Heart Disease Prediction ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Import heart disease dataset using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import dexplot as dxp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <th>target</th>\n",
       "      <th>Unnamed: 14</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
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       "      <td>1</td>\n",
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       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>trestbps-thalach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.046698</td>\n",
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       "      <td>162</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>trestbps-chol</td>\n",
       "      <td>chol</td>\n",
       "      <td>0.123174</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
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       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\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",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
       "\n",
       "   ca  thal  target  Unnamed: 14       Unnamed: 15 Unnamed: 16  Unnamed: 17  \n",
       "0   0     1       1          NaN               NaN         NaN          NaN  \n",
       "1   0     2       1          NaN               NaN         NaN          NaN  \n",
       "2   0     2       1          NaN               NaN         NaN          NaN  \n",
       "3   0     2       1          NaN               NaN         NaN          NaN  \n",
       "4   0     2       1          NaN               NaN         NaN          NaN  \n",
       "5   0     1       1          NaN               NaN         NaN          NaN  \n",
       "6   0     2       1          NaN               NaN         NaN          NaN  \n",
       "7   0     3       1          NaN  trestbps-thalach         NaN    -0.046698  \n",
       "8   0     3       1          NaN     trestbps-chol        chol     0.123174  \n",
       "9   0     2       1          NaN               NaN         NaN          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart = pd.read_csv(\"D:\\Desktop\\heart.csv\")\n",
    "heart.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Clean the dataset by dropping unnecessery data values and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "heart.drop([\"Unnamed: 14\",\"Unnamed: 15\",\"Unnamed: 16\",\"Unnamed: 17\"],axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <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",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\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",
       "   ca  thal  target  \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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Plot relationship among labels in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dxp.scatter(x =\"chol\",y =\"trestbps\",data = heart,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dxp.line(x='trestbps', y='thalach', data=heart, aggfunc='median')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Describe your features( input data) and labels(output data) from among your dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = heart.drop([\"target\"],axis = 1)\n",
    "labels = heart[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Import train_test_split module from Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Divide the dataset to be trained(60%),tested(20%) and validated(20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(features,labels,test_size = 0.4,random_state = 1)\n",
    "X_test,X_val,y_test,y_val = train_test_split(X_test,y_test,test_size = 0.5,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6\n",
      "0.2\n",
      "0.2\n"
     ]
    }
   ],
   "source": [
    "for dataset in (y_train,y_test,y_val):\n",
    "    print(round(len(dataset)/len(labels),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Random Forest Classifier and Cross-Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10-Fold Cross-Validation for Accuracy Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Range in percentage:-\n",
      "73.68 %\n",
      "88.89 %\n",
      "88.89 %\n",
      "88.89 %\n",
      "77.78 %\n",
      "83.33 %\n",
      "88.89 %\n",
      "94.44 %\n",
      "100.0 %\n",
      "88.89 %\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "score = cross_val_score(rf,X_train,y_train,cv = 10)\n",
    "print(\"Accuracy Range in percentage:-\")\n",
    "for i in (score):\n",
    "    print(round((i*100),2),\"%\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Predict your test values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 0 1\n",
      " 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Accuracy of the model as per the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for above dataset:  73.77049180327869 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for above dataset: \",((rf.score(X_test,y_test))*100),\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Classification report of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.74      0.75        31\n",
      "           1       0.74      0.77      0.75        30\n",
      "\n",
      "    accuracy                           0.75        61\n",
      "   macro avg       0.75      0.75      0.75        61\n",
      "weighted avg       0.75      0.75      0.75        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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