market basket analysi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[['Milk','Onion','Bread','Butter','Eggs','Yogurt'],\n",
    "     ['Cheese','Onion','Bread','Butter','Eggs','Yogurt'],\n",
    "     ['Milk','Apple','Butter','Eggs'],\n",
    "     ['Milk','Orange','Corn','Butter','Yogurt'],\n",
    "     ['Corn','Onion','Onion','Butter','Icecream','Eggs']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\admin\\anaconda3\\lib\\site-packages (0.18.0)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (1.1.3)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (1.19.2)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (0.23.2)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (3.3.2)\n",
      "Requirement already satisfied: setuptools in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (50.3.1.post20201107)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mlxtend) (0.17.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2020.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2.8.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.3->mlxtend) (2.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.3.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (8.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2020.6.20)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.24.2->mlxtend) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "te= TransactionEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datat=te.fit(data).transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True,  True, False, False,  True, False,  True,  True,\n",
       "        False,  True],\n",
       "       [False,  True,  True,  True, False,  True, False, False,  True,\n",
       "        False,  True],\n",
       "       [ True, False,  True, False, False,  True, False,  True, False,\n",
       "        False, False],\n",
       "       [False, False,  True, False,  True, False, False,  True, False,\n",
       "         True,  True],\n",
       "       [False, False,  True, False,  True,  True,  True, False,  True,\n",
       "        False, False]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "productdata=pd.DataFrame(datat,columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apple</th>\n",
       "      <th>Bread</th>\n",
       "      <th>Butter</th>\n",
       "      <th>Cheese</th>\n",
       "      <th>Corn</th>\n",
       "      <th>Eggs</th>\n",
       "      <th>Icecream</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Orange</th>\n",
       "      <th>Yogurt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apple  Bread  Butter  Cheese   Corn   Eggs  Icecream   Milk  Onion  Orange  \\\n",
       "0  False   True    True   False  False   True     False   True   True   False   \n",
       "1  False   True    True    True  False   True     False  False   True   False   \n",
       "2   True  False    True   False  False   True     False   True  False   False   \n",
       "3  False  False    True   False   True  False     False   True  False    True   \n",
       "4  False  False    True   False   True   True      True  False   True   False   \n",
       "\n",
       "   Yogurt  \n",
       "0    True  \n",
       "1    True  \n",
       "2   False  \n",
       "3    True  \n",
       "4   False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "productdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Market basket analysis basically uses 3 different matrix measurnment -support,confident and lift\n",
    "#support- it gives a fraction of records which contains factor,the factor can be either 1 or multiple,basically support\n",
    "#tells us about frequent combition of factors.support equal to= no. of records or transactions containing factor (a or b )or both (a,b)/no. of total records\n",
    "#confidence is equal to no of record containig both a & b/no.of records containing(a), it tells us about how often the factors a & b \n",
    "#occurs together given the no. of times a occurs.\n",
    "#suppose a is bread and b is butter  the confidence will detect the no.of records when bread and butter together be purchased /no of times  when alone bread was purchased\n",
    "# lift indicates the strength of a role over the random occurence of a & b, the greater the value of the lift , the more is the strength\n",
    "# if lift is less than 1 there is no association if lift is equal to 1 this means that there is a very poor association \n",
    "# if lift is greater than 1 that means it is good association,if lift is equal to 3 it means that if you buy a,the chances of buying b is\n",
    "# 3times, lift = support of x union y /support of x*support of y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determining the frequent factors\n",
    "frequent_factors = apriori(productdata,use_colnames=True,min_support=0.1,max_len=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtering the factors on the basis of conditions\n",
    "rules = association_rules(frequent_factors,metric ='lift',min_threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  antecedents consequents  antecedent support  consequent support  support  \\\n",
      "0     (Apple)    (Butter)                 0.2                 1.0      0.2   \n",
      "3     (Apple)      (Eggs)                 0.2                 0.8      0.2   \n",
      "4     (Apple)      (Milk)                 0.2                 0.6      0.2   \n",
      "6     (Bread)    (Butter)                 0.4                 1.0      0.4   \n",
      "9    (Cheese)     (Bread)                 0.2                 0.4      0.2   \n",
      "8     (Bread)    (Cheese)                 0.4                 0.2      0.2   \n",
      "7    (Butter)     (Bread)                 1.0                 0.4      0.4   \n",
      "5      (Milk)     (Apple)                 0.6                 0.2      0.2   \n",
      "2      (Eggs)     (Apple)                 0.8                 0.2      0.2   \n",
      "1    (Butter)     (Apple)                 1.0                 0.2      0.2   \n",
      "\n",
      "   confidence      lift  leverage  conviction  \n",
      "0    1.000000  1.000000      0.00         inf  \n",
      "3    1.000000  1.250000      0.04         inf  \n",
      "4    1.000000  1.666667      0.08         inf  \n",
      "6    1.000000  1.000000      0.00         inf  \n",
      "9    1.000000  2.500000      0.12         inf  \n",
      "8    0.500000  2.500000      0.12    1.600000  \n",
      "7    0.400000  1.000000      0.00    1.000000  \n",
      "5    0.333333  1.666667      0.08    1.200000  \n",
      "2    0.250000  1.250000      0.04    1.066667  \n",
      "1    0.200000  1.000000      0.00    1.000000  \n"
     ]
    }
   ],
   "source": [
    "#Displaying the first 10 records\n",
    "print(rules.head(10).sort_values(by='confidence',ascending =False))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lift of cheese & bread:\n",
    "# support of(cheese union bread)/support of cheese* support of bread\n",
    "# 0.2/(0.2*0.4)=2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Butter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Butter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Milk)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt, Butter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Butter, Onion)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support               itemsets\n",
       "0       1.0               (Butter)\n",
       "1       0.8                 (Eggs)\n",
       "2       0.6                 (Milk)\n",
       "3       0.6                (Onion)\n",
       "4       0.6               (Yogurt)\n",
       "5       0.8         (Eggs, Butter)\n",
       "6       0.6         (Butter, Milk)\n",
       "7       0.6        (Butter, Onion)\n",
       "8       0.6       (Yogurt, Butter)\n",
       "9       0.6          (Eggs, Onion)\n",
       "10      0.6  (Eggs, Butter, Onion)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apriori(productdata, use_colnames=True, min_support=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>(Butter)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Milk)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Onion)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Butter)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Milk)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt, Butter)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Butter, Onion)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support               itemsets  length\n",
       "0       1.0               (Butter)       1\n",
       "1       0.8                 (Eggs)       1\n",
       "2       0.6                 (Milk)       1\n",
       "3       0.6                (Onion)       1\n",
       "4       0.6               (Yogurt)       1\n",
       "5       0.8         (Eggs, Butter)       2\n",
       "6       0.6         (Butter, Milk)       2\n",
       "7       0.6        (Butter, Onion)       2\n",
       "8       0.6       (Yogurt, Butter)       2\n",
       "9       0.6          (Eggs, Onion)       2\n",
       "10      0.6  (Eggs, Butter, Onion)       3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets =apriori(productdata, min_support=0.6,use_colnames=True)\n",
    "frequent_itemsets['length']= frequent_itemsets['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Butter)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Milk)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Yogurt, Butter)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Eggs, Butter, Onion)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support               itemsets  length\n",
       "5       0.8         (Eggs, Butter)       2\n",
       "6       0.6         (Butter, Milk)       2\n",
       "7       0.6        (Butter, Onion)       2\n",
       "8       0.6       (Yogurt, Butter)       2\n",
       "9       0.6          (Eggs, Onion)       2\n",
       "10      0.6  (Eggs, Butter, Onion)       3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[(frequent_itemsets['length']>=2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.8</td>\n",
       "      <td>(Eggs, Butter)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support        itemsets  length\n",
       "5      0.8  (Eggs, Butter)       2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[(frequent_itemsets['length']>=2)& (frequent_itemsets['support']>=0.7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Butter, Onion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support         itemsets  length\n",
       "7      0.6  (Butter, Onion)       2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets[(frequent_itemsets['itemsets']=={'Onion','Butter'})]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "target ='{\\'Milk\\'}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_milk = rules[rules['antecedents'].astype(str).str.contains(target,na=False)].sort_values(by='confidence',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>25</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Butter)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Yogurt)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Yogurt, Butter)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Apple)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(Milk)</td>\n",
       "      <td>(Orange)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    antecedents       consequents  antecedent support  consequent support  \\\n",
       "25       (Milk)          (Butter)                 0.6                 1.0   \n",
       "51       (Milk)          (Yogurt)                 0.6                 0.6   \n",
       "221      (Milk)  (Yogurt, Butter)                 0.6                 0.6   \n",
       "5        (Milk)           (Apple)                 0.6                 0.2   \n",
       "48       (Milk)          (Orange)                 0.6                 0.2   \n",
       "\n",
       "     support  confidence      lift  leverage  conviction  \n",
       "25       0.6    1.000000  1.000000      0.00         inf  \n",
       "51       0.4    0.666667  1.111111      0.04         1.2  \n",
       "221      0.4    0.666667  1.111111      0.04         1.2  \n",
       "5        0.2    0.333333  1.666667      0.08         1.2  \n",
       "48       0.2    0.333333  1.666667      0.08         1.2  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_milk.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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