Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0abe58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries and specify that graphs should be plotted inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "# warnings reported for function updates, ignore them\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08582f90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diabetes_binary</th>\n",
       "      <th>HighBP</th>\n",
       "      <th>HighChol</th>\n",
       "      <th>CholCheck</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>HeartDiseaseorAttack</th>\n",
       "      <th>PhysActivity</th>\n",
       "      <th>Fruits</th>\n",
       "      <th>...</th>\n",
       "      <th>AnyHealthcare</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
       "      <th>PhysHlth</th>\n",
       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Diabetes_binary  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",
       "   HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\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  ...            1.0   \n",
       "3                   0.0           1.0     1.0  ...            1.0   \n",
       "4                   0.0           1.0     1.0  ...            1.0   \n",
       "\n",
       "   NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  \\\n",
       "0          0.0      5.0      18.0      15.0       1.0  0.0   9.0        4.0   \n",
       "1          1.0      3.0       0.0       0.0       0.0  0.0   7.0        6.0   \n",
       "2          1.0      5.0      30.0      30.0       1.0  0.0   9.0        4.0   \n",
       "3          0.0      2.0       0.0       0.0       0.0  0.0  11.0        3.0   \n",
       "4          0.0      2.0       3.0       0.0       0.0  0.0  11.0        5.0   \n",
       "\n",
       "   Income  \n",
       "0     3.0  \n",
       "1     1.0  \n",
       "2     8.0  \n",
       "3     6.0  \n",
       "4     4.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = 'diabetes_binary_health_indicators_BRFSS2015.csv'\n",
    "df = pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca1abf1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Diabetes_binary', axis=1)\n",
    "y = df['Diabetes_binary']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ee63d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split data into training and test, if test size is not specified, default value  of 0.25 is applied\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f49b67c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "124a9c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             param_grid={&#x27;max_depth&#x27;: range(1, 10),\n",
       "                         &#x27;max_leaf_nodes&#x27;: range(2, 11),\n",
       "                         &#x27;min_samples_split&#x27;: range(2, 20)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: range(1, 10),\n",
       "                         &#x27;max_leaf_nodes&#x27;: range(2, 11),\n",
       "                         &#x27;min_samples_split&#x27;: range(2, 20)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeClassifier(random_state=0),\n",
       "             param_grid={'max_depth': range(1, 10),\n",
       "                         'max_leaf_nodes': range(2, 11),\n",
       "                         'min_samples_split': range(2, 20)})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GridSearch + CV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "opt_tree = DecisionTreeClassifier(random_state = 0)\n",
    "\n",
    "dt_params = {'max_depth':  range(1,10)         , \n",
    "             'min_samples_split':   range(2,20), \n",
    "             'max_leaf_nodes':    range(2,11)   }\n",
    "\n",
    "grid_tree = GridSearchCV(opt_tree, dt_params)\n",
    "grid_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd585ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.47551496, 0.02250068, 0.        , 0.10984988, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.36993573, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.02219874, 0.        ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_tree.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "594fbce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 4, 'max_leaf_nodes': 10, 'min_samples_split': 2}\n",
      "DecisionTreeClassifier(max_depth=4, max_leaf_nodes=10, random_state=0)\n"
     ]
    }
   ],
   "source": [
    "# Get the best hyperparameters\n",
    "best_params = grid_tree.best_params_\n",
    "print(best_params)\n",
    "\n",
    "# Get the best model\n",
    "best_dt_model = grid_tree.best_estimator_\n",
    "print(best_dt_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae31d1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the decision tree\n",
    "from sklearn import tree\n",
    "fig = plt.figure(figsize=(20,10)) # set a proper figure size (in case that the figure is too small to read or ratio is not proper)\n",
    "\n",
    "tree.plot_tree(grid_tree.best_estimator_, \n",
    "               feature_names = cancer.feature_names, # specify variable names \n",
    "               class_names = cancer.target_names, # specify class (Y) names\n",
    "               filled = True, impurity = False) # whether to color the boxes, whether to report gini index\n",
    "             #   fontsize = 12) # set fontsize to read\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2cf099d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Diabetes_binary'], dtype='<U23')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = np.array(df.columns.tolist()[1:],dtype='<U23')\n",
    "target_names=np.array(df.columns.tolist()[:1],dtype='<U23')\n",
    "target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3edd56ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(max_depth=4, max_leaf_nodes=10, random_state=0)\n"
     ]
    }
   ],
   "source": [
    "print(grid_tree.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3e255272",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = best_dt_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2d326f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c10f3297",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GridSearch + CV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "opt_tree = DecisionTreeClassifier(random_state = 0)\n",
    "\n",
    "dt_params = {'max_depth':  range(1,10)         , \n",
    "             'min_samples_split':   range(2,0), \n",
    "             'max_leaf_nodes':    range(2,11)   }\n",
    "\n",
    "grid_tree = GridSearchCV(opt_tree, dt_params)\n",
    "grid_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "481e927a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = best_dt_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7cff6192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[53071  1586]\n",
      " [ 7055  1708]]\n",
      "Test accuracy is 0.863749605802586\n"
     ]
    }
   ],
   "source": [
    "confusion_mat = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "print(f'Confusion Matrix:\\n{confusion_mat}')\n",
    "print(\"Test accuracy is\", accuracy)\n",
    "\n",
    "# Decision Tree - Metrics (max depth = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a7a4e40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df3b421",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a585c44d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b9e08a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ef1ebd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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