apriori_association_rules.ipynb

{
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  {
   "cell_type": "code",
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       "      <th></th>\n",
       "      <th>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
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       "      <th>CholCheck</th>\n",
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       "      <th>NoDocbcCost</th>\n",
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       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
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      "text/plain": [
       "        Diabetes_012  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0                0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
       "1                0.0     0.0       0.0        0.0  25.0     1.0     0.0   \n",
       "2                0.0     1.0       1.0        1.0  28.0     0.0     0.0   \n",
       "3                0.0     1.0       0.0        1.0  27.0     0.0     0.0   \n",
       "4                0.0     1.0       1.0        1.0  24.0     0.0     0.0   \n",
       "...              ...     ...       ...        ...   ...     ...     ...   \n",
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       "253679           2.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "        HeartDiseaseorAttack  PhysActivity  Fruits  Veggies  \\\n",
       "0                        0.0           0.0     0.0      1.0   \n",
       "1                        0.0           1.0     0.0      0.0   \n",
       "2                        0.0           0.0     1.0      0.0   \n",
       "3                        0.0           1.0     1.0      1.0   \n",
       "4                        0.0           1.0     1.0      1.0   \n",
       "...                      ...           ...     ...      ...   \n",
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       "253678                   0.0           0.0     1.0      1.0   \n",
       "253679                   1.0           1.0     1.0      0.0   \n",
       "\n",
       "        HvyAlcoholConsump  AnyHealthcare  NoDocbcCost  GenHlth  MentHlth  \\\n",
       "0                     0.0            1.0          0.0      5.0      18.0   \n",
       "1                     0.0            0.0          1.0      3.0       0.0   \n",
       "2                     0.0            1.0          1.0      5.0      30.0   \n",
       "3                     0.0            1.0          0.0      2.0       0.0   \n",
       "4                     0.0            1.0          0.0      2.0       3.0   \n",
       "...                   ...            ...          ...      ...       ...   \n",
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       "\n",
       "        PhysHlth  DiffWalk  Sex   Age  Education  Income  \n",
       "0           15.0       1.0  0.0   9.0        4.0     3.0  \n",
       "1            0.0       0.0  0.0   7.0        6.0     1.0  \n",
       "2           30.0       1.0  0.0   9.0        4.0     8.0  \n",
       "3            0.0       0.0  0.0  11.0        3.0     6.0  \n",
       "4            0.0       0.0  0.0  11.0        5.0     4.0  \n",
       "...          ...       ...  ...   ...        ...     ...  \n",
       "253675       5.0       0.0  1.0   5.0        6.0     7.0  \n",
       "253676       0.0       1.0  0.0  11.0        2.0     4.0  \n",
       "253677       0.0       0.0  0.0   2.0        5.0     2.0  \n",
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       "253679       0.0       0.0  0.0   9.0        6.0     2.0  \n",
       "\n",
       "[253680 rows x 22 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 500)\n",
    "diabetes = pd.read_csv('diabetes_012_health_indicators_BRFSS2015.csv')\n",
    "diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smoker rates per income category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income\n",
       "8.0    0.362970\n",
       "7.0    0.440640\n",
       "6.0    0.472745\n",
       "5.0    0.493722\n",
       "4.0    0.514974\n",
       "3.0    0.526072\n",
       "2.0    0.564797\n",
       "1.0    0.521252\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate rates of smoking for each income level\n",
    "smoking_rates_by_income_category = diabetes[diabetes['Smoker'] == 1]['Income'].value_counts() / diabetes['Income'].value_counts()\n",
    "smoking_rates_by_income_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Income\n",
       "8.0    0.637030\n",
       "7.0    0.559360\n",
       "6.0    0.527255\n",
       "5.0    0.506278\n",
       "4.0    0.485026\n",
       "3.0    0.473928\n",
       "2.0    0.435203\n",
       "1.0    0.478748\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_smoking_rates_by_income_category = diabetes[diabetes['Smoker'] == 0]['Income'].value_counts() / diabetes['Income'].value_counts()\n",
    "non_smoking_rates_by_income_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "apriori_features = ['Smoker', 'Fruits', 'Veggies', 'HvyAlcoholConsump', 'NoDocbcCost', 'Sex', 'Age', 'Education',\n",
    "       'Income', 'PhysHlth', 'MentHlth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_21992/557596157.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  diabetes_apriori_df['Education'] = diabetes['Education'].astype('category')\n",
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_21992/557596157.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  diabetes_apriori_df['Income'] = diabetes['Income'].astype('category')\n",
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_21992/557596157.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  diabetes_apriori_df['Age'] = diabetes['Age'].astype('category')\n"
     ]
    }
   ],
   "source": [
    "# apriori and association rules\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "diabetes_apriori_df = diabetes[apriori_features]\n",
    "diabetes_apriori_df['Education'] = diabetes['Education'].astype('category')\n",
    "diabetes_apriori_df['Income'] = diabetes['Income'].astype('category')\n",
    "diabetes_apriori_df['Age'] = diabetes['Age'].astype('category')\n",
    "diabetes_apriori_df = pd.get_dummies(diabetes_apriori_df)\n",
    "diabetes_apriori_df = diabetes_apriori_df.astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    support    itemsets\n",
      "0  0.443169    (Smoker)\n",
      "1  0.634256    (Fruits)\n",
      "2  0.811420   (Veggies)\n",
      "3  0.440342       (Sex)\n",
      "4  0.369079  (PhysHlth)\n"
     ]
    }
   ],
   "source": [
    "# Apply the Apriori algorithm\n",
    "frequent_itemsets = apriori(diabetes_apriori_df, min_support=0.1, use_colnames=True)\n",
    "\n",
    "# Check if 'support' column exists\n",
    "print(frequent_itemsets.head())\n",
    "\n",
    "# Generate association rules (this should work if the support column is present)\n",
    "rules = association_rules(frequent_itemsets, 10, metric=\"confidence\", min_threshold=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>representativity</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>certainty</th>\n",
       "      <th>kulczynski</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Smoker)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.443169</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.353635</td>\n",
       "      <td>0.797968</td>\n",
       "      <td>0.983422</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.005961</td>\n",
       "      <td>0.933419</td>\n",
       "      <td>-0.029384</td>\n",
       "      <td>0.392511</td>\n",
       "      <td>-0.071330</td>\n",
       "      <td>0.616895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.562567</td>\n",
       "      <td>0.886972</td>\n",
       "      <td>1.093111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.047919</td>\n",
       "      <td>1.668435</td>\n",
       "      <td>0.232894</td>\n",
       "      <td>0.637030</td>\n",
       "      <td>0.400636</td>\n",
       "      <td>0.790142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Sex)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.440342</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.344726</td>\n",
       "      <td>0.782859</td>\n",
       "      <td>0.964801</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.012577</td>\n",
       "      <td>0.868467</td>\n",
       "      <td>-0.061199</td>\n",
       "      <td>0.380057</td>\n",
       "      <td>-0.151455</td>\n",
       "      <td>0.603851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(PhysHlth)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.369079</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.291458</td>\n",
       "      <td>0.789689</td>\n",
       "      <td>0.973219</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.008020</td>\n",
       "      <td>0.896672</td>\n",
       "      <td>-0.041793</td>\n",
       "      <td>0.327834</td>\n",
       "      <td>-0.115234</td>\n",
       "      <td>0.574442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(MentHlth)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.307474</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.243590</td>\n",
       "      <td>0.792231</td>\n",
       "      <td>0.976351</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.005900</td>\n",
       "      <td>0.907642</td>\n",
       "      <td>-0.033794</td>\n",
       "      <td>0.278292</td>\n",
       "      <td>-0.101756</td>\n",
       "      <td>0.546217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Age_9.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.131047</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.106382</td>\n",
       "      <td>0.811786</td>\n",
       "      <td>1.000451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>1.001943</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.127238</td>\n",
       "      <td>0.001939</td>\n",
       "      <td>0.471446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Age_10.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.126908</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.102791</td>\n",
       "      <td>0.809965</td>\n",
       "      <td>0.998206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>0.992342</td>\n",
       "      <td>-0.002054</td>\n",
       "      <td>0.123024</td>\n",
       "      <td>-0.007717</td>\n",
       "      <td>0.468322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Education_4.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.247359</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.182403</td>\n",
       "      <td>0.737402</td>\n",
       "      <td>0.908780</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.018309</td>\n",
       "      <td>0.718133</td>\n",
       "      <td>-0.117672</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>-0.392499</td>\n",
       "      <td>0.481099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Education_5.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.275583</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.223802</td>\n",
       "      <td>0.812101</td>\n",
       "      <td>1.000840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>1.003626</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.259269</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.543958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.423072</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.368429</td>\n",
       "      <td>0.870841</td>\n",
       "      <td>1.073231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025139</td>\n",
       "      <td>1.460061</td>\n",
       "      <td>0.118271</td>\n",
       "      <td>0.425406</td>\n",
       "      <td>0.315097</td>\n",
       "      <td>0.662448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Income_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.143764</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.115732</td>\n",
       "      <td>0.805018</td>\n",
       "      <td>0.992110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000920</td>\n",
       "      <td>0.967166</td>\n",
       "      <td>-0.009203</td>\n",
       "      <td>0.137867</td>\n",
       "      <td>-0.033949</td>\n",
       "      <td>0.473824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(Income_7.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.170368</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.141513</td>\n",
       "      <td>0.830630</td>\n",
       "      <td>1.023675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>1.113421</td>\n",
       "      <td>0.027876</td>\n",
       "      <td>0.168413</td>\n",
       "      <td>0.101867</td>\n",
       "      <td>0.502516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Income_8.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.356295</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.310911</td>\n",
       "      <td>0.872623</td>\n",
       "      <td>1.075427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021806</td>\n",
       "      <td>1.480484</td>\n",
       "      <td>0.108958</td>\n",
       "      <td>0.362873</td>\n",
       "      <td>0.324545</td>\n",
       "      <td>0.627896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Smoker, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.262500</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.230913</td>\n",
       "      <td>0.879668</td>\n",
       "      <td>1.084110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017915</td>\n",
       "      <td>1.567171</td>\n",
       "      <td>0.105199</td>\n",
       "      <td>0.273916</td>\n",
       "      <td>0.361907</td>\n",
       "      <td>0.582124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(Sex, Smoker)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.218243</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.168744</td>\n",
       "      <td>0.773192</td>\n",
       "      <td>0.952888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.008343</td>\n",
       "      <td>0.831452</td>\n",
       "      <td>-0.059482</td>\n",
       "      <td>0.196005</td>\n",
       "      <td>-0.202715</td>\n",
       "      <td>0.490577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(PhysHlth, Smoker)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.180625</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.139916</td>\n",
       "      <td>0.774623</td>\n",
       "      <td>0.954651</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.006646</td>\n",
       "      <td>0.836732</td>\n",
       "      <td>-0.054798</td>\n",
       "      <td>0.164196</td>\n",
       "      <td>-0.195126</td>\n",
       "      <td>0.473529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(MentHlth, Smoker)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.146531</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.113982</td>\n",
       "      <td>0.777870</td>\n",
       "      <td>0.958653</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.004916</td>\n",
       "      <td>0.848964</td>\n",
       "      <td>-0.048104</td>\n",
       "      <td>0.135055</td>\n",
       "      <td>-0.177906</td>\n",
       "      <td>0.459171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(Smoker, Education_5.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.135478</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.110194</td>\n",
       "      <td>0.813373</td>\n",
       "      <td>1.002407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>1.010465</td>\n",
       "      <td>0.002777</td>\n",
       "      <td>0.131700</td>\n",
       "      <td>0.010356</td>\n",
       "      <td>0.474588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Smoker, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.145104</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.126202</td>\n",
       "      <td>0.869736</td>\n",
       "      <td>1.071870</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008462</td>\n",
       "      <td>1.447682</td>\n",
       "      <td>0.078432</td>\n",
       "      <td>0.151992</td>\n",
       "      <td>0.309240</td>\n",
       "      <td>0.512635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(Income_8.0, Smoker)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.129324</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.112658</td>\n",
       "      <td>0.871125</td>\n",
       "      <td>1.073581</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007721</td>\n",
       "      <td>1.463280</td>\n",
       "      <td>0.078718</td>\n",
       "      <td>0.136046</td>\n",
       "      <td>0.316604</td>\n",
       "      <td>0.504983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                antecedents consequents  antecedent support  \\\n",
       "0                  (Smoker)   (Veggies)            0.443169   \n",
       "1                  (Fruits)   (Veggies)            0.634256   \n",
       "2                     (Sex)   (Veggies)            0.440342   \n",
       "3                (PhysHlth)   (Veggies)            0.369079   \n",
       "4                (MentHlth)   (Veggies)            0.307474   \n",
       "5                 (Age_9.0)   (Veggies)            0.131047   \n",
       "6                (Age_10.0)   (Veggies)            0.126908   \n",
       "7           (Education_4.0)   (Veggies)            0.247359   \n",
       "8           (Education_5.0)   (Veggies)            0.275583   \n",
       "9           (Education_6.0)   (Veggies)            0.423072   \n",
       "10             (Income_6.0)   (Veggies)            0.143764   \n",
       "11             (Income_7.0)   (Veggies)            0.170368   \n",
       "12             (Income_8.0)   (Veggies)            0.356295   \n",
       "13         (Smoker, Fruits)   (Veggies)            0.262500   \n",
       "14            (Sex, Smoker)   (Veggies)            0.218243   \n",
       "15       (PhysHlth, Smoker)   (Veggies)            0.180625   \n",
       "16       (MentHlth, Smoker)   (Veggies)            0.146531   \n",
       "17  (Smoker, Education_5.0)   (Veggies)            0.135478   \n",
       "18  (Smoker, Education_6.0)   (Veggies)            0.145104   \n",
       "19     (Income_8.0, Smoker)   (Veggies)            0.129324   \n",
       "\n",
       "    consequent support   support  confidence      lift  representativity  \\\n",
       "0              0.81142  0.353635    0.797968  0.983422               1.0   \n",
       "1              0.81142  0.562567    0.886972  1.093111               1.0   \n",
       "2              0.81142  0.344726    0.782859  0.964801               1.0   \n",
       "3              0.81142  0.291458    0.789689  0.973219               1.0   \n",
       "4              0.81142  0.243590    0.792231  0.976351               1.0   \n",
       "5              0.81142  0.106382    0.811786  1.000451               1.0   \n",
       "6              0.81142  0.102791    0.809965  0.998206               1.0   \n",
       "7              0.81142  0.182403    0.737402  0.908780               1.0   \n",
       "8              0.81142  0.223802    0.812101  1.000840               1.0   \n",
       "9              0.81142  0.368429    0.870841  1.073231               1.0   \n",
       "10             0.81142  0.115732    0.805018  0.992110               1.0   \n",
       "11             0.81142  0.141513    0.830630  1.023675               1.0   \n",
       "12             0.81142  0.310911    0.872623  1.075427               1.0   \n",
       "13             0.81142  0.230913    0.879668  1.084110               1.0   \n",
       "14             0.81142  0.168744    0.773192  0.952888               1.0   \n",
       "15             0.81142  0.139916    0.774623  0.954651               1.0   \n",
       "16             0.81142  0.113982    0.777870  0.958653               1.0   \n",
       "17             0.81142  0.110194    0.813373  1.002407               1.0   \n",
       "18             0.81142  0.126202    0.869736  1.071870               1.0   \n",
       "19             0.81142  0.112658    0.871125  1.073581               1.0   \n",
       "\n",
       "    leverage  conviction  zhangs_metric   jaccard  certainty  kulczynski  \n",
       "0  -0.005961    0.933419      -0.029384  0.392511  -0.071330    0.616895  \n",
       "1   0.047919    1.668435       0.232894  0.637030   0.400636    0.790142  \n",
       "2  -0.012577    0.868467      -0.061199  0.380057  -0.151455    0.603851  \n",
       "3  -0.008020    0.896672      -0.041793  0.327834  -0.115234    0.574442  \n",
       "4  -0.005900    0.907642      -0.033794  0.278292  -0.101756    0.546217  \n",
       "5   0.000048    1.001943       0.000518  0.127238   0.001939    0.471446  \n",
       "6  -0.000185    0.992342      -0.002054  0.123024  -0.007717    0.468322  \n",
       "7  -0.018309    0.718133      -0.117672  0.208133  -0.392499    0.481099  \n",
       "8   0.000188    1.003626       0.001158  0.259269   0.003613    0.543958  \n",
       "9   0.025139    1.460061       0.118271  0.425406   0.315097    0.662448  \n",
       "10 -0.000920    0.967166      -0.009203  0.137867  -0.033949    0.473824  \n",
       "11  0.003273    1.113421       0.027876  0.168413   0.101867    0.502516  \n",
       "12  0.021806    1.480484       0.108958  0.362873   0.324545    0.627896  \n",
       "13  0.017915    1.567171       0.105199  0.273916   0.361907    0.582124  \n",
       "14 -0.008343    0.831452      -0.059482  0.196005  -0.202715    0.490577  \n",
       "15 -0.006646    0.836732      -0.054798  0.164196  -0.195126    0.473529  \n",
       "16 -0.004916    0.848964      -0.048104  0.135055  -0.177906    0.459171  \n",
       "17  0.000265    1.010465       0.002777  0.131700   0.010356    0.474588  \n",
       "18  0.008462    1.447682       0.078432  0.151992   0.309240    0.512635  \n",
       "19  0.007721    1.463280       0.078718  0.136046   0.316604    0.504983  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "rules.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>representativity</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>certainty</th>\n",
       "      <th>kulczynski</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.562567</td>\n",
       "      <td>0.886972</td>\n",
       "      <td>1.093111</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.047919</td>\n",
       "      <td>1.668435</td>\n",
       "      <td>0.232894</td>\n",
       "      <td>0.637030</td>\n",
       "      <td>0.400636</td>\n",
       "      <td>0.790142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Education_5.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.275583</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.223802</td>\n",
       "      <td>0.812101</td>\n",
       "      <td>1.000840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>1.003626</td>\n",
       "      <td>0.001158</td>\n",
       "      <td>0.259269</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.543958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.423072</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.368429</td>\n",
       "      <td>0.870841</td>\n",
       "      <td>1.073231</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025139</td>\n",
       "      <td>1.460061</td>\n",
       "      <td>0.118271</td>\n",
       "      <td>0.425406</td>\n",
       "      <td>0.315097</td>\n",
       "      <td>0.662448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(Income_8.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.356295</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.310911</td>\n",
       "      <td>0.872623</td>\n",
       "      <td>1.075427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021806</td>\n",
       "      <td>1.480484</td>\n",
       "      <td>0.108958</td>\n",
       "      <td>0.362873</td>\n",
       "      <td>0.324545</td>\n",
       "      <td>0.627896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Smoker, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.262500</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.230913</td>\n",
       "      <td>0.879668</td>\n",
       "      <td>1.084110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017915</td>\n",
       "      <td>1.567171</td>\n",
       "      <td>0.105199</td>\n",
       "      <td>0.273916</td>\n",
       "      <td>0.361907</td>\n",
       "      <td>0.582124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(Sex, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.257490</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.222994</td>\n",
       "      <td>0.866029</td>\n",
       "      <td>1.067300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014061</td>\n",
       "      <td>1.407617</td>\n",
       "      <td>0.084924</td>\n",
       "      <td>0.263612</td>\n",
       "      <td>0.289579</td>\n",
       "      <td>0.570424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(PhysHlth, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.224799</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.196488</td>\n",
       "      <td>0.874060</td>\n",
       "      <td>1.077198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014081</td>\n",
       "      <td>1.497376</td>\n",
       "      <td>0.092447</td>\n",
       "      <td>0.233989</td>\n",
       "      <td>0.332165</td>\n",
       "      <td>0.558106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(MentHlth, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.182580</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.160797</td>\n",
       "      <td>0.880692</td>\n",
       "      <td>1.085371</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012648</td>\n",
       "      <td>1.580612</td>\n",
       "      <td>0.096225</td>\n",
       "      <td>0.192987</td>\n",
       "      <td>0.367334</td>\n",
       "      <td>0.539430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(Education_5.0, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.170715</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.151723</td>\n",
       "      <td>0.888748</td>\n",
       "      <td>1.095299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013201</td>\n",
       "      <td>1.695068</td>\n",
       "      <td>0.104919</td>\n",
       "      <td>0.182708</td>\n",
       "      <td>0.410053</td>\n",
       "      <td>0.537866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Fruits, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.293508</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.270467</td>\n",
       "      <td>0.921498</td>\n",
       "      <td>1.135661</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.032309</td>\n",
       "      <td>2.402243</td>\n",
       "      <td>0.169083</td>\n",
       "      <td>0.324122</td>\n",
       "      <td>0.583722</td>\n",
       "      <td>0.627412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Income_8.0, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.239483</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.221685</td>\n",
       "      <td>0.925681</td>\n",
       "      <td>1.140817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.027364</td>\n",
       "      <td>2.537457</td>\n",
       "      <td>0.162304</td>\n",
       "      <td>0.267342</td>\n",
       "      <td>0.605905</td>\n",
       "      <td>0.599444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(Sex, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.195080</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.164183</td>\n",
       "      <td>0.841618</td>\n",
       "      <td>1.037217</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>1.190668</td>\n",
       "      <td>0.044577</td>\n",
       "      <td>0.194919</td>\n",
       "      <td>0.160135</td>\n",
       "      <td>0.521979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(Sex, Income_8.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.180274</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.151608</td>\n",
       "      <td>0.840987</td>\n",
       "      <td>1.036438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005330</td>\n",
       "      <td>1.185939</td>\n",
       "      <td>0.042889</td>\n",
       "      <td>0.180468</td>\n",
       "      <td>0.156786</td>\n",
       "      <td>0.513915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(Income_8.0, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.235805</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.210281</td>\n",
       "      <td>0.891757</td>\n",
       "      <td>1.099008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018944</td>\n",
       "      <td>1.742189</td>\n",
       "      <td>0.117887</td>\n",
       "      <td>0.251248</td>\n",
       "      <td>0.426009</td>\n",
       "      <td>0.575454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(Income_8.0, Fruits, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.165476</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.154663</td>\n",
       "      <td>0.934656</td>\n",
       "      <td>1.151877</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020393</td>\n",
       "      <td>2.885970</td>\n",
       "      <td>0.157997</td>\n",
       "      <td>0.188102</td>\n",
       "      <td>0.653496</td>\n",
       "      <td>0.562632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            antecedents consequents  antecedent support  \\\n",
       "1                              (Fruits)   (Veggies)            0.634256   \n",
       "8                       (Education_5.0)   (Veggies)            0.275583   \n",
       "9                       (Education_6.0)   (Veggies)            0.423072   \n",
       "12                         (Income_8.0)   (Veggies)            0.356295   \n",
       "13                     (Smoker, Fruits)   (Veggies)            0.262500   \n",
       "20                        (Sex, Fruits)   (Veggies)            0.257490   \n",
       "21                   (PhysHlth, Fruits)   (Veggies)            0.224799   \n",
       "22                   (MentHlth, Fruits)   (Veggies)            0.182580   \n",
       "24              (Education_5.0, Fruits)   (Veggies)            0.170715   \n",
       "26              (Fruits, Education_6.0)   (Veggies)            0.293508   \n",
       "28                 (Income_8.0, Fruits)   (Veggies)            0.239483   \n",
       "31                 (Sex, Education_6.0)   (Veggies)            0.195080   \n",
       "32                    (Sex, Income_8.0)   (Veggies)            0.180274   \n",
       "36          (Income_8.0, Education_6.0)   (Veggies)            0.235805   \n",
       "41  (Income_8.0, Fruits, Education_6.0)   (Veggies)            0.165476   \n",
       "\n",
       "    consequent support   support  confidence      lift  representativity  \\\n",
       "1              0.81142  0.562567    0.886972  1.093111               1.0   \n",
       "8              0.81142  0.223802    0.812101  1.000840               1.0   \n",
       "9              0.81142  0.368429    0.870841  1.073231               1.0   \n",
       "12             0.81142  0.310911    0.872623  1.075427               1.0   \n",
       "13             0.81142  0.230913    0.879668  1.084110               1.0   \n",
       "20             0.81142  0.222994    0.866029  1.067300               1.0   \n",
       "21             0.81142  0.196488    0.874060  1.077198               1.0   \n",
       "22             0.81142  0.160797    0.880692  1.085371               1.0   \n",
       "24             0.81142  0.151723    0.888748  1.095299               1.0   \n",
       "26             0.81142  0.270467    0.921498  1.135661               1.0   \n",
       "28             0.81142  0.221685    0.925681  1.140817               1.0   \n",
       "31             0.81142  0.164183    0.841618  1.037217               1.0   \n",
       "32             0.81142  0.151608    0.840987  1.036438               1.0   \n",
       "36             0.81142  0.210281    0.891757  1.099008               1.0   \n",
       "41             0.81142  0.154663    0.934656  1.151877               1.0   \n",
       "\n",
       "    leverage  conviction  zhangs_metric   jaccard  certainty  kulczynski  \n",
       "1   0.047919    1.668435       0.232894  0.637030   0.400636    0.790142  \n",
       "8   0.000188    1.003626       0.001158  0.259269   0.003613    0.543958  \n",
       "9   0.025139    1.460061       0.118271  0.425406   0.315097    0.662448  \n",
       "12  0.021806    1.480484       0.108958  0.362873   0.324545    0.627896  \n",
       "13  0.017915    1.567171       0.105199  0.273916   0.361907    0.582124  \n",
       "20  0.014061    1.407617       0.084924  0.263612   0.289579    0.570424  \n",
       "21  0.014081    1.497376       0.092447  0.233989   0.332165    0.558106  \n",
       "22  0.012648    1.580612       0.096225  0.192987   0.367334    0.539430  \n",
       "24  0.013201    1.695068       0.104919  0.182708   0.410053    0.537866  \n",
       "26  0.032309    2.402243       0.169083  0.324122   0.583722    0.627412  \n",
       "28  0.027364    2.537457       0.162304  0.267342   0.605905    0.599444  \n",
       "31  0.005891    1.190668       0.044577  0.194919   0.160135    0.521979  \n",
       "32  0.005330    1.185939       0.042889  0.180468   0.156786    0.513915  \n",
       "36  0.018944    1.742189       0.117887  0.251248   0.426009    0.575454  \n",
       "41  0.020393    2.885970       0.157997  0.188102   0.653496    0.562632  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 100)\n",
    "good_rules = rules[\n",
    "    (rules['confidence'] > 0.8) &  # High confidence\n",
    "    (rules['lift'] > 1) &        # Positive association\n",
    "    (rules['support'] > 0.15) &     # Sufficient support\n",
    "    (rules['conviction'] > 1) &    # Stronger association\n",
    "    (rules['leverage'] > 0)        # Positive leverage indicates stronger relation\n",
    "]\n",
    "\n",
    "good_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>representativity</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>certainty</th>\n",
       "      <th>kulczynski</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(Veggies, Education_6.0)</td>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>0.368429</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.270467</td>\n",
       "      <td>0.734109</td>\n",
       "      <td>1.157433</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.036789</td>\n",
       "      <td>1.375540</td>\n",
       "      <td>0.215366</td>\n",
       "      <td>0.369380</td>\n",
       "      <td>0.273013</td>\n",
       "      <td>0.580270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Fruits, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.293508</td>\n",
       "      <td>0.811420</td>\n",
       "      <td>0.270467</td>\n",
       "      <td>0.921498</td>\n",
       "      <td>1.135661</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.032309</td>\n",
       "      <td>2.402243</td>\n",
       "      <td>0.169083</td>\n",
       "      <td>0.324122</td>\n",
       "      <td>0.583722</td>\n",
       "      <td>0.627412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(Income_8.0, Veggies)</td>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>0.310911</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.221685</td>\n",
       "      <td>0.713016</td>\n",
       "      <td>1.124177</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024487</td>\n",
       "      <td>1.274441</td>\n",
       "      <td>0.160300</td>\n",
       "      <td>0.306414</td>\n",
       "      <td>0.215342</td>\n",
       "      <td>0.531268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(Income_8.0, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.239483</td>\n",
       "      <td>0.811420</td>\n",
       "      <td>0.221685</td>\n",
       "      <td>0.925681</td>\n",
       "      <td>1.140817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.027364</td>\n",
       "      <td>2.537457</td>\n",
       "      <td>0.162304</td>\n",
       "      <td>0.267342</td>\n",
       "      <td>0.605905</td>\n",
       "      <td>0.599444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(Income_8.0, Education_6.0)</td>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>0.235805</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.165476</td>\n",
       "      <td>0.701750</td>\n",
       "      <td>1.106415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015916</td>\n",
       "      <td>1.226302</td>\n",
       "      <td>0.125858</td>\n",
       "      <td>0.234856</td>\n",
       "      <td>0.184540</td>\n",
       "      <td>0.481324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(Income_8.0, Veggies, Education_6.0)</td>\n",
       "      <td>(Fruits)</td>\n",
       "      <td>0.210281</td>\n",
       "      <td>0.634256</td>\n",
       "      <td>0.154663</td>\n",
       "      <td>0.735509</td>\n",
       "      <td>1.159641</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021292</td>\n",
       "      <td>1.382824</td>\n",
       "      <td>0.174321</td>\n",
       "      <td>0.224191</td>\n",
       "      <td>0.276842</td>\n",
       "      <td>0.489680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(Income_8.0, Fruits, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.165476</td>\n",
       "      <td>0.811420</td>\n",
       "      <td>0.154663</td>\n",
       "      <td>0.934656</td>\n",
       "      <td>1.151877</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020393</td>\n",
       "      <td>2.885970</td>\n",
       "      <td>0.157997</td>\n",
       "      <td>0.188102</td>\n",
       "      <td>0.653496</td>\n",
       "      <td>0.562632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             antecedents consequents  antecedent support  \\\n",
       "25              (Veggies, Education_6.0)    (Fruits)            0.368429   \n",
       "26               (Fruits, Education_6.0)   (Veggies)            0.293508   \n",
       "27                 (Income_8.0, Veggies)    (Fruits)            0.310911   \n",
       "28                  (Income_8.0, Fruits)   (Veggies)            0.239483   \n",
       "29           (Income_8.0, Education_6.0)    (Fruits)            0.235805   \n",
       "40  (Income_8.0, Veggies, Education_6.0)    (Fruits)            0.210281   \n",
       "41   (Income_8.0, Fruits, Education_6.0)   (Veggies)            0.165476   \n",
       "\n",
       "    consequent support   support  confidence      lift  representativity  \\\n",
       "25            0.634256  0.270467    0.734109  1.157433               1.0   \n",
       "26            0.811420  0.270467    0.921498  1.135661               1.0   \n",
       "27            0.634256  0.221685    0.713016  1.124177               1.0   \n",
       "28            0.811420  0.221685    0.925681  1.140817               1.0   \n",
       "29            0.634256  0.165476    0.701750  1.106415               1.0   \n",
       "40            0.634256  0.154663    0.735509  1.159641               1.0   \n",
       "41            0.811420  0.154663    0.934656  1.151877               1.0   \n",
       "\n",
       "    leverage  conviction  zhangs_metric   jaccard  certainty  kulczynski  \n",
       "25  0.036789    1.375540       0.215366  0.369380   0.273013    0.580270  \n",
       "26  0.032309    2.402243       0.169083  0.324122   0.583722    0.627412  \n",
       "27  0.024487    1.274441       0.160300  0.306414   0.215342    0.531268  \n",
       "28  0.027364    2.537457       0.162304  0.267342   0.605905    0.599444  \n",
       "29  0.015916    1.226302       0.125858  0.234856   0.184540    0.481324  \n",
       "40  0.021292    1.382824       0.174321  0.224191   0.276842    0.489680  \n",
       "41  0.020393    2.885970       0.157997  0.188102   0.653496    0.562632  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_strict_rules = rules[\n",
    "    (rules['confidence'] > 0.7) &  # High confidence\n",
    "    (rules['lift'] > 1.1) &        # Positive association\n",
    "    (rules['support'] > 0.1) &     # Sufficient support\n",
    "    (rules['antecedent support'] > 0.15)       # Positive leverage indicates stronger relation\n",
    "]\n",
    "\n",
    "less_strict_rules"
   ]
  }
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