DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "afd65812",
   "metadata": {},
   "source": [
    "# ML Model\n",
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d8825ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. IMPORTS Done\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import zscore\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, r2_score, auc\n",
    "import matplotlib.colors as mcolors\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b06996dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 14)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trtbps    303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalachh  303 non-null    int64  \n",
      " 8   exng      303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slp       303 non-null    int64  \n",
      " 11  caa       303 non-null    int64  \n",
      " 12  thall     303 non-null    int64  \n",
      " 13  output    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    },
    {
     "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trtbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalachh</th>\n",
       "      <th>exng</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slp</th>\n",
       "      <th>caa</th>\n",
       "      <th>thall</th>\n",
       "      <th>output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
       "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
       "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
       "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
       "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
       "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
       "\n",
       "   caa  thall  output  \n",
       "0    0      1       1  \n",
       "1    0      2       1  \n",
       "2    0      2       1  \n",
       "3    0      2       1  \n",
       "4    0      2       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartML = pd.read_csv(\"heartML.csv\")\n",
    "print(heartML.shape)\n",
    "heartML.info()\n",
    "heartML.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b46c1cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns that are not necessary for analysis\n",
    "# Drop Age and Sex [Remainder: Reasons for Heart attack]\n",
    "\n",
    "def removeC(tempDf, NotUsed):\n",
    "    \n",
    "    useCols = []\n",
    "    data = {}\n",
    "    for col in tempDf.columns:\n",
    "        if col not in NotUsed:\n",
    "            data[col] = tempDf[col]\n",
    "\n",
    "    newDf = pd.DataFrame(data) \n",
    "    return newDf\n",
    "\n",
    "heartMLNotUsed = ['age', 'sex']\n",
    "heartML = removeC(heartML, heartMLNotUsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1caf97f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "heartML = heartML.rename(columns = {'cp':'chest_pain','trtbps': 'resting_bp(mmhg)','chol': 'cholestoral(mg/dl)','fbs': 'fasting_blood_sugar(1/0)',\n",
    "                                    'thalachh': 'max_heart_rate','exng': 'exercise_anigna(1/0)','caa': 'num_major_vessels(0-3)'\n",
    "                                    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0347f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "heartML.drop_duplicates(inplace=True)\n",
    "print(sum(heartML.duplicated()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dc182d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chest_pain                  0\n",
       "resting_bp(mmhg)            0\n",
       "cholestoral(mg/dl)          0\n",
       "fasting_blood_sugar(1/0)    0\n",
       "restecg                     0\n",
       "max_heart_rate              0\n",
       "exercise_anigna(1/0)        0\n",
       "oldpeak                     0\n",
       "slp                         0\n",
       "num_major_vessels(0-3)      0\n",
       "thall                       0\n",
       "output                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heartML.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22a4a900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    164\n",
      "0    138\n",
      "Name: output, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of target values')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot\n",
    "print(heartML['output'].value_counts())\n",
    "plt.figure(figsize = (8, 5))\n",
    "sns.countplot(x = heartML['output'])\n",
    "plt.xlabel(\"Output\", size = 12)\n",
    "plt.ylabel(\"Count\", size = 12)\n",
    "plt.title(\"Distribution of target values\", size = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8445b630",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    164\n",
      "0    138\n",
      "Name: output, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of target values')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot\n",
    "print(heartML['output'].value_counts())\n",
    "plt.figure(figsize = (8, 5))\n",
    "sns.countplot(x = heartML['output'])\n",
    "plt.xlabel(\"Output\", size = 12)\n",
    "plt.ylabel(\"Count\", size = 12)\n",
    "plt.title(\"Distribution of target values\", size = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a02eb048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap\n",
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(heartML.corr(), annot = True)\n",
    "plt.savefig('heatmap.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28f50c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the model\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed56c04a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but DecisionTreeClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# preprocessing the dataset\n",
    "X = heartML.iloc[:,heartML.columns != 'output']\n",
    "y = heartML.output\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=5, stratify=y)\n",
    "\n",
    "# standard scaler\n",
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Training the model\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f8e6bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3ebb512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Training Set is 100.0\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the training set\n",
    "train_acc = model.score(X_train_scaled, y_train)\n",
    "print(\"The Accuracy for Training Set is {}\".format(train_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02d96fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 45.90163934426229\n"
     ]
    }
   ],
   "source": [
    "# Evaluating on test set\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e280884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      1.00      0.63        28\n",
      "           1       0.00      0.00      0.00        33\n",
      "\n",
      "    accuracy                           0.46        61\n",
      "   macro avg       0.23      0.50      0.31        61\n",
      "weighted avg       0.21      0.46      0.29        61\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cee7aba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
       " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
       " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
       " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
       " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
       " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
       " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
       " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
       " Text(0.5384615384615384, 0.25, 'X[0] <= 6.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
       " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
       " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
       " Text(0.7692307692307693, 0.4166666666666667, 'X[1] <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
       " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X, y)\n",
    "tree.plot_tree(clf)"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}