Heart fail.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "09f7d206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "fb6728ed",
   "metadata": {},
   "outputs": [
    {
     "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>anaemia</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>high_blood_pressure</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoking</th>\n",
       "      <th>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263358.03</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162000.00</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327000.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  75.0        0                       582         0                 20   \n",
       "1  55.0        0                      7861         0                 38   \n",
       "2  65.0        0                       146         0                 20   \n",
       "3  50.0        1                       111         0                 20   \n",
       "4  65.0        1                       160         1                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1  265000.00               1.9           130    1   \n",
       "1                    0  263358.03               1.1           136    1   \n",
       "2                    0  162000.00               1.3           129    1   \n",
       "3                    0  210000.00               1.9           137    1   \n",
       "4                    0  327000.00               2.7           116    0   \n",
       "\n",
       "   smoking  time  DEATH_EVENT  \n",
       "0        0     4            1  \n",
       "1        0     6            1  \n",
       "2        1     7            1  \n",
       "3        0     7            1  \n",
       "4        0     8            1  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0a78ece9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
       "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
       "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
       "       'DEATH_EVENT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cfc3f3b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    203\n",
       "1     96\n",
       "Name: DEATH_EVENT, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DEATH_EVENT\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c6cbb0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         0\n",
       "anaemia                     0\n",
       "creatinine_phosphokinase    0\n",
       "diabetes                    0\n",
       "ejection_fraction           0\n",
       "high_blood_pressure         0\n",
       "platelets                   0\n",
       "serum_creatinine            0\n",
       "serum_sodium                0\n",
       "sex                         0\n",
       "smoking                     0\n",
       "time                        0\n",
       "DEATH_EVENT                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6874ca64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='creatinine_phosphokinase', ylabel='Density'>"
      ]
     },
     "execution_count": 49,
     "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": [
    "sns.distplot(df[\"creatinine_phosphokinase\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "491a5511",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "66700c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "86c8c2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "e32bc3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "926f61af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7888888888888889"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "pred=lr.predict(x_test)\n",
    "\n",
    "score=accuracy_score(y_test,pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "70b6a3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  3],\n",
       "       [16, 21]], dtype=int64)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "6a4a5e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "60107997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv=SVC()\n",
    "sv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "d17c42eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7666666666666667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[49,  4],\n",
       "       [17, 20]], dtype=int64)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_sv=sv.predict(x_test)\n",
    "score=accuracy_score(y_test,pred_sv)\n",
    "print(score)\n",
    "confusion_matrix(y_test,pred_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "22ecaf4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "043ae2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[41, 12],\n",
       "       [18, 19]], dtype=int64)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_dt=dt.predict(x_test)\n",
    "score=accuracy_score(y_test,pred_dt)\n",
    "print(score)\n",
    "confusion_matrix(y_test,pred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "257f9a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "69a8e121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7666666666666667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[49,  4],\n",
       "       [17, 20]], dtype=int64)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_rf=rf.predict(x_test)\n",
    "score=accuracy_score(y_test,pred_rf)\n",
    "print(score)\n",
    "confusion_matrix(y_test,pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "c755801e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "4f2a2e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7333333333333333"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe_lr=RFE(estimator=rf,n_features_to_select=9,verbose=1)\n",
    "rfe_lr.fit(x_train,y_train)\n",
    "rfe_lr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0e4a791",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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