diabetes_apriori_complete_dataset.ipynb

{
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       "      <th></th>\n",
       "      <th>Diabetes_012</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
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       "      <th>PhysActivity</th>\n",
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       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
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       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Income</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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       "253676           2.0     1.0       1.0        1.0  18.0     0.0     0.0   \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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       "253677                   0.0           1.0     1.0      0.0   \n",
       "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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       "253679                0.0            1.0          0.0      2.0       0.0   \n",
       "\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",
       "253678       0.0       0.0  1.0   7.0        5.0     1.0  \n",
       "253679       0.0       0.0  0.0   9.0        6.0     2.0  \n",
       "\n",
       "[253680 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Diabetes_012', 'HighBP', 'HighChol', 'CholCheck', 'BMI', 'Smoker',\n",
       "       'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',\n",
       "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
       "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
       "       'Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "apriori_features = ['Diabetes_012', 'Smoker', 'Fruits', 'Veggies', 'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'Sex', 'Age', 'Education',\n",
    "       'Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_44044/1210794731.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['Diabetes_012'] = diabetes['Diabetes_012'].astype('category')\n",
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_44044/1210794731.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['Education'] = diabetes['Education'].astype('category')\n",
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_44044/1210794731.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['Income'] = diabetes['Income'].astype('category')\n",
      "/var/folders/tw/xhv51_3109b07x000pvllsdh0000gn/T/ipykernel_44044/1210794731.py:9: 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['Diabetes_012'] = diabetes['Diabetes_012'].astype('category')\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": 5,
   "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  0.951053  (AnyHealthcare)\n",
      "4  0.440342            (Sex)\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>249</th>\n",
       "      <td>(Education_6.0, Fruits)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.293508</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.264601</td>\n",
       "      <td>0.901514</td>\n",
       "      <td>1.164446</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.037368</td>\n",
       "      <td>2.292705</td>\n",
       "      <td>0.199892</td>\n",
       "      <td>0.329472</td>\n",
       "      <td>0.563834</td>\n",
       "      <td>0.621644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>(Income_8.0, Fruits)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.239483</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.218508</td>\n",
       "      <td>0.912414</td>\n",
       "      <td>1.178526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.033100</td>\n",
       "      <td>2.578052</td>\n",
       "      <td>0.199183</td>\n",
       "      <td>0.274792</td>\n",
       "      <td>0.612110</td>\n",
       "      <td>0.597326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>(Education_6.0, Fruits, Income_8.0)</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",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>(Education_6.0, Diabetes_012_0.0, Fruits)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.263395</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.238304</td>\n",
       "      <td>0.904741</td>\n",
       "      <td>1.168615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034384</td>\n",
       "      <td>2.370388</td>\n",
       "      <td>0.195880</td>\n",
       "      <td>0.298145</td>\n",
       "      <td>0.578128</td>\n",
       "      <td>0.606274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>(Income_8.0, Diabetes_012_0.0, Fruits)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.219442</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.200650</td>\n",
       "      <td>0.914367</td>\n",
       "      <td>1.181048</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030759</td>\n",
       "      <td>2.636846</td>\n",
       "      <td>0.196391</td>\n",
       "      <td>0.253030</td>\n",
       "      <td>0.620759</td>\n",
       "      <td>0.586769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>(Income_8.0, Fruits, AnyHealthcare, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.163852</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.153193</td>\n",
       "      <td>0.934947</td>\n",
       "      <td>1.152236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020240</td>\n",
       "      <td>2.898861</td>\n",
       "      <td>0.158013</td>\n",
       "      <td>0.186348</td>\n",
       "      <td>0.655037</td>\n",
       "      <td>0.561872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>(Income_8.0, Fruits, Education_6.0)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.165476</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.153193</td>\n",
       "      <td>0.925771</td>\n",
       "      <td>1.195777</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025081</td>\n",
       "      <td>3.041926</td>\n",
       "      <td>0.196189</td>\n",
       "      <td>0.194782</td>\n",
       "      <td>0.671261</td>\n",
       "      <td>0.561822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>(Fruits, Income_8.0, Diabetes_012_0.0, Education_6.0)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.153173</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.143425</td>\n",
       "      <td>0.936356</td>\n",
       "      <td>1.153973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019137</td>\n",
       "      <td>2.963064</td>\n",
       "      <td>0.157563</td>\n",
       "      <td>0.174659</td>\n",
       "      <td>0.662511</td>\n",
       "      <td>0.556557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>(Income_8.0, AnyHealthcare, Education_6.0, Diabetes_012_0.0, Fruits)</td>\n",
       "      <td>(Veggies)</td>\n",
       "      <td>0.151628</td>\n",
       "      <td>0.81142</td>\n",
       "      <td>0.142014</td>\n",
       "      <td>0.936592</td>\n",
       "      <td>1.154263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.018980</td>\n",
       "      <td>2.974061</td>\n",
       "      <td>0.157532</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>0.663759</td>\n",
       "      <td>0.555805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>(Fruits, Income_8.0, Diabetes_012_0.0, Education_6.0)</td>\n",
       "      <td>(Veggies, AnyHealthcare)</td>\n",
       "      <td>0.153173</td>\n",
       "      <td>0.77420</td>\n",
       "      <td>0.142014</td>\n",
       "      <td>0.927143</td>\n",
       "      <td>1.197550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.023427</td>\n",
       "      <td>3.099230</td>\n",
       "      <td>0.194800</td>\n",
       "      <td>0.180826</td>\n",
       "      <td>0.677339</td>\n",
       "      <td>0.555288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              antecedents  \\\n",
       "249                                               (Education_6.0, Fruits)   \n",
       "254                                                  (Income_8.0, Fruits)   \n",
       "272                                   (Education_6.0, Fruits, Income_8.0)   \n",
       "406                             (Education_6.0, Diabetes_012_0.0, Fruits)   \n",
       "415                                (Income_8.0, Diabetes_012_0.0, Fruits)   \n",
       "420                    (Income_8.0, Fruits, AnyHealthcare, Education_6.0)   \n",
       "422                                   (Income_8.0, Fruits, Education_6.0)   \n",
       "426                 (Fruits, Income_8.0, Diabetes_012_0.0, Education_6.0)   \n",
       "467  (Income_8.0, AnyHealthcare, Education_6.0, Diabetes_012_0.0, Fruits)   \n",
       "471                 (Fruits, Income_8.0, Diabetes_012_0.0, Education_6.0)   \n",
       "\n",
       "                  consequents  antecedent support  consequent support  \\\n",
       "249  (Veggies, AnyHealthcare)            0.293508             0.77420   \n",
       "254  (Veggies, AnyHealthcare)            0.239483             0.77420   \n",
       "272                 (Veggies)            0.165476             0.81142   \n",
       "406  (Veggies, AnyHealthcare)            0.263395             0.77420   \n",
       "415  (Veggies, AnyHealthcare)            0.219442             0.77420   \n",
       "420                 (Veggies)            0.163852             0.81142   \n",
       "422  (Veggies, AnyHealthcare)            0.165476             0.77420   \n",
       "426                 (Veggies)            0.153173             0.81142   \n",
       "467                 (Veggies)            0.151628             0.81142   \n",
       "471  (Veggies, AnyHealthcare)            0.153173             0.77420   \n",
       "\n",
       "      support  confidence      lift  representativity  leverage  conviction  \\\n",
       "249  0.264601    0.901514  1.164446               1.0  0.037368    2.292705   \n",
       "254  0.218508    0.912414  1.178526               1.0  0.033100    2.578052   \n",
       "272  0.154663    0.934656  1.151877               1.0  0.020393    2.885970   \n",
       "406  0.238304    0.904741  1.168615               1.0  0.034384    2.370388   \n",
       "415  0.200650    0.914367  1.181048               1.0  0.030759    2.636846   \n",
       "420  0.153193    0.934947  1.152236               1.0  0.020240    2.898861   \n",
       "422  0.153193    0.925771  1.195777               1.0  0.025081    3.041926   \n",
       "426  0.143425    0.936356  1.153973               1.0  0.019137    2.963064   \n",
       "467  0.142014    0.936592  1.154263               1.0  0.018980    2.974061   \n",
       "471  0.142014    0.927143  1.197550               1.0  0.023427    3.099230   \n",
       "\n",
       "     zhangs_metric   jaccard  certainty  kulczynski  \n",
       "249       0.199892  0.329472   0.563834    0.621644  \n",
       "254       0.199183  0.274792   0.612110    0.597326  \n",
       "272       0.157997  0.188102   0.653496    0.562632  \n",
       "406       0.195880  0.298145   0.578128    0.606274  \n",
       "415       0.196391  0.253030   0.620759    0.586769  \n",
       "420       0.158013  0.186348   0.655037    0.561872  \n",
       "422       0.196189  0.194782   0.671261    0.561822  \n",
       "426       0.157563  0.174659   0.662511    0.556557  \n",
       "467       0.157532  0.172969   0.663759    0.555805  \n",
       "471       0.194800  0.180826   0.677339    0.555288  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', 100)\n",
    "good_rules = rules[\n",
    "    (rules['confidence'] > 0.9) &  # High confidence\n",
    "    (rules['lift'] > 1.15) &        # Positive association\n",
    "    (rules['support'] > 0.1) &     # Sufficient support\n",
    "    (rules['conviction'] > 1) &    # Stronger association\n",
    "    (rules['leverage'] > 0)        # Positive leverage indicates stronger relation\n",
    "]\n",
    "\n",
    "good_rules"
   ]
  }
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