th>\n",
       "      <th>size_median</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_median</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>1064397</td>\n",
       "      <td>7.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>168</td>\n",
       "      <td>34b</td>\n",
       "      <td>129.88</td>\n",
       "      <td>129.00</td>\n",
       "      <td>4.87</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.68</td>\n",
       "      <td>64.00</td>\n",
       "      <td>8.08</td>\n",
       "      <td>8.00</td>\n",
       "      <td>29.57</td>\n",
       "      <td>29.00</td>\n",
       "      <td>63.77</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123793</td>\n",
       "      <td>20.00</td>\n",
       "      <td>435.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>501</td>\n",
       "      <td>34b</td>\n",
       "      <td>133.64</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.78</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.87</td>\n",
       "      <td>65.00</td>\n",
       "      <td>10.08</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.68</td>\n",
       "      <td>31.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>10</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>1378631</td>\n",
       "      <td>15.00</td>\n",
       "      <td>226.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>248</td>\n",
       "      <td>34c</td>\n",
       "      <td>139.42</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.81</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>maxi</td>\n",
       "      <td>65.90</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.69</td>\n",
       "      <td>9.00</td>\n",
       "      <td>32.47</td>\n",
       "      <td>31.00</td>\n",
       "      <td>67.14</td>\n",
       "      <td>6</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>1213427</td>\n",
       "      <td>15.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>172</td>\n",
       "      <td>34b</td>\n",
       "      <td>132.06</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.82</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.90</td>\n",
       "      <td>66.00</td>\n",
       "      <td>9.26</td>\n",
       "      <td>8.00</td>\n",
       "      <td>29.80</td>\n",
       "      <td>29.00</td>\n",
       "      <td>59.12</td>\n",
       "      <td>9</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>127865</td>\n",
       "      <td>28.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>438</td>\n",
       "      <td>34b</td>\n",
       "      <td>134.43</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.76</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.72</td>\n",
       "      <td>65.00</td>\n",
       "      <td>11.68</td>\n",
       "      <td>12.00</td>\n",
       "      <td>35.64</td>\n",
       "      <td>34.00</td>\n",
       "      <td>68.75</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>870184</td>\n",
       "      <td>1.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>99</td>\n",
       "      <td>34c</td>\n",
       "      <td>135.56</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.86</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.35</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.00</td>\n",
       "      <td>32.52</td>\n",
       "      <td>31.00</td>\n",
       "      <td>60.19</td>\n",
       "      <td>7</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>1003076</td>\n",
       "      <td>3.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>81</td>\n",
       "      <td>34b</td>\n",
       "      <td>137.58</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.89</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.83</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.31</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.28</td>\n",
       "      <td>31.00</td>\n",
       "      <td>55.79</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>241461</td>\n",
       "      <td>6.00</td>\n",
       "      <td>173.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>181</td>\n",
       "      <td>34d</td>\n",
       "      <td>141.25</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.79</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>66.17</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.56</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.01</td>\n",
       "      <td>30.00</td>\n",
       "      <td>71.35</td>\n",
       "      <td>6</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>1309537</td>\n",
       "      <td>7.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>83</td>\n",
       "      <td>34c</td>\n",
       "      <td>138.06</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.88</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.88</td>\n",
       "      <td>65.00</td>\n",
       "      <td>13.87</td>\n",
       "      <td>13.00</td>\n",
       "      <td>31.48</td>\n",
       "      <td>30.00</td>\n",
       "      <td>58.58</td>\n",
       "      <td>10</td>\n",
       "      <td>Summer</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>1687082</td>\n",
       "      <td>9.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>218</td>\n",
       "      <td>34c</td>\n",
       "      <td>131.89</td>\n",
       "      <td>132.50</td>\n",
       "      <td>4.78</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.55</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.14</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.08</td>\n",
       "      <td>30.00</td>\n",
       "      <td>66.33</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id  fit_small    fit  fit_large  user_count bust_size_top  \\\n",
       "1397  1064397       7.00 153.00       8.00         168           34b   \n",
       "1      123793      20.00 435.00      46.00         501           34b   \n",
       "1867  1378631      15.00 226.00       7.00         248           34c   \n",
       "1623  1213427      15.00 150.00       7.00         172           34b   \n",
       "10     127865      28.00 400.00      10.00         438           34b   \n",
       "1110   870184       1.00  88.00      10.00          99           34c   \n",
       "1295  1003076       3.00  75.00       3.00          81           34b   \n",
       "220    241461       6.00 173.00       2.00         181           34d   \n",
       "1767  1309537       7.00  69.00       7.00          83           34c   \n",
       "2315  1687082       9.00 200.00       9.00         218           34c   \n",
       "\n",
       "      weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "1397       129.88         129.00            4.87        wedding   \n",
       "1          133.64         130.00            4.78        wedding   \n",
       "1867       139.42         135.00            4.81        wedding   \n",
       "1623       132.06         130.00            4.82        wedding   \n",
       "10         134.43         135.00            4.76        wedding   \n",
       "1110       135.56         135.00            4.86        wedding   \n",
       "1295       137.58         135.00            4.89        wedding   \n",
       "220        141.25         135.00            4.79        wedding   \n",
       "1767       138.06         135.00            4.88        wedding   \n",
       "2315       131.89         132.50            4.78        wedding   \n",
       "\n",
       "     rented_for_all body_type_top category_top  height_mean  height_median  \\\n",
       "1397      [wedding]      athletic         gown        64.68          64.00   \n",
       "1         [wedding]      athletic         gown        64.87          65.00   \n",
       "1867      [wedding]      athletic         maxi        65.90          66.00   \n",
       "1623      [wedding]      athletic         gown        65.90          66.00   \n",
       "10        [wedding]     hourglass         gown        64.72          65.00   \n",
       "1110      [wedding]     hourglass        dress        65.35          65.00   \n",
       "1295      [wedding]      athletic        dress        65.83          66.00   \n",
       "220       [wedding]     hourglass        dress        66.17          66.00   \n",
       "1767      [wedding]     hourglass         gown        64.88          65.00   \n",
       "2315      [wedding]      athletic         gown        65.55          65.00   \n",
       "\n",
       "      size_mean  size_median  age_mean  age_median  review_length_average  \\\n",
       "1397       8.08         8.00     29.57       29.00                  63.77   \n",
       "1         10.08         8.00     31.68       31.00                  76.00   \n",
       "1867      11.69         9.00     32.47       31.00                  67.14   \n",
       "1623       9.26         8.00     29.80       29.00                  59.12   \n",
       "10        11.68        12.00     35.64       34.00                  68.75   \n",
       "1110       9.96         8.00     32.52       31.00                  60.19   \n",
       "1295      11.31        12.00     32.28       31.00                  55.79   \n",
       "220       13.56        12.00     32.01       30.00                  71.35   \n",
       "1767      13.87        13.00     31.48       30.00                  58.58   \n",
       "2315       9.14         8.00     31.08       30.00                  66.33   \n",
       "\n",
       "      review_month_top review_season_top  score  \n",
       "1397                10              Fall   4.77  \n",
       "1                   10            Summer   4.75  \n",
       "1867                 6            Summer   4.75  \n",
       "1623                 9            Spring   4.73  \n",
       "10                  10              Fall   4.72  \n",
       "1110                 7            Summer   4.72  \n",
       "1295                10              Fall   4.72  \n",
       "220                  6            Summer   4.71  \n",
       "1767                10            Summer   4.71  \n",
       "2315                 5            Spring   4.71  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_wedding = filter_popular_recommendation(rented_for='wedding')\n",
    "column_list, operator_list, condition_list = reset_condition()\n",
    "top10_wedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Content-Based Recommenders\n",
    "\n",
    "Content-based recommendation systems are based on the idea that if a user likes an item, the user will also like  items similar to it. To measure the similarity between the items, I calculate the Pearson correlation using numerical and categorical features from the table `item_data` created earlier. Then, I complete a `similarity_matrix` of all the items to use in the function `content_based_similarity` I define, which generates content-based recommendations for any `item_id`. Lastly, I use the text features later to create a **text review-based recommender** using Natural Language Processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def item_similarity(item_df):\n",
    "    '''\n",
    "    Measures pearson correlation of items from the table of item data and returns a similarity matrix.\n",
    "    '''\n",
    "    item_df = item_df.drop(['fit_small', 'fit_large', 'weight_mean', 'rented_for_all', 'height_mean', 'size_mean', \n",
    "                            'age_mean', 'review_month_top'], axis=1)\n",
    "    \n",
    "    similarity_features = item_df[['item_id', 'fit', 'user_count', 'weight_median', 'rating_average', 'rented_for_top', \n",
    "                                 'body_type_top', 'category_top', 'height_median', 'size_median', 'age_median', \n",
    "                                 'review_length_average', 'review_season_top']]\n",
    "    similarity_features = similarity_features.set_index('item_id')\n",
    "    similarity_features = pd.get_dummies(similarity_features, columns=['rented_for_top', 'body_type_top', 'category_top', 'review_season_top'])\n",
    "    \n",
    "    similarity_matrix = similarity_features.T\n",
    "    similarity_matrix = similarity_matrix.corr(method='pearson')\n",
    "    \n",
    "    return similarity_features, similarity_matrix\n",
    "\n",
    "pd.set_option('display.max_columns', 30)\n",
    "\n",
    "def content_based_similarity(similarity_matrix, item_id, n=20):\n",
    "    '''\n",
    "    Returns the most similar item recommendations to the given item based on the similarity matrix.\n",
    "    '''\n",
    "    recommendations = similarity_matrix[item_id].sort_values(ascending=False)\n",
    "    recommendations = recommendations.drop([item_id], axis=0).index\n",
    "    \n",
    "    recommendations_list = []\n",
    "    for i in range(n):\n",
    "        recommendations_list.append(recommendations[i])\n",
    "    \n",
    "    display(item_data.loc[item_data['item_id']==item_id])\n",
    "    print(f'----------------------------------------\\nTop {n} Recommendations for Item #{item_id}:')\n",
    "                          \n",
    "    recommendations_df = item_data.loc[item_data['item_id'].isin(recommendations_list)]\n",
    "    return recommendations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity_features, similarity_matrix = item_similarity(item_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965009</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2965924</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966087</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5850 rows × 5850 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "item_id  123373   123793   124204   124553   125424   125465   125564   \\\n",
       "item_id                                                                  \n",
       "123373      1.00     0.99     1.00     0.98     0.99     1.00     1.00   \n",
       "123793      0.99     1.00     0.99     0.96     0.97     1.00     0.99   \n",
       "124204      1.00     0.99     1.00     0.98     0.99     1.00     1.00   \n",
       "124553      0.98     0.96     0.98     1.00     1.00     0.98     0.99   \n",
       "125424      0.99     0.97     0.99     1.00     1.00     0.98     1.00   \n",
       "...          ...      ...      ...      ...      ...      ...      ...   \n",
       "2963850     0.25     0.14     0.23     0.34     0.35     0.21     0.28   \n",
       "2964470     0.18     0.07     0.16     0.27     0.28     0.14     0.21   \n",
       "2965009     0.19     0.08     0.17     0.28     0.29     0.15     0.22   \n",
       "2965924     0.16     0.05     0.14     0.24     0.25     0.12     0.19   \n",
       "2966087     0.19     0.08     0.17     0.29     0.29     0.15     0.23   \n",
       "\n",
       "item_id  126335   127081   127495   127865   128730   128959   129831   \\\n",
       "item_id                                                                  \n",
       "123373      0.99     0.96     1.00     0.99     0.98     1.00     0.93   \n",
       "123793      1.00     0.92     0.98     1.00     0.96     0.98     0.88   \n",
       "124204      0.99     0.95     0.99     0.99     0.98     1.00     0.92   \n",
       "124553      0.97     0.97     0.99     0.96     0.98     0.98     0.96   \n",
       "125424      0.97     0.98     1.00     0.96     0.99     0.99     0.96   \n",
       "...          ...      ...      ...      ...      ...      ...      ...   \n",
       "2963850     0.13     0.50     0.33     0.16     0.42     0.31     0.59   \n",
       "2964470     0.06     0.43     0.26     0.08     0.35     0.24     0.52   \n",
       "2965009     0.07     0.44     0.27     0.10     0.36     0.25     0.53   \n",
       "2965924     0.04     0.42     0.23     0.07     0.32     0.22     0.49   \n",
       "2966087     0.07     0.45     0.27     0.09     0.36     0.25     0.54   \n",
       "\n",
       "item_id  130259   ...  2959486  2959777  2960025  2960913  2960940  2960969  \\\n",
       "item_id           ...                                                         \n",
       "123373      1.00  ...     0.26     0.16     0.25     0.17     0.19     0.28   \n",
       "123793      0.99  ...     0.14     0.05     0.14     0.06     0.08     0.17   \n",
       "124204      1.00  ...     0.23     0.13     0.22     0.15     0.16     0.25   \n",
       "124553      0.99  ...     0.35     0.25     0.33     0.26     0.28     0.37   \n",
       "125424      0.99  ...     0.36     0.26     0.34     0.27     0.29     0.38   \n",
       "...          ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "2963850     0.22  ...     1.00     0.97     0.98     0.98     0.99     1.00   \n",
       "2964470     0.15  ...     0.99     0.99     0.97     0.98     0.99     0.99   \n",
       "2965009     0.16  ...     0.99     0.99     0.96     0.97     1.00     0.99   \n",
       "2965924     0.13  ...     0.91     0.81     0.96     0.96     0.86     0.87   \n",
       "2966087     0.16  ...     0.99     0.98     0.98     0.99     0.99     0.99   \n",
       "\n",
       "item_id  2961855  2962646  2963344  2963601  2963850  2964470  2965009  \\\n",
       "item_id                                                                  \n",
       "123373      0.17     0.24     0.24     0.16     0.25     0.18     0.19   \n",
       "123793      0.06     0.13     0.13     0.05     0.14     0.07     0.08   \n",
       "124204      0.15     0.22     0.21     0.14     0.23     0.16     0.17   \n",
       "124553      0.26     0.34     0.33     0.25     0.34     0.27     0.28   \n",
       "125424      0.27     0.35     0.34     0.26     0.35     0.28     0.29   \n",
       "...          ...      ...      ...      ...      ...      ...      ...   \n",
       "2963850     0.97     1.00     1.00     0.99     1.00     0.99     0.99   \n",
       "2964470     0.98     0.99     1.00     0.99     0.99     1.00     0.99   \n",
       "2965009     0.99     1.00     1.00     1.00     0.99     0.99     1.00   \n",
       "2965924     0.80     0.88     0.88     0.87     0.90     0.90     0.86   \n",
       "2966087     0.97     0.99     0.99     0.99     0.99     1.00     0.99   \n",
       "\n",
       "item_id  2965924  2966087  \n",
       "item_id                    \n",
       "123373      0.16     0.19  \n",
       "123793      0.05     0.08  \n",
       "124204      0.14     0.17  \n",
       "124553      0.24     0.29  \n",
       "125424      0.25     0.29  \n",
       "...          ...      ...  \n",
       "2963850     0.90     0.99  \n",
       "2964470     0.90     1.00  \n",
       "2965009     0.86     0.99  \n",
       "2965924     1.00     0.91  \n",
       "2966087     0.91     1.00  \n",
       "\n",
       "[5850 rows x 5850 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>bust_size_top</th>\n",
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       "      <th>rented_for_all</th>\n",
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       "      <th>0</th>\n",
       "      <td>123373</td>\n",
       "      <td>73.00</td>\n",
       "      <td>566.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>686</td>\n",
       "      <td>36d</td>\n",
       "      <td>140.67</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.40</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.39</td>\n",
       "      <td>65.00</td>\n",
       "      <td>15.12</td>\n",
       "      <td>13.00</td>\n",
       "      <td>34.36</td>\n",
       "      <td>33.00</td>\n",
       "      <td>66.08</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
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      ],
      "text/plain": [
       "   item_id  fit_small    fit  fit_large  user_count bust_size_top  \\\n",
       "0   123373      73.00 566.00      47.00         686           36d   \n",
       "\n",
       "   weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "0       140.67         135.00            4.40  formal affair   \n",
       "\n",
       "                                      rented_for_all body_type_top  \\\n",
       "0  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "\n",
       "  category_top  height_mean  height_median  size_mean  size_median  age_mean  \\\n",
       "0         gown        65.39          65.00      15.12        13.00     34.36   \n",
       "\n",
       "   age_median  review_length_average  review_month_top review_season_top  \n",
       "0       33.00                  66.08                12            Winter  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Top 20 Recommendations for Item #123373:\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124204</td>\n",
       "      <td>42.00</td>\n",
       "      <td>630.00</td>\n",
       "      <td>123.00</td>\n",
       "      <td>795</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.88</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.15</td>\n",
       "      <td>65.00</td>\n",
       "      <td>10.97</td>\n",
       "      <td>8.00</td>\n",
       "      <td>33.33</td>\n",
       "      <td>33.00</td>\n",
       "      <td>62.13</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>125465</td>\n",
       "      <td>150.00</td>\n",
       "      <td>720.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>883</td>\n",
       "      <td>34b</td>\n",
       "      <td>143.82</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.83</td>\n",
       "      <td>66.00</td>\n",
       "      <td>16.92</td>\n",
       "      <td>13.00</td>\n",
       "      <td>32.70</td>\n",
       "      <td>31.00</td>\n",
       "      <td>64.22</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>125564</td>\n",
       "      <td>98.00</td>\n",
       "      <td>443.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>607</td>\n",
       "      <td>34b</td>\n",
       "      <td>142.99</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.43</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, everyday, other, party, weddin...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.48</td>\n",
       "      <td>65.00</td>\n",
       "      <td>17.05</td>\n",
       "      <td>16.00</td>\n",
       "      <td>38.16</td>\n",
       "      <td>36.00</td>\n",
       "      <td>61.73</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>128959</td>\n",
       "      <td>44.00</td>\n",
       "      <td>447.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>516</td>\n",
       "      <td>34c</td>\n",
       "      <td>140.82</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.29</td>\n",
       "      <td>65.00</td>\n",
       "      <td>14.20</td>\n",
       "      <td>13.00</td>\n",
       "      <td>35.57</td>\n",
       "      <td>34.00</td>\n",
       "      <td>70.76</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>130259</td>\n",
       "      <td>54.00</td>\n",
       "      <td>643.00</td>\n",
       "      <td>216.00</td>\n",
       "      <td>913</td>\n",
       "      <td>36c</td>\n",
       "      <td>148.22</td>\n",
       "      <td>140.00</td>\n",
       "      <td>4.38</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.50</td>\n",
       "      <td>65.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>36.15</td>\n",
       "      <td>35.00</td>\n",
       "      <td>65.43</td>\n",
       "      <td>1</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>131117</td>\n",
       "      <td>71.00</td>\n",
       "      <td>798.00</td>\n",
       "      <td>112.00</td>\n",
       "      <td>981</td>\n",
       "      <td>34b</td>\n",
       "      <td>139.21</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.52</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.91</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.54</td>\n",
       "      <td>12.00</td>\n",
       "      <td>31.31</td>\n",
       "      <td>31.00</td>\n",
       "      <td>70.12</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>131533</td>\n",
       "      <td>70.00</td>\n",
       "      <td>973.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1091</td>\n",
       "      <td>34b</td>\n",
       "      <td>140.68</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.81</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.00</td>\n",
       "      <td>33.32</td>\n",
       "      <td>31.00</td>\n",
       "      <td>64.30</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>136860</td>\n",
       "      <td>66.00</td>\n",
       "      <td>618.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>708</td>\n",
       "      <td>34c</td>\n",
       "      <td>138.95</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>sheath</td>\n",
       "      <td>65.79</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.19</td>\n",
       "      <td>12.00</td>\n",
       "      <td>33.36</td>\n",
       "      <td>32.00</td>\n",
       "      <td>62.59</td>\n",
       "      <td>6</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>137585</td>\n",
       "      <td>74.00</td>\n",
       "      <td>932.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>1100</td>\n",
       "      <td>34b</td>\n",
       "      <td>129.47</td>\n",
       "      <td>129.00</td>\n",
       "      <td>4.63</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>sheath</td>\n",
       "      <td>64.81</td>\n",
       "      <td>65.00</td>\n",
       "      <td>7.93</td>\n",
       "      <td>8.00</td>\n",
       "      <td>30.77</td>\n",
       "      <td>31.00</td>\n",
       "      <td>57.05</td>\n",
       "      <td>4</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>138431</td>\n",
       "      <td>87.00</td>\n",
       "      <td>395.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>488</td>\n",
       "      <td>34c</td>\n",
       "      <td>139.79</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.63</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.67</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.86</td>\n",
       "      <td>12.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>66.25</td>\n",
       "      <td>4</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>144051</td>\n",
       "      <td>59.00</td>\n",
       "      <td>416.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>500</td>\n",
       "      <td>36c</td>\n",
       "      <td>146.32</td>\n",
       "      <td>140.00</td>\n",
       "      <td>4.52</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>sheath</td>\n",
       "      <td>65.54</td>\n",
       "      <td>65.00</td>\n",
       "      <td>19.34</td>\n",
       "      <td>16.00</td>\n",
       "      <td>34.70</td>\n",
       "      <td>33.00</td>\n",
       "      <td>55.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>146684</td>\n",
       "      <td>31.00</td>\n",
       "      <td>425.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>494</td>\n",
       "      <td>34b</td>\n",
       "      <td>133.21</td>\n",
       "      <td>131.50</td>\n",
       "      <td>4.50</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.88</td>\n",
       "      <td>65.00</td>\n",
       "      <td>10.89</td>\n",
       "      <td>11.00</td>\n",
       "      <td>34.02</td>\n",
       "      <td>33.00</td>\n",
       "      <td>69.59</td>\n",
       "      <td>1</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>152662</td>\n",
       "      <td>39.00</td>\n",
       "      <td>404.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>485</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.36</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.66</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.71</td>\n",
       "      <td>66.00</td>\n",
       "      <td>12.29</td>\n",
       "      <td>12.00</td>\n",
       "      <td>33.24</td>\n",
       "      <td>32.00</td>\n",
       "      <td>70.60</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>152836</td>\n",
       "      <td>174.00</td>\n",
       "      <td>585.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>794</td>\n",
       "      <td>34b</td>\n",
       "      <td>125.80</td>\n",
       "      <td>125.00</td>\n",
       "      <td>4.34</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>petite</td>\n",
       "      <td>mini</td>\n",
       "      <td>63.97</td>\n",
       "      <td>64.00</td>\n",
       "      <td>7.89</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.92</td>\n",
       "      <td>32.00</td>\n",
       "      <td>53.74</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>153475</td>\n",
       "      <td>41.00</td>\n",
       "      <td>408.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>519</td>\n",
       "      <td>34c</td>\n",
       "      <td>139.58</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.55</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.66</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.68</td>\n",
       "      <td>12.00</td>\n",
       "      <td>34.06</td>\n",
       "      <td>33.00</td>\n",
       "      <td>66.26</td>\n",
       "      <td>4</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>154002</td>\n",
       "      <td>37.00</td>\n",
       "      <td>537.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>623</td>\n",
       "      <td>34c</td>\n",
       "      <td>135.25</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.56</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.43</td>\n",
       "      <td>65.00</td>\n",
       "      <td>11.22</td>\n",
       "      <td>9.00</td>\n",
       "      <td>33.78</td>\n",
       "      <td>33.00</td>\n",
       "      <td>78.80</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>166633</td>\n",
       "      <td>128.00</td>\n",
       "      <td>615.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>804</td>\n",
       "      <td>34b</td>\n",
       "      <td>126.02</td>\n",
       "      <td>125.00</td>\n",
       "      <td>4.33</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>athletic</td>\n",
       "      <td>mini</td>\n",
       "      <td>64.09</td>\n",
       "      <td>64.00</td>\n",
       "      <td>7.45</td>\n",
       "      <td>8.00</td>\n",
       "      <td>30.84</td>\n",
       "      <td>31.00</td>\n",
       "      <td>59.31</td>\n",
       "      <td>6</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>168592</td>\n",
       "      <td>29.00</td>\n",
       "      <td>402.00</td>\n",
       "      <td>98.00</td>\n",
       "      <td>529</td>\n",
       "      <td>34c</td>\n",
       "      <td>135.33</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.65</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, party, other, wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.50</td>\n",
       "      <td>66.00</td>\n",
       "      <td>9.92</td>\n",
       "      <td>8.00</td>\n",
       "      <td>32.64</td>\n",
       "      <td>32.00</td>\n",
       "      <td>61.09</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>168610</td>\n",
       "      <td>44.00</td>\n",
       "      <td>429.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>532</td>\n",
       "      <td>34b</td>\n",
       "      <td>135.79</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.55</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.58</td>\n",
       "      <td>66.00</td>\n",
       "      <td>11.10</td>\n",
       "      <td>9.00</td>\n",
       "      <td>31.60</td>\n",
       "      <td>31.00</td>\n",
       "      <td>65.23</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>1076484</td>\n",
       "      <td>76.00</td>\n",
       "      <td>437.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>555</td>\n",
       "      <td>36c</td>\n",
       "      <td>142.37</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.51</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.23</td>\n",
       "      <td>65.00</td>\n",
       "      <td>15.47</td>\n",
       "      <td>12.00</td>\n",
       "      <td>34.40</td>\n",
       "      <td>33.00</td>\n",
       "      <td>59.17</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_id  fit_small    fit  fit_large  user_count bust_size_top  \\\n",
       "2      124204      42.00 630.00     123.00         795           34b   \n",
       "5      125465     150.00 720.00      13.00         883           34b   \n",
       "6      125564      98.00 443.00      66.00         607           34b   \n",
       "12     128959      44.00 447.00      25.00         516           34c   \n",
       "14     130259      54.00 643.00     216.00         913           36c   \n",
       "16     131117      71.00 798.00     112.00         981           34b   \n",
       "17     131533      70.00 973.00      48.00        1091           34b   \n",
       "28     136860      66.00 618.00      24.00         708           34c   \n",
       "29     137585      74.00 932.00      94.00        1100           34b   \n",
       "30     138431      87.00 395.00       6.00         488           34c   \n",
       "41     144051      59.00 416.00      25.00         500           36c   \n",
       "48     146684      31.00 425.00      38.00         494           34b   \n",
       "62     152662      39.00 404.00      42.00         485           34b   \n",
       "63     152836     174.00 585.00      35.00         794           34b   \n",
       "65     153475      41.00 408.00      70.00         519           34c   \n",
       "66     154002      37.00 537.00      49.00         623           34c   \n",
       "88     166633     128.00 615.00      61.00         804           34b   \n",
       "91     168592      29.00 402.00      98.00         529           34c   \n",
       "92     168610      44.00 429.00      59.00         532           34b   \n",
       "1972  1076484      76.00 437.00      42.00         555           36c   \n",
       "\n",
       "      weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "2          136.88         135.00            4.65          party   \n",
       "5          143.82         138.00            4.69  formal affair   \n",
       "6          142.99         138.00            4.43  formal affair   \n",
       "12         140.82         138.00            4.65  formal affair   \n",
       "14         148.22         140.00            4.38        wedding   \n",
       "16         139.21         135.00            4.52  formal affair   \n",
       "17         140.68         138.00            4.69  formal affair   \n",
       "28         138.95         135.00            4.62        wedding   \n",
       "29         129.47         129.00            4.63        wedding   \n",
       "30         139.79         138.00            4.63  formal affair   \n",
       "41         146.32         140.00            4.52        wedding   \n",
       "48         133.21         131.50            4.50  formal affair   \n",
       "62         136.36         135.00            4.66  formal affair   \n",
       "63         125.80         125.00            4.34          party   \n",
       "65         139.58         135.00            4.55  formal affair   \n",
       "66         135.25         135.00            4.56  formal affair   \n",
       "88         126.02         125.00            4.33        wedding   \n",
       "91         135.33         135.00            4.65  formal affair   \n",
       "92         135.79         135.00            4.55        wedding   \n",
       "1972       142.37         138.00            4.51        wedding   \n",
       "\n",
       "                                         rented_for_all body_type_top  \\\n",
       "2     [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "5     [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "6     [formal affair, everyday, other, party, weddin...     hourglass   \n",
       "12    [formal affair, work, everyday, other, party, ...     hourglass   \n",
       "14    [formal affair, work, everyday, other, party, ...     hourglass   \n",
       "16    [formal affair, work, other, party, wedding, d...      athletic   \n",
       "17    [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "28    [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "29    [formal affair, other, party, wedding, date, v...      athletic   \n",
       "30    [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "41    [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "48    [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "62    [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "63    [formal affair, other, party, wedding, date, v...        petite   \n",
       "65         [formal affair, other, party, wedding, date]     hourglass   \n",
       "66         [formal affair, other, party, wedding, date]     hourglass   \n",
       "88    [formal affair, work, other, party, wedding, d...      athletic   \n",
       "91               [formal affair, party, other, wedding]     hourglass   \n",
       "92         [formal affair, other, party, wedding, date]     hourglass   \n",
       "1972  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "\n",
       "     category_top  height_mean  height_median  size_mean  size_median  \\\n",
       "2           dress        65.15          65.00      10.97         8.00   \n",
       "5            gown        65.83          66.00      16.92        13.00   \n",
       "6            gown        65.48          65.00      17.05        16.00   \n",
       "12           gown        65.29          65.00      14.20        13.00   \n",
       "14          dress        65.50          65.00      18.00        16.00   \n",
       "16           gown        65.91          66.00      12.54        12.00   \n",
       "17           gown        65.81          66.00      13.81        12.00   \n",
       "28         sheath        65.79          66.00      12.19        12.00   \n",
       "29         sheath        64.81          65.00       7.93         8.00   \n",
       "30           gown        65.67          66.00      13.86        12.00   \n",
       "41         sheath        65.54          65.00      19.34        16.00   \n",
       "48           gown        64.88          65.00      10.89        11.00   \n",
       "62           gown        65.71          66.00      12.29        12.00   \n",
       "63           mini        63.97          64.00       7.89         8.00   \n",
       "65           gown        65.66          66.00      13.68        12.00   \n",
       "66           gown        65.43          65.00      11.22         9.00   \n",
       "88           mini        64.09          64.00       7.45         8.00   \n",
       "91           gown        65.50          66.00       9.92         8.00   \n",
       "92           gown        65.58          66.00      11.10         9.00   \n",
       "1972        dress        65.23          65.00      15.47        12.00   \n",
       "\n",
       "      age_mean  age_median  review_length_average  review_month_top  \\\n",
       "2        33.33       33.00                  62.13                12   \n",
       "5        32.70       31.00                  64.22                 4   \n",
       "6        38.16       36.00                  61.73                11   \n",
       "12       35.57       34.00                  70.76                11   \n",
       "14       36.15       35.00                  65.43                 1   \n",
       "16       31.31       31.00                  70.12                 4   \n",
       "17       33.32       31.00                  64.30                 5   \n",
       "28       33.36       32.00                  62.59                 6   \n",
       "29       30.77       31.00                  57.05                 4   \n",
       "30       34.00       32.00                  66.25                 4   \n",
       "41       34.70       33.00                  55.15                10   \n",
       "48       34.02       33.00                  69.59                 1   \n",
       "62       33.24       32.00                  70.60                 5   \n",
       "63       31.92       32.00                  53.74                 5   \n",
       "65       34.06       33.00                  66.26                 4   \n",
       "66       33.78       33.00                  78.80                 5   \n",
       "88       30.84       31.00                  59.31                 6   \n",
       "91       32.64       32.00                  61.09                 4   \n",
       "92       31.60       31.00                  65.23                 5   \n",
       "1972     34.40       33.00                  59.17                10   \n",
       "\n",
       "     review_season_top  \n",
       "2               Winter  \n",
       "5               Spring  \n",
       "6               Winter  \n",
       "12              Winter  \n",
       "14              Winter  \n",
       "16              Spring  \n",
       "17              Spring  \n",
       "28              Spring  \n",
       "29              Winter  \n",
       "30              Winter  \n",
       "41              Winter  \n",
       "48              Winter  \n",
       "62              Spring  \n",
       "63              Winter  \n",
       "65              Winter  \n",
       "66              Winter  \n",
       "88              Spring  \n",
       "91              Spring  \n",
       "92              Spring  \n",
       "1972            Winter  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example item\n",
    "content_based_similarity(similarity_matrix, 123373)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Text Review-Based Recommender\n",
    "\n",
    "To recommend items based on text reviews, Natural Language Processing (NLP) is used to:\n",
    "- Clean the text by removing stopwords and performing lemmatization.\n",
    "- Create the Term Frequency-Inverse Document Frequency (TF-IDF) vectors for the *documents*, which are the reviews.\n",
    "- Compute the pairwise cosine similarity from the constructed matrix of TF-IDF scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/czarinaluna/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/czarinaluna/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Import the Natural Language Toolkit (nltk)\n",
    "import re\n",
    "import nltk\n",
    "\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "stopwords = nltk.corpus.stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "lemmatizer = nltk.stem.WordNetLemmatizer()\n",
    "\n",
    "def preprocess(text):\n",
    "    '''\n",
    "    Text preprocessing to standardize, remove special characters and stopwords, and lemmatize.\n",
    "    '''\n",
    "    text = text.apply(lambda x: x.lower())\n",
    "    text = text.apply(lambda x: re.sub(r'([^A-Za-z0-9|\\s|[:punct:]]*)', '', x))\n",
    "    text = text.apply(lambda x: x.replace('[^a-zA-Z#]', ' '))\n",
    "    text = text.apply(lambda x: ' '.join([i for i in x.split() if len(i)>3]))\n",
    "    text = text.apply(lambda x: x.split())\n",
    "    text = text.apply(lambda x: [lemmatizer.lemmatize(word) for word in x])\n",
    "    text = text.apply(lambda x: [word for word in x if word not in stopwords])\n",
    "    text = text.apply(lambda x: ' '.join(x))\n",
    "    \n",
    "    return text\n",
    "\n",
    "def create_text_df(df=data, item_df=item_data, text_review=True, category=False):\n",
    "    '''\n",
    "    Creates new feature combining review summary and review text, to add to item data.\n",
    "    '''\n",
    "    if item_df is None:\n",
    "        item_df = create_item_df(df)\n",
    "        \n",
    "    text_df = df.copy()\n",
    "    text_df['review'] = text_df['review_summary'] + ' ' + text_df['review_text']\n",
    "    text_df['review'] = text_df['review'].fillna('')\n",
    "    text_df['review'] = preprocess(text_df['review'])\n",
    "    \n",
    "    if text_review:\n",
    "        text_df = text_df[['item_id', 'review']].groupby('item_id').agg(' '.join).reset_index()\n",
    "        text_item_df = item_df.merge(text_df, on='item_id')\n",
    "        \n",
    "    if text_review == False and category == True:\n",
    "        text_df = text_df[['item_id', 'rented_for']].groupby('item_id').agg(' '.join).reset_index()\n",
    "        text_item_df = item_df.merge(text_df, on='item_id')\n",
    "        \n",
    "    return text_item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new dataframe for text item data\n",
    "text_item_data = create_text_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "count = CountVectorizer()\n",
    "tfidf = TfidfVectorizer(ngram_range=(1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \"The TF-IDF score is the frequency of a word occurring in a document, down-weighted by the number of documents in which it occurs.\" [(Aditya Sharma)](https://www.datacamp.com/community/tutorials/recommender-systems-python])\n",
    "\n",
    "Finally, to compute the cosine similarity score between the text reviews, the dot product between each TF-IDF vector is calculated in the function `text_based_recommendation` below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity, linear_kernel\n",
    "\n",
    "def text_based_recommendation(text_item_df, item_id, n=10, text_review=True, category=False):\n",
    "    '''\n",
    "    Returns the most similar item recommendations to the given item based on text reviews.\n",
    "    '''\n",
    "    if text_review:\n",
    "        tfidf_matrix = tfidf.fit_transform(text_item_df['review'])\n",
    "        cosine_similarity_ = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "\n",
    "    if text_review == False and category == True:\n",
    "        count_matrix = count.fit_transform(text_item_df['rented_for'])\n",
    "        cosine_similarity_ = cosine_similarity(count_matrix, count_matrix)\n",
    "        \n",
    "    indices = pd.Series(text_item_df.index, index=text_item_df['item_id']).drop_duplicates()\n",
    "    idx = indices[item_id]\n",
    "    \n",
    "    similarity_scores = list(enumerate(cosine_similarity_[idx]))\n",
    "    similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    top_similarity_scores = similarity_scores[1:n+1]\n",
    "    item_indices = [i[0] for i in top_similarity_scores]\n",
    "    top_text_based_recommendations = text_item_df['item_id'].iloc[item_indices]\n",
    "    \n",
    "    display(item_data.loc[item_data['item_id']==item_id])\n",
    "    print(f'----------------------------------------\\nTop {n} Recommendations for Item #{item_id}:')\n",
    "                          \n",
    "    recommendations_df = item_data.loc[item_data['item_id'].isin(top_text_based_recommendations)]\n",
    "    return recommendations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th>bust_size_top</th>\n",
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       "      <th>rented_for_all</th>\n",
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       "      <th>0</th>\n",
       "      <td>123373</td>\n",
       "      <td>73.00</td>\n",
       "      <td>566.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>686</td>\n",
       "      <td>36d</td>\n",
       "      <td>140.67</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.40</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.39</td>\n",
       "      <td>65.00</td>\n",
       "      <td>15.12</td>\n",
       "      <td>13.00</td>\n",
       "      <td>34.36</td>\n",
       "      <td>33.00</td>\n",
       "      <td>66.08</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  fit_small    fit  fit_large  user_count bust_size_top  \\\n",
       "0   123373      73.00 566.00      47.00         686           36d   \n",
       "\n",
       "   weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "0       140.67         135.00            4.40  formal affair   \n",
       "\n",
       "                                      rented_for_all body_type_top  \\\n",
       "0  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "\n",
       "  category_top  height_mean  height_median  size_mean  size_median  age_mean  \\\n",
       "0         gown        65.39          65.00      15.12        13.00     34.36   \n",
       "\n",
       "   age_median  review_length_average  review_month_top review_season_top  \n",
       "0       33.00                  66.08                12            Winter  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Top 10 Recommendations for Item #123373:\n"
     ]
    },
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       "      <th>1</th>\n",
       "      <td>123793</td>\n",
       "      <td>65.00</td>\n",
       "      <td>1497.00</td>\n",
       "      <td>152.00</td>\n",
       "      <td>1714</td>\n",
       "      <td>34b</td>\n",
       "      <td>132.98</td>\n",
       "      <td>130.00</td>\n",
       "      <td>4.77</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.06</td>\n",
       "      <td>65.00</td>\n",
       "      <td>9.72</td>\n",
       "      <td>8.00</td>\n",
       "      <td>31.31</td>\n",
       "      <td>31.00</td>\n",
       "      <td>74.41</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>125465</td>\n",
       "      <td>150.00</td>\n",
       "      <td>720.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>883</td>\n",
       "      <td>34b</td>\n",
       "      <td>143.82</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.83</td>\n",
       "      <td>66.00</td>\n",
       "      <td>16.92</td>\n",
       "      <td>13.00</td>\n",
       "      <td>32.70</td>\n",
       "      <td>31.00</td>\n",
       "      <td>64.22</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>125564</td>\n",
       "      <td>98.00</td>\n",
       "      <td>443.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>607</td>\n",
       "      <td>34b</td>\n",
       "      <td>142.99</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.43</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, everyday, other, party, weddin...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.48</td>\n",
       "      <td>65.00</td>\n",
       "      <td>17.05</td>\n",
       "      <td>16.00</td>\n",
       "      <td>38.16</td>\n",
       "      <td>36.00</td>\n",
       "      <td>61.73</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>127865</td>\n",
       "      <td>78.00</td>\n",
       "      <td>1278.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1393</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.03</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.72</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.17</td>\n",
       "      <td>65.00</td>\n",
       "      <td>12.04</td>\n",
       "      <td>12.00</td>\n",
       "      <td>36.35</td>\n",
       "      <td>35.00</td>\n",
       "      <td>68.93</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>131533</td>\n",
       "      <td>70.00</td>\n",
       "      <td>973.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1091</td>\n",
       "      <td>34b</td>\n",
       "      <td>140.68</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.69</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.81</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.00</td>\n",
       "      <td>33.32</td>\n",
       "      <td>31.00</td>\n",
       "      <td>64.30</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>132738</td>\n",
       "      <td>60.00</td>\n",
       "      <td>937.00</td>\n",
       "      <td>580.00</td>\n",
       "      <td>1577</td>\n",
       "      <td>34b</td>\n",
       "      <td>144.50</td>\n",
       "      <td>138.00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.56</td>\n",
       "      <td>66.00</td>\n",
       "      <td>15.45</td>\n",
       "      <td>13.00</td>\n",
       "      <td>32.14</td>\n",
       "      <td>32.00</td>\n",
       "      <td>78.31</td>\n",
       "      <td>5</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>139086</td>\n",
       "      <td>7.00</td>\n",
       "      <td>331.00</td>\n",
       "      <td>178.00</td>\n",
       "      <td>516</td>\n",
       "      <td>34b</td>\n",
       "      <td>137.44</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.61</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.45</td>\n",
       "      <td>65.00</td>\n",
       "      <td>11.99</td>\n",
       "      <td>11.00</td>\n",
       "      <td>35.49</td>\n",
       "      <td>34.00</td>\n",
       "      <td>61.45</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>140321</td>\n",
       "      <td>6.00</td>\n",
       "      <td>288.00</td>\n",
       "      <td>156.00</td>\n",
       "      <td>450</td>\n",
       "      <td>34c</td>\n",
       "      <td>140.17</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.54</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.70</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.66</td>\n",
       "      <td>12.00</td>\n",
       "      <td>35.42</td>\n",
       "      <td>33.50</td>\n",
       "      <td>55.51</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>145906</td>\n",
       "      <td>76.00</td>\n",
       "      <td>1154.00</td>\n",
       "      <td>242.00</td>\n",
       "      <td>1472</td>\n",
       "      <td>34b</td>\n",
       "      <td>135.60</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.51</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.78</td>\n",
       "      <td>66.00</td>\n",
       "      <td>10.59</td>\n",
       "      <td>9.00</td>\n",
       "      <td>30.36</td>\n",
       "      <td>30.00</td>\n",
       "      <td>64.90</td>\n",
       "      <td>4</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>153475</td>\n",
       "      <td>41.00</td>\n",
       "      <td>408.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>519</td>\n",
       "      <td>34c</td>\n",
       "      <td>139.58</td>\n",
       "      <td>135.00</td>\n",
       "      <td>4.55</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.66</td>\n",
       "      <td>66.00</td>\n",
       "      <td>13.68</td>\n",
       "      <td>12.00</td>\n",
       "      <td>34.06</td>\n",
       "      <td>33.00</td>\n",
       "      <td>66.26</td>\n",
       "      <td>4</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_id  fit_small     fit  fit_large  user_count bust_size_top  \\\n",
       "1    123793      65.00 1497.00     152.00        1714           34b   \n",
       "5    125465     150.00  720.00      13.00         883           34b   \n",
       "6    125564      98.00  443.00      66.00         607           34b   \n",
       "10   127865      78.00 1278.00      37.00        1393           34b   \n",
       "17   131533      70.00  973.00      48.00        1091           34b   \n",
       "20   132738      60.00  937.00     580.00        1577           34b   \n",
       "31   139086       7.00  331.00     178.00         516           34b   \n",
       "33   140321       6.00  288.00     156.00         450           34c   \n",
       "47   145906      76.00 1154.00     242.00        1472           34b   \n",
       "65   153475      41.00  408.00      70.00         519           34c   \n",
       "\n",
       "    weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "1        132.98         130.00            4.77  formal affair   \n",
       "5        143.82         138.00            4.69  formal affair   \n",
       "6        142.99         138.00            4.43  formal affair   \n",
       "10       136.03         135.00            4.72  formal affair   \n",
       "17       140.68         138.00            4.69  formal affair   \n",
       "20       144.50         138.00            4.62  formal affair   \n",
       "31       137.44         135.00            4.61  formal affair   \n",
       "33       140.17         135.00            4.54  formal affair   \n",
       "47       135.60         135.00            4.51  formal affair   \n",
       "65       139.58         135.00            4.55  formal affair   \n",
       "\n",
       "                                       rented_for_all body_type_top  \\\n",
       "1   [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "5   [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "6   [formal affair, everyday, other, party, weddin...     hourglass   \n",
       "10  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "17  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "20  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "31       [formal affair, other, party, wedding, date]     hourglass   \n",
       "33  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "47  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "65       [formal affair, other, party, wedding, date]     hourglass   \n",
       "\n",
       "   category_top  height_mean  height_median  size_mean  size_median  age_mean  \\\n",
       "1          gown        65.06          65.00       9.72         8.00     31.31   \n",
       "5          gown        65.83          66.00      16.92        13.00     32.70   \n",
       "6          gown        65.48          65.00      17.05        16.00     38.16   \n",
       "10         gown        65.17          65.00      12.04        12.00     36.35   \n",
       "17         gown        65.81          66.00      13.81        12.00     33.32   \n",
       "20         gown        65.56          66.00      15.45        13.00     32.14   \n",
       "31         gown        65.45          65.00      11.99        11.00     35.49   \n",
       "33         gown        65.70          66.00      13.66        12.00     35.42   \n",
       "47         gown        65.78          66.00      10.59         9.00     30.36   \n",
       "65         gown        65.66          66.00      13.68        12.00     34.06   \n",
       "\n",
       "    age_median  review_length_average  review_month_top review_season_top  \n",
       "1        31.00                  74.41                 5            Winter  \n",
       "5        31.00                  64.22                 4            Spring  \n",
       "6        36.00                  61.73                11            Winter  \n",
       "10       35.00                  68.93                11            Winter  \n",
       "17       31.00                  64.30                 5            Spring  \n",
       "20       32.00                  78.31                 5            Winter  \n",
       "31       34.00                  61.45                12            Winter  \n",
       "33       33.50                  55.51                11            Winter  \n",
       "47       30.00                  64.90                 4            Winter  \n",
       "65       33.00                  66.26                 4            Winter  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same example item\n",
    "text_based_recommendation(text_item_data, 123373, n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Key Differences** between the text-based recommendations and the content-based recommendations to the same item:\n",
    "\n",
    "|Feature|Content-based|Text-based|Item|\n",
    "|---|---|---:|:---|\n",
    "|rating_average|4.38 - 4.69|4.43 - 4.77|4.40|\n",
    "|rented_for_top|party, formal affair, wedding|formal affair (across the board)|formal affair|\n",
    "|body_type_top|hourglass, athlete|hourglass (across the board)|hourglass|\n",
    "|category_top|dress, gown, sheath|gown (across the board)|gown|\n",
    "\n",
    "***\n",
    "\n",
    "### Collaborative Filtering Systems\n",
    "\n",
    "Collaborative filtering systems recommend items to a user based on the user's past ratings *and* on the past ratings and preferences of other similar users. I apply the different implementations of collaborative filtering recommendation systems using the Python library [`surprise`](https://surprise.readthedocs.io/en/stable/index.html):\n",
    "\n",
    "\n",
    "|Prediction Algorithm|Description|\n",
    "|:---|:---|\n",
    "|[Normal Predictor](https://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.random_pred.NormalPredictor)|Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal.\n",
    "|[Baseline Only](https://surprise.readthedocs.io/en/stable/basic_algorithms.html#surprise.prediction_algorithms.baseline_only.BaselineOnly)|Algorithm predicting the baseline estimate for given user and item.|\n",
    "|[KNN Basic](https://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBasic)|A basic collaborative filtering algorithm.|\n",
    "|[KNN Baseline](https://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNBaseline)|A basic collaborative filtering algorithm, taking into account the mean ratings of each user.|\n",
    "|[KNN with Means](https://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithMeans)|A basic collaborative filtering algorithm, taking into account the z-score normalization of each user.|\n",
    "|[KNN with Z-Score](https://surprise.readthedocs.io/en/stable/knn_inspired.html#surprise.prediction_algorithms.knns.KNNWithZScore)|A basic collaborative filtering algorithm taking into account a *baseline* rating.\n",
    "|[Single Value Decomposition](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD)|The famous SVD algorithm, as popularized by Simon Funk during the Netflix Prize. When baselines are not used, this is equivalent to Probabilistic Matrix Factorization.|\n",
    "|[Single Value Decomposition ++](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp)|The SVD++ algorithm, an extension of SVD taking into account implicit ratings.|\n",
    "|[Non-Negative Matrix Factorization](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)|A collaborative filtering algorithm based on Non-negative Matrix Factorization.|\n",
    "|[SlopeOne](https://surprise.readthedocs.io/en/stable/slope_one.html#surprise.prediction_algorithms.slope_one.SlopeOne)|A simple yet accurate collaborative filtering algorithm.|\n",
    "|[CoClustering](https://surprise.readthedocs.io/en/stable/co_clustering.html#surprise.prediction_algorithms.co_clustering.CoClustering)|A collaborative filtering algorithm based on co-clustering.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.rename(columns={'user_id': 'userID', 'item_id': 'itemID'})\n",
    "\n",
    "df_columns = ['userID', 'itemID', 'rating']\n",
    "df = data[df_columns]\n",
    "\n",
    "# Only use items with more than 25 ratings\n",
    "df['reviews'] = df.groupby(['itemID'])['rating'].transform('count')\n",
    "df = df.loc[df['reviews']>25, df_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import Reader, Dataset\n",
    "\n",
    "reader = Reader(rating_scale=(1,5))\n",
    "read_data = Dataset.load_from_df(df, reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Modeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    }
   ],
   "source": [
    "from surprise import NormalPredictor, BaselineOnly, SVD, SVDpp, NMF, SlopeOne, CoClustering\n",
    "from surprise.model_selection import cross_validate\n",
    "\n",
    "from surprise.prediction_algorithms import knns\n",
    "sim_cos = {'name':'cosine', 'user_based':False}\n",
    "\n",
    "evaluation = []\n",
    "recommendation_systems = [NormalPredictor(), BaselineOnly(), knns.KNNBasic(sim_options=sim_cos), knns.KNNBaseline(sim_options=sim_cos), knns.KNNWithMeans(sim_options=sim_cos), knns.KNNWithZScore(sim_options=sim_cos), SVD(), SVDpp(), NMF(), SlopeOne(), CoClustering()]\n",
    "\n",
    "# Evaluate recommendation systems using Mean Absolute Error\n",
    "for system in recommendation_systems:\n",
    "    score = cross_validate(system, read_data, measures=['MAE'], cv=3, verbose=False)\n",
    "    evaluation.append((str(system).split(' ')[0].split('.')[-1], score['test_mae'].mean()))\n",
    "\n",
    "pd.options.display.float_format = '{:.4f}'.format    \n",
    "\n",
    "evaluation = pd.DataFrame(evaluation, columns=['system', 'mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate, I use the mean absolute error which measures the difference between the rating predicted by the model and the actual rating by the user:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>system</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NormalPredictor</td>\n",
       "      <td>0.6566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BaselineOnly</td>\n",
       "      <td>0.5398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNNBasic</td>\n",
       "      <td>0.5727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNNBaseline</td>\n",
       "      <td>0.5457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNNWithMeans</td>\n",
       "      <td>0.5624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNNWithZScore</td>\n",
       "      <td>0.5635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVD</td>\n",
       "      <td>0.5359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVDpp</td>\n",
       "      <td>0.5396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NMF</td>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SlopeOne</td>\n",
       "      <td>0.5802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CoClustering</td>\n",
       "      <td>0.5673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             system    mae\n",
       "0   NormalPredictor 0.6566\n",
       "1      BaselineOnly 0.5398\n",
       "2          KNNBasic 0.5727\n",
       "3       KNNBaseline 0.5457\n",
       "4      KNNWithMeans 0.5624\n",
       "5     KNNWithZScore 0.5635\n",
       "6               SVD 0.5359\n",
       "7             SVDpp 0.5396\n",
       "8               NMF 0.6980\n",
       "9          SlopeOne 0.5802\n",
       "10     CoClustering 0.5673"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\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",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "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>system</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNNBasic</td>\n",
       "      <td>0.5729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNNBaseline</td>\n",
       "      <td>0.5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNNWithMeans</td>\n",
       "      <td>0.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNNWithZScore</td>\n",
       "      <td>0.5583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          system    mae\n",
       "0       KNNBasic 0.5729\n",
       "1    KNNBaseline 0.5391\n",
       "2   KNNWithMeans 0.5586\n",
       "3  KNNWithZScore 0.5583"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Switch similarity measure from cosine to pearson\n",
    "sim_pearson = {'name':'pearson', 'user_based':False}\n",
    "\n",
    "pearson_evaluation = []\n",
    "pearson_knns = [knns.KNNBasic(sim_options=sim_pearson), \n",
    "                knns.KNNBaseline(sim_options=sim_pearson), \n",
    "                knns.KNNWithMeans(sim_options=sim_pearson), \n",
    "                knns.KNNWithZScore(sim_options=sim_pearson)]\n",
    "\n",
    "for system in pearson_knns:\n",
    "    pearson_score = cross_validate(system, read_data, measures=['MAE'], cv=3, verbose=False)\n",
    "    pearson_evaluation.append((str(system).split(' ')[0].split('.')[-1], pearson_score['test_mae'].mean()))\n",
    "\n",
    "pearson_evaluation = pd.DataFrame(pearson_evaluation, columns=['system', 'mae'])\n",
    "pearson_evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean absolute errors of `KNNBaseline`, `KNNWithMeans`, and `KNNWithZScore` decreased by 0.01 and `KNNBaseline` becomes second to `SVD`.\n",
    "\n",
    "***\n",
    "\n",
    "#### Hyperparamater Tuning\n",
    "\n",
    "`GridSearchCV` is performed to optimize the Single Value Decomposition models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise.model_selection import GridSearchCV\n",
    "\n",
    "def grid_search(system, params):\n",
    "    '''\n",
    "    Implements grid search and returns best cross validation scores and parameters.\n",
    "    '''\n",
    "    model = GridSearchCV(system, param_grid=params, n_jobs=-1)\n",
    "    model.fit(read_data)\n",
    "    \n",
    "    print(model.best_score)\n",
    "    print(model.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmse': 0.6785759649206107, 'mae': 0.5288288608687417}\n",
      "{'rmse': {'n_factors': 10, 'n_epochs': 100, 'lr_all': 0.001, 'reg_all': 0.1}, 'mae': {'n_factors': 10, 'n_epochs': 20, 'lr_all': 0.01, 'reg_all': 0.02}}\n"
     ]
    }
   ],
   "source": [
    "params_svd1 = {'n_factors': [10, 50, 100], 'n_epochs': [10, 20, 100], 'lr_all': [0.001, 0.005, 0.01], 'reg_all': [0.02, 0.05, 0.1]}\n",
    "grid_search(SVD, params_svd1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmse': 0.6785226031170102, 'mae': 0.5300882259833981}\n",
      "{'rmse': {'n_factors': 10, 'n_epochs': 100, 'lr_all': 0.001, 'reg_all': 0.1}, 'mae': {'n_factors': 10, 'n_epochs': 100, 'lr_all': 0.005, 'reg_all': 0.1}}\n"
     ]
    }
   ],
   "source": [
    "params_svdpp1 = {'n_factors': [10, 50, 100], 'n_epochs': [10, 20, 100], 'lr_all': [0.001, 0.005, 0.01], 'reg_all': [0.02, 0.05, 0.1]}\n",
    "grid_search(SVDpp, params_svdpp1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>system</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVD</td>\n",
       "      <td>0.5302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVDpp</td>\n",
       "      <td>0.5329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  system    mae\n",
       "0    SVD 0.5302\n",
       "1  SVDpp 0.5329"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd_evaluation = []\n",
    "\n",
    "# Evaluate tuned Singular Value Decomposition models\n",
    "for system in [SVD(n_factors=10, n_epochs=20, lr_all=0.01, reg_all=0.02), \n",
    "               SVDpp(n_factors=10, n_epochs=100, lr_all=0.005, reg_all=0.1)]:\n",
    "    svd_score = cross_validate(system, read_data, measures=['MAE'], cv=3, verbose=False)\n",
    "    svd_evaluation.append((str(system).split(' ')[0].split('.')[-1], svd_score['test_mae'].mean()))\n",
    "\n",
    "svd_evaluation = pd.DataFrame(svd_evaluation, columns=['system', 'mae'])\n",
    "svd_evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V. Results and Recommendations\n",
    "\n",
    "#### Systems Performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>system</th>\n",
       "      <th>mae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NormalPredictor</td>\n",
       "      <td>0.6566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BaselineOnly</td>\n",
       "      <td>0.5398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNNBasic</td>\n",
       "      <td>0.5727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNNBaseline</td>\n",
       "      <td>0.5457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNNWithMeans</td>\n",
       "      <td>0.5624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNNWithZScore</td>\n",
       "      <td>0.5635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVD</td>\n",
       "      <td>0.5359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVDpp</td>\n",
       "      <td>0.5396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NMF</td>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SlopeOne</td>\n",
       "      <td>0.5802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CoClustering</td>\n",
       "      <td>0.5673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KNNBasic</td>\n",
       "      <td>0.5729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KNNBaseline</td>\n",
       "      <td>0.5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>KNNWithMeans</td>\n",
       "      <td>0.5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KNNWithZScore</td>\n",
       "      <td>0.5583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SVD</td>\n",
       "      <td>0.5302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SVDpp</td>\n",
       "      <td>0.5329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             system    mae\n",
       "0   NormalPredictor 0.6566\n",
       "1      BaselineOnly 0.5398\n",
       "2          KNNBasic 0.5727\n",
       "3       KNNBaseline 0.5457\n",
       "4      KNNWithMeans 0.5624\n",
       "5     KNNWithZScore 0.5635\n",
       "6               SVD 0.5359\n",
       "7             SVDpp 0.5396\n",
       "8               NMF 0.6980\n",
       "9          SlopeOne 0.5802\n",
       "10     CoClustering 0.5673\n",
       "11         KNNBasic 0.5729\n",
       "12      KNNBaseline 0.5391\n",
       "13     KNNWithMeans 0.5586\n",
       "14    KNNWithZScore 0.5583\n",
       "15              SVD 0.5302\n",
       "16            SVDpp 0.5329"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_systems = pd.concat([evaluation, pearson_evaluation, svd_evaluation], ignore_index=True)\n",
    "all_systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show that the tuned Singular Value Decomposition algorithm attains the lowest Mean Absolute Error of 0.5302 with a rating scale of 1 to 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "plt.subplots_adjust(bottom=0.2)\n",
    "\n",
    "# Plot the Mean Absolute Error of the models\n",
    "sns.barplot(all_systems.index, all_systems['mae'], palette='tab20b') \n",
    "ax.set(xlim=[-0.5,16.5], xlabel='Recommendation System', ylabel='Mean Absolute Error')\n",
    "ax.set_title('Collaborative Filtering and Recommender Systems Evaluation', fontsize=20)\n",
    "\n",
    "labels = ['Normal Predictor', 'Baseline Only', 'KNN Basic Cosine', 'KNN Baseline Cosine', 'KNN Means Cosine', \n",
    "          'KNN Z-Score Cosine', 'Default SVD', 'Default SVD++', 'NMF', 'Slope One', 'Co-Clustering', 'KNN Basic Pearson', \n",
    "          'KNN Baseline Pearson', 'KNN Means Pearson', 'KNN Z-Score Pearson', 'Tuned SVD', 'Tuned SVD++']\n",
    "plt.xticks(all_systems.index, labels, rotation=45)\n",
    "\n",
    "plt.savefig('data/images/fig11.png', dpi=200, transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recommender Engine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svd_recommendation(user_id, n=10):\n",
    "    '''\n",
    "    Returns top item recommendations generated by the Single Value Decomposition model.\n",
    "    '''\n",
    "    unique_ids = df['itemID'].unique()\n",
    "    item_user_id = df.loc[df['userID']==user_id, 'itemID']\n",
    "    items_to_predict = np.setdiff1d(unique_ids, item_user_id)\n",
    "    \n",
    "    engine = SVD(n_factors=10, n_epochs=20, lr_all=0.01, reg_all=0.02)\n",
    "    engine.fit(read_data.build_full_trainset())\n",
    "\n",
    "    svd_recommendations = []\n",
    "    for i in items_to_predict:\n",
    "        svd_recommendations.append((i, engine.predict(uid=user_id, iid=i).est))\n",
    "\n",
    "    display(user_data.loc[user_data['user_id']==user_id])\n",
    "    print(f'----------------------------------------\\nTop {n} Recommendations for User #{user_id}:')\n",
    "    \n",
    "    svd_recommendations = pd.DataFrame(svd_recommendations, columns=['item_id', 'predicted_rating'])\n",
    "    svd_recommendations = svd_recommendations.sort_values('predicted_rating', ascending=False).head(n)\n",
    "    svd_recommendations = svd_recommendations.merge(item_data, on='item_id')\n",
    "    \n",
    "    return svd_recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>user_id</th>\n",
       "      <th>bust_size</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>weight</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>body_type</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>category_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52769</th>\n",
       "      <td>501485</td>\n",
       "      <td>32a</td>\n",
       "      <td>42</td>\n",
       "      <td>105.0000</td>\n",
       "      <td>4.9762</td>\n",
       "      <td>party</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>33.6190</td>\n",
       "      <td>4</td>\n",
       "      <td>Spring</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>[sheath, dress, gown, romper, skirt, shirtdres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76995</th>\n",
       "      <td>731517</td>\n",
       "      <td>32a</td>\n",
       "      <td>17</td>\n",
       "      <td>113.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>party</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>59.1176</td>\n",
       "      <td>9</td>\n",
       "      <td>Summer</td>\n",
       "      <td>[formal affair, work, everyday, party, date]</td>\n",
       "      <td>[down, dress, sheath, gown, suit, top, jacket,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89653</th>\n",
       "      <td>849603</td>\n",
       "      <td>32a</td>\n",
       "      <td>17</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>everyday</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>91.8824</td>\n",
       "      <td>6</td>\n",
       "      <td>Summer</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>[shift, dress, gown, pants, sweater, top]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15262</th>\n",
       "      <td>145029</td>\n",
       "      <td>32a</td>\n",
       "      <td>16</td>\n",
       "      <td>115.0000</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>everyday</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>43.0000</td>\n",
       "      <td>14.1875</td>\n",
       "      <td>8</td>\n",
       "      <td>Summer</td>\n",
       "      <td>[work, everyday, vacation, other]</td>\n",
       "      <td>[dress, romper, maxi, skirt, culottes, jumpsui...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50530</th>\n",
       "      <td>480611</td>\n",
       "      <td>32a</td>\n",
       "      <td>12</td>\n",
       "      <td>125.0000</td>\n",
       "      <td>4.5833</td>\n",
       "      <td>party</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "      <td>[party, wedding]</td>\n",
       "      <td>[sheath, dress, skirt]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id bust_size  rating_count   weight  rating_average  \\\n",
       "52769   501485       32a            42 105.0000          4.9762   \n",
       "76995   731517       32a            17 113.0000          5.0000   \n",
       "89653   849603       32a            17 100.0000          5.0000   \n",
       "15262   145029       32a            16 115.0000          4.5000   \n",
       "50530   480611       32a            12 125.0000          4.5833   \n",
       "\n",
       "      rented_for_top body_type category_top  height  size     age  \\\n",
       "52769          party    petite        dress 62.0000     4 27.0000   \n",
       "76995          party    petite        dress 62.0000     1 25.0000   \n",
       "89653       everyday    petite        dress 62.0000     1 27.0000   \n",
       "15262       everyday    petite        dress 62.0000     4 43.0000   \n",
       "50530          party    petite        dress 62.0000     1 35.0000   \n",
       "\n",
       "       review_length_average  review_month_top review_season_top  \\\n",
       "52769                33.6190                 4            Spring   \n",
       "76995                59.1176                 9            Summer   \n",
       "89653                91.8824                 6            Summer   \n",
       "15262                14.1875                 8            Summer   \n",
       "50530                80.0000                 4              Fall   \n",
       "\n",
       "                                          rented_for_all  \\\n",
       "52769  [formal affair, work, everyday, other, party, ...   \n",
       "76995       [formal affair, work, everyday, party, date]   \n",
       "89653  [formal affair, work, everyday, other, party, ...   \n",
       "15262                  [work, everyday, vacation, other]   \n",
       "50530                                   [party, wedding]   \n",
       "\n",
       "                                            category_all  \n",
       "52769  [sheath, dress, gown, romper, skirt, shirtdres...  \n",
       "76995  [down, dress, sheath, gown, suit, top, jacket,...  \n",
       "89653          [shift, dress, gown, pants, sweater, top]  \n",
       "15262  [dress, romper, maxi, skirt, culottes, jumpsui...  \n",
       "50530                             [sheath, dress, skirt]  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample user\n",
    "sample = user_data.sort_values('rating_count', ascending=False)\n",
    "sample.loc[((user_data['bust_size']=='32a') & (user_data['height']==62))].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
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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>bust_size</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>weight</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>body_type</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height</th>\n",
       "      <th>size</th>\n",
       "      <th>age</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>category_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50530</th>\n",
       "      <td>480611</td>\n",
       "      <td>32a</td>\n",
       "      <td>12</td>\n",
       "      <td>125.0000</td>\n",
       "      <td>4.5833</td>\n",
       "      <td>party</td>\n",
       "      <td>petite</td>\n",
       "      <td>dress</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>Fall</td>\n",
       "      <td>[party, wedding]</td>\n",
       "      <td>[sheath, dress, skirt]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id bust_size  rating_count   weight  rating_average  \\\n",
       "50530   480611       32a            12 125.0000          4.5833   \n",
       "\n",
       "      rented_for_top body_type category_top  height  size     age  \\\n",
       "50530          party    petite        dress 62.0000     1 35.0000   \n",
       "\n",
       "       review_length_average  review_month_top review_season_top  \\\n",
       "50530                80.0000                 4              Fall   \n",
       "\n",
       "         rented_for_all            category_all  \n",
       "50530  [party, wedding]  [sheath, dress, skirt]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Top 10 Recommendations for User #480611:\n"
     ]
    },
    {
     "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>item_id</th>\n",
       "      <th>predicted_rating</th>\n",
       "      <th>fit_small</th>\n",
       "      <th>fit</th>\n",
       "      <th>fit_large</th>\n",
       "      <th>user_count</th>\n",
       "      <th>bust_size_top</th>\n",
       "      <th>weight_mean</th>\n",
       "      <th>weight_median</th>\n",
       "      <th>rating_average</th>\n",
       "      <th>rented_for_top</th>\n",
       "      <th>rented_for_all</th>\n",
       "      <th>body_type_top</th>\n",
       "      <th>category_top</th>\n",
       "      <th>height_mean</th>\n",
       "      <th>height_median</th>\n",
       "      <th>size_mean</th>\n",
       "      <th>size_median</th>\n",
       "      <th>age_mean</th>\n",
       "      <th>age_median</th>\n",
       "      <th>review_length_average</th>\n",
       "      <th>review_month_top</th>\n",
       "      <th>review_season_top</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1215281</td>\n",
       "      <td>4.8961</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>71</td>\n",
       "      <td>34d</td>\n",
       "      <td>131.7606</td>\n",
       "      <td>130.0000</td>\n",
       "      <td>4.9577</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, party, other, wedding]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>gown</td>\n",
       "      <td>64.9437</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>10.0423</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>32.8592</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>55.4507</td>\n",
       "      <td>10</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1451390</td>\n",
       "      <td>4.8904</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>29</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.5862</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>4.9655</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, everyday, party, wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>maxi</td>\n",
       "      <td>66.2069</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>13.7931</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>34.8276</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>62.7241</td>\n",
       "      <td>7</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2546911</td>\n",
       "      <td>4.8897</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>29</td>\n",
       "      <td>34c</td>\n",
       "      <td>130.7931</td>\n",
       "      <td>130.0000</td>\n",
       "      <td>4.9310</td>\n",
       "      <td>everyday</td>\n",
       "      <td>[work, everyday, party, wedding, date, vacation]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>pant</td>\n",
       "      <td>64.8276</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>9.4483</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>35.0690</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>35.7931</td>\n",
       "      <td>10</td>\n",
       "      <td>Summer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1186923</td>\n",
       "      <td>4.8640</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>91.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>103</td>\n",
       "      <td>34c</td>\n",
       "      <td>137.4757</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>4.9223</td>\n",
       "      <td>party</td>\n",
       "      <td>[formal affair, other, party, wedding, date, v...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.3301</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>13.5340</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>30.3301</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>65.6990</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1547051</td>\n",
       "      <td>4.8606</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>48</td>\n",
       "      <td>34c</td>\n",
       "      <td>137.2917</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>4.9167</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, party, other, wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>65.2708</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>11.6875</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>34.0833</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>55.8958</td>\n",
       "      <td>11</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>371022</td>\n",
       "      <td>4.8581</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>53.0000</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>62</td>\n",
       "      <td>34b</td>\n",
       "      <td>138.8065</td>\n",
       "      <td>137.5000</td>\n",
       "      <td>4.9194</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, party, wedding, date]</td>\n",
       "      <td>athletic</td>\n",
       "      <td>dress</td>\n",
       "      <td>65.0645</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>12.6613</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>36.2419</td>\n",
       "      <td>33.5000</td>\n",
       "      <td>55.7097</td>\n",
       "      <td>12</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1200223</td>\n",
       "      <td>4.8574</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>42</td>\n",
       "      <td>34c</td>\n",
       "      <td>142.9286</td>\n",
       "      <td>140.0000</td>\n",
       "      <td>4.9048</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>sheath</td>\n",
       "      <td>65.7857</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>15.9048</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>43.1667</td>\n",
       "      <td>41.5000</td>\n",
       "      <td>51.5476</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>386314</td>\n",
       "      <td>4.8559</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>37</td>\n",
       "      <td>38d</td>\n",
       "      <td>171.7568</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>4.9189</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, other, party, wedding, d...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>sheath</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>55.0270</td>\n",
       "      <td>11</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>740349</td>\n",
       "      <td>4.8526</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>29</td>\n",
       "      <td>34b</td>\n",
       "      <td>136.2069</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>4.8621</td>\n",
       "      <td>wedding</td>\n",
       "      <td>[formal affair, work, everyday, other, party, ...</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>shift</td>\n",
       "      <td>65.1034</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>12.7586</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>35.6897</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>41.0345</td>\n",
       "      <td>5</td>\n",
       "      <td>Spring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1328898</td>\n",
       "      <td>4.8524</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>42.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>44</td>\n",
       "      <td>34b</td>\n",
       "      <td>135.7500</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>4.9091</td>\n",
       "      <td>formal affair</td>\n",
       "      <td>[formal affair, other, wedding]</td>\n",
       "      <td>hourglass</td>\n",
       "      <td>gown</td>\n",
       "      <td>66.0455</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>9.6136</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>33.8182</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>63.2727</td>\n",
       "      <td>10</td>\n",
       "      <td>Winter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  predicted_rating  fit_small     fit  fit_large  user_count  \\\n",
       "0  1215281            4.8961     4.0000 62.0000     5.0000          71   \n",
       "1  1451390            4.8904     0.0000 27.0000     2.0000          29   \n",
       "2  2546911            4.8897     1.0000 28.0000     0.0000          29   \n",
       "3  1186923            4.8640     9.0000 91.0000     3.0000         103   \n",
       "4  1547051            4.8606     1.0000 44.0000     3.0000          48   \n",
       "5   371022            4.8581     1.0000 53.0000     8.0000          62   \n",
       "6  1200223            4.8574     3.0000 37.0000     2.0000          42   \n",
       "7   386314            4.8559     0.0000 28.0000     9.0000          37   \n",
       "8   740349            4.8526     1.0000 21.0000     7.0000          29   \n",
       "9  1328898            4.8524     1.0000 42.0000     1.0000          44   \n",
       "\n",
       "  bust_size_top  weight_mean  weight_median  rating_average rented_for_top  \\\n",
       "0           34d     131.7606       130.0000          4.9577        wedding   \n",
       "1           34b     136.5862       135.0000          4.9655        wedding   \n",
       "2           34c     130.7931       130.0000          4.9310       everyday   \n",
       "3           34c     137.4757       135.0000          4.9223          party   \n",
       "4           34c     137.2917       135.0000          4.9167        wedding   \n",
       "5           34b     138.8065       137.5000          4.9194  formal affair   \n",
       "6           34c     142.9286       140.0000          4.9048        wedding   \n",
       "7           38d     171.7568       150.0000          4.9189        wedding   \n",
       "8           34b     136.2069       135.0000          4.8621        wedding   \n",
       "9           34b     135.7500       135.0000          4.9091  formal affair   \n",
       "\n",
       "                                      rented_for_all body_type_top  \\\n",
       "0             [formal affair, party, other, wedding]      athletic   \n",
       "1    [formal affair, work, everyday, party, wedding]     hourglass   \n",
       "2   [work, everyday, party, wedding, date, vacation]     hourglass   \n",
       "3  [formal affair, other, party, wedding, date, v...     hourglass   \n",
       "4             [formal affair, party, other, wedding]     hourglass   \n",
       "5       [formal affair, other, party, wedding, date]      athletic   \n",
       "6  [formal affair, work, everyday, other, party, ...     hourglass   \n",
       "7  [formal affair, work, other, party, wedding, d...     hourglass   \n",
       "8  [formal affair, work, everyday, other, party, ...     hourglass   \n",
       "9                    [formal affair, other, wedding]     hourglass   \n",
       "\n",
       "  category_top  height_mean  height_median  size_mean  size_median  age_mean  \\\n",
       "0         gown      64.9437        65.0000    10.0423       8.0000   32.8592   \n",
       "1         maxi      66.2069        66.0000    13.7931      12.0000   34.8276   \n",
       "2         pant      64.8276        65.0000     9.4483       8.0000   35.0690   \n",
       "3        dress      65.3301        65.0000    13.5340      14.0000   30.3301   \n",
       "4         gown      65.2708        65.0000    11.6875      12.0000   34.0833   \n",
       "5        dress      65.0645        65.0000    12.6613      12.0000   36.2419   \n",
       "6       sheath      65.7857        66.0000    15.9048      16.0000   43.1667   \n",
       "7       sheath      65.0000        65.0000    33.0000      32.0000   39.0000   \n",
       "8        shift      65.1034        65.0000    12.7586      12.0000   35.6897   \n",
       "9         gown      66.0455        66.0000     9.6136       8.0000   33.8182   \n",
       "\n",
       "   age_median  review_length_average  review_month_top review_season_top  \n",
       "0     32.0000                55.4507                10              Fall  \n",
       "1     35.0000                62.7241                 7            Summer  \n",
       "2     35.0000                35.7931                10            Summer  \n",
       "3     30.0000                65.6990                11            Winter  \n",
       "4     32.0000                55.8958                11            Spring  \n",
       "5     33.5000                55.7097                12            Winter  \n",
       "6     41.5000                51.5476                11            Winter  \n",
       "7     39.0000                55.0270                11            Winter  \n",
       "8     35.0000                41.0345                 5            Spring  \n",
       "9     32.0000                63.2727                10            Winter  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd_recommendation(480611)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VI. Further Research\n",
    "\n",
    "For further research, the data should be updated with more recent rentals, and more features should be added such as prices for the products as well as product description."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contact\n",
    "\n",
    "Feel free to contact me for any questions and connect with me on [Linkedin](https://www.linkedin.com/in/czarinagluna/)."
   ]
  }
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