09. Clustering Grocery Items.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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_name</th>\n",
       "      <th>Item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>coffee</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tea</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>juice</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>soda</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sandwich loaves</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Item_name  Item_id\n",
       "0           coffee       43\n",
       "1              tea       23\n",
       "2            juice       38\n",
       "3             soda        9\n",
       "4  sandwich loaves       39"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_to_id = pd.read_csv('./data/grocery/item_to_id.csv')\n",
    "item_to_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48 entries, 0 to 47\n",
      "Data columns (total 2 columns):\n",
      "Item_name    48 non-null object\n",
      "Item_id      48 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 848.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "item_to_id.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]\n"
     ]
    }
   ],
   "source": [
    "# Unique ids\n",
    "print(sorted(item_to_id['Item_id'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>222087</td>\n",
       "      <td>27,26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1343649</td>\n",
       "      <td>6,47,17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404134</td>\n",
       "      <td>18,12,23,22,27,43,38,20,35,1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1110200</td>\n",
       "      <td>9,23,2,20,26,47,37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>224107</td>\n",
       "      <td>31,18,5,13,1,21,48,16,26,2,44,32,20,37,42,35,4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id                                                 id\n",
       "0   222087                                              27,26\n",
       "1  1343649                                            6,47,17\n",
       "2   404134                       18,12,23,22,27,43,38,20,35,1\n",
       "3  1110200                                 9,23,2,20,26,47,37\n",
       "4   224107  31,18,5,13,1,21,48,16,26,2,44,32,20,37,42,35,4..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history = pd.read_csv('./data/grocery/purchase_history.csv')\n",
    "history.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39474 entries, 0 to 39473\n",
      "Data columns (total 2 columns):\n",
      "user_id    39474 non-null int64\n",
      "id         39474 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 616.9+ KB\n"
     ]
    }
   ],
   "source": [
    "history.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref: https://github.com/stasi009/TakeHomeDataChallenges/blob/master/09.ClusterGrocery/cluster_grocery.ipynb\n",
    "def id_to_item(df):\n",
    "    \"\"\" function to convert id into counts \"\"\"\n",
    "    # 'sum' here is adding two lists into one big list\n",
    "    ids = df['id'].str.split(',').sum()\n",
    "    id_list = [0 for i in range(1, 49)]\n",
    "    for i in ids:\n",
    "        id_list[int(i) - 1] += 1\n",
    "        \n",
    "    return pd.Series(id_list, index=list(range(1, 49)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>39</th>\n",
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       "      <th>48</th>\n",
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       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>113</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         1   2   3   4   5   6   7   8   9   10 ...  39  40  41  42  43  44  \\\n",
       "user_id                                         ...                           \n",
       "47        0   1   1   1   0   0   0   0   0   0 ...   0   0   0   0   0   1   \n",
       "68        0   0   0   0   0   1   0   0   0   1 ...   1   0   0   1   0   0   \n",
       "113       0   0   1   0   0   0   0   0   1   0 ...   0   0   0   0   1   0   \n",
       "123       0   0   0   1   0   0   0   0   0   1 ...   0   0   0   0   0   0   \n",
       "223       1   1   0   0   0   1   0   0   0   0 ...   0   0   1   0   0   0   \n",
       "\n",
       "         45  46  47  48  \n",
       "user_id                  \n",
       "47        1   1   0   0  \n",
       "68        0   0   0   0  \n",
       "113       0   1   0   0  \n",
       "123       0   0   0   0  \n",
       "223       1   0   0   0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get user_item count matrix\n",
    "user_item_count = history.groupby('user_id').apply(id_to_item)\n",
    "\n",
    "user_item_count.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The customer who bought the most items overall in her lifetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4445</th>\n",
       "      <td>269335</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6111</th>\n",
       "      <td>367872</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>397623</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9918</th>\n",
       "      <td>599172</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6291</th>\n",
       "      <td>377284</td>\n",
       "      <td>63</td>\n",
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      ],
      "text/plain": [
       "      user_id  count\n",
       "4445   269335     72\n",
       "6111   367872     70\n",
       "6623   397623     64\n",
       "9918   599172     64\n",
       "6291   377284     63"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_count = user_item_count.sum(axis=1).reset_index().rename(columns={0: 'count'})\n",
    "user_count.sort_values(by='count', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the customer who bought the most items is 269335 with 72 items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For each item, the customer who bought that product the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_id</th>\n",
       "      <th>Item_name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>sugar</td>\n",
       "      <td>31625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>lettuce</td>\n",
       "      <td>31625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>pet items</td>\n",
       "      <td>154960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>baby items</td>\n",
       "      <td>5289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>waffles</td>\n",
       "      <td>217277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>poultry</td>\n",
       "      <td>334664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>sandwich bags</td>\n",
       "      <td>175865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>butter</td>\n",
       "      <td>151926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>soda</td>\n",
       "      <td>269335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>carrots</td>\n",
       "      <td>618914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>cereals</td>\n",
       "      <td>367872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>shampoo</td>\n",
       "      <td>557904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>bagels</td>\n",
       "      <td>653800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>eggs</td>\n",
       "      <td>172120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>aluminum foil</td>\n",
       "      <td>143741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>73071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>beef</td>\n",
       "      <td>366155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>laundry detergent</td>\n",
       "      <td>917199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>shaving cream</td>\n",
       "      <td>31625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>grapefruit</td>\n",
       "      <td>885474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>cheeses</td>\n",
       "      <td>884172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>frozen vegetables</td>\n",
       "      <td>1199670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>tea</td>\n",
       "      <td>920002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>paper towels</td>\n",
       "      <td>189913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>cherries</td>\n",
       "      <td>68282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>spaghetti sauce</td>\n",
       "      <td>967573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>dishwashing</td>\n",
       "      <td>956666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>canned vegetables</td>\n",
       "      <td>204624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>hand soap</td>\n",
       "      <td>394348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>flour</td>\n",
       "      <td>21779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>pasta</td>\n",
       "      <td>289360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>apples</td>\n",
       "      <td>109578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>toilet paper</td>\n",
       "      <td>1310207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>tortillas</td>\n",
       "      <td>305916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>soap</td>\n",
       "      <td>450482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>ice cream</td>\n",
       "      <td>269335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>dinner rolls</td>\n",
       "      <td>46757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>juice</td>\n",
       "      <td>255546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>sandwich loaves</td>\n",
       "      <td>599172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>berries</td>\n",
       "      <td>38872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>ketchup</td>\n",
       "      <td>133355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>cucumbers</td>\n",
       "      <td>80215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>coffee</td>\n",
       "      <td>996380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>broccoli</td>\n",
       "      <td>31625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>cauliflower</td>\n",
       "      <td>1198106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>bananas</td>\n",
       "      <td>1218645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>pork</td>\n",
       "      <td>384935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>335841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Item_id          Item_name  User_id\n",
       "0         1              sugar    31625\n",
       "1         2            lettuce    31625\n",
       "2         3          pet items   154960\n",
       "3         4         baby items     5289\n",
       "4         5            waffles   217277\n",
       "5         6            poultry   334664\n",
       "6         7      sandwich bags   175865\n",
       "7         8             butter   151926\n",
       "8         9               soda   269335\n",
       "9        10            carrots   618914\n",
       "10       11            cereals   367872\n",
       "11       12            shampoo   557904\n",
       "12       13             bagels   653800\n",
       "13       14               eggs   172120\n",
       "14       15      aluminum foil   143741\n",
       "15       16               milk    73071\n",
       "16       17               beef   366155\n",
       "17       18  laundry detergent   917199\n",
       "18       19      shaving cream    31625\n",
       "19       20         grapefruit   885474\n",
       "20       21            cheeses   884172\n",
       "21       22  frozen vegetables  1199670\n",
       "22       23                tea   920002\n",
       "23       24       paper towels   189913\n",
       "24       25           cherries    68282\n",
       "25       26    spaghetti sauce   967573\n",
       "26       27       dishwashing    956666\n",
       "27       28  canned vegetables   204624\n",
       "28       29          hand soap   394348\n",
       "29       30              flour    21779\n",
       "30       31              pasta   289360\n",
       "31       32             apples   109578\n",
       "32       33       toilet paper  1310207\n",
       "33       34          tortillas   305916\n",
       "34       35               soap   450482\n",
       "35       36          ice cream   269335\n",
       "36       37       dinner rolls    46757\n",
       "37       38              juice   255546\n",
       "38       39    sandwich loaves   599172\n",
       "39       40            berries    38872\n",
       "40       41            ketchup   133355\n",
       "41       42          cucumbers    80215\n",
       "42       43             coffee   996380\n",
       "43       44           broccoli    31625\n",
       "44       45        cauliflower  1198106\n",
       "45       46            bananas  1218645\n",
       "46       47               pork   384935\n",
       "47       48             yogurt   335841"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_user_most = user_item_count.apply(np.argmax, axis=0).reset_index()\n",
    "item_user_most = item_user_most.rename(columns={'index': 'Item_id', 0: 'User_id'})\n",
    "\n",
    "df = pd.merge(left=item_user_most, right=item_to_id, on='Item_id', how='left')\n",
    "df[['Item_id', 'Item_name', 'User_id']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>user_id</th>\n",
       "      <th>47</th>\n",
       "      <th>68</th>\n",
       "      <th>113</th>\n",
       "      <th>123</th>\n",
       "      <th>223</th>\n",
       "      <th>281</th>\n",
       "      <th>323</th>\n",
       "      <th>394</th>\n",
       "      <th>420</th>\n",
       "      <th>453</th>\n",
       "      <th>...</th>\n",
       "      <th>1499502</th>\n",
       "      <th>1499523</th>\n",
       "      <th>1499563</th>\n",
       "      <th>1499702</th>\n",
       "      <th>1499727</th>\n",
       "      <th>1499730</th>\n",
       "      <th>1499778</th>\n",
       "      <th>1499797</th>\n",
       "      <th>1499817</th>\n",
       "      <th>1499974</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24885 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id  47       68       113      123      223      281      323      \\\n",
       "1              0        0        0        0        1        0        1   \n",
       "2              1        0        0        0        1        0        0   \n",
       "3              1        0        1        0        0        1        0   \n",
       "4              1        0        0        1        0        0        0   \n",
       "5              0        0        0        0        0        0        0   \n",
       "\n",
       "user_id  394      420      453       ...     1499502  1499523  1499563  \\\n",
       "1              1        0        1   ...           0        0        1   \n",
       "2              1        1        1   ...           1        0        1   \n",
       "3              0        0        0   ...           0        0        0   \n",
       "4              0        0        0   ...           0        1        0   \n",
       "5              0        0        0   ...           0        0        0   \n",
       "\n",
       "user_id  1499702  1499727  1499730  1499778  1499797  1499817  1499974  \n",
       "1              0        0        0        0        0        1        0  \n",
       "2              0        0        0        1        0        2        0  \n",
       "3              0        0        0        0        1        0        0  \n",
       "4              0        0        0        0        0        0        0  \n",
       "5              0        0        0        0        0        0        1  \n",
       "\n",
       "[5 rows x 24885 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the feature matrix: item_id, user_id\n",
    "feature = user_item_count.T\n",
    "feature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# determine the best number of clusters\n",
    "clusters = range(2, 30)\n",
    "inertias = []\n",
    "silhouettes = []\n",
    "\n",
    "for n_clusters in clusters:\n",
    "    kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42, n_jobs=-1)\n",
    "    kmeans = kmeans.fit(feature)\n",
    "    label = kmeans.predict(feature)\n",
    "    \n",
    "    inertias.append(kmeans.inertia_)\n",
    "    silhouettes.append(silhouette_score(feature, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "ax[0].plot(clusters, inertias, 'o-', label='Sum of Squared Distances')\n",
    "ax[0].grid(True)\n",
    "ax[1].plot(clusters, silhouettes, 'o-', label='Silhouette Coefficient')\n",
    "ax[1].grid(True)\n",
    "plt.legend(fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems 21 clusters is the best choice. Now, let's apply PCA on the data and visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build K-Means model\n",
    "kmeans = KMeans(n_clusters=21, init='k-means++', random_state=42, n_jobs=-1)\n",
    "kmeans = kmeans.fit(feature)\n",
    "label = kmeans.predict(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA for dimension reduction\n",
    "pca = PCA(n_components=2, random_state=42)\n",
    "pca = pca.fit(feature)\n",
    "pca_feature = pca.transform(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization in 2D\n",
    "colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 16))\n",
    "for j in range(len(pca_feature)):\n",
    "    x = pca_feature[j][0]\n",
    "    y = pca_feature[j][1]\n",
    "    ax.plot(x, y, 'o', color=colors[label[j] % 8])\n",
    "    name = item_to_id[item_to_id['Item_id'] == j + 1]['Item_name']\n",
    "    ax.annotate(xy=(x+0.2, y+0.2), s=name.values[0], fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \t ['sandwich bags', 'aluminum foil', 'paper towels', 'toilet paper']\n",
      "1 \t ['sugar']\n",
      "2 \t ['bagels']\n",
      "3 \t ['juice']\n",
      "4 \t ['baby items', 'waffles', 'butter', 'cereals', 'shampoo', 'eggs', 'milk', 'laundry detergent', 'shaving cream', 'cheeses', 'frozen vegetables', 'spaghetti sauce', 'dishwashing\\xa0', 'canned vegetables', 'hand soap', 'flour', 'pasta', 'soap', 'ice cream', 'ketchup', 'yogurt']\n",
      "5 \t ['cucumbers']\n",
      "6 \t ['broccoli']\n",
      "7 \t ['cauliflower']\n",
      "8 \t ['tea']\n",
      "9 \t ['berries']\n",
      "10 \t ['lettuce']\n",
      "11 \t ['bananas']\n",
      "12 \t ['poultry', 'beef', 'pork']\n",
      "13 \t ['sandwich loaves']\n",
      "14 \t ['soda']\n",
      "15 \t ['grapefruit', 'cherries']\n",
      "16 \t ['pet items']\n",
      "17 \t ['carrots']\n",
      "18 \t ['coffee']\n",
      "19 \t ['tortillas', 'dinner rolls']\n",
      "20 \t ['apples']\n"
     ]
    }
   ],
   "source": [
    "# print useful information\n",
    "df = pd.DataFrame({'Item_id': list(range(1, 49)), 'label': label})\n",
    "df = pd.merge(left=df, right=item_to_id, on='Item_id', how='left')\n",
    "\n",
    "for i in range(0, 21):\n",
    "    print(i, '\\t', list(df[df['label'] == i]['Item_name']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above clustering is based on the raw feature, namely the user purchase for each item. There are other methods for this problem. For example, this notebook contains another method: [notebook link](https://github.com/stasi009/TakeHomeDataChallenges/blob/master/09.ClusterGrocery/cluster_grocery.ipynb).\n",
    "\n",
    "With 21 clusters, some cluster looks very weird. Since there are only 48 different items, with one business expert in related area, it only take a few time to manually determine the best cluster numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}