DataAnalysisonBakeryTransactions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ed85b75",
   "metadata": {},
   "source": [
    "# Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efd8626a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#basic libraries\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#machine learning extensions\n",
    "import mlxtend"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a32691",
   "metadata": {},
   "source": [
    "# Explore the Bakery Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae2b9dcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "items = pd.read_csv('BreadBasket_DMS.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb3d78a",
   "metadata": {},
   "source": [
    "Here is a link to [\"Bakery Data\"](https://www.kaggle.com/sulmansarwar/transactions-from-a-bakery)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "266764e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Cookies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:08:41</td>\n",
       "      <td>4</td>\n",
       "      <td>Muffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:13:03</td>\n",
       "      <td>5</td>\n",
       "      <td>Coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:13:03</td>\n",
       "      <td>5</td>\n",
       "      <td>Pastry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:13:03</td>\n",
       "      <td>5</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item\n",
       "0  2016-10-30  09:58:11            1          Bread\n",
       "1  2016-10-30  10:05:34            2   Scandinavian\n",
       "2  2016-10-30  10:05:34            2   Scandinavian\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate\n",
       "4  2016-10-30  10:07:57            3            Jam\n",
       "5  2016-10-30  10:07:57            3        Cookies\n",
       "6  2016-10-30  10:08:41            4         Muffin\n",
       "7  2016-10-30  10:13:03            5         Coffee\n",
       "8  2016-10-30  10:13:03            5         Pastry\n",
       "9  2016-10-30  10:13:03            5          Bread"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db434667",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21283</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:30:09</td>\n",
       "      <td>9681</td>\n",
       "      <td>Tea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21284</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:30:09</td>\n",
       "      <td>9681</td>\n",
       "      <td>Spanish Brunch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21285</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:30:09</td>\n",
       "      <td>9681</td>\n",
       "      <td>Christmas common</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21286</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:32:58</td>\n",
       "      <td>9682</td>\n",
       "      <td>Muffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21287</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:32:58</td>\n",
       "      <td>9682</td>\n",
       "      <td>Tacos/Fajita</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21288</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:32:58</td>\n",
       "      <td>9682</td>\n",
       "      <td>Coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21289</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:32:58</td>\n",
       "      <td>9682</td>\n",
       "      <td>Tea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21290</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:57:06</td>\n",
       "      <td>9683</td>\n",
       "      <td>Coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21291</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>14:57:06</td>\n",
       "      <td>9683</td>\n",
       "      <td>Pastry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21292</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>15:04:24</td>\n",
       "      <td>9684</td>\n",
       "      <td>Smoothies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Transaction              Item\n",
       "21283  2017-04-09  14:30:09         9681               Tea\n",
       "21284  2017-04-09  14:30:09         9681    Spanish Brunch\n",
       "21285  2017-04-09  14:30:09         9681  Christmas common\n",
       "21286  2017-04-09  14:32:58         9682            Muffin\n",
       "21287  2017-04-09  14:32:58         9682      Tacos/Fajita\n",
       "21288  2017-04-09  14:32:58         9682            Coffee\n",
       "21289  2017-04-09  14:32:58         9682               Tea\n",
       "21290  2017-04-09  14:57:06         9683            Coffee\n",
       "21291  2017-04-09  14:57:06         9683            Pastry\n",
       "21292  2017-04-09  15:04:24         9684         Smoothies"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c7a5b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21293, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ea6c6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21293 entries, 0 to 21292\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Date         21293 non-null  object\n",
      " 1   Time         21293 non-null  object\n",
      " 2   Transaction  21293 non-null  int64 \n",
      " 3   Item         21293 non-null  object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 665.5+ KB\n"
     ]
    }
   ],
   "source": [
    "items.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d2a246",
   "metadata": {},
   "source": [
    "# Cleaning the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59d25257",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    20507\n",
      "True       786\n",
      "Name: Item, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "x = items['Item'] == 'NONE'\n",
    "print(x.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1139e71e",
   "metadata": {},
   "source": [
    "### Drop the items with values \"NONE\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b515a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "items= items.drop(items[items.Item == 'NONE'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a0dfff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20507, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b9b5e8",
   "metadata": {},
   "source": [
    "# Explore the data for Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "214bc1e3",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>date_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-10-30 09:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30 10:05:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30 10:05:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>2016-10-30 10:07:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2016-10-30 10:07:57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item           date_time\n",
       "0  2016-10-30  09:58:11            1          Bread 2016-10-30 09:58:11\n",
       "1  2016-10-30  10:05:34            2   Scandinavian 2016-10-30 10:05:34\n",
       "2  2016-10-30  10:05:34            2   Scandinavian 2016-10-30 10:05:34\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate 2016-10-30 10:07:57\n",
       "4  2016-10-30  10:07:57            3            Jam 2016-10-30 10:07:57"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items['date_time'] = pd.to_datetime(items['Date'] + ' ' + items['Time'])\n",
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02de7e22",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>date_time</th>\n",
       "      <th>year_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17737</th>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>12:24:38</td>\n",
       "      <td>8083</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2017-03-13 12:24:38</td>\n",
       "      <td>2017-3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14221</th>\n",
       "      <td>2017-02-16</td>\n",
       "      <td>11:23:06</td>\n",
       "      <td>6638</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>2017-02-16 11:23:06</td>\n",
       "      <td>2017-2</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>2016-11-12</td>\n",
       "      <td>12:38:17</td>\n",
       "      <td>1151</td>\n",
       "      <td>Hearty &amp; Seasonal</td>\n",
       "      <td>2016-11-12 12:38:17</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10291</th>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>09:37:27</td>\n",
       "      <td>4896</td>\n",
       "      <td>Granola</td>\n",
       "      <td>2017-01-19 09:37:27</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16593</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>14:28:29</td>\n",
       "      <td>7587</td>\n",
       "      <td>Cake</td>\n",
       "      <td>2017-03-04 14:28:29</td>\n",
       "      <td>2017-3</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18322</th>\n",
       "      <td>2017-03-18</td>\n",
       "      <td>12:38:34</td>\n",
       "      <td>8366</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>2017-03-18 12:38:34</td>\n",
       "      <td>2017-3</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8993</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>11:41:57</td>\n",
       "      <td>4302</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2017-01-07 11:41:57</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5076</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>17:30:41</td>\n",
       "      <td>2416</td>\n",
       "      <td>Cake</td>\n",
       "      <td>2016-12-01 17:30:41</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11567</th>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>11:04:11</td>\n",
       "      <td>5494</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2017-01-28 11:04:11</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20926</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>09:14:59</td>\n",
       "      <td>9510</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2017-04-07 09:14:59</td>\n",
       "      <td>2017-4</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Transaction               Item  \\\n",
       "17737  2017-03-13  12:24:38         8083             Coffee   \n",
       "14221  2017-02-16  11:23:06         6638      Hot chocolate   \n",
       "2417   2016-11-12  12:38:17         1151  Hearty & Seasonal   \n",
       "10291  2017-01-19  09:37:27         4896            Granola   \n",
       "16593  2017-03-04  14:28:29         7587               Cake   \n",
       "18322  2017-03-18  12:38:34         8366            Brownie   \n",
       "8993   2017-01-07  11:41:57         4302              Bread   \n",
       "5076   2016-12-01  17:30:41         2416               Cake   \n",
       "11567  2017-01-28  11:04:11         5494             Coffee   \n",
       "20926  2017-04-07  09:14:59         9510              Bread   \n",
       "\n",
       "                date_time year_month  hour  day_of_week       day  \n",
       "17737 2017-03-13 12:24:38     2017-3    12            0    Monday  \n",
       "14221 2017-02-16 11:23:06     2017-2    11            3  Thursday  \n",
       "2417  2016-11-12 12:38:17    2016-11    12            5  Saturday  \n",
       "10291 2017-01-19 09:37:27     2017-1     9            3  Thursday  \n",
       "16593 2017-03-04 14:28:29     2017-3    14            5  Saturday  \n",
       "18322 2017-03-18 12:38:34     2017-3    12            5  Saturday  \n",
       "8993  2017-01-07 11:41:57     2017-1    11            5  Saturday  \n",
       "5076  2016-12-01 17:30:41    2016-12    17            3  Thursday  \n",
       "11567 2017-01-28 11:04:11     2017-1    11            5  Saturday  \n",
       "20926 2017-04-07 09:14:59     2017-4     9            4    Friday  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items['year_month'] = items['date_time'].map(lambda x: str(x.year) + '-' + str(x.month))\n",
    "items['hour'] = items['date_time'].dt.hour\n",
    "items['day_of_week'] = items['date_time'].dt.dayofweek\n",
    "items['day'] = items['date_time'].dt.day_name()\n",
    "items.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd56f21a",
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>date_time</th>\n",
       "      <th>year_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day</th>\n",
       "      <th>weekend_vs_weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>14:36:39</td>\n",
       "      <td>873</td>\n",
       "      <td>Hearty &amp; Seasonal</td>\n",
       "      <td>2016-11-08 14:36:39</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13241</th>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>13:47:08</td>\n",
       "      <td>6228</td>\n",
       "      <td>Soup</td>\n",
       "      <td>2017-02-08 13:47:08</td>\n",
       "      <td>2017-2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>2016-11-16</td>\n",
       "      <td>09:37:17</td>\n",
       "      <td>1417</td>\n",
       "      <td>Pastry</td>\n",
       "      <td>2016-11-16 09:37:17</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11437</th>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>14:23:41</td>\n",
       "      <td>5444</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2017-01-27 14:23:41</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>Friday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5727</th>\n",
       "      <td>2016-12-06</td>\n",
       "      <td>13:36:54</td>\n",
       "      <td>2730</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-12-06 13:36:54</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4903</th>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>11:14:46</td>\n",
       "      <td>2329</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-11-30 11:14:46</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6109</th>\n",
       "      <td>2016-12-10</td>\n",
       "      <td>11:41:15</td>\n",
       "      <td>2919</td>\n",
       "      <td>Vegan mincepie</td>\n",
       "      <td>2016-12-10 11:41:15</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3907</th>\n",
       "      <td>2016-11-21</td>\n",
       "      <td>15:47:22</td>\n",
       "      <td>1841</td>\n",
       "      <td>Alfajores</td>\n",
       "      <td>2016-11-21 15:47:22</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7918</th>\n",
       "      <td>2016-12-24</td>\n",
       "      <td>11:31:29</td>\n",
       "      <td>3800</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2016-12-24 11:31:29</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8956</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>10:12:02</td>\n",
       "      <td>4285</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2017-01-07 10:12:02</td>\n",
       "      <td>2017-1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Transaction               Item  \\\n",
       "1845   2016-11-08  14:36:39          873  Hearty & Seasonal   \n",
       "13241  2017-02-08  13:47:08         6228               Soup   \n",
       "3009   2016-11-16  09:37:17         1417             Pastry   \n",
       "11437  2017-01-27  14:23:41         5444             Coffee   \n",
       "5727   2016-12-06  13:36:54         2730              Bread   \n",
       "4903   2016-11-30  11:14:46         2329              Bread   \n",
       "6109   2016-12-10  11:41:15         2919     Vegan mincepie   \n",
       "3907   2016-11-21  15:47:22         1841          Alfajores   \n",
       "7918   2016-12-24  11:31:29         3800             Coffee   \n",
       "8956   2017-01-07  10:12:02         4285             Coffee   \n",
       "\n",
       "                date_time year_month  hour  day_of_week        day  \\\n",
       "1845  2016-11-08 14:36:39    2016-11    14            1    Tuesday   \n",
       "13241 2017-02-08 13:47:08     2017-2    13            2  Wednesday   \n",
       "3009  2016-11-16 09:37:17    2016-11     9            2  Wednesday   \n",
       "11437 2017-01-27 14:23:41     2017-1    14            4     Friday   \n",
       "5727  2016-12-06 13:36:54    2016-12    13            1    Tuesday   \n",
       "4903  2016-11-30 11:14:46    2016-11    11            2  Wednesday   \n",
       "6109  2016-12-10 11:41:15    2016-12    11            5   Saturday   \n",
       "3907  2016-11-21 15:47:22    2016-11    15            0     Monday   \n",
       "7918  2016-12-24 11:31:29    2016-12    11            5   Saturday   \n",
       "8956  2017-01-07 10:12:02     2017-1    10            5   Saturday   \n",
       "\n",
       "      weekend_vs_weekday  \n",
       "1845             weekday  \n",
       "13241            weekday  \n",
       "3009             weekday  \n",
       "11437            weekday  \n",
       "5727             weekday  \n",
       "4903             weekday  \n",
       "6109             weekend  \n",
       "3907             weekday  \n",
       "7918             weekend  \n",
       "8956             weekend  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items['weekend_vs_weekday'] =items['date_time'].apply(\\\n",
    "                                                     lambda x: 'weekend' if x.dayofweek // 5 == 1 else 'weekday')\n",
    "items.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a64a5576",
   "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>year_month</th>\n",
       "      <th>Transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10</td>\n",
       "      <td>32288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11</td>\n",
       "      <td>5599892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12</td>\n",
       "      <td>10827910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-1</td>\n",
       "      <td>16519571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-2</td>\n",
       "      <td>25607531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month  Transaction\n",
       "0    2016-10        32288\n",
       "1    2016-11      5599892\n",
       "2    2016-12     10827910\n",
       "3     2017-1     16519571\n",
       "4     2017-2     25607531"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcation_by_month = items[['year_month',\n",
    "                              'Transaction']].groupby('year_month', as_index=False).sum()\n",
    "transcation_by_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ed465e",
   "metadata": {},
   "source": [
    "This is the total number of transactions that occurred in this bakery on a monthly basis. We used groupby operation on the year_month column and calculated the sum. We see that the transactions are steadily increasing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3499301b",
   "metadata": {},
   "source": [
    "### Monthly Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bce6ba41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transcations by month')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "sns.barplot(x ='year_month', y ='Transaction', data =transcation_by_month)\n",
    "plt.ticklabel_format(style = 'plain', axis ='y')\n",
    "plt.title('Number of transcations by month')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27292ee3",
   "metadata": {},
   "source": [
    "Year_month versus number of transactions, and this bar plot shows you that we just have a few months' worth of data in this bakery dataset, and of all of the months, March of 2017 had the highest number of transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e151a24e",
   "metadata": {},
   "source": [
    "### Daily Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "215da400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transcations by day')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 8])\n",
    "sns.boxplot(x ='day', y ='Transaction', data = items)\n",
    "plt.ticklabel_format(style = 'plain', axis ='y')\n",
    "plt.title('Number of transcations by day')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e0199b",
   "metadata": {},
   "source": [
    "You can see that the median across the days are more or less the same. The box itself represents the inter-quartile region; each of the boxes are of similar size as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c68c7ea",
   "metadata": {},
   "source": [
    "### Weekend vs Weekday Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c335d5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weekend vs Weekday')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,5])\n",
    "sns.boxplot(x ='weekend_vs_weekday', y='Transaction', data= items)\n",
    "plt.ticklabel_format(style='plain', axis='y')\n",
    "plt.title(\"Weekend vs Weekday\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1daa9b5",
   "metadata": {},
   "source": [
    "This box plot shows us that there is not much of a difference.The median values for weekends versus weekdays in terms of number of transactions is more or less the same, but if you consider the fact that there are more weekdays than weekends in the week, you can draw the conclusion that the bakery is rather busy on weekends. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a7a119",
   "metadata": {},
   "source": [
    "### Hourly Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0000cf07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sales by Hour')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "plt.plot(items[['hour','Transaction']].groupby('hour').sum())\n",
    "plt.ticklabel_format(style='plain', axis='y')\n",
    "plt.title('Sales by Hour')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6016b009",
   "metadata": {},
   "source": [
    "The number of transactions in this bakery varies based on the hour. The line plot shows that the bakery opens at around eight in the morning, reaches the peak number of transactions at around 12, and closes at around 6 (x-asis 18:00) or 7 (x-axis 19:00) in the evening."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1097a2",
   "metadata": {},
   "source": [
    "### Top 10 Items Sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9da8d215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 10 Items Sold')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12,6))\n",
    "items['Item'].value_counts().sort_values(ascending = False).head(10).plot.bar()\n",
    "\n",
    "plt.xlabel('Food Item', fontsize =10)\n",
    "plt.ylabel('Number of Transactions', fontsize =10)\n",
    "\n",
    "ax.tick_params(labelsize = 10)\n",
    "plt.title('Top 10 Items Sold', fontsize = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0cab461",
   "metadata": {},
   "source": [
    "We can see that coffee is by far the most popular; it's present in the most number of transactions, then comes bread and then tea."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a67c45",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "265dd683",
   "metadata": {},
   "source": [
    "# Setting up the Data for Assosciation Rule Mining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3519de58",
   "metadata": {},
   "source": [
    "Group our data by transaction. So, use the group-by operation on the transaction problem, and apply an aggregation to the other columns in the data frame and the aggregation simply joins the data available in the other columns, separated by the comma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7b7bac2f",
   "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>Transaction</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Item</th>\n",
       "      <th>year_month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekend_vs_weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-10-30,2016-10-30</td>\n",
       "      <td>10:05:34,10:05:34</td>\n",
       "      <td>Scandinavian,Scandinavian</td>\n",
       "      <td>2016-10,2016-10</td>\n",
       "      <td>Sunday,Sunday</td>\n",
       "      <td>weekend,weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016-10-30,2016-10-30,2016-10-30</td>\n",
       "      <td>10:07:57,10:07:57,10:07:57</td>\n",
       "      <td>Hot chocolate,Jam,Cookies</td>\n",
       "      <td>2016-10,2016-10,2016-10</td>\n",
       "      <td>Sunday,Sunday,Sunday</td>\n",
       "      <td>weekend,weekend,weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:08:41</td>\n",
       "      <td>Muffin</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016-10-30,2016-10-30,2016-10-30</td>\n",
       "      <td>10:13:03,10:13:03,10:13:03</td>\n",
       "      <td>Coffee,Pastry,Bread</td>\n",
       "      <td>2016-10,2016-10,2016-10</td>\n",
       "      <td>Sunday,Sunday,Sunday</td>\n",
       "      <td>weekend,weekend,weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction                              Date                        Time  \\\n",
       "0            1                        2016-10-30                    09:58:11   \n",
       "1            2             2016-10-30,2016-10-30           10:05:34,10:05:34   \n",
       "2            3  2016-10-30,2016-10-30,2016-10-30  10:07:57,10:07:57,10:07:57   \n",
       "3            4                        2016-10-30                    10:08:41   \n",
       "4            5  2016-10-30,2016-10-30,2016-10-30  10:13:03,10:13:03,10:13:03   \n",
       "\n",
       "                        Item               year_month                   day  \\\n",
       "0                      Bread                  2016-10                Sunday   \n",
       "1  Scandinavian,Scandinavian          2016-10,2016-10         Sunday,Sunday   \n",
       "2  Hot chocolate,Jam,Cookies  2016-10,2016-10,2016-10  Sunday,Sunday,Sunday   \n",
       "3                     Muffin                  2016-10                Sunday   \n",
       "4        Coffee,Pastry,Bread  2016-10,2016-10,2016-10  Sunday,Sunday,Sunday   \n",
       "\n",
       "        weekend_vs_weekday  \n",
       "0                  weekend  \n",
       "1          weekend,weekend  \n",
       "2  weekend,weekend,weekend  \n",
       "3                  weekend  \n",
       "4  weekend,weekend,weekend  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_per_transcation = items.groupby('Transaction').agg(','.join).reset_index()\n",
    "items_per_transcation.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7aff0e5",
   "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>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian,Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate,Jam,Cookies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Muffin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Coffee,Pastry,Bread</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction                       Item\n",
       "0            1                      Bread\n",
       "1            2  Scandinavian,Scandinavian\n",
       "2            3  Hot chocolate,Jam,Cookies\n",
       "3            4                     Muffin\n",
       "4            5        Coffee,Pastry,Bread"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_per_transcation = items_per_transcation.drop(['Date','Time', 'year_month','day','weekend_vs_weekday'], axis=1)\n",
    "items_per_transcation.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a725a851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                              Bread\n",
       "1                          Scandinavian,Scandinavian\n",
       "2                          Hot chocolate,Jam,Cookies\n",
       "3                                             Muffin\n",
       "4                                Coffee,Pastry,Bread\n",
       "                            ...                     \n",
       "9460                                           Bread\n",
       "9461    Truffles,Tea,Spanish Brunch,Christmas common\n",
       "9462                  Muffin,Tacos/Fajita,Coffee,Tea\n",
       "9463                                   Coffee,Pastry\n",
       "9464                                       Smoothies\n",
       "Name: Item, Length: 9465, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_per_transcation.Item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec49408e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Bread'],\n",
       " ['Scandinavian', 'Scandinavian'],\n",
       " ['Hot chocolate', 'Jam', 'Cookies'],\n",
       " ['Muffin'],\n",
       " ['Coffee', 'Pastry', 'Bread'],\n",
       " ['Medialuna', 'Pastry', 'Muffin'],\n",
       " ['Medialuna', 'Pastry', 'Coffee', 'Tea'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Bread', 'Muffin'],\n",
       " ['Scandinavian', 'Medialuna'],\n",
       " ['Bread', 'Medialuna', 'Bread'],\n",
       " ['Jam', 'Coffee', 'Tartine', 'Pastry', 'Tea'],\n",
       " ['Basket', 'Bread', 'Coffee'],\n",
       " ['Bread', 'Medialuna', 'Pastry'],\n",
       " ['Mineral water', 'Scandinavian'],\n",
       " ['Bread', 'Medialuna', 'Coffee'],\n",
       " ['Hot chocolate'],\n",
       " ['Farm House'],\n",
       " ['Farm House', 'Bread'],\n",
       " ['Bread', 'Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Medialuna', 'Bread'],\n",
       " ['Jam'],\n",
       " ['Scandinavian', 'Muffin'],\n",
       " ['Bread'],\n",
       " ['Scandinavian'],\n",
       " ['Fudge'],\n",
       " ['Scandinavian'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread', 'Jam'],\n",
       " ['Bread'],\n",
       " ['Basket'],\n",
       " ['Scandinavian', 'Muffin'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Muffin', 'Scandinavian'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread', 'Tea'],\n",
       " ['Scandinavian'],\n",
       " ['Juice', 'Tartine', 'Coffee', 'Muffin'],\n",
       " ['Scandinavian'],\n",
       " ['Bread', 'Tea'],\n",
       " ['Scandinavian', 'Fudge'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Coffee', 'Hot chocolate', 'Medialuna'],\n",
       " ['Coffee'],\n",
       " [\"Ella's Kitchen Pouches\", 'Juice', 'Bread', 'Muffin', 'Jam'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Coffee', 'Medialuna'],\n",
       " ['Bread', 'Victorian Sponge'],\n",
       " ['Bread'],\n",
       " ['Scandinavian'],\n",
       " ['Bread'],\n",
       " ['Frittata', 'Coffee', 'Tea', 'Hearty & Seasonal'],\n",
       " ['Coffee', 'Frittata'],\n",
       " ['Scandinavian'],\n",
       " ['Victorian Sponge', 'Hot chocolate', 'Tea', 'Soup'],\n",
       " ['Tea'],\n",
       " ['Cookies', 'Coffee', 'Juice', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Pick and Mix Bowls',\n",
       "  'Hearty & Seasonal',\n",
       "  'Hearty & Seasonal',\n",
       "  'Coffee',\n",
       "  'Smoothies',\n",
       "  'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Cake'],\n",
       " ['Tartine', 'Mighty Protein', 'Tea', 'Coffee'],\n",
       " ['Hearty & Seasonal', 'Frittata', 'Mineral water'],\n",
       " ['Hearty & Seasonal', 'Mineral water', 'Muffin'],\n",
       " ['Frittata', 'Coffee', 'Tea', 'Scandinavian', 'Chicken sand'],\n",
       " ['Bread', 'Tea', 'Victorian Sponge'],\n",
       " ['Fudge'],\n",
       " ['Muffin'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Jam', 'Frittata'],\n",
       " ['Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Fudge'],\n",
       " ['Fudge', 'Tea', 'Coffee', 'Muffin'],\n",
       " ['Frittata', 'Bread', 'Coffee'],\n",
       " ['Coffee', 'Cake'],\n",
       " ['Tartine', 'Bread'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee', 'Pastry', 'Medialuna'],\n",
       " ['Juice'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee', 'Jam'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Tea'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Coke'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Tea', 'Pastry', 'Farm House'],\n",
       " ['Pastry', 'Pastry', 'Coffee', 'Juice'],\n",
       " ['Coffee', 'Pastry', 'Juice'],\n",
       " ['Farm House'],\n",
       " ['Pastry', 'Pastry', 'Coffee', 'Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee', 'Medialuna', 'Muffin', 'Coffee'],\n",
       " ['Coffee', 'Tea', 'Cake'],\n",
       " ['Coffee', 'Coffee', 'Pastry', 'Pastry'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Farm House', 'Muffin'],\n",
       " ['Bread', 'Muffin', 'Pastry'],\n",
       " ['Pastry'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Cake'],\n",
       " ['Farm House'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Cookies', 'Cake', 'Coffee', 'My-5 Fruit Shoot'],\n",
       " ['Cookies', 'Cookies', 'Tea', 'Pastry'],\n",
       " ['Coffee', 'Coffee', 'Muffin'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Cookies'],\n",
       " ['Jam', 'Coffee', 'Coffee', 'Juice'],\n",
       " ['Coffee', 'Soup'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Jam'],\n",
       " ['Farm House'],\n",
       " ['Tartine', 'Coffee', 'Tea'],\n",
       " ['Coffee', 'Coffee', 'Cake'],\n",
       " ['Coffee', 'Coffee', 'Muffin'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Tea', 'Juice', 'Pick and Mix Bowls', 'Cookies'],\n",
       " ['Hearty & Seasonal', 'Soup'],\n",
       " ['Hearty & Seasonal', 'Tea', 'Farm House'],\n",
       " ['Bread'],\n",
       " ['Tea', 'Soup'],\n",
       " ['Farm House'],\n",
       " ['Soup', 'Hearty & Seasonal', 'Coffee'],\n",
       " ['Bread', 'Muffin', 'Hearty & Seasonal'],\n",
       " ['Coffee', 'Coffee', 'Cookies'],\n",
       " ['Bread'],\n",
       " ['Tea', 'Muffin'],\n",
       " ['Tea'],\n",
       " ['Soup', 'Hearty & Seasonal'],\n",
       " ['Coffee', 'Tea', 'Focaccia'],\n",
       " ['Sandwich'],\n",
       " ['Tartine', 'Coffee'],\n",
       " ['Fudge', 'Coffee', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coke', 'Soup'],\n",
       " ['Muffin'],\n",
       " ['Tea', 'Coffee', 'Cookies', 'Muffin'],\n",
       " ['Mighty Protein', 'Hearty & Seasonal', 'Coffee', 'Coke'],\n",
       " ['Tea', 'Muffin', 'Sandwich'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Bread', 'Hot chocolate', 'Cookies'],\n",
       " ['Mineral water', 'Muffin'],\n",
       " ['Tea'],\n",
       " ['Muffin'],\n",
       " ['Coffee', 'Juice'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Sandwich', 'Hearty & Seasonal', 'Bread', 'Mineral water'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Mineral water'],\n",
       " ['Coffee'],\n",
       " ['Soup', 'Tea'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Bread', 'Muffin'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Cookies', 'Coffee'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Jam', 'Tea'],\n",
       " ['Tea', 'Tea'],\n",
       " ['Pastry', 'Pastry', \"Ella's Kitchen Pouches\"],\n",
       " ['Pastry'],\n",
       " ['Farm House', 'Tea'],\n",
       " ['Tea', 'Victorian Sponge'],\n",
       " ['Tea', 'Pastry'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Coffee', 'Pastry', 'Pastry'],\n",
       " ['Soup', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Bread'],\n",
       " ['Bread', 'Scandinavian'],\n",
       " ['Bread', 'Cake', 'Cookies'],\n",
       " ['Coffee', 'Coffee', 'Tea'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Scandinavian', 'Bread'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Scandinavian', 'Coffee', 'Pastry'],\n",
       " ['Hearty & Seasonal', 'Coffee', 'Muffin'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Soup', 'Coffee'],\n",
       " ['Coffee', 'Cookies', 'Tartine'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Tea', 'Tea'],\n",
       " ['Pastry', 'Coffee'],\n",
       " ['Tartine', 'Coffee'],\n",
       " ['Bread', 'Cookies'],\n",
       " ['Sandwich'],\n",
       " ['Tartine'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Bread', 'Scandinavian'],\n",
       " ['Scandinavian'],\n",
       " ['Alfajores', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Coke', 'Soup'],\n",
       " ['Cake'],\n",
       " ['Tea', 'Sandwich', 'Soup'],\n",
       " ['Coffee'],\n",
       " ['Hearty & Seasonal', 'Bread'],\n",
       " ['Alfajores'],\n",
       " ['Cake'],\n",
       " ['Tea', 'Tea', 'Bread'],\n",
       " ['Coffee', 'Tea', 'Bread', 'Soup'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Fudge'],\n",
       " ['Coffee', 'Sandwich'],\n",
       " ['Tea', 'Bread', 'Cookies', 'Pastry'],\n",
       " ['Muffin'],\n",
       " ['Scandinavian'],\n",
       " ['Jam', 'Tea'],\n",
       " ['Juice', 'Tea', 'Alfajores'],\n",
       " ['Cake', 'Muffin', 'Coffee', 'Soup'],\n",
       " ['Hot chocolate', 'Cake', 'Coffee'],\n",
       " ['Cake', 'Cookies'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Alfajores', 'Coffee', 'Coffee', 'Coffee', 'Juice'],\n",
       " ['Coffee', 'Tea', 'Soup'],\n",
       " ['Hot chocolate'],\n",
       " ['Cookies'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Bread', 'Eggs'],\n",
       " ['Scandinavian'],\n",
       " ['Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread', 'Coffee', 'Pastry'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Pastry'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Hot chocolate', 'Pastry'],\n",
       " ['Coke'],\n",
       " ['Coffee', 'Jam'],\n",
       " ['Tea'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Coffee', 'Alfajores', 'Farm House'],\n",
       " ['Tea', 'Pastry'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Jam'],\n",
       " ['Coffee', 'Tea', 'Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Bread', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Bread', 'Bread'],\n",
       " ['Bread', 'Coffee', 'Pastry'],\n",
       " ['Pastry', 'Coffee'],\n",
       " ['Coffee', 'Farm House'],\n",
       " ['Farm House', 'Farm House', 'Pastry'],\n",
       " ['Bread', 'Juice', 'Alfajores'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Bread', 'Alfajores'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Cookies'],\n",
       " ['Hearty & Seasonal'],\n",
       " ['Coffee'],\n",
       " ['Jam', 'Tartine', 'Bread'],\n",
       " ['Farm House', 'Scandinavian'],\n",
       " ['Hearty & Seasonal', 'Coffee', 'Muffin', 'Scandinavian'],\n",
       " ['Tartine', 'Pick and Mix Bowls', 'Coffee', 'Coke'],\n",
       " ['Bread'],\n",
       " ['Scandinavian', 'Farm House', 'Alfajores', 'Coffee', 'Coffee'],\n",
       " ['Juice', 'Mineral water', 'Soup'],\n",
       " ['Soup', 'Tea'],\n",
       " ['Juice', 'Tea', 'Cookies', 'Soup'],\n",
       " ['Hearty & Seasonal', 'Tea'],\n",
       " ['Fudge'],\n",
       " ['Coffee', 'Mineral water', 'Juice', 'Soup'],\n",
       " ['Hearty & Seasonal'],\n",
       " ['Hearty & Seasonal', 'Smoothies'],\n",
       " ['Hearty & Seasonal', 'Tea'],\n",
       " ['Coffee', 'Sandwich', 'Sandwich'],\n",
       " ['Cookies', 'Farm House'],\n",
       " ['Soup', 'Tea', 'Juice'],\n",
       " ['Soup'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Cookies', 'Tea'],\n",
       " ['Soup', 'Hearty & Seasonal', 'Bread'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Coffee'],\n",
       " ['Fudge'],\n",
       " ['Tea'],\n",
       " ['Soup', 'Tea'],\n",
       " ['Juice'],\n",
       " ['Alfajores'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Alfajores'],\n",
       " ['Juice', 'Alfajores', 'Cookies', 'Pick and Mix Bowls', 'Mineral water'],\n",
       " ['Coffee', 'Alfajores'],\n",
       " ['Fudge'],\n",
       " ['Mighty Protein', 'Juice'],\n",
       " ['Coffee', 'Mineral water', 'Alfajores'],\n",
       " ['Mighty Protein', 'Coffee'],\n",
       " ['Alfajores'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Medialuna', 'Medialuna'],\n",
       " ['Medialuna', 'Bread'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Coffee', 'Pastry', 'Pastry', 'Coffee'],\n",
       " ['Medialuna'],\n",
       " ['Bread', 'Tea', 'Sandwich'],\n",
       " ['Bread'],\n",
       " ['Jam', 'Coffee', 'Farm House'],\n",
       " ['Focaccia'],\n",
       " ['Bread'],\n",
       " ['Tartine', 'Pastry', 'Bread', 'My-5 Fruit Shoot'],\n",
       " ['Farm House', 'Pastry'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Jam', 'Coffee'],\n",
       " ['Farm House'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Coffee', 'Tea', 'Bread'],\n",
       " ['Jam'],\n",
       " ['Pastry', 'Medialuna', 'Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Tea'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Pastry'],\n",
       " ['Farm House'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Brownie', 'Alfajores', 'Fudge'],\n",
       " ['Bread', 'Fudge', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee', 'Jam', 'Pastry'],\n",
       " ['Soup', 'Coffee'],\n",
       " ['Smoothies', 'Soup', 'Hearty & Seasonal', 'Pastry'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Pastry', 'Medialuna'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Bread', 'Cookies'],\n",
       " ['Coffee',\n",
       "  'Coffee',\n",
       "  'Bread',\n",
       "  'Hearty & Seasonal',\n",
       "  'Pick and Mix Bowls',\n",
       "  'Sandwich',\n",
       "  'Focaccia'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Sandwich', 'Brownie'],\n",
       " ['Brownie', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Sandwich', 'Bread', 'Coffee'],\n",
       " ['Coffee', 'Hot chocolate'],\n",
       " ['Tea'],\n",
       " ['Sandwich', 'Coffee', 'Brownie'],\n",
       " ['Brownie', 'Pastry', 'Focaccia', 'Farm House'],\n",
       " ['Cake', 'Focaccia'],\n",
       " ['Soup', 'Hearty & Seasonal', 'Mineral water'],\n",
       " ['Hearty & Seasonal'],\n",
       " ['Hearty & Seasonal'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Coffee'],\n",
       " ['Tea', 'Cake'],\n",
       " ['Coffee', 'Soup'],\n",
       " ['Bread', 'Focaccia', 'Fudge'],\n",
       " ['Hot chocolate', 'Coffee', 'Alfajores'],\n",
       " ['Brownie', 'Pastry', 'Cake'],\n",
       " ['Medialuna', 'Alfajores', 'Dulce de Leche'],\n",
       " ['Dulce de Leche', 'Alfajores'],\n",
       " ['Bread', 'Cake'],\n",
       " ['Cake', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Sandwich'],\n",
       " ['Bread', 'Coffee', 'Alfajores', 'Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Alfajores'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Dulce de Leche'],\n",
       " ['Farm House'],\n",
       " ['Fudge'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Alfajores', 'Juice', \"Ella's Kitchen Pouches\", 'Brownie'],\n",
       " ['Alfajores'],\n",
       " ['Alfajores', 'Brownie', 'Cookies', 'Bread', 'Juice'],\n",
       " ['Cake', 'Cookies'],\n",
       " ['Pick and Mix Bowls'],\n",
       " ['Coffee'],\n",
       " ['Farm House'],\n",
       " ['Tea'],\n",
       " ['Coffee'],\n",
       " ['Focaccia', 'Farm House'],\n",
       " ['Dulce de Leche'],\n",
       " ['Juice', 'My-5 Fruit Shoot', \"Ella's Kitchen Pouches\", 'Cookies'],\n",
       " ['Bread'],\n",
       " ['Hot chocolate'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Medialuna'],\n",
       " ['Bread', 'Tea', 'Jam'],\n",
       " ['Farm House'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Medialuna', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Medialuna', 'Pastry', 'Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Jam'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Hot chocolate'],\n",
       " ['Bread', 'Medialuna'],\n",
       " ['Bread', 'Jam'],\n",
       " ['Jam', 'Bread'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Bread', 'Farm House'],\n",
       " ['Bread', 'Coffee', 'Coffee'],\n",
       " ['Tea', 'Pastry'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Pastry', 'Brownie'],\n",
       " ['Farm House'],\n",
       " ['Coffee'],\n",
       " ['Tea', 'Coffee', 'Honey', 'Cookies'],\n",
       " ['Tea'],\n",
       " ['Bread'],\n",
       " ['Brownie'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Tea', 'Tea'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Bread', 'Bread'],\n",
       " ['Bread', 'The BART'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Mineral water', 'Coffee', 'Coffee', 'Tartine'],\n",
       " ['Brownie', 'Coffee'],\n",
       " ['Brownie', 'Juice'],\n",
       " ['Coffee',\n",
       "  'Coffee',\n",
       "  'Bread',\n",
       "  'Tea',\n",
       "  'Alfajores',\n",
       "  'Medialuna',\n",
       "  'Pastry',\n",
       "  'Jam'],\n",
       " ['Bread'],\n",
       " ['Fudge', 'Fudge'],\n",
       " ['Coffee', 'Pick and Mix Bowls'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Soup', 'Scandinavian', 'Coffee', 'Mineral water'],\n",
       " ['Juice', 'Mineral water', 'Sandwich', 'Pick and Mix Bowls'],\n",
       " ['Bread', 'Pastry', 'Bread'],\n",
       " ['Hearty & Seasonal', 'Hearty & Seasonal', 'Coffee', 'Coffee'],\n",
       " ['Bread', 'Hearty & Seasonal', 'Coffee', 'Mineral water'],\n",
       " ['Coffee', 'Tartine', 'Sandwich', 'Brownie'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Alfajores', 'Coffee', 'Tartine'],\n",
       " ['Mighty Protein', 'Coffee'],\n",
       " ['Coffee', 'Hot chocolate'],\n",
       " ['Soup', 'Jam', 'Coffee', 'Coffee'],\n",
       " ['Mineral water', 'Brownie'],\n",
       " ['Scandinavian', 'Coffee'],\n",
       " ['Scandinavian', 'Coffee'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee', 'Coffee', 'Hot chocolate', 'Brownie', 'Hearty & Seasonal'],\n",
       " ['Tea', 'Cake'],\n",
       " ['Medialuna'],\n",
       " ['Alfajores', 'Tea', 'Coffee'],\n",
       " ['Coffee', 'Juice', 'Brownie', 'Alfajores'],\n",
       " ['Coffee', 'Sandwich'],\n",
       " ['Bread', 'Soup'],\n",
       " ['Soup', 'Bread'],\n",
       " ['Bread'],\n",
       " ['Tea', 'Brownie', 'Cookies'],\n",
       " ['Coffee', 'Bread', 'Brownie'],\n",
       " ['Brownie'],\n",
       " ['Cookies', 'Bread', 'Jam'],\n",
       " ['Coffee'],\n",
       " ['Tea', 'Brownie', 'Tea'],\n",
       " ['Alfajores', 'Medialuna', 'Scandinavian'],\n",
       " ['Hot chocolate', 'Coffee', 'Alfajores', 'Cookies'],\n",
       " ['Brownie'],\n",
       " ['Fudge'],\n",
       " ['Tea', 'Brownie'],\n",
       " ['Dulce de Leche', 'Fudge'],\n",
       " ['Bread', 'Scandinavian', 'Fudge', 'Granola', 'Pastry', 'Coffee'],\n",
       " ['Bread', 'Fudge'],\n",
       " ['Hot chocolate', 'Coffee', 'Medialuna', 'Medialuna'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Medialuna'],\n",
       " ['Bread', 'Coffee', 'Medialuna', 'Jam'],\n",
       " ['Jam', 'Coffee', 'Brownie'],\n",
       " ['Bread', 'Coffee', 'Jam'],\n",
       " ['Scandinavian'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Coffee', 'Coffee', 'Scandinavian'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Jam', 'Brownie', 'Coffee', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Brownie', 'Hot chocolate'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Bread', 'Medialuna'],\n",
       " ['Coffee', 'Hot chocolate', 'Brownie'],\n",
       " ['Scandinavian'],\n",
       " ['Basket', 'Bread'],\n",
       " ['Coffee', 'Bread', 'Brownie'],\n",
       " ['Cake', 'Fudge'],\n",
       " ['Coffee', 'Hot chocolate', 'Coffee', 'Basket'],\n",
       " ['Scandinavian'],\n",
       " ['Scandinavian'],\n",
       " ['Coffee', 'Bread', 'Coffee', 'Cake', 'Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Medialuna'],\n",
       " ['Bread', 'Pastry'],\n",
       " ['Bread', 'Brownie'],\n",
       " ['Medialuna', 'Farm House'],\n",
       " ['Bread', 'Medialuna'],\n",
       " ['Brownie', 'Medialuna', 'Hot chocolate', 'Smoothies', 'Coffee'],\n",
       " ['Bread', 'Tea', 'Coffee', 'Brownie', 'Medialuna'],\n",
       " ['Medialuna', 'Medialuna', 'Cookies'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Bread', 'Fudge'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Pastry', 'Bread', 'Brownie'],\n",
       " ['Bread', 'Coffee', 'Bread'],\n",
       " ['Coffee', 'Coffee', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Medialuna', 'Coffee'],\n",
       " ['My-5 Fruit Shoot', 'Coffee', 'Brownie'],\n",
       " ['Coffee', 'Fudge'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Basket'],\n",
       " ['Farm House'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Scandinavian', 'Brownie', 'My-5 Fruit Shoot'],\n",
       " ['Tartine', 'Coffee', 'Hot chocolate', 'Brownie'],\n",
       " ['Brownie', 'Bread', 'Hot chocolate'],\n",
       " ['Jam', 'Fudge'],\n",
       " ['Coffee'],\n",
       " ['Fudge'],\n",
       " ['Mighty Protein', 'Hearty & Seasonal', 'Coffee', 'Tea', 'Cake'],\n",
       " ['Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Cake', 'Victorian Sponge', 'Scandinavian'],\n",
       " ['Tea', 'Cake'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Frittata'],\n",
       " ['Coffee', 'Coffee', 'My-5 Fruit Shoot', 'Brownie'],\n",
       " ['Focaccia', 'Fairy Doors'],\n",
       " ['Tea', 'Coffee', 'Coffee'],\n",
       " ['Hot chocolate', 'Hearty & Seasonal', 'Pick and Mix Bowls', 'Brownie'],\n",
       " ['Frittata', 'Coffee'],\n",
       " ['Bread', 'Brownie'],\n",
       " ['Frittata'],\n",
       " ['Scandinavian'],\n",
       " ['Bread'],\n",
       " ['Farm House'],\n",
       " ['Pick and Mix Bowls', 'Jam', 'Soup', 'Brownie'],\n",
       " ['Hearty & Seasonal', 'Coffee', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Frittata'],\n",
       " ['Scandinavian'],\n",
       " ['Tartine', 'Hot chocolate', 'Coffee'],\n",
       " ['Coffee', 'Mighty Protein'],\n",
       " ['Frittata', 'Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Hearty & Seasonal', 'Coffee', 'Mineral water'],\n",
       " ['Scandinavian'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Hearty & Seasonal', 'Soup', 'Bread', 'Coffee'],\n",
       " ['Brownie', 'Cake', 'Tea'],\n",
       " ['Mighty Protein', 'Coffee'],\n",
       " ['Coffee', 'Tea', 'Brownie', 'Soup'],\n",
       " ['Cake'],\n",
       " ['Hot chocolate', 'Coffee', 'Cake'],\n",
       " ['Hearty & Seasonal', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Frittata', 'Coffee', 'Brownie'],\n",
       " ['Sandwich', 'Frittata', 'Coke', 'Mineral water', 'Brownie'],\n",
       " ['Granola', 'Scandinavian'],\n",
       " ['Coffee', 'Coke', 'Coffee'],\n",
       " ['Tea', 'Tea', 'Brownie', 'Cake'],\n",
       " ['Fudge'],\n",
       " ['Coffee'],\n",
       " ['Brownie'],\n",
       " ['Cake'],\n",
       " ['Tea'],\n",
       " ['Coffee', 'Coffee', 'Coffee', 'Soup', 'Sandwich', 'Cake'],\n",
       " ['Coffee', 'Bread', 'Brownie'],\n",
       " ['Bread', 'Coffee', 'Brownie'],\n",
       " ['Coffee', 'Bread', 'Brownie'],\n",
       " ['Brownie', 'Coffee'],\n",
       " ['Coffee', 'Soup', 'Brownie'],\n",
       " ['Fudge'],\n",
       " ['Bread', 'Focaccia'],\n",
       " ['Bread', 'Farm House', 'Jam'],\n",
       " ['Hot chocolate', 'Brownie'],\n",
       " ['Hot chocolate'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Medialuna', 'Pastry'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Pastry', 'Bread'],\n",
       " ['Coffee', 'Mineral water'],\n",
       " ['Pastry', 'Bread', 'Hot chocolate'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Jam', 'Coffee', 'Coffee', 'Bread'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Medialuna', 'Coffee', 'Coffee', 'Focaccia', 'Hearty & Seasonal'],\n",
       " ['Bread', 'Farm House', 'Coffee', 'Jam'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Tea', 'Pastry', 'Medialuna'],\n",
       " ['Pastry', 'Medialuna', 'Coffee'],\n",
       " ['Coffee', 'Hot chocolate', 'Basket', 'Farm House'],\n",
       " ['Pastry', 'Farm House'],\n",
       " ['Hot chocolate', 'Coffee', 'My-5 Fruit Shoot'],\n",
       " ['Pastry', 'Medialuna', 'Muffin'],\n",
       " ['Farm House'],\n",
       " ['Muffin', 'Pastry', 'Focaccia'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Focaccia'],\n",
       " ['Coffee', 'Hot chocolate', 'Pastry', 'Jam'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Pastry', 'Bread'],\n",
       " ['Tea'],\n",
       " ['Hot chocolate', 'Coffee', 'Mineral water'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Jam', 'Coffee', 'Coffee'],\n",
       " ['Muffin'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Muffin', 'Hot chocolate', 'Coffee'],\n",
       " ['Muffin', 'Fudge'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Focaccia'],\n",
       " ['Bread', 'Tea'],\n",
       " ['Coffee', 'Hot chocolate'],\n",
       " ['Coffee', 'Tartine', 'Coffee', 'Pick and Mix Bowls', 'Jam'],\n",
       " ['Jam'],\n",
       " ['Tartine'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Coffee',\n",
       "  'Coffee',\n",
       "  'Mineral water',\n",
       "  'Juice',\n",
       "  'Tartine',\n",
       "  'Pick and Mix Bowls'],\n",
       " ['Bread', 'Tea', 'Juice'],\n",
       " ['Coffee', 'Coffee', 'Cake'],\n",
       " ['Farm House'],\n",
       " ['Farm House'],\n",
       " ['Cake', 'Brownie', 'Coffee', 'Coffee'],\n",
       " ['Focaccia', 'Coffee'],\n",
       " ['Tea', 'Coffee', 'Muffin'],\n",
       " ['Coffee'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Hearty & Seasonal', 'Cake', 'Coffee'],\n",
       " ['Bread', 'Tea', 'Cake'],\n",
       " ['Mighty Protein', 'Coffee'],\n",
       " ['Hot chocolate', 'Muffin'],\n",
       " ['Coffee', 'Coffee', 'Soup', 'Mighty Protein'],\n",
       " ['Jam'],\n",
       " ['Coffee', 'Alfajores'],\n",
       " ['Cake', 'Hot chocolate', 'Alfajores'],\n",
       " ['Hot chocolate', 'Tea', 'Muffin'],\n",
       " ['Brownie'],\n",
       " ['Cake', 'Coffee'],\n",
       " ['Victorian Sponge', 'Alfajores', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Tartine', 'Tea', 'Tartine'],\n",
       " ['Mighty Protein', 'Tea'],\n",
       " ['Bread', 'Fudge'],\n",
       " ['Medialuna', 'Tartine', 'Coffee'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Pastry', 'Bread'],\n",
       " ['Coffee', 'Tea', 'Alfajores', 'Alfajores'],\n",
       " ['Alfajores', 'Alfajores', 'Coffee'],\n",
       " ['Coffee', 'Alfajores'],\n",
       " ['Alfajores', 'Pastry'],\n",
       " ['Alfajores'],\n",
       " ['Pastry', 'Tea', 'Alfajores', 'Hearty & Seasonal'],\n",
       " ['Tea', 'Sandwich'],\n",
       " ['Empanadas', 'Tea'],\n",
       " ['Alfajores', 'Alfajores', 'Tea'],\n",
       " ['Alfajores'],\n",
       " ['Pastry', 'Dulce de Leche'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Farm House', 'Coffee'],\n",
       " ['Bread', 'Scandinavian', 'Medialuna'],\n",
       " ['Bread'],\n",
       " ['Pastry', 'Tea', 'Alfajores'],\n",
       " ['Pastry', 'Coffee'],\n",
       " ['Bread', 'Pastry', 'Muffin', 'Farm House'],\n",
       " ['Medialuna', 'Tea'],\n",
       " ['Alfajores'],\n",
       " ['Sandwich', 'Tea'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Bread', 'Coffee', 'Pastry'],\n",
       " ['Coffee', 'Coffee', 'Muffin', 'Alfajores', 'Empanadas'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Pastry', 'Medialuna', 'Tea'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Muffin'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Coffee', 'Bread', 'Pastry', 'Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Muffin'],\n",
       " ['Muffin', 'Hot chocolate', 'Scandinavian', 'Bread'],\n",
       " ['Farm House'],\n",
       " ['Muffin'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee', 'Cookies'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Muffin'],\n",
       " ['Coffee', 'Tea', 'Keeping It Local'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Bread'],\n",
       " ['Tartine', 'Scandinavian'],\n",
       " ['Coffee', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coke'],\n",
       " ['Coffee', 'Muffin'],\n",
       " ['Coffee', 'Bread', 'Coffee', 'Medialuna', 'Muffin', 'Scandinavian'],\n",
       " ['Bread'],\n",
       " ['Coke'],\n",
       " ['Bread', 'Scandinavian'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Scandinavian', 'Bread'],\n",
       " ['Fudge', 'Fudge'],\n",
       " ['Soup', 'Hearty & Seasonal', 'Coffee'],\n",
       " ['Coke'],\n",
       " ['Coffee', 'Cookies', 'Tea'],\n",
       " ['Pastry', 'Tea', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Coffee', 'Coffee', 'Muffin', 'Sandwich', 'Soup'],\n",
       " ['Tea', 'Coffee', 'Muffin'],\n",
       " ['Alfajores'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Medialuna'],\n",
       " ['Empanadas', 'Bread', 'Scandinavian'],\n",
       " ['Coffee', 'Tartine'],\n",
       " ['Tea'],\n",
       " ['Scandinavian'],\n",
       " ['Muffin'],\n",
       " ['Scandinavian', 'Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Cookies', 'Scandinavian'],\n",
       " ['Coffee', 'Bread', 'Empanadas', 'Medialuna'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Bread'],\n",
       " ['Pastry', 'Tea'],\n",
       " ['Coffee', 'Coffee', 'Pastry'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Coffee', 'Keeping It Local'],\n",
       " ['Fudge'],\n",
       " ['Tartine', 'Coffee', 'Coffee'],\n",
       " ['Hot chocolate', 'Coffee', 'Pastry', 'Medialuna'],\n",
       " ['Alfajores', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Coffee', 'Medialuna'],\n",
       " ['Coke'],\n",
       " ['Coffee', 'Coffee', 'Alfajores'],\n",
       " ['Muffin'],\n",
       " ['Farm House', 'Brownie', 'Fudge'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Tea', 'Muffin', 'Farm House'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Pastry', 'Brownie'],\n",
       " ['Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Pastry', 'Coffee', 'Juice'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Tea', 'Alfajores'],\n",
       " ['Alfajores', 'Coffee'],\n",
       " ['Scandinavian'],\n",
       " ['Pastry', 'Medialuna'],\n",
       " ['Brownie'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Soup'],\n",
       " ['Art Tray', 'Coffee', 'Coffee', 'Brownie'],\n",
       " ['Coffee'],\n",
       " ['Farm House'],\n",
       " ['Soup', 'Art Tray', 'Alfajores', 'Frittata'],\n",
       " ['Sandwich', 'Coffee'],\n",
       " ['Tea'],\n",
       " ['Farm House'],\n",
       " ['Scandinavian', 'Muffin'],\n",
       " ['Farm House'],\n",
       " ['Scandinavian'],\n",
       " ['Soup'],\n",
       " ['Hearty & Seasonal', 'Tea', 'Bread'],\n",
       " ['Hot chocolate'],\n",
       " ['Coffee', 'Sandwich', 'Hearty & Seasonal'],\n",
       " ['Brownie', 'Hearty & Seasonal', 'Sandwich', 'Coffee'],\n",
       " ['Medialuna', 'Coffee'],\n",
       " ['Coffee', 'Brownie', 'Soup'],\n",
       " ['Sandwich', 'Sandwich', 'Alfajores', 'Alfajores', 'Focaccia'],\n",
       " ['Farm House', 'Tartine'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Hot chocolate', 'Tea', 'Muffin'],\n",
       " ['Pastry'],\n",
       " ['Coffee', 'Brownie', 'Hearty & Seasonal', 'Soup', 'Bowl Nic Pitt'],\n",
       " ['Coffee'],\n",
       " ['Alfajores', 'Medialuna', 'Scandinavian', 'Farm House'],\n",
       " ['Soup', 'Coffee'],\n",
       " ['Tea', 'Tea'],\n",
       " ['Coffee', 'Tea', 'Cookies'],\n",
       " ['Tea', 'Muffin'],\n",
       " ['Coffee', 'Coffee', 'Brownie'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Farm House'],\n",
       " ['Coffee', 'Bread Pudding'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Hot chocolate'],\n",
       " ['Coffee', 'Hot chocolate', 'Farm House'],\n",
       " ['Pastry', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Pastry', 'Coffee'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Hot chocolate'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Focaccia', 'Coffee', 'Medialuna', 'Bread'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Keeping It Local'],\n",
       " ['Bread', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Coffee', 'Medialuna'],\n",
       " ['Coffee', 'Cookies', 'Tartine'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Bread', 'Keeping It Local', 'Coffee'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Tea'],\n",
       " ['Juice', 'Coke', 'Soup'],\n",
       " ['Bread'],\n",
       " ['Brownie'],\n",
       " ['Brownie', 'Bread', 'Coffee'],\n",
       " ['Coffee', 'Pastry', 'Bread', 'Hearty & Seasonal'],\n",
       " ['Bread', 'Coffee', 'Brownie'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Cookies'],\n",
       " ['Fudge', 'Eggs'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Muffin'],\n",
       " ['Soup'],\n",
       " ['Coffee', 'Bread Pudding'],\n",
       " ['Bread', 'Tartine'],\n",
       " ['Sandwich', 'Coffee', 'Fudge', 'Jam', 'Bread Pudding'],\n",
       " ['Bread Pudding', 'Soup', 'Coffee'],\n",
       " ['Hearty & Seasonal', 'Bread'],\n",
       " ['Coffee', 'Coffee', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Bread', 'Soup'],\n",
       " ['Soup', 'Tea', 'Focaccia'],\n",
       " ['Coffee', 'My-5 Fruit Shoot', 'Cookies'],\n",
       " ['Coffee', 'Coffee'],\n",
       " ['Coffee', 'Sandwich', 'Tea'],\n",
       " ['Scandinavian'],\n",
       " ['Adjustment'],\n",
       " ['Coffee'],\n",
       " ['Medialuna', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Pastry'],\n",
       " ['Farm House'],\n",
       " ['Medialuna', 'Brownie'],\n",
       " ['Bread', 'Pastry', 'Brownie'],\n",
       " ['Pastry', 'Bread', 'Cookies'],\n",
       " ['Bread', 'Bread'],\n",
       " ['Cookies', 'Pastry', 'Coffee', 'Juice'],\n",
       " ['Pastry', 'Cookies', 'Coffee'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Bread'],\n",
       " ['Tartine', 'Coffee'],\n",
       " ['Coffee', 'Medialuna'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Coffee', 'Alfajores', 'Cookies'],\n",
       " ['Bread', 'Coffee', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Coffee', 'Tea', 'Keeping It Local', 'Jam', 'Cookies'],\n",
       " ['Pastry', 'Coffee', 'Juice'],\n",
       " ['Medialuna', 'Bread', 'Focaccia'],\n",
       " ['Pastry', 'Medialuna', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Cookies', 'Truffles', 'Brownie', 'Bread'],\n",
       " ['Art Tray', 'Tea'],\n",
       " ['Bread'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Brownie'],\n",
       " ['Bread', 'Tea', 'Empanadas'],\n",
       " ['Bread', 'Hot chocolate'],\n",
       " ['Coffee', 'Cookies'],\n",
       " ['Bread'],\n",
       " ['Tartine', 'Sandwich', 'Tea', 'Bread'],\n",
       " ['Soup', 'Sandwich', 'Bread'],\n",
       " ['Soup', 'Bread'],\n",
       " ['Fudge'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Empanadas', 'Soup'],\n",
       " ['Bread', 'Coffee', 'Brownie'],\n",
       " ['Coffee', 'Smoothies', 'Juice', 'Soup', 'Hearty & Seasonal'],\n",
       " ['Alfajores',\n",
       "  'Sandwich',\n",
       "  'Hot chocolate',\n",
       "  'Coffee',\n",
       "  'Scandinavian',\n",
       "  'Cookies'],\n",
       " ['Tea', 'Coffee'],\n",
       " ['Bread', 'Cookies'],\n",
       " ['Alfajores', 'Cookies', 'Bread', 'Bread'],\n",
       " ['Tea', 'Brownie'],\n",
       " ['Tea', 'Bread'],\n",
       " ['Coffee', 'Alfajores'],\n",
       " ['Bread'],\n",
       " ['Empanadas', 'Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Hot chocolate'],\n",
       " ['Coffee'],\n",
       " ['Coffee', 'Brownie'],\n",
       " ['Tea', 'Hot chocolate', 'Brownie', 'Truffles'],\n",
       " ['Coffee', 'Keeping It Local'],\n",
       " ['Coffee'],\n",
       " ['Alfajores'],\n",
       " ['Scandinavian', 'Chimichurri Oil'],\n",
       " ['Bread', 'Truffles'],\n",
       " ['Brownie', 'Focaccia'],\n",
       " ['Coffee', 'Bread'],\n",
       " ['Coffee', 'Tea', 'Art Tray', 'Cookies'],\n",
       " ['Coffee'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Alfajores', 'Alfajores', 'Coke'],\n",
       " ['Bread'],\n",
       " ['Bread'],\n",
       " ['Bread', 'Pastry', 'Medialuna', 'Coffee'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Keeping It Local'],\n",
       " ['Bread'],\n",
       " ['Coffee', 'Scandinavian'],\n",
       " ['Bread', 'Farm House', 'Pastry', 'Medialuna'],\n",
       " ['Medialuna'],\n",
       " ['Coffee'],\n",
       " ['Bread', 'Farm House'],\n",
       " ['Bread'],\n",
       " ['Keeping It Local', 'Bread'],\n",
       " ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert items into list form\n",
    "items_list = [item.split(',') for item in items_per_transcation['Item']]\n",
    "items_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db686fe",
   "metadata": {},
   "source": [
    "Set this items list up in a separate data frame with just one column. As below -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8aa08fb5",
   "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>Items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Bread]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Scandinavian, Scandinavian]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Hot chocolate, Jam, Cookies]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Muffin]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[Coffee, Pastry, Bread]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[Medialuna, Pastry, Muffin]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[Medialuna, Pastry, Coffee, Tea]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[Pastry, Bread]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[Bread, Muffin]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[Scandinavian, Medialuna]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Items\n",
       "0                           [Bread]\n",
       "1      [Scandinavian, Scandinavian]\n",
       "2     [Hot chocolate, Jam, Cookies]\n",
       "3                          [Muffin]\n",
       "4           [Coffee, Pastry, Bread]\n",
       "5       [Medialuna, Pastry, Muffin]\n",
       "6  [Medialuna, Pastry, Coffee, Tea]\n",
       "7                   [Pastry, Bread]\n",
       "8                   [Bread, Muffin]\n",
       "9         [Scandinavian, Medialuna]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_list_df = pd.DataFrame({'Items': items_list})\n",
    "items_list_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b243025c",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36de4119",
   "metadata": {},
   "source": [
    "# Using the Apriori Algorithm to Generate Frequent Itemsets\n",
    "\n",
    "### Encode transction data in the form of a Python list to a NumPy array of booleans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "faf5753f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "transcencoder = TransactionEncoder()\n",
    "transcencoder_array = transcencoder.fit(items_list).transform(items_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05a5fcfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcencoder_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ffccedd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Adjustment',\n",
       " 'Afternoon with the baker',\n",
       " 'Alfajores',\n",
       " 'Argentina Night',\n",
       " 'Art Tray',\n",
       " 'Bacon',\n",
       " 'Baguette',\n",
       " 'Bakewell',\n",
       " 'Bare Popcorn',\n",
       " 'Basket',\n",
       " 'Bowl Nic Pitt',\n",
       " 'Bread',\n",
       " 'Bread Pudding',\n",
       " 'Brioche and salami',\n",
       " 'Brownie',\n",
       " 'Cake',\n",
       " 'Caramel bites',\n",
       " 'Cherry me Dried fruit',\n",
       " 'Chicken Stew',\n",
       " 'Chicken sand',\n",
       " 'Chimichurri Oil',\n",
       " 'Chocolates',\n",
       " 'Christmas common',\n",
       " 'Coffee',\n",
       " 'Coffee granules ',\n",
       " 'Coke',\n",
       " 'Cookies',\n",
       " 'Crepes',\n",
       " 'Crisps',\n",
       " 'Drinking chocolate spoons ',\n",
       " 'Duck egg',\n",
       " 'Dulce de Leche',\n",
       " 'Eggs',\n",
       " \"Ella's Kitchen Pouches\",\n",
       " 'Empanadas',\n",
       " 'Extra Salami or Feta',\n",
       " 'Fairy Doors',\n",
       " 'Farm House',\n",
       " 'Focaccia',\n",
       " 'Frittata',\n",
       " 'Fudge',\n",
       " 'Gift voucher',\n",
       " 'Gingerbread syrup',\n",
       " 'Granola',\n",
       " 'Hack the stack',\n",
       " 'Half slice Monster ',\n",
       " 'Hearty & Seasonal',\n",
       " 'Honey',\n",
       " 'Hot chocolate',\n",
       " 'Jam',\n",
       " 'Jammie Dodgers',\n",
       " 'Juice',\n",
       " 'Keeping It Local',\n",
       " 'Kids biscuit',\n",
       " 'Lemon and coconut',\n",
       " 'Medialuna',\n",
       " 'Mighty Protein',\n",
       " 'Mineral water',\n",
       " 'Mortimer',\n",
       " 'Muesli',\n",
       " 'Muffin',\n",
       " 'My-5 Fruit Shoot',\n",
       " 'Nomad bag',\n",
       " 'Olum & polenta',\n",
       " 'Panatone',\n",
       " 'Pastry',\n",
       " 'Pick and Mix Bowls',\n",
       " 'Pintxos',\n",
       " 'Polenta',\n",
       " 'Postcard',\n",
       " 'Raspberry shortbread sandwich',\n",
       " 'Raw bars',\n",
       " 'Salad',\n",
       " 'Sandwich',\n",
       " 'Scandinavian',\n",
       " 'Scone',\n",
       " 'Siblings',\n",
       " 'Smoothies',\n",
       " 'Soup',\n",
       " 'Spanish Brunch',\n",
       " 'Spread',\n",
       " 'Tacos/Fajita',\n",
       " 'Tartine',\n",
       " 'Tea',\n",
       " 'The BART',\n",
       " 'The Nomad',\n",
       " 'Tiffin',\n",
       " 'Toast',\n",
       " 'Truffles',\n",
       " 'Tshirt',\n",
       " \"Valentine's card\",\n",
       " 'Vegan Feast',\n",
       " 'Vegan mincepie',\n",
       " 'Victorian Sponge']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcencoder.columns_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c877ee0b",
   "metadata": {},
   "source": [
    "<b>Turn the encoded array into a pandas 'DataFrame' for our convenience<b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f98a4bb5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9464</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9465 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adjustment  Afternoon with the baker  Alfajores  Argentina Night  \\\n",
       "0          False                     False      False            False   \n",
       "1          False                     False      False            False   \n",
       "2          False                     False      False            False   \n",
       "3          False                     False      False            False   \n",
       "4          False                     False      False            False   \n",
       "...          ...                       ...        ...              ...   \n",
       "9460       False                     False      False            False   \n",
       "9461       False                     False      False            False   \n",
       "9462       False                     False      False            False   \n",
       "9463       False                     False      False            False   \n",
       "9464       False                     False      False            False   \n",
       "\n",
       "      Art Tray  Bacon  Baguette  Bakewell  Bare Popcorn  Basket  ...  \\\n",
       "0        False  False     False     False         False   False  ...   \n",
       "1        False  False     False     False         False   False  ...   \n",
       "2        False  False     False     False         False   False  ...   \n",
       "3        False  False     False     False         False   False  ...   \n",
       "4        False  False     False     False         False   False  ...   \n",
       "...        ...    ...       ...       ...           ...     ...  ...   \n",
       "9460     False  False     False     False         False   False  ...   \n",
       "9461     False  False     False     False         False   False  ...   \n",
       "9462     False  False     False     False         False   False  ...   \n",
       "9463     False  False     False     False         False   False  ...   \n",
       "9464     False  False     False     False         False   False  ...   \n",
       "\n",
       "      The BART  The Nomad  Tiffin  Toast  Truffles  Tshirt  Valentine's card  \\\n",
       "0        False      False   False  False     False   False             False   \n",
       "1        False      False   False  False     False   False             False   \n",
       "2        False      False   False  False     False   False             False   \n",
       "3        False      False   False  False     False   False             False   \n",
       "4        False      False   False  False     False   False             False   \n",
       "...        ...        ...     ...    ...       ...     ...               ...   \n",
       "9460     False      False   False  False     False   False             False   \n",
       "9461     False      False   False  False      True   False             False   \n",
       "9462     False      False   False  False     False   False             False   \n",
       "9463     False      False   False  False     False   False             False   \n",
       "9464     False      False   False  False     False   False             False   \n",
       "\n",
       "      Vegan Feast  Vegan mincepie  Victorian Sponge  \n",
       "0           False           False             False  \n",
       "1           False           False             False  \n",
       "2           False           False             False  \n",
       "3           False           False             False  \n",
       "4           False           False             False  \n",
       "...           ...             ...               ...  \n",
       "9460        False           False             False  \n",
       "9461        False           False             False  \n",
       "9462        False           False             False  \n",
       "9463        False           False             False  \n",
       "9464        False           False             False  \n",
       "\n",
       "[9465 rows x 94 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df = pd.DataFrame(transcencoder_array, columns =transcencoder.columns_)\n",
    "items_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6bc2984",
   "metadata": {},
   "source": [
    "The rows are the transactions and the columns are the items that we could buy from the bakery. A cell contains a true or false value, indicating whether or not a particular item was present within a transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "941600bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9465, 94)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b3c729",
   "metadata": {},
   "source": [
    "### Trancation - Coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7b2e315",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>4528 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adjustment  Afternoon with the baker  Alfajores  Argentina Night  \\\n",
       "4          False                     False      False            False   \n",
       "6          False                     False      False            False   \n",
       "11         False                     False      False            False   \n",
       "12         False                     False      False            False   \n",
       "15         False                     False      False            False   \n",
       "...          ...                       ...        ...              ...   \n",
       "9454       False                     False      False            False   \n",
       "9455       False                     False      False            False   \n",
       "9458       False                     False      False            False   \n",
       "9462       False                     False      False            False   \n",
       "9463       False                     False      False            False   \n",
       "\n",
       "      Art Tray  Bacon  Baguette  Bakewell  Bare Popcorn  Basket  ...  \\\n",
       "4        False  False     False     False         False   False  ...   \n",
       "6        False  False     False     False         False   False  ...   \n",
       "11       False  False     False     False         False   False  ...   \n",
       "12       False  False     False     False         False    True  ...   \n",
       "15       False  False     False     False         False   False  ...   \n",
       "...        ...    ...       ...       ...           ...     ...  ...   \n",
       "9454     False  False     False     False         False   False  ...   \n",
       "9455     False  False     False     False         False   False  ...   \n",
       "9458     False  False     False     False         False   False  ...   \n",
       "9462     False  False     False     False         False   False  ...   \n",
       "9463     False  False     False     False         False   False  ...   \n",
       "\n",
       "      The BART  The Nomad  Tiffin  Toast  Truffles  Tshirt  Valentine's card  \\\n",
       "4        False      False   False  False     False   False             False   \n",
       "6        False      False   False  False     False   False             False   \n",
       "11       False      False   False  False     False   False             False   \n",
       "12       False      False   False  False     False   False             False   \n",
       "15       False      False   False  False     False   False             False   \n",
       "...        ...        ...     ...    ...       ...     ...               ...   \n",
       "9454     False      False   False  False     False   False             False   \n",
       "9455     False      False   False  False     False   False             False   \n",
       "9458     False      False   False   True     False   False             False   \n",
       "9462     False      False   False  False     False   False             False   \n",
       "9463     False      False   False  False     False   False             False   \n",
       "\n",
       "      Vegan Feast  Vegan mincepie  Victorian Sponge  \n",
       "4           False           False             False  \n",
       "6           False           False             False  \n",
       "11          False           False             False  \n",
       "12          False           False             False  \n",
       "15          False           False             False  \n",
       "...           ...             ...               ...  \n",
       "9454        False           False             False  \n",
       "9455        False           False             False  \n",
       "9458        False           False             False  \n",
       "9462        False           False             False  \n",
       "9463        False           False             False  \n",
       "\n",
       "[4528 rows x 94 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee_df = items_df[items_df['Coffee'] == True]\n",
    "coffee_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd509cf7",
   "metadata": {},
   "source": [
    "There are a total of about 4500 transactions, which include coffee. With about 9500 transactions in total, almost 50% of them contain coffee. So coffee, will definitely be in our frequent itemset. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b972325d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4       True\n",
       "6       True\n",
       "11      True\n",
       "12      True\n",
       "15      True\n",
       "        ... \n",
       "9454    True\n",
       "9455    True\n",
       "9458    True\n",
       "9462    True\n",
       "9463    True\n",
       "Name: Coffee, Length: 4528, dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee_df['Coffee']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a8dbfd",
   "metadata": {},
   "source": [
    "Within this coffee_df, you'll find that all of the column values of the coffee column are set to true."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3567f22",
   "metadata": {},
   "source": [
    "### Transaction - Bread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d81fbe57",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "      Adjustment  Afternoon with the baker  Alfajores  Argentina Night  \\\n",
       "0          False                     False      False            False   \n",
       "4          False                     False      False            False   \n",
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       "...          ...                       ...        ...              ...   \n",
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       "\n",
       "      Art Tray  Bacon  Baguette  Bakewell  Bare Popcorn  Basket  ...  \\\n",
       "0        False  False     False     False         False   False  ...   \n",
       "4        False  False     False     False         False   False  ...   \n",
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       "10       False  False     False     False         False   False  ...   \n",
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       "9459     False  False     False     False         False   False  ...   \n",
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       "\n",
       "      The BART  The Nomad  Tiffin  Toast  Truffles  Tshirt  Valentine's card  \\\n",
       "0        False      False   False  False     False   False             False   \n",
       "4        False      False   False  False     False   False             False   \n",
       "7        False      False   False  False     False   False             False   \n",
       "8        False      False   False  False     False   False             False   \n",
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       "...        ...        ...     ...    ...       ...     ...               ...   \n",
       "9449     False      False   False  False     False   False             False   \n",
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       "9460     False      False   False  False     False   False             False   \n",
       "\n",
       "      Vegan Feast  Vegan mincepie  Victorian Sponge  \n",
       "0           False           False             False  \n",
       "4           False           False             False  \n",
       "7           False           False             False  \n",
       "8           False           False             False  \n",
       "10          False           False             False  \n",
       "...           ...             ...               ...  \n",
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       "\n",
       "[3097 rows x 94 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bread_df = items_df[items_df['Bread'] == True]\n",
    "bread_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37cc4f8",
   "metadata": {},
   "source": [
    "There are roughly a total of about 3000 transactions, which include bread. This is also a frequently bought item."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a254654c",
   "metadata": {},
   "source": [
    "### Transaction - Jam, Coffee & Bread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0b0e882d",
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2498</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adjustment  Afternoon with the baker  Alfajores  Argentina Night  \\\n",
       "474        False                     False       True            False   \n",
       "520        False                     False      False            False   \n",
       "522        False                     False      False            False   \n",
       "640        False                     False      False            False   \n",
       "643        False                     False      False            False   \n",
       "938        False                     False      False            False   \n",
       "1383       False                     False      False            False   \n",
       "2257       False                     False      False            False   \n",
       "2498       False                     False      False            False   \n",
       "2832       False                     False      False            False   \n",
       "6777       False                     False      False            False   \n",
       "\n",
       "      Art Tray  Bacon  Baguette  Bakewell  Bare Popcorn  Basket  ...  \\\n",
       "474      False  False     False     False         False   False  ...   \n",
       "520      False  False     False     False         False   False  ...   \n",
       "522      False  False     False     False         False   False  ...   \n",
       "640      False  False     False     False         False   False  ...   \n",
       "643      False  False     False     False         False   False  ...   \n",
       "938      False  False     False     False         False   False  ...   \n",
       "1383     False  False     False     False         False   False  ...   \n",
       "2257     False  False     False     False         False   False  ...   \n",
       "2498     False  False     False     False         False   False  ...   \n",
       "2832     False  False     False     False         False   False  ...   \n",
       "6777     False  False     False     False         False   False  ...   \n",
       "\n",
       "      The BART  The Nomad  Tiffin  Toast  Truffles  Tshirt  Valentine's card  \\\n",
       "474      False      False   False  False     False   False             False   \n",
       "520      False      False   False  False     False   False             False   \n",
       "522      False      False   False  False     False   False             False   \n",
       "640      False      False   False  False     False   False             False   \n",
       "643      False      False   False  False     False   False             False   \n",
       "938      False      False   False  False     False   False             False   \n",
       "1383     False      False   False  False     False   False             False   \n",
       "2257     False      False   False  False     False   False             False   \n",
       "2498     False      False   False  False     False   False             False   \n",
       "2832     False      False   False  False     False   False             False   \n",
       "6777     False      False   False  False     False   False             False   \n",
       "\n",
       "      Vegan Feast  Vegan mincepie  Victorian Sponge  \n",
       "474         False           False             False  \n",
       "520         False           False             False  \n",
       "522         False           False             False  \n",
       "640         False           False             False  \n",
       "643         False           False             False  \n",
       "938         False           False             False  \n",
       "1383        False           False             False  \n",
       "2257        False           False             False  \n",
       "2498        False           False             False  \n",
       "2832        False           False             False  \n",
       "6777        False           False             False  \n",
       "\n",
       "[11 rows x 94 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jam_coffee_bread_df = items_df[(items_df['Jam'] == True) &\n",
    "                               (items_df['Coffee'] == True) &\n",
    "                                (items_df['Bread'] == True)]\n",
    "jam_coffee_bread_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871dbc26",
   "metadata": {},
   "source": [
    "It has only 11 rows. This is clearly not a frequent itemset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d4ab4769",
   "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>Jam</th>\n",
       "      <th>Coffee</th>\n",
       "      <th>Bread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2498</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6777</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Jam  Coffee  Bread\n",
       "474   True    True   True\n",
       "520   True    True   True\n",
       "522   True    True   True\n",
       "640   True    True   True\n",
       "643   True    True   True\n",
       "938   True    True   True\n",
       "1383  True    True   True\n",
       "2257  True    True   True\n",
       "2498  True    True   True\n",
       "2832  True    True   True\n",
       "6777  True    True   True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jam_coffee_bread = jam_coffee_bread_df[['Jam', 'Coffee', 'Bread']]\n",
    "jam_coffee_bread"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c13a88",
   "metadata": {},
   "source": [
    "### Use Apriori algorithm available in mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3154d7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the apriori and association rules\n",
    "from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8b1d5b",
   "metadata": {},
   "source": [
    "1 Minimum Support - 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "270187a1",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.327205</td>\n",
       "      <td>(Bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.103856</td>\n",
       "      <td>(Cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.478394</td>\n",
       "      <td>(Coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.142631</td>\n",
       "      <td>(Tea)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support  itemsets\n",
       "0  0.327205   (Bread)\n",
       "1  0.103856    (Cake)\n",
       "2  0.478394  (Coffee)\n",
       "3  0.142631     (Tea)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_frequent = apriori(items_df,\n",
    "                         min_support =0.1,\n",
    "                         use_colnames=True)\n",
    "### alternatively:\n",
    "#items_frequent = fpgrowth(items_df, min_support=0.6, use_colnames=True)\n",
    "#items_frequent = fpmax(items_df, min_support=0.6, use_colnames=True)\n",
    "items_frequent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c10573",
   "metadata": {},
   "source": [
    "We see that there are no combinations of items that meet this 10% min support threshold. Bread is present in about thirty-two percent of the transactions, coffee in about 47%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89923d86",
   "metadata": {},
   "source": [
    "2 Minimum Support - 0.09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f461295a",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.327205</td>\n",
       "      <td>(Bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.103856</td>\n",
       "      <td>(Cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.478394</td>\n",
       "      <td>(Coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.142631</td>\n",
       "      <td>(Tea)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.090016</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support         itemsets\n",
       "0  0.327205          (Bread)\n",
       "1  0.103856           (Cake)\n",
       "2  0.478394         (Coffee)\n",
       "3  0.142631            (Tea)\n",
       "4  0.090016  (Coffee, Bread)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_frequent = apriori(items_df,\n",
    "                         min_support =0.09,\n",
    "                         use_colnames=True)\n",
    "items_frequent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317bd7ff",
   "metadata": {},
   "source": [
    "3 Minimum Support - 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f5e762d8",
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.327205</td>\n",
       "      <td>(Bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.103856</td>\n",
       "      <td>(Cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.478394</td>\n",
       "      <td>(Coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.054411</td>\n",
       "      <td>(Cookies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.058320</td>\n",
       "      <td>(Hot chocolate)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.061807</td>\n",
       "      <td>(Medialuna)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.086107</td>\n",
       "      <td>(Pastry)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.071844</td>\n",
       "      <td>(Sandwich)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.142631</td>\n",
       "      <td>(Tea)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.090016</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.054728</td>\n",
       "      <td>(Coffee, Cake)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     support         itemsets\n",
       "0   0.327205          (Bread)\n",
       "1   0.103856           (Cake)\n",
       "2   0.478394         (Coffee)\n",
       "3   0.054411        (Cookies)\n",
       "4   0.058320  (Hot chocolate)\n",
       "5   0.061807      (Medialuna)\n",
       "6   0.086107         (Pastry)\n",
       "7   0.071844       (Sandwich)\n",
       "8   0.142631            (Tea)\n",
       "9   0.090016  (Coffee, Bread)\n",
       "10  0.054728   (Coffee, Cake)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_frequent = apriori(items_df,\n",
    "                         min_support =0.05,\n",
    "                         use_colnames=True)\n",
    "items_frequent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "897a49f5",
   "metadata": {},
   "source": [
    "4 Minimum Support - 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3535538d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036344</td>\n",
       "      <td>(Alfajores)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016059</td>\n",
       "      <td>(Baguette)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.327205</td>\n",
       "      <td>(Bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.040042</td>\n",
       "      <td>(Brownie)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.103856</td>\n",
       "      <td>(Cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.023666</td>\n",
       "      <td>(Coffee, Toast)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.014369</td>\n",
       "      <td>(Sandwich, Tea)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(Coffee, Bread, Cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.011199</td>\n",
       "      <td>(Coffee, Bread, Pastry)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(Coffee, Tea, Cake)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     support                 itemsets\n",
       "0   0.036344              (Alfajores)\n",
       "1   0.016059               (Baguette)\n",
       "2   0.327205                  (Bread)\n",
       "3   0.040042                (Brownie)\n",
       "4   0.103856                   (Cake)\n",
       "..       ...                      ...\n",
       "56  0.023666          (Coffee, Toast)\n",
       "57  0.014369          (Sandwich, Tea)\n",
       "58  0.010037    (Coffee, Bread, Cake)\n",
       "59  0.011199  (Coffee, Bread, Pastry)\n",
       "60  0.010037      (Coffee, Tea, Cake)\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_frequent = apriori(items_df,\n",
    "                         min_support =0.01,\n",
    "                         use_colnames=True)\n",
    "items_frequent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a0d879",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6baf0a4",
   "metadata": {},
   "source": [
    "# Association Rule Mining"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1fa7da8",
   "metadata": {},
   "source": [
    "1 Level of confidence is above 70 percent threshold (min_threshold=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a3d9275a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Toast)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support   support  \\\n",
       "0     (Toast)    (Coffee)            0.033597            0.478394  0.023666   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "0    0.704403  1.472431  0.007593    1.764582  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association = association_rules(items_frequent,\n",
    "                 metric = \"confidence\",\n",
    "                 min_threshold=0.7\n",
    "                 )\n",
    "association"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb28237",
   "metadata": {},
   "source": [
    "The customers at the bakery, if they buy Toast, there is a 70% probability of them including coffee as a part of the same transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66dfbc8",
   "metadata": {},
   "source": [
    "2 Level of confidence is above 50 percent threshold (min_threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d0de7599",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Alfajores)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Cookies)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Juice)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Medialuna)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Scone)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Spanish Brunch)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.300235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Toast)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents consequents  antecedent support  consequent support  \\\n",
       "0        (Alfajores)    (Coffee)            0.036344            0.478394   \n",
       "1             (Cake)    (Coffee)            0.103856            0.478394   \n",
       "2          (Cookies)    (Coffee)            0.054411            0.478394   \n",
       "3    (Hot chocolate)    (Coffee)            0.058320            0.478394   \n",
       "4            (Juice)    (Coffee)            0.038563            0.478394   \n",
       "5        (Medialuna)    (Coffee)            0.061807            0.478394   \n",
       "6           (Pastry)    (Coffee)            0.086107            0.478394   \n",
       "7         (Sandwich)    (Coffee)            0.071844            0.478394   \n",
       "8            (Scone)    (Coffee)            0.034548            0.478394   \n",
       "9   (Spanish Brunch)    (Coffee)            0.018172            0.478394   \n",
       "10           (Toast)    (Coffee)            0.033597            0.478394   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "0   0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "1   0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "2   0.028209    0.518447  1.083723  0.002179    1.083174  \n",
       "3   0.029583    0.507246  1.060311  0.001683    1.058553  \n",
       "4   0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "5   0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "6   0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "7   0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "8   0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "9   0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "10  0.023666    0.704403  1.472431  0.007593    1.764582  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association = association_rules(items_frequent,\n",
    "                 metric = \"confidence\",\n",
    "                 min_threshold=0.5\n",
    "                 )\n",
    "association"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b794dfc1",
   "metadata": {},
   "source": [
    "The customers at the bakery, if they buy pastries or sandwiches or scones, there is a 50% probability of them including coffee as a part of the same transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e93328",
   "metadata": {},
   "source": [
    "3 Level of confidence is above 30 percent threshold (min_threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee611fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Alfajores)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
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       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.338650</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.017305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Brownie)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.024293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Cookies)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Juice)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Medialuna)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Muffin)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.489011</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.020777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Scone)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Soup)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.034443</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>0.460123</td>\n",
       "      <td>0.961807</td>\n",
       "      <td>-0.000629</td>\n",
       "      <td>0.966156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Spanish Brunch)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.300235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Tea)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.349630</td>\n",
       "      <td>0.730840</td>\n",
       "      <td>-0.018366</td>\n",
       "      <td>0.802014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(Toast)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Bread, Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.023349</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.429864</td>\n",
       "      <td>0.898557</td>\n",
       "      <td>-0.001133</td>\n",
       "      <td>0.914880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Bread, Pastry)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.384058</td>\n",
       "      <td>0.802807</td>\n",
       "      <td>-0.002751</td>\n",
       "      <td>0.846843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Tea, Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>0.882582</td>\n",
       "      <td>-0.001335</td>\n",
       "      <td>0.902779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents consequents  antecedent support  consequent support  \\\n",
       "0        (Alfajores)    (Coffee)            0.036344            0.478394   \n",
       "1           (Pastry)     (Bread)            0.086107            0.327205   \n",
       "2          (Brownie)    (Coffee)            0.040042            0.478394   \n",
       "3             (Cake)    (Coffee)            0.103856            0.478394   \n",
       "4          (Cookies)    (Coffee)            0.054411            0.478394   \n",
       "5    (Hot chocolate)    (Coffee)            0.058320            0.478394   \n",
       "6            (Juice)    (Coffee)            0.038563            0.478394   \n",
       "7        (Medialuna)    (Coffee)            0.061807            0.478394   \n",
       "8           (Muffin)    (Coffee)            0.038457            0.478394   \n",
       "9           (Pastry)    (Coffee)            0.086107            0.478394   \n",
       "10        (Sandwich)    (Coffee)            0.071844            0.478394   \n",
       "11           (Scone)    (Coffee)            0.034548            0.478394   \n",
       "12            (Soup)    (Coffee)            0.034443            0.478394   \n",
       "13  (Spanish Brunch)    (Coffee)            0.018172            0.478394   \n",
       "14             (Tea)    (Coffee)            0.142631            0.478394   \n",
       "15           (Toast)    (Coffee)            0.033597            0.478394   \n",
       "16     (Bread, Cake)    (Coffee)            0.023349            0.478394   \n",
       "17   (Bread, Pastry)    (Coffee)            0.029160            0.478394   \n",
       "18       (Tea, Cake)    (Coffee)            0.023772            0.478394   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "0   0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "1   0.029160    0.338650  1.034977  0.000985    1.017305  \n",
       "2   0.019651    0.490765  1.025860  0.000495    1.024293  \n",
       "3   0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "4   0.028209    0.518447  1.083723  0.002179    1.083174  \n",
       "5   0.029583    0.507246  1.060311  0.001683    1.058553  \n",
       "6   0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "7   0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "8   0.018806    0.489011  1.022193  0.000408    1.020777  \n",
       "9   0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "10  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "11  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "12  0.015848    0.460123  0.961807 -0.000629    0.966156  \n",
       "13  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "14  0.049868    0.349630  0.730840 -0.018366    0.802014  \n",
       "15  0.023666    0.704403  1.472431  0.007593    1.764582  \n",
       "16  0.010037    0.429864  0.898557 -0.001133    0.914880  \n",
       "17  0.011199    0.384058  0.802807 -0.002751    0.846843  \n",
       "18  0.010037    0.422222  0.882582 -0.001335    0.902779  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association = association_rules(items_frequent,\n",
    "                 metric = \"confidence\",\n",
    "                 min_threshold=0.3\n",
    "                 )\n",
    "association"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "86d82168",
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Alfajores)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.004936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Alfajores)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Bread)</td>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.089119</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.003306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.338650</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.017305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Brownie)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.001080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Brownie)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.024293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Cake)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.114399</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.011905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>(Cake)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>1.883874</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>1.114125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.109868</td>\n",
       "      <td>1.883874</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>1.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Tea)</td>\n",
       "      <td>(Cake)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.604781</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>1.075372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.228891</td>\n",
       "      <td>1.604781</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>1.111865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Cookies)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.058966</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.004841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Cookies)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.061837</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.003749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Juice)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.004705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Juice)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Medialuna)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.073542</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.012667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(Medialuna)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(Muffin)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.489011</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.020777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Muffin)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.099382</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.014740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.079947</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.008807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Scone)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.037765</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.003343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(Scone)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Spanish Brunch)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.004682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(Spanish Brunch)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.300235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>(Toast)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.049470</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.016699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(Toast)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>(Tea)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.402222</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>1.071712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(Tea)</td>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.100741</td>\n",
       "      <td>1.402222</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>1.032134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>(Cake)</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.111502</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.008606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.124413</td>\n",
       "      <td>1.444872</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>1.043749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.130061</td>\n",
       "      <td>1.444872</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>1.046033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(Coffee, Tea)</td>\n",
       "      <td>(Cake)</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.201271</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>1.121962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(Coffee, Cake)</td>\n",
       "      <td>(Tea)</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.183398</td>\n",
       "      <td>1.285822</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>1.049923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(Tea)</td>\n",
       "      <td>(Coffee, Cake)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.070370</td>\n",
       "      <td>1.285822</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>1.016827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee, Tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>1.051779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "0           (Coffee)       (Alfajores)            0.478394   \n",
       "1        (Alfajores)          (Coffee)            0.036344   \n",
       "2            (Bread)          (Pastry)            0.327205   \n",
       "3           (Pastry)           (Bread)            0.086107   \n",
       "4           (Coffee)         (Brownie)            0.478394   \n",
       "5          (Brownie)          (Coffee)            0.040042   \n",
       "6           (Coffee)            (Cake)            0.478394   \n",
       "7             (Cake)          (Coffee)            0.103856   \n",
       "8    (Hot chocolate)            (Cake)            0.058320   \n",
       "9             (Cake)   (Hot chocolate)            0.103856   \n",
       "10             (Tea)            (Cake)            0.142631   \n",
       "11            (Cake)             (Tea)            0.103856   \n",
       "12          (Coffee)         (Cookies)            0.478394   \n",
       "13         (Cookies)          (Coffee)            0.054411   \n",
       "14          (Coffee)   (Hot chocolate)            0.478394   \n",
       "15   (Hot chocolate)          (Coffee)            0.058320   \n",
       "16          (Coffee)           (Juice)            0.478394   \n",
       "17           (Juice)          (Coffee)            0.038563   \n",
       "18          (Coffee)       (Medialuna)            0.478394   \n",
       "19       (Medialuna)          (Coffee)            0.061807   \n",
       "20          (Muffin)          (Coffee)            0.038457   \n",
       "21          (Coffee)          (Muffin)            0.478394   \n",
       "22          (Coffee)          (Pastry)            0.478394   \n",
       "23          (Pastry)          (Coffee)            0.086107   \n",
       "24          (Coffee)        (Sandwich)            0.478394   \n",
       "25        (Sandwich)          (Coffee)            0.071844   \n",
       "26          (Coffee)           (Scone)            0.478394   \n",
       "27           (Scone)          (Coffee)            0.034548   \n",
       "28          (Coffee)  (Spanish Brunch)            0.478394   \n",
       "29  (Spanish Brunch)          (Coffee)            0.018172   \n",
       "30          (Coffee)           (Toast)            0.478394   \n",
       "31           (Toast)          (Coffee)            0.033597   \n",
       "32        (Sandwich)             (Tea)            0.071844   \n",
       "33             (Tea)        (Sandwich)            0.142631   \n",
       "34   (Coffee, Bread)            (Cake)            0.090016   \n",
       "35            (Cake)   (Coffee, Bread)            0.103856   \n",
       "36   (Coffee, Bread)          (Pastry)            0.090016   \n",
       "37          (Pastry)   (Coffee, Bread)            0.086107   \n",
       "38     (Coffee, Tea)            (Cake)            0.049868   \n",
       "39    (Coffee, Cake)             (Tea)            0.054728   \n",
       "40             (Tea)    (Coffee, Cake)            0.142631   \n",
       "41            (Cake)     (Coffee, Tea)            0.103856   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "0             0.036344  0.019651    0.041078  1.130235  0.002264    1.004936  \n",
       "1             0.478394  0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "2             0.086107  0.029160    0.089119  1.034977  0.000985    1.003306  \n",
       "3             0.327205  0.029160    0.338650  1.034977  0.000985    1.017305  \n",
       "4             0.040042  0.019651    0.041078  1.025860  0.000495    1.001080  \n",
       "5             0.478394  0.019651    0.490765  1.025860  0.000495    1.024293  \n",
       "6             0.103856  0.054728    0.114399  1.101515  0.005044    1.011905  \n",
       "7             0.478394  0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "8             0.103856  0.011410    0.195652  1.883874  0.005354    1.114125  \n",
       "9             0.058320  0.011410    0.109868  1.883874  0.005354    1.057910  \n",
       "10            0.103856  0.023772    0.166667  1.604781  0.008959    1.075372  \n",
       "11            0.142631  0.023772    0.228891  1.604781  0.008959    1.111865  \n",
       "12            0.054411  0.028209    0.058966  1.083723  0.002179    1.004841  \n",
       "13            0.478394  0.028209    0.518447  1.083723  0.002179    1.083174  \n",
       "14            0.058320  0.029583    0.061837  1.060311  0.001683    1.003749  \n",
       "15            0.478394  0.029583    0.507246  1.060311  0.001683    1.058553  \n",
       "16            0.038563  0.020602    0.043065  1.116750  0.002154    1.004705  \n",
       "17            0.478394  0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "18            0.061807  0.035182    0.073542  1.189878  0.005614    1.012667  \n",
       "19            0.478394  0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "20            0.478394  0.018806    0.489011  1.022193  0.000408    1.020777  \n",
       "21            0.038457  0.018806    0.039311  1.022193  0.000408    1.000888  \n",
       "22            0.086107  0.047544    0.099382  1.154168  0.006351    1.014740  \n",
       "23            0.478394  0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "24            0.071844  0.038246    0.079947  1.112792  0.003877    1.008807  \n",
       "25            0.478394  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "26            0.034548  0.018067    0.037765  1.093107  0.001539    1.003343  \n",
       "27            0.478394  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "28            0.018172  0.010882    0.022747  1.251766  0.002189    1.004682  \n",
       "29            0.478394  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "30            0.033597  0.023666    0.049470  1.472431  0.007593    1.016699  \n",
       "31            0.478394  0.023666    0.704403  1.472431  0.007593    1.764582  \n",
       "32            0.142631  0.014369    0.200000  1.402222  0.004122    1.071712  \n",
       "33            0.071844  0.014369    0.100741  1.402222  0.004122    1.032134  \n",
       "34            0.103856  0.010037    0.111502  1.073621  0.000688    1.008606  \n",
       "35            0.090016  0.010037    0.096643  1.073621  0.000688    1.007336  \n",
       "36            0.086107  0.011199    0.124413  1.444872  0.003448    1.043749  \n",
       "37            0.090016  0.011199    0.130061  1.444872  0.003448    1.046033  \n",
       "38            0.103856  0.010037    0.201271  1.937977  0.004858    1.121962  \n",
       "39            0.142631  0.010037    0.183398  1.285822  0.002231    1.049923  \n",
       "40            0.054728  0.010037    0.070370  1.285822  0.002231    1.016827  \n",
       "41            0.049868  0.010037    0.096643  1.937977  0.004858    1.051779  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association = association_rules(items_frequent,\n",
    "                 metric = \"lift\",\n",
    "                 min_threshold= 1\n",
    "                 )\n",
    "association"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b00725",
   "metadata": {},
   "source": [
    "## Sorting the Association Rules \n",
    "### All rules with confidence greater than 50% sorted on the basis of confidence in the descending order, and these are the top 10 rules. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8cdcaf14",
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(Toast)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(Spanish Brunch)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.300235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(Medialuna)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Alfajores)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Juice)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(Sandwich)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(Scone)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Cookies)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents consequents  antecedent support  consequent support  \\\n",
       "31           (Toast)    (Coffee)            0.033597            0.478394   \n",
       "29  (Spanish Brunch)    (Coffee)            0.018172            0.478394   \n",
       "19       (Medialuna)    (Coffee)            0.061807            0.478394   \n",
       "23          (Pastry)    (Coffee)            0.086107            0.478394   \n",
       "1        (Alfajores)    (Coffee)            0.036344            0.478394   \n",
       "17           (Juice)    (Coffee)            0.038563            0.478394   \n",
       "25        (Sandwich)    (Coffee)            0.071844            0.478394   \n",
       "7             (Cake)    (Coffee)            0.103856            0.478394   \n",
       "27           (Scone)    (Coffee)            0.034548            0.478394   \n",
       "13         (Cookies)    (Coffee)            0.054411            0.478394   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "31  0.023666    0.704403  1.472431  0.007593    1.764582  \n",
       "29  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "19  0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "23  0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "1   0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "17  0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "25  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "7   0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "27  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "13  0.028209    0.518447  1.083723  0.002179    1.083174  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association[association['confidence'] >= 0.5].sort_values('confidence',\n",
    "                                                       ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6719ef",
   "metadata": {},
   "source": [
    " The rules in which we have the highest confidence generally tend to involve coffee."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c59418",
   "metadata": {},
   "source": [
    "### Filter Operations (antecedents and the consequents in this association rules data frame are frozen sets so apply filters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9628e27e",
   "metadata": {},
   "source": [
    " 1 Association rules where the antecedents include cake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b1a629be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Hot chocolate)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.109868</td>\n",
       "      <td>1.883874</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>1.057910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.228891</td>\n",
       "      <td>1.604781</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>1.111865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee, Tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>1.051779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   antecedents      consequents  antecedent support  consequent support  \\\n",
       "7       (Cake)         (Coffee)            0.103856            0.478394   \n",
       "9       (Cake)  (Hot chocolate)            0.103856            0.058320   \n",
       "11      (Cake)            (Tea)            0.103856            0.142631   \n",
       "35      (Cake)  (Coffee, Bread)            0.103856            0.090016   \n",
       "41      (Cake)    (Coffee, Tea)            0.103856            0.049868   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "7   0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "9   0.011410    0.109868  1.883874  0.005354    1.057910  \n",
       "11  0.023772    0.228891  1.604781  0.008959    1.111865  \n",
       "35  0.010037    0.096643  1.073621  0.000688    1.007336  \n",
       "41  0.010037    0.096643  1.937977  0.004858    1.051779  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association[association['antecedents'] == {'Cake'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c621f91a",
   "metadata": {},
   "source": [
    " 2 Association rules where the consequents include bread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b42e5736",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.02916</td>\n",
       "      <td>0.33865</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.017305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support  support  \\\n",
       "3    (Pastry)     (Bread)            0.086107            0.327205  0.02916   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "3     0.33865  1.034977  0.000985    1.017305  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association[association['consequents'] == {'Bread'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3c09b3",
   "metadata": {},
   "source": [
    " 3 Association rules where the consequents include bread and coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3119d5ec",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
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       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(Cake)</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(Pastry)</td>\n",
       "      <td>(Coffee, Bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.130061</td>\n",
       "      <td>1.444872</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>1.046033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   antecedents      consequents  antecedent support  consequent support  \\\n",
       "35      (Cake)  (Coffee, Bread)            0.103856            0.090016   \n",
       "37    (Pastry)  (Coffee, Bread)            0.086107            0.090016   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "35  0.010037    0.096643  1.073621  0.000688    1.007336  \n",
       "37  0.011199    0.130061  1.444872  0.003448    1.046033  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association[association['consequents'] == {'Bread', 'Coffee'}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74dd4042",
   "metadata": {},
   "source": [
    " 4 Association rules where the antecedents include cake and coffee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "53b7f452",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(Coffee, Cake)</td>\n",
       "      <td>(Tea)</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.183398</td>\n",
       "      <td>1.285822</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>1.049923</td>\n",
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       "       antecedents consequents  antecedent support  consequent support  \\\n",
       "39  (Coffee, Cake)       (Tea)            0.054728            0.142631   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "39  0.010037    0.183398  1.285822  0.002231    1.049923  "
      ]
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     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "association[association['antecedents'] == {'Cake', 'Coffee'}]"
   ]
  }
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