Association_rule_miningAmrutesh_V_Saraf.ipynb

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      "Collecting apyori\n",
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      "  Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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   "source": [
    "import pandas as pd\n",
    "from apyori import apriori"
   ]
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     "text": [
      "num of records= 20507\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('bread_basket.csv')\n",
    "data = data[[\"Transaction\",\"Item\"]]\n",
    "num_records=len(data)\n",
    "print(\"num of records=\",num_records)"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20502</th>\n",
       "      <td>9682</td>\n",
       "      <td>Coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20503</th>\n",
       "      <td>9682</td>\n",
       "      <td>Tea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20504</th>\n",
       "      <td>9683</td>\n",
       "      <td>Coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20505</th>\n",
       "      <td>9683</td>\n",
       "      <td>Pastry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20506</th>\n",
       "      <td>9684</td>\n",
       "      <td>Smoothies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20507 rows × 2 columns</p>\n",
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      "text/plain": [
       "       Transaction           Item\n",
       "0                1          Bread\n",
       "1                2   Scandinavian\n",
       "2                2   Scandinavian\n",
       "3                3  Hot chocolate\n",
       "4                3            Jam\n",
       "...            ...            ...\n",
       "20502         9682         Coffee\n",
       "20503         9682            Tea\n",
       "20504         9683         Coffee\n",
       "20505         9683         Pastry\n",
       "20506         9684      Smoothies\n",
       "\n",
       "[20507 rows x 2 columns]"
      ]
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   "source": [
    "observations_data = [] \n",
    "\n",
    "for i in range(num_records):\n",
    "  observations_data.append([str(data.values[i,j]) for j in range(len(data.columns))])"
   ]
  },
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   "outputs": [
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     "data": {
      "text/plain": [
       "[RelationRecord(items=frozenset({'Bread'}), support=0.16213975715609305, ordered_statistics=[OrderedStatistic(items_base=frozenset(), items_add=frozenset({'Bread'}), confidence=0.16213975715609305, lift=1.0)]),\n",
       " RelationRecord(items=frozenset({'Coffee'}), support=0.26678695079728876, ordered_statistics=[OrderedStatistic(items_base=frozenset(), items_add=frozenset({'Coffee'}), confidence=0.26678695079728876, lift=1.0)])]"
      ]
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     "execution_count": 30,
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   ],
   "source": [
    "associations_data = apriori(observations_data,confidence=0.1,support=0.1)\n",
    "associations_data = list(associations_data)\n",
    "associations_data\n"
   ]
  },
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       "      <th>0</th>\n",
       "      <td>(Bread)</td>\n",
       "      <td>0.162140</td>\n",
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       "      items   support                          ordered_statistics\n",
       "0   (Bread)  0.162140   [((), (Bread), 0.16213975715609305, 1.0)]\n",
       "1  (Coffee)  0.266787  [((), (Coffee), 0.26678695079728876, 1.0)]"
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     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "associations_df = pd.DataFrame(associations_data)\n",
    "associations_df"
   ]
  }
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