frequently_bought_together_v1.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "700d6f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries.\n",
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "069e41e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Excel data source as Pandas dataframe.\n",
    "df = pd.read_excel(\"./data/groceries_dataset_sample_v1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1467f406",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>ItemDescription</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9246351480504524</td>\n",
       "      <td>1000</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9246351480504524</td>\n",
       "      <td>1000</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>pastry</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9246351480504524</td>\n",
       "      <td>1000</td>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>salty snack</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2066741216007356</td>\n",
       "      <td>1000</td>\n",
       "      <td>2015-03-15</td>\n",
       "      <td>sausage</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1000</td>\n",
       "      <td>2015-03-15</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          InvoiceNo  CustomerID InvoiceDate ItemDescription  Quantity\n",
       "0  9246351480504524        1000  2014-06-24      whole milk         2\n",
       "1  9246351480504524        1000  2014-06-24          pastry         3\n",
       "2  9246351480504524        1000  2014-06-24     salty snack         2\n",
       "3  2066741216007356        1000  2015-03-15         sausage         4\n",
       "4  2066741216007356        1000  2015-03-15      whole milk         3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the header of the dataframe.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ba67837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1920 entries, 0 to 1919\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   InvoiceNo        1920 non-null   int64         \n",
      " 1   CustomerID       1920 non-null   int64         \n",
      " 2   InvoiceDate      1920 non-null   datetime64[ns]\n",
      " 3   ItemDescription  1920 non-null   object        \n",
      " 4   Quantity         1920 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 75.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check dataframe information.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "364cb084",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo          0\n",
       "CustomerID         0\n",
       "InvoiceDate        0\n",
       "ItemDescription    0\n",
       "Quantity           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Check any null record in the dataframe.\n",
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7aea905",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Quantity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>1.920000e+03</td>\n",
       "      <td>1920.000000</td>\n",
       "      <td>1920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.422356e+15</td>\n",
       "      <td>1099.888021</td>\n",
       "      <td>2.948958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.683089e+15</td>\n",
       "      <td>57.965179</td>\n",
       "      <td>1.428329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.002270e+15</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.103041e+15</td>\n",
       "      <td>1051.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.335092e+15</td>\n",
       "      <td>1102.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.825146e+15</td>\n",
       "      <td>1148.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999248e+15</td>\n",
       "      <td>1200.000000</td>\n",
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      ],
      "text/plain": [
       "          InvoiceNo   CustomerID     Quantity\n",
       "count  1.920000e+03  1920.000000  1920.000000\n",
       "mean   5.422356e+15  1099.888021     2.948958\n",
       "std    2.683089e+15    57.965179     1.428329\n",
       "min    1.002270e+15  1000.000000     1.000000\n",
       "25%    3.103041e+15  1051.000000     2.000000\n",
       "50%    5.335092e+15  1102.000000     3.000000\n",
       "75%    7.825146e+15  1148.000000     4.000000\n",
       "max    9.999248e+15  1200.000000     5.000000"
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "# Obtain the descriptive statistics of the dataframe.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04c98e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min   2014-01-02\n",
       "max   2015-12-29\n",
       "Name: InvoiceDate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Obtain the earliest and latest date of InvoiceDate in the dataframe.\n",
    "df['InvoiceDate'].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e93910c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a matrix to show the purchased items in each invoice number.\n",
    "basket = (df.groupby(['InvoiceNo', 'ItemDescription'])['Quantity'].sum()\n",
    "          .unstack().reset_index().fillna(0).set_index('InvoiceNo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9eb67ac4",
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       "ItemDescription   Instant food products  UHT-milk  artif. sweetener  \\\n",
       "InvoiceNo                                                             \n",
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       "\n",
       "ItemDescription   baking powder  bathroom cleaner  beef  berries  beverages  \\\n",
       "InvoiceNo                                                                     \n",
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       "InvoiceNo                                      ...                             \n",
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       "\n",
       "ItemDescription   whipped/sour cream  whisky  white bread  white wine  \\\n",
       "InvoiceNo                                                               \n",
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       "\n",
       "ItemDescription   whole milk  yogurt  zwieback  \n",
       "InvoiceNo                                       \n",
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       "\n",
       "[10 rows x 145 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the header of the matrix.\n",
    "basket.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e89d3586",
   "metadata": {},
   "outputs": [
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       "ItemDescription   Instant food products  UHT-milk  artif. sweetener  \\\n",
       "InvoiceNo                                                             \n",
       "1002269524162774                      0         0                 0   \n",
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       "\n",
       "ItemDescription   baking powder  bathroom cleaner  beef  berries  beverages  \\\n",
       "InvoiceNo                                                                     \n",
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       "\n",
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       "InvoiceNo                                      ...                             \n",
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       "InvoiceNo                                                               \n",
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       "\n",
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       "InvoiceNo                                       \n",
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       "[5 rows x 145 columns]"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the function to encode the sum of the item purchase quantity to a binary number.\n",
    "def encode(x):\n",
    "  if x < 1:\n",
    "    return 0\n",
    "  else:\n",
    "    return 1\n",
    "\n",
    "# Apply the function to encode the sum of the item purchase quantity to a binary number.\n",
    "basket = basket.applymap(encode)\n",
    "basket.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "673f2961",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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      "text/plain": [
       "          antecedents         consequents  antecedent support  \\\n",
       "0           (berries)  (other vegetables)            1.000000   \n",
       "1  (other vegetables)           (berries)            0.266667   \n",
       "2           (berries)       (white bread)            1.000000   \n",
       "3       (white bread)           (berries)            0.266667   \n",
       "4           (berries)            (yogurt)            1.000000   \n",
       "5            (yogurt)           (berries)            0.200000   \n",
       "\n",
       "   consequent support   support  confidence  lift  leverage  conviction  \n",
       "0            0.266667  0.266667    0.266667   1.0       0.0         1.0  \n",
       "1            1.000000  0.266667    1.000000   1.0       0.0         inf  \n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "# Define the sample item in the basket.\n",
    "item_in_basket = basket.loc[basket['berries']==1]\n",
    "# Applying the Apriori algorithm, creating the association rules for the sample item in the basket.\n",
    "frequent_itemsets = apriori(item_in_basket, min_support=0.15, use_colnames=True)\n",
    "# Obtaining a frequent itemset by calling the association_rules function.\n",
    "frequent_itemsets_association_rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "# Sorting the result by Lift and Support metric.\n",
    "frequent_itemsets_association_rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67181c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function to obtain the frequently bought together items.\n",
    "def frequently_bought_together(item):\n",
    "    # Obtain the item name that is already in the basket.\n",
    "    item_in_basket = basket.loc[basket[item]==1]\n",
    "    # Applying the Apriori algorithm, creating the association rules for the sample item in the basket.\n",
    "    frequent_itemsets = apriori(item_in_basket, min_support=0.15, use_colnames=True)\n",
    "    # Obtaining a frequent itemset by calling the association_rules function.\n",
    "    rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "    # Filter the result by confidence less than 1.\n",
    "    filtered_rules = rules[(rules['confidence']<1)]\n",
    "    # Sorting the result by Lift and Support metric.\n",
    "    filtered_rules.sort_values(['lift','support'],ascending=False).reset_index(drop=True)\n",
    "    print('Items frequently bought together with {0}'.format(item))\n",
    "    return filtered_rules['consequents'].unique()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bda1801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Items frequently bought together with berries\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([frozenset({'other vegetables'}), frozenset({'white bread'}),\n",
       "       frozenset({'yogurt'})], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply the function to obtain the frequently bought together items.\n",
    "frequently_bought_together('berries')"
   ]
  }
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