Neural_Network_Multi-Layer-Perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33e77ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\All Semister\\\\8A\\\\Neural Network\\\\Lab'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20449773",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4c1fbe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart failure.csv\")    # data loading\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2941e8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape   # total object/sample or row = 299 and features/columns = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fea2ad89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 299 entries, 0 to 298\n",
      "Data 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  \n",
      "dtypes: float64(3), int64(10)\n",
      "memory usage: 30.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c1cbc09",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()    # missing value checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a37e07eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename({\"DEATH_EVENT\":\"death\"}, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a17f303",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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</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",
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       "      <th>2</th>\n",
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       "      <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  \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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da481dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    203\n",
       "1     96\n",
       "Name: death, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.death.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fdc0c2",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d9d1a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fce958a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='death', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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.countplot(x=\"death\", data=df)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1ba65a",
   "metadata": {},
   "source": [
    "### death analysis with respect to diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2ba27cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diabetes', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATU0lEQVR4nO3df5BdZX3H8fcXElgjoQESKWaJCYLWoBLIAhaBUbHlhxKigglTNUicyEgpSv2BdQaQDjP+oKUItU5akNChCYhigAFaRFNgkB+JRAkJlAgImwkhRMU4GpD47R978nANm3DZ7L3nhvt+zezsOc95znO+m9nZT86v50ZmIkkSwA51FyBJ6hyGgiSpMBQkSYWhIEkqDAVJUjGi7gK2xdixY3PixIl1lyFJ25UlS5Y8k5njBtu2XYfCxIkTWbx4cd1lSNJ2JSJ+saVtXj6SJBWGgiSpaFkoRMTlEfF0RCxraPt6RDwUET+LiOsiYkzDti9GxMqIeDgijm5VXZKkLWvlPYUrgEuBKxvabgW+mJkvRMRXgS8CX4iIycBMYH/g9cAPIuJNmbmxhfVJ0pD94Q9/oL+/nw0bNtRdyhb19PTQ29vLyJEjm96nZaGQmbdHxMTN2v6nYfVu4MRq+QRgQWY+BzwWESuBQ4Aft6o+SdoW/f39jB49mokTJxIRdZfzEpnJunXr6O/vZ9KkSU3vV+c9hVOBm6vl8cCTDdv6q7aXiIg5EbE4IhavXbu2xSVK0uA2bNjAHnvs0ZGBABAR7LHHHq/4TKaWUIiILwEvAFe90n0zc25m9mVm37hxgz5mK0lt0amBsMlQ6mv7ewoRcQrwfuCofHHe7lXA3g3deqs2SVIbtfVMISKOAT4PTMvM3zVsuh6YGRE7R8QkYD/g3nbWJkmtct5553HhhRe+4v0WLVrEXXfdVdZPOeUUrr322uEs7SVadqYQEfOBdwFjI6IfOJeBp412Bm6tTmvuzszTMvPBiLgGWM7AZaXT2/Xk0dTPXfnynbrEkq9/rO4SJDVYtGgRu+yyC4cddljbjtmyM4XMPDkz98rMkZnZm5mXZea+mbl3Zk6pvk5r6H9BZr4xM9+cmTdvbWxJ6nQXXHABb3rTmzj88MN5+OGHAfj5z3/OMcccw9SpUzniiCN46KGHALjhhhs49NBDOfDAA3nve9/LmjVrePzxx/nWt77FRRddxJQpU7jjjjsAuP322znssMPYZ599WnLW4BvNkjTMlixZwoIFC1i6dCk33XQT9913HwBz5szhkksuYcmSJVx44YV86lOfAuDwww/n7rvv5v7772fmzJl87WtfY+LEiZx22ml85jOfYenSpRxxxBEArF69mjvvvJMbb7yRs88+e9hr364nxJOkTnTHHXfwgQ98gFGjRgEwbdo0NmzYwF133cVJJ51U+j333HPAwDsPM2bMYPXq1Tz//PNbfa9g+vTp7LDDDkyePJk1a9YMe+2GgiS1wR//+EfGjBnD0qVLX7LtjDPO4KyzzmLatGksWrSI8847b4vj7LzzzmX5xQc4h4+XjyRpmB155JF8//vf5/e//z3r16/nhhtuYNSoUUyaNInvfOc7wMAf9J/+9KcAPPvss4wfP/C+7rx588o4o0ePZv369W2t3VCQpGF20EEHMWPGDA444ACOPfZYDj74YACuuuoqLrvsMg444AD2339/Fi5cCAw8snrSSScxdepUxo4dW8Y5/vjjue666/7kRnOrRStOP9qlr68vt/VDdnwk9UU+kio1b8WKFbzlLW+pu4yXNVidEbEkM/sG6++ZgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVPhGsyQNg+F+vL2ZR8RvueUWzjzzTDZu3MgnPvGJYZkLyTMFSdoObdy4kdNPP52bb76Z5cuXM3/+fJYvX77N4xoKkrQduvfee9l3333ZZ5992GmnnZg5c2Z5Q3pbGAqStB1atWoVe+/94qcY9/b2smrVtn+KsaEgSSoMBUnaDo0fP54nn3yyrPf395eZVreFoSBJ26GDDz6YRx55hMcee4znn3+eBQsWMG3atG0e10dSJWkYtHuW4REjRnDppZdy9NFHs3HjRk499VT233//bR93GGqTJNXguOOO47jjjhvWMb18JEkqDAVJUmEoSJIKQ0GSVBgKkqSiZaEQEZdHxNMRsayhbfeIuDUiHqm+71a1R0R8IyJWRsTPIuKgVtUlSdqyVj6SegVwKdA4n+zZwG2Z+ZWIOLta/wJwLLBf9XUo8G/Vd0naLjxx/tuGdbwJ5zzwsn1OPfVUbrzxRl73utexbNmyl+3fjJadKWTm7cAvN2s+AZhXLc8Dpje0X5kD7gbGRMRerapNkl4NTjnlFG655ZZhHbPd9xT2zMzV1fJTwJ7V8njgyYZ+/VXbS0TEnIhYHBGL165d27pKJanDHXnkkey+++7DOmZtN5ozM4Ecwn5zM7MvM/vGjRvXgsokqXu1OxTWbLosVH1/umpfBezd0K+3apMktVG7Q+F6YFa1PAtY2ND+seoppHcAzzZcZpIktUnLnj6KiPnAu4CxEdEPnAt8BbgmImYDvwA+XHW/CTgOWAn8Dvh4q+qSJG1Zy0IhM0/ewqajBumbwOmtqkWSWq2ZR0iH28knn8yiRYt45pln6O3t5ctf/jKzZ8/epjGdOluStlPz588f9jGd5kKSVBgKkqTCUJCkIRq4Hdq5hlKfoSBJQ9DT08O6des6Nhgyk3Xr1tHT0/OK9vNGsyQNQW9vL/39/XTydDs9PT309va+on0MBUkagpEjRzJp0qS6yxh2Xj6SJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgqnzpY61NTPXVl3CR1jydc/VncJXcMzBUlSYShIkgpDQZJUGAqSpKKWUIiIz0TEgxGxLCLmR0RPREyKiHsiYmVEXB0RO9VRmyR1s7aHQkSMB/4O6MvMtwI7AjOBrwIXZea+wK+A2e2uTZK6XV2Xj0YAr4mIEcAoYDXwHuDaavs8YHo9pUlS92p7KGTmKuBC4AkGwuBZYAnw68x8oerWD4wfbP+ImBMRiyNi8dq1a9tRsiR1jTouH+0GnABMAl4PvBY4ptn9M3NuZvZlZt+4ceNaVKUkdac6Lh+9F3gsM9dm5h+A7wHvBMZUl5MAeoFVNdQmSV2tjlB4AnhHRIyKiACOApYDPwJOrPrMAhbWUJskdbU67incw8AN5Z8AD1Q1zAW+AJwVESuBPYDL2l2bJHW7WibEy8xzgXM3a34UOKSGciRJFd9oliQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVDQVChFxWzNtkqTt21Y/ZCcieoBRwNiI2A2IatOuwPgW1yZJarOX++S1TwKfBl4PLOHFUPgNcGnrypIk1WGroZCZFwMXR8QZmXlJm2qSJNWkqc9ozsxLIuIwYGLjPpl5ZYvqkiTVoKlQiIj/BN4ILAU2Vs0JGAqS9CrSVCgAfcDkzMxWFiNJqlez7yksA/68lYVIkurX7JnCWGB5RNwLPLepMTOntaQqSVItmg2F81pZhCSpMzT79NH/troQSVL9mp3mYn1E/Kb62hARGyPiN0M9aESMiYhrI+KhiFgREX8ZEbtHxK0R8Uj1fbehji9JGpqmQiEzR2fmrpm5K/Aa4EPAN7fhuBcDt2TmXwAHACuAs4HbMnM/4LZqXZLURq94ltQc8H3g6KEcMCL+DDgSuKwa7/nM/DVwAjCv6jYPmD6U8SVJQ9fsy2sfbFjdgYH3FjYM8ZiTgLXAtyPiAAbmVDoT2DMzV1d9ngL2HOL4kqQhavbpo+Mbll8AHmfgf/ZDPeZBwBmZeU9EXMxml4oyMyNi0BflImIOMAdgwoQJQyxBkjSYZp8++vgwHrMf6M/Me6r1axkIhTURsVdmro6IvYCnt1DLXGAuQF9fn29YS9Iwavbpo96IuC4inq6+vhsRvUM5YGY+BTwZEW+umo4ClgPXA7OqtlnAwqGML0kaumYvH30b+C/gpGr9I1XbXw3xuGcAV0XETsCjwMcZCKhrImI28Avgw0McW5I0RM2GwrjM/HbD+hUR8emhHjQzlzJws3pzRw11TEnStmv2kdR1EfGRiNix+voIsK6VhUmS2q/ZUDiVgcs5TwGrgROBU1pUkySpJs1ePjofmJWZvwKIiN2BCxkIC71KPHH+2+ouoWNMOOeBukuQatHsmcLbNwUCQGb+EjiwNSVJkurSbCjs0DhBXXWm0OxZhiRpO9HsH/Z/An4cEd+p1k8CLmhNSZKkujT7RvOVEbEYeE/V9MHMXN66siRJdWj6ElAVAgaBJL2KveKpsyVJr16GgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqagtFCJix4i4PyJurNYnRcQ9EbEyIq6OiJ3qqk2SulWdZwpnAisa1r8KXJSZ+wK/AmbXUpUkdbFaQiEieoH3Af9RrQfwHuDaqss8YHodtUlSN6vrTOFfgM8Df6zW9wB+nZkvVOv9wPga6pKkrjai3QeMiPcDT2fmkoh41xD2nwPMAZgwYcLwFiepIz1x/tvqLqFjTDjngZaOX8eZwjuBaRHxOLCAgctGFwNjImJTSPUCqwbbOTPnZmZfZvaNGzeuHfVKUtdoeyhk5hczszczJwIzgR9m5t8APwJOrLrNAha2uzZJ6nad9J7CF4CzImIlA/cYLqu5HknqOm2/p9AoMxcBi6rlR4FD6qxHkrpdJ50pSJJqZihIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUtD0UImLviPhRRCyPiAcj4syqffeIuDUiHqm+79bu2iSp29VxpvAC8PeZORl4B3B6REwGzgZuy8z9gNuqdUlSG7U9FDJzdWb+pFpeD6wAxgMnAPOqbvOA6e2uTZK6Xa33FCJiInAgcA+wZ2aurjY9Bey5hX3mRMTiiFi8du3a9hQqSV2itlCIiF2A7wKfzszfNG7LzARysP0yc25m9mVm37hx49pQqSR1j1pCISJGMhAIV2Xm96rmNRGxV7V9L+DpOmqTpG5Wx9NHAVwGrMjMf27YdD0wq1qeBSxsd22S1O1G1HDMdwIfBR6IiKVV2z8AXwGuiYjZwC+AD9dQmyR1tbaHQmbeCcQWNh/VzlokSX/KN5olSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqOi4UIuKYiHg4IlZGxNl11yNJ3aSjQiEidgT+FTgWmAycHBGT661KkrpHR4UCcAiwMjMfzczngQXACTXXJEldY0TdBWxmPPBkw3o/cGhjh4iYA8ypVn8bEQ+3qbZXvTfAWOCZuuvoCOdG3RWogb+bDYbnd/MNW9rQaaHwsjJzLjC37jpejSJicWb21V2HtDl/N9un0y4frQL2bljvrdokSW3QaaFwH7BfREyKiJ2AmcD1NdckSV2joy4fZeYLEfG3wH8DOwKXZ+aDNZfVTbwsp07l72abRGbWXYMkqUN02uUjSVKNDAVJUmEoyKlF1LEi4vKIeDoiltVdS7cwFLqcU4uow10BHFN3Ed3EUJBTi6hjZebtwC/rrqObGAoabGqR8TXVIqlmhoIkqTAU5NQikgpDQU4tIqkwFLpcZr4AbJpaZAVwjVOLqFNExHzgx8CbI6I/ImbXXdOrndNcSJIKzxQkSYWhIEkqDAVJUmEoSJIKQ0GSVHTUJ69JnSIizgN+C+wK3J6ZP9hK30XAZzNzcZNjTwFen5k3bXul0vAyFKStyMxzWjDsFKAPMBTUcbx8JFUi4ksR8X8RcSfw5qrtiog4sVo+JyLui4hlETE3IqJh949GxNJq2yFV/9dWnwdwb0TcHxEnVG+Nnw/MqPrPGKxftf/+VdvSiPhZROzX3n8RdSNDQQIiYioDU3xMAY4DDh6k26WZeXBmvhV4DfD+hm2jMnMK8Cng8qrtS8APM/MQ4N3A14GRwDnA1Zk5JTOvHqxfRLwWOA24uBq3j4EZbKWW8vKRNOAI4LrM/B1ARAw2/9O7I+LzwChgd+BB4IZq23wYmP8/InaNiDHAXwPTIuKzVZ8eYMIg426p34+BL0VEL/C9zHxkG39G6WUZClITIqIH+CbQl5lPVjeiexq6bD5fTAIBfCgzH95srEM3H36wfsCKiLgHeB9wU0R8MjN/uI0/irRVXj6SBtwOTI+I10TEaOD4zbZvCoBnImIX4MTNts8AiIjDgWcz81kGJhk8Y9O9h4g4sOq7HhjdsO+g/SJiH+DRzPwGsBB4+7b/mNLWGQoSkJk/Aa4GfgrczMCU4o3bfw38O7CMgT/i9202xIaIuB/4FrBpJs9/ZOAews8i4sFqHeBHwORNN5q30u/DwLKIWAq8FbhyWH5YaSucJVWSVHimIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKn4f87mDMtIBWJwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"diabetes\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23eff334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    174\n",
       "1    125\n",
       "Name: diabetes, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diabetes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8565691",
   "metadata": {},
   "source": [
    "### death analysis with respect to anaemia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb05d4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='anaemia', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATJElEQVR4nO3df5BdZX3H8fcXkriNoJhkpZglJghag8qPLD+KQFVoCVQCWpAwbU1MbGRERGmrWGcE7TDjDyq1UHXSooQOTQQsP0exFE2BwQDZEiUkUKKgbCaGEJWm1RASvv3jnjxZw0ZuNnvv2eS+XzM7e85zftxvMjv72eec5zwnMhNJkgD2qrsASdLIYShIkgpDQZJUGAqSpMJQkCQVo+ouYFdMmDAhJ0+eXHcZkrRb6evreyYzuwfbtluHwuTJk1m6dGndZUjSbiUifrKjbV4+kiQVhoIkqTAUJEnFbn1PQZLq8vzzz9Pf38/GjRvrLmWHurq66OnpYfTo0U0fYyhI0hD09/ez7777MnnyZCKi7nJeJDNZv349/f39TJkypenjvHwkSUOwceNGxo8fPyIDASAiGD9+/E73ZFoWChHxtYh4OiKWD2j7QkQ8GhE/jIibImK/Ads+ERGrIuKxiDilVXVJ0nAZqYGw1VDqa2VP4Rpg+nZtdwJvysy3AP8NfAIgIqYCM4FDq2O+HBF7t7A2SdIgWhYKmXk38PPt2v49MzdXq0uAnmr5DGBRZj6XmU8Aq4CjW1WbJLXTpZdeyuWXX77Txy1evJj77ruvrM+ePZsbb7xxOEt7kTpvNM8BvlEtT6QRElv1V20vEhHzgHkAkyZN2uUipv31tbt8jj1F3xfeW3cJkgZYvHgx++yzD8cdd1zbPrOWG80R8UlgM3Ddzh6bmfMzszcze7u7B526Q5Jqd9lll/H617+e448/nsceewyAH/3oR0yfPp1p06Zxwgkn8OijjwJw2223ccwxx3DEEUdw8skns3btWp588km++tWvcsUVV3D44Ydzzz33AHD33Xdz3HHHcdBBB7Wk19D2UIiI2cA7gT/Nbe8CXQ0cOGC3nqpNknY7fX19LFq0iGXLlvGtb32LBx98EIB58+Zx5ZVX0tfXx+WXX84HP/hBAI4//niWLFnCQw89xMyZM/n85z/P5MmTOe+88/joRz/KsmXLOOGEEwBYs2YN9957L7fffjsXX3zxsNfe1stHETEd+BjwB5n5qwGbbgX+NSK+CLwGOAR4oJ21SdJwueeee3jXu97F2LFjAZgxYwYbN27kvvvu4+yzzy77Pffcc0DjmYdzzjmHNWvWsGnTpt/6XMGZZ57JXnvtxdSpU1m7du2w196yUIiIhcDbgAkR0Q9cQmO00cuAO6uhUksy87zMfCQirgdW0LisdH5mbmlVbZLUbi+88AL77bcfy5Yte9G2Cy64gIsuuogZM2awePFiLr300h2e52Uve1lZ3naxZfi0cvTRuZl5QGaOzsyezLw6Mw/OzAMz8/Dq67wB+1+Wma/LzDdk5rdbVZcktdqJJ57IzTffzK9//Ws2bNjAbbfdxtixY5kyZQo33HAD0PiF/oMf/ACAZ599lokTG2NrFixYUM6z7777smHDhrbW7hPNkjTMjjzySM455xwOO+wwTj31VI466igArrvuOq6++moOO+wwDj30UG655RagMWT17LPPZtq0aUyYMKGc5/TTT+emm276jRvNrRat6H60S29vb+7qS3YckrqNQ1Kl5q1cuZI3vvGNdZfxkgarMyL6MrN3sP3tKUiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWv45SkYTDcw9ubGSJ+xx13cOGFF7Jlyxbe//73D8tcSPYUJGk3tGXLFs4//3y+/e1vs2LFChYuXMiKFSt2+byGgiTthh544AEOPvhgDjroIMaMGcPMmTPLE9K7wlCQpN3Q6tWrOfDAbW8c6OnpYfXqXX/jgKEgSSoMBUnaDU2cOJGnnnqqrPf395eZVneFoSBJu6GjjjqKxx9/nCeeeIJNmzaxaNEiZsyYscvndUiqJA2Dds8yPGrUKK666ipOOeUUtmzZwpw5czj00EN3/bzDUJskqQannXYap5122rCe08tHkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlS4ZBUSRoGP/3Mm4f1fJM+9fBL7jNnzhxuv/12Xv3qV7N8+fJh+Vx7CpK0m5o9ezZ33HHHsJ6zZaEQEV+LiKcjYvmAtnERcWdEPF59f1XVHhHxDxGxKiJ+GBFHtqouSdpTnHjiiYwbN25Yz9nKnsI1wPTt2i4G7srMQ4C7qnWAU4FDqq95wFdaWJckaQdaFgqZeTfw8+2azwAWVMsLgDMHtF+bDUuA/SLigFbVJkkaXLvvKeyfmWuq5Z8B+1fLE4GnBuzXX7W9SETMi4ilEbF03bp1ratUkjpQbTeaMzOBHMJx8zOzNzN7u7u7W1CZJHWudg9JXRsRB2Tmmury0NNV+2rgwAH79VRtkrRbaGYI6XA799xzWbx4Mc888ww9PT18+tOfZu7cubt0znaHwq3ALOCz1fdbBrR/KCIWAccAzw64zCRJGsTChQuH/ZwtC4WIWAi8DZgQEf3AJTTC4PqImAv8BHhPtfu3gNOAVcCvgPe1qi5J0o61LBQy89wdbDppkH0TOL9VtUiSmuMTzZI0RI2/Z0euodRnKEjSEHR1dbF+/foRGwyZyfr16+nq6tqp45wQT5KGoKenh/7+fkby81JdXV309PTs1DGGgiQNwejRo5kyZUrdZQw7Lx9JkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFaPqLkDS4Kb99bV1lzBi9H3hvXWX0DHsKUiSilpCISI+GhGPRMTyiFgYEV0RMSUi7o+IVRHxjYgYU0dtktTJ2h4KETER+DDQm5lvAvYGZgKfA67IzIOBXwBz212bJHW6ui4fjQJ+JyJGAWOBNcA7gBur7QuAM+spTZI6V9tDITNXA5cDP6URBs8CfcAvM3NztVs/MHGw4yNiXkQsjYil69ata0fJktQx6rh89CrgDGAK8Brg5cD0Zo/PzPmZ2ZuZvd3d3S2qUpI6Ux2Xj04GnsjMdZn5PPBvwFuB/arLSQA9wOoaapOkjlZHKPwUODYixkZEACcBK4DvAWdV+8wCbqmhNknqaHXcU7ifxg3l/wIermqYD3wcuCgiVgHjgavbXZskdbpanmjOzEuAS7Zr/jFwdA3lSJIqPtEsSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQ0FQoRcVczbZKk3dtvnSU1IrpovEN5QvXGtKg2vYIdvC5TkrT7eqmpsz8AfITGazP72BYK/wNc1bqyJEl1+K2hkJlfAr4UERdk5pVtqkmSVJOmXrKTmVdGxHHA5IHHZOa1LapLklSDpkIhIv4FeB2wDNhSNSdgKEjSHqTZ13H2AlMzM1tZjCSpXs0+p7Ac+N1WFiJJql+zPYUJwIqIeAB4bmtjZs5oSVWSpFo0GwqXtrIISdLI0Ozoo/9sdSGSpPo1O/poA43RRgBjgNHA/2XmK1pVmCSp/ZrtKey7dTkiAjgDOLZVRUmS6rHTs6Rmw83AKUP90IjYLyJujIhHI2JlRPx+RIyLiDsj4vHq+6uGen5J0tA0e/no3QNW96Lx3MLGXfjcLwF3ZOZZETGGxqR7fwPclZmfjYiLgYuBj+/CZ0iSdlKzo49OH7C8GXiSxiWknRYRrwROBGYDZOYmYFNEnAG8rdptAbAYQ0GS2qrZewrvG8bPnAKsA74eEYfRmH31QmD/zFxT7fMzYP/BDo6IecA8gEmTJg1jWZKkZl+y0xMRN0XE09XXNyOiZ4ifOQo4EvhKZh4B/B+NS0VFNZ3GoFNqZOb8zOzNzN7u7u4hliBJGkyzN5q/DtxK470KrwFuq9qGoh/oz8z7q/UbaYTE2og4AKD6/vQQzy9JGqJmQ6E7M7+emZurr2uAIf2Znpk/A56KiDdUTScBK2iEzqyqbRZwy1DOL0kaumZvNK+PiD8DFlbr5wLrd+FzLwCuq0Ye/Rh4H42Auj4i5gI/Ad6zC+eXJA1Bs6EwB7gSuILGtf77qEYPDUVmLqMxrHV7Jw31nJKkXddsKHwGmJWZvwCIiHHA5TTCQpK0h2j2nsJbtgYCQGb+HDiiNSVJkurSbCjsNXDaiaqn0GwvQ5K0m2j2F/vfAd+PiBuq9bOBy1pTkiSpLs0+0XxtRCwF3lE1vTszV7SuLElSHZq+BFSFgEEgSXuwnZ46W5K05zIUJEmFoSBJKgwFSVJhKEiSCkNBklT4VLKKn37mzXWXMGJM+tTDdZcg1cKegiSpMBQkSYWhIEkqDAVJUmEoSJIKRx9JGvEcGbdNq0fG2VOQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVNQWChGxd0Q8FBG3V+tTIuL+iFgVEd+IiDF11SZJnarOnsKFwMoB658DrsjMg4FfAHNrqUqSOlgtoRARPcAfA/9crQfwDuDGapcFwJl11CZJnayunsLfAx8DXqjWxwO/zMzN1Xo/MHGwAyNiXkQsjYil69ata3mhktRJ2h4KEfFO4OnM7BvK8Zk5PzN7M7O3u7t7mKuTpM5Wx4R4bwVmRMRpQBfwCuBLwH4RMarqLfQAq2uoTZI6Wtt7Cpn5iczsyczJwEzgu5n5p8D3gLOq3WYBt7S7NknqdCPpOYWPAxdFxCoa9xiurrkeSeo4tb5PITMXA4ur5R8DR9dZjyR1upHUU5Ak1cxQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpKLtoRARB0bE9yJiRUQ8EhEXVu3jIuLOiHi8+v6qdtcmSZ2ujp7CZuAvM3MqcCxwfkRMBS4G7srMQ4C7qnVJUhu1PRQyc01m/le1vAFYCUwEzgAWVLstAM5sd22S1OlqvacQEZOBI4D7gf0zc0216WfA/js4Zl5ELI2IpevWrWtPoZLUIWoLhYjYB/gm8JHM/J+B2zIzgRzsuMycn5m9mdnb3d3dhkolqXPUEgoRMZpGIFyXmf9WNa+NiAOq7QcAT9dRmyR1sjpGHwVwNbAyM784YNOtwKxqeRZwS7trk6RON6qGz3wr8OfAwxGxrGr7G+CzwPURMRf4CfCeGmqTpI7W9lDIzHuB2MHmk9pZiyTpN/lEsySpMBQkSYWhIEkqDAVJUmEoSJIKQ0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlSYShIkgpDQZJUGAqSpMJQkCQVhoIkqTAUJEmFoSBJKgwFSVJhKEiSCkNBklQYCpKkwlCQJBWGgiSpMBQkScWIC4WImB4Rj0XEqoi4uO56JKmTjKhQiIi9gX8ETgWmAudGxNR6q5KkzjGiQgE4GliVmT/OzE3AIuCMmmuSpI4xqu4CtjMReGrAej9wzMAdImIeMK9a/d+IeKxNte3xXgsTgGfqrmNEuCTqrkAD+LM5wPD8bL52RxtGWii8pMycD8yvu449UUQszczeuuuQtufPZvuMtMtHq4EDB6z3VG2SpDYYaaHwIHBIREyJiDHATODWmmuSpI4xoi4fZebmiPgQ8B1gb+BrmflIzWV1Ei/LaaTyZ7NNIjPrrkGSNEKMtMtHkqQaGQqSpMJQkFOLaMSKiK9FxNMRsbzuWjqFodDhnFpEI9w1wPS6i+gkhoKcWkQjVmbeDfy87jo6iaGgwaYWmVhTLZJqZihIkgpDQU4tIqkwFOTUIpIKQ6HDZeZmYOvUIiuB651aRCNFRCwEvg+8ISL6I2Ju3TXt6ZzmQpJU2FOQJBWGgiSpMBQkSYWhIEkqDAVJUmEoSCNIRHwmIk6uuw51LoekSpIKewpSJSJujoi+iHgkIuZVbf8bEZdFxA8iYklE7F+1nx4R90fEQxHxHwPaX169A+CBatsZVfvs6vx3RsSTEfGhiLio2mdJRIyr9rsmIs6qlj8VEQ9GxPKImB8RUc//jDqJoSBtMyczpwG9wIcjYjzwcmBJZh4G3A38RbXvvcCxmXkEjenGP1a1fxL4bmYeDbwd+EJEvLza9ibg3cBRwGXAr6rjvw+8d5B6rsrMozLzTcDvAO8c3n+u9GKj6i5AGkE+HBHvqpYPBA4BNgG3V219wB9Wyz3ANyLiAGAM8ETV/kfAjIj4q2q9C5hULX8vMzcAGyLiWeC2qv1h4C2D1PP2iPgYMBYYBzwy4BipJewpSEBEvA04Gfj9qlfwEI1f6M/nthtvW9j2h9SVNP6SfzPwgWpfgAD+JDMPr74mZebKattzAz7yhQHrL7DdH2gR0QV8GTir+ox/GvAZUssYClLDK4FfZOavIuL3gGOb2H/rFOOzBrR/B7hg6/X/iDhiiPVsDYBnImIf4KwhnkfaKYaC1HAHMCoiVgKfBZa8xP6XAjdERB/wzID2vwVGAz+MiEeq9Z2Wmb+k0TtYTiNoHhzKeaSd5ZBUSVJhT0GSVBgKkqTCUJAkFYaCJKkwFCRJhaEgSSoMBUlS8f8JCf5O6W7YlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"anaemia\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b10d1726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    170\n",
       "1    129\n",
       "Name: anaemia, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.anaemia.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d2f215",
   "metadata": {},
   "source": [
    "### death analysis with respect to high_blood_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e66de42",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='high_blood_pressure', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWQElEQVR4nO3dfbRddX3n8fcHEowIykMig7lgguADqAhclCIwncJUQA1oRZLpjCCxKUsGrXZq0a4lkZFZRZlhUDo6tCBgmSBilQcBZdAUEAETCQKJFASEmwkQolKqPEj8zh9nZ3Mb8nBJ7jnnJvf9Wuuss/dvP33PWnfdz/ntvc9vp6qQJAlgi34XIEkaOwwFSVLLUJAktQwFSVLLUJAktSb0u4CNMXny5Jo2bVq/y5CkTcrChQsfr6opa1q2SYfCtGnTWLBgQb/LkKRNSpKfr22Zp48kSa2uhUKS85M8luSuNSz78ySVZHIznyRfSHJfkp8k2bdbdUmS1q6bPYULgMNXb0yyC/CHwEPDmo8A9mhec4AvdbEuSdJadO2aQlXdkGTaGhadBXwCuHxY21HARdUZc+OWJNsl2bmqlnWrPknaGL/97W8ZGhri6aef7ncpazVp0iQGBgaYOHHiiLfp6YXmJEcBS6vqjiTDF00FHh42P9S0vSAUksyh05tg11137V6xkrQOQ0NDbLvttkybNo3V/p+NCVXFihUrGBoaYvr06SPermcXmpNsDXwK+PTG7Keqzq2qwaoanDJljXdUSVLXPf300+y4445jMhAAkrDjjju+6J5ML3sKrwGmA6t6CQPAj5O8FVgK7DJs3YGmTZLGrLEaCKtsSH096ylU1Z1V9cqqmlZV0+icItq3qh4BrgA+0NyFdADwhNcTJKn3unlL6jzgh8Drkgwlmb2O1a8G7gfuA/4W+HC36pKkXps7dy5nnnnmi95u/vz53Hzzze388ccfz2WXXTaapb1AN+8+mrWe5dOGTRdwUrdqWZf9/uKifhx2TFr4+Q/0uwRJw8yfP59tttmGAw88sGfH9BfNktQFp59+Oq997Ws56KCDuOeeewD42c9+xuGHH85+++3HwQcfzE9/+lMArrzySt72trexzz77cNhhh/Hoo4/y4IMP8uUvf5mzzjqLt7zlLdx4440A3HDDDRx44IHstttuXek1GAqSNMoWLlzIJZdcwqJFi7j66qv50Y9+BMCcOXP44he/yMKFCznzzDP58Ic7Z8oPOuggbrnlFm6//XZmzpzJ5z73OaZNm8aJJ57Ixz72MRYtWsTBBx8MwLJly7jpppu46qqrOOWUU0a99k16QDxJGotuvPFG3vOe97D11lsDMGPGDJ5++mluvvlmjjnmmHa9Z555Buj85uHYY49l2bJlPPvss+v8XcHRRx/NFltswZ577smjjz466rUbCpLUA7/73e/YbrvtWLRo0QuWnXzyyXz84x9nxowZzJ8/n7lz5651Py95yUva6c7l2NHl6SNJGmWHHHII3/rWt3jqqad48sknufLKK9l6662ZPn06X//614HOP/Q77rgDgCeeeIKpU6cCcOGFF7b72XbbbXnyySd7WruhIEmjbN999+XYY49l77335ogjjmD//fcH4OKLL+a8885j7733Zq+99uLyyztDwM2dO5djjjmG/fbbj8mTJ7f7efe73803v/nNf3WhudvSje5HrwwODtbGPmTHW1Kf5y2p0sgtWbKEN7zhDf0uY73WVGeShVU1uKb17SlIklqGgiSpZShIklqGgiSpZShIklqGgiSp5S+aJWkUjPbt7SO5Rfzaa6/lox/9KCtXruRDH/rQqIyFZE9BkjZBK1eu5KSTTuKaa65h8eLFzJs3j8WLF2/0fg0FSdoE3Xbbbey+++7stttubLXVVsycObP9hfTGMBQkaRO0dOlSdtnl+UfbDwwMsHTpxj/a3lCQJLUMBUnaBE2dOpWHH364nR8aGmpHWt0YhoIkbYL2339/7r33Xh544AGeffZZLrnkEmbMmLHR+/WWVEkaBb0eZXjChAmcc845vOMd72DlypWccMIJ7LXXXhu/31GobY2SnA+8C3isqt7YtH0eeDfwLPAz4INV9atm2SeB2cBK4CNV9Z1u1SZJm4MjjzySI488clT32c3TRxcAh6/Wdh3wxqp6M/BPwCcBkuwJzAT2arb5X0m27GJtkqQ16FooVNUNwC9Wa/tuVT3XzN4CDDTTRwGXVNUzVfUAcB/w1m7VJklas35eaD4BuKaZngo8PGzZUNMmSeqhvoRCkr8CngMu3oBt5yRZkGTB8uXLR784SRrHeh4KSY6ncwH6j+v5B0QvBXYZttpA0/YCVXVuVQ1W1eCUKVO6WqskjTc9DYUkhwOfAGZU1W+GLboCmJnkJUmmA3sAt/WyNklSd29JnQf8PjA5yRBwKp27jV4CXJcE4JaqOrGq7k5yKbCYzmmlk6pqZbdqk6TR9tBpbxrV/e366TvXu84JJ5zAVVddxStf+UruuuuuUTluN+8+mlVVO1fVxKoaqKrzqmr3qtqlqt7SvE4ctv7pVfWaqnpdVV2zrn1LkuD444/n2muvHdV9OsyFJG2iDjnkEHbYYYdR3aehIElqGQqSpJahIElqGQqSpJZDZ0vSKBjJLaSjbdasWcyfP5/HH3+cgYEBPvOZzzB79uyN2qehIEmbqHnz5o36Pj19JElqGQqSpJahIEkb6PkxPcemDanPUJCkDTBp0iRWrFgxZoOhqlixYgWTJk16Udt5oVmSNsDAwABDQ0OM5ee6TJo0iYGBgfWvOIyhIEkbYOLEiUyfPr3fZYw6Tx9JklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSp1bVQSHJ+kseS3DWsbYck1yW5t3nfvmlPki8kuS/JT5Ls2626JElr182ewgXA4au1nQJcX1V7ANc38wBHAHs0rznAl7pYlyRpLboWClV1A/CL1ZqPAi5spi8Ejh7WflF13AJsl2TnbtUmSVqzXl9T2KmqljXTjwA7NdNTgYeHrTfUtEmSeqhvF5qrM97six5zNsmcJAuSLBjLoxNK0qao16Hw6KrTQs37Y037UmCXYesNNG0vUFXnVtVgVQ1OmTKlq8VK0njT61C4AjiumT4OuHxY+weau5AOAJ4YdppJktQjXXueQpJ5wO8Dk5MMAacCfw1cmmQ28HPg/c3qVwNHAvcBvwE+2K26JElr17VQqKpZa1l06BrWLeCkbtUiSRoZf9EsSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKkVl9CIcnHktyd5K4k85JMSjI9ya1J7kvytSRb9aM2SRrPeh4KSaYCHwEGq+qNwJbATOAM4Kyq2h34JTC717VJ0njXr9NHE4CXJpkAbA0sA/4AuKxZfiFwdH9Kk6Txq+ehUFVLgTOBh+iEwRPAQuBXVfVcs9oQMHVN2yeZk2RBkgXLly/vRcmSNG6MKBSSXD+SthHua3vgKGA68CrgZcDhI92+qs6tqsGqGpwyZcqGlCBJWosJ61qYZBKd0zuTm3/maRa9nLV8kx+Bw4AHqmp5c4x/AN4ObJdkQtNbGACWbuD+JUkbaJ2hAPwp8Gd0vtEv5PlQ+GfgnA085kPAAUm2Bp4CDgUWAN8H3gdcAhwHXL6B+5ckbaB1hkJVnQ2cneTkqvriaBywqm5NchnwY+A54HbgXODbwCVJPtu0nTcax5Mkjdz6egoAVNUXkxwITBu+TVVdtCEHrapTgVNXa74feOuG7E+SNDpGFApJvgq8BlgErGyaC9igUJAkjU0jCgVgENizqqqbxUiS+mukv1O4C/g33SxEktR/I+0pTAYWJ7kNeGZVY1XN6EpVkqS+GGkozO1mEZKksWGkdx/9Y7cLkST130jvPnqSzt1GAFsBE4FfV9XLu1WYJKn3RtpT2HbVdJLQGbvogG4VJUnqjxc9Smp1fAt4x+iXI0nqp5GePnrvsNkt6Pxu4emuVCRJ6puR3n307mHTzwEP0jmFJEnajIz0msIHu12IJKn/RvqQnYEk30zyWPP6RpKBbhcnSeqtkV5o/gpwBZ3nKrwKuLJpkyRtRkYaClOq6itV9VzzugDwWZiStJkZ6YXmFUn+IzCvmZ8FrOhOSZIA9vsLR6ZfZeHnP9DvEsaNkfYUTgDeDzwCLKPz2Mzju1STJKlPRtpTOA04rqp+CZBkB+BMOmEhSdpMjLSn8OZVgQBQVb8A9ulOSZKkfhlpKGyRZPtVM01PYaS9DEnSJmKk/9j/O/DDJF9v5o8BTu9OSZKkfhlRT6GqLgLeCzzavN5bVV/d0IMm2S7JZUl+mmRJkt9LskOS65Lc27xvv/49SZJG04hHSa2qxVV1TvNavJHHPRu4tqpeD+wNLAFOAa6vqj2A65t5SVIPveihszdWklcAhwDnAVTVs1X1KzoD7F3YrHYhcHSva5Ok8a7noQBMB5YDX0lye5K/S/IyYKeqWtas8wiw05o2TjInyYIkC5YvX96jkiVpfOhHKEwA9gW+VFX7AL9mtVNFVVU8//hPVlt2blUNVtXglCmOtCFJo6kfoTAEDFXVrc38ZXRC4tEkOwM074/1oTZJGtd6HgpV9QjwcJLXNU2HAovpjMJ6XNN2HHB5r2uTpPGuXz9AOxm4OMlWwP3AB+kE1KVJZgM/pzPWkiSph/oSClW1iM5znld3aI9L0TAPnfamfpcwZuz66Tv7XYLUF/24piBJGqMMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLX6FgpJtkxye5KrmvnpSW5Ncl+SryXZql+1SdJ41c+ewkeBJcPmzwDOqqrdgV8Cs/tSlSSNY30JhSQDwDuBv2vmA/wBcFmzyoXA0f2oTZLGs371FP4n8Angd838jsCvquq5Zn4ImLqmDZPMSbIgyYLly5d3vVBJGk96HgpJ3gU8VlULN2T7qjq3qgaranDKlCmjXJ0kjW8T+nDMtwMzkhwJTAJeDpwNbJdkQtNbGACW9qE2SWPQQ6e9qd8ljBm7fvrOru6/5z2FqvpkVQ1U1TRgJvC9qvpj4PvA+5rVjgMu73VtkjTejaXfKfwl8PEk99G5xnBen+uRpHGnH6ePWlU1H5jfTN8PvLWf9UjSeDeWegqSpD4zFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktTqeSgk2SXJ95MsTnJ3ko827TskuS7Jvc379r2uTZLGu370FJ4D/ryq9gQOAE5KsidwCnB9Ve0BXN/MS5J6qOehUFXLqurHzfSTwBJgKnAUcGGz2oXA0b2uTZLGu75eU0gyDdgHuBXYqaqWNYseAXbqV12SNF71LRSSbAN8A/izqvrn4cuqqoBay3ZzkixIsmD58uU9qFSSxo++hEKSiXQC4eKq+oem+dEkOzfLdwYeW9O2VXVuVQ1W1eCUKVN6U7AkjRP9uPsowHnAkqr6H8MWXQEc10wfB1ze69okabyb0Idjvh34T8CdSRY1bZ8C/hq4NMls4OfA+/tQmySNaz0Phaq6CchaFh/ay1okSf+av2iWJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSa8yFQpLDk9yT5L4kp/S7HkkaT8ZUKCTZEvgb4AhgT2BWkj37W5UkjR9jKhSAtwL3VdX9VfUscAlwVJ9rkqRxY0K/C1jNVODhYfNDwNuGr5BkDjCnmf2XJPf0qLbN3qthMvB4v+sYE05NvyvQMP5tDjM6f5uvXtuCsRYK61VV5wLn9ruOzVGSBVU12O86pNX5t9k7Y+300VJgl2HzA02bJKkHxloo/AjYI8n0JFsBM4Er+lyTJI0bY+r0UVU9l+Q/A98BtgTOr6q7+1zWeOJpOY1V/m32SKqq3zVIksaIsXb6SJLUR4aCJKllKMihRTRmJTk/yWNJ7up3LeOFoTDOObSIxrgLgMP7XcR4YijIoUU0ZlXVDcAv+l3HeGIoaE1Di0ztUy2S+sxQkCS1DAU5tIiklqEghxaR1DIUxrmqeg5YNbTIEuBShxbRWJFkHvBD4HVJhpLM7ndNmzuHuZAktewpSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoaExJMm1NwyQnOS3JYevZdm6S/7Ixx2mWzU8yOLKK13mM45Ocs7H7kXppTD2jWVqbqvp0v2vohyQTmh8YblbH0thlT0Fj0ZZJ/jbJ3Um+m+SlSS5I8j6AJEcm+WmShUm+kOSqYdvu2XzTvz/JR9ZznAlJLk6yJMllSbZefYUks5LcmeSuJGeMoP2DSf4pyW3A29d18OYzfTnJgmabdzXtxye5Isn3gOuTvKx52MxtSW5PclSz3l5N26IkP0myR7Put5Pc0dR2bLPug0kmN9ODSeY303OTfDXJD4CvJpmS5BtJftS81vkZtPmxp6CxaA9gVlX9SZJLgT9atSDJJOB/A4dU1QPNMAjDvR74d8C2wD1JvlRVv13LcV4HzK6qHyQ5H/gwcOawY70KOAPYD/gl8N0kRwO3raX9VuAzTfsTwPeB29fzWafReabFa4DvJ9m9ad8XeHNV/SLJfwO+V1UnJNkOuC3J/wVOBM6uqoubcau2BI4E/l9VvbP5DK9Yz/Gh83Clg6rqqST/Bzirqm5Ksiud4U/eMIJ9aDNhT0Fj0QNVtaiZXkjnH+cqrwfur6oHmvnVQ+HbVfVMVT0OPAbstI7jPFxVP2im/x44aLXl+wPzq2p5c1rlYuCQdbS/bVj7s8DXRvBZL62q31XVvcD9zecDuK6qVj1c5g+BU5IsAuYDk4Bd6YwJ9Kkkfwm8uqqeAu4E/n2SM5IcXFVPjKCGK5ptAQ4DzmmOdQXw8iTbjGAf2kzYU9BY9Myw6ZXASzdi23X9ja8+8Fc/BgJbWw2/HtYW4I+q6p7V1l2S5FbgncDVSf60qr6XZF86PYbPJrm+qk4DnuP5L4GTVtvP8GNtARxQVU9v4OfRJs6egjY19wC7JZnWzB+7EfvaNcnvNdP/AbhpteW3Af82yeTmWdazgH9cR/utTfuOSSYCx4yghmOSbJHkNcBuzedb3XeAk5MEIMk+zftudHpNXwAuB97cnPL6TVX9PfB5OqehAB6kc1oLhp2OW4PvAievmknylhF8Bm1GDAVtUprTHB8Grk2yEHiSzvn7DXEPcFKSJcD2wJdWO9Yy4BQ61wbuABZW1eXraZ9L57TOD+gMRb4+D9EJmWuAE9fyDf2/AhOBnyS5u5kHeD9wV3Oq543ARcCb6FxzWAScCny2WfczwNlJFtDpQa3NR4DB5sL1YjrXLTSOOHS2NjlJtqmqf2m+Of8NcG9VndXvul6sJBcAV1XVZf2uRVrFnoI2RX/SfBO+G3gFnbuRJI0CewrarCXZEbh+DYsOraoVParhr3jh9YWvV9XpvTi+9GIYCpKklqePJEktQ0GS1DIUJEktQ0GS1Pr/v5pT5F2Yg7gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"high_blood_pressure\", data=df, hue=\"death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "208a1d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    194\n",
       "1    105\n",
       "Name: high_blood_pressure, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.high_blood_pressure.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa616fdd",
   "metadata": {},
   "source": [
    "### high_blood_pressure analysis with respect to diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e487cbdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diabetes', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "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.countplot(x=\"diabetes\", data=df, hue=\"high_blood_pressure\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10f0516",
   "metadata": {},
   "source": [
    "### Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "790aee95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm          # Choosing Colormaps\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap( df.corr(), annot=True, cmap=plt.cm.Reds)    # for correlation measure and drow\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "253fb402",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from above correlation's graphical view, smoking and sex are max correlated among them-self. \n",
    "# But, we think, this correlation is not enough to drop a column ( smoking or sex )."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f272d1ab",
   "metadata": {},
   "source": [
    "# Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b63104b",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.preprocessing import MinMaxScaler    # normalization scaling technique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d914bcb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalization = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7547d92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"age\"] = normalization.fit_transform(df[[\"age\"]])\n",
    "df[\"creatinine_phosphokinase\"] = normalization.fit_transform(df[[\"creatinine_phosphokinase\"]])\n",
    "df[\"ejection_fraction\"] = normalization.fit_transform(df[[\"ejection_fraction\"]])\n",
    "df[\"platelets\"] = normalization.fit_transform(df[[\"platelets\"]])\n",
    "df[\"serum_sodium\"] = normalization.fit_transform(df[[\"serum_sodium\"]]) \n",
    "df[\"time\"] = normalization.fit_transform(df[[\"time\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a8a5559",
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>1</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>1</td>\n",
       "      <td>0.157474</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.946619</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0</td>\n",
       "      <td>0.099770</td>\n",
       "      <td>0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288833</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.373665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004593</td>\n",
       "      <td>0</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0</td>\n",
       "      <td>0.278700</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288256</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008803</td>\n",
       "      <td>0</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>1</td>\n",
       "      <td>0.353861</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.054545</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042740</td>\n",
       "      <td>0</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0</td>\n",
       "      <td>0.256880</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330961</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "294  0.400000        0                  0.004848         1           0.363636   \n",
       "140  0.727273        0                  0.099770         0           0.363636   \n",
       "97   0.545455        1                  0.004593         0           0.696970   \n",
       "76   0.545455        0                  0.008803         0           0.696970   \n",
       "125  0.054545        1                  0.042740         0           0.545455   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "294                    1   0.157474               1.1      0.857143    1   \n",
       "140                    0   0.288833               1.1      0.600000    1   \n",
       "97                     0   0.278700               1.1      0.657143    0   \n",
       "76                     1   0.353861               0.8      0.771429    0   \n",
       "125                    0   0.256880               1.3      0.628571    0   \n",
       "\n",
       "     smoking      time  death  \n",
       "294        1  0.946619      0  \n",
       "140        0  0.373665      1  \n",
       "97         0  0.288256      0  \n",
       "76         1  0.249110      0  \n",
       "125        0  0.330961      0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)    # randomly 5 sample calling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2ce67b",
   "metadata": {},
   "source": [
    "# split data into x(independent value) and y(dependent or target value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed0a8f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"death\", axis=1)     # feature columns / data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36ded399",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"death\"]    # target column / data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0b2d78b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: death, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d1099f69",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0</td>\n",
       "      <td>0.071319</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1</td>\n",
       "      <td>0.290823</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.272727</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0</td>\n",
       "      <td>0.288833</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015693</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0</td>\n",
       "      <td>0.165960</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.181818</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011227</td>\n",
       "      <td>0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0</td>\n",
       "      <td>0.224148</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.454545</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017479</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0</td>\n",
       "      <td>0.365984</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.085714</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0  0.636364        0                  0.071319         0           0.090909   \n",
       "1  0.272727        0                  1.000000         0           0.363636   \n",
       "2  0.454545        0                  0.015693         0           0.090909   \n",
       "3  0.181818        1                  0.011227         0           0.090909   \n",
       "4  0.454545        1                  0.017479         1           0.090909   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                    1   0.290823               1.9      0.485714    1   \n",
       "1                    0   0.288833               1.1      0.657143    1   \n",
       "2                    0   0.165960               1.3      0.457143    1   \n",
       "3                    0   0.224148               1.9      0.685714    1   \n",
       "4                    0   0.365984               2.7      0.085714    0   \n",
       "\n",
       "   smoking      time  \n",
       "0        0  0.000000  \n",
       "1        0  0.007117  \n",
       "2        1  0.010676  \n",
       "3        0  0.010676  \n",
       "4        0  0.014235  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b0a795",
   "metadata": {},
   "source": [
    "# split data according to traning and testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb0918c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff981b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, train_size=.70, random_state=42)  # training data: 70% & testing: 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "df3a55cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain: (209, 12), xtest: (90, 12) \n",
      "ytrain: (209,),  ytest: (90,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"xtrain: {xtrain.shape}, xtest: {xtest.shape} \\nytrain: {ytrain.shape},  ytest: {ytest.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36fd76c6",
   "metadata": {},
   "source": [
    "# apply Neural network, multi-layer perceptron classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9c4edf4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a33e551",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MLPClassifier(max_iter=500, solver='adam', random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6486422a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(max_iter=500, random_state=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(xtrain, ytrain)  # train the model with 70% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c39eddf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = clf.predict(xtest)   # predict corresponding y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7820aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c91d7a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(ytest)     # actual y values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80755c55",
   "metadata": {},
   "source": [
    "# Evaluation of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45484954",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, RocCurveDisplay, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0da7e4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7555555555555555"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, pred)  # overall model's accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "01e38842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.89      0.81        53\n",
      "           1       0.78      0.57      0.66        37\n",
      "\n",
      "    accuracy                           0.76        90\n",
      "   macro avg       0.76      0.73      0.73        90\n",
      "weighted avg       0.76      0.76      0.75        90\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print( classification_report(ytest, pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "140c0dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[47,  6],\n",
       "       [16, 21]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by analyzing the confusion matrix, we can know actually what is going on. For example - precision, recall etc\n",
    "\n",
    "test_confusion_matrix = confusion_matrix(ytest, pred)\n",
    "test_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "546acd25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(clf, xtest, ytest)    # graphical view of confusion matrix\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d5b333c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47,  6, 16, 21], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, pred).reshape(-1)   # convert into one dimentional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "aea0bd08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Negative: 21,   False Positive: 6, \n",
      "False Negative: 16,   True Positive: 47\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(ytest, pred).reshape(-1)   # multi-variable assign at a same time of confusion matrix\n",
    "\n",
    "print(f\"True Negative: {tp},   False Positive: {fp}, \\nFalse Negative: {fn},   True Positive: {tn}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beadcad0",
   "metadata": {},
   "source": [
    "### compare precision and recall with classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d601c6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.746031746031746"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision of 0, 0 mean's not death; Negeative prediction value\n",
    "precision_of_0 = (tn) / (tn+fn)\n",
    "precision_of_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4f347411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision of 1, 1 mean's death; Positive prediction value\n",
    "precision_of_1 = (tp) / (tp+fp)\n",
    "precision_of_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2ce2bbde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8867924528301887"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recall of 0, 0 mean's not death; true negative rate\n",
    "recall_of_0 = (tn) / (tn+fp)\n",
    "recall_of_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "878a5b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5675675675675675"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recall of 1, 1 mean's death; true positive rate\n",
    "recall_of_1 = (tp) / (tp+fn)\n",
    "recall_of_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153b195e",
   "metadata": {},
   "source": [
    "### ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "36a7a446",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "# Receiver Operating Characteristic\n",
    "# The heigher the AUC(Area under the curve), the better the performance of the model \n",
    "\n",
    "RocCurveDisplay.from_estimator(clf, xtest, ytest)\n",
    "plt.plot([0,1],[0,1],linestyle='dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5117aec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# From this curve AUC is 0.81, so our model's performance is excellent. [ 0.80 to 0.90 range is called excellent ]\n",
    "# But to considering overall accuracy, precision and recall, we can say that, \n",
    "# for this dataset and using multi-layer perceptron, this model is acceptable. [ 0.70 to 0.80 range is called acceptable ]"
   ]
  }
 ],
 "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}