market-basket-analysis-for-online-retail-dataset.ipynb

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     "output_type": "stream",
     "text": [
      "/kaggle/input/onlineretail/OnlineRetail.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
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    },
    "tags": []
   },
   "source": [
    "## Load and Examine Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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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",
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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",
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       "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  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "retail = pd.read_csv('../input/onlineretail/OnlineRetail.csv', encoding = 'unicode_escape')\n",
    "retail.head()"
   ]
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   "source": [
    "## Preprocessing\n",
    "\n",
    "There is a little cleanup, we need to do. First, some of the descriptions have spaces that need to be removed. We’ll also drop the rows that don’t have invoice numbers and remove the credit transactions (those with invoice numbers containing C)."
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   "source": [
    "retail['Description'] = retail['Description'].str.strip()\n",
    "retail.dropna(axis=0, subset=['InvoiceNo'], inplace=True)\n",
    "retail['InvoiceNo'] = retail['InvoiceNo'].astype('str')\n",
    "retail = retail[~retail['InvoiceNo'].str.contains('C')]"
   ]
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   "source": [
    "## Consolidate Items into 1 Transaction Per Row\n",
    "\n",
    "After the cleanup, we need to consolidate the items into 1 transaction per row with each product 1 hot encoded. For the sake of keeping the data set small, I’m only looking at sales for France. However, in additional code below, I will compare these results to sales from Germany. Further country comparisons would be interesting to investigate."
   ]
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   "source": [
    "basket = (retail[retail['Country'] ==\"France\"]\n",
    "          .groupby(['InvoiceNo', 'Description'])['Quantity']\n",
    "          .sum().unstack().reset_index().fillna(0)\n",
    "          .set_index('InvoiceNo'))"
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       "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
       "InvoiceNo                                                         \n",
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       "InvoiceNo                                                                 \n",
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       "537463                             0.0                              0.0   \n",
       "\n",
       "Description  12 PENCIL SMALL TUBE WOODLAND  \\\n",
       "InvoiceNo                                    \n",
       "536370                                 0.0   \n",
       "536852                                 0.0   \n",
       "536974                                 0.0   \n",
       "537065                                 0.0   \n",
       "537463                                 0.0   \n",
       "\n",
       "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\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 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\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 PENCILS TALL TUBE WOODLAND  ...  WRAP VINTAGE PETALS  DESIGN  \\\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 COAT RACK PARIS FASHION  YELLOW GIANT GARDEN THERMOMETER  \\\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 SHARK HELICOPTER  ZINC  STAR T-LIGHT HOLDER  \\\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 FOLKART SLEIGH BELLS  ZINC HERB GARDEN CONTAINER  \\\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 METAL HEART DECORATION  ZINC T-LIGHT HOLDER STAR LARGE  \\\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 STARS SMALL  \n",
       "InvoiceNo                                     \n",
       "536370                                   0.0  \n",
       "536852                                   0.0  \n",
       "536974                                   0.0  \n",
       "537065                                   0.0  \n",
       "537463                                   0.0  \n",
       "\n",
       "[5 rows x 1563 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016643,
     "end_time": "2020-10-21T13:44:11.646470",
     "exception": false,
     "start_time": "2020-10-21T13:44:11.629827",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Postprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016621,
     "end_time": "2020-10-21T13:44:11.680075",
     "exception": false,
     "start_time": "2020-10-21T13:44:11.663454",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are a lot of zeros in the data but we also need to make sure any positive values are converted to a 1 and anything less the 0 is set to 0. This step will complete the one hot encoding of the data and remove the postage column (since that charge is not one we wish to explore):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:11.721678Z",
     "iopub.status.busy": "2020-10-21T13:44:11.720702Z",
     "iopub.status.idle": "2020-10-21T13:44:12.326072Z",
     "shell.execute_reply": "2020-10-21T13:44:12.325281Z"
    },
    "papermill": {
     "duration": 0.629095,
     "end_time": "2020-10-21T13:44:12.326210",
     "exception": false,
     "start_time": "2020-10-21T13:44:11.697115",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def encode_units(x):\n",
    "    if x <= 0:\n",
    "        return 0\n",
    "    if x >= 1:\n",
    "        return 1\n",
    "\n",
    "basket_sets = basket.applymap(encode_units)\n",
    "basket_sets.drop('POSTAGE', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01693,
     "end_time": "2020-10-21T13:44:12.360654",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.343724",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Generate Frequent Itemsets\n",
    "\n",
    "Now that the data is structured properly, we can generate frequent item sets that have a support of at least 7% (this number was chosen so that I could get enough useful examples):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:12.402090Z",
     "iopub.status.busy": "2020-10-21T13:44:12.401044Z",
     "iopub.status.idle": "2020-10-21T13:44:12.426501Z",
     "shell.execute_reply": "2020-10-21T13:44:12.425918Z"
    },
    "papermill": {
     "duration": 0.048647,
     "end_time": "2020-10-21T13:44:12.426641",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.377994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "frequent_itemsets = apriori(basket_sets, min_support=0.07, use_colnames=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017231,
     "end_time": "2020-10-21T13:44:12.461358",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.444127",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Building Association Rules Using Frequent Itemset\n",
    "\n",
    "The final step is to generate the rules with their corresponding support, confidence and lift:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:12.503837Z",
     "iopub.status.busy": "2020-10-21T13:44:12.502666Z",
     "iopub.status.idle": "2020-10-21T13:44:12.529498Z",
     "shell.execute_reply": "2020-10-21T13:44:12.528759Z"
    },
    "papermill": {
     "duration": 0.050997,
     "end_time": "2020-10-21T13:44:12.529633",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.478636",
     "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    antecedents                   consequents  \\\n",
       "0   (ALARM CLOCK BAKELIKE PINK)  (ALARM CLOCK BAKELIKE GREEN)   \n",
       "1  (ALARM CLOCK BAKELIKE GREEN)   (ALARM CLOCK BAKELIKE PINK)   \n",
       "2  (ALARM CLOCK BAKELIKE GREEN)    (ALARM CLOCK BAKELIKE RED)   \n",
       "3    (ALARM CLOCK BAKELIKE RED)  (ALARM CLOCK BAKELIKE GREEN)   \n",
       "4   (ALARM CLOCK BAKELIKE PINK)    (ALARM CLOCK BAKELIKE RED)   \n",
       "\n",
       "   antecedent support  consequent support   support  confidence      lift  \\\n",
       "0            0.102041            0.096939  0.073980    0.725000  7.478947   \n",
       "1            0.096939            0.102041  0.073980    0.763158  7.478947   \n",
       "2            0.096939            0.094388  0.079082    0.815789  8.642959   \n",
       "3            0.094388            0.096939  0.079082    0.837838  8.642959   \n",
       "4            0.102041            0.094388  0.073980    0.725000  7.681081   \n",
       "\n",
       "   leverage  conviction  \n",
       "0  0.064088    3.283859  \n",
       "1  0.064088    3.791383  \n",
       "2  0.069932    4.916181  \n",
       "3  0.069932    5.568878  \n",
       "4  0.064348    3.293135  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
    "rules.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017592,
     "end_time": "2020-10-21T13:44:12.565443",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.547851",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualizing Relationship between Support, Confidence and Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:12.608599Z",
     "iopub.status.busy": "2020-10-21T13:44:12.607847Z",
     "iopub.status.idle": "2020-10-21T13:44:14.007048Z",
     "shell.execute_reply": "2020-10-21T13:44:14.006139Z"
    },
    "papermill": {
     "duration": 1.423642,
     "end_time": "2020-10-21T13:44:14.007209",
     "exception": false,
     "start_time": "2020-10-21T13:44:12.583567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5qcVshbLBrjkQZNOuridXrWsOEND6sBJnU0cP40/Xn8pD727CH3D8/WePoCTMjmLnHB9t3c/CByvY3ejj/GNG828XTWeEVsRLSuEmghbnnC+0kFjYK5QNatleD7OPKGDDzoZO+8bkZZCmZiGJs6x0D0cV5fAvF0wD6FOT0K4GH9c/tpLdjaErgec/2s4Xy8dx2tFD+vuddCOWK5QNapneVK474yiyu7iD+AfnT9UNZRJVTb4AvkBr7wd2ISXF+tEv4Do1bx5MCpJ8YrlC2aA3Oi+DFxafwiUziykpyOT40uE89rUTOGVyoW4ok6jY2+jjlbU7+M4jH/DPz6ymalcjzf7+JYS+GJaZxuK5kw89L8pN56SjRsb8dSUx2WC7Wbi8vNxVVFQM6Gs2tgRo9AXwpqaQH8aNPiLhaGoJ8MvXPuG/Xv+/G8q8qSk8v/hkJhXlxvz19zX52N3gY0d9M0cV5VCYk87B5l8ZeszsPedceVf7euwjaFswpttM4ZybEWFsg0J2uodszQ8vUVbfHOD+N6sO2+ZrDfLzVzbwk0tnxPw9l5/lJT/Ly5FFOTF9HUl8vb3TLmj791tt/z7U9u+V9LxesYj0wtcaxNfF6LOavQfwBYKoG0oGSo+JwDn3KYCZneScO6ndrpvM7C3g32IZnMhQlpmWSunIbKp2NR62/cKZY8O+M7i2voXNexppDULpyGwKc5U9pO/CvfbMNrOTnXN/BjCzE4Hs2IUlMvSNyPFy79/P4aoH/krN/mYAzp0+iguPHRvWYIQddc184Z532LwndHE+alg6S795kiadkz4LNxFcCzxgZnltz/cB18QmJJHkYGYcWZjD09edRENLK15PCtne1LAGJARagzz07qeHkgDAjroWfvXqBv7p89PITBvyM8BIFIU719B7wLFmNozQSKP9sQ1LJDmkpBiFuRkU9nGQUHMgSGV15z/DNdvqafa1KhFIn/Q2amiBc+5hM7uhw3YAnHN3xTA2EelGtjeV844Zw+sf1x62/ZyyUeRmaoSb9E1vN5QdnBs3t5uHiMSBmXF22Si+WF5CioUmnDtv+mgu/8w4PCma/kT6prevDke2/bvGOff7WAeTiHyBVnY1+GgJtOL1pJKb7tFc75IQhmd7+acLyrjh7Mk4QvNj6b0p/dFbIvicmf0AuJnQ/EJJZVd9Cy+t2c5P/7SevU1+vKkpXDK7mBvPnqy1YSWqGloCpKUY6X1s28/NSCM3Qx/+EpneEsGLwC5Cw0fr2m03wDnnhsUssgTw0db93PJk5aHnvtYgj63YAsCtn5vCsExNNyGR2d3Qwluf7OLJD2ooyEpj0akTKSnIJEcf7jKAeruh7PvA983saefcRQMUU0LYWdfML1/d0OW+pe9X850zJzFMw7UlArsbWrj+sZW8uWHXoW1LP9jKf145m3PKRuHR2thx4ff7qa6uprm5Od6h9EtGRgYlJSWkpYX/ZSLc4aODOgnUNfupbw6ws66ZMXmZDMvwkNXLPC7+Vkf13gPd7mvp55TBIgdt2dN0WBI46F+eXk35EQVqfoyT6upqcnNzmTBhwqCbhM85x+7du6murqa0tDTs88JdqnKemW0ws/1mVmdm9R2aihLW/gM+7n+zipN//CqX/OfbnPKTV3l1/U4O+AI9npeVnsrMcfld7svPSiND47QlQis27e1ye21DCy0BrYAXL83NzYwYMWLQJQEIjSYbMWJEn69mwr32/AlwoXMuzzk3zDmXO1j6B/Y0+lmybAMHZ9v2tzq+9/tV1DX3nAgKsrx8/++OJr2LlchuOHsyI7PVPyCROWpU17N+Zqal4tUKeHE1GJPAQf2JPdx32w7n3No+l54APt5R32lbsz9IY0vPiQCgJD+TZ799MueUjaIwN52Z4/K576pyzj9mDF5dEUiEpo/NY9zwzh1Ni06dSJ6GgcoACvcWxAozewx4Cmg5uNE5tzQmUUXRpC7mWs9ISyHb23vVM9M9TB6Vy48vnUGTL0BaaorabSVqCnPT+f3XPsvdr3zMS6t3kJcZGjV03vQxanpMcjk5OTQ0NFBTU8N3vvMdnnjiCQDmz5/P6tWrufrqqzEzFi1aRFZWVi+l9S7cRDCM0PoD57Tb5oCETwQjsr1cd+ZR/MernwDgSTF+cumxDMsK/zb8gmwvBWoKkhgYnZfJDz8/jRvPPpoUM4Zne/ux/rAMVWPHjj2UBLZv387bb7/Np59+CsCECRNYsGDBwCUC59zVEb9SnORleVl0ykSu+Mw4duxvprggk2GZaWSmaT4WSQyZXg+ZYVyhSvLZtGkTF1xwAZWVlZxzzjns3LmTmTNncskll1BTU8MZZ5zByJEjee211yJ6nbDefWZWAvwSOInQlcCfgcXOueqIXn2ADMtMY1hmGiUFkWdOEZF4eOaZZ7jgggtYuXIlAL/5zW947bXXGDlyZMRlh9tZ/BvgGWAsUAw827atR2Z2rpmtN7NPzOymLvZ/38xWtj0qzazVzIb3pQIiIhKZcBNBoXPuN865QNvjt0BhTyeYWSrwK+A8oAyYb2Zl7Y9xzv3UOTfTOTeT0HxGy51ze/pcCxER6bdwE8EuM1tgZqltjwXA7l7OOQ74xDm30TnnAx4FerpDeT7wSJjxiIgktdzcXOrrOw+P749wE8E1wBeB7cA24DKgtw7kYmBLu+fVbds6MbMs4FzgD93sX2RmFWZWUVtb29UhIiJJZdGiRZx33nmcccYZEZcVbiL4EXCVc67QOVdEKDH8sJdzuhoD57o59vPAW901Cznn7nXOlTvnygsLe2yREhEZ9BoaGoDQENHKyspOPwN8+9vfZt26dRGPGILwE8EM59yhiVHaPrBn9XJONTCu3fMSoKabY69AzUIiInERbiJIMbOCg0/aRvb0NvR0BTDJzErNzEvow/6ZjgeZWR5wGvB0mLGIiEgUhXsXy53A22b2BKHmnS8C/97TCc65gJldB/wJSAUecM6tNrOvt+2/p+3QS4CXnHON/amAiIhEJtw7i39nZhXAmYTa/uc559aEcd4LwAsdtt3T4flvgd+GGa+IiERZ2Pe1t33w9/rhLyIig4smPRcRSXJKBCIiCWbLli2cccYZTJ06lWnTprFkyZKYvp6mPBQRiVAgEGDfvn3k5+fj8UT+serxeLjzzjuZPXs29fX1zJkzh7PPPpuysrLeT+4HXRGIiERg1apVnHXWWVx44YWcddZZrFq1KuIyx4wZw+zZs4HQVBJTp05l69atEZfbHSUCEZF+CgQCLF68mIaGBnw+Hw0NDSxevJjW1taovcamTZv44IMPOP7446NWZkdKBCIi/bRv3z58Pt9h23w+H3v37u3mjL5paGjg0ksv5ec//znDhg2LSpldUSIQEemn/Px8vN7Dl7H1er0UFBR0c0b4/H4/l156KVdeeSXz5s2LuLyeKBGIiPSTx+NhyZIl5OTk4PV6ycnJYcmSJaSmpkZUrnOOa6+9lqlTp3LDDTdEKdruadSQiEgEjj32WJYtW8bevXspKCiIOAkAvPXWWzz00EMcc8wxzJw5E4DbbruNz33ucxGX3RUlAhGRCKWmpkZl7eCDTj75ZJzrbtb+6FPTkIhIklMiEBFJckoEIiJJTolARCTJqbNYEsqeRh9VuxrwpqZSUpBJQba395NEJCJKBJIw9h/wc/sf1/L7imoAbjh7Ml89ZSKZ3siH44lI99Q0JAmjxd/KHz/afuj5s6tqaPQF4hiRSPy0trYya9YsLrjggpi/lhKBJAyvJ4VTJ/3fWOwzpxSRmaarAUl8lZWVvPDCC1RWVkatzCVLljB16tSoldcTNQ1JwsjP8vKji6dz6ZwS0j2pTBs7jOx0vUUlsd11110sXbqUlJQUgsEg8+bNi3haiOrqap5//nluvfVW7rrrrihF2j1dEUhCGZGTztypozh50kh1FEvCq6ysZOnSpTQ3N9PU1ERzczNLly6N+Mrg+uuv5yc/+QkpKQPzEa1EICLST5s3b+70YZ2SksLmzZv7XeZzzz1HUVERc+bMiTS8sCkRiIj00/jx4wkGg4dtCwaDjB8/vt9lvvXWWzzzzDNMmDCBK664gldffZUFCxZEGmqPlAhERPpp+vTpzJs3j4yMDLKyssjIyGDevHlMnz6932XefvvtVFdXs2nTJh599FHOPPNMHn744ShG3Zl64kREInDDDTdwzjnnsHnzZsaPHx9REogXJQIRkQhNnz49Jgng9NNP5/TTT496uR2paUhEJMkpEYiIJDklAhGRJKdEICKS5JQIRESSnEYNiSSp3Q0tLFu3k3Xb6rjy+CMoLsgkQ5P8JSVdEYgkoYZmP7e/sI5/eOJDHnhrE+cueYOddS3xDmvQqqqqYvny5VRVVUWtzH379nHZZZcxZcoUpk6dyjvvvBO1sjvSFYFIEmryt/LshzWHnvtbHW9uqOXKEUfEMarBZ9++fdx4442sW7cOj8dDIBBgypQp3HnnneTn50dU9uLFizn33HN54okn8Pl8NDU1RSnqznRFIDKI7W308dLq7Tz3YQ17GsP/Rp+WksK0sXmHbZtenNfN0dKdG2+8kdWrV9PS0kJjYyMtLS2sXr2aG2+8MaJy6+rqeOONN7j22msB8Hq9ESeWnigRiAxSwaDj0RWbWfTQe1z3vx/wn6/9jWZ/a1jnFmR7+eWXZnHW1CImFeVwx7xjOGJEVowjHlo2btzIunXrCAQOX0UvEAiwbt26iJqJNm7cSGFhIVdffTWzZs1i4cKFNDY2Rhpyt2KaCMzsXDNbb2afmNlN3RxzupmtNLPVZrY8lvGIDCWBYJDVNXWHnq/dXocvEOzhjMMV52dy9+UzeWTRCXxhTgn5WVr/oS+2bNmCx9N163paWlpEU1EHAgHef/99vvGNb/DBBx+QnZ3NHXfc0e/yehOzPgIzSwV+BZwNVAMrzOwZ59yadsfkA/8JnOuc22xmRbGKR2So8XpSueHsyexr8hF0jh+cX0ZuRt/+pHMz0siNUXxD3fjx4ztdDRzk9/sjmoq6pKSEkpISjj/+eAAuu+yymCaCWF4RHAd84pzb6JzzAY8CF3U45kvAUufcZgDn3M4YxiMy5ORnebn6pFKu+mwpRbnpmFm8Q0oapaWlTJkypdNVgcfjYcqUKZSWlva77NGjRzNu3DjWr18PwLJlyygrK4so3p7EMhEUA1vaPa9u29beZKDAzF43s/fM7MtdFWRmi8yswswqamtrYxSuyOASDDqeeG8L1z5Ywdcefo9739hIS5h9BBIdd955J9OmTSM9PZ2cnBzS09OZNm0ad955Z8Rl//KXv+TKK69kxowZrFy5kltuuSUKEXctlsNHu/pq4rp4/TnAXCATeMfM3nXOfXzYSc7dC9wLUF5e3rEMkaQUCAZZtWX/oeeVNftpCQRJ101hAyY/P5/777+fqqqqQ+sRRHIl0N7MmTOpqKiISlm9iWUiqAbGtXteAtR0ccwu51wj0GhmbwDHAh8jIj3yelL57tmTee/Tvfhbg9x83tQ+9xFIdJSWlkYtAcRDLN81K4BJZlYKbAWuINQn0N7TwH+YmQfwAscDd8cwJpEhZeLIbJ799kk4YESWV30E0i8xSwTOuYCZXQf8CUgFHnDOrTazr7ftv8c5t9bMXgQ+BILAfc65yljFJDLUpKQYhbkZ8Q5DBrmYXkc6514AXuiw7Z4Oz38K/DSWcYiISPd0Z7GISJJTIhARiZBzDr/fj3ODc1CjEoGISD/V19dz9913c9ppp3HiiSdy2mmncffdd1NfXx9x2XfffTfTpk1j+vTpzJ8/n+bm5ihE3LWkSATN/lZ21DXz6e5GdtY1E2gNfz4WEZGu1NfXs2DBAh5//HGamppwztHU1MTjjz/OggULIkoGW7du5Re/+AUVFRVUVlbS2trKo48+GsXoDzfkE0FDc4A/Vm7jjJ+9zmk/fZ3zlrzJyi378LfqDkwR6b/77ruPnTt34vf7D9vu9/vZuXMn9913X0TlBwIBDhw4QCAQoKmpibFjx0ZUXk+GfCLYd8DHDY+voskX+uDf3ejjmgdXsLfR38uZIiJdc87x5JNPdkoCB/n9fp566ql+9xkUFxfzve99j/HjxzNmzBjy8vI455xzIgm5R0M+Eaypqdl4/ikAAApQSURBVKPj76LuQICGlq5nDRQR6c3Bb+s9aWpq6nZ20t7s3buXp59+mqqqKmpqamhsbOThhx/uV1nhGPKJoKvFNjwpRpZX87GISP94PB4yMzN7PCYrK6vb9Qp688orr1BaWkphYSFpaWnMmzePt99+u19lhWPIJ4Ki3Aw+P2PMYdtuPGcyOelpcYpIRAY7M+OSSy4hLa3rz5G0tDQuvvjifk/5MX78eN59991DndDLli1j6tSpkYTcoyE/Q1VBtpd/vWg6i049knXb65g1voDCXC85mpxLRCKwcOFCXn/99U4dxmlpaRQVFbFw4cJ+l3388cdz2WWXMXv2bDweD7NmzWLRokXRCLtLNthugCgvL3cDNTWriCSftWvXhv3tu76+nvvuu4+nnnqKpqYmsrKyuPjii1m4cCG5ufFb+62rOpjZe8658q6O19diEZF+ys3N5bvf/S7XX389gUAAj8czKGeAVSIQEYmQmXXbXzAYDPnOYhER6ZkSgYhIklMiEBFJcuojEBGJ0NatW6mtraWwsJDi4uJ4h9NnSgQiIv20Zs0abrvtNqqqqkhLS8Pv91NaWsott9xCWVlZv8u95ppreO655ygqKqKyMrR67549e7j88svZtGkTEyZM4PHHH6egoCAq9VDTkIhIP6xZs4ZFixaxbt06WlpaaGhooKWlhXXr1rFo0SLWrFnT77K/8pWv8OKLLx627Y477mDu3Lls2LCBuXPncscdd0RahUOUCERE+uG2227rdrGY5uZmbr/99n6XfeqppzJ8+PDDtj399NNcddVVAFx11VU89dRT/S6/IyUCEZE+2rp1K1VVVT0es3HjRrZu3Rq119yxYwdjxoTmTRszZgw7d+6MWtlKBCIifVRbW9vrDWRpaWnU1tYOUESRUSKQhOILtLKzrpmd9c20BgfXPFiSPAoLC7tdlOYgv99PYWFh1F5z1KhRbNu2DYBt27ZRVFQUtbKVCCRh+AKtVGzay1l3L+f8X/yZ9dvrCCoZSAIqLi6mtLS0x2MmTpwY1aGkF154IQ8++CAADz74IBdddFHUylYikISxr8nPtx/5gLoDAWrrW/j+Ex+yt8kX77BEunTLLbeQkZHR5b6MjAxuvvnmfpc9f/58PvvZz7J+/XpKSkq4//77uemmm3j55ZeZNGkSL7/8MjfddFO/y+9I9xFIwjCDrPRUdjeGnmene0hJGXwzOUpyKCsr49577+X2229n48aNh+4jmDhxIjfffHNE9xE88sgjXW5ftmxZv8vsiRKBJIyROen85ivHcdMfPiQ9LYWfXDqDgixvvMMS6VZZWRkPPfSQ7iwWiRYz46iiHH795XJSDPKUBGSQKC4uHpQJ4CAlAkk4BdlKABJfzrlBucAMhGLvK3UWi4i0k5GRwe7du/v1gRpvzjl2797dbSd2d3RFICLSTklJCdXV1YPmZrCOMjIyKCkp6dM5SgQiIu2kpaX1eo/AUKOmIRGRJKdEICKS5JQIRESSnA22nnEzqwU+7Wb3SGDXAIYTb8lU32SqK6i+Q1m86nqEc67LWfAGXSLoiZlVOOfK4x3HQEmm+iZTXUH1HcoSsa5qGhIRSXJKBCIiSW6oJYJ74x3AAEum+iZTXUH1HcoSrq5Dqo9ARET6bqhdEYiISB8pEYiIJLlBkQjM7FwzW29mn5hZp/XZLOQXbfs/NLPZbduPNrOV7R51Znb9wNegb/pb37Z93zWz1WZWaWaPmFnfpiGMgwjru7itrquHyO92ipm9Y2YtZva9vpybiCKs7wNmttPMKgcu4sj0t75mNs7MXjOztW3v5cUDGrhzLqEfQCrwN2Ai4AVWAWUdjvkc8EfAgBOAv3RTznZCN1XEvV6xqC9QDFQBmW3PHwe+Eu86xbC+04FKIIvQBIqvAJPiXacI61oEfAb4d+B7fTk30R6R1Ldt36nAbKAy3nUZgN/vGGB228+5wMcD+fsdDFcExwGfOOc2Oud8wKPARR2OuQj4nQt5F8g3szEdjpkL/M05191dyYki0vp6gEwz8xD6gKwZqMD7KZL6TgXedc41OecCwHLgkoEMvo96ratzbqdzbgXg7+u5CSiS+uKcewPYMyCRRke/6+uc2+ace7/t53pgLaEvdgNiMCSCYmBLu+fVdP4PCueYK4CuV4ROLP2ur3NuK/AzYDOwDdjvnHsphrFGQyS/30rgVDMbYWZZhK4cxsUw1kiFU9dYnBsvgzHmSESlvmY2AZgF/CUqUYVhMCSCrtaL6zjmtcdjzMwLXAj8PopxxUq/62tmBYS+gZQCY4FsM1sQ5fiird/1dc6tBX4MvAy8SOhSPBDd8KIqnLrG4tx4GYwxRyLi+ppZDvAH4HrnXF1UogrDYEgE1Rz+La+Ezs0dvR1zHvC+c25HTCKMrkjqexZQ5Zyrdc75gaXAiTGMNRoi+v065+53zs12zp1KqBlhQwxjjVQ4dY3FufEyGGOORET1NbM0Qkngf5xzS6McW48GQyJYAUwys9K2b/ZXAM90OOYZ4Mtto0tOINQksq3d/vkMjmYhiKy+m4ETzCzLQitvzyXU1pjIIvr9mllR27/jgXkk9u85nLrG4tx4GYwxR6Lf9W37e70fWOucuyuGMXYt3j3t4TwItf1+TKhH/ta2bV8Hvt72swG/atv/EVDe7twsYDeQF+96DFB9/xVYR6j9/CEgPd71iXF93wTWEGoWmhvvukShrqMJfbOsA/a1/Tysu3MT/RFhfR8h1Nflb9t+bbzrE6v6AicTakb6EFjZ9vjcQMWtKSZERJLcYGgaEhGRGFIiEBFJckoEIiJJTolARCTJKRGIiCQ5JQKRBGFm17dNlSEyoDR8VCQBmNnBmSvLnXO74h2PJBddEUjSM7NsM3vezFa1rW1wuZltMrORbfvLzez1tp9/aGYPmdmrZrbBzL7atv10M3vDzJ40szVmdo+ZpbTtm29mH7WV/eN2r9tgZv9mZn8BbiU0P9RrZvbaQP8fSHLzxDsAkQRwLlDjnDsfwMzyCE1m150ZhNZFyAY+MLPn27YfB5QBnxKaBG+emb3dVtYcYC/wkpld7Jx7qu38SufcP7e97jXAGboikIGmKwKR0LQVZ5nZj83sFOfc/l6Of9o5d6DtA/s1QgkA4K8uNBd9K6HpEU4mtAjJ6y40EWAA+B9CC64AtBKaZEwkrnRFIEnPOfexmc0hNE/M7Wb2EqHprA9+Ueq43GfHjjXXw/aupiY+qLktaYjEla4IJOmZ2VigyTn3MKGFfWYDmwg15wBc2uGUi8wsw8xGAKcTmnUS4Li2mSdTgMuBPxNaXOQ0MxvZ1iE8n9BKal2pJ7RMociA0hWBCBwD/NTMgoRmuvwGkAncb2a30HmlqL8CzwPjgR8552rMbDLwDnBHW3lvAE8654JmdjOhJiQDXnDOPd1NHPcCfzSzbc65M6JbRZHuafioSB+Y2Q+BBufczzpsP53QYuQXxCMukUioaUhEJMnpikBEJMnpikBEJMkpEYiIJDklAhGRJKdEICKS5JQIRESS3P8H5Vm/3UieqTAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn under its standard alias\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Generate scatterplot using support and confidence\n",
    "sns.scatterplot(x = \"support\", y = \"confidence\", \n",
    "                size = \"lift\", data = rules)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020403,
     "end_time": "2020-10-21T13:44:14.051461",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.031058",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Filtering Rules Dataframe\n",
    "\n",
    "Now, the tricky part is figuring out what this tells us. For instance, we can see that there are quite a few rules with a high lift value which means that it occurs more frequently than would be expected given the number of transaction and product combinations. We can also see several where the confidence is high as well. This part of the analysis is where the domain knowledge will come in handy.\n",
    "\n",
    "We can filter the dataframe using standard pandas code. In this case, look for a large lift (6) and high confidence (.8):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:14.115440Z",
     "iopub.status.busy": "2020-10-21T13:44:14.114166Z",
     "iopub.status.idle": "2020-10-21T13:44:14.118380Z",
     "shell.execute_reply": "2020-10-21T13:44:14.118909Z"
    },
    "papermill": {
     "duration": 0.047597,
     "end_time": "2020-10-21T13:44:14.119077",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.071480",
     "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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES)</td>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS)</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>6.030769</td>\n",
       "      <td>0.085121</td>\n",
       "      <td>4.336735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS)</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES)</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>6.968889</td>\n",
       "      <td>0.104878</td>\n",
       "      <td>7.852041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES)</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS)</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>6.968889</td>\n",
       "      <td>0.104878</td>\n",
       "      <td>21.556122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER PLATES)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.127551</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.644000</td>\n",
       "      <td>0.086474</td>\n",
       "      <td>34.897959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...</td>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS)</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.137755</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>7.077778</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>34.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(SET/6 RED SPOTTY PAPER CUPS, SET/6 RED SPOTTY...</td>\n",
       "      <td>(SET/20 RED RETROSPOT PAPER NAPKINS)</td>\n",
       "      <td>0.122449</td>\n",
       "      <td>0.132653</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>6.125000</td>\n",
       "      <td>0.083247</td>\n",
       "      <td>4.625850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          antecedents  \\\n",
       "2                        (ALARM CLOCK BAKELIKE GREEN)   \n",
       "3                          (ALARM CLOCK BAKELIKE RED)   \n",
       "17                    (SET/6 RED SPOTTY PAPER PLATES)   \n",
       "18                      (SET/6 RED SPOTTY PAPER CUPS)   \n",
       "19                    (SET/6 RED SPOTTY PAPER PLATES)   \n",
       "20  (SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...   \n",
       "21  (SET/20 RED RETROSPOT PAPER NAPKINS, SET/6 RED...   \n",
       "22  (SET/6 RED SPOTTY PAPER CUPS, SET/6 RED SPOTTY...   \n",
       "\n",
       "                             consequents  antecedent support  \\\n",
       "2             (ALARM CLOCK BAKELIKE RED)            0.096939   \n",
       "3           (ALARM CLOCK BAKELIKE GREEN)            0.094388   \n",
       "17  (SET/20 RED RETROSPOT PAPER NAPKINS)            0.127551   \n",
       "18       (SET/6 RED SPOTTY PAPER PLATES)            0.137755   \n",
       "19         (SET/6 RED SPOTTY PAPER CUPS)            0.127551   \n",
       "20       (SET/6 RED SPOTTY PAPER PLATES)            0.102041   \n",
       "21         (SET/6 RED SPOTTY PAPER CUPS)            0.102041   \n",
       "22  (SET/20 RED RETROSPOT PAPER NAPKINS)            0.122449   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \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",
       "17            0.132653  0.102041    0.800000  6.030769  0.085121    4.336735  \n",
       "18            0.127551  0.122449    0.888889  6.968889  0.104878    7.852041  \n",
       "19            0.137755  0.122449    0.960000  6.968889  0.104878   21.556122  \n",
       "20            0.127551  0.099490    0.975000  7.644000  0.086474   34.897959  \n",
       "21            0.137755  0.099490    0.975000  7.077778  0.085433   34.489796  \n",
       "22            0.132653  0.099490    0.812500  6.125000  0.083247    4.625850  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[ (rules['lift'] >= 6) &\n",
    "       (rules['confidence'] >= 0.8) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019467,
     "end_time": "2020-10-21T13:44:14.160216",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.140749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Further Analysis\n",
    " \n",
    "You may want to look at how much opportunity there is to use the popularity of one product to drive sales of another. For instance, we can see that we sell 340 Green Alarm clocks but only 316 Red Alarm Clocks so maybe we can drive more Red Alarm Clock sales through recommendations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:14.208803Z",
     "iopub.status.busy": "2020-10-21T13:44:14.207715Z",
     "iopub.status.idle": "2020-10-21T13:44:14.212718Z",
     "shell.execute_reply": "2020-10-21T13:44:14.213277Z"
    },
    "papermill": {
     "duration": 0.03317,
     "end_time": "2020-10-21T13:44:14.213462",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.180292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALARM CLOCK BAKELIKE GREEN :  340.0\n",
      "ALARM CLOCK BAKELIKE RED :  316.0\n"
     ]
    }
   ],
   "source": [
    "print(\"ALARM CLOCK BAKELIKE GREEN : \",basket['ALARM CLOCK BAKELIKE GREEN'].sum())\n",
    "print(\"ALARM CLOCK BAKELIKE RED : \",basket['ALARM CLOCK BAKELIKE RED'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020062,
     "end_time": "2020-10-21T13:44:14.253794",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.233732",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Popular Product Combinations by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:14.332562Z",
     "iopub.status.busy": "2020-10-21T13:44:14.331157Z",
     "iopub.status.idle": "2020-10-21T13:44:14.400006Z",
     "shell.execute_reply": "2020-10-21T13:44:14.398692Z"
    },
    "papermill": {
     "duration": 0.126478,
     "end_time": "2020-10-21T13:44:14.400167",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.273689",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Description</th>\n",
       "      <th>10 COLOUR SPACEBOY PEN</th>\n",
       "      <th>12 COLOURED PARTY BALLOONS</th>\n",
       "      <th>12 IVORY ROSE PEG PLACE SETTINGS</th>\n",
       "      <th>12 MESSAGE CARDS WITH ENVELOPES</th>\n",
       "      <th>12 PENCIL SMALL TUBE WOODLAND</th>\n",
       "      <th>12 PENCILS SMALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS SMALL TUBE SKULL</th>\n",
       "      <th>12 PENCILS TALL TUBE POSY</th>\n",
       "      <th>12 PENCILS TALL TUBE RED RETROSPOT</th>\n",
       "      <th>12 PENCILS TALL TUBE SKULLS</th>\n",
       "      <th>...</th>\n",
       "      <th>YULETIDE IMAGES GIFT WRAP SET</th>\n",
       "      <th>ZINC  HEART T-LIGHT HOLDER</th>\n",
       "      <th>ZINC  STAR T-LIGHT HOLDER</th>\n",
       "      <th>ZINC BOX SIGN HOME</th>\n",
       "      <th>ZINC FOLKART SLEIGH BELLS</th>\n",
       "      <th>ZINC HEART LATTICE T-LIGHT HOLDER</th>\n",
       "      <th>ZINC METAL HEART DECORATION</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STAR LARGE</th>\n",
       "      <th>ZINC T-LIGHT HOLDER STARS SMALL</th>\n",
       "      <th>ZINC WILLIE WINKIE  CANDLE STICK</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "Description  10 COLOUR SPACEBOY PEN  12 COLOURED PARTY BALLOONS  \\\n",
       "InvoiceNo                                                         \n",
       "536527                          0.0                         0.0   \n",
       "536840                          0.0                         0.0   \n",
       "536861                          0.0                         0.0   \n",
       "536967                          0.0                         0.0   \n",
       "536983                          0.0                         0.0   \n",
       "\n",
       "Description  12 IVORY ROSE PEG PLACE SETTINGS  \\\n",
       "InvoiceNo                                       \n",
       "536527                                    0.0   \n",
       "536840                                    0.0   \n",
       "536861                                    0.0   \n",
       "536967                                    0.0   \n",
       "536983                                    0.0   \n",
       "\n",
       "Description  12 MESSAGE CARDS WITH ENVELOPES  12 PENCIL SMALL TUBE WOODLAND  \\\n",
       "InvoiceNo                                                                     \n",
       "536527                                   0.0                            0.0   \n",
       "536840                                   0.0                            0.0   \n",
       "536861                                   0.0                            0.0   \n",
       "536967                                   0.0                            0.0   \n",
       "536983                                   0.0                            0.0   \n",
       "\n",
       "Description  12 PENCILS SMALL TUBE RED RETROSPOT  12 PENCILS SMALL TUBE SKULL  \\\n",
       "InvoiceNo                                                                       \n",
       "536527                                       0.0                          0.0   \n",
       "536840                                       0.0                          0.0   \n",
       "536861                                       0.0                          0.0   \n",
       "536967                                       0.0                          0.0   \n",
       "536983                                       0.0                          0.0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE POSY  12 PENCILS TALL TUBE RED RETROSPOT  \\\n",
       "InvoiceNo                                                                    \n",
       "536527                             0.0                                 0.0   \n",
       "536840                             0.0                                 0.0   \n",
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       "536983                             0.0                                 0.0   \n",
       "\n",
       "Description  12 PENCILS TALL TUBE SKULLS  ...  YULETIDE IMAGES GIFT WRAP SET  \\\n",
       "InvoiceNo                                 ...                                  \n",
       "536527                               0.0  ...                            0.0   \n",
       "536840                               0.0  ...                            0.0   \n",
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       "536967                               0.0  ...                            0.0   \n",
       "536983                               0.0  ...                            0.0   \n",
       "\n",
       "Description  ZINC  HEART T-LIGHT HOLDER  ZINC  STAR T-LIGHT HOLDER  \\\n",
       "InvoiceNo                                                            \n",
       "536527                              0.0                        0.0   \n",
       "536840                              0.0                        0.0   \n",
       "536861                              0.0                        0.0   \n",
       "536967                              0.0                        0.0   \n",
       "536983                              0.0                        0.0   \n",
       "\n",
       "Description  ZINC BOX SIGN HOME  ZINC FOLKART SLEIGH BELLS  \\\n",
       "InvoiceNo                                                    \n",
       "536527                      0.0                        0.0   \n",
       "536840                      0.0                        0.0   \n",
       "536861                      0.0                        0.0   \n",
       "536967                      0.0                        0.0   \n",
       "536983                      0.0                        0.0   \n",
       "\n",
       "Description  ZINC HEART LATTICE T-LIGHT HOLDER  ZINC METAL HEART DECORATION  \\\n",
       "InvoiceNo                                                                     \n",
       "536527                                     0.0                          0.0   \n",
       "536840                                     0.0                          0.0   \n",
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       "536983                                     0.0                          0.0   \n",
       "\n",
       "Description  ZINC T-LIGHT HOLDER STAR LARGE  ZINC T-LIGHT HOLDER STARS SMALL  \\\n",
       "InvoiceNo                                                                      \n",
       "536527                                  0.0                              0.0   \n",
       "536840                                  0.0                              0.0   \n",
       "536861                                  0.0                              0.0   \n",
       "536967                                  0.0                              0.0   \n",
       "536983                                  0.0                              0.0   \n",
       "\n",
       "Description  ZINC WILLIE WINKIE  CANDLE STICK  \n",
       "InvoiceNo                                      \n",
       "536527                                    0.0  \n",
       "536840                                    0.0  \n",
       "536861                                    0.0  \n",
       "536967                                    0.0  \n",
       "536983                                    0.0  \n",
       "\n",
       "[5 rows x 1695 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket2 = (retail[retail['Country'] ==\"Germany\"]\n",
    "          .groupby(['InvoiceNo', 'Description'])['Quantity']\n",
    "          .sum().unstack().reset_index().fillna(0)\n",
    "          .set_index('InvoiceNo'))\n",
    "\n",
    "\n",
    "basket2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:14.486000Z",
     "iopub.status.busy": "2020-10-21T13:44:14.467017Z",
     "iopub.status.idle": "2020-10-21T13:44:15.177710Z",
     "shell.execute_reply": "2020-10-21T13:44:15.177096Z"
    },
    "papermill": {
     "duration": 0.754791,
     "end_time": "2020-10-21T13:44:15.177901",
     "exception": false,
     "start_time": "2020-10-21T13:44:14.423110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "basket_sets2 = basket2.applymap(encode_units)\n",
    "basket_sets2.drop('POSTAGE', inplace=True, axis=1)\n",
    "frequent_itemsets2 = apriori(basket_sets2, min_support=0.05, use_colnames=True)\n",
    "rules2 = association_rules(frequent_itemsets2, metric=\"lift\", min_threshold=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:15.240687Z",
     "iopub.status.busy": "2020-10-21T13:44:15.235309Z",
     "iopub.status.idle": "2020-10-21T13:44:15.446948Z",
     "shell.execute_reply": "2020-10-21T13:44:15.446266Z"
    },
    "papermill": {
     "duration": 0.247163,
     "end_time": "2020-10-21T13:44:15.447088",
     "exception": false,
     "start_time": "2020-10-21T13:44:15.199925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn under its standard alias\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Generate scatterplot using support and confidence\n",
    "sns.scatterplot(x = \"support\", y = \"confidence\", \n",
    "                size = \"lift\", data = rules2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:15.513939Z",
     "iopub.status.busy": "2020-10-21T13:44:15.513121Z",
     "iopub.status.idle": "2020-10-21T13:44:15.518430Z",
     "shell.execute_reply": "2020-10-21T13:44:15.517825Z"
    },
    "papermill": {
     "duration": 0.048525,
     "end_time": "2020-10-21T13:44:15.518585",
     "exception": false,
     "start_time": "2020-10-21T13:44:15.470060",
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(PLASTERS IN TIN CIRCUS PARADE)</td>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
       "      <td>0.115974</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.067834</td>\n",
       "      <td>0.584906</td>\n",
       "      <td>4.242887</td>\n",
       "      <td>0.051846</td>\n",
       "      <td>2.076984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(PLASTERS IN TIN SPACEBOY)</td>\n",
       "      <td>(PLASTERS IN TIN WOODLAND ANIMALS)</td>\n",
       "      <td>0.107221</td>\n",
       "      <td>0.137856</td>\n",
       "      <td>0.061269</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>4.145125</td>\n",
       "      <td>0.046488</td>\n",
       "      <td>2.011670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(RED RETROSPOT CHARLOTTE BAG)</td>\n",
       "      <td>(WOODLAND CHARLOTTE BAG)</td>\n",
       "      <td>0.070022</td>\n",
       "      <td>0.126915</td>\n",
       "      <td>0.059081</td>\n",
       "      <td>0.843750</td>\n",
       "      <td>6.648168</td>\n",
       "      <td>0.050194</td>\n",
       "      <td>5.587746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        antecedents                         consequents  \\\n",
       "0   (PLASTERS IN TIN CIRCUS PARADE)  (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
       "6        (PLASTERS IN TIN SPACEBOY)  (PLASTERS IN TIN WOODLAND ANIMALS)   \n",
       "10    (RED RETROSPOT CHARLOTTE BAG)            (WOODLAND CHARLOTTE BAG)   \n",
       "\n",
       "    antecedent support  consequent support   support  confidence      lift  \\\n",
       "0             0.115974            0.137856  0.067834    0.584906  4.242887   \n",
       "6             0.107221            0.137856  0.061269    0.571429  4.145125   \n",
       "10            0.070022            0.126915  0.059081    0.843750  6.648168   \n",
       "\n",
       "    leverage  conviction  \n",
       "0   0.051846    2.076984  \n",
       "6   0.046488    2.011670  \n",
       "10  0.050194    5.587746  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules2[ (rules2['lift'] >= 4) &\n",
    "        (rules2['confidence'] >= 0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-21T13:44:15.577237Z",
     "iopub.status.busy": "2020-10-21T13:44:15.574280Z",
     "iopub.status.idle": "2020-10-21T13:44:15.829924Z",
     "shell.execute_reply": "2020-10-21T13:44:15.830515Z"
    },
    "papermill": {
     "duration": 0.288865,
     "end_time": "2020-10-21T13:44:15.830703",
     "exception": false,
     "start_time": "2020-10-21T13:44:15.541838",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import seaborn under its standard alias\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Transform the DataFrame of rules into a matrix using the lift metric\n",
    "\n",
    "pivot = rules2.pivot(index = 'consequents', columns = 'antecedents', values= 'lift')\n",
    "\n",
    "# Generate a heatmap with annotations on and the colorbar off\n",
    "\n",
    "sns.heatmap(pivot, annot = True, cbar=False)\n",
    "plt.yticks(rotation=0)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
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   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "## References\n",
    "\n",
    "https://pbpython.com/market-basket-analysis.html"
   ]
  }
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