Heart_Failure_Det.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\n"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('dataset/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n0  75.0        0                       582         0                 20   \n1  55.0        0                      7861         0                 38   \n2  65.0        0                       146         0                 20   \n3  50.0        1                       111         0                 20   \n4  65.0        1                       160         1                 20   \n\n   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n0                    1  265000.00               1.9           130    1   \n1                    0  263358.03               1.1           136    1   \n2                    0  162000.00               1.3           129    1   \n3                    0  210000.00               1.9           137    1   \n4                    0  327000.00               2.7           116    0   \n\n   smoking  time  DEATH_EVENT  \n0        0     4            1  \n1        0     6            1  \n2        1     7            1  \n3        0     7            1  \n4        0     8            1  ",
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     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
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     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 299 entries, 0 to 298\nData columns (total 13 columns):\n #   Column                    Non-Null Count  Dtype  \n---  ------                    --------------  -----  \n 0   age                       299 non-null    float64\n 1   anaemia                   299 non-null    int64  \n 2   creatinine_phosphokinase  299 non-null    int64  \n 3   diabetes                  299 non-null    int64  \n 4   ejection_fraction         299 non-null    int64  \n 5   high_blood_pressure       299 non-null    int64  \n 6   platelets                 299 non-null    float64\n 7   serum_creatinine          299 non-null    float64\n 8   serum_sodium              299 non-null    int64  \n 9   sex                       299 non-null    int64  \n 10  smoking                   299 non-null    int64  \n 11  time                      299 non-null    int64  \n 12  DEATH_EVENT               299 non-null    int64  \ndtypes: float64(3), int64(10)\nmemory usage: 30.5 KB\n"
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   ],
   "source": [
    "train_df.info()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "sns.countplot(train_df['DEATH_EVENT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x14a13331548>"
     },
     "metadata": {},
     "execution_count": 12
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 288x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "sns.distplot(train_df['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x14a1514d148>"
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"420.507812\" xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"459.371094\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"520.894531\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"582.173828\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"621.382812\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"649.166016\" xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"712.544922\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"740.328125\" xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"803.707031\" xlink:href=\"#DejaVuSans-101\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_9\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"319.512091\" xlink:href=\"#m00b59cc041\" y=\"228.439219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_9\">\r\n      <!-- serum_sodium -->\r\n      <g transform=\"translate(322.132404 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id=\"text_11\">\r\n      <!-- smoking -->\r\n      <g transform=\"translate(363.47762 277.762656)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"52.099609\" xlink:href=\"#DejaVuSans-109\"/>\r\n       <use x=\"149.511719\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"210.693359\" xlink:href=\"#DejaVuSans-107\"/>\r\n       <use x=\"268.603516\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"296.386719\" xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"359.765625\" xlink:href=\"#DejaVuSans-103\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_12\">\r\n     <g id=\"line2d_12\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"381.321322\" xlink:href=\"#m00b59cc041\" y=\"228.439219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_12\">\r\n      <!-- time -->\r\n      <g transform=\"translate(384.080697 258.031406)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"39.208984\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"66.992188\" xlink:href=\"#DejaVuSans-109\"/>\r\n       <use x=\"164.404297\" xlink:href=\"#DejaVuSans-101\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_13\">\r\n     <g id=\"line2d_13\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"401.924399\" xlink:href=\"#m00b59cc041\" y=\"228.439219\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_13\">\r\n      <!-- DEATH_EVENT -->\r\n      <defs>\r\n       <path d=\"M 19.671875 64.796875 \r\nL 19.671875 8.109375 \r\nL 31.59375 8.109375 \r\nQ 46.6875 8.109375 53.6875 14.9375 \r\nQ 60.6875 21.78125 60.6875 36.53125 \r\nQ 60.6875 51.171875 53.6875 57.984375 \r\nQ 46.6875 64.796875 31.59375 64.796875 \r\nz\r\nM 9.8125 72.90625 \r\nL 30.078125 72.90625 \r\nQ 51.265625 72.90625 61.171875 64.09375 \r\nQ 71.09375 55.28125 71.09375 36.53125 \r\nQ 71.09375 17.671875 61.125 8.828125 \r\nQ 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xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"438.179688\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"499.703125\" xlink:href=\"#DejaVuSans-95\"/>\r\n       <use x=\"549.703125\" xlink:href=\"#DejaVuSans-112\"/>\r\n       <use x=\"613.179688\" xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"676.558594\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"737.740234\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"789.839844\" xlink:href=\"#DejaVuSans-112\"/>\r\n       <use x=\"853.316406\" xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"916.695312\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"977.876953\" xlink:href=\"#DejaVuSans-107\"/>\r\n       <use x=\"1035.787109\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"1063.570312\" xlink:href=\"#DejaVuSans-110\"/>\r\n       <use x=\"1126.949219\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"1188.228516\" xlink:href=\"#DejaVuSans-115\"/>\r\n       <use x=\"1240.328125\" xlink:href=\"#DejaVuSans-101\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_17\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"144.385937\" xlink:href=\"#m643affe274\" y=\"69.540757\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- diabetes -->\r\n      <g transform=\"translate(94.346875 73.339976)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-100\"/>\r\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"91.259766\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"152.539062\" xlink:href=\"#DejaVuSans-98\"/>\r\n       <use x=\"216.015625\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"277.539062\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"316.748047\" xlink:href=\"#DejaVuSans-101\"/>\r\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-115\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g 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xlink:href=\"#DejaVuSans-114\"/>\r\n       <use x=\"523.681641\" xlink:href=\"#DejaVuSans-97\"/>\r\n       <use x=\"584.960938\" xlink:href=\"#DejaVuSans-99\"/>\r\n       <use x=\"639.941406\" xlink:href=\"#DejaVuSans-116\"/>\r\n       <use x=\"679.150391\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"706.933594\" xlink:href=\"#DejaVuSans-111\"/>\r\n       <use x=\"768.115234\" xlink:href=\"#DejaVuSans-110\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_19\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"144.385937\" xlink:href=\"#m643affe274\" y=\"102.993065\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_19\">\r\n      <!-- high_blood_pressure -->\r\n      <g transform=\"translate(34.689062 106.792284)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-104\"/>\r\n       <use x=\"63.378906\" xlink:href=\"#DejaVuSans-105\"/>\r\n       <use x=\"91.162109\" xlink:href=\"#DejaVuSans-103\"/>\r\n      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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.heatmap(train_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop('time',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x14a114dc7c8>"
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train_df.boxplot(figsize=(5,5),rot=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_df.drop('DEATH_EVENT',axis=1)\n",
    "y = train_df.DEATH_EVENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_val,y_train,y_val = train_test_split(x,y,test_size=0.2,random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = RobustScaler()\n",
    "x_train = rs.fit_transform(x_train)\n",
    "x_val = rs.transform(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "DecisionTreeClassifier()"
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "score_dt = accuracy_score(y_pred_dt,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "RandomForestClassifier()"
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = rf.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_rf = accuracy_score(y_pred_rf,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "KNeighborsClassifier()"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "kn = KNeighborsClassifier()\n",
    "kn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_kn = kn.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_kn = accuracy_score(y_pred_kn,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "SVR(C=3, gamma='auto')"
     },
     "metadata": {},
     "execution_count": 88
    }
   ],
   "source": [
    "svr =SVR(gamma='auto',kernel='rbf',C=3)\n",
    "svr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svr = svr.predict(x_val)\n",
    "y_pred_svr = np.round(y_pred_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_svr = accuracy_score(y_pred_svr,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "GradientBoostingClassifier()"
     },
     "metadata": {},
     "execution_count": 93
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier()\n",
    "gb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gb =gb.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_gb = accuracy_score(y_pred_gb,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression()"
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "log = LogisticRegression()\n",
    "log.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_log = log.predict(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_log = accuracy_score(y_pred_log,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "The Accuracy of Decision Tree Model is: 0.7166666666666667\nThe Accuracy of Random Forest Model is: 0.65\nThe Accuracy of KNeighbors Classifier Model is: 0.6166666666666667\nThe Accuracy of Support Vector Machine Model is: 0.7\nThe Accuracy of Gradient Boosting Classifier Model is: 0.7\nThe Accuracy of Logistic Regression Model is: 0.6333333333333333\n"
    }
   ],
   "source": [
    "print(f\"The Accuracy of Decision Tree Model is: {score_dt}\")\n",
    "print(f\"The Accuracy of Random Forest Model is: {score_rf}\")\n",
    "print(f\"The Accuracy of KNeighbors Classifier Model is: {score_kn}\")\n",
    "print(f\"The Accuracy of Support Vector Machine Model is: {score_svr}\")\n",
    "print(f\"The Accuracy of Gradient Boosting Classifier Model is: {score_gb}\")\n",
    "print(f\"The Accuracy of Logistic Regression Model is: {score_log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}