Association rules (movies).ipynb

{
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  {
   "cell_type": "markdown",
   "id": "c1bb4625",
   "metadata": {},
   "source": [
    "# Problem Statement (Movies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1aa3af",
   "metadata": {},
   "source": [
    "Prepare rules for the data set\n",
    "\n",
    "1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence value\n",
    "\n",
    "2) Change the minimum length in apriori algorithm\n",
    "\n",
    "3) Visulize the obtained rules using different plots "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e1067f",
   "metadata": {},
   "source": [
    "# Theory of Association Rules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123837fb",
   "metadata": {},
   "source": [
    "Association rules determines dependencies among different items. It is used for stocking purposes and for placement of items in stores ie. Market Basket Analysis.\n",
    "\n",
    "Rule form: antecedent --> consequent [support, confidence] & lift \n",
    "eg: Bread --> Milk says if a customer buys bread then high chances that he will buy milk too.\n",
    "\n",
    "There are 3 factors in considering imp of a rule, which are: \n",
    "\n",
    "1) Support = no. of transactions with both antecedent and consequent /  no. of total transactions\n",
    "support indicates volume / popularity of an item. \n",
    "\n",
    "2) Confidence =  no. of transactions with both antecedent and consequent / no. of transactions with antecedent items.\n",
    "\n",
    "confidence checks binding \n",
    "\n",
    "3) Lift = support(overall) / support(antecedent) * support(consequent) \n",
    "\n",
    "lift checks importance of rule\n",
    "\n",
    "lift > 1 means consequent likely to be bought with x\n",
    "\n",
    "lift < 1 means consequent unlikely to be bought with x\n",
    "\n",
    "lift = 1 means no association between x and y"
   ]
  },
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   "source": [
    "# importing necessary libraries\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
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   "execution_count": 2,
   "id": "b217f909",
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     "end_time": "2022-11-10T14:21:20.722431Z",
     "start_time": "2022-11-10T14:21:20.698651Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
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       "      <th>Sixth Sense</th>\n",
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       "      <th>LOTR1</th>\n",
       "      <th>Harry Potter1</th>\n",
       "      <th>Patriot</th>\n",
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       "      <th>4</th>\n",
       "      <td>Gladiator</td>\n",
       "      <td>Patriot</td>\n",
       "      <td>Sixth Sense</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Harry Potter1</td>\n",
       "      <td>Harry Potter2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Gladiator</td>\n",
       "      <td>Patriot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "              V1             V2             V3          V4     V5  \\\n",
       "0    Sixth Sense          LOTR1  Harry Potter1  Green Mile  LOTR2   \n",
       "1      Gladiator        Patriot     Braveheart         NaN    NaN   \n",
       "2          LOTR1          LOTR2            NaN         NaN    NaN   \n",
       "3      Gladiator        Patriot    Sixth Sense         NaN    NaN   \n",
       "4      Gladiator        Patriot    Sixth Sense         NaN    NaN   \n",
       "5      Gladiator        Patriot    Sixth Sense         NaN    NaN   \n",
       "6  Harry Potter1  Harry Potter2            NaN         NaN    NaN   \n",
       "7      Gladiator        Patriot            NaN         NaN    NaN   \n",
       "8      Gladiator        Patriot    Sixth Sense         NaN    NaN   \n",
       "9    Sixth Sense           LOTR      Gladiator  Green Mile    NaN   \n",
       "\n",
       "   Sixth Sense  Gladiator  LOTR1  Harry Potter1  Patriot  LOTR2  \\\n",
       "0            1          0      1              1        0      1   \n",
       "1            0          1      0              0        1      0   \n",
       "2            0          0      1              0        0      1   \n",
       "3            1          1      0              0        1      0   \n",
       "4            1          1      0              0        1      0   \n",
       "5            1          1      0              0        1      0   \n",
       "6            0          0      0              1        0      0   \n",
       "7            0          1      0              0        1      0   \n",
       "8            1          1      0              0        1      0   \n",
       "9            1          1      0              0        0      0   \n",
       "\n",
       "   Harry Potter2  LOTR  Braveheart  Green Mile  \n",
       "0              0     0           0           1  \n",
       "1              0     0           1           0  \n",
       "2              0     0           0           0  \n",
       "3              0     0           0           0  \n",
       "4              0     0           0           0  \n",
       "5              0     0           0           0  \n",
       "6              1     0           0           0  \n",
       "7              0     0           0           0  \n",
       "8              0     0           0           0  \n",
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     "execution_count": 2,
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   ],
   "source": [
    "movie = pd.read_csv('my_movies.csv')\n",
    "movie "
   ]
  },
  {
   "cell_type": "code",
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   "id": "a97f42de",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Sixth Sense  Gladiator  LOTR1  Harry Potter1  Patriot  LOTR2  \\\n",
       "0            1          0      1              1        0      1   \n",
       "1            0          1      0              0        1      0   \n",
       "2            0          0      1              0        0      1   \n",
       "3            1          1      0              0        1      0   \n",
       "4            1          1      0              0        1      0   \n",
       "5            1          1      0              0        1      0   \n",
       "6            0          0      0              1        0      0   \n",
       "7            0          1      0              0        1      0   \n",
       "8            1          1      0              0        1      0   \n",
       "9            1          1      0              0        0      0   \n",
       "\n",
       "   Harry Potter2  LOTR  Braveheart  Green Mile  \n",
       "0              0     0           0           1  \n",
       "1              0     0           1           0  \n",
       "2              0     0           0           0  \n",
       "3              0     0           0           0  \n",
       "4              0     0           0           0  \n",
       "5              0     0           0           0  \n",
       "6              1     0           0           0  \n",
       "7              0     0           0           0  \n",
       "8              0     0           0           0  \n",
       "9              0     1           0           1  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we only need transaction encoded data\n",
    "movie = movie.iloc[:,5:]\n",
    "movie "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ca0029",
   "metadata": {},
   "source": [
    "Capturing trends and buying patterns can help movie store in several ways.\n",
    "If we find pairs of movies bought together then it store manager can place them on same shelf to promote buyers to buy both movies. Advertisements on X could be targeted at buyers who purchase Y. Discount on X movie can raise sell of Y movie. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1314d873",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
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   "id": "e0d65dcc",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2022-11-10T14:21:20.738348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7546c440",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:20.770389Z",
     "start_time": "2022-11-10T14:21:20.754341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   Sixth Sense    10 non-null     int64\n",
      " 1   Gladiator      10 non-null     int64\n",
      " 2   LOTR1          10 non-null     int64\n",
      " 3   Harry Potter1  10 non-null     int64\n",
      " 4   Patriot        10 non-null     int64\n",
      " 5   LOTR2          10 non-null     int64\n",
      " 6   Harry Potter2  10 non-null     int64\n",
      " 7   LOTR           10 non-null     int64\n",
      " 8   Braveheart     10 non-null     int64\n",
      " 9   Green Mile     10 non-null     int64\n",
      "dtypes: int64(10)\n",
      "memory usage: 928.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# movie.describe() wont make much sense since data is categorical\n",
    "movie.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca57e535",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:20.786342Z",
     "start_time": "2022-11-10T14:21:20.770389Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie.duplicated().sum()\n",
    "#Though we have duplicate entries, we do not remove them because this is transaction data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "641e07ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:20.794348Z",
     "start_time": "2022-11-10T14:21:20.786342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gladiator        7\n",
       "Sixth Sense      6\n",
       "Patriot          6\n",
       "LOTR1            2\n",
       "Harry Potter1    2\n",
       "LOTR2            2\n",
       "Green Mile       2\n",
       "Harry Potter2    1\n",
       "LOTR             1\n",
       "Braveheart       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# popularity wise movies\n",
    "list = movie.sum()\n",
    "list= list.sort_values(ascending=False)\n",
    "list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9f41c78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:20.939304Z",
     "start_time": "2022-11-10T14:21:20.794348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "sns.barplot(x= list.index, y=list.values, order=list.index )\n",
    "plt.title('Movie Popularity( Purchase)',  fontsize = 18)\n",
    "plt.yticks(fontsize = 14)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2610d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-03T05:13:27.891378Z",
     "start_time": "2022-11-03T05:13:27.883338Z"
    }
   },
   "source": [
    "### Most bought movies are Gladiator, Sixth Sense, Patriot          "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25475fcd",
   "metadata": {},
   "source": [
    "# Apriori Algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b9af1c",
   "metadata": {},
   "source": [
    "First step in generation of association rules is to get all the frequent itemsets on which binary partitions can be performed to get the antecedent and the consequent.\n",
    "\n",
    "Size of an itemset can vary from one to the total number of items that we have. Now, we seek only frequent itemsets from this and not all, hence we set minimum threshold value.\n",
    "\n",
    "eg: (Toothpaste, Toothbrush) might not be frequent itemsets if it occurs only 1 in 20 transactions ie 1/20 = 0.05 but our min_support is 0.1 ie 10%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb4f5288",
   "metadata": {},
   "source": [
    "## 1) Association rules with 10% Support and 25% confidence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4cc21dfc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:20.971921Z",
     "start_time": "2022-11-10T14:21:20.939304Z"
    }
   },
   "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>0.6</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7</td>\n",
       "      <td>(Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Harry Potter1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(LOTR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Braveheart)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.5</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Sixth Sense, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Gladiator, LOTR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Gladiator, Braveheart)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Gladiator, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR2, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Harry Potter2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Braveheart, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(LOTR2, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(LOTR, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, Gladiator, LOTR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, Gladiator, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR1, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, Green Mile, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Gladiator, Braveheart, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Gladiator, LOTR, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR2, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(LOTR2, LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR2, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, Gladiator, LOTR, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR1, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Sixth Sense, LOTR1, Green Mile, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, Green Mile, LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR2, LOTR1, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.1</td>\n",
       "      <td>(Harry Potter1, LOTR1, Green Mile, LOTR2, Sixt...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                                           itemsets\n",
       "0       0.6                                      (Sixth Sense)\n",
       "1       0.7                                        (Gladiator)\n",
       "2       0.2                                            (LOTR1)\n",
       "3       0.2                                    (Harry Potter1)\n",
       "4       0.6                                          (Patriot)\n",
       "5       0.2                                            (LOTR2)\n",
       "6       0.1                                    (Harry Potter2)\n",
       "7       0.1                                             (LOTR)\n",
       "8       0.1                                       (Braveheart)\n",
       "9       0.2                                       (Green Mile)\n",
       "10      0.5                           (Sixth Sense, Gladiator)\n",
       "11      0.1                               (Sixth Sense, LOTR1)\n",
       "12      0.1                       (Harry Potter1, Sixth Sense)\n",
       "13      0.4                             (Sixth Sense, Patriot)\n",
       "14      0.1                               (Sixth Sense, LOTR2)\n",
       "15      0.1                                (Sixth Sense, LOTR)\n",
       "16      0.2                          (Sixth Sense, Green Mile)\n",
       "17      0.6                               (Gladiator, Patriot)\n",
       "18      0.1                                  (Gladiator, LOTR)\n",
       "19      0.1                            (Gladiator, Braveheart)\n",
       "20      0.1                            (Gladiator, Green Mile)\n",
       "21      0.1                             (Harry Potter1, LOTR1)\n",
       "22      0.2                                     (LOTR2, LOTR1)\n",
       "23      0.1                                (LOTR1, Green Mile)\n",
       "24      0.1                             (Harry Potter1, LOTR2)\n",
       "25      0.1                     (Harry Potter1, Harry Potter2)\n",
       "26      0.1                        (Harry Potter1, Green Mile)\n",
       "27      0.1                              (Braveheart, Patriot)\n",
       "28      0.1                                (LOTR2, Green Mile)\n",
       "29      0.1                                 (LOTR, Green Mile)\n",
       "30      0.4                  (Sixth Sense, Gladiator, Patriot)\n",
       "31      0.1                     (Sixth Sense, Gladiator, LOTR)\n",
       "32      0.1               (Sixth Sense, Gladiator, Green Mile)\n",
       "33      0.1                (Harry Potter1, Sixth Sense, LOTR1)\n",
       "34      0.1                        (Sixth Sense, LOTR1, LOTR2)\n",
       "35      0.1                   (Sixth Sense, LOTR1, Green Mile)\n",
       "36      0.1                (Harry Potter1, Sixth Sense, LOTR2)\n",
       "37      0.1           (Harry Potter1, Sixth Sense, Green Mile)\n",
       "38      0.1                   (Sixth Sense, Green Mile, LOTR2)\n",
       "39      0.1                    (Sixth Sense, LOTR, Green Mile)\n",
       "40      0.1                   (Gladiator, Braveheart, Patriot)\n",
       "41      0.1                      (Gladiator, LOTR, Green Mile)\n",
       "42      0.1                      (Harry Potter1, LOTR2, LOTR1)\n",
       "43      0.1                 (Harry Potter1, LOTR1, Green Mile)\n",
       "44      0.1                         (LOTR2, LOTR1, Green Mile)\n",
       "45      0.1                 (Harry Potter1, LOTR2, Green Mile)\n",
       "46      0.1         (Sixth Sense, Gladiator, LOTR, Green Mile)\n",
       "47      0.1         (Harry Potter1, Sixth Sense, LOTR1, LOTR2)\n",
       "48      0.1    (Harry Potter1, Sixth Sense, LOTR1, Green Mile)\n",
       "49      0.1            (Sixth Sense, LOTR1, Green Mile, LOTR2)\n",
       "50      0.1    (Harry Potter1, Sixth Sense, Green Mile, LOTR2)\n",
       "51      0.1          (Harry Potter1, LOTR2, LOTR1, Green Mile)\n",
       "52      0.1  (Harry Potter1, LOTR1, Green Mile, LOTR2, Sixt..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 10% support\n",
    "frequent_items = apriori(movie, min_support=0.1, use_colnames=True)\n",
    "frequent_items \n",
    "\n",
    "#For (Gladiator, Sixth Sense) support is 0.5 , which means Gladiator, Sixth Sense appear together 50% times in total transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2bb058e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.004370Z",
     "start_time": "2022-11-10T14:21:20.971921Z"
    }
   },
   "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>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Harry Potter1)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>212</th>\n",
       "      <td>(LOTR2, Sixth Sense)</td>\n",
       "      <td>(Harry Potter1, LOTR1, Green Mile)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>(Harry Potter1)</td>\n",
       "      <td>(LOTR2, Sixth Sense, LOTR1, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(Harry Potter1, LOTR2, Sixth Sense, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Harry Potter1, LOTR2, Sixth Sense, LOTR1)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR1, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents                                      consequents  \\\n",
       "0           (Sixth Sense)                                      (Gladiator)   \n",
       "1             (Gladiator)                                    (Sixth Sense)   \n",
       "2                 (LOTR1)                                    (Sixth Sense)   \n",
       "3         (Harry Potter1)                                    (Sixth Sense)   \n",
       "4           (Sixth Sense)                                        (Patriot)   \n",
       "..                    ...                                              ...   \n",
       "212  (LOTR2, Sixth Sense)               (Harry Potter1, LOTR1, Green Mile)   \n",
       "213       (Harry Potter1)          (LOTR2, Sixth Sense, LOTR1, Green Mile)   \n",
       "214               (LOTR1)  (Harry Potter1, LOTR2, Sixth Sense, Green Mile)   \n",
       "215          (Green Mile)       (Harry Potter1, LOTR2, Sixth Sense, LOTR1)   \n",
       "216               (LOTR2)  (Harry Potter1, Sixth Sense, LOTR1, Green Mile)   \n",
       "\n",
       "     antecedent support  consequent support  support  confidence       lift  \\\n",
       "0                   0.6                 0.7      0.5    0.833333   1.190476   \n",
       "1                   0.7                 0.6      0.5    0.714286   1.190476   \n",
       "2                   0.2                 0.6      0.1    0.500000   0.833333   \n",
       "3                   0.2                 0.6      0.1    0.500000   0.833333   \n",
       "4                   0.6                 0.6      0.4    0.666667   1.111111   \n",
       "..                  ...                 ...      ...         ...        ...   \n",
       "212                 0.1                 0.1      0.1    1.000000  10.000000   \n",
       "213                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "214                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "215                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "216                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "\n",
       "     leverage  conviction  \n",
       "0        0.08         1.8  \n",
       "1        0.08         1.4  \n",
       "2       -0.02         0.8  \n",
       "3       -0.02         0.8  \n",
       "4        0.04         1.2  \n",
       "..        ...         ...  \n",
       "212      0.09         inf  \n",
       "213      0.08         1.8  \n",
       "214      0.08         1.8  \n",
       "215      0.08         1.8  \n",
       "216      0.08         1.8  \n",
       "\n",
       "[217 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 25% Confidence\n",
    "rules = association_rules(frequent_items, metric='confidence', min_threshold=0.25)\n",
    "rules "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b6b9dd8",
   "metadata": {},
   "source": [
    "Antecedent support variable tells us probability of antecedent products alone\n",
    "\n",
    "Consequents support variable tells us probability of consequents products alone\n",
    "\n",
    "The support value is the value of the two products (Antecedents and Consequents)\n",
    "\n",
    "Confidence is an indication of how often the rule has been found to be true.\n",
    "\n",
    "The ratio of the observed support to that expected if X and Y were independent.\n",
    "\n",
    "A leverage value of 0 indicates independence. Range will be [-1 1]\n",
    "\n",
    "A high conviction value means that the consequent is highly depending on the antecedent and range [0 inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50473e34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.028739Z",
     "start_time": "2022-11-10T14:21:21.004370Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>165</th>\n",
       "      <td>(Harry Potter1, Sixth Sense)</td>\n",
       "      <td>(LOTR2, Green Mile)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(Harry Potter1, LOTR1)</td>\n",
       "      <td>(Sixth Sense, LOTR2)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>(LOTR2, Green Mile)</td>\n",
       "      <td>(Sixth Sense, LOTR1)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>(Gladiator, Green Mile)</td>\n",
       "      <td>(Sixth Sense, LOTR)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>(LOTR)</td>\n",
       "      <td>(Sixth Sense, Gladiator, Green Mile)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</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>6</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Harry Potter1)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(Sixth Sense, Green Mile)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      antecedents                           consequents  \\\n",
       "165  (Harry Potter1, Sixth Sense)                   (LOTR2, Green Mile)   \n",
       "127        (Harry Potter1, LOTR1)                  (Sixth Sense, LOTR2)   \n",
       "157           (LOTR2, Green Mile)                  (Sixth Sense, LOTR1)   \n",
       "118       (Gladiator, Green Mile)                   (Sixth Sense, LOTR)   \n",
       "120                        (LOTR)  (Sixth Sense, Gladiator, Green Mile)   \n",
       "..                            ...                                   ...   \n",
       "6                         (LOTR2)                         (Sixth Sense)   \n",
       "3                 (Harry Potter1)                         (Sixth Sense)   \n",
       "2                         (LOTR1)                         (Sixth Sense)   \n",
       "14                   (Green Mile)                           (Gladiator)   \n",
       "41      (Sixth Sense, Green Mile)                           (Gladiator)   \n",
       "\n",
       "     antecedent support  consequent support  support  confidence       lift  \\\n",
       "165                 0.1                 0.1      0.1         1.0  10.000000   \n",
       "127                 0.1                 0.1      0.1         1.0  10.000000   \n",
       "157                 0.1                 0.1      0.1         1.0  10.000000   \n",
       "118                 0.1                 0.1      0.1         1.0  10.000000   \n",
       "120                 0.1                 0.1      0.1         1.0  10.000000   \n",
       "..                  ...                 ...      ...         ...        ...   \n",
       "6                   0.2                 0.6      0.1         0.5   0.833333   \n",
       "3                   0.2                 0.6      0.1         0.5   0.833333   \n",
       "2                   0.2                 0.6      0.1         0.5   0.833333   \n",
       "14                  0.2                 0.7      0.1         0.5   0.714286   \n",
       "41                  0.2                 0.7      0.1         0.5   0.714286   \n",
       "\n",
       "     leverage  conviction  \n",
       "165      0.09         inf  \n",
       "127      0.09         inf  \n",
       "157      0.09         inf  \n",
       "118      0.09         inf  \n",
       "120      0.09         inf  \n",
       "..        ...         ...  \n",
       "6       -0.02         0.8  \n",
       "3       -0.02         0.8  \n",
       "2       -0.02         0.8  \n",
       "14      -0.04         0.6  \n",
       "41      -0.04         0.6  \n",
       "\n",
       "[217 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.sort_values('lift', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73795bd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.060918Z",
     "start_time": "2022-11-10T14:21:21.028739Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(LOTR)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.04</td>\n",
       "      <td>inf</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>212</th>\n",
       "      <td>(LOTR2, Sixth Sense)</td>\n",
       "      <td>(Harry Potter1, LOTR1, Green Mile)</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.09</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>(Harry Potter1)</td>\n",
       "      <td>(LOTR2, Sixth Sense, LOTR1, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(Harry Potter1, LOTR2, Sixth Sense, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Harry Potter1, LOTR2, Sixth Sense, LOTR1)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(Harry Potter1, Sixth Sense, LOTR1, Green Mile)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              antecedents                                      consequents  \\\n",
       "0           (Sixth Sense)                                      (Gladiator)   \n",
       "1             (Gladiator)                                    (Sixth Sense)   \n",
       "4           (Sixth Sense)                                        (Patriot)   \n",
       "5               (Patriot)                                    (Sixth Sense)   \n",
       "7                  (LOTR)                                    (Sixth Sense)   \n",
       "..                    ...                                              ...   \n",
       "212  (LOTR2, Sixth Sense)               (Harry Potter1, LOTR1, Green Mile)   \n",
       "213       (Harry Potter1)          (LOTR2, Sixth Sense, LOTR1, Green Mile)   \n",
       "214               (LOTR1)  (Harry Potter1, LOTR2, Sixth Sense, Green Mile)   \n",
       "215          (Green Mile)       (Harry Potter1, LOTR2, Sixth Sense, LOTR1)   \n",
       "216               (LOTR2)  (Harry Potter1, Sixth Sense, LOTR1, Green Mile)   \n",
       "\n",
       "     antecedent support  consequent support  support  confidence       lift  \\\n",
       "0                   0.6                 0.7      0.5    0.833333   1.190476   \n",
       "1                   0.7                 0.6      0.5    0.714286   1.190476   \n",
       "4                   0.6                 0.6      0.4    0.666667   1.111111   \n",
       "5                   0.6                 0.6      0.4    0.666667   1.111111   \n",
       "7                   0.1                 0.6      0.1    1.000000   1.666667   \n",
       "..                  ...                 ...      ...         ...        ...   \n",
       "212                 0.1                 0.1      0.1    1.000000  10.000000   \n",
       "213                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "214                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "215                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "216                 0.2                 0.1      0.1    0.500000   5.000000   \n",
       "\n",
       "     leverage  conviction  \n",
       "0        0.08         1.8  \n",
       "1        0.08         1.4  \n",
       "4        0.04         1.2  \n",
       "5        0.04         1.2  \n",
       "7        0.04         inf  \n",
       "..        ...         ...  \n",
       "212      0.09         inf  \n",
       "213      0.08         1.8  \n",
       "214      0.08         1.8  \n",
       "215      0.08         1.8  \n",
       "216      0.08         1.8  \n",
       "\n",
       "[210 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lift Ratio > 1 indicates good influential rule \n",
    "rules[(rules.lift>1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d36dea",
   "metadata": {},
   "source": [
    "## Visualisation of obtained rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f08de94d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.342151Z",
     "start_time": "2022-11-10T14:21:21.060918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.barplot(\"support\",\"confidence\",data=rules)\n",
    "plt.title(\"support vs confidence\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.scatter(rules['support'],rules['confidence'])\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3a31065",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.455583Z",
     "start_time": "2022-11-10T14:21:21.342151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.scatter(rules['lift'],rules['confidence'])\n",
    "plt.xlabel('lift')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bfedeab9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:21.479878Z",
     "start_time": "2022-11-10T14:21:21.455583Z"
    }
   },
   "outputs": [],
   "source": [
    "matrix = rules.pivot('antecedents','consequents','lift') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b9f716c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.610340Z",
     "start_time": "2022-11-10T14:21:21.479878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='consequents', ylabel='antecedents'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.heatmap(matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36b3c88",
   "metadata": {},
   "source": [
    "# 2) Association rules with 20% Support and 40% confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cad2fad7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.634355Z",
     "start_time": "2022-11-10T14:21:22.610340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>0.6</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7</td>\n",
       "      <td>(Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Harry Potter1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.5</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(Sixth Sense, Green Mile)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.2</td>\n",
       "      <td>(LOTR2, LOTR1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    support                           itemsets\n",
       "0       0.6                      (Sixth Sense)\n",
       "1       0.7                        (Gladiator)\n",
       "2       0.2                            (LOTR1)\n",
       "3       0.2                    (Harry Potter1)\n",
       "4       0.6                          (Patriot)\n",
       "5       0.2                            (LOTR2)\n",
       "6       0.2                       (Green Mile)\n",
       "7       0.5           (Sixth Sense, Gladiator)\n",
       "8       0.4             (Sixth Sense, Patriot)\n",
       "9       0.2          (Sixth Sense, Green Mile)\n",
       "10      0.6               (Gladiator, Patriot)\n",
       "11      0.2                     (LOTR2, LOTR1)\n",
       "12      0.4  (Sixth Sense, Gladiator, Patriot)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 20% support\n",
    "frequent_items = apriori(movie, min_support=0.2, use_colnames=True)\n",
    "frequent_items \n",
    "#For (Patriot, Sixth Sense) support is 0.4 , which means Patriot, Sixth Sense appear together 40% times in total transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "598ad6a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.674354Z",
     "start_time": "2022-11-10T14:21:22.634355Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.08</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "4               (Green Mile)             (Sixth Sense)                 0.2   \n",
       "5                (Gladiator)                 (Patriot)                 0.7   \n",
       "6                  (Patriot)               (Gladiator)                 0.6   \n",
       "7                    (LOTR2)                   (LOTR1)                 0.2   \n",
       "8                    (LOTR1)                   (LOTR2)                 0.2   \n",
       "9   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "10    (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "11      (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "12             (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "13               (Gladiator)    (Sixth Sense, Patriot)                 0.7   \n",
       "14                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "4                  0.6      0.2    1.000000  1.666667      0.08         inf  \n",
       "5                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "6                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "7                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "8                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "9                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "10                 0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "11                 0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "12                 0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "13                 0.4      0.4    0.571429  1.428571      0.12         1.4  \n",
       "14                 0.5      0.4    0.666667  1.333333      0.10         1.5  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 40% Confidence\n",
    "rules = association_rules(frequent_items, metric='confidence', min_threshold=0.4)\n",
    "rules "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153997c5",
   "metadata": {},
   "source": [
    "Antecedent support variable tells us probability of antecedent products alone\n",
    "\n",
    "Consequents support variable tells us probability of consequents products alone\n",
    "\n",
    "The support value is the value of the two products (Antecedents and Consequents)\n",
    "\n",
    "Confidence is an indication of how often the rule has been found to be true.\n",
    "\n",
    "The ratio of the observed support to that expected if X and Y were independent.\n",
    "\n",
    "A leverage value of 0 indicates independence. Range will be [-1 1]\n",
    "\n",
    "A high conviction value means that the consequent is highly depending on the antecedent and range [0 inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dfec9fea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.714450Z",
     "start_time": "2022-11-10T14:21:22.674354Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <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>7</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.08</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "7                    (LOTR2)                   (LOTR1)                 0.2   \n",
       "8                    (LOTR1)                   (LOTR2)                 0.2   \n",
       "4               (Green Mile)             (Sixth Sense)                 0.2   \n",
       "5                (Gladiator)                 (Patriot)                 0.7   \n",
       "6                  (Patriot)               (Gladiator)                 0.6   \n",
       "10    (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "13               (Gladiator)    (Sixth Sense, Patriot)                 0.7   \n",
       "9   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "14                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "11      (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "12             (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "7                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "8                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "4                  0.6      0.2    1.000000  1.666667      0.08         inf  \n",
       "5                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "6                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "10                 0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "13                 0.4      0.4    0.571429  1.428571      0.12         1.4  \n",
       "9                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "14                 0.5      0.4    0.666667  1.333333      0.10         1.5  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "11                 0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "12                 0.6      0.4    0.666667  1.111111      0.04         1.2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.sort_values('lift', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69618450",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.747240Z",
     "start_time": "2022-11-10T14:21:22.714450Z"
    }
   },
   "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>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Green Mile)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.08</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(LOTR1)</td>\n",
       "      <td>(LOTR2)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.16</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "4               (Green Mile)             (Sixth Sense)                 0.2   \n",
       "5                (Gladiator)                 (Patriot)                 0.7   \n",
       "6                  (Patriot)               (Gladiator)                 0.6   \n",
       "7                    (LOTR2)                   (LOTR1)                 0.2   \n",
       "8                    (LOTR1)                   (LOTR2)                 0.2   \n",
       "9   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "10    (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "11      (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "12             (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "13               (Gladiator)    (Sixth Sense, Patriot)                 0.7   \n",
       "14                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "4                  0.6      0.2    1.000000  1.666667      0.08         inf  \n",
       "5                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "6                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "7                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "8                  0.2      0.2    1.000000  5.000000      0.16         inf  \n",
       "9                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "10                 0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "11                 0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "12                 0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "13                 0.4      0.4    0.571429  1.428571      0.12         1.4  \n",
       "14                 0.5      0.4    0.666667  1.333333      0.10         1.5  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lift Ratio > 1 indicates good influential rule \n",
    "rules[(rules.lift>1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53bc069",
   "metadata": {},
   "source": [
    "## Visualization of obtained rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3df9aa21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:22.965416Z",
     "start_time": "2022-11-10T14:21:22.747240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.barplot(\"support\",\"confidence\",data=rules)\n",
    "plt.title(\"support vs confidence\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.scatter(rules['support'],rules['confidence'])\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4fea6754",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.061437Z",
     "start_time": "2022-11-10T14:21:22.965416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.scatter(rules['lift'],rules['confidence'])\n",
    "plt.xlabel('lift')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06611c73",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.078047Z",
     "start_time": "2022-11-10T14:21:23.061437Z"
    }
   },
   "outputs": [],
   "source": [
    "matrix = rules.pivot('antecedents','consequents','lift') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "343e943f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.310747Z",
     "start_time": "2022-11-10T14:21:23.078047Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='consequents', ylabel='antecedents'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.heatmap(matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201ebb49",
   "metadata": {},
   "source": [
    "# 3) Association rules with 30% Support and 60% confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1556769e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.327008Z",
     "start_time": "2022-11-10T14:21:23.310747Z"
    }
   },
   "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>0.6</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7</td>\n",
       "      <td>(Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.4</td>\n",
       "      <td>(Sixth Sense, Gladiator, Patriot)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   support                           itemsets\n",
       "0      0.6                      (Sixth Sense)\n",
       "1      0.7                        (Gladiator)\n",
       "2      0.6                          (Patriot)\n",
       "3      0.5           (Sixth Sense, Gladiator)\n",
       "4      0.4             (Sixth Sense, Patriot)\n",
       "5      0.6               (Gladiator, Patriot)\n",
       "6      0.4  (Sixth Sense, Gladiator, Patriot)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 30% support\n",
    "frequent_items = apriori(movie, min_support=0.3, use_colnames=True)\n",
    "frequent_items \n",
    "#For (Patriot, Gladiator, Sixth Sense) support is 0.4 , which means Patriot, Gladiator, Sixth Sense appear together 40% times in total transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "501ad530",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.359522Z",
     "start_time": "2022-11-10T14:21:23.327008Z"
    }
   },
   "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>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "4                (Gladiator)                 (Patriot)                 0.7   \n",
       "5                  (Patriot)               (Gladiator)                 0.6   \n",
       "6   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "7     (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "8       (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "9              (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "10                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "4                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "5                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "6                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "7                  0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "8                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "9                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "10                 0.5      0.4    0.666667  1.333333      0.10         1.5  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with 60% Confidence\n",
    "rules = association_rules(frequent_items, metric='confidence', min_threshold=0.6)\n",
    "rules "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a602c87",
   "metadata": {},
   "source": [
    "Antecedent support variable tells us probability of antecedent products alone\n",
    "\n",
    "Consequents support variable tells us probability of consequents products alone\n",
    "\n",
    "The support value is the value of the two products (Antecedents and Consequents)\n",
    "\n",
    "Confidence is an indication of how often the rule has been found to be true.\n",
    "\n",
    "The ratio of the observed support to that expected if X and Y were independent.\n",
    "\n",
    "A leverage value of 0 indicates independence. Range will be [-1 1]\n",
    "\n",
    "A high conviction value means that the consequent is highly depending on the antecedent and range [0 inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9147888e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.391615Z",
     "start_time": "2022-11-10T14:21:23.359522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>4</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "4                (Gladiator)                 (Patriot)                 0.7   \n",
       "5                  (Patriot)               (Gladiator)                 0.6   \n",
       "7     (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "6   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "10                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "8       (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "9              (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "4                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "5                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "7                  0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "6                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "10                 0.5      0.4    0.666667  1.333333      0.10         1.5  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "8                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "9                  0.6      0.4    0.666667  1.111111      0.04         1.2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.sort_values('lift', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad574251",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.423710Z",
     "start_time": "2022-11-10T14:21:23.391615Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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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>0</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.18</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Sixth Sense, Patriot)</td>\n",
       "      <td>(Gladiator)</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>0.12</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Sixth Sense)</td>\n",
       "      <td>(Gladiator, Patriot)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Patriot)</td>\n",
       "      <td>(Sixth Sense, Gladiator)</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents               consequents  antecedent support  \\\n",
       "0              (Sixth Sense)               (Gladiator)                 0.6   \n",
       "1                (Gladiator)             (Sixth Sense)                 0.7   \n",
       "2              (Sixth Sense)                 (Patriot)                 0.6   \n",
       "3                  (Patriot)             (Sixth Sense)                 0.6   \n",
       "4                (Gladiator)                 (Patriot)                 0.7   \n",
       "5                  (Patriot)               (Gladiator)                 0.6   \n",
       "6   (Sixth Sense, Gladiator)                 (Patriot)                 0.5   \n",
       "7     (Sixth Sense, Patriot)               (Gladiator)                 0.4   \n",
       "8       (Gladiator, Patriot)             (Sixth Sense)                 0.6   \n",
       "9              (Sixth Sense)      (Gladiator, Patriot)                 0.6   \n",
       "10                 (Patriot)  (Sixth Sense, Gladiator)                 0.6   \n",
       "\n",
       "    consequent support  support  confidence      lift  leverage  conviction  \n",
       "0                  0.7      0.5    0.833333  1.190476      0.08         1.8  \n",
       "1                  0.6      0.5    0.714286  1.190476      0.08         1.4  \n",
       "2                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "3                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "4                  0.6      0.6    0.857143  1.428571      0.18         2.8  \n",
       "5                  0.7      0.6    1.000000  1.428571      0.18         inf  \n",
       "6                  0.6      0.4    0.800000  1.333333      0.10         2.0  \n",
       "7                  0.7      0.4    1.000000  1.428571      0.12         inf  \n",
       "8                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "9                  0.6      0.4    0.666667  1.111111      0.04         1.2  \n",
       "10                 0.5      0.4    0.666667  1.333333      0.10         1.5  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lift Ratio > 1 indicates good influential rule \n",
    "rules[(rules.lift>1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa653b7",
   "metadata": {},
   "source": [
    "## Visualization of obtained rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08a06bfc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.649296Z",
     "start_time": "2022-11-10T14:21:23.423710Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "sns.barplot(\"support\",\"confidence\",data=rules)\n",
    "plt.title(\"support vs confidence\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.scatter(rules['support'],rules['confidence'])\n",
    "plt.xlabel('support')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a1742ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.746653Z",
     "start_time": "2022-11-10T14:21:23.649296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.scatter(rules['lift'],rules['confidence'])\n",
    "plt.xlabel('lift')\n",
    "plt.ylabel('confidence') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2a76e560",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.762623Z",
     "start_time": "2022-11-10T14:21:23.746653Z"
    }
   },
   "outputs": [],
   "source": [
    "matrix = rules.pivot('antecedents','consequents','lift') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fa6421e9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:21:23.939388Z",
     "start_time": "2022-11-10T14:21:23.762623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='consequents', ylabel='antecedents'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xDElWacd+4qA6Cyc5J4nZlZLm2Hknnc0hH/jikNvv/r87OXC7z/DZLfZm+uEn8YEvDR9skCbCNlv+M0cd8p/D1pk1axaHfvM4NlhvnXEalSRJmk8dD2w+zPazgLWqam1gZ+CYQds3raq1q2pUMQaDYRPjI8CWVbX9QMEkDKpsAzwRDEuyFPD6qjpnUL09gXWr6pTBDSTZHNgL2KKqVgPWAc4Hlhnr4KpqelUdOEK1TYA5CoYN/jpU1Q3t2FdPsnJH+WM034zbzUn7kgTwxz9cy0P3PzTk9j9degP/eOBhAG669I8s9cLnj9fQpFFbd+01eO5zlhi2zg9Oms4/b7IBSz1vyfEZlCRJmvtq9tgeo+miiTXcM8z2h6qeWLxsMWBMC5kZDBtnSY4CVgKmJ7k/ybQkZwInJFkhyVltJtVZSV7S7nN5x2NGko2TLNamEV6U5LIk72jr7pjk5CS/THJjkq+05VOSHJ/k6jZ9cK+2fOW27iVJzk2yaptNtTVwUNvnysC7gF92OaSFq6rzL7oZwMDrTwP7VNXtAFU1q6qOraobupyXI9tUx2uSfL6jfPM28+w84F86yndMckT7/O1Jft+eh18nWSbJisBuwF7tMWw4zPk9PskhSX4LfHnQMQz4G7DkoLKfAdsjSfPQRtu9mavOvmyihyHNsb/ddTdnnXM+79lmy4keiiRJGosxTpNMsmv79/7AY9dnMowk2ya5HjiNJjtsQAFntnGNUbU92bKRel5V7dZmTG0K7A68HXhjVc1I8nPghKr6TpKdgcOAbdo0QJK8Hfg4TXbV54HfVNXO7bTDPyT5ddvN2sCrgUeBG5IcDrwAWK6qVm/bWrKtOw3YrapuTPI64JtV9aYk04FTq+qktv5ngZM6jyXJAgyKxlbVjztergZcOspT8+mquifJFOCsJGsCfwS+BbwJ+BPw4yH2PY8ma62SfAj4eFV9rA08PlRVB7fj7Xp+2zZeAbylqmYN0cdsYMqgsquB1w51QO034a4ARx99NLvu+oy+3yX1sVXXX50Nt3sz//2uT0/0UKQ59uWvH81e/74zU6YMfvuUJEnzkxrjAvpVNY0m9jDWdk4BTkmzfNMXgbe0mzaoqjuSvAD4VZLru8xqewqDYRNvelXNaJ+vz5PZT98FvjJQKcnLgYOAN1XVzCSbAVsn2aetsgjwkvb5WVV1f7vftcAKwDXASm1g7DSaqOniNNMIT0wy0NWzhhjnssBdg8rWBm4bzUEmWaM9piWATw0KmgG8pw0eLdj29SqazMVbqurGto3v0QaXBlke+HGSZYGFgVuGGMaQ5xc4cZhAGMDtwJrAHwYKqmpWkseSLFFVDw7eYdA3fO/di1bSPLX8qiuw04H/ziE7/icP3zf0lEppsrrm+hvZ93PNigb33v8A515wEVOmTOHNG83xcp6SJGkijXIR/PFSVee0s9yWrqq7q+qOtvzOJKcA6wEGwya5h4fZVgBJFgN+Auwy8EUGArxz8JTDNrvr0Y6iWcCCVXVvkrWAtwIfBd5Ds97XfQOZZyOYQRNwG+hnd5rg3J7D7HMNzTphv62qq4C126mNiw4a80uBfYDXtuM8vqOv0XzXHQ4cUlXTk2wCHDCKfQa3PdzXAeAo4Iwkr6uqXTrKnwU8Msr+JGlUlnrR0ux+1L58a6/D+Nstf5no4UjPyBknHf/E80//51fZeIP1DIRJkqRnJMnLgJvaGWHr0CTC/L2NlyxQVQ+2zzcDvjBSewbDJpfzgffSZC1tTzP9D+A44Liq6rwn+RnAHkn2aC+GV1fVkIvKJFkaeKyqfprkJuD4qnogyS1J3l1VJ6ZJD1uzqq4AHqTJ4hpwHfAy4GyAqjoiya+AXwFHD9Htl4CDk7yjqgYyyBbtUu85NMGo+5MsA2zR9nM98NIkK1fVTcD7hujnuTSZWwAf6Ch/sG17wFDndzT2oQlG/nCgIMnzgbuqauYctCNJfPiwvVj19aux+POW4KsXTONnh/6YKQs1U8nO/v6ZvGPqu1n8eUvw/v9sYu+zHp/FF7b+xEQOWXqafT93IBdddiX33fcAb95mBz7ywffz+OOPA7Ddtm+b4NFJkqS5ZpSL4I9Fkh/S3ARv6SS3AZ8DFgKoqqOAdwL/lmQmTbLOdm0sZBmaqZPQxLh+UFXd1jt/CoNhk8tU4Ngk+9JMSdwpyQo0i9e/ol3nCuBDNPNjvwZc2QaxbgW2Gqbt5YDj2nW+AD7Z/r89cGSS/WkutB8BV7T/fyvJ1Lb/04AP03H70qq6Ic1dJruqql8k+Sfg9HYtsPto1tk6Y1C9K5JcRpNJdjPwu7b8kXbq5GlJ7qYJXq3epasDaKZ63g5cCLy0Lf85cFKamwvsQZfzO/TpeprnATcOKtsU+MUctCFJABw99dBhtx+335Ect9+R4zQa6Zk56PP7jbruf+3/sXk4EkmSNE+NwzTJqhoq+WVg+5dpbng3uPxmYK057S9P3plSGl57R8etquq+jrIHgOWr6oEJG9g81gYbrwLeXVXXdZSfDHyy290xu/AbTfO9nVZ850QPQZorpl180EQPQRqzhZZeaaKHIEl6UkauMv96+ID3jenv2cUO+OGkOz8LjFxFesLHeHKR/gEHA+cl2XYCxjPPJVkFuJwmW+6PHeULAz8bZSBMkiRJkiRNEk6T1KhV1e+7lH2BUSxON79qg11PS7msqseAE8Z/RJIkSZIkjaNJdjfJucFgmCRJkiRJkrobhwX0x5vBMEmSJEmSJHVnZpgkSZIkSZL6Rc3uvcwwF9CXJEmSJElS3zAzTJIkSZIkSd05TVKSJEmSJEl9w2CYJEmSJEmS+oZ3k5QkSZIkSVLf6MHMMBfQlyRJkiRJUt8wM0ySJEmSJEldVQ9mhhkMkyRJkiRJUncGwyRJkiRJktQ3ZvfeAvquGSZJkiRJkqS+YWaYJEmSJEmSunOapCRJkiRJkvqGwTBJkiRJkiT1iyqDYZIkSZIkSeoXPZgZ5gL6kiRJkiRJ6htmhkmSJEmSJKm7HswMMxgmjYOZd9880UOQxuy7d1w40UOQ5orjll5poocgSWo9tPfWEz0EacwWP2T6RA9hniqDYZIkSZIkSeobBsMkSZIkSZLUN2ZP9ADmPhfQlyRJkiRJUt8wM0ySJEmSJElduWaYJEmSJEmS+ofBMEmSJEmSJPUN1wyTJEmSJEmS5l9mhkmSJEmSJKkr1wyTJEmSJElS/+jBaZIGwyRJkiRJktSVmWGSJEmSJEnqHz2YGeYC+pIkSZIkSeobZoZJkiRJkiSpq+rBzDCDYZIkSZIkSerOYJgkSZIkSZL6hZlhkiRJkiRJ6h89GAxzAX1JkiRJkiT1DTPDJEmSJEmS1JXTJCVJkiRJktQ3ejEY5jRJSZIkSZIkdVWzx/YYjSTHJrkzydVDbH9HkiuTXJ7k4iRv7Ni2eZIbkvwpyX6j6c9gmCRJkiRJkibS8cDmw2w/C1irqtYGdgaOAUgyBfgGsAXwKuB9SV41UmcGwyRJkiRJktRdZWyP0XRRdQ5wzzDbH6qqal8uBgw8Xw/4U1XdXFWPAT8C3jFSfwbDJM039v/vQ9jobe9lmx12G7beVdfdwJobvo0zf3vuOI1MmjPfmvZV7rjtCi6/7Kyu21dZZWXOO2c6Dz94M3vv9eFxHp0kSZrfPGu7qTz78yew6L6Hd90+ZbXXseg+h7Hox77Gont9lQVe+spxHqHmZ+MxTXI0kmyb5HrgNJrsMIDlgP/rqHZbWzYsg2HzkSRTk1yX5PsTPZahJNlmcEpikq8l2WhQ2WeSXJ9k2/b1jkkOaJ8fkOT2di7w1Um2HqHPT42w/RdJlhxm+5JJPtLxesUkZ3e8Predk/zCQfv9KMnLh+tbc9c2W/4zRx3yn8PWmTVrFod+8zg2WG+dcRqVNOdOOOEnvG2r7Yfcfs8997HnXp/hkEOPHsdRSZKk+dXMi87ikWkHDLl91o1XMOPgqcz46p48+qPDWeQ9e4zf4DTfq9kZ0yPJru3f1AOPXZ/ROKpOqapVgW2AL7bF3VLPqkvZUxgMm798BNiyqp74CyrJZLsj6DY083QBSLIU8Po25bHTnsC6VXXKEO0c2s4FfjdwbJLhrtWuwbA0FqiqLavqvmH2X5Lm3HZVVRsCFwNvG7TpSODjw7SruWzdtdfguc9ZYtg6PzhpOv+8yQYs9bwlx2dQ0jNw7nm/55577xty+113/Z2LL7mCmTNnjt+gJEnSfGv2zddQ/3ho6AqPPfLk84WfxShiBdITxpoZVlXTqmrdjse0MY2niS+snGRpmkywF3dsXh64Y6Q2RhUMS7JBksXa5zskOSTJCs9gzHqGkhwFrARMT3J/kmlJzgROSLJCkrPaOyucleQl7T6XdzxmJNk4yWLtXRouSnJZkne0dXdMcnKSXya5MclX2vIpSY5vM7SuSrJXW75yW/eSNnNq1SRvALYGDmr7XBl4F/DLLoe0cFV1/rSeATztp3dVXQc8Diyd5Gdtf9cMRJKTHAgs2vb3/Tar67ok3wQuBV6c5Nb2m4Qke7fHcnWSPdtuDqT5Rro8yUHALJ4+V/mvNEGzTucCb5mEAcm+9be77uasc87nPdtsOdFDkSRJkiaVKWu8nmd/4pssustneeRHh030cKQ5kuRlSdI+XwdYGPg7cBHw8iQvTbIw8F5g+kjtjfaP+COBtZKsRZMJ823gBGDjOT8EPRNVtVuSzYFNgd2BtwNvrKoZSX4OnFBV30myM3AYsE2bWUWSt9N83c4HPg/8pqp2bqcO/iHJr9tu1gZeDTwK3JDkcOAFwHJVtXrb1pJt3WnAblV1Y5LXAd+sqjclmQ6cWlUntfU/C5zUeSxtltdTPoqoqh93O+627dnAXcDOVXVPkkWBi5L8tKr2S7J7x7GuCKwC7FRVH2nLBtp6DbAT8DqaVMrfJ/kfYD9g9YE2Wv8yaCizgSmDxjw7yZ+AtYBLuo1f4+vLXz+avf59Z6ZMmTJyZUmSJKmPzLrqQv5x1YUssNJqLLzF9jxy1GcnekiaT9QoF8EfiyQ/BDahSYS5DfgcsFDTfx0FvBP4tyQzaZJptmsX1H88ye7AGTR/sx9bVdeM1N9og2GPV1W1WURfr6pvJ/nAHB6b5q7pVTWjfb4+TwZvvgt8ZaBSu6bVQcCbqmpmks2ArZPs01ZZBHhJ+/ysqrq/3e9aYAXgGmClNjB2GnBmksWBNwAnDgSagGcNMc5laQJZndamSWUczl5JdgAepL3I06yZtm27/cXAy2kiwYP9uaou7FL+RuCUqnq4PcaTgQ0ZRdQYuJ0mEDnYncCL6BIMa7PXdgX45lf/kw/92/tG0Y3G4prrb2Tfzx0IwL33P8C5F1zElClTePNGb5jgkUmSJEmTw+ybr2GB5y8Liy0BDz840cPRfGBuLoI/ZB9Vw/7BXFVfBr48xLZfAL+Yk/5GGwx7MMkngR2AjZJMoY3QacI8PMy2Amintv4E2KWqBubMBnhnVd3QuUObgfVoR9EsYMGqurfNCHwr8FHgPTTrfd03KJNqKDNoAm4D/exOE5zbc4T9Dq2qgzv22wR4C7B+Vf0jzQL3i3TfdchzM5Zw9snAZ5Nc3y7YN2ARmmN8mnYe9DSAmXff7KT8cXDGScc/8fzT//lVNt5gPQNhkiRJ6ntZelnq7r8AsMByK8GCCxoI06jV7HmfGTbeRhsM2w74V+CDVfXXdk2qg+bdsDSHzqeZF/tdYHvgvLb8OOC4qjq3o+4ZwB5J9mizrV5dVZcN1XC71tZjVfXTJDcBx1fVA0luSfLuqjqxnbe7ZlVdQZPJ1bnC+XXAy4CzAarqiCS/An4FzMlt0p4L3NsGwlYFXt+xbWaShapqpJWmzwGOb9cZC7At8P4uY+7m34BfVtWHB5W/giZ7TuNg388dyEWXXcl99z3Am7fZgY988P08/vjjAGy37eD7G0iT1/e++w023mh9ll56KW69+WI+/4WDWWih5jOmad/6Lsss80/8/oLTec5zFmf27NlM3WMX1lhrEx58cJiFcSVJUt961g77MOVlq5PFnsOzP3ssj53xQ1igWTrk8Qt+yYJrrs+C674JZj0OMx/jkRO+MkKLUm8bbTBsr6r6xMCLqvrfJKvNozFpzk2luePivjRTEndKc4ODdwGvaNcRA/gQze1HvwZc2QaxbgW2Gqbt5YDj8uTdHD/Z/r89cGSS/WmyBH8EXNH+/60kU9v+TwM+DBwz0GBV3ZDmLpNz4pfAbkmuBG4AOqdBTmuP51Lg00M1UFWXJjke+ENbdMxAIDDJ75JcDZxeVft22f15wI2dBUmWAWZU1V/m8Fj0DB30+f1GXfe/9v/YPByJNDY7vP+jw27/29/uYsWV1h2n0UiSpPndo987eNjtM39zMjN/c/I4jUa9pnpwnlNqFEeV5NKqWmdQ2ZVVteY8G5l6RpLzgK2q6r6OsgeA5avqgQkb2Bxo7055VVUd2VG2F/BAVX17pP2dJqlesOiLNpzoIUhzxeOP3T7RQ5AktR7ae+uJHoI0ZosfMr335hF2+PM6bxnT37MrXPrrSXd+FhhuY5J/T3IVsEqSKzsetwBXjc8Q1QM+xpOL9A84GDivY0H8SSvJOcBqwKmDNt0HfGfcByRJkiRJ0jip2RnTYzIaaZrkD4DTgS8BnfOTHqyqe+bZqNRTqur3Xcq+AHxhAoYzx6pqoyHKjxvvsUiSJEmSNJ56cZrksMGwqrofuB94X3sHyWXafRZPsnhV/e84jFGSJEmSJEmaK0a1gH6S3YEDgL8Bs9viAlwzTJIkSZIkqUdN1qmOYzHau0nuCaxSVX+fh2ORJEmSJEnSJFLVv8Gw/6OZLilJkiRJkqQ+UbNHrjO/GW0w7Gbg7CSnAY8OFFbVIfNkVJIkSZIkSZpws/s4M+x/28fC7UOSJEmSJEma74wqGFZVnwdIslhVPTxvhyRJkiRJkqTJoBfXDFtgNJWSrJ/kWuC69vVaSb45T0cmSZIkSZKkCVWzM6bHZDSqYBjwNeCtwN8BquoKYKN5NCZJkiRJkiRNAlVje0xGow2GUVX/N6ho1lweiyRJkiRJkjRPjXYB/f9L8gagkiwMTKWdMilJkiRJkqTeNFmnOo7FaINhuwFfB5YDbgPOBD46rwYlSZIkSZKkiTe7BxfQH+3dJO8Gtp/HY5EkSZIkSdIk0ot3kxw2GJbkcGDI5c6qaupcH5EkSZIkSZImhcm6CP5YjLSA/sXAJcAiwDrAje1jbVxAX5IkSZIkSfOZYTPDquo7AEl2BDatqpnt66No1g2TJEmSJElSj+rbNcOAFwFLAPe0rxdvyyRJkiRJktSj+m7NsA4HApcl+W37emPggHkyIkmSJEmSJE0Kvbhm2GjvJnlcktOB17VF+1XVX+fdsCRJkiRJkjTRenGa5EgL6AOQJMBbgLWq6v8BCydZb56OTJIkSZIkSZrLUqPId0tyJDAbeFNVvTLJ84Azq+q183qAUo/owcRSSZo/PbT31hM9BGnMFj9k+kQPQZL0pN5Lnepw0XLbjunv2dfefsqkOz+jXTPsdVW1TpLLAKrq3iQLz8NxSZIkSZIkaYL14jTJ0QbDZiaZQpvdkuSfaDLFJEmSJEmS1KN6cZrTqNYMAw4DTgFekOS/gPOA/55no5IkSZIkSZLmgdHeTfL7SS4B3kwzF3abqrpuno5MkiRJkiRJE6pvp0kmeT1wTVV9o329RJLXVdXv5+noJEmSJEmSNGGqB4Nho50meSTwUMfrh9sySZIkSZIk9ajZY3xMRqNdQD9V9cSaaVU1O8lo95UkSZIkSdJ8qOjfzLCbk0xNslD7+A/g5nk5MEmSJEmSJGluG20wbDfgDcDtwG3A64Bd59WgJEmSJEmSNPFm19gek9Fo7yZ5J/DeeTwWSZIkSZIkTSKz+3WaZJJXJDkrydXt6zWT7D9vhyZJkiRJkqSJVGRMj8lotNMkvwV8EpgJUFVXYqaYJEmSJElST+vFu0mONhj27Kr6w6Cyx+f2YCRJkiRJkqR5aVRrhgF3J1kZKIAk7wL+Ms9GJUmSJEmSpAk3Wac6jsVog2EfBaYBqya5HbgF2H6ejUqSJEmSJEkTbrJOdRyL0QbDqqrekmQxYIGqejDJS+flwCRJkiRJkjSxejEYNto1w34KUFUPV9WDbdlJ82ZIkiRJkiRJ6hdJjk1yZ5Krh9i+fZIr28f5Sdbq2HZrkquSXJ7k4tH0N2xmWJJVgdWA5yb5l45NzwEWGU0HkiRJkiRJmj+N05phxwNHACcMsf0WYOOqujfJFjRLeb2uY/umVXX3aDsbaZrkKsBWwJLA2zvKHwR2GW0nkiRJkiRJmv/MHodYWFWdk2TFYbaf3/HyQmD5sfQ3bDCsqv4f8P+SrF9VF4ylI0mSJEmSJM1fZo8xMyzJrsCuHUXTqmraGJr8IHB6x+sCzkxSwNGjaXu0C+j/KcmngBU796mqnUc/VkmSJEmSJM1Paqz7N8GpsQS/npBkU5pg2Bs7ijeoqjuSvAD4VZLrq+qc4doZbTDs/wHnAr8GZj2TAUuSJKl3PGu7qUx51brUQ/cz46A9nrZ9ymqvY+EttoeaDbNn8ejPjmH2LddNwEglSVIvSLImcAywRVX9faC8qu5o/78zySnAesCwwbDR3k3y2VX1iar6SVX9dOAxwiCnJrkuyfdH2ceESLJNklcNKvtako0GlX0myfVJtm1f75jkgPb5KknObu9ccF2SaW35ukkOm5P+23bWHWGfBZIcluTq9o4JFyV56Rwd+DOU5NY5qZNkmSQ/SHJzkkuSXNBxDjdJcuoc9v/E+UnyiyRLjlD/U3PSfsd+t3Y8P6T9um46qM7uSXZ6Ju1LkjS/m3nRWTwy7YAht8+68QpmHDyVGV/dk0d/dDiLvOfpATNJkjT5zR7jY25I8hLgZOD9VfXHjvLFkiwx8BzYDOh6R8pOo80MOzXJllX1izkY60doonW3dBYmWbCqHp+Ddua1bYBTgWsBkiwFvL6q9hxUb09ghap6qEsbhwGHtmuskWQNgKq6GBjptp5P6X+UtgNeBKxZVbOTLA88PAf7j4skAX4GfKeq/rUtWwHYem60X1VbjqLap4D/Hm2b7ZifMiG6qvZO8gdgZ+C3HZuOBX4HHDfa9iVJ6hWzb76GPO8FQ1d47JEnny/8LMY+yUKSJE2E2Zn3K+gn+SGwCbB0ktuAzwELAVTVUcBngecD32z+bOfxqloXWAY4pS1bEPhBVf1ypP5GGwz7D+BTSR4FZtIEC6qqnjPEQRwFrARMT3Is8Fya4M2KwN1JPkkTSPgn4C5gp6r63ySXdzSzCrA5TTDpcGCNdrwHVNX/S7IjTVDl2cDKwClV9fEkU4BvA+vS/NZ1bFUdmmRl4Bttn/+guRvmUm0bGyfZH3gn8Gag24lbeFAgbAYw8HpZ4LaBDVV1VXseNgH2qaqt2gyxu6vqC0neCnwa2K9L/wDvTvJNmrt4frCqzh00lmWBv1TV7La/J/pOshnweeBZwE3tuX2ozXT6Ds1dQRcC3l1V1yfZGPj6wNCBjarqwST7Au9p2zmlqj7X1rmry7kZbKDOm4DH2gt34Nz8mebr+RRJ1gO+BixKc253qqobkixKE2x6FXBdu31gn1uBdavq7iQ/A14MLAJ8vaqmJTkQWLS9rq6pqu2T7E0T1AI4pqq+1t6x4nSaQNf6NAHKwcf5V5qvxxOq6h9Jbk2yXlX9YRTnRZKkvjJljdfzrC3/jSzxXGZ86wsTPRxJkvQMjMfHWVX1vhG2fwj4UJfym4G15rS/UQXDqmqJNmPq5TTBhpHq75Zkc2DTNlBxAPAa4I1VNSPJz4ETquo7SXamyazapqrWBkjyduDjwPk0gZ3fVNXO7ZS4PyT5ddvV2sCrgUeBG5IcDrwAWK6qVm/bWrKtOw3YrapuTPI64JtV9aYk04FTq+qktv5ngZM6jyfJAgz6+lfVjzteHgr8Jsn5wJnAcVV136DTsh9wUZJz2+Pdsqpu6tI/wIJVtV6SLWmioW8Z1NZPgPOSbAicBXyvqi5LsjSwP/CWqno4ySeAvYGB3z7vrqp1knwE2IfmQtoH+GhV/S7J4sAjbUDt5TTzbEMT1Nyoqs6pqtcygo46qwGXjlS/dT1NIO7xJG+hyeZ6J/DvwD+qas12fvBQ7e1cVfe0wbOLkvy0qvZLsnvHdfUaYCfgde1x/T7J/wD30gRfd6qqj7TtDT7O2cCULv1eDGwIGAyTJGmQWVddyD+uupAFVlqNhbfYnkeO+uxED0mSJM2huTXVcTIZ1ZphST4E/A9NxtQB7f9z+tvM9Kqa0T5fH/hB+/y7dNwFIMnLgYOA7apqJs18z/3a7J6zaYJxL2mrn1VV91fVIzTTDFcAbgZWSnJ4G5B7oA3yvAE4sW3naJrsqm6W5elZQWvTkfk1WFUdB7wSOJEmre/CJM8aVGcgG+1XwBFVddNQ7dHMgwW4hCabbnB/t9EEbz5Jc12eleTNwOtpMqh+1x7nB2jOyXDt/g44JMlUYMl2Cutm7eMymuDTqjTBsTFJ8o0kVyS5qMvm59J8fa6mCS6u1pZvBHwPoKquBK4covmpSa4ALqTJEOs23jfSZLk93Gb5nUwTyAL4c1VdOMzwbwdekWRwMPhOmqzHp0mya5KLk1w8bdpcuXGGJEnzpdk3X8MCz18WFltioociSZI0R9MkXwtcWFWbJlmVJmNrTgy3plXBE4ud/QTYZeBuADQZPO+sqhs6d2izux7tKJpFk1F1b5K1gLcCH6WZ6rcncN9AhtAIZtCR/ZZkd5rg3J7D7dSO91jg2Dags3qXamsAf2eI4EmHgeOaxRBfo6p6lGZq3+lJ/kYzte9M4FfDpBc+rd2qOjDJacCWNEG8t9Cc8y9V1dEjjHMk1/Dk1E+q6qNt9lq3ddS+CPy2qrZtpy2e3bFt2KzMdjrqW4D126mLZ9M9g3G4ic7DrrnWZvFdC/xvkjcPTIVt+5kxxD6dt491oRRJUl/J0stSd/8FgAWWWwkWXBAefnCCRyVJkubU7Hm/ZNi4G+3dJB9ps69I8qyqup4mM+mZOh94b/t8e+C89vlxNFMMO9fIOgPYo13YnCSvHq7hNtiyQHu3y88A61TVA8AtSd7d1kkbMAN4EOj8mPI64GUDL6rqCJrMsE8P0+fmSRZqn7+QZlG32wfVWQH4GM20zi3aYF63/keUZJ0kL2qfLwCsCfyZJitqgyQva7c9O8krRmhr5aq6qqq+TBOkWpXmnO/cZtSRZLkkT1shN8n1Iwz1N8AiSf69o+zZQ9R9Lk+esx07ys+huUZIsjrNsXbb9942ELYqTYbcgJkDX5u2rW3a87IYsC0weD22rtrrZSWaKbhXdWx6BaO4U4UkSb3mWTvsw6L/8RUWeMFyPPuzx7Lg6/6ZBdffnAXX3xyABddcn0U/fgSLfuxrPOudu/HICV+Z4BFLkqRnYjYZ02MyGm1m2G3t2ls/A36V5F7gjmH3GN5UmgyqfWkX0G+DRe+imYo2sMD5h2gyhr4GXNkGxG4Fthqm7eWA49ogETRTCaEJqByZZqH6hYAfAVe0/3+rnSb4LuA04MPAMQMNtgu5LzVMn5sBX08ycNukfavqr21gZuAOhd+mWUz/jiQfBI5P8tou/Y/GC9p9BqZi/oFm6uUjaW4s8MOObfsDf+zSxoA9k2xKky12LXB6VT2a5JXABW0M8iFgB5opgbTHtDTDZ1pRVZVkG+DQJB+n+Vo/DHyiS/WvAN9Js8D9bzrKj6T5el4JXE73tbl+CezW1rmBJig4YBrNtXNpu4D+8R1tHNOutbbicMfReh5wazt1t9MGzHmWpCRJ871Hv3fwsNtn/uZkZv7m5GHrSJKkya8Xpzmlas4OK83dB58L/LKqHpsno5pgSc4DtupcBD/JA8DybZZZ30uyFbBSVR020WMZD0neQzNdd7uOslcDe1fV+0fRRC/+/JCk+dJDe2890UOQxmzxQ6ZP9BAkSU+anOlPc8n3XrTDmP6e3eGO70268zPazLAnVNX/zIuBTDIfo1mk/76OsoNp7uD4uao6ZUJGNYlU1akTPYbxkuQQYGOaO4J2WppmKq4kSZIkST2pF9cMm+NgWD+oqt93KfsC8IUJGI4mWFXtPUT5r8Z7LJIkSZIkjafZEz2AecBgmCRJkiRJkrrqxTV/DIZJkiRJkiSpq16cJrnAyFUkSZIkSZKk3mBmmCRJkiRJkrpyzTBJkiRJkiT1DYNhkiRJkiRJ6hvVg2uGGQyTJEmSJElSV72YGeYC+pIkSZIkSeobZoZJkiRJkiSpq17MDDMYJkmSJEmSpK5qogcwDxgMkyRJkiRJUleze3ABfdcMkyRJkiRJUt8wM0ySJEmSJElduWaYJEmSJEmS+obBMEmSJEmSJPUNF9CXJEmSJElS33ABfUmSJEmSJGk+ZmaYJEmSJEmSunLNMEmSJEmSJPUN1wyTJEmSJElS35jdg+Ewg2HSOJh5980TPQRpzBZaeqWJHoI0Vyx+yPSJHoIkSZImkMEwSZIkSZIkdeWaYZIkSZIkSeobvTdJ0mCYJEmSJEmShmBmmCRJkiRJkvrG7Ez0COa+BSZ6AJIkSZIkSdJ4MTNMkiRJkiRJXc3uwVXDDIZJkiRJkiSpq94LhRkMkyRJkiRJ0hBcQF+SJEmSJEl9oxenSbqAviRJkiRJkvqGmWGSJEmSJEnqqvfywgyGSZIkSZIkaQiuGSZJkiRJkqS+4ZphkiRJkiRJ0lyU5Ngkdya5eojt2ye5sn2cn2Stjm2bJ7khyZ+S7Dea/gyGSZIkSZIkqasa42OUjgc2H2b7LcDGVbUm8EVgGkCSKcA3gC2AVwHvS/KqkTozGCZJkiRJkqSuZo/xMRpVdQ5wzzDbz6+qe9uXFwLLt8/XA/5UVTdX1WPAj4B3jNSfwTBJkiRJkiR1VWP8l2TXJBd3PHYd45A+CJzePl8O+L+Obbe1ZcNyAX1JkiRJkiR1Nda7SVbVNNppjWOVZFOaYNgbB4q6dTlSOwbDJEmSJEmSNKklWRM4Btiiqv7eFt8GvLij2vLAHSO1ZTBMkiRJkiRJXc2ek2Xw55EkLwFOBt5fVX/s2HQR8PIkLwVuB94L/OtI7RkMkyRJkiRJUlfjEQpL8kNgE2DpJLcBnwMWAqiqo4DPAs8HvpkE4PGqWreqHk+yO3AGMAU4tqquGak/g2GSJEmSJEnqajwyw6rqfSNs/xDwoSG2/QL4xZz0590kJc039v/vQ9jobe9lmx12G7beVdfdwJobvo0zf3vuOI1MkiRJknrT7DE+JqNJFwxLMjXJdUm+P9FjGU6SbZK8alDZ15JsNKjsM0muT7Jt+3rHJAe0z1dJcnaSy9tjntaWr5vksDnpv21n3RH2WSDJYUmuTnJVkovaebXzXJJb56ROklntebk6yYlJnj3MfpskecMw27dOst8IfQ8+n8cn2aR9/q9JbkjysUH7rJHk+JGOS3PPNlv+M0cd8p/D1pk1axaHfvM4NlhvnXEalSRJkiRpfjLpgmHAR4Atq2r7zsIkk21K5zZAZ/BkKeD1VXXOoHp7AutW1Sld2jgMOLSq1q6qVwKHA1TVxVU1dU76H6XtgBcBa1bVGsC2wH1z2MZ4mdGel9WBx4DhUoE2AboGw5IsWFXTq+rAEfrbhiHOZ1X9ANiY5mvZWX4VsHy7kJ/Gwbprr8Fzn7PEsHV+cNJ0/nmTDVjqeUuOz6AkSZIkqYfVGP9NRpMqGJbkKGAlYHqSvZIckGRakjOBE5KskOSsJFe2/7+k3e/yjseMJBsnWSzJsW3202VJ3tHW3THJyUl+meTGJF9py6e02UADWVN7teUrt3UvSXJuklXbLKStgYPaPlcG3gX8ssthLVxVD3W8ngEMvF6W5jagwBPBlYFMp1Pb54cl+Wz7/K1Jzhmif4B3J/lDkj8m2bDLWJYF/lJVs9v+bquqe9u2N0tyQZJL20ysxdvyW5N8vi2/KsmqbfnGHef8siRLtOX7tuf8yiSf7+j7rqG/8iPWORd4WZK3J/l929+vkyyTZEWaQNle7Vg2bL+OhyT5LfDl9mt+RDu+p11DQ5zP+2mCcANfm78CS3YZ289p7lahSeBvd93NWeecz3u22XKihyJJkiRJPcFpkvNYVe0G3AFsWlWHtsWvAd5RVf8KHAGcUFVrAt+nyayizSBaG/gMcDFwPvBp4DdV9VpgU5pAx2Jtm2vTZEmtAWyX5MVt2XJVtXqbNXVcW3casEdVvQbYB/hmVZ0PTAf2bfu+CdgAuKTzeJIswKAbL1TVj6vq4PblocBvkpzeBv+W7HJa9mvHuGl7vDsN0T/AglW1Hk0G0+e6tPUT4O1twOerSV7djnNpYH/gLVW1TnsO9+7Y7+62/Mj2HND+/9H2vG8IzEiyGfByYL32fL4m7bTR9uswrG512ozALYCrgPNosu9eDfwI+HhV3QocxZMZdgOLRL2iPZ6PDWryaddQt/NZVf/Rlj9lOF2GfXF7/E+TZNckFye5+JgTfjjS4Wsu+PLXj2avf9+ZKVOmTPRQJEmSJKkn9GJm2GSbetjN9Kqa0T5fH/iX9vl3ga8MVErycuAg4E1VNbMNzGydZCB4swgwMJ3trKq6v93vWmAF4BpgpSSHA6cBZ7bZUW8ATkyeiIM8a4hxLsvTM5vWpiPza7CqOi7JGcDmwDuADydZa1CdfyTZBTgH2Ksj8NXNye3/lwArdunvtiSrAG9qH2cleTewKM0Uwd+1x7kwcMEQ7Q6c/98Bh6RZ2+3ktu3NgM2Ay9o6i9MExwZPHR2NRZNc3j4/F/g2sArw4yTLtmO8ZZj9T6yqWV3Kh7yGRuHeJC+rqj91lN1JM/X0aapqGk0wlZl33zw5fwL0mGuuv5F9P9fMiL33/gc494KLmDJlCm/eaMgl5SRJkiRJfWZ+CIY9PMy2Amgzvn4C7FJVd7TbAryzqm7o3CHJ64BHO4pm0WRU3dsGot4KfBR4D02G1X1t9tNIZtAE3Ab62Z0mOLfncDu14z0WODbJ1cDqXaqtAfydIYIuHQaOaxZDfG2r6lHgdOD0JH+jWSvrTOBXw9zK9GntVtWBSU4DtgQuTPIWmnP+pao6eoRxjsaMwee9DVQeUlXT0yxuf8Aw+w933XSakyDV14ArkuxRVce2ZYvQfO01CZxx0vFPPP/0f36VjTdYz0CYJEmSJI3BZJ3qOBaTaprkKJzPk+szbU8zbQ6aKY3HdUyRAzgD2CNtqtPAlMChtFMFF6iqn9JMt1ynqh4Abmmzp0hjIHPrQaBzJe/rgJcNvKiqI2gywz49TJ+bJ1moff5C4PnA7YPqrAB8DHg1sEUbzOvW/4iSrJPkRe3zBYA1gT8DFwIbJHlZu+3ZSV4xQlsrV9VVVfVlmqmCq9Kc85071htbLskLuux7/ZyMu8NzefL8fKCjfE7OxVDX0Gja+BTw8o5AGDTTMa8eZd8ao30/dyDbf3gvbv3f23jzNjvw05+fwY9POY0fn3LaRA9NkiRJknrS7KoxPSaj+SEzrNNUmgyqfWmmJO7UBoveBbwiyc5tvQ8BX6TJ5LmyDYjdCmw1TNvLAce1QSKAT7b/bw8cmWR/YCGataquaP//VpKpbf+nAR8GjhlosKpuSHOXyaFsBnw9ySPt632r6q8di9SHZnrgPlV1R5IPAscneW2X/kfjBe0+A1M9/wAcUVWPJNkR+GHHtv2BPw7T1p7tOmazgGuB06vq0SSvBC5oY5APATvQTCWkPaal6b721mgcQDNl9XaaAN5L2/KfAyeluUnCHiO08bRrqC1/yvkcYjrqszoyDwdsSvO11zg46PP7jbruf+0/eLk4SZIkSdKcmpzhrLFJTdIo3fwoyXnAVlV1X0fZA8DybZZZ30uyFbBSVR020WOZE22G21VVtUxH2bOA/wHeWFWPD7e/a4apFyy09EoTPQRJkiRpMnqmCR/zhX9dYdsx/T37gz+fMunOz/w2TXKy+xhPLtI/4GDgvCTbTsB4Jp2qOnU+DIT9K/BrmjXgOr0E2G+kQJgkSZIkSZo8zAyTxoGZYeoFZoZJkiRJXU26zKe56X0rbDOmv2d/+OefTbrzM7+tGSZJkiRJkqRx0ot3kzQYJkmSJEmSpK5m9+AS+gbDJEmSJEmS1FX1YDDMBfQlSZIkSZLUN8wMkyRJkiRJUleuGSZJkiRJkqS+UdV70yQNhkmSJEmSJKmrXlxA3zXDJEmSJEmS1DfMDJMkSZIkSVJXrhkmSZIkSZKkvlE9OE3SYJgkSZIkSZK66sU1wwyGSZIkSZIkqatevJukC+hLkiRJkiSpb5gZJkmSJEmSpK5cQF+SJEmSJEl9wwX0JUmSJEmS1DdcQF+SJEmSJEl9wwX0JUmSJEmSpPmYmWGSJEmSJEnqymmSkp6RhZZeaaKHII3ZggsvN9FDkOaKxx+7faKHIEmSNN9wAX1JkiRJkiT1jdmuGSZJkiRJkiTNv8wMkyRJkiRJUle9lxdmMEySJEmSJElDcAF9SZIkSZIk9Q2DYZIkSZIkSeob5QL6kiRJkiRJ0vzLzDBJkiRJkiR15TRJSZIkSZIk9Y0yGCZJkiRJkqR+4ZphkiRJkiRJ6huzqTE9RiPJsUnuTHL1ENtXTXJBkkeT7DNo261JrkpyeZKLR9OfwTBJkiRJkiRNpOOBzYfZfg8wFTh4iO2bVtXaVbXuaDozGCZJkiRJkqSuqmpMj1H2cQ5NwGuo7XdW1UXAzLlxTAbDJEmSJEmS1NV4TJMcowLOTHJJkl1Hs4ML6EuSJEmSJKmrsd5Nsg1QdQapplXVtDE1+lQbVNUdSV4A/CrJ9W2m2ZAMhkmSJEmSJGmeaANfczP4Nbj9O9r/70xyCrAeMGwwzGmSkiRJkiRJ6mp21Zge81KSxZIsMfAc2AzoekfKTmaGSZIkSZIkqauxTpMcjSQ/BDYBlk5yG/A5YCGAqjoqyQuBi4HnALOT7Am8ClgaOCUJNDGuH1TVL0fqz2CYJEmSJEmSuprX2V0AVfW+Ebb/FVi+y6YHgLXmtD+DYZIkSZIkSepqPDLDxptrhkmSJEmSJKlvmBkmSZIkSZKkrsZjmuR4MzNMkqRx9q1pX+WO267g8svO6rp9lVVW5rxzpvPwgzez914fHufRSZIkSU+qMf6bjOb7YFiSqUmuS/L9iR7LcJJsk+RVg8q+lmSjQWWfSXJ9km3b1zsmOaBj+w5JrkxyTZIrkhyTZMl229lJ1p2DMW2S5NT2+dZJ9htF/TeMtv2O/Z5yDMO0fXxH/buSXJ7k2iS7jLDvnkmePcz2Ywaf+y51PjXo9a0dz3+U5Kokqw2qc3CSNw3XriR1c8IJP+FtW20/5PZ77rmPPff6DIccevQ4jkqSJEl6utlVY3pMRvN9MAz4CLBlVT3lr4okk20K6DY0t/0EIMlSwOur6pxB9fYE1q2qUwY3kGRzYC9gi6paDVgHOB9YZqyDq6rpVXXgCNU2AeYoGDaGr8OPq2rtts//TjLcMe4JdA2GJZlSVR+qqmtH6O9TQ22oqvcC3wfeO2jT4cCwAURJ6ubc837PPffeN+T2u+76OxdfcgUzZ84cv0FJkiRJXZgZNskkOQpYCZieZK8kBySZluRM4IQkKyQ5q82kOivJS9r9Lu94zEiycZLFkhyb5KIklyV5R1t3xyQnJ/llkhuTfKUtn5Lk+CRXt1lDe7XlK7d1L0lybpJV22yqrYGD2j5XBt4F/LLLYS1cVQ91vJ4BDLz+NLBPVd0OUFWzqurYqrqhy7k5MsnFbQbZ5zvKN28zz84D/qWjfMckR7TP357k9+15+HWSZZKsCOwG7NUew4bDnN/jkxyS5LfAlwcdw1AeA+4fXFhVdwI3ASt0O6YkU4EXAb9t+yPJQ0m+kOT3wPqdGXNJ3td+va5O8uW27EBg0fa4BjIM7xo0lL8CSw4a25+B5yd54QjHJkmSJEmSJonJlj01R6pqtzZbatOqurudivca4I1VNSPJz4ETquo7SXYGDgO2aTOOSPJ24OM02VWfB35TVTu30w7/kOTXbVdrA68GHgVuSHI48AJguapavW1rybbuNGC3qroxyeuAb1bVm5JMB06tqpPa+p8FTuo8niQLwFPDplX1446XqwGXjvL0fLqq7kkyBTgryZrAH4FvAW8C/gT8eIh9z6PJWqskHwI+XlUfa4OPD1XVwe14u57fto1XAG+pqlmjGWxVnU/zdXiKJCvRBDz/1O2YquqwJHvTXgPtbosBV1fVZ9s2Btp6EU1w7jXAvcCZSbapqv2S7D5wXbTjee2gocwGpnQZ+qXABsBPR3OckiRJkiTNT6pmT/QQ5rr5OjNsCNOrakb7fH3gB+3z7wJvHKiU5OXAQcB2VTUT2AzYL8nlwNnAIsBL2upnVdX9VfUIcC2wAnAzsFKSw9uA3ANJFqeZRnhi287RwLJDjHNZnp59tDZw22gOMskabSbTTUm261LlPUkuBS6jCaK9ClgVuKWqbqyqAr43RPPLA2ckuQrYt92/myHPL3DiaANhQ9iuPYc/BD5cVfcMcUzdzKJ7cOq1wNlVdVdVPU4z9XGjLvW6uR1YrQ1YdrqTJjPtaZLs2mayXTxt2rRRdiNJkiRJ0uQxmxrTYzKarzPDhvDwMNsKIMliwE+AXarqjnZbgHcOnnLYZnc92lE0C1iwqu5NshbwVuCjwHto1q66rzPDaBgzaAJuA/3sThOc23OYfa6hWSfst1V1FbB2O7Vx0UFjfimwD/DadpzHd/Q1mivxcOCQqpqeZBPggFHsM7jt4b4Oo/Hjqtp94MUIxzTYI0ME4jKG8ZwDLAH8b5K1qurvbfkiNF/Lp6mqaTSZgjC68y5JkiRJ0qRSk3QR/LHoxcywTufz5KLn29NM/wM4Djiuqs7tqHsGsEfaOXVJXj1cw0mWBhaoqp8CnwHWqaoHgFuSvLutkzZgBvAgTTBlwHXAywZeVNURNJlhnx6m2y8BBydZvqNs0S71nkMTjLo/zcLzW7Tl1wMvbdcsA3jfEP08lyYTCuADHeWDj2Go8zukJNsm+dJI9boY6pi6jWsovwc2TrJ0O9XyfcD/tNtmJllomH23BP4GvLgjEAbNdNCrR3kMkgTA9777Dc47ZzqrvGJlbr35Ynba8b3susv72XWX9wOwzDL/xK03X8ye/7Ern/rkf3DrzRezxBKLT/CoJUmSpN7Qi5lhnaYCxybZl2ZK4k5JVqBZvP4V7TpXAB8Cvgh8DbiyDYjdCmw1TNvLAcd1TJv7ZPv/9sCRSfYHFgJ+BFzR/v+tdsH3dwGnAR8GjhlosKpuSHOXya6q6hdJ/gk4vQ3m3EcTiDljUL0rklxGk0l2M/C7tvyRJLsCpyW5myZ4tXqXrg6gmep5O3Ah8NK2/OfASWluLrAHXc7v0KfrCSsDD4yi3lMMdUytaTTn5C9VtekwbfwlySeB39Jkif2iqv5fRxtXJrl08J1JW88DbqqOkHgbPHsZcPGcHo+k/rbD+z867Pa//e0uVlxp3XEajSRJkjS0yTrVcSzSi+lu84s0d3Tcqqru6yh7AFi+zTLrOUm+B+xVVYPXS5vUknwceH5VfaKjbFuajMDPjKIJv9E031tw4eUmegjSXPH4Y7ePXEmSJGn0xrIkz6S33PNWG9Pfs7ffe82kOz+9Pk1ysvsYTy7SP+Bg4Lw20NJzqmqH+TAQ9iNgW5rF/DstCHx1/EckSZIkSdL4mF01psdkZGaYND78RtN8z8ww9QozwyRJ0lw26TKf5qYXLvnKMf09+9f7rpt058fMMEmSJEmSJPWNXl9AX5IkSZIkSc9QL84oNBgmSZIkSZKkrnrxbpIGwyRJkiRJktRVL2aGuWaYJEmSJEmS+oaZYZIkSZIkSepqdg9mhhkMkyRJkiRJUle9OE3SYJgkSZIkSZK6cgF9SZIkSZIk9Y1ezAxzAX1JkiRJkiT1DTPDJEmSJEmS1JUL6EuSJEmSJKlvlGuGSZIkSZIkqV+YGSZJkiRJkqS+4QL6kiRJkiRJ0nzMzDBJkiRJkiR15ZphkiRJkiRJ6hu9OE3SYJgkSZIkSZK66sVgmGuGSZIkSZIkqW+YGSZJkiRJkqSuei8vDNKL6W6S+k+SXatq2kSPQxoLr2P1Cq9l9QqvZfUCr2Pp6ZwmKalX7DrRA5DmAq9j9QqvZfUKr2X1Aq9jaRCDYZIkSZIkSeobBsMkSZIkSZLUNwyGSeoVroOgXuB1rF7htaxe4bWsXuB1LA3iAvqSJEmSJEnqG2aGSZIkSZIkqW8YDJMkSZIkSVLfMBgm9bgkU5Ncl+T7Ez2WoSTZJsmrBpV9LclGg8o+k+T6JNu2r3dMckDH9h2SXJnkmiRXJDkmyZLttrOTrDsHY9okyant862T7DeK+m8Ybfsd+z1xDElWacd+4qA6Cyc5J8mCc9r+/GZ+uF5h7lyz7df77CSXt8c8rS1fN8lhc9L/aK7vJAskOSzJ1UmuSnJRkpfO0YE/Q0lu7Xj+o7b/1QbVOTjJm8ZjPJPV/HD9z6Vr/4Akt7fX/tVJth6hz0+NsP0XAz/rh9i+ZJKPdLxeMcnZHa/PTXJxkhcO2u9HSV4+XN+T3fxwTYG/B3QewzBtH99R/672++faJLuMsO+eSZ49zPZjBp/7LnU+Nej1rR3P5+hnej9dk/36Pj+aOkmWSfKDJDcnuSTJBR3n8InvvTno/4nzM9J7Qltn2PeVYfa7teP5Ie3XddNBdXZPstMzaV/jy2CY1Ps+AmxZVdsPFGTyBVW2ATrf8JcCXl9V5wyqtyewblWdMriBJJsDewFbVNVqwDrA+cAyYx1cVU2vqgNHqLYJMEe/BA/+OlTVDe3YV0+yckf5Y8BZwHZz0v586mnXK/TmNQscBhxaVWtX1SuBwwGq6uKqmjon/Y/SdsCLgDWrag1gW+C+OWxjzKrqvcD3gfcO2nQ4MOwfm32gL35etw6tqrWBdwPHJhnud9Kuf7SksUBVbVlV9w2z/5I057arqtoQuBh426BNRwIfH6bd+UHf/Ezthd8D5sCP2++fTYD/TjLcMe4JdA2GJZlSVR+qqmtH6G/IwMEz+JneN9ckffo+P5IkAX4GnFNVK1XVa2iun+XnRvujeE+AYa7pbgbebwb1szfweWDnQdWPBUb6+moSMBgm9bAkRwErAdOT3J9kWpIzgROSrJDkrPYT1LOSvKTd5/KOx4wkGydZLMmx7SdMlyV5R1t3xyQnJ/llkhuTfKUtn5Lk+I5PpvZqy1du616S5pP4VdN8iro1cFDb58rAu4BfdjmkhavqoY7XM4CB158G9qmq2wGqalZVHVtVN3Q5L0emyQK4JsnnO8o3bz/dOw/4l47yHZMc0T5/e5Lft+fh1+0nWysCuwF7tcew4TDn9/j2k6TfAl8edAwD/kbzx1unnwHb08MGXa97pcke6eVrdlngtoENVXVV22dnNsJhST7bPn9rmgzBbv0DvDvJH5L8McmGXcayLPCXqprd9ndbVd3btr1Zmk9lL01yYpLF2/Jbk3y+Lb8qyapt+cYd5/yyJEu05fu25/zKzu8t4K5BY/krg67xqvoz8PwMys7pF+mvn9dPqKrrgMeBpZP8rO3vmiS7tuM4EFi07e/7abK6rkvyTeBS4MXtdbp0W3/v9liuTrJn282BwMptGwcBs4B7Bg3ladckcC7wlky+P9JHJf33M7WXfg8Y7DHg/sGFVXUncBOwQrdjSjKVJjjy27Y/kjyU5AtJfg+sn6dm1Lyv/XpdneTLbdlTvgfbrp/Rz/Q+vCb7+X2+m4E6bwIeq6qjOs7Nn6vq8ME7JFkvyfntGM5PskpbvmiarMQrk/wYWLRjn873hBHfV9qyp713pMv7TZfj7Hbt/wO4Ncl6ozgnmkhV5cOHjx5+ALcCSwMHAJcAi7blPwc+0D7fGfjZoP3eTvOHwELAfwM7tOVLAn8EFgN2BG4GngssAvyZ5o3iNcCvOtpasv3/LODl7fPXAb9pnx8PvKuj/neAtw8azwLAA8Mc5z3Ac4fZfjbNJ3cAS7X/T2nL12zH/3/Ay4EAPwFObevtCBzRPn8ePHEn3g8BX22fH0DzS/hAf13Pb3uspwJThhnrb4D1BpVNAe6a6OtpvK7XjnPay9fsTjR/3JxOk80w0OcmHdfes4FrgE2BG4CVh+j/7I5rcUvg1136W749v5cDXwVe3ZYvDZwDLNa+/gTw2Y6vxx7t848Ax3R8LTZony8OLAhsRnPr9rTHfiqw0RDH/m/AN7uUfwt450RfhxN9/ffBtX8A7c/Ltu072utm4GfzosDVwPPb1w917LsiMJsmQ2PweXsNcFV7vIvTfO+8ut3n6hHO/WeBj3cp/xXwmom+NsZ6TXWc916+rnrm94ARvqadY1kJuBNYqtsxDb4G2tcFvGfweaEJmv0v8E80P9N/A2wz+HtwiDGN+mc6/XVN9u37/AjXy1SajLmhtneen+cAC7bP3wL8tH2+N3Bs+3xNmg9V1u04poFrbDTvK8O9dzzl/abLWDcCftGl/NPAx57J97iP8XuYGSb1l+lVNaN9vj7wg/b5d4E3DlRKs0bKQcB2VTWT5s1vvySX07wZLwK8pK1+VlXdX1WPANcCK9D8ErJSksPTTFt4oP0E6g3AiW07R9N8gtXNsjz9k5e16fh0bThJ1mg/7bkpSbephe9JcilwGbAaTRr6qsAtVXVjNe9i3xui+eWBM5JcBezb7t/NkOcXOLGqZg1zCLfTvLE/oa3/2MAnc32kZ6/ZqjoOeCVwIs0vfhcmedagOv8AdqH5g/yIqrppqPaAk9v/L6H5BW5wf7cBqwCfpPnl7qwkbwZeT/M98Lv2OD9Ac06Ga/d3wCFpsg6WrKrHac75ZjTfV5fSfE8Ntd7S7cBqefrUuDtp/iBTD1/7rb3atg9ux17A1CRXABfS/PE61PXz56q6sEv5G4FTqurhajI1Tga6ZU9087Sfu61euyZ7/boaGP/8/nvASLZrz+EPgQ9X1T1DHFM3s4Cfdil/LXB2Vd3V/kz/Ps0f+qMxlp/pPXtN9vn7/Kgl+UaaNf4u6rL5uTRfn6uBQ3ny+20j2u/RqroSuHKI5kfzvjLce8dQ7zcDbgdekWSRQeW99t7Rk+bLtG9Jz9jDw2wrgCSL0XwauktV3dFuC80ne0+ZapDkdcCjHUWzaD69uTfJWsBbgY8C76FZU+G+ata4GMkMml9qBvrZneYXoD2H2ecamvVBfltNGvraaaY0LNpZKc1CovsAr23HeXxHXzWKsR0OHFJV05NsQvOp5mh0tj3c1wHgKJpftF9XVZ0L4z4LeGSU/fWKXr5macd7LM2aSVcDq3eptgbwd0b+pWrguGYxxPt7VT1K8wn16Un+RrMmyZk0n5a/b7TtVtWBSU6j+XT6wiRvoTnnX6qqo0cYJzSfUC8B/G+Starq7235IjTnUj1+7dNkBRzcsd8mNJ/6r19V/0izwP3gPy4GDHVuMorxDuVk4LNJrq+qVTvKe+2a7OXrqpd+DxjJj6tq94EXIxzTYI8MEYgby/fPWH6m9/I12c/v88O5Bnhnx5g/2k5rvLhL3S/SfE9vm2Y68tkd24b9fp2D95Xhrv1hv1er6qYk19Jc+29uf/ZA77139CQzw6T+dT5PLna6PXBe+/w44LiqOrej7hnAHkkCkOTVwzXcvqEtUFU/BT4DrFNVDwC3JHl3WyftLyUAD9L8EjXgOuBlAy+q6giaT98+PUy3XwIOTtK5+OaiXeo9h+aN7f40C85u0ZZfD7w0T67LMNQvDM+l+RQImk/WBgw+hqHO72jsQ/ML3xOBsCTPp5kmOXMO2uk1PXXNplmbZqH2+QuB5/PktTVQZwXgYzTp+lu0v+R3639ESdZJ8qL2+QI0WTB/pvm0dIMkL2u3PTvJK0Zoa+Wquqqqvkzzy+uqNOd85zy5DslySV4wRBNb0qyN9+KOP5oAXkEzjUFP1VPX/hCeC9zb/sGyKk0mw4CZA98rIzgH2Ka9hhejWTz63C5j7ubfgF8OCoRBc01eM6ojmP/02nU13/8ekGTbJF8aqV4XQx1Tt3EN5ffAxkmWTjKF5vj/p9020vfg3PqZ3lPXZL++zye5foSh/gZYJMm/d5QNdcfTzu+3HTvKz6FdSzfJ6nTP7B3t+8pQ7x0jaq+XlYDlOgJh4O8z8wWDYVL/mgrslORK4P3Af7RvyO+ieaMbWDRzXZpPZRYCrmw/1friCG0vB5ydJhX7eJp0bWjetD6YJl35GuAdbfmPgH3TLI65MnAaTTr5E9pP/pYaqsOq+gXNXXtOT3Or8fNpPuE6Y1C9K2jSu6+h+aTud235I8CuwGlpFs798xBdHUCTrn0ucHdH+c+BbdtztiFdzu9QY+/iecCNg8o2BX4xB230op66ZmmmGlzdtn0GsG9V/XVgY/sL/rdp1qC5A/ggcEyaVPzB/Y/GC4Cft+fjSpr1NY6oqrtofsH8YXtuL6T5pXc4e6ZZZPYKmk8+T6+qM2mmt1yQZvrQSQz9i/zzgJvaqUgDx7sQzR8a3T4Z7ne9du1380tgwfYYv0hzHQ6Y1h7P97vu+WS/l7bH8AeaP+yPqarL2j/Of9deswcNsfvTfu62QYUZVfWXOTyW+UVPXVc98nvAysADo6j3FEMdU2sazTn57Qht/IXm6/Rb4Arg0qr6fx1tDPc9OLd+pvfUNUkfvs+nCToOm2XYXifb0ARfb0nyB5o12T7RpfpXgC8l+R3NengDjgQWb4/n4zQ/9wcb1fvKUO8dwx1Dh+cBt3b5sHoD4NejbEMTJB0/syRp0mh/Ed2qOm6NnOQBYPn2k7ye1P5idBXw7mrusjZQfjLwycHTATR59Os1+0wk+TjNIraf6CjbluaT+s9M3Mj0TPTCtZ/mbmFXVdWRHWV70SyO/e2JG1n/6oXrak4l+R6wVxu8mG/0y8/0frwm51SSrYCVquqwiR7LeEjyHprputt1lL0a2Luq3j9xI9NomBkmabL6GE8uhDrgYOC89hesnpPmdtGX03wi+8eO8oVp7qhkIGxy67tr9plI8iOaKQg/HLRpQZq7X2n+M19f+0nOoVmU+dRBm+6jyVbQxJivr6tnoqp2mA8DYf30M73vrsk5VVWn9lEg7BCabLZjBm1ammYqriY5M8MkSZIkSZLUN8wMkyRJkiRJUt8wGCZJkiRJkqS+YTBMkiRJ840k2yR51USPQ5Ikzb8MhkmSJGl+sg1gMEySJD1jBsMkSZJ6VJJ/S3JlkiuSfDfJCknOasvOSvKStt7xSQ5Lcn6Sm5O8qy1fNsk5SS5PcnWSDdvyzZJckOTSJCcmWbwt3zzJ9UnOa9s7tS0/IMk+HeO6OsmK7fMdkvyh7ePoJFPa8oeS/Fc79guTLJPkDcDWwEFt/ZWTTE1ybXtMPxrH0ytJkuZTBsMkSZJ6UJLVgE8Db6qqtYD/AI4ATqiqNYHvA4d17LIs8EZgK+DAtuxfgTOqam1gLeDyJEsD+wNvqap1gIuBvZMsAnwLeDuwIfDCUYzxlcB2wAZtH7OA7dvNiwEXtmM/B9ilqs4HpgP7VtXaVXUTsB/w6vaYdpuzsyRJkvrRghM9AEmSJM0TbwJOqqq7AarqniTrA//Sbv8u8JWO+j+rqtnAtUmWacsuAo5NslC7/fIkG9NMU/xdEoCFgQuAVYFbqupGgCTfA3YdYYxvBl4DXNS2tShwZ7vtMeDU9vklwD8P0caVwPeT/Az42Qj9SZIkGQyTJEnqUQFqhDqd2x8dtC9VdU6SjYC3Ad9NchBwL/CrqnrfUzpL1h6mv8d56oyERTr6+U5VfbLLPjOraqC9WQz9e+vbgI1opk9+JslqVfX4EHUlSZKcJilJktSjzgLek+T5AEmWAs4H3ttu3x44b7gGkqwA3FlV3wK+DawDXAhskORlbZ1nJ3kFcD3w0iQrt7t3BstubfclyTrASzvG+K4kLxgYY9vncB4ElmjrLwC8uKp+C3wcWBJYfIT9JUlSnzMzTJIkqQdV1TVJ/gv4nySzgMuAqTTTHvcF7gJ2GqGZTYB9k8wEHgL+raruSrIj8MMkz2rr7V9Vf0yyK3BakrtpAm2rt9t/Cvxbkstppl7+sR3jtUn2B85sA1szgY8Cfx5mTD8CvpVkKk1g79tJnkuTZXZoVd03itMjSZL6WJ7MPpckSZLmjiSbAPtU1VYTPBRJkqSncJqkJEmSJEmS+oaZYZIkSZIkSeobZoZJkiRJkiSpbxgMkyRJkiRJUt8wGCZJkiRJkqS+YTBMkiRJkiRJfcNgmCRJkiRJkvqGwTBJkiRJkiT1jf8PxGQO8shXoEgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.heatmap(matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a8b8ac",
   "metadata": {},
   "source": [
    "Association rules with 10% Support and 25% confidence then no. of rules 217\n",
    "\n",
    "Association rules with 20% Support and 40% confidence then no. of rules 14\n",
    "    \n",
    "Association rules with 30% Support and 60% confidence then no. of rules 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8aae1a1",
   "metadata": {},
   "source": [
    "## As the support and confidence threshold increases, no of rules decreases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51d19d7",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}