Market Basket Analysis of Grocery Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "import csv\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>waffles</th>\n",
       "      <th>liquor</th>\n",
       "      <th>meat spreads</th>\n",
       "      <th>frozen chicken</th>\n",
       "      <th>pastry</th>\n",
       "      <th>nut snack</th>\n",
       "      <th>organic sausage</th>\n",
       "      <th>beverages</th>\n",
       "      <th>onions</th>\n",
       "      <th>herbs</th>\n",
       "      <th>...</th>\n",
       "      <th>grapes</th>\n",
       "      <th>specialty fat</th>\n",
       "      <th>whole milk</th>\n",
       "      <th>dessert</th>\n",
       "      <th>specialty bar</th>\n",
       "      <th>cake bar</th>\n",
       "      <th>pasta</th>\n",
       "      <th>photo/film</th>\n",
       "      <th>canned fruit</th>\n",
       "      <th>pudding powder</th>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   waffles  liquor  meat spreads  frozen chicken  pastry  nut snack  \\\n",
       "0        0       0             0               0       0          0   \n",
       "1        0       0             0               0       0          0   \n",
       "2        0       0             0               0       0          0   \n",
       "3        0       0             1               0       0          0   \n",
       "4        0       0             0               0       0          0   \n",
       "\n",
       "   organic sausage  beverages  onions  herbs  ...  grapes  specialty fat  \\\n",
       "0                0          0       0      0  ...       0              0   \n",
       "1                0          0       0      0  ...       0              0   \n",
       "2                0          0       0      0  ...       0              0   \n",
       "3                0          0       0      0  ...       0              0   \n",
       "4                0          0       0      0  ...       0              0   \n",
       "\n",
       "   whole milk  dessert  specialty bar  cake bar  pasta  photo/film  \\\n",
       "0           0        0              0         0      0           0   \n",
       "1           0        0              0         0      0           0   \n",
       "2           1        0              0         0      0           0   \n",
       "3           0        0              0         0      0           0   \n",
       "4           1        0              0         0      0           0   \n",
       "\n",
       "   canned fruit  pudding powder  \n",
       "0             0               0  \n",
       "1             0               0  \n",
       "2             0               0  \n",
       "3             0               0  \n",
       "4             0               0  \n",
       "\n",
       "[5 rows x 169 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grocery_items = set()\n",
    "with open(\"grocery_dataset.txt\") as f:\n",
    "    reader = csv.reader(f, delimiter=\",\")\n",
    "    for i, line in enumerate(reader):\n",
    "        grocery_items.update(line)\n",
    "\n",
    "output_list = list()\n",
    "with open(\"grocery_dataset.txt\") as f:\n",
    "    reader = csv.reader(f, delimiter=\",\")\n",
    "    for i, line in enumerate(reader):\n",
    "        row_val = {item:0 for item in grocery_items}\n",
    "        row_val.update({item:1 for item in line})\n",
    "        output_list.append(row_val)\n",
    "grocery = pd.DataFrame(output_list)\n",
    "\n",
    "grocery.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['waffles',\n",
       " 'liquor',\n",
       " 'meat spreads',\n",
       " 'frozen chicken',\n",
       " 'pastry',\n",
       " 'nut snack',\n",
       " 'organic sausage',\n",
       " 'beverages',\n",
       " 'onions',\n",
       " 'herbs',\n",
       " 'bottled beer',\n",
       " 'detergent',\n",
       " 'pickled vegetables',\n",
       " 'yogurt',\n",
       " 'abrasive cleaner',\n",
       " 'ketchup',\n",
       " 'frozen vegetables',\n",
       " 'kitchen towels',\n",
       " 'frankfurter',\n",
       " 'chicken',\n",
       " 'flower soil/fertilizer',\n",
       " 'dishes',\n",
       " 'dog food',\n",
       " 'sliced cheese',\n",
       " 'frozen dessert',\n",
       " 'rolls/buns',\n",
       " 'root vegetables',\n",
       " 'liqueur',\n",
       " 'specialty chocolate',\n",
       " 'honey',\n",
       " 'nuts/prunes',\n",
       " 'chewing gum',\n",
       " 'newspapers',\n",
       " 'soda',\n",
       " 'turkey',\n",
       " 'seasonal products',\n",
       " 'frozen fruits',\n",
       " 'semi-finished bread',\n",
       " 'curd',\n",
       " 'soap',\n",
       " 'liquor (appetizer)',\n",
       " 'packaged fruit/vegetables',\n",
       " 'potato products',\n",
       " 'bathroom cleaner',\n",
       " 'liver loaf',\n",
       " 'butter',\n",
       " 'beef',\n",
       " 'fruit/vegetable juice',\n",
       " 'fish',\n",
       " 'specialty vegetables',\n",
       " 'coffee',\n",
       " 'ice cream',\n",
       " 'margarine',\n",
       " 'oil',\n",
       " 'specialty cheese',\n",
       " 'hygiene articles',\n",
       " 'prosecco',\n",
       " 'bottled water',\n",
       " 'decalcifier',\n",
       " 'cereals',\n",
       " 'salty snack',\n",
       " 'zwieback',\n",
       " 'roll products ',\n",
       " 'cleaner',\n",
       " 'cream cheese ',\n",
       " 'cream',\n",
       " 'rum',\n",
       " 'condensed milk',\n",
       " 'ham',\n",
       " 'candy',\n",
       " 'sugar',\n",
       " 'canned vegetables',\n",
       " 'pet care',\n",
       " 'sauces',\n",
       " 'male cosmetics',\n",
       " 'frozen potato products',\n",
       " 'brandy',\n",
       " 'whipped/sour cream',\n",
       " 'dental care',\n",
       " 'rice',\n",
       " 'rubbing alcohol',\n",
       " 'berries',\n",
       " 'soups',\n",
       " 'spread cheese',\n",
       " 'tea',\n",
       " 'salad dressing',\n",
       " 'long life bakery product',\n",
       " 'dish cleaner',\n",
       " 'whisky',\n",
       " 'canned fish',\n",
       " 'brown bread',\n",
       " 'curd cheese',\n",
       " 'misc. beverages',\n",
       " 'pot plants',\n",
       " 'chocolate marshmallow',\n",
       " 'sweet spreads',\n",
       " 'sparkling wine',\n",
       " 'baking powder',\n",
       " 'hair spray',\n",
       " 'tidbits',\n",
       " 'ready soups',\n",
       " 'domestic eggs',\n",
       " 'hamburger meat',\n",
       " 'kitchen utensil',\n",
       " 'flour',\n",
       " 'artif. sweetener',\n",
       " 'pip fruit',\n",
       " 'chocolate',\n",
       " 'UHT-milk',\n",
       " 'baby food',\n",
       " 'preservation products',\n",
       " 'butter milk',\n",
       " 'hard cheese',\n",
       " 'red/blush wine',\n",
       " 'skin care',\n",
       " 'popcorn',\n",
       " 'sound storage medium',\n",
       " 'canned beer',\n",
       " 'cocoa drinks',\n",
       " 'frozen fish',\n",
       " 'white wine',\n",
       " 'toilet cleaner',\n",
       " 'flower (seeds)',\n",
       " 'make up remover',\n",
       " 'organic products',\n",
       " 'light bulbs',\n",
       " 'tropical fruit',\n",
       " 'white bread',\n",
       " 'sausage',\n",
       " 'processed cheese',\n",
       " 'soft cheese',\n",
       " 'softener',\n",
       " 'cookware',\n",
       " 'other vegetables',\n",
       " 'female sanitary products',\n",
       " 'bags',\n",
       " 'house keeping products',\n",
       " 'mustard',\n",
       " 'cooking chocolate',\n",
       " 'spices',\n",
       " 'napkins',\n",
       " 'mayonnaise',\n",
       " 'finished products',\n",
       " 'citrus fruit',\n",
       " 'jam',\n",
       " 'pork',\n",
       " 'meat',\n",
       " 'salt',\n",
       " 'snack products',\n",
       " 'baby cosmetics',\n",
       " 'cat food',\n",
       " 'shopping bags',\n",
       " 'Instant food products',\n",
       " 'vinegar',\n",
       " 'cling film/bags',\n",
       " 'syrup',\n",
       " 'candles',\n",
       " 'frozen meals',\n",
       " 'instant coffee',\n",
       " 'grapes',\n",
       " 'specialty fat',\n",
       " 'whole milk',\n",
       " 'dessert',\n",
       " 'specialty bar',\n",
       " 'cake bar',\n",
       " 'pasta',\n",
       " 'photo/film',\n",
       " 'canned fruit',\n",
       " 'pudding powder']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grocery.columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View Top Sold items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Item :  43367\n",
      "\n",
      "Item Summary:\n",
      "          item_name  item_count\n",
      "0        whole milk        2513\n",
      "1  other vegetables        1903\n",
      "2        rolls/buns        1809\n",
      "3              soda        1715\n",
      "4            yogurt        1372\n"
     ]
    }
   ],
   "source": [
    "total_item_count = sum(grocery.sum())\n",
    "print(\"Total Item : \", total_item_count)\n",
    "print()\n",
    "item_summary = grocery.sum().sort_values(ascending = False).reset_index()\n",
    "item_summary.rename(columns={item_summary.columns[0]:'item_name',item_summary.columns[1]:'item_count'}, inplace=True)\n",
    "print(\"Item Summary:\")\n",
    "print(item_summary.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Top Sold Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": [
    "objects = (list(item_summary['item_name'].head(n=20)))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = list(item_summary['item_count'].head(n=20))\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects, rotation='vertical')\n",
    "plt.ylabel('Item count')\n",
    "plt.title('Item sales distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating frequent itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequent_itemsets = apriori(grocery, min_support=0.047, use_colnames=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>(root vegetables)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.047382</td>\n",
       "      <td>0.434701</td>\n",
       "      <td>2.246605</td>\n",
       "      <td>0.026291</td>\n",
       "      <td>1.426693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.047382</td>\n",
       "      <td>0.244877</td>\n",
       "      <td>2.246605</td>\n",
       "      <td>0.026291</td>\n",
       "      <td>1.179941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.074835</td>\n",
       "      <td>0.292877</td>\n",
       "      <td>1.513634</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>1.140548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(other vegetables)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.193493</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.074835</td>\n",
       "      <td>0.386758</td>\n",
       "      <td>1.513634</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>1.214013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.219260</td>\n",
       "      <td>1.571735</td>\n",
       "      <td>0.020379</td>\n",
       "      <td>1.102157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(yogurt)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.139502</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.401603</td>\n",
       "      <td>1.571735</td>\n",
       "      <td>0.020379</td>\n",
       "      <td>1.244132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.048907</td>\n",
       "      <td>0.448694</td>\n",
       "      <td>1.756031</td>\n",
       "      <td>0.021056</td>\n",
       "      <td>1.350401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(root vegetables)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.108998</td>\n",
       "      <td>0.048907</td>\n",
       "      <td>0.191405</td>\n",
       "      <td>1.756031</td>\n",
       "      <td>0.021056</td>\n",
       "      <td>1.101913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(rolls/buns)</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.183935</td>\n",
       "      <td>0.056634</td>\n",
       "      <td>0.221647</td>\n",
       "      <td>1.205032</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>1.048452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(rolls/buns)</td>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>0.183935</td>\n",
       "      <td>0.255516</td>\n",
       "      <td>0.056634</td>\n",
       "      <td>0.307905</td>\n",
       "      <td>1.205032</td>\n",
       "      <td>0.009636</td>\n",
       "      <td>1.075696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          antecedents         consequents  antecedent support  \\\n",
       "0   (root vegetables)  (other vegetables)            0.108998   \n",
       "1  (other vegetables)   (root vegetables)            0.193493   \n",
       "2        (whole milk)  (other vegetables)            0.255516   \n",
       "3  (other vegetables)        (whole milk)            0.193493   \n",
       "4        (whole milk)            (yogurt)            0.255516   \n",
       "5            (yogurt)        (whole milk)            0.139502   \n",
       "6   (root vegetables)        (whole milk)            0.108998   \n",
       "7        (whole milk)   (root vegetables)            0.255516   \n",
       "8        (whole milk)        (rolls/buns)            0.255516   \n",
       "9        (rolls/buns)        (whole milk)            0.183935   \n",
       "\n",
       "   consequent support   support  confidence      lift  leverage  conviction  \n",
       "0            0.193493  0.047382    0.434701  2.246605  0.026291    1.426693  \n",
       "1            0.108998  0.047382    0.244877  2.246605  0.026291    1.179941  \n",
       "2            0.193493  0.074835    0.292877  1.513634  0.025394    1.140548  \n",
       "3            0.255516  0.074835    0.386758  1.513634  0.025394    1.214013  \n",
       "4            0.139502  0.056024    0.219260  1.571735  0.020379    1.102157  \n",
       "5            0.255516  0.056024    0.401603  1.571735  0.020379    1.244132  \n",
       "6            0.255516  0.048907    0.448694  1.756031  0.021056    1.350401  \n",
       "7            0.108998  0.048907    0.191405  1.756031  0.021056    1.101913  \n",
       "8            0.183935  0.056634    0.221647  1.205032  0.009636    1.048452  \n",
       "9            0.255516  0.056634    0.307905  1.205032  0.009636    1.075696  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#viewing top 100 rules\n",
    "my_rules.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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