market-basket-analysis.ipynb

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    "# Market Basket Analysis\n",
    "\n",
    "By using `Association rules` derived from `frequent itemsets` identified by `Apriori`, Market Basket Analysis helps retailers discover hidden patterns in customer buying habits, leading to better product placement, targeted promotions, and improved inventory management.\n",
    "\n",
    "I will use the **[Online Retail Dataset](https://archive.ics.uci.edu/dataset/352/online+retail)** from UCI machine learning repository\n",
    "\n",
    "These are the steps I will follow:\n",
    "\n",
    "1. Number of Transactions by Country\n",
    "2. Working with Transactions from France\n",
    "3. Binary Encoding\n",
    "4. Training the Model\n",
    "5. Making Recommendations\n",
    "\n",
    "***I'll briefly explain Association rules and the Apriori algorithm. If you're already familiar with these concepts, you can skip ahead.***"
   ]
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    "## Association Rules:\n",
    "\n",
    "Imagine a grocery store. An association rule might state: \"If a customer buys bread, then they are also likely to buy butter.\" This rule captures the relationship between frequently bought together items.\n",
    "\n",
    "Association rules are written in the format A -> B, where A (antecedent) is the item purchased first and B (consequent) is the item purchased together with A.\n",
    "\n",
    "## Apriori:\n",
    "\n",
    "The Apriori algorithm efficiently finds frequently bought together items (itemsets) in your transaction data. `mlxtend` library provides a user-friendly implementation. You simply:\n",
    "\n",
    "* Set Minimum Support: Define the minimum number of times an itemset needs to appear to be considered frequent (e.g., 10% of transactions).\n",
    "* Run Apriori: Use `mlxtend.frequent_patterns.apriori` on your transaction data.\n",
    "* Get Frequent Itemsets: Apriori identifies groups of items (bread & butter, milk & cereal) that frequently appear together, exceeding the minimum support threshold.\n",
    "\n",
    "## Interpreting the results \n",
    "\n",
    "Choosing the Right Metric:\n",
    "\n",
    "1. `Support`: A good starting point to identify frequently bought-together itemsets.\n",
    "2. `Confidence`: Helps refine the analysis by focusing on how likely B is purchased with A.\n",
    "3. `Lift`: Measures how much more likely it is to buy B given A compared to buying them independently. A higher lift indicates a stronger association.\n",
    "\n",
    "In essence:\n",
    "\n",
    "1. Use higher support to identify commonly purchased itemsets.\n",
    "2. Use higher confidence to identify strong conditional relationships between items.\n",
    "3. Use higher lift to identify itemsets where co-purchases are more frequent than expected by chance."
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   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
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   "source": [
    "# Load Dataset\n",
    "retail = pd.read_excel('/kaggle/input/online-retail-data/Online Retail.xlsx')\n",
    "df = retail.copy() # make a copy of the dataset\n",
    "\n",
    "df.head()"
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   "source": [
    "# Data Cleaning"
   ]
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   InvoiceNo    541909 non-null  object        \n",
      " 1   StockCode    541909 non-null  object        \n",
      " 2   Description  540455 non-null  object        \n",
      " 3   Quantity     541909 non-null  int64         \n",
      " 4   InvoiceDate  541909 non-null  datetime64[ns]\n",
      " 5   UnitPrice    541909 non-null  float64       \n",
      " 6   CustomerID   406829 non-null  float64       \n",
      " 7   Country      541909 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
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   "source": [
    "df.info() # 541,909 rows and 8 columns"
   ]
  },
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       "InvoiceNo       0.0\n",
       "StockCode       0.0\n",
       "Description     0.3\n",
       "Quantity        0.0\n",
       "InvoiceDate     0.0\n",
       "UnitPrice       0.0\n",
       "CustomerID     24.9\n",
       "Country         0.0\n",
       "dtype: float64"
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     "execution_count": 4,
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   "source": [
    "# Percentage of Missing values\n",
    "(df.isnull().mean()*100).round(1)"
   ]
  },
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       "      <th>InvoiceNo</th>\n",
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       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
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       "      <th>622</th>\n",
       "      <td>536414</td>\n",
       "      <td>22139</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2010-12-01 11:52:00</td>\n",
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       "      <td>United Kingdom</td>\n",
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       "      <th>1970</th>\n",
       "      <td>536545</td>\n",
       "      <td>21134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 14:32:00</td>\n",
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       "      <th>1971</th>\n",
       "      <td>536546</td>\n",
       "      <td>22145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 14:33:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>536547</td>\n",
       "      <td>37509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 14:33:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>536549</td>\n",
       "      <td>85226A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-12-01 14:34:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     InvoiceNo StockCode Description  Quantity         InvoiceDate  UnitPrice  \\\n",
       "622     536414     22139         NaN        56 2010-12-01 11:52:00        0.0   \n",
       "1970    536545     21134         NaN         1 2010-12-01 14:32:00        0.0   \n",
       "1971    536546     22145         NaN         1 2010-12-01 14:33:00        0.0   \n",
       "1972    536547     37509         NaN         1 2010-12-01 14:33:00        0.0   \n",
       "1987    536549    85226A         NaN         1 2010-12-01 14:34:00        0.0   \n",
       "\n",
       "      CustomerID         Country  \n",
       "622          NaN  United Kingdom  \n",
       "1970         NaN  United Kingdom  \n",
       "1971         NaN  United Kingdom  \n",
       "1972         NaN  United Kingdom  \n",
       "1987         NaN  United Kingdom  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "# Inspect the rows with missing values in 'Description' column\n",
    "missing_descriptions = df['Description'].isnull()\n",
    "missing_data = df[missing_descriptions]\n",
    "missing_data.head()"
   ]
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     "data": {
      "text/plain": [
       "InvoiceNo       0.0\n",
       "StockCode       0.0\n",
       "Description     0.0\n",
       "Quantity        0.0\n",
       "InvoiceDate     0.0\n",
       "UnitPrice       0.0\n",
       "CustomerID     24.7\n",
       "Country         0.0\n",
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   "source": [
    "#  Remove any rows from df where the 'Description' value is missing\n",
    "df = df.dropna(axis = 0, subset = ['Description'])\n",
    "\n",
    "# Percentage of Missing values\n",
    "(df.isnull().mean()*100).round(1)"
   ]
  },
  {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
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       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
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       "      <td>2010-12-01 08:26:00</td>\n",
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       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
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       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
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       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
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       "      <td>2010-12-01 08:26:00</td>\n",
       "      <td>2.75</td>\n",
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       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
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       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
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       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
       "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
       "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
       "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['InvoiceNo'] = df['InvoiceNo'].astype('str') #converting invoice number to be string\n",
    "df = df[~df['InvoiceNo'].str.contains('C')] #remove the credit transactions \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4dd028f",
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   "source": [
    "# 1. Number of Transactions by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bce989ec",
   "metadata": {
    "_cell_guid": "b3420749-1f31-4e67-b881-c9ba745aca99",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United Kingdom          486168\n",
       "Germany                   9042\n",
       "France                    8408\n",
       "EIRE                      7894\n",
       "Spain                     2485\n",
       "Netherlands               2363\n",
       "Belgium                   2031\n",
       "Switzerland               1967\n",
       "Portugal                  1501\n",
       "Australia                 1185\n",
       "Norway                    1072\n",
       "Italy                      758\n",
       "Channel Islands            748\n",
       "Finland                    685\n",
       "Cyprus                     614\n",
       "Sweden                     451\n",
       "Unspecified                446\n",
       "Austria                    398\n",
       "Denmark                    380\n",
       "Poland                     330\n",
       "Japan                      321\n",
       "Israel                     295\n",
       "Hong Kong                  284\n",
       "Singapore                  222\n",
       "Iceland                    182\n",
       "USA                        179\n",
       "Canada                     151\n",
       "Greece                     145\n",
       "Malta                      112\n",
       "United Arab Emirates        68\n",
       "European Community          60\n",
       "RSA                         58\n",
       "Lebanon                     45\n",
       "Lithuania                   35\n",
       "Brazil                      32\n",
       "Czech Republic              25\n",
       "Bahrain                     18\n",
       "Saudi Arabia                 9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b206dad",
   "metadata": {
    "_cell_guid": "c75183fb-7b40-454c-91dd-ed5b8c575025",
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    "tags": []
   },
   "source": [
    "# 2. Working with Transactions from France"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3819db4",
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      "text/plain": [
       "Description   50'S CHRISTMAS GIFT BAG LARGE   DOLLY GIRL BEAKER  \\\n",
       "InvoiceNo                                                         \n",
       "536370                                  0.0                 0.0   \n",
       "536852                                  0.0                 0.0   \n",
       "536974                                  0.0                 0.0   \n",
       "537065                                  0.0                 0.0   \n",
       "537463                                  0.0                 0.0   \n",
       "\n",
       "Description   I LOVE LONDON MINI BACKPACK   NINE DRAWER OFFICE TIDY  \\\n",
       "InvoiceNo                                                             \n",
       "536370                                0.0                       0.0   \n",
       "536852                                0.0                       0.0   \n",
       "536974                                0.0                       0.0   \n",
       "537065                                0.0                       0.0   \n",
       "537463                                0.0                       0.0   \n",
       "\n",
       "Description   SET 2 TEA TOWELS I LOVE LONDON    SPACEBOY BABY GIFT SET  \\\n",
       "InvoiceNo                                                                \n",
       "536370                                   24.0                      0.0   \n",
       "536852                                    0.0                      0.0   \n",
       "536974                                    0.0                      0.0   \n",
       "537065                                    0.0                      0.0   \n",
       "537463                                    0.0                      0.0   \n",
       "\n",
       "Description   TRELLIS COAT RACK  10 COLOUR SPACEBOY PEN  \\\n",
       "InvoiceNo                                                 \n",
       "536370                      0.0                     0.0   \n",
       "536852                      0.0                     0.0   \n",
       "536974                      0.0                     0.0   \n",
       "537065                      0.0                     0.0   \n",
       "537463                      0.0                     0.0   \n",
       "\n",
       "Description  12 COLOURED PARTY BALLOONS  12 EGG HOUSE PAINTED WOOD  ...  \\\n",
       "InvoiceNo                                                           ...   \n",
       "536370                              0.0                        0.0  ...   \n",
       "536852                              0.0                        0.0  ...   \n",
       "536974                              0.0                        0.0  ...   \n",
       "537065                              0.0                        0.0  ...   \n",
       "537463                              0.0                        0.0  ...   \n",
       "\n",
       "Description  WRAP VINTAGE PETALS  DESIGN  YELLOW COAT RACK PARIS FASHION  \\\n",
       "InvoiceNo                                                                  \n",
       "536370                               0.0                             0.0   \n",
       "536852                               0.0                             0.0   \n",
       "536974                               0.0                             0.0   \n",
       "537065                               0.0                             0.0   \n",
       "537463                               0.0                             0.0   \n",
       "\n",
       "Description  YELLOW GIANT GARDEN THERMOMETER  YELLOW SHARK HELICOPTER  \\\n",
       "InvoiceNo                                                               \n",
       "536370                                   0.0                      0.0   \n",
       "536852                                   0.0                      0.0   \n",
       "536974                                   0.0                      0.0   \n",
       "537065                                   0.0                      0.0   \n",
       "537463                                   0.0                      0.0   \n",
       "\n",
       "Description  ZINC  STAR T-LIGHT HOLDER   ZINC FOLKART SLEIGH BELLS  \\\n",
       "InvoiceNo                                                            \n",
       "536370                              0.0                        0.0   \n",
       "536852                              0.0                        0.0   \n",
       "536974                              0.0                        0.0   \n",
       "537065                              0.0                        0.0   \n",
       "537463                              0.0                        0.0   \n",
       "\n",
       "Description  ZINC HERB GARDEN CONTAINER  ZINC METAL HEART DECORATION  \\\n",
       "InvoiceNo                                                              \n",
       "536370                              0.0                          0.0   \n",
       "536852                              0.0                          0.0   \n",
       "536974                              0.0                          0.0   \n",
       "537065                              0.0                          0.0   \n",
       "537463                              0.0                          0.0   \n",
       "\n",
       "Description  ZINC T-LIGHT HOLDER STAR LARGE  ZINC T-LIGHT HOLDER STARS SMALL  \n",
       "InvoiceNo                                                                     \n",
       "536370                                  0.0                              0.0  \n",
       "536852                                  0.0                              0.0  \n",
       "536974                                  0.0                              0.0  \n",
       "537065                                  0.0                              0.0  \n",
       "537463                                  0.0                              0.0  \n",
       "\n",
       "[5 rows x 1564 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter dataframe and select transactions where 'Country' is 'France'\n",
    "# Groups transactions based on 'InvoiceNo', then by 'Description'\n",
    "# Within each group, calculate the total 'Quantity' of each product (identified by description) purchased in each 'InvoiceNo'.\n",
    "france_basket = df[df['Country'] =='France'] \\\n",
    "               .groupby(['InvoiceNo','Description'])['Quantity'] \\\n",
    "               .sum().unstack().reset_index().fillna(0) \\\n",
    "               .set_index('InvoiceNo')\n",
    "\n",
    "print(france_basket.shape)  # (392 rows and 1564 columns)\n",
    "print() # blank line\n",
    "france_basket.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8b1928",
   "metadata": {
    "_cell_guid": "7b5adafc-f379-4c87-b4ee-85cb1869c27e",
    "_uuid": "605127af-8b02-44b8-aab7-9eb339071e76",
    "papermill": {
     "duration": 0.010917,
     "end_time": "2024-06-10T17:31:36.166537",
     "exception": false,
     "start_time": "2024-06-10T17:31:36.155620",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Binary Encoding\n",
    "\n",
    "***Focus on Presence/Absence***: The function encodes positive values (indicating a purchase) to True, essentially signifying the item's presence in the transaction.\n",
    "\n",
    "***Handling Non-Positive Values***: It encodes everything less than or equal to 0 (including 0) to False, representing the item's absence in the transaction.\n",
    "\n",
    "Focusing on presence/absence simplifies market basket analysis by focusing on co-occurrence patterns, not quantities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "219a1046",
   "metadata": {
    "_cell_guid": "9ee83f97-897f-4c72-acd0-ee569bf127b7",
    "_uuid": "78d69747-a22c-4f6a-a180-ab0a9673f2ad",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-06-10T17:31:36.190633Z",
     "iopub.status.busy": "2024-06-10T17:31:36.190212Z",
     "iopub.status.idle": "2024-06-10T17:31:36.588022Z",
     "shell.execute_reply": "2024-06-10T17:31:36.586739Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.412954,
     "end_time": "2024-06-10T17:31:36.590664",
     "exception": false,
     "start_time": "2024-06-10T17:31:36.177710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Description\n",
       "50'S CHRISTMAS GIFT BAG LARGE      bool\n",
       "DOLLY GIRL BEAKER                  bool\n",
       "I LOVE LONDON MINI BACKPACK        bool\n",
       "NINE DRAWER OFFICE TIDY            bool\n",
       "SET 2 TEA TOWELS I LOVE LONDON     bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_encode_units(x):\n",
    "  \"\"\"Converts positive values to True and everything else to False.\"\"\"\n",
    "  return x > 0  # This returns True for positive values and False otherwise\n",
    "\n",
    "my_basket_sets = france_basket.map(my_encode_units)\n",
    "my_basket_sets.drop('POSTAGE', inplace=True, axis=1) # Remove \"postage\" as an item\n",
    "my_basket_sets.dtypes.head()  # boolean datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "175e16bb",
   "metadata": {
    "_cell_guid": "9661324c-76b2-4958-add2-b7e0f2363b93",
    "_uuid": "cf01308f-8a15-4b0d-adb4-3b5658245815",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-06-10T17:31:36.615020Z",
     "iopub.status.busy": "2024-06-10T17:31:36.614618Z",
     "iopub.status.idle": "2024-06-10T17:31:37.051146Z",
     "shell.execute_reply": "2024-06-10T17:31:37.049843Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.453228,
     "end_time": "2024-06-10T17:31:37.055272",
     "exception": false,
     "start_time": "2024-06-10T17:31:36.602044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use this to count the Transactions per Product (in this case 'Description')\n",
    "transaction_counts = my_basket_sets.sum(axis=0)  \n",
    "\n",
    "top_20_products = transaction_counts.sort_values(ascending=False).head(20)  # Get top 20 by count\n",
    "\n",
    "# Create a bar chart with Matplotlib\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Define a colormap (you can choose a different colormap from 'coolwarm')\n",
    "cmap = cm.coolwarm\n",
    "\n",
    "# Normalize the data values for the colormap (optional)\n",
    "norm = plt.Normalize(top_20_products.min(), top_20_products.max())\n",
    "\n",
    "# Create the bar chart with colormap\n",
    "plt.bar(top_20_products.index, top_20_products.values, color=cmap(norm(top_20_products.values)))\n",
    "plt.title('Top 20 Most Frequent Products in France')\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Transaction Count')\n",
    "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for readability if needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da448c59",
   "metadata": {
    "_cell_guid": "908026b3-506d-4140-9443-5b10ff5504ce",
    "_uuid": "492579d6-360d-45a3-9f1e-21c7458012e7",
    "papermill": {
     "duration": 0.013279,
     "end_time": "2024-06-10T17:31:37.081959",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.068680",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Training the Model\n",
    "\n",
    "Adjust the min_support value if you want to. You will see different results.\n",
    "\n",
    "`min_support=0.05`: This parameter sets the minimum support threshold. Here, it means an itemset needs to appear in at least 5% of transactions to be considered frequent.\n",
    "\n",
    "A higher support value like 10% gives less rules while a lower one like 1% gives more than 6,000 rules!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "05dd232e",
   "metadata": {
    "_cell_guid": "d84fcb86-f710-4f29-bbc1-e95b75f1cdad",
    "_uuid": "fa3c04ed-ed16-4bab-81e9-4123f1aadac8",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-06-10T17:31:37.110721Z",
     "iopub.status.busy": "2024-06-10T17:31:37.110049Z",
     "iopub.status.idle": "2024-06-10T17:31:37.159909Z",
     "shell.execute_reply": "2024-06-10T17:31:37.158697Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.06706,
     "end_time": "2024-06-10T17:31:37.162597",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.095537",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>7.478947</td>\n",
       "      <td>0.064088</td>\n",
       "      <td>3.283859</td>\n",
       "      <td>0.964734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.763158</td>\n",
       "      <td>7.478947</td>\n",
       "      <td>0.064088</td>\n",
       "      <td>3.791383</td>\n",
       "      <td>0.959283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>8.642959</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>4.916181</td>\n",
       "      <td>0.979224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>8.642959</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>5.568878</td>\n",
       "      <td>0.976465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>7.681081</td>\n",
       "      <td>0.064348</td>\n",
       "      <td>3.293135</td>\n",
       "      <td>0.968652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.783784</td>\n",
       "      <td>7.681081</td>\n",
       "      <td>0.064348</td>\n",
       "      <td>4.153061</td>\n",
       "      <td>0.960466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.158163</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>3.417611</td>\n",
       "      <td>0.036092</td>\n",
       "      <td>1.336856</td>\n",
       "      <td>0.840303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND )</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.158163</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>3.417611</td>\n",
       "      <td>0.036092</td>\n",
       "      <td>1.832233</td>\n",
       "      <td>0.781127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(CHILDRENS CUTLERY DOLLY GIRL )</td>\n",
       "      <td>(CHILDRENS CUTLERY SPACEBOY )</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>12.962963</td>\n",
       "      <td>0.058856</td>\n",
       "      <td>8.690476</td>\n",
       "      <td>0.993846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(CHILDRENS CUTLERY SPACEBOY )</td>\n",
       "      <td>(CHILDRENS CUTLERY DOLLY GIRL )</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>12.962963</td>\n",
       "      <td>0.058856</td>\n",
       "      <td>12.535714</td>\n",
       "      <td>0.991123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             antecedents  \\\n",
       "0            (ALARM CLOCK BAKELIKE PINK)   \n",
       "1           (ALARM CLOCK BAKELIKE GREEN)   \n",
       "2           (ALARM CLOCK BAKELIKE GREEN)   \n",
       "3            (ALARM CLOCK BAKELIKE RED )   \n",
       "4            (ALARM CLOCK BAKELIKE PINK)   \n",
       "5            (ALARM CLOCK BAKELIKE RED )   \n",
       "6  (ROUND SNACK BOXES SET OF4 WOODLAND )   \n",
       "7            (ALARM CLOCK BAKELIKE RED )   \n",
       "8        (CHILDRENS CUTLERY DOLLY GIRL )   \n",
       "9          (CHILDRENS CUTLERY SPACEBOY )   \n",
       "\n",
       "                             consequents  antecedent support  \\\n",
       "0           (ALARM CLOCK BAKELIKE GREEN)            0.102041   \n",
       "1            (ALARM CLOCK BAKELIKE PINK)            0.096939   \n",
       "2            (ALARM CLOCK BAKELIKE RED )            0.096939   \n",
       "3           (ALARM CLOCK BAKELIKE GREEN)            0.094388   \n",
       "4            (ALARM CLOCK BAKELIKE RED )            0.102041   \n",
       "5            (ALARM CLOCK BAKELIKE PINK)            0.094388   \n",
       "6            (ALARM CLOCK BAKELIKE RED )            0.158163   \n",
       "7  (ROUND SNACK BOXES SET OF4 WOODLAND )            0.094388   \n",
       "8          (CHILDRENS CUTLERY SPACEBOY )            0.071429   \n",
       "9        (CHILDRENS CUTLERY DOLLY GIRL )            0.068878   \n",
       "\n",
       "   consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "0            0.096939  0.073980    0.725000   7.478947  0.064088    3.283859   \n",
       "1            0.102041  0.073980    0.763158   7.478947  0.064088    3.791383   \n",
       "2            0.094388  0.079082    0.815789   8.642959  0.069932    4.916181   \n",
       "3            0.096939  0.079082    0.837838   8.642959  0.069932    5.568878   \n",
       "4            0.094388  0.073980    0.725000   7.681081  0.064348    3.293135   \n",
       "5            0.102041  0.073980    0.783784   7.681081  0.064348    4.153061   \n",
       "6            0.094388  0.051020    0.322581   3.417611  0.036092    1.336856   \n",
       "7            0.158163  0.051020    0.540541   3.417611  0.036092    1.832233   \n",
       "8            0.068878  0.063776    0.892857  12.962963  0.058856    8.690476   \n",
       "9            0.071429  0.063776    0.925926  12.962963  0.058856   12.535714   \n",
       "\n",
       "   zhangs_metric  \n",
       "0       0.964734  \n",
       "1       0.959283  \n",
       "2       0.979224  \n",
       "3       0.976465  \n",
       "4       0.968652  \n",
       "5       0.960466  \n",
       "6       0.840303  \n",
       "7       0.781127  \n",
       "8       0.993846  \n",
       "9       0.991123  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate frequent itemsets\n",
    "my_frequent_itemsets = apriori(my_basket_sets, min_support=0.05, use_colnames=True)\n",
    "\n",
    "# Generate rules\n",
    "my_rules = association_rules(my_frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "\n",
    "print(my_rules.shape) # 86 rows so 86 association rules\n",
    "\n",
    "# View first 10 rules\n",
    "my_rules.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95df97ae",
   "metadata": {
    "_cell_guid": "59a27995-7281-4b37-9562-37beec690111",
    "_uuid": "1e2b78f7-ce41-4b94-9543-9ea821e72fe1",
    "papermill": {
     "duration": 0.013693,
     "end_time": "2024-06-10T17:31:37.190870",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.177177",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 5. Making Recommendations\n",
    "\n",
    "You can sort the rules in descending order by `Lift` and make relevant recommendations. Also consider the `support` and `confidence` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e9ae4f6",
   "metadata": {
    "_cell_guid": "dd8ae6cd-ae5e-4f9f-b339-a79f99b336ed",
    "_uuid": "f266e3bf-ed97-41af-97fc-c5a44164a399",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2024-06-10T17:31:37.220924Z",
     "iopub.status.busy": "2024-06-10T17:31:37.220509Z",
     "iopub.status.idle": "2024-06-10T17:31:37.251937Z",
     "shell.execute_reply": "2024-06-10T17:31:37.250675Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.049812,
     "end_time": "2024-06-10T17:31:37.254735",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.204923",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(PACK OF 6 SKULL PAPER CUPS)</td>\n",
       "      <td>(PACK OF 6 SKULL PAPER PLATES)</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.056122</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>14.254545</td>\n",
       "      <td>0.047441</td>\n",
       "      <td>4.719388</td>\n",
       "      <td>0.993188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(PACK OF 6 SKULL PAPER PLATES)</td>\n",
       "      <td>(PACK OF 6 SKULL PAPER CUPS)</td>\n",
       "      <td>0.056122</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>14.254545</td>\n",
       "      <td>0.047441</td>\n",
       "      <td>10.298469</td>\n",
       "      <td>0.985135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(CHILDRENS CUTLERY SPACEBOY )</td>\n",
       "      <td>(CHILDRENS CUTLERY DOLLY GIRL )</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>12.962963</td>\n",
       "      <td>0.058856</td>\n",
       "      <td>12.535714</td>\n",
       "      <td>0.991123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(CHILDRENS CUTLERY DOLLY GIRL )</td>\n",
       "      <td>(CHILDRENS CUTLERY SPACEBOY )</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.068878</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>12.962963</td>\n",
       "      <td>0.058856</td>\n",
       "      <td>8.690476</td>\n",
       "      <td>0.993846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>9.133271</td>\n",
       "      <td>0.056793</td>\n",
       "      <td>6.565689</td>\n",
       "      <td>0.961653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>9.133271</td>\n",
       "      <td>0.056793</td>\n",
       "      <td>2.855230</td>\n",
       "      <td>0.983324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>8.892922</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>6.547194</td>\n",
       "      <td>0.958457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>8.892922</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>2.706829</td>\n",
       "      <td>0.982825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>8.642959</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>5.568878</td>\n",
       "      <td>0.976465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>8.642959</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>4.916181</td>\n",
       "      <td>0.979224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          antecedents  \\\n",
       "39                       (PACK OF 6 SKULL PAPER CUPS)   \n",
       "38                     (PACK OF 6 SKULL PAPER PLATES)   \n",
       "9                       (CHILDRENS CUTLERY SPACEBOY )   \n",
       "8                     (CHILDRENS CUTLERY DOLLY GIRL )   \n",
       "68  (ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...   \n",
       "73                        (ALARM CLOCK BAKELIKE RED )   \n",
       "69  (ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...   \n",
       "72                       (ALARM CLOCK BAKELIKE GREEN)   \n",
       "3                         (ALARM CLOCK BAKELIKE RED )   \n",
       "2                        (ALARM CLOCK BAKELIKE GREEN)   \n",
       "\n",
       "                                          consequents  antecedent support  \\\n",
       "39                     (PACK OF 6 SKULL PAPER PLATES)            0.063776   \n",
       "38                       (PACK OF 6 SKULL PAPER CUPS)            0.056122   \n",
       "9                     (CHILDRENS CUTLERY DOLLY GIRL )            0.068878   \n",
       "8                       (CHILDRENS CUTLERY SPACEBOY )            0.071429   \n",
       "68                        (ALARM CLOCK BAKELIKE RED )            0.073980   \n",
       "73  (ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...            0.094388   \n",
       "69                       (ALARM CLOCK BAKELIKE GREEN)            0.073980   \n",
       "72  (ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...            0.096939   \n",
       "3                        (ALARM CLOCK BAKELIKE GREEN)            0.094388   \n",
       "2                         (ALARM CLOCK BAKELIKE RED )            0.096939   \n",
       "\n",
       "    consequent support   support  confidence       lift  leverage  conviction  \\\n",
       "39            0.056122  0.051020    0.800000  14.254545  0.047441    4.719388   \n",
       "38            0.063776  0.051020    0.909091  14.254545  0.047441   10.298469   \n",
       "9             0.071429  0.063776    0.925926  12.962963  0.058856   12.535714   \n",
       "8             0.068878  0.063776    0.892857  12.962963  0.058856    8.690476   \n",
       "68            0.094388  0.063776    0.862069   9.133271  0.056793    6.565689   \n",
       "73            0.073980  0.063776    0.675676   9.133271  0.056793    2.855230   \n",
       "69            0.096939  0.063776    0.862069   8.892922  0.056604    6.547194   \n",
       "72            0.073980  0.063776    0.657895   8.892922  0.056604    2.706829   \n",
       "3             0.096939  0.079082    0.837838   8.642959  0.069932    5.568878   \n",
       "2             0.094388  0.079082    0.815789   8.642959  0.069932    4.916181   \n",
       "\n",
       "    zhangs_metric  \n",
       "39       0.993188  \n",
       "38       0.985135  \n",
       "9        0.991123  \n",
       "8        0.993846  \n",
       "68       0.961653  \n",
       "73       0.983324  \n",
       "69       0.958457  \n",
       "72       0.982825  \n",
       "3        0.976465  \n",
       "2        0.979224  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort by lift (highest first)\n",
    "sorted_lift_rules = my_rules.sort_values(by='lift', ascending=False)\n",
    "sorted_lift_rules.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe455be9",
   "metadata": {
    "_cell_guid": "31dcdb05-b25a-49fa-ba7a-9b68495e758e",
    "_uuid": "7771200c-c7b5-4b5c-9a53-b382546e1b37",
    "papermill": {
     "duration": 0.014222,
     "end_time": "2024-06-10T17:31:37.283354",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.269132",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Strong Co-occurrence of Party Supplies\n",
    "\n",
    "Looking at the first two rules:\n",
    "\n",
    "* Both rules involve \"PACK OF 6 SKULL PAPER CUPS\" and \"PACK OF 6 SKULL PAPER PLATES\" with the same support value (0.051020) and lift value (14.254545)\n",
    "* `Lift: Highlighting a Non-Random Relationship`\n",
    "* A lift value greater than 1 indicates a positive association, but a value this high (14.25) suggests a very **strong, non-random relationship** between the two products.\n",
    "\n",
    "These rules highlight a strong co-occurrence pattern between the two skull-themed party supplies, indicating that customers who buy one item are very likely to also buy the other.  Here's how a **Store manager can leverage this information**:\n",
    "\n",
    "1. **Strategic Product Placement**: Consider recommending \"PACK OF 6 SKULL PAPER PLATES\" next to \"PACK OF 6 SKULL PAPER CUPS\". This close proximity can create a visual reminder for customers browsing either product, potentially leading to impulse purchases of the complementary item (cups if looking at plates, and vice versa).\n",
    "\n",
    "2.  **Ensure Adequate Stock**: Given the high confidence values (especially the 90.9% confidence for plates leading to cups), it's crucial to ensure the store has sufficient stock of both products to meet this customer demand. Running out of either product could lead to lost sales opportunities.\n",
    "\n",
    "3.  **Bundled Promotions**: While the co-occurrence is strong, you might consider a bundled promotion (e.g., \"Party Pack: Skull Paper Cups & Plates\") to further incentivize customers to buy both items together. However, analyse your profit margins before implementing this strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39965ca",
   "metadata": {
    "_cell_guid": "e8e7e3d7-9b34-452a-8208-fe50f71963ec",
    "_uuid": "99e68191-1c8a-4244-ac7b-a45f2cc43906",
    "papermill": {
     "duration": 0.01416,
     "end_time": "2024-06-10T17:31:37.312131",
     "exception": false,
     "start_time": "2024-06-10T17:31:37.297971",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Product-Specific Recommendations\n",
    "\n",
    "This section is relevant if you want to see rules on **Specific products** and make Recommendations based on the `support`, `confidence` and `lift` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac55cff7",
   "metadata": {
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    "# Join list elements with comma. Helps when converting to string datatype\n",
    "my_rules['antecedents_str'] = my_rules['antecedents'].apply(lambda x: ','.join(x))  \n",
    "my_rules['consequents_str'] = my_rules['consequents'].apply(lambda x: ','.join(x))"
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    {
     "data": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "      <th>zhangs_metric</th>\n",
       "      <th>antecedents_str</th>\n",
       "      <th>consequents_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.096939</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.815789</td>\n",
       "      <td>8.642959</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>4.916181</td>\n",
       "      <td>0.979224</td>\n",
       "      <td>ALARM CLOCK BAKELIKE GREEN</td>\n",
       "      <td>ALARM CLOCK BAKELIKE RED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>7.681081</td>\n",
       "      <td>0.064348</td>\n",
       "      <td>3.293135</td>\n",
       "      <td>0.968652</td>\n",
       "      <td>ALARM CLOCK BAKELIKE PINK</td>\n",
       "      <td>ALARM CLOCK BAKELIKE RED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(ROUND SNACK BOXES SET OF4 WOODLAND )</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.158163</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>3.417611</td>\n",
       "      <td>0.036092</td>\n",
       "      <td>1.336856</td>\n",
       "      <td>0.840303</td>\n",
       "      <td>ROUND SNACK BOXES SET OF4 WOODLAND</td>\n",
       "      <td>ALARM CLOCK BAKELIKE RED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE RED )</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>0.094388</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.862069</td>\n",
       "      <td>9.133271</td>\n",
       "      <td>0.056793</td>\n",
       "      <td>6.565689</td>\n",
       "      <td>0.961653</td>\n",
       "      <td>ALARM CLOCK BAKELIKE PINK,ALARM CLOCK BAKELIKE...</td>\n",
       "      <td>ALARM CLOCK BAKELIKE RED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>(ALARM CLOCK BAKELIKE PINK)</td>\n",
       "      <td>(ALARM CLOCK BAKELIKE GREEN, ALARM CLOCK BAKEL...</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.063776</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>7.903226</td>\n",
       "      <td>0.055706</td>\n",
       "      <td>2.455782</td>\n",
       "      <td>0.972727</td>\n",
       "      <td>ALARM CLOCK BAKELIKE PINK</td>\n",
       "      <td>ALARM CLOCK BAKELIKE GREEN,ALARM CLOCK BAKELIK...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                          antecedents  \\\n",
       "2                        (ALARM CLOCK BAKELIKE GREEN)   \n",
       "4                         (ALARM CLOCK BAKELIKE PINK)   \n",
       "6               (ROUND SNACK BOXES SET OF4 WOODLAND )   \n",
       "68  (ALARM CLOCK BAKELIKE PINK, ALARM CLOCK BAKELI...   \n",
       "71                        (ALARM CLOCK BAKELIKE PINK)   \n",
       "\n",
       "                                          consequents  antecedent support  \\\n",
       "2                         (ALARM CLOCK BAKELIKE RED )            0.096939   \n",
       "4                         (ALARM CLOCK BAKELIKE RED )            0.102041   \n",
       "6                         (ALARM CLOCK BAKELIKE RED )            0.158163   \n",
       "68                        (ALARM CLOCK BAKELIKE RED )            0.073980   \n",
       "71  (ALARM CLOCK BAKELIKE GREEN, ALARM CLOCK BAKEL...            0.102041   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "2             0.094388  0.079082    0.815789  8.642959  0.069932    4.916181   \n",
       "4             0.094388  0.073980    0.725000  7.681081  0.064348    3.293135   \n",
       "6             0.094388  0.051020    0.322581  3.417611  0.036092    1.336856   \n",
       "68            0.094388  0.063776    0.862069  9.133271  0.056793    6.565689   \n",
       "71            0.079082  0.063776    0.625000  7.903226  0.055706    2.455782   \n",
       "\n",
       "    zhangs_metric                                    antecedents_str  \\\n",
       "2        0.979224                         ALARM CLOCK BAKELIKE GREEN   \n",
       "4        0.968652                          ALARM CLOCK BAKELIKE PINK   \n",
       "6        0.840303                ROUND SNACK BOXES SET OF4 WOODLAND    \n",
       "68       0.961653  ALARM CLOCK BAKELIKE PINK,ALARM CLOCK BAKELIKE...   \n",
       "71       0.972727                          ALARM CLOCK BAKELIKE PINK   \n",
       "\n",
       "                                      consequents_str  \n",
       "2                           ALARM CLOCK BAKELIKE RED   \n",
       "4                           ALARM CLOCK BAKELIKE RED   \n",
       "6                           ALARM CLOCK BAKELIKE RED   \n",
       "68                          ALARM CLOCK BAKELIKE RED   \n",
       "71  ALARM CLOCK BAKELIKE GREEN,ALARM CLOCK BAKELIK...  "
      ]
     },
     "execution_count": 15,
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   ],
   "source": [
    "# Example on 'ALARM CLOCK BAKELIKE RED' as the consequent\n",
    "alarm_clock_red = my_rules[my_rules['consequents_str'].str.contains(\"ALARM CLOCK BAKELIKE RED\")]  # replace with any product\n",
    "alarm_clock_red.head()\n",
    "\n",
    "# # Uncomment this code if you want 'ALARM CLOCK BAKELIKE RED' as the antecedent\n",
    "# alarm_red = my_rules[my_rules['antecedents_str'].str.contains(\"ALARM CLOCK BAKELIKE RED\")]  # replace with any product\n",
    "# alarm_red.head()"
   ]
  },
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   "source": [
    "## Strong Association for \"ALARM CLOCK BAKELIKE RED\"\n",
    "\n",
    "The rule suggests a strong association between buying \"ALARM CLOCK BAKELIKE GREEN\" or \"ALARM CLOCK BAKELIKE PINK\" (antecedents) and also buying \"ALARM CLOCK BAKELIKE RED\" (consequent)\n",
    "\n",
    "1. **Support** (0.0638): This indicates that out of all transactions, approximately 6.4% involved customers buying at least one of the green or pink alarm clocks along with the red alarm clock.\n",
    "\n",
    "1. **Confidence** (0.862): If a customer buys \"ALARM CLOCK BAKELIKE GREEN\" or \"ALARM CLOCK BAKELIKE PINK\", there's an 86.2% chance they also purchase the \"ALARM CLOCK BAKELIKE RED\". This suggests a strong tendency for customers who like these similar alarm clocks to also consider the red one.\n",
    "\n",
    "1. **Lift** (9.13): This is the most crucial metric. A lift value this high (over 9) signifies that customers who buy the green or pink clocks are 9.13 times more likely to also buy the red clock compared to if they hadn't purchased the green or pink ones. This indicates a very strong, non-random association.\n",
    "\n",
    "Recommendation:\n",
    "\n",
    "* **Cross-Selling**: The store manager should focus on cross-selling the \"ALARM CLOCK BAKELIKE RED\" whenever a customer shows interest in or purchases either the green or pink version.\n",
    "\n",
    "* **Visual Merchandising**: Consider creating an eye-catching display that showcases all three alarm clock colors together. This can further emphasize the color options and potentially encourage indecisive customers to buy more than one.\n",
    "\n",
    "* **Inventory Management**: Ensure there's adequate stock of all three colored alarm clocks, especially the red one, given the high likelihood of customers buying it alongside the green or pink versions."
   ]
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   "source": [
    "Feel free to perform further datetime-specific analyses and transactions in other Countries.\n",
    "\n",
    "***Explore and be teachable***"
   ]
  }
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