Association Rules Book Update.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "833bdb2e",
   "metadata": {},
   "source": [
    "# Association Rules Book Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "76dd43d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori,association_rules\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b2a856",
   "metadata": {},
   "source": [
    "# Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbdcf77",
   "metadata": {},
   "source": [
    "### Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "731e21ee",
   "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>ChildBks</th>\n",
       "      <th>YouthBks</th>\n",
       "      <th>CookBks</th>\n",
       "      <th>DoItYBks</th>\n",
       "      <th>RefBks</th>\n",
       "      <th>ArtBks</th>\n",
       "      <th>GeogBks</th>\n",
       "      <th>ItalCook</th>\n",
       "      <th>ItalAtlas</th>\n",
       "      <th>ItalArt</th>\n",
       "      <th>Florence</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1997</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1999</th>\n",
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       "</table>\n",
       "<p>2000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ChildBks  YouthBks  CookBks  DoItYBks  RefBks  ArtBks  GeogBks  \\\n",
       "0            0         1        0         1       0       0        1   \n",
       "1            1         0        0         0       0       0        0   \n",
       "2            0         0        0         0       0       0        0   \n",
       "3            1         1        1         0       1       0        1   \n",
       "4            0         0        1         0       0       0        1   \n",
       "...        ...       ...      ...       ...     ...     ...      ...   \n",
       "1995         0         0        1         0       0       1        1   \n",
       "1996         0         0        0         0       0       0        0   \n",
       "1997         0         0        0         0       0       0        0   \n",
       "1998         0         0        1         0       0       0        0   \n",
       "1999         0         0        0         0       0       0        0   \n",
       "\n",
       "      ItalCook  ItalAtlas  ItalArt  Florence  \n",
       "0            0          0        0         0  \n",
       "1            0          0        0         0  \n",
       "2            0          0        0         0  \n",
       "3            0          0        0         0  \n",
       "4            0          0        0         0  \n",
       "...        ...        ...      ...       ...  \n",
       "1995         1          0        1         1  \n",
       "1996         0          0        0         0  \n",
       "1997         0          0        0         0  \n",
       "1998         0          0        0         0  \n",
       "1999         0          0        0         0  \n",
       "\n",
       "[2000 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book = pd.read_csv(\"book.csv\")\n",
    "book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "180d2162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChildBks     0\n",
       "YouthBks     0\n",
       "CookBks      0\n",
       "DoItYBks     0\n",
       "RefBks       0\n",
       "ArtBks       0\n",
       "GeogBks      0\n",
       "ItalCook     0\n",
       "ItalAtlas    0\n",
       "ItalArt      0\n",
       "Florence     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ec4d6eb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ChildBks</th>\n",
       "      <th>YouthBks</th>\n",
       "      <th>CookBks</th>\n",
       "      <th>DoItYBks</th>\n",
       "      <th>RefBks</th>\n",
       "      <th>ArtBks</th>\n",
       "      <th>GeogBks</th>\n",
       "      <th>ItalCook</th>\n",
       "      <th>ItalAtlas</th>\n",
       "      <th>ItalArt</th>\n",
       "      <th>Florence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
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       "      <td>0.276000</td>\n",
       "      <td>0.113500</td>\n",
       "      <td>0.037000</td>\n",
       "      <td>0.048500</td>\n",
       "      <td>0.108500</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.49534</td>\n",
       "      <td>0.450086</td>\n",
       "      <td>0.410578</td>\n",
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       "      <td>0.447129</td>\n",
       "      <td>0.317282</td>\n",
       "      <td>0.188809</td>\n",
       "      <td>0.214874</td>\n",
       "      <td>0.311089</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          ChildBks     YouthBks     CookBks     DoItYBks       RefBks  \\\n",
       "count  2000.000000  2000.000000  2000.00000  2000.000000  2000.000000   \n",
       "mean      0.423000     0.247500     0.43100     0.282000     0.214500   \n",
       "std       0.494159     0.431668     0.49534     0.450086     0.410578   \n",
       "min       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.00000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     1.00000     1.000000     0.000000   \n",
       "max       1.000000     1.000000     1.00000     1.000000     1.000000   \n",
       "\n",
       "            ArtBks      GeogBks     ItalCook    ItalAtlas      ItalArt  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean      0.241000     0.276000     0.113500     0.037000     0.048500   \n",
       "std       0.427797     0.447129     0.317282     0.188809     0.214874   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "          Florence  \n",
       "count  2000.000000  \n",
       "mean      0.108500  \n",
       "std       0.311089  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b21e043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChildBks     0\n",
       "YouthBks     0\n",
       "CookBks      0\n",
       "DoItYBks     0\n",
       "RefBks       0\n",
       "ArtBks       0\n",
       "GeogBks      0\n",
       "ItalCook     0\n",
       "ItalAtlas    0\n",
       "ItalArt      0\n",
       "Florence     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e98f2d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "item_sets = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "813f5892",
   "metadata": {},
   "outputs": [],
   "source": [
    "te = TransactionEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6132b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "te_ary = te.fit(book).transform(book)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3065e825",
   "metadata": {},
   "outputs": [],
   "source": [
    "ap = pd.DataFrame(te_ary, columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f44f23f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ap.sum().to_frame('Frequency').sort_values('Frequency',ascending=False)[:25].plot(kind='bar',\n",
    "                                                                                  figsize=(12,8),\n",
    "                                                                                  title=\"Frequent Items\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8497b384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Word cloud of most frequent books\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (13, 13)\n",
    "wordcloud = WordCloud(background_color = 'white', width = 1200,  height = 1200, max_words = 121).generate(str(book))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.title('Most Popular Books',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfe54e5",
   "metadata": {},
   "source": [
    "# Apriori Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3db17d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ap_0_5 = {}\n",
    "ap_1 = {}\n",
    "ap_5 = {}\n",
    "ap_1_0 = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fbdb3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "confidence = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5badf678",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_rules(df,confidence,support):\n",
    "    ap = {}\n",
    "    for i in confidence:\n",
    "        ap_i =apriori(book,support,True)\n",
    "        rule= association_rules(ap_i,min_threshold=i)\n",
    "        ap[i] = len(rule.antecedents)\n",
    "    return pd.Series(ap).to_frame(\"Support: %s\"%support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5996dfe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "confs = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3428d705",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in [0.001,0.005,0.01,0.05,0.1]:\n",
    "    ap_i = gen_rules(ap,confidence=confidence,support=i)\n",
    "    confs.append(ap_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a2a3a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_conf = pd.concat(confs,axis=1)\n",
    "all_conf.plot(figsize=(8,8),grid=True)\n",
    "plt.ylabel('Rules')\n",
    "plt.xlabel('Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc892fc",
   "metadata": {},
   "source": [
    "## Lower the Confidence level Higher the no. of rules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90b5ac8",
   "metadata": {},
   "source": [
    "## Apriori Algorithm for min_support = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b184d14",
   "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",
       "    }\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4230</td>\n",
       "      <td>(ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2475</td>\n",
       "      <td>(YouthBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4310</td>\n",
       "      <td>(CookBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2820</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2145</td>\n",
       "      <td>(RefBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2410</td>\n",
       "      <td>(ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2760</td>\n",
       "      <td>(GeogBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1135</td>\n",
       "      <td>(ItalCook)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1085</td>\n",
       "      <td>(Florence)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.1650</td>\n",
       "      <td>(YouthBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.2560</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1840</td>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.1515</td>\n",
       "      <td>(RefBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.1625</td>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1950</td>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.1620</td>\n",
       "      <td>(YouthBks, CookBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.1155</td>\n",
       "      <td>(YouthBks, DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.1010</td>\n",
       "      <td>(YouthBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.1205</td>\n",
       "      <td>(YouthBks, GeogBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.1875</td>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.1525</td>\n",
       "      <td>(CookBks, RefBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.1670</td>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.1925</td>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.1135</td>\n",
       "      <td>(CookBks, ItalCook)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.1055</td>\n",
       "      <td>(RefBks, DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1235</td>\n",
       "      <td>(DoItYBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.1325</td>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.1105</td>\n",
       "      <td>(GeogBks, RefBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.1275</td>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.1290</td>\n",
       "      <td>(YouthBks, CookBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.1460</td>\n",
       "      <td>(CookBks, DoItYBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.1225</td>\n",
       "      <td>(CookBks, RefBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.1265</td>\n",
       "      <td>(CookBks, ChildBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.1495</td>\n",
       "      <td>(CookBks, GeogBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.1045</td>\n",
       "      <td>(GeogBks, DoItYBks, ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.1020</td>\n",
       "      <td>(GeogBks, ChildBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.1015</td>\n",
       "      <td>(CookBks, DoItYBks, ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.1085</td>\n",
       "      <td>(CookBks, DoItYBks, GeogBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.1035</td>\n",
       "      <td>(CookBks, GeogBks, ArtBks)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                       itemsets\n",
       "0    0.4230                     (ChildBks)\n",
       "1    0.2475                     (YouthBks)\n",
       "2    0.4310                      (CookBks)\n",
       "3    0.2820                     (DoItYBks)\n",
       "4    0.2145                       (RefBks)\n",
       "5    0.2410                       (ArtBks)\n",
       "6    0.2760                      (GeogBks)\n",
       "7    0.1135                     (ItalCook)\n",
       "8    0.1085                     (Florence)\n",
       "9    0.1650           (YouthBks, ChildBks)\n",
       "10   0.2560            (CookBks, ChildBks)\n",
       "11   0.1840           (DoItYBks, ChildBks)\n",
       "12   0.1515             (RefBks, ChildBks)\n",
       "13   0.1625             (ChildBks, ArtBks)\n",
       "14   0.1950            (GeogBks, ChildBks)\n",
       "15   0.1620            (YouthBks, CookBks)\n",
       "16   0.1155           (YouthBks, DoItYBks)\n",
       "17   0.1010             (YouthBks, ArtBks)\n",
       "18   0.1205            (YouthBks, GeogBks)\n",
       "19   0.1875            (CookBks, DoItYBks)\n",
       "20   0.1525              (CookBks, RefBks)\n",
       "21   0.1670              (CookBks, ArtBks)\n",
       "22   0.1925             (CookBks, GeogBks)\n",
       "23   0.1135            (CookBks, ItalCook)\n",
       "24   0.1055             (RefBks, DoItYBks)\n",
       "25   0.1235             (DoItYBks, ArtBks)\n",
       "26   0.1325            (GeogBks, DoItYBks)\n",
       "27   0.1105              (GeogBks, RefBks)\n",
       "28   0.1275              (GeogBks, ArtBks)\n",
       "29   0.1290  (YouthBks, CookBks, ChildBks)\n",
       "30   0.1460  (CookBks, DoItYBks, ChildBks)\n",
       "31   0.1225    (CookBks, RefBks, ChildBks)\n",
       "32   0.1265    (CookBks, ChildBks, ArtBks)\n",
       "33   0.1495   (CookBks, GeogBks, ChildBks)\n",
       "34   0.1045  (GeogBks, DoItYBks, ChildBks)\n",
       "35   0.1020    (GeogBks, ChildBks, ArtBks)\n",
       "36   0.1015    (CookBks, DoItYBks, ArtBks)\n",
       "37   0.1085   (CookBks, DoItYBks, GeogBks)\n",
       "38   0.1035     (CookBks, GeogBks, ArtBks)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets1 = apriori(book, min_support=0.1, use_colnames=True)\n",
    "frequent_itemsets1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "39253b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>0</th>\n",
       "      <td>0.4230</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2475</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4310</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2820</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2145</td>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2410</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2760</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1135</td>\n",
       "      <td>(ItalCook)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1085</td>\n",
       "      <td>(Florence)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.1650</td>\n",
       "      <td>(YouthBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.2560</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1840</td>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.1515</td>\n",
       "      <td>(RefBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.1625</td>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1950</td>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.1620</td>\n",
       "      <td>(YouthBks, CookBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.1155</td>\n",
       "      <td>(YouthBks, DoItYBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.1010</td>\n",
       "      <td>(YouthBks, ArtBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.1205</td>\n",
       "      <td>(YouthBks, GeogBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.1875</td>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.1525</td>\n",
       "      <td>(CookBks, RefBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.1670</td>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.1925</td>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.1135</td>\n",
       "      <td>(CookBks, ItalCook)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.1055</td>\n",
       "      <td>(RefBks, DoItYBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1235</td>\n",
       "      <td>(DoItYBks, ArtBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.1325</td>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.1105</td>\n",
       "      <td>(GeogBks, RefBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.1275</td>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.1290</td>\n",
       "      <td>(YouthBks, CookBks, ChildBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.1460</td>\n",
       "      <td>(CookBks, DoItYBks, ChildBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.1225</td>\n",
       "      <td>(CookBks, RefBks, ChildBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.1265</td>\n",
       "      <td>(CookBks, ChildBks, ArtBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.1495</td>\n",
       "      <td>(CookBks, GeogBks, ChildBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.1045</td>\n",
       "      <td>(GeogBks, DoItYBks, ChildBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.1020</td>\n",
       "      <td>(GeogBks, ChildBks, ArtBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.1015</td>\n",
       "      <td>(CookBks, DoItYBks, ArtBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.1085</td>\n",
       "      <td>(CookBks, DoItYBks, GeogBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.1035</td>\n",
       "      <td>(CookBks, GeogBks, ArtBks)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                       itemsets  length\n",
       "0    0.4230                     (ChildBks)       1\n",
       "1    0.2475                     (YouthBks)       1\n",
       "2    0.4310                      (CookBks)       1\n",
       "3    0.2820                     (DoItYBks)       1\n",
       "4    0.2145                       (RefBks)       1\n",
       "5    0.2410                       (ArtBks)       1\n",
       "6    0.2760                      (GeogBks)       1\n",
       "7    0.1135                     (ItalCook)       1\n",
       "8    0.1085                     (Florence)       1\n",
       "9    0.1650           (YouthBks, ChildBks)       2\n",
       "10   0.2560            (CookBks, ChildBks)       2\n",
       "11   0.1840           (DoItYBks, ChildBks)       2\n",
       "12   0.1515             (RefBks, ChildBks)       2\n",
       "13   0.1625             (ChildBks, ArtBks)       2\n",
       "14   0.1950            (GeogBks, ChildBks)       2\n",
       "15   0.1620            (YouthBks, CookBks)       2\n",
       "16   0.1155           (YouthBks, DoItYBks)       2\n",
       "17   0.1010             (YouthBks, ArtBks)       2\n",
       "18   0.1205            (YouthBks, GeogBks)       2\n",
       "19   0.1875            (CookBks, DoItYBks)       2\n",
       "20   0.1525              (CookBks, RefBks)       2\n",
       "21   0.1670              (CookBks, ArtBks)       2\n",
       "22   0.1925             (CookBks, GeogBks)       2\n",
       "23   0.1135            (CookBks, ItalCook)       2\n",
       "24   0.1055             (RefBks, DoItYBks)       2\n",
       "25   0.1235             (DoItYBks, ArtBks)       2\n",
       "26   0.1325            (GeogBks, DoItYBks)       2\n",
       "27   0.1105              (GeogBks, RefBks)       2\n",
       "28   0.1275              (GeogBks, ArtBks)       2\n",
       "29   0.1290  (YouthBks, CookBks, ChildBks)       3\n",
       "30   0.1460  (CookBks, DoItYBks, ChildBks)       3\n",
       "31   0.1225    (CookBks, RefBks, ChildBks)       3\n",
       "32   0.1265    (CookBks, ChildBks, ArtBks)       3\n",
       "33   0.1495   (CookBks, GeogBks, ChildBks)       3\n",
       "34   0.1045  (GeogBks, DoItYBks, ChildBks)       3\n",
       "35   0.1020    (GeogBks, ChildBks, ArtBks)       3\n",
       "36   0.1015    (CookBks, DoItYBks, ArtBks)       3\n",
       "37   0.1085   (CookBks, DoItYBks, GeogBks)       3\n",
       "38   0.1035     (CookBks, GeogBks, ArtBks)       3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets1 = apriori(book, min_support = 0.1, use_colnames=True)\n",
    "frequent_itemsets1['length'] = frequent_itemsets1['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c15bd2",
   "metadata": {},
   "source": [
    "## Rules when min_support = 0.1 and min_threshold for lift is 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be469354",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>(YouthBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.576044</td>\n",
       "      <td>0.060308</td>\n",
       "      <td>1.731000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>1.576044</td>\n",
       "      <td>0.060308</td>\n",
       "      <td>1.233750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.652482</td>\n",
       "      <td>1.542511</td>\n",
       "      <td>0.064714</td>\n",
       "      <td>1.660347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.619760</td>\n",
       "      <td>2.245509</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>1.904063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.811765</td>\n",
       "      <td>1.883445</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>3.022812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.240139</td>\n",
       "      <td>1.883445</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>1.148237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>2.245509</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>1.332800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.429461</td>\n",
       "      <td>2.230964</td>\n",
       "      <td>0.057107</td>\n",
       "      <td>1.415327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          antecedents         consequents  antecedent support  \\\n",
       "0          (YouthBks)          (ChildBks)              0.2475   \n",
       "1          (ChildBks)          (YouthBks)              0.4230   \n",
       "2           (CookBks)          (ChildBks)              0.4310   \n",
       "3          (ChildBks)           (CookBks)              0.4230   \n",
       "4          (DoItYBks)          (ChildBks)              0.2820   \n",
       "..                ...                 ...                 ...   \n",
       "95  (CookBks, ArtBks)           (GeogBks)              0.1670   \n",
       "96  (GeogBks, ArtBks)           (CookBks)              0.1275   \n",
       "97          (CookBks)   (GeogBks, ArtBks)              0.4310   \n",
       "98          (GeogBks)   (CookBks, ArtBks)              0.2760   \n",
       "99           (ArtBks)  (CookBks, GeogBks)              0.2410   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0               0.4230   0.1650    0.666667  1.576044  0.060308    1.731000  \n",
       "1               0.2475   0.1650    0.390071  1.576044  0.060308    1.233750  \n",
       "2               0.4230   0.2560    0.593968  1.404179  0.073687    1.421069  \n",
       "3               0.4310   0.2560    0.605201  1.404179  0.073687    1.441240  \n",
       "4               0.4230   0.1840    0.652482  1.542511  0.064714    1.660347  \n",
       "..                 ...      ...         ...       ...       ...         ...  \n",
       "95              0.2760   0.1035    0.619760  2.245509  0.057408    1.904063  \n",
       "96              0.4310   0.1035    0.811765  1.883445  0.048547    3.022812  \n",
       "97              0.1275   0.1035    0.240139  1.883445  0.048547    1.148237  \n",
       "98              0.1670   0.1035    0.375000  2.245509  0.057408    1.332800  \n",
       "99              0.1925   0.1035    0.429461  2.230964  0.057107    1.415327  \n",
       "\n",
       "[100 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules1 = association_rules(frequent_itemsets1, metric=\"lift\", min_threshold=0.5)\n",
    "rules1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a8741119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<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>28</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ItalCook)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>0.263341</td>\n",
       "      <td>2.320186</td>\n",
       "      <td>0.064582</td>\n",
       "      <td>1.203406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(ItalCook)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.064582</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.627692</td>\n",
       "      <td>2.274247</td>\n",
       "      <td>0.057150</td>\n",
       "      <td>1.944628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.369565</td>\n",
       "      <td>2.274247</td>\n",
       "      <td>0.057150</td>\n",
       "      <td>1.328448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.421162</td>\n",
       "      <td>2.246196</td>\n",
       "      <td>0.056313</td>\n",
       "      <td>1.403674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.652482</td>\n",
       "      <td>1.542511</td>\n",
       "      <td>0.064714</td>\n",
       "      <td>1.660347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>1.518667</td>\n",
       "      <td>0.055328</td>\n",
       "      <td>1.647105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.375870</td>\n",
       "      <td>1.518667</td>\n",
       "      <td>0.055328</td>\n",
       "      <td>1.205678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           antecedents          consequents  antecedent support  \\\n",
       "28           (CookBks)           (ItalCook)              0.4310   \n",
       "29          (ItalCook)            (CookBks)              0.1135   \n",
       "78  (ChildBks, ArtBks)            (GeogBks)              0.1625   \n",
       "79           (GeogBks)   (ChildBks, ArtBks)              0.2760   \n",
       "87            (ArtBks)  (CookBks, DoItYBks)              0.2410   \n",
       "..                 ...                  ...                 ...   \n",
       "4           (DoItYBks)           (ChildBks)              0.2820   \n",
       "12          (YouthBks)            (CookBks)              0.2475   \n",
       "13           (CookBks)           (YouthBks)              0.4310   \n",
       "3           (ChildBks)            (CookBks)              0.4230   \n",
       "2            (CookBks)           (ChildBks)              0.4310   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "28              0.1135   0.1135    0.263341  2.320186  0.064582    1.203406  \n",
       "29              0.4310   0.1135    1.000000  2.320186  0.064582         inf  \n",
       "78              0.2760   0.1020    0.627692  2.274247  0.057150    1.944628  \n",
       "79              0.1625   0.1020    0.369565  2.274247  0.057150    1.328448  \n",
       "87              0.1875   0.1015    0.421162  2.246196  0.056313    1.403674  \n",
       "..                 ...      ...         ...       ...       ...         ...  \n",
       "4               0.4230   0.1840    0.652482  1.542511  0.064714    1.660347  \n",
       "12              0.4310   0.1620    0.654545  1.518667  0.055328    1.647105  \n",
       "13              0.2475   0.1620    0.375870  1.518667  0.055328    1.205678  \n",
       "3               0.4310   0.2560    0.605201  1.404179  0.073687    1.441240  \n",
       "2               0.4230   0.2560    0.593968  1.404179  0.073687    1.421069  \n",
       "\n",
       "[100 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules1.sort_values('lift',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1d3c5325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ICAAAQEdAAAAAOgICAADQERAAAICOgAAAAHQEBAAAoCMgAAAAHQEBAADoCAgAAEBn76gbMI5OzvVy7MSZnD7bz8H9k5mdmc6tB6ZG3SwAANgwFYQ1OjnXy9Hjp9Lrz+fA1L70+vM5evxUTs71Rt00AADYMBWENTp24kymJicyNTmRJN3XYyfODL2KoHIBAMCwqSCs0emz/Vy/b3muun7f3pw+2x/q91W5AABgKwgIa3Rw/2TOnb+wbNu58xdycP/kUL/v0srFVVXd/WMnzgz1+wIAsLsICGs0OzOdXn8+vf58nmituz87Mz3U7zuqygUAALuLgLBGtx6Yyh2335KpyYnM9c5nanIid9x+y9DHAoyqcgEAwO5ikPI63HpgassHB8/OTOfo8VNJFioH585fSK8/nyO3HdrSdgAAsLMJCEOy2TMOLVYulr7nkdsOmcUIAIBNJSAMweKMQ1OTE8tmHNpoV6RRVC4AANhdjEEYAjMOAQAwrlQQhuD02X4OTO1bts2MQ2tnYTgAgK2ngjAEZhzaOAvDAQCMhoAwBKNaK2En0U0LAGA0BIQhGNVaCTuJheEAAEbDGIQhMePQxhzcP5lefz5TkxPdNt20AACGT0BgW5qdmc5r33Fv/vDRx/LYhcdzzd49ueG6a3Lk65876qYBAOxoQ+1iVFWzVfWRqrqvql5zkeenqupXqur9VXVPVX3XMNvDeHmitSRJpZY9BgBgeIZWQaiqPUlen+QlSR5IcldVvb219qElu31fkg+11v5yVd2Y5CNV9fOttc8Oq12byTScw3PsxJk882nX5vmH9nfbev35HDtxxjEGABiiYVYQXpjkvtbaRwcn/G9J8rIV+7Qk11dVJbkuycNJLmQMmIZzuAxSBgAYjWEGhINJ7l/y+IHBtqV+LMmtSR5M8sEkf7u19sQQ27RpTMM5XNaSAAAYjWEOUq6LbFvZifzrk7wvyV9M8uwk76yq/95ae2TZG1XdkeSOJLn55ps3v6XrYLXk4Zqdmc7R46eSLBzXc+cvpNefz5HbDg3l++kuBgCwYJgVhAeS3LTk8aEsVAqW+q4kv9QW3JfkVJIvWflGrbWjrbXDrbXDN95449AavBaucA/XVq4lobsYAMDnDLOCcFeS51TVLUlOJ3l5km9fsc/Hknxtkv9eVdNJvjjJR4fYpk2z1Ve4d6OtWktiaXexJN1XA6IBgN1oaAGhtXahql6d5B1J9iR5Y2vtnqp61eD5NyT54SQ/XVUfzEKXpL/fWvvEsNq0mRavcC/tlnLktkNOKMeQ7mKroxsWAOwOQ10orbV2Z5I7V2x7w5L7Dyb5S8NswzBZLXlnsGrzlS12w5qanFjWDWtY3b4AgNEZ6kJpMA5mZ6bT68+n15/PE61192dnpkfdtG3DrF0AsHsICOx6WzkgelxZlwIAdo+hdjHic/Tf3t50F7s83bAAYPdQQdgCptFk3OmGBQC7h4CwBfTfZtzphgUAu4cuRlvANJrsBLphAcDuoIKwBay6DADAuBAQtoD+2wAAjAtdjLaAVZe3P7NMAQAsEBC2iP7b25dVggEAPkdAYEdbTWVg6SxTSbqvx06cERCWUGUBgN3BGAR2rNWuP2GV4CuzlgcA7B4qCOxYq60MWCX4yjZaZVF9AIDxoYLAjrXayoBZpq5sI1UW1QcAGC8CAjvWatefsErwlW1kLQ8riQPAeNHFiB1rdmY6R4+fSrJwtfvc+Qvp9edz5LZDn7evWaYuby3HciUriQPAeFFBYMdSGdg8GzmWVhIHgPGigsCOpjKwedZ7LDdSfQAAtp4KAjBUKjkAMF5UEIChU8kBgPEhIABDZx0EABgfuhgBQ2UdBAAYLwICMFTWQQCA8SIgAEO1kVWYAYCtJyAAQ2UdBAAYLwICMFSzM9Pp9efT68/nida6+7Mz06NuGgBwEQICMFTWQQCA8WKaU2DorIMAAONDQGBHM/8+AMDa6GLEjmX+fQCAtRMQ2LHMvw8AsHYCAjuW+fcBANZOQGDHMv8+AMDaCQjsWObfBwBYOwGBHcv8+wAAa2eaU3Y08+8DAKyNCgIAANAREAAAgI6AAAAAdAQEAACgIyAAAAAdAQEAAOgICAAAQEdAAAAAOgICAADQERAAAICOgAAAAHQEBAAAoCMgAAAAHQEBAADoCAgAAEBHQAAAADoCAgAA0BEQAACAjoAAAAB0BAQAAKAjIAAAAB0BAQAA6AgIAABAR0AAAAA6AgIAANAREAAAgI6AAAAAdAQEAACgIyAAAAAdAQEAAOgICAAAQGfvqBsAsNLJuV6OnTiT02f7Obh/MrMz07n1wNSomwUAu4IKArCtnJzr5ejxU+n153Ngal96/fkcPX4qJ+d6o24aAOwKAgKwrRw7cSZTkxOZmpzIVVXd/WMnzoy6aQCwKwgIwLZy+mw/1+9b3vvx+n17c/psf0QtAoDdxRgEYOSWjjn42MOfyfyFx/OsG67rnj93/kIO7p8cYQthY4yrAcaJCgIwUivHHHzB9dfkDz52Nn/yiUfzRGvp9efT689ndmZ61E2FdTGuBhg3KgjASC0dc5Akt9y4UDmYe+SxTOzdk4P7J3PktkOutjK2Vv6OL349duKM32tgWxIQgJE6fbafA1P7lm175g3X5uqJPXnt//yCEbUKNs/FfseNqwG2s6F2Maqq2ar6SFXdV1Wvucjz/0dVvW9wO1FVj1fVU4fZJmB7Obh/MufOX1i2zZgDdhK/48C4GVpAqKo9SV6f5KVJnpfkFVX1vKX7tNb+dWvty1prX5bkB5P8dmvt4WG1Cdh+Zmemu3EGxhywE/kdB8bNMCsIL0xyX2vto621zyZ5S5KXXWb/VyR58xDbA2xDtx6Yyh2335KpyYnM9c5nanIid9x+i77Z7Bh+x4FxM8wxCAeT3L/k8QNJXnSxHavqSUlmk7x6iO0BtqlbD0w5WWLsrGXqUr/jwDgZZgWhLrKtXWLfv5zk/7tU96KquqOq7q6qux966KFNayAArIepS4GdbJgB4YEkNy15fCjJg5fY9+W5TPei1trR1trh1trhG2+8cRObCABrt3Tq0ququvvHTpwZddMANmyYAeGuJM+pqluq6uoshIC3r9ypqqaS/IUkvzzEtgDApjl9tp/r9y3vpWvqUmCnGNoYhNbahap6dZJ3JNmT5I2ttXuq6lWD598w2PVbkvx6a+3Tw2oLwDhYS592Ruvg/sn0+vPdomeJqUuBnWOo6yC01u5srT23tfbs1to/H2x7w5JwkNbaT7fWXj7MdgBsd/q0jxdTlwI72VADAgCro0/7eDF1KbCTDXOaUwBW6fTZfg5M7Vu2TZ/27c3UpcBOpYIAsA0c3D+Zc+cvLNumTzsAoyAgAGwD+rQDsF0ICADbgD7tAGwXxiAAbBP6tAOwHaggAAAAHQEBAADoCAgAAEBHQAAAADoCAgAA0BEQAACAjoAAAAB0BAQAAKBjoTSADTg518uxE2dy+mw/B/dPZnZm2mJnAIw1FQSAdTo518vR46fS68/nwNS+9PrzOXr8VE7O9UbdNABYNwEBYJ2OnTiTqcmJTE1O5Kqq7v6xE2dG3TQAWDcBAWCdTp/t5/p9y3tqXr9vb06f7Y+oRQCwcQICwDod3D+Zc+cvLNt27vyFHNw/OaIWAcDGCQgA6zQ7M51efz69/nyeaK27PzszPeqmAcC6mcUIYJ1uPTCVO26/ZdksRkduO3TRWYzMdgTAuBAQADbg1gNTVzzRX5ztaGpyYtlsR3fcfouQAMC2IyAADNnS2Y6SdF+PnTgjIGwRFRyA1TMGAWDIzHY0WtarAFgbAQFgyMx2NFrWqwBYGwEBYMjMdjRaKjgAayMgAAzZ4mxHU5MTmeudz9TkhAHKW0gFB2BtDFIG2AKrme2I4Zidmc7R46eSLFQOzp2/kF5/PkduOzTilgFsTyoIAOxoKjgAa6OCAMCOp4IDsHoqCAAAQEcFAWAdLLwFwE4lIACs0eLCW1OTE8sW3tKvfbiEMoCtoYsRwBpZeGvrWQ0ZYOsICABrZOGtrSeUAWwdAQFgjSy8tfWEMoCtIyAArNHszHR6/fn0+vN5orXu/uzM9KibtmMJZQBbR0AAWCMLb209oQxg65jFCGAdLLy1tRZD2dJZjI7cdsi/AcAQrDogVNUzkzyntfYbVTWZZG9r7dzwmgYAnyOUAWyNVXUxqqrvTfKLSf7DYNOhJG8bUpsAAIARWe0YhO9L8lVJHkmS1tofJXn6sBoFAACMxmoDwmOttc8uPqiqvUnacJoEAACMymoDwm9X1Q8lmayqlyR5a5JfGV6zAACAUVhtQHhNkoeSfDDJ30xyZ5J/OKxGAQAAo7HaWYwmk7yxtfYTSVJVewbbPjOshgEAAFtvtRWE38xCIFg0meQ3Nr85AADAKK02IOxrrT26+GBw/0nDaRIAADAqqw0In66qr1h8UFV/Lkl/OE0CAABGZbVjEL4/yVur6sHB4wNJjgylRQAAwMisKiC01u6qqi9J8sVJKsmHW2vzQ20ZAACw5VZbQUiS25I8a/CaL6+qtNZ+diitAgAARmJVAaGq3pTk2Unel+TxweaWREAAAIAdZLUVhMNJntdaa8NszLg5OdfLsRNncvpsPwf3T2Z2Zjq3HpgadbMAAGDdVjuL0YkkXzDMhoybk3O9HD1+Kr3+fA5M7UuvP5+jx0/l5Fxv1E0DAIB1W20F4YYkH6qq30/y2OLG1to3D6VVY+DYiTOZmpzI1OREknRfj504o4oAAMDYWm1A+CfDbMQ4On22nwNT+5Ztu37f3pw+a3kIAADG12qnOf3tqnpmkue01n6jqp6UZM9wm7a9Hdw/mV5/vqscJMm58xdycP/kCFsFAAAbs6oxCFX1vUl+Mcl/GGw6mORtQ2rTWJidmU6vP59efz5PtNbdn52ZHnXTAABg3VY7SPn7knxVkkeSpLX2R0mePqxGjYNbD0zljttvydTkROZ65zM1OZE7br/F+AMAAMbaascgPNZa+2xVJUmqam8W1kHY1W49MHXJQGAKVAAAxtFqKwi/XVU/lGSyql6S5K1JfmV4zRpvpkAFAGBcrTYgvCbJQ0k+mORvJrkzyT8cVqPG3dIpUK+q6u4fO3Fm1E0DAIDLWu0sRk8k+YnBjSswBSoAAOPqsgGhqj6Yy4w1aK09f9NbtAOYAhUAgHF1pQrCNw2+ft/g65sGX/9aks8MpUU7wOzMdI4eP5VkoXJw7vyF9PrzOXLboRG3DAAALu+yYxBaa3/aWvvTJF/VWvt7rbUPDm6vSfL1W9PE8WMKVAAAxtVqpzm9tqq+urX2O0lSVX8+ybXDa9b4u9wUqAAAsF2tNiB8d5I3VtXiGe/ZJH9jKC0CAABGZrWzGL03yQuq6slJqrVmQn8AANiBrjSL0Xe01n6uqv73FduTJK21fzPEtm1bVkkGAGCnutJCaU8afL3+ErddxyrJAADsZFfqYvTswdcPtdbeOuzGjIOlqyQn6b4eO3FGFQEAgMsah54oV6ogfENVTST5wa1ozDg4fbaf6/ctz1VWSQYA4ErGpSfKlSoIx5J8IgvTnD6yZHslaa21Jw+tZduUVZIBAFiPcemJcqWF0v6P1tpUkl9trT15ye361YSDqpqtqo9U1X1V9ZpL7PPiqnpfVd1TVb+9zp9jy8zOTKfXn0+vP58nWuvuz85Mj7ppAABsY+PSE+VKXYySJK21l631jatqT5LXJ3lpkucleUVVPW/FPvuT/HiSb26tfWmS/3mt32erWSUZAID1OLh/MufOX1i2bTv2RFnVOghV9a1JfiTJ07PQvWg1XYxemOS+1tpHB+/xliQvS/KhJft8e5Jfaq19LAtv+PE1/wQjYJVkAADWanZmOkePn0qyUDk4d/5Cev35HLnt0IhbttyqKghJ/lUWrvJPraGL0cEk9y95/MBg21LPTfKUqnpXVb23ql65yvYAAMBYGZeeKKuqICQ501o7ucb3rotsaxf5/n8uydcmmUzyu1X1e621e5e9UdUdSe5IkptvvnmNzQAAgO1hHHqirDYg3F1V/2+StyV5bHFja+2XLvOaB5LctOTxoSQPXmSfT7TWPp3k01V1PMkLkiwLCK21o0mOJsnhw4dXhgwAAGCTrDYgPDnJZ5L8pSXbWpLLBYS7kjynqm5JcjrJy7Mw5mCpX07yY1W1N8nVSV6U5HWrbBMAAIyVcVgobVUBobX2XWt949bahap6dZJ3JNmT5I2ttXuq6lWD59/QWjtZVceSfCDJE0l+srV2Yq3fCwAAtrvFhdKmJieWLZS23cYhrHYWo0NJ/n2Sr8pC5eB3kvzt1toDl3tda+3OJHeu2PaGFY//dZJ/vYY2AwDA2NkRC6Ut8VNJ3p7kGVmYiehXBtsAAIBV2FELpSW5sbX2U621C4PbTye5cYjtAgCAHWVcFkpbbUD4RFV9R1XtGdy+I8knh9kwAADYSWZnptPrz6fXn88TrXX3Z2emR920ZVYbEP5Gkr+a5M+SzCX5tiRrHrgMAAC71U5bKO2Hk/wvrbVPJUlVPTXJa7MQHAAAgFUYh4XSVltBeP5iOEiS1trDSb58OE0CAABGZbUVhKuq6ikrKgirfS2wy43DojAAwILVVhD+7yTvrqofrqp/luTdSf7V8JoF7BSLi8L0+vPLFoU5OdcbddMAgItY7UrKP1tVdyf5i0kqybe21j401JYBO8K4LAoDACxYdTehQSAQCoA1OX22nwNT+5Zt246LwgAAC4wjGCP6cTOODu6fTK8/31UOku25KAwAsGC1YxAYMf24GVfjsigMALBAQBgTS/txX1XV3T924syomwaXNS6LwgAAC3QxGhP6cTPOxmFRGABggQrCmDi4fzLnzl9Ytk0/bgAANpuAMCb04wYAYCsICGNCP24AALaCMQhjZLf14zat6/bi3wMAdgcVBLYl07puL/49AGD3UEHYIq6+rs3SaV2TdF+PnTjjuI2Afw8A2D1UELaAq69rd/psP9fvW55fTes6Ov49AGD3EBC2gEXO1s60rtuLfw8A2D0EhC3g6uvamdZ1e/HvAQC7hzEIW+Dg/sn0+vNdv+1kfVdfd9M4hsVpXZf+vEduO7Rjf97tzr8HAOwe1VobdRvW5PDhw+3uu+8edTPWZHEMwtTkRK7ftzfnzl9Irz+/pnUMNuM9AABgUVW9t7V2eOV2XYy2wGYscmYcAwAAW0EXoy2y0UXOTp/t58DUvmXbjGMAAGCzqSCMCbPIAACwFQSEMWEWGQAAtoKAMCY2YxwDAABciTEIY2Sj4xgAAOBKVBAAAICOgAAAAHQEBAAAoCMgAAAAHQEBAADoCAgAAEBHQAAAADoCAgAA0BEQAACAjoAAAAB09o66AcDOd3Kul2MnzuT02X4O7p/M7Mx0bj0wNepmAQAXISCsg5MdWL2Tc70cPX4qU5MTOTC1L73+fI4eP5U7br/F3w0AbEO6GK3R4slOrz+/7GTn5Fxv1E2DbenYiTOZmpzI1ORErqrq7h87cWbUTQMALkJAWCMnO7A2p8/2c/2+5cXK6/ftzemz/RG1CAC4HF2M1uj02X4OTO1btu1iJzu6IcGCg/sn0+vPZ2pyott27vyFHNw/OcJWAQCXIiCs0WpOdi7W5/q177g300++Jp99vAkM7CqzM9M5evxUkoUwfe78hfT68zly26ERtwwAuBhdjNZodmY6vf58ev35PNFad392ZrrbZ2U3pPnHH8+pT3w69zz4iHEL7Dq3HpjKHbffkqnJicz1zmdqcsIAZQDYxlQQ1mjxZGdp96Ejtx1adrKzshvSfR//dK67Zk/mH2/duIVkIUg4SWI3uPXAlN91ABgTAsI6XOlkZ2U3pEfOz+fqqyrXLRmoaZAmAADbkS5GQ7CyG9LVe67Ko489ni+68dpuH4M0AQDYjgSEIVjZ53rmGU/OzU97Uq7eu+eS4xYAAGA70MVoSFZ2Q1o57enKcQtsDdPPAgBcnoCwRQzSHL2LTT979PgpM+oAACyhixG7hlWwAQCuTAWBHW1pl6J7HuzlBYemli1yZzYpAIDlVBDYsRa7FPX68zkwtS9X77kqd536VB46d77bx2xSAADLqSBsgAGv29vSLkVJMnPwyfndP3449zz4SG5/7jU5d/5Cev35HLnt0IhbCgCwfaggrNPKq9OLA15PzvVG3TQGTp/t5/oli9PdcN2+vOgLn5L5x1vmeuczNTlhgDIAwAoqCOu08ur04tdjJ8444dwmVq5onSTX7N2blzxvOn/nJc9dtq9qEADAAhWEdVp5dTox4HW7Wbmi9aUWqFMNAgD4HAFhnQ7un8y58xeWbTPgdXtZuaL1pboUmf4UAOBzdDFap9mZ6Rw9firJQuXAgNftaTUL1J0+28+BqX3LtqkGfT7dsABgd1BBWKfVXp1m+1MNujLdsABg91BB2IDVXJ3eaq7yrp1q0JUZlA8Au4cKwg7iKu/6qAZdmUH5ALB7qCDsIDv9Ku8wqyPbsRq0nVxsyljdsABgZ1JB2EF28lVe1ZHRWu2UsQDA+BMQdpCdPNjWVKSjpRsWAOweuhjtIDt5sK2pSEdPNywA2B1UEHaQnXyVdydXRwAAthMVhB1mp17l3cnVEQCA7UQFgbGwk6sjAADbiQoCY2OY1RELzAEALFBBYNczhSoAwOcMNSBU1WxVfaSq7quq11zk+RdXVa+q3je4/aNhtgcuxhSqAACfM7QuRlW1J8nrk7wkyQNJ7qqqt7fWPrRi1//eWvumYbUDrsQUqgAAnzPMCsILk9zXWvtoa+2zSd6S5GVD/H6wLqZQBQD4nGEGhINJ7l/y+IHBtpX+x6p6f1X9WlV96RDbAxc1OzOdXn8+vf58nmituz87Mz3qpgEAbLlhBoS6yLa24vEfJHlma+0FSf59krdd9I2q7qiqu6vq7oceemhzW8muZwpVAIDPGeY0pw8kuWnJ40NJHly6Q2vtkSX376yqH6+qG1prn1ix39EkR5Pk8OHDK0MGbNhOXWAOAGCthhkQ7krynKq6JcnpJC9P8u1Ld6iqL0hyprXWquqFWahofHKIbWKXsb4BAMDaDK2LUWvtQpJXJ3lHkpNJfqG1dk9VvaqqXjXY7duSnKiq9yf5d0le3lpTIWBTWN8AAGDthrqScmvtziR3rtj2hiX3fyzJjw2zDduVK9vDt3R9gyTd12MnzjjWAACXYCXlEXBle2ucPtvP9fuWZ2DrGwAAXJ6AMAJW7t0a1jcAAFg7AWEEXNneGtY3AABYu6GOQeDiDu6fTK8/3/WJT3bfle2tGIOxuL7B0u9z5LZDxh8AAFyGgDACszPTOXr8VJKFysG58xfS68/nyG2HRtyyrbE4BmNqcmLZGIxhLE5mfQMAgLXRxWgEdvvKvcZgAABsXyoII7Kbr2yfPtvPgal9y7YZgwEAsD2oILDlzC4EALB9CQhsObMLAQBsX7oYseV22uxCVsUGAHYSAYGRWM0YjHE48d7KGZkAALaCLkZsS4sn3r3+/LIT75NzvVE3bRkzMgEAO42AwLY0LifeVsUGAHYaAYFtaVxOvM3IBADsNAIC29K4nHibkQkA2GkEBLalcTnx3u2rYgMAO49ZjNiWxmkq1N28KjYAsPMICGxbTrwBALaegAAbNA7rNQAArJYxCLAB47JeAwDAaqkgcEWukF/a0vUaknRfj5044xgBAGNJBYHLcoX88sZlvQYAgNUSELiscVnReFTGZb0GAIDVEhC4LFfIL29c1msAAFgtYxC4rIP7J9Prz3d965PxvkK+2eMpxmm9BgCA1RAQuKzZmekcPX4qyULl4Nz5C+n153PktkMjbtnaLY6nmJqcWDaeYqMrH1uvAbgSkz0A40QXIy5r8Qr51ORE5nrnMzU5seET6lExngIYBZM9AONGBYEr2ilXyE+f7efA1L5l24ynAIbNdMjAuFFBYNcw4xAwCiZ7AMaNgMCuYcYhYBRcnADGjYDArrGTxlMA48PFCWDcGIPArrJTxlMA48N0yMC4ERAAYMhcnADGiS5GAABAR0AAAAA6AgIAANAREAAAgI5BygCwDifnestmJpqdmTYQGdgRVBAAYI1OzvVy9Pip9PrzOTC1L73+fI4eP5WTc71RNw1gwwQEAFijYyfOZGpyIlOTE7mqqrt/7MSZUTcNYMMEBABYo9Nn+7l+3/Jeutfv25vTZ/sjahHA5hEQAGCNDu6fzLnzF5ZtO3f+Qg7unxxRiwA2j4AAAGs0OzOdXn8+vf58nmituz87Mz3qpgFsmIAAAGt064Gp3HH7LZmanMhc73ymJidyx+23mMUI2BFMcwoA63DrgSmBANiRBATGhjnHAQCGT0BgJNZ6sr845/jU5MSyOceV9AEANpeAwJZbz8n+0jnHk3Rfj504IyDALrGRKqIKJMDqGaTMllvPAkPmHIfdbSMrF1v1GGBtBAS23HpO9s05DrvbRlYutuoxwNoICGy59Zzsm3McdreNVBFVIAHWRkBgy63nZN+c47C7baSKqAIJsDYGKQ+JAXGXtniyv/T4HLnt0BWPjznHYfeanZnO0eOnkixc/T93/kJ6/fkcue3QUF8LsBtVa23UbViTw4cPt7vvvnvUzbispbP0LP0wcsUbYP3MYgSwuarqva21wyu3qyAMgSk5ATbfRqqIKpAAq2cMwhAYEAcAwLgSEIbAgDgAAMaVLkYbcKk+rQbEAQAwrlQQ1ulyK3OakhMAgHGlgrAOJ+d6+cdv/1AefvSzeep1V+eLbrw2N16/L8nnBiIbEAcAwDgSENbg5Fwvb/rdP83v3PfJnDs/n2dMXZPH5h/PH3zsbL7i5v152nXXGIgMAMBY08VolRa7FN3z4CN5yuTeXL3nqpw++1ieaC3X7L0q9z30aQORAQAYewLCKi2ubTD/eMs1E3ty4/XXpJJ8/JHHcvWeysOPfja9/nxmZ6ZH3VQAAFg3AWGVFtc2uG7f3jx24Ylce83ePOMp+/J4a3n4M/N56nVXG4gMAMDYMwZhlQ7un0yvP58vuvHa/MHHziZJ9lTl4P7JfOGN1wkHAADsCCoIqzQ7M51efz5X792TL7tpIQic7V/IzDOeLBwAALBjqCCs0uLaBsdOnMmjj13Ii7/46d3CaAAAsFMICGtgbQMAAHY6AYErOjnXy7ETZ3L6bD8H90+qnAAA7GDGIHBZi+s/9PrzOTC1L73+fI4eP5WTc71RNw0AgCFQQViD3XglfXH9h6nJiSTpvh47cWbH/+wAALuRCsIq7dYr6YvrPyx1/b69OX22P6IWAQAwTALCKi29kn5VVXf/2Ikzo27aUB3cP5lz5y8s23bu/IUc3D85ohYBADBMAsIq7dYr6YvrP/T683mite7+7Mz0qJsGAMAQCAirtFuvpC+u/zA1OZG53vlMTU5YGA4AYAczSHmVZmemc/T4qSQLlYNz5y+k15/PkdsOjbhlw2f9BwCA3WOoFYSqmq2qj1TVfVX1msvsd1tVPV5V3zbM9mzEuFxJPznXy+veeW9+4K3vz+veee+OH0QNAMDmGloFoar2JHl9kpckeSDJXVX19tbahy6y348kecew2rJZtvuV9MWZlqYmJ5bNtLQdgwwAANvTMCsIL0xyX2vto621zyZ5S5KXXWS/v5XkPyf5+BDbsivs1pmWAADYPMMMCAeT3L/k8QODbZ2qOpjkW5K84XJvVFV3VNXdVXX3Qw89tOkN3Sl260xLAABsnmEGhLrItrbi8Y8m+futtccv90attaOttcOttcM33njjZrVvx9mtMy0BALB5hhkQHkhy05LHh5I8uGKfw0neUlV/kuTbkvx4Vf2VIbZpR7NmAQAAGzXMaU7vSvKcqrolyekkL0/y7Ut3aK3dsni/qn46yX9trb1tiG0aayfnejl24kxOn+3n4P7JzM5MLxt8vDjT0tJ9jtx2yABlAABWbWgBobV2oapenYXZifYkeWNr7Z6qetXg+cuOO2C51c5QtN1nWgIAYHsb6kJprbU7k9y5YttFg0Fr7TuH2ZZxt3SGoiTd12MnzggEAABsGispj4nTZ/s5MLVv2TYzFMH4uFIXQQDYLoa6kjKbxwxFML4Wuwj2+vPLugha6RyA7UgFYUzMzkzn6PFTSRYqB+fOX0ivP58jtx0accuAK1ltF0FVBgC2AxWEMbE4Q9HU5ETmeuczNTnxeQOUge1pNYsYqjIAsF0ICABDtpougkurDFdVdfePnTiz1c0FYJcTEMaEq4uwPZ2c6+V177w3P/DW9+d177z3on+Tq1nEcDVVBgDYCgLCmHB1Ebaf1Qb31XQRNBEBANuFQcpjwjSnsP2sZX2SKy1iaCICALYLFYQx4eoibD+b2S3IRAQAbBcqCFtko9MXuroI28/B/ZPp9ee7ykGyseB+pSoDAGwFFYQtsBkDjF1dhO1nNYOPAWDcqCBsgbX0U74cVxdhe1kM7kurg0duO+TvFICxJiAMydIuRfc82MsLDk0t64ZggDGMt5XdBr/7q58lGACwI+hiNAQruxRdveeq3HXqU3no3Plun4998tP52MOfuezc6cD2ZF0SAHYyAWEIVq5ZMHPwyWlJ7nnwkTzRWv7kE4/mDz52Nl9w/TVOLmAMWZcEgJ1MQBiClVMf3nDdvrzoC5+S+cdb5nrnM/fIY/nym/bnlhuvc3IBY8iqxwDsZALCEFxszYLPPPZ4rhucUJw7P58nXbNn2fNOLmB8WJcEgJ1MQNiAk3O9vO6d937eOIKVUx+u7FJ0sTEJTi5gfJjeFICdTEBYp8sNUly5ZsHKLkUrxyQ4uYDxYl0SAHYy05yu05XWNli6ZsEPvPX9OTC1r3vt4piE99//SOZ6582dDmPIuiQA7FQCwjqdPttfdtKfXHocwcH9k+n155etg3DN3r15yfOm83de8tyhtxUAAFZLF6N1WssgRf2VAQAYFwLCOq3lpF9/ZQAAxoUuRuu0eNJ/7MSZnD7bv+I4Av2VAQAYBwLCBjjpBwBgpxEQ1uHkXG9Z5WB2ZlpQAABgRzAGYY0ut/4BAACMOxWENbrS+gfA2qnKAcD2oYKwRqfP9nP9vuW56lLrHwBXpioHANuLgLBGa1n/ALiypVW5q6q6+8dOnBl10wBgV9LFaI1mZ6Zz9PipJAuVg3PnL6TXn8+R2w6NuGUwni62Kvn5+Qt5z6lP6nIEACOggrBGFj2DtTs518vr3nlvfuCt78/r3nnvsu5DK6tyD507n7tOfSpX77lKlyMAGAEVhHWw/gGs3uIYg6nJiWUn/IvBemVV7p4HH0lLMnPwyV2Xo8REAACwVVQQgKG60hiDlVW5+cdbXvSFT8kN132u25GJAABg66ggAEN1sTEGK0/4l1blXvfOe9Przy/b30QAALB1VBCAoVrrzF+zM9Pp9efT68/nida6+7Mz01vRXADY9QQEYKhmZ6Zz/8Ofybs+8vG84565vOsjH8/9D3/mkif8JgIAgNHSxQgYuidaS5JUatnjSzERAACMjoAArMrJuV6OnTiz5rUJjp04k2c+7do8/9D+bluvP29WIgDYpnQxAq5ocarSXn9+zWsTnD7bz/X7ll+LMCsRAGxfAgJwRVeaqvRy1jpIGQAYLQEBuKKNVAHMSgQA40VAAK5oI1UAsxIBwHgxSBm4otmZ6bz2HffmDx99LI9deDzX7N2TG667Jke+/rmrer1ZiQBgfKggAKuy1qlKAYDxpIIAXJGpSgFg91BBAK7IVKUAsHsICMAVmaoUAHYPAQG4IlOVAsDuISAAV2SqUgDYPQxSBlbFVKUAsDuoIAAAAB0BAQAA6AgIAABAR0AAAAA6AgIAANAREAAAgI6AAAAAdAQEAACgIyAAAAAdAQEAAOgICAAAQEdAAAAAOgICAADQERAAAICOgAAAAHQEBAAAoCMgAAAAHQEBAADoCAgAAEBHQAAAADp7R90A2A5OzvVy7MSZnD7bz8H9k5mdmc6tB6ZG3SwAgC2ngsCud3Kul6PHT6XXn8+BqX3p9edz9PipnJzrjbppAABbTkBg1zt24kymJicyNTmRq6q6+8dOnBl10wAAtpyAwK53+mw/1+9b3tvu+n17c/psf0QtAgAYnaEGhKqaraqPVNV9VfWaizz/sqr6QFW9r6rurqqvHmZ74GIO7p/MufMXlm07d/5CDu6fHFGLAABGZ2iDlKtqT5LXJ3lJkgeS3FVVb2+tfWjJbr+Z5O2ttVZVz0/yC0m+ZFhtYvdZzeDj2ZnpvPYd9+YPH30sj114PNfs3ZMbrrsmR77+uSNqNQDA6AyzgvDCJPe11j7aWvtskrckednSHVprj7bW2uDhtUlaYJOsZfDxE4Nfw0otewwAsNsMc5rTg0nuX/L4gSQvWrlTVX1Lkn+R5OlJvvFib1RVdyS5I0luvvnmTW8oO9PSwcdJuq/HTpxZVkU4duJMnvm0a/P8Q/u7bb3+/OftBwCwGwyzglAX2fZ5l2Vba/+ltfYlSf5Kkh++2Bu11o621g631g7feOONm9tKdqzVDj42SBkA4HOGGRAeSHLTkseHkjx4qZ1ba8eTPLuqbhhim9hFVjv42CBlAIDPGWZAuCvJc6rqlqq6OsnLk7x96Q5V9UVVVYP7X5Hk6iSfHGKb2EVmZ6Zz/8Ofybs+8vG84565vOsjH8/9D38mszPTn7dfrz+fXn8+T7TW3V+5HwDAbjC0MQittQtV9eok70iyJ8kbW2v3VNWrBs+/Icn/lOSVVTWfpJ/kyJJBy7Bhqxl8fOuBqdxx+y3LZjs6ctsh4w8AgF2pxu18/PDhw+3uu+8edTMYA697573p9ee7wclJusd/5yWmMAUAdreqem9r7fDK7cOcxQg2ZDVrGFzO6bP9HJjat2ybwccAAJc31JWUYb3WsobBpRh8DACwdgIC29LSNQyuquruHztxZtXvYfAxAMDaCQhsS5uxNsHi4OOpyYnM9c5nanIid9x+i8HHAACXYQwC29LB/ZOfN8B4Pd2Dbj0wJRAAAKyBgMC2NDsznde+49784aOP5bELj+eavXtyw3XX5MjXm30IAGCYdDFi21rNGgYAAGwuFQS2pWMnzuSZT7s2zz+0v9vW68/n2IkzugwBAAyRCgLb0mYMUgYAYO0EBLYlaxgAAIyGgMC2ZA0DAIDREBDYlqxhAAAwGgYps21ZwwAAYOupIAAAAB0BAQAA6AgIAABAR0AAAAA6AgIAANAREAAAgI6AAAAAdAQEAACgIyAAAAAdKynvcifnejl24kxOn+3n4P7JzM5MW70YAGAXU0HYxU7O9XL0+Kn0+vM5MLUvvf58jh4/lZNzvVE3DQCAEREQdrFjJ85kanIiU5MTuaqqu3/sxJlRNw0AgBEREHax02f7uX7f8l5m1+/bm9Nn+yNqEQAAoyYg7GIH90/m3PkLy7adO38hB/dPjqhFAACMmoCwi83OTKfXn0+vP58nWuvuz85Mj7ppAACMiICwi916YCp33H5LpiYnMtc7n6nJidxx+y1mMQIA2MVMc7rL3XpgSiAAAKCjggAAAHQEBAAAoKOL0Rix6jEAAMOmgjAmrHoMAMBWEBDGhFWPAQDYCgLCmLDqMQAAW8EYhC2y0fEDB/dPptefz9TkRLfNqscAAGw2FYQtsBnjB6x6DADAVhAQtsBmjB+w6jEAAFtBF6MtcPpsPwem9i3btp7xA1Y9BgBg2FQQtsDB/ZM5d/7Csm3GDwAAsB2pIGzAagcez85M5+jxU0kWKgfnzl9Irz+fI7cd2uomAwDAZQkI67Q48HhqcmLZwOPFcQErw8PX3Xpj7j3z6e7xkdsO6S4EAMC2IyCs09KBx0m6r4sDj1eGh984+ZBBxQAAbHvGIKzT5RYus+oxAADjSkBYp8sNPLbqMQAA40pAWIeTc7184tz5/NaHP553feTjOfNIf9nCZWYtAgBgXAkIa7Q4OHli75581Rc9NUny7j9+OJ+98Hg3xsCqxwAAjCuDlNdo+eDkiTz9iyfT689nanKiG4C8uOrx0lmMzFoEAMA4EBDWaLWrIlv1GACAcaSL0RoZXwAAwE4mIKyR8QUAAOxkAsIaLY4vmJqcyFzvfKYmJyyABgDAjmEMwjoYXwAAwE6lggAAAHQEBAAAoCMgAAAAHQEBAADoCAgAAEBHQAAAADoCAgAA0BEQAACAjoAAAAB0BAQAAKAjIAAAAB0BAQAA6AgIAABAR0AAAAA6AgIAANAREAAAgI6AAAAAdAQEAACgIyAAAAAdAQEAAOgICAAAQGeoAaGqZqvqI1V1X1W95iLP/7Wq+sDg9u6qesEw2wMAAFze0AJCVe1J8vokL03yvCSvqKrnrdjtVJK/0Fp7fpIfTnJ0WO0BAACubJgVhBcmua+19tHW2meTvCXJy5bu0Fp7d2vtU4OHv5fk0BDbAwAAXMEwA8LBJPcvefzAYNulfHeSXxtiewAAgCvYO8T3rotsaxfdseprshAQvvoSz9+R5I4kufnmmzerfQAAwArDrCA8kOSmJY8PJXlw5U5V9fwkP5nkZa21T17sjVprR1trh1trh2+88cahNBYAAEiqtYte1N/4G1ftTXJvkq9NcjrJXUm+vbV2z5J9bk7yW0le2Vp79yrf96Ekf7r5Lb6sG5J8You/507nmG4+x3TzOaabzzHdfI7p5nNMh8Nx3XwbPabPbK193tX3oXUxaq1dqKpXJ3lHkj1J3thau6eqXjV4/g1J/lGSpyX58apKkguttcNXeN8tLyFU1d1Xahdr45huPsd08zmmm88x3XyO6eZzTIfDcd18wzqmwxyDkNbanUnuXLHtDUvuf0+S7xlmGwAAgNWzkjIAANAREFbHAm6bzzHdfI7p5nNMN59juvkc083nmA6H47r5hnJMhzZIGQAAGD8qCAAAQGdXB4Sqmq2qj1TVfVX1mos8/yVV9btV9VhV/cBaXrtbrfeYVtVNVfXfqupkVd1TVX97a1u+vW3kd3Xw/J6q+sOq+q9b0+Ltb4N///ur6her6sOD39n/cetavn1t8Jj+ncHf/omqenNV7du6lm9fqzimf62qPjC4vbuqXrDa1+5W6z2mPqcubSO/p4PnfUatsMG//Y1/RrXWduUtC1Ov/nGSL0xydZL3J3nein2enuS2JP88yQ+s5bW78bbBY3ogyVcM7l+fhTU0dv0x3ehxXfL8/57kPyX5r6P+ebbDbaPHNMnPJPmewf2rk+wf9c806tsG//4PJjmVZHLw+BeSfOeof6ZR31Z5TP98kqcM7r80yXtW+9rdeNvgMfU5tcnHdMnzPqM28ZhuxmfUbq4gvDDJfa21j7bWPpvkLUletnSH1trHW2t3JZlf62t3qXUf09baXGvtDwb3zyU5mYWTBjb2u5qqOpTkG7OwYjkL1n1Mq+rJSW5P8h8H+322tXZ2S1q9vW3o9zQL025P1sIim09K8uCwGzwGVnNM391a+9Tg4e8lObTa1+5S6z6mPqcuaSO/pz6jLm7dx3SzPqN2c0A4mOT+JY8fyOr/0Dfy2p1sU45LVT0ryZcnec/mNGvsbfS4/miSv5fkiU1s07jbyDH9wiQPJfmpQUn8J6vq2s1u4Bha9zFtrZ1O8tokH0syl6TXWvv1TW/h+FnrMf3uJL+2ztfuFhs5ph2fU8ts9Jj+aHxGrbSRY7opn1G7OSDURbatdkqnjbx2J9vwcamq65L85yTf31p7ZFNaNf7WfVyr6puSfLy19t7NbdLY28jv6t4kX5Hk/2mtfXmSTyfRv3tjv6dPycLVsVuSPCPJtVX1HZvYtnG16mNaVV+ThZOEv7/W1+4yGzmmi9t9Ti237mPqM+qSNvJ7uimfUbs5IDyQ5KYljw9l9SXtjbx2J9vQcamqiSz8p/vzrbVf2uS2jbONHNevSvLNVfUnWShR/sWq+rnNbd5Y2ujf/wOttcUrh7+Yhf+Md7uNHNOvS3KqtfZQa20+yS9loX/tbreqY1pVz89C94yXtdY+uZbX7kIbOaY+py5uI8fUZ9TFbfRvf8OfUbs5INyV5DlVdUtVXZ3k5UnevgWv3cnWfVyqqrLQX+5ka+3fDLGN42jdx7W19oOttUOttWcNXvdbrTVXZjd2TP8syf1V9cWDTV+b5EPDaeZY2cj/ix9L8pVV9aTB/wVfm4X+3bvdFY9pVd2chUD111tr967ltbvUuo+pz6lLWvcx9Rl1SRs5ppvzGbXZI6/H6ZbkG7IwC8EfJ/kHg22vSvKqwf0vyEISeyTJ2cH9J1/qtW7rP6ZJvjoL5bMPJHnf4PYNo/55tsttI7+rS97jxTFDxKYc0yRfluTuwe/r2zKYSWK33zZ4TP9pkg8nOZHkTUmuGfXPsx1uqzimP5nkU0v+37z7cq91W/8x9Tm1+cd0xXv4jNqkY7oZn1FWUgYAADq7uYsRAACwgoAAAAB0BAQAAKAjIAAAAB0BAQAA6AgIAGwrVfX9VfWkUbcDYLcyzSkA20ZV7cnCvN+HW2ufGHV7AHYjFQQAkiRVdW1V/WpVvb+qTlTVkar6k6q6YfD84ap61+D+P6mqN1XVb1XVH1XV9w62v7iqjlfVf6mqD1XVG6rqqsFzr6iqDw7e+0eWfN9Hq+qfVdV7kvyDJM9I8t+q6r9t9TEAINk76gYAsG3MJnmwtfaNSVJVU0l+5DL7Pz/JVya5NskfVtWvDra/MMnzkvxpkmNJvrWq3j14rz+XhdU/f72q/kpr7W2D159orf2jwff9G0m+RgUBYDRUEABY9MEkX1dVP1JV/7/WWu8K+/9ya60/OJH/b1kIBkny+621j7bWHk/y5iRfneS2JO9qrT3UWruQ5OeT3D7Y//Ek/3nTfxoA1kUFAYAkSWvt3qr6c0m+Icm/qKpfT3Ihn7uYtG/lSy7x+GLb6zLf+vwgTACwDaggAJAkqapnJPlMa+3nkrw2yVck+ZMsdAtKkv9pxUteVlX7quppSV6c5K7B9hdW1S2DsQdHkvxOkvck+QtVdcNgIPIrkvz2JZpyLsn1m/NTAbBWKggALPofkvzrqnoiyXyS/zXJZJL/WFU/lIWT/KV+P8mvJrk5yQ+31h6squcm+d0k/3LwfseT/JfW2hNV9YNZ6IpUSe5srf3yJdpxNMmvVdVca+1rNvdHBOBKTHMKwJpV1T9J8mhr7bUrtr84yQ+01r5pBM0CYBPoYgQAAHRUEAAAgI4KAgAA0BEQAACAjoAAAAB0BAQAAKAjIAAAAB0BAQAA6Pz/AW6GUbs0bmOyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules1['support'], rules1['confidence'], alpha=0.5)\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence')\n",
    "plt.title('Support vs Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58f00bb",
   "metadata": {},
   "source": [
    "## Rules when min_support = 0.1 and min_threshold for confidence is 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d9c31813",
   "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",
       "    }\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>(YouthBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.576044</td>\n",
       "      <td>0.060308</td>\n",
       "      <td>1.731000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.652482</td>\n",
       "      <td>1.542511</td>\n",
       "      <td>0.064714</td>\n",
       "      <td>1.660347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>0.706294</td>\n",
       "      <td>1.669725</td>\n",
       "      <td>0.060767</td>\n",
       "      <td>1.964548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.674274</td>\n",
       "      <td>1.594028</td>\n",
       "      <td>0.060557</td>\n",
       "      <td>1.771427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.706522</td>\n",
       "      <td>1.670264</td>\n",
       "      <td>0.078252</td>\n",
       "      <td>1.966074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>1.518667</td>\n",
       "      <td>0.055328</td>\n",
       "      <td>1.647105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.664894</td>\n",
       "      <td>1.542677</td>\n",
       "      <td>0.065958</td>\n",
       "      <td>1.697968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.710956</td>\n",
       "      <td>1.649549</td>\n",
       "      <td>0.060050</td>\n",
       "      <td>1.968556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.692946</td>\n",
       "      <td>1.607763</td>\n",
       "      <td>0.063129</td>\n",
       "      <td>1.853095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.697464</td>\n",
       "      <td>1.618245</td>\n",
       "      <td>0.073544</td>\n",
       "      <td>1.880766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(ItalCook)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.320186</td>\n",
       "      <td>0.064582</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.512448</td>\n",
       "      <td>1.817192</td>\n",
       "      <td>0.055538</td>\n",
       "      <td>1.472664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1105</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>1.866491</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>1.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.529046</td>\n",
       "      <td>1.916832</td>\n",
       "      <td>0.060984</td>\n",
       "      <td>1.537304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(YouthBks, CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>1.882497</td>\n",
       "      <td>0.060474</td>\n",
       "      <td>2.832545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(YouthBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>1.813963</td>\n",
       "      <td>0.057885</td>\n",
       "      <td>2.607917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.503906</td>\n",
       "      <td>2.035985</td>\n",
       "      <td>0.065640</td>\n",
       "      <td>1.516850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.521212</td>\n",
       "      <td>2.035985</td>\n",
       "      <td>0.065640</td>\n",
       "      <td>1.553924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.778667</td>\n",
       "      <td>1.840820</td>\n",
       "      <td>0.066687</td>\n",
       "      <td>2.606928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.570312</td>\n",
       "      <td>2.022385</td>\n",
       "      <td>0.073808</td>\n",
       "      <td>1.670982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.793478</td>\n",
       "      <td>1.841017</td>\n",
       "      <td>0.066696</td>\n",
       "      <td>2.755158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.517730</td>\n",
       "      <td>2.022385</td>\n",
       "      <td>0.073808</td>\n",
       "      <td>1.542706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(CookBks, RefBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.803279</td>\n",
       "      <td>1.899004</td>\n",
       "      <td>0.057993</td>\n",
       "      <td>2.933083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(RefBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.808581</td>\n",
       "      <td>1.876058</td>\n",
       "      <td>0.057204</td>\n",
       "      <td>2.972534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.571096</td>\n",
       "      <td>2.230842</td>\n",
       "      <td>0.067588</td>\n",
       "      <td>1.734652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.757485</td>\n",
       "      <td>1.790745</td>\n",
       "      <td>0.055859</td>\n",
       "      <td>2.379235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.778462</td>\n",
       "      <td>1.806175</td>\n",
       "      <td>0.056462</td>\n",
       "      <td>2.568403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.524896</td>\n",
       "      <td>2.050376</td>\n",
       "      <td>0.064804</td>\n",
       "      <td>1.565974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.776623</td>\n",
       "      <td>1.835989</td>\n",
       "      <td>0.068072</td>\n",
       "      <td>2.583081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.583984</td>\n",
       "      <td>2.115885</td>\n",
       "      <td>0.078844</td>\n",
       "      <td>1.740319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>1.778809</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>2.438571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>2.115885</td>\n",
       "      <td>0.078844</td>\n",
       "      <td>1.623273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.788679</td>\n",
       "      <td>1.864490</td>\n",
       "      <td>0.048452</td>\n",
       "      <td>2.730446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.535897</td>\n",
       "      <td>1.900346</td>\n",
       "      <td>0.049510</td>\n",
       "      <td>1.547072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.567935</td>\n",
       "      <td>2.057735</td>\n",
       "      <td>0.053716</td>\n",
       "      <td>1.675673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>2.170444</td>\n",
       "      <td>0.055005</td>\n",
       "      <td>1.591452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.891253</td>\n",
       "      <td>0.048067</td>\n",
       "      <td>2.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.627692</td>\n",
       "      <td>2.274247</td>\n",
       "      <td>0.057150</td>\n",
       "      <td>1.944628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.541333</td>\n",
       "      <td>2.246196</td>\n",
       "      <td>0.056313</td>\n",
       "      <td>1.654797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.607784</td>\n",
       "      <td>2.155264</td>\n",
       "      <td>0.054406</td>\n",
       "      <td>1.830626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(DoItYBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.821862</td>\n",
       "      <td>1.906873</td>\n",
       "      <td>0.048272</td>\n",
       "      <td>3.194159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.578667</td>\n",
       "      <td>2.096618</td>\n",
       "      <td>0.056750</td>\n",
       "      <td>1.718354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>1.998711</td>\n",
       "      <td>0.054215</td>\n",
       "      <td>1.645417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.818868</td>\n",
       "      <td>1.899926</td>\n",
       "      <td>0.051392</td>\n",
       "      <td>3.141354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.537662</td>\n",
       "      <td>2.230964</td>\n",
       "      <td>0.057107</td>\n",
       "      <td>1.641657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.619760</td>\n",
       "      <td>2.245509</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>1.904063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.811765</td>\n",
       "      <td>1.883445</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>3.022812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             antecedents          consequents  antecedent support  \\\n",
       "0             (YouthBks)           (ChildBks)              0.2475   \n",
       "1              (CookBks)           (ChildBks)              0.4310   \n",
       "2             (ChildBks)            (CookBks)              0.4230   \n",
       "3             (DoItYBks)           (ChildBks)              0.2820   \n",
       "4               (RefBks)           (ChildBks)              0.2145   \n",
       "5               (ArtBks)           (ChildBks)              0.2410   \n",
       "6              (GeogBks)           (ChildBks)              0.2760   \n",
       "7             (YouthBks)            (CookBks)              0.2475   \n",
       "8             (DoItYBks)            (CookBks)              0.2820   \n",
       "9               (RefBks)            (CookBks)              0.2145   \n",
       "10              (ArtBks)            (CookBks)              0.2410   \n",
       "11             (GeogBks)            (CookBks)              0.2760   \n",
       "12            (ItalCook)            (CookBks)              0.1135   \n",
       "13              (ArtBks)           (DoItYBks)              0.2410   \n",
       "14              (RefBks)            (GeogBks)              0.2145   \n",
       "15              (ArtBks)            (GeogBks)              0.2410   \n",
       "16   (YouthBks, CookBks)           (ChildBks)              0.1620   \n",
       "17  (YouthBks, ChildBks)            (CookBks)              0.1650   \n",
       "18   (CookBks, ChildBks)           (YouthBks)              0.2560   \n",
       "19            (YouthBks)  (CookBks, ChildBks)              0.2475   \n",
       "20   (CookBks, DoItYBks)           (ChildBks)              0.1875   \n",
       "21   (CookBks, ChildBks)           (DoItYBks)              0.2560   \n",
       "22  (DoItYBks, ChildBks)            (CookBks)              0.1840   \n",
       "23            (DoItYBks)  (CookBks, ChildBks)              0.2820   \n",
       "24     (CookBks, RefBks)           (ChildBks)              0.1525   \n",
       "25    (RefBks, ChildBks)            (CookBks)              0.1515   \n",
       "26              (RefBks)  (CookBks, ChildBks)              0.2145   \n",
       "27     (CookBks, ArtBks)           (ChildBks)              0.1670   \n",
       "28    (ChildBks, ArtBks)            (CookBks)              0.1625   \n",
       "29              (ArtBks)  (CookBks, ChildBks)              0.2410   \n",
       "30    (CookBks, GeogBks)           (ChildBks)              0.1925   \n",
       "31   (CookBks, ChildBks)            (GeogBks)              0.2560   \n",
       "32   (GeogBks, ChildBks)            (CookBks)              0.1950   \n",
       "33             (GeogBks)  (CookBks, ChildBks)              0.2760   \n",
       "34   (GeogBks, DoItYBks)           (ChildBks)              0.1325   \n",
       "35   (GeogBks, ChildBks)           (DoItYBks)              0.1950   \n",
       "36  (DoItYBks, ChildBks)            (GeogBks)              0.1840   \n",
       "37   (GeogBks, ChildBks)             (ArtBks)              0.1950   \n",
       "38     (GeogBks, ArtBks)           (ChildBks)              0.1275   \n",
       "39    (ChildBks, ArtBks)            (GeogBks)              0.1625   \n",
       "40   (CookBks, DoItYBks)             (ArtBks)              0.1875   \n",
       "41     (CookBks, ArtBks)           (DoItYBks)              0.1670   \n",
       "42    (DoItYBks, ArtBks)            (CookBks)              0.1235   \n",
       "43   (CookBks, DoItYBks)            (GeogBks)              0.1875   \n",
       "44    (CookBks, GeogBks)           (DoItYBks)              0.1925   \n",
       "45   (GeogBks, DoItYBks)            (CookBks)              0.1325   \n",
       "46    (CookBks, GeogBks)             (ArtBks)              0.1925   \n",
       "47     (CookBks, ArtBks)            (GeogBks)              0.1670   \n",
       "48     (GeogBks, ArtBks)            (CookBks)              0.1275   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0               0.4230   0.1650    0.666667  1.576044  0.060308    1.731000  \n",
       "1               0.4230   0.2560    0.593968  1.404179  0.073687    1.421069  \n",
       "2               0.4310   0.2560    0.605201  1.404179  0.073687    1.441240  \n",
       "3               0.4230   0.1840    0.652482  1.542511  0.064714    1.660347  \n",
       "4               0.4230   0.1515    0.706294  1.669725  0.060767    1.964548  \n",
       "5               0.4230   0.1625    0.674274  1.594028  0.060557    1.771427  \n",
       "6               0.4230   0.1950    0.706522  1.670264  0.078252    1.966074  \n",
       "7               0.4310   0.1620    0.654545  1.518667  0.055328    1.647105  \n",
       "8               0.4310   0.1875    0.664894  1.542677  0.065958    1.697968  \n",
       "9               0.4310   0.1525    0.710956  1.649549  0.060050    1.968556  \n",
       "10              0.4310   0.1670    0.692946  1.607763  0.063129    1.853095  \n",
       "11              0.4310   0.1925    0.697464  1.618245  0.073544    1.880766  \n",
       "12              0.4310   0.1135    1.000000  2.320186  0.064582         inf  \n",
       "13              0.2820   0.1235    0.512448  1.817192  0.055538    1.472664  \n",
       "14              0.2760   0.1105    0.515152  1.866491  0.051298    1.493250  \n",
       "15              0.2760   0.1275    0.529046  1.916832  0.060984    1.537304  \n",
       "16              0.4230   0.1290    0.796296  1.882497  0.060474    2.832545  \n",
       "17              0.4310   0.1290    0.781818  1.813963  0.057885    2.607917  \n",
       "18              0.2475   0.1290    0.503906  2.035985  0.065640    1.516850  \n",
       "19              0.2560   0.1290    0.521212  2.035985  0.065640    1.553924  \n",
       "20              0.4230   0.1460    0.778667  1.840820  0.066687    2.606928  \n",
       "21              0.2820   0.1460    0.570312  2.022385  0.073808    1.670982  \n",
       "22              0.4310   0.1460    0.793478  1.841017  0.066696    2.755158  \n",
       "23              0.2560   0.1460    0.517730  2.022385  0.073808    1.542706  \n",
       "24              0.4230   0.1225    0.803279  1.899004  0.057993    2.933083  \n",
       "25              0.4310   0.1225    0.808581  1.876058  0.057204    2.972534  \n",
       "26              0.2560   0.1225    0.571096  2.230842  0.067588    1.734652  \n",
       "27              0.4230   0.1265    0.757485  1.790745  0.055859    2.379235  \n",
       "28              0.4310   0.1265    0.778462  1.806175  0.056462    2.568403  \n",
       "29              0.2560   0.1265    0.524896  2.050376  0.064804    1.565974  \n",
       "30              0.4230   0.1495    0.776623  1.835989  0.068072    2.583081  \n",
       "31              0.2760   0.1495    0.583984  2.115885  0.078844    1.740319  \n",
       "32              0.4310   0.1495    0.766667  1.778809  0.065455    2.438571  \n",
       "33              0.2560   0.1495    0.541667  2.115885  0.078844    1.623273  \n",
       "34              0.4230   0.1045    0.788679  1.864490  0.048452    2.730446  \n",
       "35              0.2820   0.1045    0.535897  1.900346  0.049510    1.547072  \n",
       "36              0.2760   0.1045    0.567935  2.057735  0.053716    1.675673  \n",
       "37              0.2410   0.1020    0.523077  2.170444  0.055005    1.591452  \n",
       "38              0.4230   0.1020    0.800000  1.891253  0.048067    2.885000  \n",
       "39              0.2760   0.1020    0.627692  2.274247  0.057150    1.944628  \n",
       "40              0.2410   0.1015    0.541333  2.246196  0.056313    1.654797  \n",
       "41              0.2820   0.1015    0.607784  2.155264  0.054406    1.830626  \n",
       "42              0.4310   0.1015    0.821862  1.906873  0.048272    3.194159  \n",
       "43              0.2760   0.1085    0.578667  2.096618  0.056750    1.718354  \n",
       "44              0.2820   0.1085    0.563636  1.998711  0.054215    1.645417  \n",
       "45              0.4310   0.1085    0.818868  1.899926  0.051392    3.141354  \n",
       "46              0.2410   0.1035    0.537662  2.230964  0.057107    1.641657  \n",
       "47              0.2760   0.1035    0.619760  2.245509  0.057408    1.904063  \n",
       "48              0.4310   0.1035    0.811765  1.883445  0.048547    3.022812  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules2 = association_rules(frequent_itemsets1, metric=\"confidence\", min_threshold=0.5)\n",
    "rules2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "846da833",
   "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",
       "    }\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>12</th>\n",
       "      <td>(ItalCook)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.320186</td>\n",
       "      <td>0.064582</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(DoItYBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.821862</td>\n",
       "      <td>1.906873</td>\n",
       "      <td>0.048272</td>\n",
       "      <td>3.194159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.818868</td>\n",
       "      <td>1.899926</td>\n",
       "      <td>0.051392</td>\n",
       "      <td>3.141354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.811765</td>\n",
       "      <td>1.883445</td>\n",
       "      <td>0.048547</td>\n",
       "      <td>3.022812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(RefBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.808581</td>\n",
       "      <td>1.876058</td>\n",
       "      <td>0.057204</td>\n",
       "      <td>2.972534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(CookBks, RefBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.803279</td>\n",
       "      <td>1.899004</td>\n",
       "      <td>0.057993</td>\n",
       "      <td>2.933083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(GeogBks, ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.891253</td>\n",
       "      <td>0.048067</td>\n",
       "      <td>2.885000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(YouthBks, CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>1.882497</td>\n",
       "      <td>0.060474</td>\n",
       "      <td>2.832545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.793478</td>\n",
       "      <td>1.841017</td>\n",
       "      <td>0.066696</td>\n",
       "      <td>2.755158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(GeogBks, DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.788679</td>\n",
       "      <td>1.864490</td>\n",
       "      <td>0.048452</td>\n",
       "      <td>2.730446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(YouthBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>1.813963</td>\n",
       "      <td>0.057885</td>\n",
       "      <td>2.607917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.778667</td>\n",
       "      <td>1.840820</td>\n",
       "      <td>0.066687</td>\n",
       "      <td>2.606928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.778462</td>\n",
       "      <td>1.806175</td>\n",
       "      <td>0.056462</td>\n",
       "      <td>2.568403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.776623</td>\n",
       "      <td>1.835989</td>\n",
       "      <td>0.068072</td>\n",
       "      <td>2.583081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>1.778809</td>\n",
       "      <td>0.065455</td>\n",
       "      <td>2.438571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.757485</td>\n",
       "      <td>1.790745</td>\n",
       "      <td>0.055859</td>\n",
       "      <td>2.379235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>0.710956</td>\n",
       "      <td>1.649549</td>\n",
       "      <td>0.060050</td>\n",
       "      <td>1.968556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.706522</td>\n",
       "      <td>1.670264</td>\n",
       "      <td>0.078252</td>\n",
       "      <td>1.966074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>0.706294</td>\n",
       "      <td>1.669725</td>\n",
       "      <td>0.060767</td>\n",
       "      <td>1.964548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.697464</td>\n",
       "      <td>1.618245</td>\n",
       "      <td>0.073544</td>\n",
       "      <td>1.880766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.692946</td>\n",
       "      <td>1.607763</td>\n",
       "      <td>0.063129</td>\n",
       "      <td>1.853095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.674274</td>\n",
       "      <td>1.594028</td>\n",
       "      <td>0.060557</td>\n",
       "      <td>1.771427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.576044</td>\n",
       "      <td>0.060308</td>\n",
       "      <td>1.731000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.664894</td>\n",
       "      <td>1.542677</td>\n",
       "      <td>0.065958</td>\n",
       "      <td>1.697968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>1.518667</td>\n",
       "      <td>0.055328</td>\n",
       "      <td>1.647105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.652482</td>\n",
       "      <td>1.542511</td>\n",
       "      <td>0.064714</td>\n",
       "      <td>1.660347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(ChildBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.627692</td>\n",
       "      <td>2.274247</td>\n",
       "      <td>0.057150</td>\n",
       "      <td>1.944628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.619760</td>\n",
       "      <td>2.245509</td>\n",
       "      <td>0.057408</td>\n",
       "      <td>1.904063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(CookBks, ArtBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.607784</td>\n",
       "      <td>2.155264</td>\n",
       "      <td>0.054406</td>\n",
       "      <td>1.830626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.583984</td>\n",
       "      <td>2.115885</td>\n",
       "      <td>0.078844</td>\n",
       "      <td>1.740319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.578667</td>\n",
       "      <td>2.096618</td>\n",
       "      <td>0.056750</td>\n",
       "      <td>1.718354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1225</td>\n",
       "      <td>0.571096</td>\n",
       "      <td>2.230842</td>\n",
       "      <td>0.067588</td>\n",
       "      <td>1.734652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.570312</td>\n",
       "      <td>2.022385</td>\n",
       "      <td>0.073808</td>\n",
       "      <td>1.670982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(DoItYBks, ChildBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.1840</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.567935</td>\n",
       "      <td>2.057735</td>\n",
       "      <td>0.053716</td>\n",
       "      <td>1.675673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1085</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>1.998711</td>\n",
       "      <td>0.054215</td>\n",
       "      <td>1.645417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>2.115885</td>\n",
       "      <td>0.078844</td>\n",
       "      <td>1.623273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(CookBks, DoItYBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1015</td>\n",
       "      <td>0.541333</td>\n",
       "      <td>2.246196</td>\n",
       "      <td>0.056313</td>\n",
       "      <td>1.654797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(CookBks, GeogBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>0.537662</td>\n",
       "      <td>2.230964</td>\n",
       "      <td>0.057107</td>\n",
       "      <td>1.641657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.535897</td>\n",
       "      <td>1.900346</td>\n",
       "      <td>0.049510</td>\n",
       "      <td>1.547072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1275</td>\n",
       "      <td>0.529046</td>\n",
       "      <td>1.916832</td>\n",
       "      <td>0.060984</td>\n",
       "      <td>1.537304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1265</td>\n",
       "      <td>0.524896</td>\n",
       "      <td>2.050376</td>\n",
       "      <td>0.064804</td>\n",
       "      <td>1.565974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(GeogBks, ChildBks)</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.523077</td>\n",
       "      <td>2.170444</td>\n",
       "      <td>0.055005</td>\n",
       "      <td>1.591452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.521212</td>\n",
       "      <td>2.035985</td>\n",
       "      <td>0.065640</td>\n",
       "      <td>1.553924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0.517730</td>\n",
       "      <td>2.022385</td>\n",
       "      <td>0.073808</td>\n",
       "      <td>1.542706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>0.1105</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>1.866491</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>1.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.2820</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>0.512448</td>\n",
       "      <td>1.817192</td>\n",
       "      <td>0.055538</td>\n",
       "      <td>1.472664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.1290</td>\n",
       "      <td>0.503906</td>\n",
       "      <td>2.035985</td>\n",
       "      <td>0.065640</td>\n",
       "      <td>1.516850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             antecedents          consequents  antecedent support  \\\n",
       "12            (ItalCook)            (CookBks)              0.1135   \n",
       "42    (DoItYBks, ArtBks)            (CookBks)              0.1235   \n",
       "45   (GeogBks, DoItYBks)            (CookBks)              0.1325   \n",
       "48     (GeogBks, ArtBks)            (CookBks)              0.1275   \n",
       "25    (RefBks, ChildBks)            (CookBks)              0.1515   \n",
       "24     (CookBks, RefBks)           (ChildBks)              0.1525   \n",
       "38     (GeogBks, ArtBks)           (ChildBks)              0.1275   \n",
       "16   (YouthBks, CookBks)           (ChildBks)              0.1620   \n",
       "22  (DoItYBks, ChildBks)            (CookBks)              0.1840   \n",
       "34   (GeogBks, DoItYBks)           (ChildBks)              0.1325   \n",
       "17  (YouthBks, ChildBks)            (CookBks)              0.1650   \n",
       "20   (CookBks, DoItYBks)           (ChildBks)              0.1875   \n",
       "28    (ChildBks, ArtBks)            (CookBks)              0.1625   \n",
       "30    (CookBks, GeogBks)           (ChildBks)              0.1925   \n",
       "32   (GeogBks, ChildBks)            (CookBks)              0.1950   \n",
       "27     (CookBks, ArtBks)           (ChildBks)              0.1670   \n",
       "9               (RefBks)            (CookBks)              0.2145   \n",
       "6              (GeogBks)           (ChildBks)              0.2760   \n",
       "4               (RefBks)           (ChildBks)              0.2145   \n",
       "11             (GeogBks)            (CookBks)              0.2760   \n",
       "10              (ArtBks)            (CookBks)              0.2410   \n",
       "5               (ArtBks)           (ChildBks)              0.2410   \n",
       "0             (YouthBks)           (ChildBks)              0.2475   \n",
       "8             (DoItYBks)            (CookBks)              0.2820   \n",
       "7             (YouthBks)            (CookBks)              0.2475   \n",
       "3             (DoItYBks)           (ChildBks)              0.2820   \n",
       "39    (ChildBks, ArtBks)            (GeogBks)              0.1625   \n",
       "47     (CookBks, ArtBks)            (GeogBks)              0.1670   \n",
       "41     (CookBks, ArtBks)           (DoItYBks)              0.1670   \n",
       "2             (ChildBks)            (CookBks)              0.4230   \n",
       "1              (CookBks)           (ChildBks)              0.4310   \n",
       "31   (CookBks, ChildBks)            (GeogBks)              0.2560   \n",
       "43   (CookBks, DoItYBks)            (GeogBks)              0.1875   \n",
       "26              (RefBks)  (CookBks, ChildBks)              0.2145   \n",
       "21   (CookBks, ChildBks)           (DoItYBks)              0.2560   \n",
       "36  (DoItYBks, ChildBks)            (GeogBks)              0.1840   \n",
       "44    (CookBks, GeogBks)           (DoItYBks)              0.1925   \n",
       "33             (GeogBks)  (CookBks, ChildBks)              0.2760   \n",
       "40   (CookBks, DoItYBks)             (ArtBks)              0.1875   \n",
       "46    (CookBks, GeogBks)             (ArtBks)              0.1925   \n",
       "35   (GeogBks, ChildBks)           (DoItYBks)              0.1950   \n",
       "15              (ArtBks)            (GeogBks)              0.2410   \n",
       "29              (ArtBks)  (CookBks, ChildBks)              0.2410   \n",
       "37   (GeogBks, ChildBks)             (ArtBks)              0.1950   \n",
       "19            (YouthBks)  (CookBks, ChildBks)              0.2475   \n",
       "23            (DoItYBks)  (CookBks, ChildBks)              0.2820   \n",
       "14              (RefBks)            (GeogBks)              0.2145   \n",
       "13              (ArtBks)           (DoItYBks)              0.2410   \n",
       "18   (CookBks, ChildBks)           (YouthBks)              0.2560   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "12              0.4310   0.1135    1.000000  2.320186  0.064582         inf  \n",
       "42              0.4310   0.1015    0.821862  1.906873  0.048272    3.194159  \n",
       "45              0.4310   0.1085    0.818868  1.899926  0.051392    3.141354  \n",
       "48              0.4310   0.1035    0.811765  1.883445  0.048547    3.022812  \n",
       "25              0.4310   0.1225    0.808581  1.876058  0.057204    2.972534  \n",
       "24              0.4230   0.1225    0.803279  1.899004  0.057993    2.933083  \n",
       "38              0.4230   0.1020    0.800000  1.891253  0.048067    2.885000  \n",
       "16              0.4230   0.1290    0.796296  1.882497  0.060474    2.832545  \n",
       "22              0.4310   0.1460    0.793478  1.841017  0.066696    2.755158  \n",
       "34              0.4230   0.1045    0.788679  1.864490  0.048452    2.730446  \n",
       "17              0.4310   0.1290    0.781818  1.813963  0.057885    2.607917  \n",
       "20              0.4230   0.1460    0.778667  1.840820  0.066687    2.606928  \n",
       "28              0.4310   0.1265    0.778462  1.806175  0.056462    2.568403  \n",
       "30              0.4230   0.1495    0.776623  1.835989  0.068072    2.583081  \n",
       "32              0.4310   0.1495    0.766667  1.778809  0.065455    2.438571  \n",
       "27              0.4230   0.1265    0.757485  1.790745  0.055859    2.379235  \n",
       "9               0.4310   0.1525    0.710956  1.649549  0.060050    1.968556  \n",
       "6               0.4230   0.1950    0.706522  1.670264  0.078252    1.966074  \n",
       "4               0.4230   0.1515    0.706294  1.669725  0.060767    1.964548  \n",
       "11              0.4310   0.1925    0.697464  1.618245  0.073544    1.880766  \n",
       "10              0.4310   0.1670    0.692946  1.607763  0.063129    1.853095  \n",
       "5               0.4230   0.1625    0.674274  1.594028  0.060557    1.771427  \n",
       "0               0.4230   0.1650    0.666667  1.576044  0.060308    1.731000  \n",
       "8               0.4310   0.1875    0.664894  1.542677  0.065958    1.697968  \n",
       "7               0.4310   0.1620    0.654545  1.518667  0.055328    1.647105  \n",
       "3               0.4230   0.1840    0.652482  1.542511  0.064714    1.660347  \n",
       "39              0.2760   0.1020    0.627692  2.274247  0.057150    1.944628  \n",
       "47              0.2760   0.1035    0.619760  2.245509  0.057408    1.904063  \n",
       "41              0.2820   0.1015    0.607784  2.155264  0.054406    1.830626  \n",
       "2               0.4310   0.2560    0.605201  1.404179  0.073687    1.441240  \n",
       "1               0.4230   0.2560    0.593968  1.404179  0.073687    1.421069  \n",
       "31              0.2760   0.1495    0.583984  2.115885  0.078844    1.740319  \n",
       "43              0.2760   0.1085    0.578667  2.096618  0.056750    1.718354  \n",
       "26              0.2560   0.1225    0.571096  2.230842  0.067588    1.734652  \n",
       "21              0.2820   0.1460    0.570312  2.022385  0.073808    1.670982  \n",
       "36              0.2760   0.1045    0.567935  2.057735  0.053716    1.675673  \n",
       "44              0.2820   0.1085    0.563636  1.998711  0.054215    1.645417  \n",
       "33              0.2560   0.1495    0.541667  2.115885  0.078844    1.623273  \n",
       "40              0.2410   0.1015    0.541333  2.246196  0.056313    1.654797  \n",
       "46              0.2410   0.1035    0.537662  2.230964  0.057107    1.641657  \n",
       "35              0.2820   0.1045    0.535897  1.900346  0.049510    1.547072  \n",
       "15              0.2760   0.1275    0.529046  1.916832  0.060984    1.537304  \n",
       "29              0.2560   0.1265    0.524896  2.050376  0.064804    1.565974  \n",
       "37              0.2410   0.1020    0.523077  2.170444  0.055005    1.591452  \n",
       "19              0.2560   0.1290    0.521212  2.035985  0.065640    1.553924  \n",
       "23              0.2560   0.1460    0.517730  2.022385  0.073808    1.542706  \n",
       "14              0.2760   0.1105    0.515152  1.866491  0.051298    1.493250  \n",
       "13              0.2820   0.1235    0.512448  1.817192  0.055538    1.472664  \n",
       "18              0.2475   0.1290    0.503906  2.035985  0.065640    1.516850  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules2 = rules2.sort_values(['confidence', 'lift'], ascending =[False, False])\n",
    "rules2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f43d62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules2['support'], rules2['confidence'], alpha=0.5)\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence')\n",
    "plt.title('Support vs Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27412259",
   "metadata": {},
   "source": [
    "## Apriori Algorithm for min_support = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94ec46e8",
   "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",
       "    }\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4230</td>\n",
       "      <td>(ChildBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2475</td>\n",
       "      <td>(YouthBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4310</td>\n",
       "      <td>(CookBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2820</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2145</td>\n",
       "      <td>(RefBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2410</td>\n",
       "      <td>(ArtBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2760</td>\n",
       "      <td>(GeogBks)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.2560</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support             itemsets\n",
       "0   0.4230           (ChildBks)\n",
       "1   0.2475           (YouthBks)\n",
       "2   0.4310            (CookBks)\n",
       "3   0.2820           (DoItYBks)\n",
       "4   0.2145             (RefBks)\n",
       "5   0.2410             (ArtBks)\n",
       "6   0.2760            (GeogBks)\n",
       "7   0.2560  (CookBks, ChildBks)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets2 = apriori(book, min_support=0.2, use_colnames=True)\n",
    "frequent_itemsets2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ddae0988",
   "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",
       "    }\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>support</th>\n",
       "      <th>itemsets</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4230</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2475</td>\n",
       "      <td>(YouthBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4310</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2820</td>\n",
       "      <td>(DoItYBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2145</td>\n",
       "      <td>(RefBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2410</td>\n",
       "      <td>(ArtBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2760</td>\n",
       "      <td>(GeogBks)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.2560</td>\n",
       "      <td>(CookBks, ChildBks)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support             itemsets  length\n",
       "0   0.4230           (ChildBks)       1\n",
       "1   0.2475           (YouthBks)       1\n",
       "2   0.4310            (CookBks)       1\n",
       "3   0.2820           (DoItYBks)       1\n",
       "4   0.2145             (RefBks)       1\n",
       "5   0.2410             (ArtBks)       1\n",
       "6   0.2760            (GeogBks)       1\n",
       "7   0.2560  (CookBks, ChildBks)       2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequent_itemsets2 = apriori(book, min_support = 0.2, use_colnames=True)\n",
    "frequent_itemsets2['length'] = frequent_itemsets2['itemsets'].apply(lambda x: len(x))\n",
    "frequent_itemsets2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9469dc",
   "metadata": {},
   "source": [
    "## Rules when min_support = 0.2 and min_threshold for lift is 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b0c166a",
   "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",
       "    }\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>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support  support  \\\n",
       "0   (CookBks)  (ChildBks)               0.431               0.423    0.256   \n",
       "1  (ChildBks)   (CookBks)               0.423               0.431    0.256   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "0    0.593968  1.404179  0.073687    1.421069  \n",
       "1    0.605201  1.404179  0.073687    1.441240  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules3 = association_rules(frequent_itemsets2, metric=\"lift\", min_threshold=0.1)\n",
    "rules3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca8fb739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules3['support'], rules3['confidence'], alpha=0.5)\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence')\n",
    "plt.title('Support vs Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40496dd",
   "metadata": {},
   "source": [
    "## Rules when min_support = 0.1 and min_threshold for confidence is 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d34b39f",
   "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",
       "    }\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>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support  support  \\\n",
       "0   (CookBks)  (ChildBks)               0.431               0.423    0.256   \n",
       "1  (ChildBks)   (CookBks)               0.423               0.431    0.256   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "0    0.593968  1.404179  0.073687    1.421069  \n",
       "1    0.605201  1.404179  0.073687    1.441240  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules4 = association_rules(frequent_itemsets2, metric=\"confidence\", min_threshold=0.5)\n",
    "rules4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7657bf9b",
   "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",
       "    }\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>1</th>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.605201</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.441240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(CookBks)</td>\n",
       "      <td>(ChildBks)</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>1.404179</td>\n",
       "      <td>0.073687</td>\n",
       "      <td>1.421069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  antecedents consequents  antecedent support  consequent support  support  \\\n",
       "1  (ChildBks)   (CookBks)               0.423               0.431    0.256   \n",
       "0   (CookBks)  (ChildBks)               0.431               0.423    0.256   \n",
       "\n",
       "   confidence      lift  leverage  conviction  \n",
       "1    0.605201  1.404179  0.073687    1.441240  \n",
       "0    0.593968  1.404179  0.073687    1.421069  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules4 = rules4.sort_values(['confidence', 'lift'], ascending =[False, False])\n",
    "rules4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "933b5e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAzjZCqOq2qPlpVV1XVWbtY5tSquqSqLquq83e3blX9QVV9pKo+VFVvqarDZrkPAADAyppZhFTVAUleluTxSR6c5KlV9eBFyxyW5OVJntTdD0nyY8tY9z1JHtrdD0vyr0leMKt9AAAAVt4sz4ScnOSq7r66u29P8vokpy9a5ieTvLm7P5kk3X3j7tbt7nd3945puQuSHDvDfQAAAFbYLCPkmCTXLHi8dZq20AOTHF5V762qi6vq6XuwbpL8XJJ3rtB4AQCAAdbNcNu1xLRe4vkfmeT7k6xP8i9VdcFy1q2q30iyI8lfLfnkVWcmOTNJjj/++D0aOAAAMDuzPBOyNclxCx4fm+S6JZbZ1N2f7+6bk7wvycN3t25VPSPJE5P8VHcvDpskSXef3d1z3T23cePGvd4ZAABgZcwyQi5KclJVnVhVByU5I8l5i5Z5W5Lvqap1VXVIkkclueKu1q2q05L8WubfzP6FGY4fAACYgZldjtXdO6rqeUneleSAJOd092VV9exp/iu6+4qq2pTkQ0nuTPKq7t6SJEutO236z5J8U5L3VFWSXNDdz57VfgAAACurdnE10z5lbm6uN2/evNrDAACANauqLu7uuZXYlr+YDgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoUQIAAAwlAgBAACGEiEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAoWYaIVV1WlV9tKquqqqzdrHMqVV1SVVdVlXn727dqrp3Vb2nqq6cvh4+y30AAABW1swipKoOSPKyJI9P8uAkT62qBy9a5rAkL0/ypO5+SJIfW8a6ZyX5h+4+Kck/TI8BAIA1YtkRUlX3r6ofmO6vr6p77WaVk5Nc1d1Xd/ftSV6f5PRFy/xkkjd39yeTpLtvXMa6pyd5zXT/NUl+ZLn7AAAArL5lRUhVPSvJG5P8+TTp2CRv3c1qxyS5ZsHjrdO0hR6Y5PCqem9VXVxVT1/Guvfp7uuTZPp61HL2AQAA+MawbpnLPTfzZycuTJLuvrKqdvfLfy0xrZd4/kcm+f4k65P8S1VdsMx17/rJq85McmaSHH/88XuyKgAAMEPLvRzrS9NlUUmSqlqX3UfB1iTHLXh8bJLrllhmU3d/vrtvTvK+JA/fzbo3VNXR0ziOTnJjltDdZ3f3XHfPbdy4cTdDBQAARlluhJxfVb+eZH1VPS7JG5L87W7WuSjJSVV1YlUdlOSMJOctWuZtSb6nqtZV1SFJHpXkit2se16SZ0z3nzFtAwAAWCOWeznWWUmemeTDSX4hyTuSvOquVujuHVX1vCTvSnJAknO6+7KqevY0/xXdfUVVbUryoSR3JnlVd29JkqXWnTb9+0n+pqqemeSTmT5RCwAAWBuqe/dvtaiqeyb5Ynd/eXp8QJJv6u4vzHh8K2Jubq43b9682sMAAIA1q6ou7u65ldjWci/H+ofMv3F8p/VJ/n4lBgAAAOxflhshB3f3bTsfTPcPmc2QAACAfdlyI+TzVfWInQ+q6pFJts9mSAAAwL5suW9Mf36SN1TVzo/JPTrJT8xkRAAAwD5tWRHS3RdV1b9P8q2Z/0OCH+nuO2Y6MgAAYJ+03DMhSfIdSU6Y1vn2qkp3/4+ZjAoAANhnLStCquq1SR6Q5JIkX54mdxIRAgAA7JHlngmZS/LgXs4fFQEAALgLy/10rC1J7jvLgQAAAPuH5Z4JOTLJ5VX1gSRf2jmxu580k1EBAAD7rOVGyItmOQgAAGD/sdyP6D2/qu6f5KTu/vuqOiTJAbMdGgAAsC9a1ntCqupZSd6Y5M+nScckeeuMxgQAAOzDlvvG9OcmeUySzyZJd1+Z5KhZDQoAANh3LTdCvtTdt+98UFXrMv93QgAAAPbIciPk/Kr69STrq+pxSd6Q5G9nNywAAGBftdwIOSvJTUk+nOQXkrwjyW/OalAAAMC+a7mfjnVnkldONwAAgLvtLiOkqj6cu3jvR3c/bMVHBAAA7NN2dybkidPX505fXzt9/akkX5jJiAAAgH3aXUZId38iSarqMd39mAWzzqqq/53kt2c5OAAAYN+z3Dem37Oqvnvng6r6riT3nM2QAACAfdmy3pie5JlJzqmqDdPjW5P83ExGBAAA7NOW++lYFyd5eFUdmqS6e9tshwUAAOyrdvfpWE/r7tdV1X9aND1J0t3/dYZjAwAA9kG7OxNyyPT1XrMeCAAAsH/YXYQ8YPp6eXe/YdaDAQAA9n27+3SsJ1TVgUleMGIwAADAvm93Z0I2Jbk58x/R+9kF0ytJd/ehMxsZAACwT7rLMyHd/f9294Ykb+/uQxfc7iVAAACAu2NZf6ywu0+f9UAAAID9w7IipKqeUlVXVtW2qvpsVX1u0eVZAAAAy7Lcv5j+kiQ/3N1XzHIwAADAvm9ZZ0KS3CBAAACAlbDcMyGbq+p/Jnlrki/tnNjdb57FoAAAgH3XciPk0CRfSPKDC6Z1EhECAADskWVFSHf/7KwHAgAA7B+W++lYx1bVW6rqxqq6oareVFXHznpwAADAvme5b0z/iyTnJblfkmOS/O00DQAAYI8sN0I2dvdfdPeO6faXSTbOcFwAAMA+arkRcnNVPa2qDphuT0vy6VkODAAA2DctN0J+LsmPJ/lUkuuT/GgSb1YHAAD22HI/ovd3kjyju29Jkqq6d5KXZj5OAAAAlm25Z0IetjNAkqS7P5Pk22czJAAAYF+23Ai5R1UdvvPBdCZkuWdRAAAAvmK5IfGHSd5fVW/M/F9K//EkvzuzUQEAAPus5f7F9P9RVZuTfF+SSvKU7r58piMDAAD2Scu+pGqKDuEBAADsleW+JwQAAGBFiBAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDzTRCquq0qvpoVV1VVWctMf/UqtpWVZdMtxcumPdLVbWlqi6rqucvmP5tVXXBtPzmqjp5lvsAAACsrHWz2nBVHZDkZUkel2Rrkouq6rzuvnzRov/c3U9ctO5DkzwryclJbk+yqare3t1XJnlJkt/q7ndW1ROmx6fOaj8AAICVNcszIScnuaq7r+7u25O8Psnpy1z3QUku6O4vdPeOJOcnefI0r5McOt3fkOS6FRwzAAAwY7OMkGOSXLPg8dZp2mLfWVWXVtU7q+oh07QtSU6pqiOq6pAkT0hy3DTv+Un+oKquSfLSJC+YyegBAICZmGWE1BLTetHjDya5f3c/PMmfJnlrknT3FUlenOQ9STYluTTJjmmd5yT5j919XJL/mOTVSz551ZnTe0Y233TTTXu5KwAAwEqZZYRszVfPXiTJsVl06VR3f7a7b5vuvyPJgVV15PT41d39iO4+Jclnklw5rfaMJG+e7r8h85d9fZ3uPru757p7buPGjSu1TwAAwF6aZYRclOSkqjqxqg5KckaS8xYuUFX3raqa7p88jefT0+Ojpq/HJ3lKknOn1a5L8r3T/e/LV+MEAABYA2b26VjdvaOqnpfkXUkOSHJOd19WVc+e5r8iyY8meU5V7UiyPckZ3b3zkq03VdURSe5I8tzuvmWa/qwkf1JV65J8McmZs9oHAABg5dVXf+ffd83NzfXmzZtXexgAALBmVdXF3T23EtvyF9MBAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADDUTCOkqk6rqo9W1VVVddYS80+tqm1Vdcl0e+GCeb9UVVuq6rKqev6i9X5x2u5lVfWSWe4DAACwstbNasNVdUCSlyV5XJKtSS6qqvO6+/JFi/5zdz9x0boPTfKsJCcnuT3Jpqp6e3dfWVWPTXJ6kod195eq6qhZ7QMAALDyZnkm5OQkV3X31d19e5LXZz4eluNBSS7o7i90944k5yd58jTvOUl+v7u/lCTdfeMKjxsAAJihWUbIMUmuWfB46zRtse+sqkur6p1V9ZBp2pYkp1TVEVV1SJInJDlumvfAJN9TVRdW1flV9R1LPXlVnVlVm6tq80033bQyewQAAOy1mV2OlaSWmNaLHn8wyf27+7aqekKStyY5qbuvqKoXJ3lPktuSXJpkx7TOuiSHJ3l0ku9I8jdV9S3d/TXb7u6zk5ydJHNzc4ufFwAAWCWzPBOyNV89e5Ekxya5buEC3f3Z7r5tuv+OJAdW1ZHT41d39yO6+5Qkn0ly5YLtvrnnfSDJnUmOnOF+AAAAK2iWEXJRkpOq6sSqOijJGUnOW7hAVd23qmq6f/I0nk9Pj4+avh6f5ClJzp1We2uS75vmPTDJQUlunuF+AAAAK2hml2N1946qel6SdyU5IMk53X1ZVT17mv+KJD+a5DlVtSPJ9iRnLLis6k1VdUSSO5I8t7tvmaafk+ScqtqS+U/OesbiS7EAAIBvXLU//P4+NzfXmzdvXu1hAADAmlVVF3f33Epsy19MBwAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQIAAAwlQgAAgKFECAAAMJQIAQAAhppphFTVaVX10aq6qqrOWmL+qVW1raoumW4vXDDvl6pqS1VdVlXPX2LdX6mqrqojZ7kPAADAylo3qw1X1QFJXpbkcUm2Jrmoqs7r7ssXLfrP3f3ERes+NMmzkpyc5PYkm6rq7d195TT/uGm7n5zV+AEAgNmY5ZmQk5Nc1d1Xd/ftSV6f5PRlrvugJBd09xe6e0eS85M8ecH8P0ryq0l6JQcMAADM3szOhCQ5Jsk1Cx5vTfKoJZb7zqq6NMl1SX6luy9LsiXJ71bVEUm2J3lCks1JUlVPSnJtd19aVTMcPgAjXHH9tmzackOuvXV7jjlsfU576H3yoKM3rPawAJihWZ4JWaoQFp+5+GCS+3f3w5P8aZK3Jkl3X5HkxUnek2RTkkuT7KiqQ5L8RpIXZjeq6syq2lxVm2+66aa7vRMAzM4V12/L2e/7WLZtvyNHbzg427bfkbPf97Fccf221R4aADM0ywjZmuS4BY+PzfzZjq/o7s92923T/XckOXDnG827+9Xd/YjuPiXJZ5JcmeQBSU5McmlVfXza5ger6r6Ln7y7z+7uue6e27hx48rvHQB7bdOWG7Jh/YHZsP7A3KPqK/c3bblhtYcGwAzN8nKsi5KcVFUnJrk2yRlJfnLhAlM83NDdXVUnZz6KPj3NO6q7b6yq45M8Jcl3dvctSY5asP7Hk8x1980z3A8AZuTaW7fn6A0Hf820ex28Ltfeun2VRgTACDOLkO7eUVXPS/KuJAckOae7L6uqZ0/zX5HkR5M8p6p2ZP69H2d0985Ltt40vSfkjiTPnQIEgH3IMYetz7btd2TD+gO/Mu1zX9yRYw5bv4qjAmDW6qu/8++75ubmevPmzas9DAAW2fmekA3rD8y9Dl6Xz31xR7ZtvyNnnnKiN6cDfIOpqou7e24ltuUvpgOwah509IacecqJ2bD+wFy/7YvZsP5AAQKwH5jle0IAYLcedPQG0QGwn3EmBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgKBECAAAMJUIAAIChRAgAADCUCAEAAIYSIQAAwFAiBAAAGEqEAAAAQ4kQAABgqOru1R7DzFXVTUk+sdrjWKOOTHLzag+CVeUYwDGAY2D/5ufPzmPg/t29cSU2uF9ECHdfVW3u7rnVHgerxzGAYwDHwP7Nz59ZHAMuxwIAAIYSIQAAwFAihN05e7UHwKpzDOAYwDGwf/PzZ8WPAe8JAQAAhnImBAAAGEqE7Geq6rSq+mhVXVVVZy0x/6eq6kPT7f1V9fBF8w+oqv9TVX+3YNqLquraqrpkuj1hxL6w5/bm519VH6+qD08/480Lpt+7qt5TVVdOXw8ftT/suRkdA14D1pC9PAYOq6o3VtVHquqKqvrOabrXgTVkRseA14E15O4eA1X1rQt+xpdU1Wer6vnTvD17Hehut/3kluSAJP+W5FuSHJTk0iQPXrTMdyU5fLr/+CQXLpr/n5L8dZK/WzDtRUl+ZbX3z222P/8kH09y5BLbfUmSs6b7ZyV58Wrvq9vwY8BrwBq5rcAx8JokPz/dPyjJYdN9rwNr5DbDY8DrwBq5rcTvgwu286nM/+2QPX4dcCZk/3Jykqu6++ruvj3J65OcvnCB7n5/d98yPbwgybE751XVsUl+KMmrBo2XlbVXP/+7cHrm/1HK9PVHVma4zMCsjgHWjrt9DFTVoUlOSfLqabnbu/vWaTmvA2vHrI4B1o6V+rfg+5P8W3fv/IPge/Q6IEL2L8ckuWbB463TtF15ZpJ3Lnj8x0l+NcmdSyz7vOmU3TlOw3/D2tuffyd5d1VdXFVnLph+n+6+Pkmmr0et0HhZebM6BhKvAWvF3hwD35LkpiR/MV2W+6qquuc0z+vA2jGrYyDxOrBW7O2/BTudkeTcBY/36HVAhOxfaolpS348WlU9NvMH3a9Nj5+Y5MbuvniJxf97kgck+bYk1yf5w5UYLCvubv/8J4/p7kdk/rTsc6vqlJUfIjM2q2PAa8DasTfHwLokj0jy37v725N8PvOXXLC2zOoY8DqwduztvwWpqoOSPCnJG+7uIETI/mVrkuMWPD42yXWLF6qqh2X+kqvTu/vT0+THJHlSVX0886ftvq+qXpck3X1Dd3+5u+9M8srMn+bjG8/e/PzT3ddNX29M8pZ89ed8Q1UdPa17dJIbZzJ6VsJMjgGvAWvK3hwDW5Ns7e4Lp8dvzPwvpInXgbVkJseA14E1Za/+LZg8PskHu/uGBdP26HVAhOxfLkpyUlWdOBXsGUnOW7hAVR2f5M1Jfrq7/3Xn9O5+QXcf290nTOv9Y3c/bVrn6AWbeHKSLbPdDe6mu/3zr6p7VtW9dt5P8oP56s/5vCTPmO4/I8nbZroX7I2ZHANeA9aUvfl34FNJrqmqb50mfX+Sy6f7XgfWjpkcA14H1pS7fQws8NR87aVYyR6+Dqy7GwNnjeruHVX1vCTvyvwnGpzT3ZdV1bOn+a9I8sIkRyR5eVUlyY7untvNpl9SVd+W+VN5H0/yC7PZA/bGXv7875PkLdO0dUn+urs3TZv+/SR/U1XPTPLJJD82cLfYAzM8BrwGrBEr8O/ALyb5q+kXl6uT/Ow03evAGjHDY8DrwBqxt8dAVR2S5HH5+p/xHr0O+IvpAADAUC7HAgAAhhIhAADAUCIEAAAYSoQAAABDiRAAAGAoEQLAmldVz58+NhKANcBH9AKwplXVAUn+Lclcd9+82uMBYPecCQHgbpv+kvrbq+rSqtpSVT9RVR+vqiOn+XNV9d7p/ouq6rVV9Y9VdWVVPWuafmpVva+q3lJVl1fVK6rqHtO8p1bVh6dtv3jB895WVb9dVRcm+Y0k90vyT1X1T6O/BwDsOX8xHYC9cVqS67r7h5KkqjYkefFdLP+wJI9Ocs8k/6eq3j5NPznJg5N8IsmmJE+pqvdP23pkkluSvLuqfqS73zqtv6W7Xzg9788leawzIQBrgzMhAOyNDyf5gap6cVV9T3dv283yb+vu7VMs/FPm4yNJPtDdV3f3l5Ocm+S7k3xHkvd2903dvSPJXyU5ZVr+y0netOJ7A8AQzoQAcLd1979W1SOTPCHJ71XVu5PsyFf/k+vgxavs4vFS0+sunvqLU7AAsAY5EwLA3VZV90vyhe5+XZKXJnlEko9n/hKqJPl/Fq1yelUdXFVHJDk1yUXT9JOr6sTpvSA/keR/JbkwyfdW1ZHTm8+fmuT8XQzlc0nutTJ7BcCsORMCwN74v5L8QVXdmeSOJM9Jsj7Jq6vq1zMfEgt9IMnbkxyf5He6+7qqemCSf0ny+9P23pfkLd19Z1W9IPOXbVWSd3T323YxjrOTvLOqru/ux67sLgKw0nxELwBDVNWLktzW3S9dNP3UJL/S3U9chWEBsApcjgUAAAzlTAgAADCUMyEAAMBQIgQAABhKhAAAAEOJEAAAYCgRAgAADCVCAACAof5/wY6+dOP2kkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rules4['support'], rules4['confidence'], alpha=0.5)\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence')\n",
    "plt.title('Support vs Confidence')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a675e041",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b68a1dfc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}