Association Rule Mining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Trans.</th>\n",
       "      <th>Bag</th>\n",
       "      <th>Blush</th>\n",
       "      <th>Nail Polish</th>\n",
       "      <th>Brushes</th>\n",
       "      <th>Concealer</th>\n",
       "      <th>Eyebrow Pencils</th>\n",
       "      <th>Bronzer</th>\n",
       "      <th>Lip liner</th>\n",
       "      <th>Mascara</th>\n",
       "      <th>Eye shadow</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>Lip Gloss</th>\n",
       "      <th>Lipstick</th>\n",
       "      <th>Eyeliner</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Trans.   Bag  Blush  Nail Polish  Brushes  Concealer  Eyebrow Pencils  \\\n",
       "0        1    0      1            1        1          1                0   \n",
       "1        2    0      0            1        0          1                0   \n",
       "2        3    0      1            0        0          1                1   \n",
       "3        4    0      0            1        1          1                0   \n",
       "4        5    0      1            0        0          1                0   \n",
       "\n",
       "   Bronzer  Lip liner  Mascara  Eye shadow  Foundation  Lip Gloss  Lipstick  \\\n",
       "0        1          1        1           0           0          0         0   \n",
       "1        1          1        0           0           1          1         0   \n",
       "2        1          1        1           1           1          1         1   \n",
       "3        1          0        0           0           1          0         0   \n",
       "4        1          1        1           1           0          1         1   \n",
       "\n",
       "   Eyeliner  \n",
       "0         1  \n",
       "1         0  \n",
       "2         0  \n",
       "3         1  \n",
       "4         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "cosmetics_df = pd.read_csv(\"Cosmetics.csv\")\n",
    "cosmetics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosmetics_df.drop('Trans. ',inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
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       "      <th>conviction</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>(Nail Polish, Eye shadow, Concealer)</td>\n",
       "      <td>(Brushes, Mascara)</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.641026</td>\n",
       "      <td>7.723200</td>\n",
       "      <td>0.043526</td>\n",
       "      <td>2.554500</td>\n",
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       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>(Brushes, Mascara)</td>\n",
       "      <td>(Nail Polish, Eye shadow, Concealer)</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>7.723200</td>\n",
       "      <td>0.043526</td>\n",
       "      <td>2.318970</td>\n",
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       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>(Nail Polish, Concealer, Mascara)</td>\n",
       "      <td>(Brushes, Eye shadow)</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>7.437156</td>\n",
       "      <td>0.043277</td>\n",
       "      <td>2.311424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2268</th>\n",
       "      <td>(Brushes, Eye shadow)</td>\n",
       "      <td>(Nail Polish, Concealer, Mascara)</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.617284</td>\n",
       "      <td>7.437156</td>\n",
       "      <td>0.043277</td>\n",
       "      <td>2.396032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>(Brushes, Eye shadow)</td>\n",
       "      <td>(Nail Polish, Mascara)</td>\n",
       "      <td>0.081</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>6.817763</td>\n",
       "      <td>0.063146</td>\n",
       "      <td>10.020857</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>(Eye shadow, Foundation)</td>\n",
       "      <td>(Bronzer)</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.336493</td>\n",
       "      <td>1.206068</td>\n",
       "      <td>0.012131</td>\n",
       "      <td>1.086650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Concealer)</td>\n",
       "      <td>(Nail Polish)</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.337104</td>\n",
       "      <td>1.203943</td>\n",
       "      <td>0.025240</td>\n",
       "      <td>1.086143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Nail Polish)</td>\n",
       "      <td>(Concealer)</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.532143</td>\n",
       "      <td>1.203943</td>\n",
       "      <td>0.025240</td>\n",
       "      <td>1.192672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2397</th>\n",
       "      <td>(Eye shadow, Lip Gloss, Foundation, Mascara)</td>\n",
       "      <td>(Concealer)</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.531532</td>\n",
       "      <td>1.202560</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>1.191115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>(Concealer)</td>\n",
       "      <td>(Eye shadow, Lip Gloss, Foundation, Mascara)</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.133484</td>\n",
       "      <td>1.202560</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>1.025948</td>\n",
       "    </tr>\n",
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       "<p>2472 rows × 9 columns</p>\n",
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      "text/plain": [
       "                                       antecedents  \\\n",
       "2255          (Nail Polish, Eye shadow, Concealer)   \n",
       "2270                            (Brushes, Mascara)   \n",
       "2257             (Nail Polish, Concealer, Mascara)   \n",
       "2268                         (Brushes, Eye shadow)   \n",
       "1343                         (Brushes, Eye shadow)   \n",
       "...                                            ...   \n",
       "622                       (Eye shadow, Foundation)   \n",
       "17                                     (Concealer)   \n",
       "16                                   (Nail Polish)   \n",
       "2397  (Eye shadow, Lip Gloss, Foundation, Mascara)   \n",
       "2422                                   (Concealer)   \n",
       "\n",
       "                                       consequents  antecedent support  \\\n",
       "2255                            (Brushes, Mascara)               0.078   \n",
       "2270          (Nail Polish, Eye shadow, Concealer)               0.083   \n",
       "2257                         (Brushes, Eye shadow)               0.083   \n",
       "2268             (Nail Polish, Concealer, Mascara)               0.081   \n",
       "1343                        (Nail Polish, Mascara)               0.081   \n",
       "...                                            ...                 ...   \n",
       "622                                      (Bronzer)               0.211   \n",
       "17                                   (Nail Polish)               0.442   \n",
       "16                                     (Concealer)               0.280   \n",
       "2397                                   (Concealer)               0.111   \n",
       "2422  (Eye shadow, Lip Gloss, Foundation, Mascara)               0.442   \n",
       "\n",
       "      consequent support  support  confidence      lift  leverage  conviction  \n",
       "2255               0.083    0.050    0.641026  7.723200  0.043526    2.554500  \n",
       "2270               0.078    0.050    0.602410  7.723200  0.043526    2.318970  \n",
       "2257               0.081    0.050    0.602410  7.437156  0.043277    2.311424  \n",
       "2268               0.083    0.050    0.617284  7.437156  0.043277    2.396032  \n",
       "1343               0.134    0.074    0.913580  6.817763  0.063146   10.020857  \n",
       "...                  ...      ...         ...       ...       ...         ...  \n",
       "622                0.279    0.071    0.336493  1.206068  0.012131    1.086650  \n",
       "17                 0.280    0.149    0.337104  1.203943  0.025240    1.086143  \n",
       "16                 0.442    0.149    0.532143  1.203943  0.025240    1.192672  \n",
       "2397               0.442    0.059    0.531532  1.202560  0.009938    1.191115  \n",
       "2422               0.111    0.059    0.133484  1.202560  0.009938    1.025948  \n",
       "\n",
       "[2472 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(cosmetics_df, min_support=0.05, use_colnames=True)\n",
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1.2)\n",
    "rules.head()\n",
    "rules.sort_values(by=['lift'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Intro</th>\n",
       "      <th>DataMining</th>\n",
       "      <th>Survey</th>\n",
       "      <th>Cat Data</th>\n",
       "      <th>Regression</th>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Intro  DataMining  Survey  Cat Data  Regression  Forecast  DOE  SW\n",
       "0      1           1       0         0           0         0    0   0\n",
       "1      0           0       1         0           0         0    0   0\n",
       "2      0           1       0         1           1         0    0   1\n",
       "3      1           0       0         0           0         0    0   0\n",
       "4      1           1       0         0           0         0    0   0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "coursetopic_df = pd.read_csv(\"coursetopic.csv\")\n",
    "coursetopic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
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       "      <th>0</th>\n",
       "      <td>(Cat Data)</td>\n",
       "      <td>(Survey)</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.186301</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.302632</td>\n",
       "      <td>1.62442</td>\n",
       "      <td>0.024222</td>\n",
       "      <td>1.166813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Survey)</td>\n",
       "      <td>(Cat Data)</td>\n",
       "      <td>0.186301</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.338235</td>\n",
       "      <td>1.62442</td>\n",
       "      <td>0.024222</td>\n",
       "      <td>1.196469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Regression)</td>\n",
       "      <td>(Cat Data)</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>1.26385</td>\n",
       "      <td>0.011439</td>\n",
       "      <td>1.074560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Cat Data)</td>\n",
       "      <td>(Regression)</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>1.26385</td>\n",
       "      <td>0.011439</td>\n",
       "      <td>1.074560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Cat Data)</td>\n",
       "      <td>(SW)</td>\n",
       "      <td>0.208219</td>\n",
       "      <td>0.221918</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.302632</td>\n",
       "      <td>1.36371</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>1.115741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    antecedents   consequents  antecedent support  consequent support  \\\n",
       "0    (Cat Data)      (Survey)            0.208219            0.186301   \n",
       "1      (Survey)    (Cat Data)            0.186301            0.208219   \n",
       "2  (Regression)    (Cat Data)            0.208219            0.208219   \n",
       "3    (Cat Data)  (Regression)            0.208219            0.208219   \n",
       "4    (Cat Data)          (SW)            0.208219            0.221918   \n",
       "\n",
       "    support  confidence     lift  leverage  conviction  \n",
       "0  0.063014    0.302632  1.62442  0.024222    1.166813  \n",
       "1  0.063014    0.338235  1.62442  0.024222    1.196469  \n",
       "2  0.054795    0.263158  1.26385  0.011439    1.074560  \n",
       "3  0.054795    0.263158  1.26385  0.011439    1.074560  \n",
       "4  0.063014    0.302632  1.36371  0.016806    1.115741  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(coursetopic_df, min_support=0.05, use_colnames=True)\n",
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1.2)\n",
    "rules.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardid</th>\n",
       "      <th>value</th>\n",
       "      <th>pmethod</th>\n",
       "      <th>sex</th>\n",
       "      <th>homeown</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>fruitveg</th>\n",
       "      <th>freshmeat</th>\n",
       "      <th>dairy</th>\n",
       "      <th>cannedveg</th>\n",
       "      <th>cannedmeat</th>\n",
       "      <th>frozenmeal</th>\n",
       "      <th>beer</th>\n",
       "      <th>wine</th>\n",
       "      <th>softdrink</th>\n",
       "      <th>fish</th>\n",
       "      <th>confectionery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39808</td>\n",
       "      <td>42.7123</td>\n",
       "      <td>CHEQUE</td>\n",
       "      <td>M</td>\n",
       "      <td>NO</td>\n",
       "      <td>27000</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67362</td>\n",
       "      <td>25.3567</td>\n",
       "      <td>CASH</td>\n",
       "      <td>F</td>\n",
       "      <td>NO</td>\n",
       "      <td>30000</td>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10872</td>\n",
       "      <td>20.6176</td>\n",
       "      <td>CASH</td>\n",
       "      <td>M</td>\n",
       "      <td>NO</td>\n",
       "      <td>13200</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26748</td>\n",
       "      <td>23.6883</td>\n",
       "      <td>CARD</td>\n",
       "      <td>F</td>\n",
       "      <td>NO</td>\n",
       "      <td>12200</td>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>T</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91609</td>\n",
       "      <td>18.8133</td>\n",
       "      <td>CARD</td>\n",
       "      <td>M</td>\n",
       "      <td>YES</td>\n",
       "      <td>11000</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cardid    value pmethod sex homeown  income  age fruitveg freshmeat dairy  \\\n",
       "0   39808  42.7123  CHEQUE   M      NO   27000   46        F         T     T   \n",
       "1   67362  25.3567    CASH   F      NO   30000   28        F         T     F   \n",
       "2   10872  20.6176    CASH   M      NO   13200   36        F         F     F   \n",
       "3   26748  23.6883    CARD   F      NO   12200   26        F         F     T   \n",
       "4   91609  18.8133    CARD   M     YES   11000   24        F         F     F   \n",
       "\n",
       "  cannedveg cannedmeat frozenmeal beer wine softdrink fish confectionery  \n",
       "0         F          F          F    F    F         F    F             T  \n",
       "1         F          F          F    F    F         F    F             T  \n",
       "2         T          F          T    T    F         F    T             F  \n",
       "3         F          F          F    F    T         F    F             F  \n",
       "4         F          F          F    F    F         F    F             F  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "retail_df = pd.read_csv(\"market basket.csv\")\n",
    "retail_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "retail_df.drop(['cardid','value','pmethod','sex','homeown','income','age'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in retail_df.columns:\n",
    "    retail_df[i] = retail_df[i].apply(lambda x: 0 if (x == 'F') else 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(fish)</td>\n",
       "      <td>(fruitveg)</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.496575</td>\n",
       "      <td>1.660787</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>1.392463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(fruitveg)</td>\n",
       "      <td>(fish)</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.484950</td>\n",
       "      <td>1.660787</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>1.374623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.570957</td>\n",
       "      <td>1.890586</td>\n",
       "      <td>0.081494</td>\n",
       "      <td>1.626877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.572848</td>\n",
       "      <td>1.890586</td>\n",
       "      <td>0.081494</td>\n",
       "      <td>1.631736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>(beer)</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.551155</td>\n",
       "      <td>1.881075</td>\n",
       "      <td>0.078221</td>\n",
       "      <td>1.575154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(beer)</td>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.569966</td>\n",
       "      <td>1.881075</td>\n",
       "      <td>0.078221</td>\n",
       "      <td>1.620802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(cannedmeat)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.248344</td>\n",
       "      <td>1.217374</td>\n",
       "      <td>0.013392</td>\n",
       "      <td>1.058996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(cannedmeat)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.367647</td>\n",
       "      <td>1.217374</td>\n",
       "      <td>0.013392</td>\n",
       "      <td>1.103814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(beer)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.580205</td>\n",
       "      <td>1.921208</td>\n",
       "      <td>0.081514</td>\n",
       "      <td>1.662715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(beer)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.562914</td>\n",
       "      <td>1.921208</td>\n",
       "      <td>0.081514</td>\n",
       "      <td>1.617530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(confectionery)</td>\n",
       "      <td>(wine)</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>1.817906</td>\n",
       "      <td>0.064788</td>\n",
       "      <td>1.490818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(wine)</td>\n",
       "      <td>(confectionery)</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.501742</td>\n",
       "      <td>1.817906</td>\n",
       "      <td>0.064788</td>\n",
       "      <td>1.453063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(cannedveg, beer)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.874251</td>\n",
       "      <td>2.894873</td>\n",
       "      <td>0.095566</td>\n",
       "      <td>5.550762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(cannedveg, frozenmeal)</td>\n",
       "      <td>(beer)</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.843931</td>\n",
       "      <td>2.880309</td>\n",
       "      <td>0.095311</td>\n",
       "      <td>4.530037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(frozenmeal, beer)</td>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>2.834401</td>\n",
       "      <td>0.094490</td>\n",
       "      <td>4.937083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>(frozenmeal, beer)</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.481848</td>\n",
       "      <td>2.834401</td>\n",
       "      <td>0.094490</td>\n",
       "      <td>1.601847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(beer)</td>\n",
       "      <td>(cannedveg, frozenmeal)</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.498294</td>\n",
       "      <td>2.880309</td>\n",
       "      <td>0.095311</td>\n",
       "      <td>1.648374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(cannedveg, beer)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.483444</td>\n",
       "      <td>2.894873</td>\n",
       "      <td>0.095566</td>\n",
       "      <td>1.612603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(fish, cannedveg)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.573034</td>\n",
       "      <td>1.897463</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>1.634789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(fish, frozenmeal)</td>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>1.870187</td>\n",
       "      <td>0.023730</td>\n",
       "      <td>1.608462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(cannedveg)</td>\n",
       "      <td>(fish, frozenmeal)</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.168317</td>\n",
       "      <td>1.870187</td>\n",
       "      <td>0.023730</td>\n",
       "      <td>1.094167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(fish, cannedveg)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.168874</td>\n",
       "      <td>1.897463</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>1.096104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(fish, beer)</td>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>1.695143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(fish, frozenmeal)</td>\n",
       "      <td>(beer)</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.896094</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>1.590750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(beer)</td>\n",
       "      <td>(fish, frozenmeal)</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.170648</td>\n",
       "      <td>1.896094</td>\n",
       "      <td>0.023630</td>\n",
       "      <td>1.097243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(frozenmeal)</td>\n",
       "      <td>(fish, beer)</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.165563</td>\n",
       "      <td>1.947799</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>1.096548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                antecedents              consequents  antecedent support  \\\n",
       "0                    (fish)               (fruitveg)               0.292   \n",
       "1                (fruitveg)                   (fish)               0.299   \n",
       "2               (cannedveg)             (frozenmeal)               0.303   \n",
       "3              (frozenmeal)              (cannedveg)               0.302   \n",
       "4               (cannedveg)                   (beer)               0.303   \n",
       "5                    (beer)              (cannedveg)               0.293   \n",
       "6              (frozenmeal)             (cannedmeat)               0.302   \n",
       "7              (cannedmeat)             (frozenmeal)               0.204   \n",
       "8                    (beer)             (frozenmeal)               0.293   \n",
       "9              (frozenmeal)                   (beer)               0.302   \n",
       "10          (confectionery)                   (wine)               0.276   \n",
       "11                   (wine)          (confectionery)               0.287   \n",
       "12        (cannedveg, beer)             (frozenmeal)               0.167   \n",
       "13  (cannedveg, frozenmeal)                   (beer)               0.173   \n",
       "14       (frozenmeal, beer)              (cannedveg)               0.170   \n",
       "15              (cannedveg)       (frozenmeal, beer)               0.303   \n",
       "16                   (beer)  (cannedveg, frozenmeal)               0.293   \n",
       "17             (frozenmeal)        (cannedveg, beer)               0.302   \n",
       "18        (fish, cannedveg)             (frozenmeal)               0.089   \n",
       "19       (fish, frozenmeal)              (cannedveg)               0.090   \n",
       "20              (cannedveg)       (fish, frozenmeal)               0.303   \n",
       "21             (frozenmeal)        (fish, cannedveg)               0.302   \n",
       "22             (fish, beer)             (frozenmeal)               0.085   \n",
       "23       (fish, frozenmeal)                   (beer)               0.090   \n",
       "24                   (beer)       (fish, frozenmeal)               0.293   \n",
       "25             (frozenmeal)             (fish, beer)               0.302   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0                0.299    0.145    0.496575  1.660787  0.057692    1.392463  \n",
       "1                0.292    0.145    0.484950  1.660787  0.057692    1.374623  \n",
       "2                0.302    0.173    0.570957  1.890586  0.081494    1.626877  \n",
       "3                0.303    0.173    0.572848  1.890586  0.081494    1.631736  \n",
       "4                0.293    0.167    0.551155  1.881075  0.078221    1.575154  \n",
       "5                0.303    0.167    0.569966  1.881075  0.078221    1.620802  \n",
       "6                0.204    0.075    0.248344  1.217374  0.013392    1.058996  \n",
       "7                0.302    0.075    0.367647  1.217374  0.013392    1.103814  \n",
       "8                0.302    0.170    0.580205  1.921208  0.081514    1.662715  \n",
       "9                0.293    0.170    0.562914  1.921208  0.081514    1.617530  \n",
       "10               0.287    0.144    0.521739  1.817906  0.064788    1.490818  \n",
       "11               0.276    0.144    0.501742  1.817906  0.064788    1.453063  \n",
       "12               0.302    0.146    0.874251  2.894873  0.095566    5.550762  \n",
       "13               0.293    0.146    0.843931  2.880309  0.095311    4.530037  \n",
       "14               0.303    0.146    0.858824  2.834401  0.094490    4.937083  \n",
       "15               0.170    0.146    0.481848  2.834401  0.094490    1.601847  \n",
       "16               0.173    0.146    0.498294  2.880309  0.095311    1.648374  \n",
       "17               0.167    0.146    0.483444  2.894873  0.095566    1.612603  \n",
       "18               0.302    0.051    0.573034  1.897463  0.024122    1.634789  \n",
       "19               0.303    0.051    0.566667  1.870187  0.023730    1.608462  \n",
       "20               0.090    0.051    0.168317  1.870187  0.023730    1.094167  \n",
       "21               0.089    0.051    0.168874  1.897463  0.024122    1.096104  \n",
       "22               0.302    0.050    0.588235  1.947799  0.024330    1.695143  \n",
       "23               0.293    0.050    0.555556  1.896094  0.023630    1.590750  \n",
       "24               0.090    0.050    0.170648  1.896094  0.023630    1.097243  \n",
       "25               0.085    0.050    0.165563  1.947799  0.024330    1.096548  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets = apriori(retail_df, min_support=0.05, use_colnames=True)\n",
    "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1.2)\n",
    "rules.to_csv('Market Basket Association Rules.csv',index = False)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruitveg</th>\n",
       "      <th>freshmeat</th>\n",
       "      <th>dairy</th>\n",
       "      <th>cannedveg</th>\n",
       "      <th>cannedmeat</th>\n",
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       "      <th>995</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     fruitveg  freshmeat  dairy  cannedveg  cannedmeat  frozenmeal  beer  \\\n",
       "0           0          1      1          0           0           0     0   \n",
       "1           0          1      0          0           0           0     0   \n",
       "2           0          0      0          1           0           1     1   \n",
       "3           0          0      1          0           0           0     0   \n",
       "4           0          0      0          0           0           0     0   \n",
       "..        ...        ...    ...        ...         ...         ...   ...   \n",
       "995         0          0      0          1           0           0     0   \n",
       "996         0          0      0          1           0           0     0   \n",
       "997         0          1      0          0           0           0     0   \n",
       "998         1          0      0          0           0           0     0   \n",
       "999         0          0      1          0           0           0     0   \n",
       "\n",
       "     wine  softdrink  fish  confectionery  \n",
       "0       0          0     0              1  \n",
       "1       0          0     0              1  \n",
       "2       0          0     1              0  \n",
       "3       1          0     0              0  \n",
       "4       0          0     0              0  \n",
       "..    ...        ...   ...            ...  \n",
       "995     0          0     0              0  \n",
       "996     0          0     1              0  \n",
       "997     0          0     0              0  \n",
       "998     1          0     0              1  \n",
       "999     0          1     0              1  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retail_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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