9. Cluster Grocery.ipynb

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  {
   "cell_type": "markdown",
   "id": "f8dc9835",
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   "source": [
    "# Goal of Analysis:\n",
    "\n",
    "### 1. find out the customers who bought the most items overall in her lifetime\n",
    "\n",
    "### 2. for each item, the customer who bought that product the most\n",
    "\n",
    "### 3. cluster items based on user co-purchase history. That is, create clusters of products that have the highest probability of being bought together. Each item can only belong to one cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd28df97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b6d6778",
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       "  <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",
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      "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"
      ]
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     "execution_count": 2,
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   "source": [
    "item_to_id = pd.read_csv('item_to_id.csv')\n",
    "item_to_id.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2885db90",
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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..."
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   "source": [
    "purchase_history = pd.read_csv('purchase_history.csv')\n",
    "purchase_history.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed72633",
   "metadata": {},
   "source": [
    "### Part 1: find out customers who bought the most items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "176aa1a5",
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       "       user_id   0   1   2   3   4   5   6   7   8  ...  17  18 19 20 21 22  \\\n",
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       "\n",
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     "execution_count": 4,
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   "source": [
    "# the rows are each user, the columns are the items they purchased\n",
    "\n",
    "purchase_expanded = pd.concat([purchase_history['user_id'], purchase_history['id'].\\\n",
    "                               str.split(',', expand=True)], axis=1).fillna(0)\n",
    "\n",
    "purchase_expanded"
   ]
  },
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   "id": "4f65c712",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499730</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499778</th>\n",
       "      <td>3214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499797</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499817</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499974</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24885 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        purchase_quantity\n",
       "user_id                  \n",
       "47                      3\n",
       "68                     23\n",
       "113                    46\n",
       "123                     4\n",
       "223                   117\n",
       "...                   ...\n",
       "1499730                45\n",
       "1499778              3214\n",
       "1499797                34\n",
       "1499817               216\n",
       "1499974                34\n",
       "\n",
       "[24885 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# next I will sum up the number of purchase for each user\n",
    "\n",
    "purchase_sum = purchase_expanded.groupby('user_id').sum()\n",
    "purchase_sum = purchase_sum.rename(columns={0:'purchase_quantity'})\n",
    "\n",
    "purchase_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ba96a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1148109    991813\n",
       "1038037        99\n",
       "562246         99\n",
       "745575        988\n",
       "639912         98\n",
       "            ...  \n",
       "1381911         1\n",
       "514469          1\n",
       "513403          1\n",
       "717090          1\n",
       "468254          1\n",
       "Name: purchase_quantity, Length: 24885, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_sum['purchase_quantity'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2dcc24",
   "metadata": {},
   "source": [
    "The user with user_id 1148109 bought 991813 items throught his/her lifetime."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8e67ef",
   "metadata": {},
   "source": [
    "### Part 2: for each item, the customer who bought that product the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5171141e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         0   1   2   3   4   5   6   7   8   9   ...  17  18 19 20 21 22 23  \\\n",
       "user_id                                          ...                          \n",
       "222087   27  26   0   0   0   0   0   0   0   0  ...   0   0  0  0  0  0  0   \n",
       "1343649   6  47  17   0   0   0   0   0   0   0  ...   0   0  0  0  0  0  0   \n",
       "404134   18  12  23  22  27  43  38  20  35   1  ...   0   0  0  0  0  0  0   \n",
       "1110200   9  23   2  20  26  47  37   0   0   0  ...   0   0  0  0  0  0  0   \n",
       "224107   31  18   5  13   1  21  48  16  26   2  ...  47  12  7  0  0  0  0   \n",
       "...      ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  .. .. .. .. .. ..   \n",
       "704794   48  42  44  10   2   0   0   0   0   0  ...   0   0  0  0  0  0  0   \n",
       "31237    23  21  26  27  41   9  47  43  20  40  ...   0   0  0  0  0  0  0   \n",
       "724925    3  39  28   4   1  43  47  27  18  25  ...   0   0  0  0  0  0  0   \n",
       "651509   13  39  20  32  37   1   3  18  34   0  ...   0   0  0  0  0  0  0   \n",
       "517744   39  40  20   3  10  42  38  43  28  25  ...   0   0  0  0  0  0  0   \n",
       "\n",
       "        24 25 26  \n",
       "user_id           \n",
       "222087   0  0  0  \n",
       "1343649  0  0  0  \n",
       "404134   0  0  0  \n",
       "1110200  0  0  0  \n",
       "224107   0  0  0  \n",
       "...     .. .. ..  \n",
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       "724925   0  0  0  \n",
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       "\n",
       "[39474 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_expanded = purchase_expanded.set_index('user_id')\n",
    "purchase_expanded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff31a35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>shampoo</td>\n",
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       "      <td>aluminum foil</td>\n",
       "      <td>2788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>beef</td>\n",
       "      <td>6638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>laundry detergent</td>\n",
       "      <td>5132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>shaving cream</td>\n",
       "      <td>7362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>grapefruit</td>\n",
       "      <td>29671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>cheeses</td>\n",
       "      <td>17017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>frozen vegetables</td>\n",
       "      <td>2085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>tea</td>\n",
       "      <td>28237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>paper towels</td>\n",
       "      <td>21269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>cherries</td>\n",
       "      <td>3332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>spaghetti sauce</td>\n",
       "      <td>3332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Item_id          Item_name  User_id\n",
       "1         1              sugar      160\n",
       "2         2            lettuce       14\n",
       "3         3          pet items       63\n",
       "4         4         baby items      190\n",
       "5         5            waffles       53\n",
       "6         6            poultry        4\n",
       "7         7      sandwich bags      105\n",
       "8         8             butter      248\n",
       "9         9               soda       18\n",
       "10       10            carrots       97\n",
       "11       11            cereals      238\n",
       "12       12            shampoo      237\n",
       "13       13             bagels      454\n",
       "14       14               eggs      197\n",
       "15       15      aluminum foil     2788\n",
       "16       16               milk     2050\n",
       "17       17               beef     6638\n",
       "18       18  laundry detergent     5132\n",
       "19       19      shaving cream     7362\n",
       "20       20         grapefruit    29671\n",
       "21       21            cheeses    17017\n",
       "22       22  frozen vegetables     2085\n",
       "23       23                tea    28237\n",
       "24       24       paper towels    21269\n",
       "25       25           cherries     3332\n",
       "26       26    spaghetti sauce     3332"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_with_user = purchase_expanded.astype('int').apply(np.argmax, axis=0).reset_index()\n",
    "item_user_most = item_with_user.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 = df[['Item_id', 'Item_name', 'User_id']]\n",
    "df.iloc[1:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d51c481",
   "metadata": {},
   "source": [
    "### Part 3: Item clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4989e9b",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_id</th>\n",
       "      <th>222087</th>\n",
       "      <th>1343649</th>\n",
       "      <th>404134</th>\n",
       "      <th>1110200</th>\n",
       "      <th>224107</th>\n",
       "      <th>409803</th>\n",
       "      <th>1296632</th>\n",
       "      <th>748280</th>\n",
       "      <th>31625</th>\n",
       "      <th>987613</th>\n",
       "      <th>...</th>\n",
       "      <th>1494796</th>\n",
       "      <th>587211</th>\n",
       "      <th>755085</th>\n",
       "      <th>922812</th>\n",
       "      <th>1055933</th>\n",
       "      <th>704794</th>\n",
       "      <th>31237</th>\n",
       "      <th>724925</th>\n",
       "      <th>651509</th>\n",
       "      <th>517744</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>9</td>\n",
       "      <td>48</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>47</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>34</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>44</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39474 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id 222087  1343649 404134  1110200 224107  409803  1296632 748280   \\\n",
       "0            27       6      18       9      31      43      18      14   \n",
       "1            26      47      12      23      18      29      15      43   \n",
       "2             0      17      23       2       5       8      31       6   \n",
       "3             0       0      22      20      13      37      11      20   \n",
       "4             0       0      27      26       1      23      20       7   \n",
       "\n",
       "user_id 31625   987613   ... 1494796 587211  755085  922812  1055933 704794   \\\n",
       "0             1      27  ...      38      13      18      46       9      48   \n",
       "1            43       1  ...       2      39      21      35      43      42   \n",
       "2            31       3  ...      44      34      17       0      15      44   \n",
       "3            37       2  ...      43      27      47       0       3      10   \n",
       "4            25      11  ...      46      11       6       0      44       2   \n",
       "\n",
       "user_id 31237   724925  651509  517744   \n",
       "0            23       3      13      39  \n",
       "1            21      39      39      40  \n",
       "2            26      28      20      20  \n",
       "3            27       4      32       3  \n",
       "4            41       1      37      10  \n",
       "\n",
       "[5 rows x 39474 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the feature matrix: item_id, user_id\n",
    "feature = purchase_expanded.T\n",
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a96f79c",
   "metadata": {},
   "source": [
    "#### I will need to use elbow method to determine the best numer of clusters before fitting the clustering model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbf11847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose best K - number of clusters\n",
    "inertias = []\n",
    "\n",
    "ks = range(1,10)\n",
    "for k in ks:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(feature)   \n",
    "    inertias.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c32a3052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1516668f190>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "fig.add_subplot(1,2,1)\n",
    "plt.plot(ks,inertias,marker='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b92630a",
   "metadata": {},
   "source": [
    "it seems that the number of clusters should be 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f863328",
   "metadata": {},
   "source": [
    "#### fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03937085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 1, 1, 1, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "label = kmeans.fit_predict(feature)\n",
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8affeb",
   "metadata": {},
   "source": [
    "#### model visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e8782a",
   "metadata": {},
   "source": [
    "I will need to reduce the dimension in order to visualize my clustering results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58f31f97",
   "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": 14,
   "id": "f6ce3b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_label0 = pca_feature[label == 0]\n",
    "filtered_label1 = pca_feature[label == 1]\n",
    "filtered_label2 = pca_feature[label == 2]\n",
    "filtered_label3 = pca_feature[label == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f80e096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea9cb41",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}