RFModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "\r\n",
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "\r\n",
    "df = pd.read_csv('heart.csv')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>212</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Preprocessing"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Modeling"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "from sklearn.model_selection import train_test_split\r\n",
    "from sklearn.ensemble import RandomForestClassifier\r\n",
    "from sklearn.metrics import confusion_matrix,classification_report\r\n",
    "from sklearn.metrics import f1_score,plot_roc_curve,accuracy_score,roc_curve,roc_auc_score,recall_score\r\n",
    "from mlxtend.plotting import plot_confusion_matrix\r\n",
    "from sklearn import metrics\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "X = df.drop('target',axis=1).values\r\n",
    "Y = df['target'].values\r\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "rf = RandomForestClassifier(random_state=50)\r\n",
    "rf.fit(X_train,Y_train)\r\n",
    "Y_pred_rf = rf.predict(X_test)\r\n",
    "score_rf = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\r\n",
    "score_rf"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "conf_mat=confusion_matrix(Y_test,Y_pred_rf)\r\n",
    "plot_confusion_matrix(conf_mat,class_names=[\"Negative\",\"Positive\"],figsize=(12,5),cmap='cividis_r'); #fn\r\n",
    "#plt.savefig(\"RFcrr.png\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "Y_pred_rf = np.around(Y_pred_rf)\r\n",
    "print(metrics.classification_report(Y_test,Y_pred_rf))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        98\n",
      "           1       1.00      1.00      1.00       107\n",
      "\n",
      "    accuracy                           1.00       205\n",
      "   macro avg       1.00      1.00      1.00       205\n",
      "weighted avg       1.00      1.00      1.00       205\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "plot_roc_curve(rf,X_test,Y_test)\r\n",
    "plt.xlabel('False Positive Rate')\r\n",
    "plt.ylabel('True Positive Rate')\r\n",
    "plt.title('Receiver Operating Characteristic Curve');\r\n",
    "#plt.savefig(\"RF.png\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "Input = (63, 1, 3,145,233,1,0,150, 0, 2.3,0,0,1)\r\n",
    "\r\n",
    "Input_array= np.asarray(Input)\r\n",
    "Input_reshaped = Input_array.reshape(1,-1)\r\n",
    "\r\n",
    "prediction = rf.predict(Input_reshaped)\r\n",
    "prediction = np.around(prediction)\r\n",
    "\r\n",
    "print(prediction)\r\n",
    "\r\n",
    "if (prediction[0]== 0):\r\n",
    "  print('The Person does not have a Heart Disease')\r\n",
    "else:\r\n",
    "  print(\"The Person will has a Heart Disease \")\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1]\n",
      "The Person will has a Heart Disease \n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "   import pickle\r\n",
    "with open('models.pkl', 'wb') as file:\r\n",
    "    pickle.dump(rf, file)"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.8 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "interpreter": {
   "hash": "951ea1177d18e5c3a13e2b09a88d23690a3438aec926dbfb7969a8e9fcaa8c82"
  }
 },
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