predict-heart-failure-survival.ipynb

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   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c080ce8",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ff1bee2",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
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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",
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       "      <td>38</td>\n",
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       "      <td>263358.03</td>\n",
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       "      <td>136</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
       "      <td>20</td>\n",
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       "      <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",
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../input/heart-failure-clinical-data/heart_failure_clinical_records_dataset.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8fa546",
   "metadata": {
    "papermill": {
     "duration": 0.061713,
     "end_time": "2022-03-28T14:16:07.572218",
     "exception": false,
     "start_time": "2022-03-28T14:16:07.510505",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Handle missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00755884",
   "metadata": {
    "papermill": {
     "duration": 0.056612,
     "end_time": "2022-03-28T14:16:07.686199",
     "exception": false,
     "start_time": "2022-03-28T14:16:07.629587",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's start by checking if there's any missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d4210f1",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-03-28T14:16:07.743262",
     "status": "completed"
    },
    "tags": []
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb388e5",
   "metadata": {
    "papermill": {
     "duration": 0.057511,
     "end_time": "2022-03-28T14:16:07.934148",
     "exception": false,
     "start_time": "2022-03-28T14:16:07.876637",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like we have no missing values!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c3d87b",
   "metadata": {
    "papermill": {
     "duration": 0.058651,
     "end_time": "2022-03-28T14:16:08.050375",
     "exception": false,
     "start_time": "2022-03-28T14:16:07.991724",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Change data types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bac2391",
   "metadata": {
    "papermill": {
     "duration": 0.057002,
     "end_time": "2022-03-28T14:16:08.165115",
     "exception": false,
     "start_time": "2022-03-28T14:16:08.108113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's see the types of the columns and see if any conversions are needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "855a1f3f",
   "metadata": {
    "execution": {
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     "end_time": "2022-03-28T14:16:08.291200",
     "exception": false,
     "start_time": "2022-03-28T14:16:08.222576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         float64\n",
       "anaemia                       int64\n",
       "creatinine_phosphokinase      int64\n",
       "diabetes                      int64\n",
       "ejection_fraction             int64\n",
       "high_blood_pressure           int64\n",
       "platelets                   float64\n",
       "serum_creatinine            float64\n",
       "serum_sodium                  int64\n",
       "sex                           int64\n",
       "smoking                       int64\n",
       "time                          int64\n",
       "DEATH_EVENT                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9218db8",
   "metadata": {
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     "duration": 0.058211,
     "end_time": "2022-03-28T14:16:08.408007",
     "exception": false,
     "start_time": "2022-03-28T14:16:08.349796",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like we should consider converting \"age\" and \"platelets\" to integers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925e15ea",
   "metadata": {
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     "duration": 0.057982,
     "end_time": "2022-03-28T14:16:08.524367",
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     "start_time": "2022-03-28T14:16:08.466385",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's strart by checking the values of \"age\" columns and see if we can convert it to integer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c7b8db7",
   "metadata": {
    "execution": {
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     "start_time": "2022-03-28T14:16:08.583287",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([75.   , 55.   , 65.   , 50.   , 90.   , 60.   , 80.   , 62.   ,\n",
       "       45.   , 49.   , 82.   , 87.   , 70.   , 48.   , 68.   , 53.   ,\n",
       "       95.   , 58.   , 94.   , 85.   , 69.   , 72.   , 51.   , 57.   ,\n",
       "       42.   , 41.   , 67.   , 79.   , 59.   , 44.   , 63.   , 86.   ,\n",
       "       66.   , 43.   , 46.   , 61.   , 81.   , 52.   , 64.   , 40.   ,\n",
       "       60.667, 73.   , 77.   , 78.   , 54.   , 47.   , 56.   ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242fa957",
   "metadata": {
    "papermill": {
     "duration": 0.06028,
     "end_time": "2022-03-28T14:16:08.782334",
     "exception": false,
     "start_time": "2022-03-28T14:16:08.722054",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like only one value in the dataset is an actual float vlue - let's converting this column to integer whould be a good choice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb409f3b",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-03-28T14:16:08.843140",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([75, 55, 65, 50, 90, 60, 80, 62, 45, 49, 82, 87, 70, 48, 68, 53, 95,\n",
       "       58, 94, 85, 69, 72, 51, 57, 42, 41, 67, 79, 59, 44, 63, 86, 66, 43,\n",
       "       46, 61, 81, 52, 64, 40, 73, 77, 78, 54, 47, 56])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age = data.age.astype(\"int64\")\n",
    "data.age.dtype\n",
    "data.age.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f200b87",
   "metadata": {
    "papermill": {
     "duration": 0.064446,
     "end_time": "2022-03-28T14:16:09.040510",
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     "start_time": "2022-03-28T14:16:08.976064",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Lets' look at the \"platelets\" column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "354c6605",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-03-28T14:16:09.102828",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "       212000.  , 186000.  , 268000.  , 147000.  , 481000.  , 290000.  ,\n",
       "       358000.  , 151000.  , 371000.  , 130000.  , 504000.  , 141000.  ,\n",
       "        62000.  , 330000.  , 248000.  , 257000.  , 533000.  , 264000.  ,\n",
       "       282000.  , 314000.  , 246000.  , 301000.  , 404000.  , 236000.  ,\n",
       "       294000.  , 233000.  , 308000.  , 198000.  , 208000.  , 133000.  ,\n",
       "       222000.  , 215000.  , 150000.  , 422000.  ,  25100.  , 232000.  ,\n",
       "       241000.  ,  51000.  , 336000.  , 543000.  , 382000.  , 179000.  ,\n",
       "       155000.  , 742000.  ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.platelets.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce175622",
   "metadata": {
    "papermill": {
     "duration": 0.064692,
     "end_time": "2022-03-28T14:16:09.300099",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.235407",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "These values seem to be very large! probably because they are in platelets/mL measure, converting it to kiloplatelets/mL would make the values more compact. Finally, we will converty it to int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a257f902",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:09.438794Z",
     "iopub.status.busy": "2022-03-28T14:16:09.437865Z",
     "iopub.status.idle": "2022-03-28T14:16:09.441613Z",
     "shell.execute_reply": "2022-03-28T14:16:09.442195Z",
     "shell.execute_reply.started": "2022-03-28T14:14:49.581330Z"
    },
    "papermill": {
     "duration": 0.080809,
     "end_time": "2022-03-28T14:16:09.442379",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.361570",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([265, 263, 162, 210, 327, 204, 127, 454, 388, 368, 253, 136, 276,\n",
       "       427,  47, 262, 166, 237,  87, 297, 289, 149, 196, 284, 153, 200,\n",
       "       360, 319, 302, 188, 228, 226, 321, 305, 329, 185, 218, 194, 310,\n",
       "       271, 451, 140, 395, 418, 351, 255, 461, 223, 216, 254, 390, 385,\n",
       "       119, 213, 274, 244, 497, 374, 122, 243, 266, 317, 283, 324, 293,\n",
       "       172, 406, 173, 304, 235, 181, 249, 219, 318, 221, 298, 286, 621,\n",
       "       850, 306, 252, 328, 164, 507, 203, 217, 300, 267, 227, 250, 295,\n",
       "       231, 211, 348, 229, 338, 242, 225, 184, 277, 362, 174, 448,  75,\n",
       "       334, 192, 220,  70, 270, 325, 176, 189, 281, 337, 105, 132, 279,\n",
       "       303, 224, 389, 365, 201, 275, 350, 309, 260, 160, 126, 259,  73,\n",
       "       377, 212, 186, 268, 147, 481, 290, 358, 151, 371, 130, 504, 141,\n",
       "        62, 330, 248, 257, 533, 264, 282, 314, 246, 301, 404, 236, 294,\n",
       "       233, 308, 198, 208, 133, 222, 215, 150, 422,  25, 232, 241,  51,\n",
       "       336, 543, 382, 179, 155, 742])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.platelets = (data.platelets / 1000).astype(\"int64\")\n",
    "data.platelets.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1c4ceb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:09.570839Z",
     "iopub.status.busy": "2022-03-28T14:16:09.570135Z",
     "iopub.status.idle": "2022-03-28T14:16:09.573340Z",
     "shell.execute_reply": "2022-03-28T14:16:09.572838Z",
     "shell.execute_reply.started": "2022-03-28T14:14:49.597689Z"
    },
    "papermill": {
     "duration": 0.069484,
     "end_time": "2022-03-28T14:16:09.573490",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.504006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,   6,   7,   8,  10,  11,  12,  13,  14,  15,  16,  20,  22,\n",
       "        23,  24,  26,  27,  28,  29,  30,  31,  32,  33,  35,  38,  40,\n",
       "        41,  42,  43,  44,  45,  50,  54,  55,  59,  60,  61,  63,  64,\n",
       "        65,  66,  67,  68,  71,  72,  73,  74,  75,  76,  77,  78,  79,\n",
       "        80,  82,  83,  85,  86,  87,  88,  90,  91,  94,  95,  96,  97,\n",
       "       100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 117,\n",
       "       118, 119, 120, 121, 123, 126, 129, 130, 134, 135, 140, 145, 146,\n",
       "       147, 148, 150, 154, 162, 170, 171, 172, 174, 175, 180, 185, 186,\n",
       "       187, 188, 192, 193, 194, 195, 196, 197, 198, 200, 201, 205, 206,\n",
       "       207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 220, 230, 231,\n",
       "       233, 235, 237, 240, 241, 244, 245, 246, 247, 250, 256, 257, 258,\n",
       "       270, 271, 278, 280, 285])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.time.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c9a1e1",
   "metadata": {
    "papermill": {
     "duration": 0.061303,
     "end_time": "2022-03-28T14:16:09.697135",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.635832",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's change the date column which indicates the number of days between medical inspection for the patients. Let's rescale this data to weeks (starting from once a month)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90465d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:09.860179Z",
     "iopub.status.busy": "2022-03-28T14:16:09.859051Z",
     "iopub.status.idle": "2022-03-28T14:16:09.881784Z",
     "shell.execute_reply": "2022-03-28T14:16:09.882868Z",
     "shell.execute_reply.started": "2022-03-28T14:14:49.613431Z"
    },
    "papermill": {
     "duration": 0.116878,
     "end_time": "2022-03-28T14:16:09.883118",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.766240",
     "status": "completed"
    },
    "tags": []
   },
   "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</td>\n",
       "      <td>0</td>\n",
       "      <td>582</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>7861</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>263</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>210</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>327</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>155</td>\n",
       "      <td>1.1</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>1.2</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>2060</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>742</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>2413</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>1.4</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>395</td>\n",
       "      <td>1.6</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  anaemia  creatinine_phosphokinase  diabetes  ejection_fraction  \\\n",
       "0     75        0                       582         0                 20   \n",
       "1     55        0                      7861         0                 38   \n",
       "2     65        0                       146         0                 20   \n",
       "3     50        1                       111         0                 20   \n",
       "4     65        1                       160         1                 20   \n",
       "..   ...      ...                       ...       ...                ...   \n",
       "294   62        0                        61         1                 38   \n",
       "295   55        0                      1820         0                 38   \n",
       "296   45        0                      2060         1                 60   \n",
       "297   45        0                      2413         0                 38   \n",
       "298   50        0                       196         0                 45   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  sex  \\\n",
       "0                      1        265               1.9           130    1   \n",
       "1                      0        263               1.1           136    1   \n",
       "2                      0        162               1.3           129    1   \n",
       "3                      0        210               1.9           137    1   \n",
       "4                      0        327               2.7           116    0   \n",
       "..                   ...        ...               ...           ...  ...   \n",
       "294                    1        155               1.1           143    1   \n",
       "295                    0        270               1.2           139    0   \n",
       "296                    0        742               0.8           138    0   \n",
       "297                    0        140               1.4           140    1   \n",
       "298                    0        395               1.6           136    1   \n",
       "\n",
       "     smoking  time  DEATH_EVENT  \n",
       "0          0     1            1  \n",
       "1          0     1            1  \n",
       "2          1     1            1  \n",
       "3          0     1            1  \n",
       "4          0     2            1  \n",
       "..       ...   ...          ...  \n",
       "294        1    39            0  \n",
       "295        0    39            0  \n",
       "296        0    40            0  \n",
       "297        1    40            0  \n",
       "298        1    41            0  \n",
       "\n",
       "[299 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.time = np.ceil(data.time / 7).astype(\"int64\")\n",
    "data.time.unique()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc1005c",
   "metadata": {
    "papermill": {
     "duration": 0.061871,
     "end_time": "2022-03-28T14:16:10.034971",
     "exception": false,
     "start_time": "2022-03-28T14:16:09.973100",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Remove columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908c3672",
   "metadata": {
    "papermill": {
     "duration": 0.07127,
     "end_time": "2022-03-28T14:16:10.179271",
     "exception": false,
     "start_time": "2022-03-28T14:16:10.108001",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally, let's check the binrary columns are actually binary (anemia, diabetes, high blood plessure, sex, smoking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3b4fc47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:10.307587Z",
     "iopub.status.busy": "2022-03-28T14:16:10.306536Z",
     "iopub.status.idle": "2022-03-28T14:16:10.319643Z",
     "shell.execute_reply": "2022-03-28T14:16:10.320302Z",
     "shell.execute_reply.started": "2022-03-28T14:14:49.638882Z"
    },
    "papermill": {
     "duration": 0.079042,
     "end_time": "2022-03-28T14:16:10.320479",
     "exception": false,
     "start_time": "2022-03-28T14:16:10.241437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                           int64\n",
       "anaemia                        bool\n",
       "creatinine_phosphokinase      int64\n",
       "diabetes                       bool\n",
       "ejection_fraction             int64\n",
       "high_blood_pressure            bool\n",
       "platelets                     int64\n",
       "serum_creatinine            float64\n",
       "serum_sodium                  int64\n",
       "sex                            bool\n",
       "smoking                        bool\n",
       "time                          int64\n",
       "DEATH_EVENT                    bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.anaemia = data.anaemia.astype(\"bool\")\n",
    "data.diabetes = data.diabetes.astype(\"bool\")\n",
    "data.high_blood_pressure = data.high_blood_pressure.astype(\"bool\")\n",
    "data.sex = data.sex.astype(\"bool\")\n",
    "data.smoking = data.smoking.astype(\"bool\")\n",
    "data.DEATH_EVENT = data.DEATH_EVENT.astype(\"bool\")\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70eeb3c1",
   "metadata": {
    "papermill": {
     "duration": 0.062196,
     "end_time": "2022-03-28T14:16:10.444947",
     "exception": false,
     "start_time": "2022-03-28T14:16:10.382751",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's try removing columns that have low correlation to the DEATH_EVNET value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "46a38808",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:10.575648Z",
     "iopub.status.busy": "2022-03-28T14:16:10.574624Z",
     "iopub.status.idle": "2022-03-28T14:16:11.093983Z",
     "shell.execute_reply": "2022-03-28T14:16:11.093249Z",
     "shell.execute_reply.started": "2022-03-28T14:14:49.653266Z"
    },
    "papermill": {
     "duration": 0.58551,
     "end_time": "2022-03-28T14:16:11.094138",
     "exception": false,
     "start_time": "2022-03-28T14:16:10.508628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation to 'WAU' value:\n",
      "DEATH_EVENT                 1.000000\n",
      "time                       -0.526917\n",
      "serum_creatinine            0.294278\n",
      "ejection_fraction          -0.268603\n",
      "age                         0.253581\n",
      "serum_sodium               -0.195204\n",
      "high_blood_pressure         0.079351\n",
      "anaemia                     0.066270\n",
      "creatinine_phosphokinase    0.062728\n",
      "platelets                  -0.049188\n",
      "smoking                    -0.012623\n",
      "sex                        -0.004316\n",
      "diabetes                   -0.001943\n",
      "Name: DEATH_EVENT, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Heart Failure Survival Heatmap')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "\n",
    "corr = data.corr().sort_values(ascending=False, key=lambda x: abs(x), by=\"DEATH_EVENT\")\n",
    "\n",
    "print(\"Correlation to 'WAU' value:\")\n",
    "print (corr[\"DEATH_EVENT\"])\n",
    "\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True,\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ")\n",
    "ax.set_title(\"Heart Failure Survival Heatmap\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8bcd074",
   "metadata": {
    "papermill": {
     "duration": 0.075423,
     "end_time": "2022-03-28T14:16:11.236409",
     "exception": false,
     "start_time": "2022-03-28T14:16:11.160986",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like \"diabetes\", \"sex\", and \"smoking\" have low correlation to the DEATH_EVENT value. Let's drop them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95bedfa6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:11.403560Z",
     "iopub.status.busy": "2022-03-28T14:16:11.402504Z",
     "iopub.status.idle": "2022-03-28T14:16:11.405774Z",
     "shell.execute_reply": "2022-03-28T14:16:11.405150Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.116999Z"
    },
    "papermill": {
     "duration": 0.087677,
     "end_time": "2022-03-28T14:16:11.405927",
     "exception": false,
     "start_time": "2022-03-28T14:16:11.318250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data.drop(columns=[\"sex\", \"diabetes\", \"smoking\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b838be75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:11.539588Z",
     "iopub.status.busy": "2022-03-28T14:16:11.538458Z",
     "iopub.status.idle": "2022-03-28T14:16:11.554218Z",
     "shell.execute_reply": "2022-03-28T14:16:11.553641Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.126108Z"
    },
    "papermill": {
     "duration": 0.082991,
     "end_time": "2022-03-28T14:16:11.554376",
     "exception": false,
     "start_time": "2022-03-28T14:16:11.471385",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75</td>\n",
       "      <td>False</td>\n",
       "      <td>582</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>265</td>\n",
       "      <td>1.9</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>7861</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>263</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>False</td>\n",
       "      <td>146</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>162</td>\n",
       "      <td>1.3</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>True</td>\n",
       "      <td>111</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>210</td>\n",
       "      <td>1.9</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>True</td>\n",
       "      <td>160</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>327</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "0   75    False                       582                 20   \n",
       "1   55    False                      7861                 38   \n",
       "2   65    False                       146                 20   \n",
       "3   50     True                       111                 20   \n",
       "4   65     True                       160                 20   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  time  \\\n",
       "0                 True        265               1.9           130     1   \n",
       "1                False        263               1.1           136     1   \n",
       "2                False        162               1.3           129     1   \n",
       "3                False        210               1.9           137     1   \n",
       "4                False        327               2.7           116     2   \n",
       "\n",
       "   DEATH_EVENT  \n",
       "0         True  \n",
       "1         True  \n",
       "2         True  \n",
       "3         True  \n",
       "4         True  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd6c9f4",
   "metadata": {
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     "start_time": "2022-03-28T14:16:11.619447",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6dcf75a7",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-03-28T14:16:11.749110",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>299.000000</td>\n",
       "      <td>299</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.00000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60.829431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>581.839465</td>\n",
       "      <td>38.083612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.327759</td>\n",
       "      <td>1.39388</td>\n",
       "      <td>136.625418</td>\n",
       "      <td>19.026756</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.894997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>970.287881</td>\n",
       "      <td>11.834841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.805105</td>\n",
       "      <td>1.03451</td>\n",
       "      <td>4.412477</td>\n",
       "      <td>11.038035</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212.500000</td>\n",
       "      <td>0.90000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>1.10000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>303.500000</td>\n",
       "      <td>1.40000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7861.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>9.40000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "count   299.000000     299                299.000000         299.000000   \n",
       "unique         NaN       2                       NaN                NaN   \n",
       "top            NaN   False                       NaN                NaN   \n",
       "freq           NaN     170                       NaN                NaN   \n",
       "mean     60.829431     NaN                581.839465          38.083612   \n",
       "std      11.894997     NaN                970.287881          11.834841   \n",
       "min      40.000000     NaN                 23.000000          14.000000   \n",
       "25%      51.000000     NaN                116.500000          30.000000   \n",
       "50%      60.000000     NaN                250.000000          38.000000   \n",
       "75%      70.000000     NaN                582.000000          45.000000   \n",
       "max      95.000000     NaN               7861.000000          80.000000   \n",
       "\n",
       "       high_blood_pressure   platelets  serum_creatinine  serum_sodium  \\\n",
       "count                  299  299.000000         299.00000    299.000000   \n",
       "unique                   2         NaN               NaN           NaN   \n",
       "top                  False         NaN               NaN           NaN   \n",
       "freq                   194         NaN               NaN           NaN   \n",
       "mean                   NaN  263.327759           1.39388    136.625418   \n",
       "std                    NaN   97.805105           1.03451      4.412477   \n",
       "min                    NaN   25.000000           0.50000    113.000000   \n",
       "25%                    NaN  212.500000           0.90000    134.000000   \n",
       "50%                    NaN  262.000000           1.10000    137.000000   \n",
       "75%                    NaN  303.500000           1.40000    140.000000   \n",
       "max                    NaN  850.000000           9.40000    148.000000   \n",
       "\n",
       "              time DEATH_EVENT  \n",
       "count   299.000000         299  \n",
       "unique         NaN           2  \n",
       "top            NaN       False  \n",
       "freq           NaN         203  \n",
       "mean     19.026756         NaN  \n",
       "std      11.038035         NaN  \n",
       "min       1.000000         NaN  \n",
       "25%      11.000000         NaN  \n",
       "50%      17.000000         NaN  \n",
       "75%      29.500000         NaN  \n",
       "max      41.000000         NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a44b234",
   "metadata": {
    "papermill": {
     "duration": 0.065226,
     "end_time": "2022-03-28T14:16:11.993006",
     "exception": false,
     "start_time": "2022-03-28T14:16:11.927780",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Ejection Fraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d68361",
   "metadata": {
    "papermill": {
     "duration": 0.065468,
     "end_time": "2022-03-28T14:16:12.124015",
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     "start_time": "2022-03-28T14:16:12.058547",
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    },
    "tags": []
   },
   "source": [
    "Let's start with the \"ejection_fraction\" column. Ejaction fraction is the percentage of blood leaving the heart at each contraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6869545d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:12.281309Z",
     "iopub.status.busy": "2022-03-28T14:16:12.259991Z",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Ejection Fraction Boxplot (before outliers removal)')]"
      ]
     },
     "execution_count": 16,
     "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.boxplot(x=data.ejection_fraction, color='teal').set(title='Ejection Fraction Boxplot (before outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3e4ecd",
   "metadata": {
    "papermill": {
     "duration": 0.067529,
     "end_time": "2022-03-28T14:16:12.596267",
     "exception": false,
     "start_time": "2022-03-28T14:16:12.528738",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like we have two outliers: 70, 80. Let's remove them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1e52d98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:12.762839Z",
     "iopub.status.busy": "2022-03-28T14:16:12.762079Z",
     "iopub.status.idle": "2022-03-28T14:16:12.917575Z",
     "shell.execute_reply": "2022-03-28T14:16:12.916467Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.381378Z"
    },
    "papermill": {
     "duration": 0.252189,
     "end_time": "2022-03-28T14:16:12.917789",
     "exception": false,
     "start_time": "2022-03-28T14:16:12.665600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Ejection Fraction Boxplot (after outliers removal)')]"
      ]
     },
     "execution_count": 17,
     "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": [
    "data = data.drop(data[data.ejection_fraction >= 70].index)\n",
    "sns.boxplot(x=data.ejection_fraction, color='teal').set(title='Ejection Fraction Boxplot (after outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9275f50d",
   "metadata": {
    "papermill": {
     "duration": 0.068138,
     "end_time": "2022-03-28T14:16:13.054631",
     "exception": false,
     "start_time": "2022-03-28T14:16:12.986493",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Platelets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afdf7028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:13.195108Z",
     "iopub.status.busy": "2022-03-28T14:16:13.194041Z",
     "iopub.status.idle": "2022-03-28T14:16:13.366689Z",
     "shell.execute_reply": "2022-03-28T14:16:13.367184Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.552664Z"
    },
    "papermill": {
     "duration": 0.24513,
     "end_time": "2022-03-28T14:16:13.367372",
     "exception": false,
     "start_time": "2022-03-28T14:16:13.122242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Platelets Boxplot (before outliers removal)')]"
      ]
     },
     "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.boxplot(x=data.platelets, color='teal').set(title='Platelets Boxplot (before outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4f3096",
   "metadata": {
    "papermill": {
     "duration": 0.068698,
     "end_time": "2022-03-28T14:16:13.504770",
     "exception": false,
     "start_time": "2022-03-28T14:16:13.436072",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems line we have lots of outliers: values below 100 kiloplatelets/mL or higher than ~400 kiloplatelets/mL. Normal platelets levels are 150-450 kiloplatelets/mL, while a value lower than 10 is considered extremely low and above 450 is considered very high. Seems like removing that value higher than 650 would be a good choice (two outliers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79961b5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:13.657109Z",
     "iopub.status.busy": "2022-03-28T14:16:13.645789Z",
     "iopub.status.idle": "2022-03-28T14:16:13.659834Z",
     "shell.execute_reply": "2022-03-28T14:16:13.660454Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.728252Z"
    },
    "papermill": {
     "duration": 0.086876,
     "end_time": "2022-03-28T14:16:13.660648",
     "exception": false,
     "start_time": "2022-03-28T14:16:13.573772",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>292</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>850</td>\n",
       "      <td>1.3</td>\n",
       "      <td>142</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>2060</td>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>742</td>\n",
       "      <td>0.8</td>\n",
       "      <td>138</td>\n",
       "      <td>40</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "109   45    False                       292                 35   \n",
       "296   45    False                      2060                 60   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  time  \\\n",
       "109                False        850               1.3           142    13   \n",
       "296                False        742               0.8           138    40   \n",
       "\n",
       "     DEATH_EVENT  \n",
       "109        False  \n",
       "296        False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.platelets > 650]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b2f90550",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:13.806542Z",
     "iopub.status.busy": "2022-03-28T14:16:13.805769Z",
     "iopub.status.idle": "2022-03-28T14:16:13.997988Z",
     "shell.execute_reply": "2022-03-28T14:16:13.997420Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.743400Z"
    },
    "papermill": {
     "duration": 0.264725,
     "end_time": "2022-03-28T14:16:13.998168",
     "exception": false,
     "start_time": "2022-03-28T14:16:13.733443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Platelets Boxplot (after outliers removal)')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l1HI+lDCW/NoTEz4HvGLFCvbaq6pmr732YsWKFS3t269+TGNZV1xxBbNmzRq0/6xZs0asQ5pOVqxYMWQMwO7x0crYG22MNht3HR27791aHXPN6hlLPpQw0e2b8AA+6KCDOO2004gITj/9dBYsWNDSvosWLRpyTGNZxxxzDKeffvqgMs4444wR65Cmk4MOOogzzjhjYHnu3LmDxkcrY2+0MTrauGt1zDWrZyz5UMJEt2/CP4SD6lVky5YtLb9KbtmyhZUrV7Jq1aqmr8b1slasWMGmTZvo6+tj9uzZU+4VVJpoK1asYOPGjfT09HDVVVfR1dU1aHy0MvZG22+4cRcRYxpzzeoZSz6UMJHti8YPsUaydOnStGHDhnFvxETrn0+biXOJsHsOePuyZRNWx7z16wHGtY5569fz1hk+BzwT+z4dRcTdKaWljev9VWRJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCOko3YDIsXry4dBOkMfGanRlmRACvXLmydBOkMfGanRmcgpCkQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSrEAJakQgxgSSqko3QDNDk6duxg3vr1E1o+MK519JcpTVcG8AywePHiCa+jp6cHgM7OznEtdzLaLpViAM8AK1euLN0ESU04ByxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklRIpJRa3zliG9ADzAeenKhGTTL7MjXZl6lpOvUFJq8/nSmlBY0rxxTAAwdFbEgpLR2XZhVmX6Ym+zI1Tae+QPn+OAUhSYUYwJJUyJ4G8DXj2oqy7MvUZF+mpunUFyjcnz2aA5YkvXhOQUhSIQawJBUy5gCOiPdGxAMRsSUiPjYRjRpPEfH5iOiNiPtr6w6MiFsj4of535fl9RERn859uy8ilpRr+VARcXhE3B4RmyLiBxFxQV7fdv2JiH0j4rsRsTH35U/y+ldFxHdym/8uImbn9fvk5S15+8KiHWgQEXtHxPci4ua83Jb9AIiIhyPi+xFxb0RsyOva7hoDiIh5EbEmIjZHRHdEnDCV+jKmAI6IvYG/BN4HHAt8ICKOnYiGjaPVwHsb1n0M+EZK6dXAN/IyVP16df75EHD1JLWxVc8BF6WUjgWWAR/J578d+/OfwEkppTcAxwHvjYhlwP8EPpVSWgQ8DZyX9z8PeDqv/1Tebyq5AOiuLbdrP/q9M6V0XO3/yLbjNQZwFfBPKaXFwBuonqOp05eUUss/wAnALbXljwMfH0sZJX6AhcD9teUHgIPz44OBB/LjzwIfaLbfVPwBvgKc3O79AfYD7gGOp/qtpI7G6w24BTghP+7I+0Xptuf2HEY1kE8CbgaiHftR68/DwPyGdW13jQEvBf698fxOpb6MdQriUGBrbfmRvK7dvCKl9Fh+/Djwivy4bfqX37q+EfgObdqf/Lb9XqAXuBV4ENieUnou71Jv70Bf8vZngJdPaoOHdyXwUeCFvPxy2rMf/RLw9Yi4OyI+lNe14zX2KmAb8Ld5euhzEbE/U6gvM/5DuFS91LXV/8WLiAOAtcCFKaUd9W3t1J+U0vMppeOo7iB/HlhctkVjFxG/BPSmlO4u3ZZx9NaU0hKqt+QfiYi31ze20TXWASwBrk4pvRHYxe7pBqB8X8YawI8Ch9eWD8vr2s0TEXEwQP63N6+f8v2LiFlU4fuFlNKX8+q27Q9ASmk7cDvVW/V5EdGRN9XbO9CXvP2lwI8nt6VNvQU4JSIeBr5ENQ1xFe3XjwEppUfzv73A31O9OLbjNfYI8EhK6Tt5eQ1VIE+Zvow1gP8VeHX+hHc28GvAjePfrAl3I3Bufnwu1Vxq//rfyJ+GLgOeqb1VKS4iAvgboDul9L9rm9quPxGxICLm5ccvoZrL7qYK4jPybo196e/jGcBt+e6lqJTSx1NKh6WUFlKNh9tSSh+kzfrRLyL2j4g5/Y+BXwDupw2vsZTS48DWiDg6r3oXsImp1Jc9mNh+P/BvVPN1K0tPtLfQ3uuBx4A+qlfE86jm3L4B/BD4Z+DAvG9Q/S+PB4HvA0tLt7+hL2+lert0H3Bv/nl/O/YHeD3wvdyX+4H/ntcfCXwX2ALcAOyT1++bl7fk7UeW7kOTPp0I3NzO/cjt3ph/ftA/xtvxGsvtOw7YkK+zfwBeNpX64q8iS1IhM/5DOEkqxQCWpEIMYEkqxACWpEIMYEkqxADWlBER6yJixD+QGBEXRsR+k1mWNFEMYLWbC6m+vGeqlSWNmQGsSRcRC/P3s34hf0frmsY70Yi4OiI2xODvCv4D4BDg9oi4Pa/7hYj4dkTcExE35O/JaKxvyD6NZeUvBlodEfdH9V24fzjxZ0IznQGsUo4GulJKxwA7gBUN21em6rtoXw+8IyJen1L6NPAjqu+qfWdEzAf+GHh3qr48ZgPwR/VChtunsSyq35g6NKX02pTS64C/nZhuS7t1jL6LNCG2ppTuzI+vA/6gYfuZ+asQO6i+s/VYql8nrVuW199ZfU0Gs4Fv78E+AA8BR0bEXwBfBb6+B32SxsQAVimNvwM/sBwRrwIuBt6cUno6IlZTfYdCowBuTSl9YIR6WtmHXM8bgPcAHwbOBH571F5IL4JTECrliIg4IT/+deBbtW1zqb679ZmIeAXV99L2exaYkx+vB94SEYtg4Ju8jmqoZ6R9BsrKUxV7pZTWUk1ZTKm/babpyQBWKQ9Qfdl3N9U3VA38/a2U0kaqb0rbDHwRuLN23DXAP0XE7SmlbcBvAtdHxH1UUwuDvtR9lH0GyqL6ywfr8l/ouI7qz21JE8pvQ9Oky39O6eaU0mtLt0UqyTtgSSrEO2BJKsQ7YEkqxACWpEIMYEkqxACWpEIMYEkq5P8DwddlTGA8SywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.drop(data[data.platelets >= 650].index)\n",
    "sns.boxplot(x=data.platelets, color='teal').set(title='Platelets Boxplot (after outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95832fa8",
   "metadata": {
    "papermill": {
     "duration": 0.070153,
     "end_time": "2022-03-28T14:16:14.140057",
     "exception": false,
     "start_time": "2022-03-28T14:16:14.069904",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Serum Sodium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "611e7b16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:14.284207Z",
     "iopub.status.busy": "2022-03-28T14:16:14.283527Z",
     "iopub.status.idle": "2022-03-28T14:16:14.473415Z",
     "shell.execute_reply": "2022-03-28T14:16:14.472777Z",
     "shell.execute_reply.started": "2022-03-28T14:14:50.925351Z"
    },
    "papermill": {
     "duration": 0.263258,
     "end_time": "2022-03-28T14:16:14.473558",
     "exception": false,
     "start_time": "2022-03-28T14:16:14.210300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Serum Sodium Boxplot (before outliers removal)')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1ISA1iSkhjAkpTEAJakJAawJCUxgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSjIvuwDlW7p0aXYJ0sCazf3DABbLli3LLkEaWLO5f3gJQpKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1ISA1iSkhjAkpTEAJakJAawJCUxgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1KSedkFSMNu3sMPs8vKlbPeBtC3dtQfBrC0GZYuXdqXdtauXQvA4sWLZ72tfq2TDGBpsyxbtiy7BA0xrwFLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1ISA1iSkhjAkpTEAJakJFFKmfyLI9YDa2evnAktAO5LbH+qhqneYaoVhqveYaoVhqveYal1cSllYe/EKQVwtohYVUo5MLuOyRqmeoepVhiueoepVhiueoep1tF4CUKSkhjAkpRk2AL409kFTNEw1TtMtcJw1TtMtcJw1TtMtT7LUF0DlqQtybAdAUvSFsMAlqQkAxXAEfH3EXFvRNzcmXZMRHw/IjZGxIGd6Usi4vGIuL79fHIAaj0jItZExI0RcWFE7NKZ9ycRcWtE/CAiDu1nrVOtd0D79s9anddHxBURsWubHhHx161vb4yIV/ez1mnUe0hEPNTp21Oya+3M+2BElIhY0MYHsm/HqTe1b6ellDIwP8CvAK8Gbu5M2wd4GXAlcGBn+pLu6wak1jcB89rwx4CPteGXAzcA2wIvAW4D5g5wvYPYt/M7wycDn2zDhwFfBwI4GLhmwOs9BLhskPq2Td8duJz6H60WDHLfjlNvat9O52egjoBLKVcBD/RMW11K+UFSSWMao9YrSilPtdGVwG5t+Ejgi6WUJ0opPwJuBV7Tt2KZcr2pxqj14c7ojsDI3eMjgX8s1Upgl4j4uf5U+nRtU6k31Wi1NmcC/5VN6xzIvm1Gq3foDFQAT8NLIuLfIuJfIuKXs4vp8Xbq0QPAi4F1nXl3tmmDpFsvDGDfRsTyiFgHHAuMnF4ObN+OUS/AayPihoj4ekTsm1Te0yLiSOCuUsoNPbMGsm/HqRcGrG8nMswBfA+wRynlPwB/BHwhIuYn1wRARCwDngI+n13LZIxS70D2bSllWSlld2qd78uuZyJj1Hsd9bkArwI+DlyUVB4AEbED8GE2/YAYWBPUO1B9OxlDG8DtdP7+Nnwt9brqS3Orgog4Efgt4NjSLkwBd1GvWY3YrU1LN1q9g9q3HZ8Hjm7DA9u3HU/XW0p5uJTySBv+GrDNyE2kJHtS70vcEBF3UPvvuoh4EYPZt2PWO4B9O6GhDeCIWBgRc9vwzwN7A7cn1/Rm6nWpI0opj3VmXQL8TkRsGxEvodb6vYwau8aqd0D7du/O6JHAmjZ8CXBCu2N/MPBQKeWevhfYY6x6I+JFERFt+DXUffD+/ldYlVJuKqUsKqUsKaUsoV5meHUp5f8ygH07Xr2D1reTkn0XsPsDnEc9/d1A7dh3AP+xDT8B/Bi4vL32aOD7wPXUU4/DB6DWW6nXzK5vP5/svH4Z9UjyB8BvDEjfjlrvgPbtBcDNwI3ApcCL22sD+JvWtzfR+abMgNb7vta3N1BvfP5idq098+/gmW8VDGTfjlNvat9O58f/iixJSYb2EoQkDTsDWJKSGMCSlMQAlqQkBrAkJTGAJSmJAaytXkRcGe1RpxHxteg8RlSaTfOyC9DWJSLmlWeewDZwSimHZdegrYdHwJqWiNgxIr7anjx1c0S8NSIOaE9PuzYiLh95dGE7wjwrIlYBH4iIsyPitzvLeqT9e0j7/Ysj4vaI+MuIODYivhcRN0XEnuPUc0yr44aIuKpN2y4i/qH97r9FxK+26dtHxBcjYnVEXAhs31nOHRGxIOpD6bsPWP/jiDitsz5nRsSqtoxfiIivRMQtEXH6jHa0tmgeAWu63gzcXUr5TYCI2Jn6OMsjSynrI+KtwHLqYy4BnlNKGTnNP3uc5b6K+hD+B6jPn/hMKeU1EfEB4P3AH47xe6cAh5ZS7upcQngvUEopr4iIpcAVEfFS4D3AY6WUfSLildT/bj1VT5ZSDmx1XQwc0Gq+LSLOLO1hRtJ4PALWdN0EvDEiPhb1ecG7A/sB34yI64GPsOkD3r80yeX+71LKPaWUJ6jPILii096ScX7vu8DZEfEuYG6b9jrgXIBSyhrqX094KfWvLIxMv5H6vIapuqRT1/c7Nd/Opk8Qk8bkEbCmpZTyw6h/I+ww4HTgn6lB9NoxfuXRzvBTtA//iJgDPKcz74nO8MbO+EbG2V5LKe+OiIOA3wSujYgDprA6o3m6xma7nvndunprdr/SpHgErGmJ+kcmHyulnAucARwELIyI17b528TYf5HgDuopO8ARwDYzUM+epZRrSimnAOupR6H/Sv1rFLRLD3tQn0Z3FfCf2/T9gFeOssgfA4si4gURsS31mcnSjPKTWtP1CuCMiNhIfVTge6hHjX/drgfPA86iPh6w198BF0fEDcA32PToeLrOaM/gDeCfqI8kXAP8bUTc1Go7sZTyRET8LfAPEbEaWA1c27uwUsqGiPgo9bnNd/HM84elGePjKCUpiZcgJCmJlyA0VKL+AdFjeiZ/uZSyPKMeaXN4CUKSkngJQpKSGMCSlMQAlqQkBrAkJfn/SKW0xifYFbEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data.serum_sodium, color='teal').set(title='Serum Sodium Boxplot (before outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb76f08",
   "metadata": {
    "papermill": {
     "duration": 0.073518,
     "end_time": "2022-03-28T14:16:14.619849",
     "exception": false,
     "start_time": "2022-03-28T14:16:14.546331",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Normal values of serum sodium are 135-145 mEq/L, while values lower than 125 indicates severe Hyponatremia. Values under 120 would probably be outliers. Let's remove them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f2481076",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:14.780443Z",
     "iopub.status.busy": "2022-03-28T14:16:14.779731Z",
     "iopub.status.idle": "2022-03-28T14:16:14.786626Z",
     "shell.execute_reply": "2022-03-28T14:16:14.786068Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.112040Z"
    },
    "papermill": {
     "duration": 0.090424,
     "end_time": "2022-03-28T14:16:14.786795",
     "exception": false,
     "start_time": "2022-03-28T14:16:14.696371",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>True</td>\n",
       "      <td>160</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>327</td>\n",
       "      <td>2.7</td>\n",
       "      <td>116</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>60</td>\n",
       "      <td>False</td>\n",
       "      <td>1211</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "      <td>263</td>\n",
       "      <td>1.8</td>\n",
       "      <td>113</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "4     65     True                       160                 20   \n",
       "199   60    False                      1211                 35   \n",
       "\n",
       "     high_blood_pressure  platelets  serum_creatinine  serum_sodium  time  \\\n",
       "4                  False        327               2.7           116     2   \n",
       "199                False        263               1.8           113    27   \n",
       "\n",
       "     DEATH_EVENT  \n",
       "4           True  \n",
       "199        False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.serum_sodium < 120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "699610fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:14.957323Z",
     "iopub.status.busy": "2022-03-28T14:16:14.956268Z",
     "iopub.status.idle": "2022-03-28T14:16:15.139967Z",
     "shell.execute_reply": "2022-03-28T14:16:15.140487Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.129610Z"
    },
    "papermill": {
     "duration": 0.276475,
     "end_time": "2022-03-28T14:16:15.140690",
     "exception": false,
     "start_time": "2022-03-28T14:16:14.864215",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Serum Sodium Boxplot (after outliers removal)')]"
      ]
     },
     "execution_count": 23,
     "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": [
    "data = data.drop(data[data.serum_sodium < 120].index)\n",
    "sns.boxplot(x=data.serum_sodium, color='teal').set(title='Serum Sodium Boxplot (after outliers removal)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b5ccc43",
   "metadata": {
    "papermill": {
     "duration": 0.075507,
     "end_time": "2022-03-28T14:16:15.291638",
     "exception": false,
     "start_time": "2022-03-28T14:16:15.216131",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Serum Creatinine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c052acef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:15.462054Z",
     "iopub.status.busy": "2022-03-28T14:16:15.446529Z",
     "iopub.status.idle": "2022-03-28T14:16:15.625704Z",
     "shell.execute_reply": "2022-03-28T14:16:15.626220Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.324786Z"
    },
    "papermill": {
     "duration": 0.259901,
     "end_time": "2022-03-28T14:16:15.626411",
     "exception": false,
     "start_time": "2022-03-28T14:16:15.366510",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Serum Creatinine Boxplot')]"
      ]
     },
     "execution_count": 24,
     "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.boxplot(x=data.serum_creatinine, color='teal').set(title='Serum Creatinine Boxplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d8da99",
   "metadata": {
    "papermill": {
     "duration": 0.092712,
     "end_time": "2022-03-28T14:16:15.810844",
     "exception": false,
     "start_time": "2022-03-28T14:16:15.718132",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Values higher than 5 indicate severe kidney damage, so logically seems that serum creatinine levels higher than 8 are extremely high values (possibly outliers). Looking at the data I decided to leave them, because patients having values higher than 8 died from heart failure and it is possible than the high value is caused by additional medical disorder that lead to their death. Removing these outliers can lead to loss of valuable data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ceb82d67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:15.971802Z",
     "iopub.status.busy": "2022-03-28T14:16:15.967319Z",
     "iopub.status.idle": "2022-03-28T14:16:15.984054Z",
     "shell.execute_reply": "2022-03-28T14:16:15.983474Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.488092Z"
    },
    "papermill": {
     "duration": 0.097917,
     "end_time": "2022-03-28T14:16:15.984211",
     "exception": false,
     "start_time": "2022-03-28T14:16:15.886294",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>80</td>\n",
       "      <td>True</td>\n",
       "      <td>123</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>388</td>\n",
       "      <td>9.4</td>\n",
       "      <td>133</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "9   80     True                       123                 35   \n",
       "\n",
       "   high_blood_pressure  platelets  serum_creatinine  serum_sodium  time  \\\n",
       "9                 True        388               9.4           133     2   \n",
       "\n",
       "   DEATH_EVENT  \n",
       "9         True  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.serum_creatinine > 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd519032",
   "metadata": {
    "papermill": {
     "duration": 0.077872,
     "end_time": "2022-03-28T14:16:16.142368",
     "exception": false,
     "start_time": "2022-03-28T14:16:16.064496",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Creatinine Phosphokinase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d04d7e1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:16.327204Z",
     "iopub.status.busy": "2022-03-28T14:16:16.324640Z",
     "iopub.status.idle": "2022-03-28T14:16:16.509223Z",
     "shell.execute_reply": "2022-03-28T14:16:16.508564Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.505349Z"
    },
    "papermill": {
     "duration": 0.28656,
     "end_time": "2022-03-28T14:16:16.509378",
     "exception": false,
     "start_time": "2022-03-28T14:16:16.222818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Creatinine Phosphokinase Boxplot')]"
      ]
     },
     "execution_count": 26,
     "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.boxplot(x=data.creatinine_phosphokinase, color='teal').set(title='Creatinine Phosphokinase Boxplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b90dc9",
   "metadata": {
    "papermill": {
     "duration": 0.084963,
     "end_time": "2022-03-28T14:16:16.683792",
     "exception": false,
     "start_time": "2022-03-28T14:16:16.598829",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Seems like creatinine phosphokinase are petty high levels and might be outliers, but looking at the data again these extreme levels might have actually led to the death event. So I decided to leave the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "33f51e34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:16.866558Z",
     "iopub.status.busy": "2022-03-28T14:16:16.865518Z",
     "iopub.status.idle": "2022-03-28T14:16:16.869390Z",
     "shell.execute_reply": "2022-03-28T14:16:16.869901Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.694211Z"
    },
    "papermill": {
     "duration": 0.098411,
     "end_time": "2022-03-28T14:16:16.870107",
     "exception": false,
     "start_time": "2022-03-28T14:16:16.771696",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>time</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>False</td>\n",
       "      <td>7861</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>263</td>\n",
       "      <td>1.1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>7702</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>390</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  anaemia  creatinine_phosphokinase  ejection_fraction  \\\n",
       "1    55    False                      7861                 38   \n",
       "60   45    False                      7702                 25   \n",
       "\n",
       "    high_blood_pressure  platelets  serum_creatinine  serum_sodium  time  \\\n",
       "1                 False        263               1.1           136     1   \n",
       "60                 True        390               1.0           139     9   \n",
       "\n",
       "    DEATH_EVENT  \n",
       "1          True  \n",
       "60         True  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.creatinine_phosphokinase > 7000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ee1078",
   "metadata": {
    "papermill": {
     "duration": 0.077946,
     "end_time": "2022-03-28T14:16:17.027627",
     "exception": false,
     "start_time": "2022-03-28T14:16:16.949681",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Check imbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9f54a1",
   "metadata": {
    "papermill": {
     "duration": 0.077451,
     "end_time": "2022-03-28T14:16:17.182798",
     "exception": false,
     "start_time": "2022-03-28T14:16:17.105347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "By looking at the data, it is clear that the data is very imbalanced, only third of the patients have actually died. In this classification problem we will care more about recall than precision, because predicting a \"no death event\" for a patient who eventually will die would be very unfortunate. Hence we would like to decrease the number of FN = high recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6fa4c735",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:17.346823Z",
     "iopub.status.busy": "2022-03-28T14:16:17.346035Z",
     "iopub.status.idle": "2022-03-28T14:16:17.536450Z",
     "shell.execute_reply": "2022-03-28T14:16:17.537111Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.708715Z"
    },
    "papermill": {
     "duration": 0.277061,
     "end_time": "2022-03-28T14:16:17.537288",
     "exception": false,
     "start_time": "2022-03-28T14:16:17.260227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Death event distribution')"
      ]
     },
     "execution_count": 28,
     "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(data.DEATH_EVENT).set_title(\"Death event distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1da744de",
   "metadata": {
    "papermill": {
     "duration": 0.080749,
     "end_time": "2022-03-28T14:16:17.696767",
     "exception": false,
     "start_time": "2022-03-28T14:16:17.616018",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Visualizaitons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0a8234",
   "metadata": {
    "papermill": {
     "duration": 0.079031,
     "end_time": "2022-03-28T14:16:17.854754",
     "exception": false,
     "start_time": "2022-03-28T14:16:17.775723",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First we can notice than the patients with high blood pressure and anaemia may infer that a death event can occur:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1567f805",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:18.025500Z",
     "iopub.status.busy": "2022-03-28T14:16:18.024465Z",
     "iopub.status.idle": "2022-03-28T14:16:18.305857Z",
     "shell.execute_reply": "2022-03-28T14:16:18.305247Z",
     "shell.execute_reply.started": "2022-03-28T14:14:51.900460Z"
    },
    "papermill": {
     "duration": 0.370974,
     "end_time": "2022-03-28T14:16:18.306047",
     "exception": false,
     "start_time": "2022-03-28T14:16:17.935073",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f268ed6c650>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//CMAP/zhD+nVqxcDBw6kT58+3H777RQVFXHdddexaNEi+vbtyyuvvEKPHj0AiIuLY+TIkfTp0+f4IHV1vP7667zwwgv079+f3r17Hx/0Bqeb6bXXXmPSpEnVKu9Lv379CA0NpX///n4ZpLZcTMbU1PrP4I2r4fp3oet5vsuowpO9IXEwXP1KYNsXhNasWUPPnj3ruxmNnq/3wXIxGeNP6z6GiKaQfGbFZUSg8zmweaazmM6Y05AFCGNqQtW5guh6HoRFVl62yzlwNA8ylwaiZcb4nQUIY2oiZz0cyIQuFXQtldV5tHO/6WtPm2SMVyxAGFMTW2c79ymjqi4bEw8JPWH7Am/bZIxHLEAYUxNb50LzDtAypeqyAElDIeN7qGVSOGPqkwUIY6pLFbbOgU4jT6TWqErSUDiaD7kbvG2bMR6wAGFMdeVsgEN7Kp+9VF7SMOd+h3UzBYOHH36Y3r17069fP9LS0liwoO7vy/Tp03n00Uf90DpnwVwwsVQbxlTX9u+c+04jq/+auK4Q3dIJEANv9KZdplrmzZvHjBkzWLx4MZGRkeTk5FBQUFCt1xYVFREW5vvjcsKECUyYMMGfTQ0adgVhTHVlLHI+7Msn56uMiHMVseN779plqiUzM5P4+PjjOYzi4+Np3749ycnJ5OTkALBo0SJGjx4NwAMPPMANN9zAyJEjueGGGxg+fDirVq06frzRo0ezaNEiXnrpJe66665ape3esmULI0aMoG/fvvzud78L4F+jeuwKwpjq2pkOHQZXf/yhVPsBztqJYwchMri6EOrFJ/dB1gr/HrNtX7iw8m6eMWPG8NBDD9GtWzfOP/98Jk2axNlnn13pa1avXs2cOXOIjo7mySef5K233uLBBx8kMzOTzMxMBg8ezMqVKwFo0aJFjdN233333fz4xz/mxhtv5JlnnvHbn8Nf7ArCmOo4dgD2rHFSZ9RU236Awu5VVRY13mnatCnp6ek8//zzJCQkMGnSJF566aVKXzNhwgSio509Pa6++urj6cDfeustJk6ceEr50rTdAFOmTGHSpEknpe1OS0vj9ttvJzMzE4C5c+dyzTVOxt8bbrjBX6fqN3YFYUx17FoCqHMFUVPt+jn3Wcuh4zC/Nuu0VMU3fS+FhoYyevRoRo8eTd++fXn55ZcJCws73i1UPpV3TEzM8Z87dOhAXFwcy5cvZ+rUqfz73/8+5fi1SdstNb0iDSC7gjCmOjLcRJAdBtb8tc07QHQryKw8VbPx1rp169iw4cR046VLl9KpUyeSk5NJT08H4J133qn0GJMmTeKvf/0r+fn59OvX75Tna5q2e+TIkUyZMgVwMrcGGwsQxlTHznRo1QWatKr5a0Wcq4is5f5vl6m2gwcPctNNN9GrVy/69evH6tWreeCBB/jjH//I3XffzeDBgwkNrSB9u2vixIlMmTKFq6++usIyNUnb/dRTT/HMM8/Qt29fdu7c6Z8T9SNL921MdTzeHTqfDVc8X7vXf/57WPBvuH8XhIb7t22nAUv3HRws3bcx/nZwDxzMgnZptT9Gu/5QXADZa/3WLGO8ZgHCmKqUTsls26f2x2jr9ldnWjeTOX1YgDCmKrudee60qUOAiOsCYdEnjtUINZTu7NNVbf7+FiCMqUrWCmieWLsB6lIhoZDQ3VlL0QhFRUWRm5trQaKeqCq5ublERUXV6HW2DsKYqmStrFv3UqnWPWHzt3U/zmkoMTGRjIwMsrOz67spjVZUVBSJiYk1eo0FCGMqU3jU2UWux8V1P1ZCD1j2JhzJg+jYuh/vNBIeHk5KSjX30DBBw9MuJhEZJyLrRGSjiNzn4/lIEZnqPr9ARJLLPd9RRA6KyK+8bKcxFcpeC1rs5Pqpq9Y9TxzTmNOAZwFCREKBZ4ALgV7ANSLSq1yxW4F9qtoVeBJ4rNzzfwM+8aqNxlTp+AwmPwSIhB7O/Z7VdT+WMQHg5RXEUGCjqm5W1QJgCnBpuTKXAi+7P08DzhM3MYmIXAZsASzDmak/u1dCeEz1txitTIskiGgKe+wKwpwevAwQHYAdZX7PcB/zWUZVi4B8IE5EmgK/AR6srAIRuU1EFonIIhv8Mp7IWgltekGIH/5XCQlxZjJlN86ZTOb0E6zTXB8AnlTVg5UVUtXnVXWwqg5OSEgITMtM46HqdDH5o3upVEJPu4Iwpw0vA8ROIKnM74nuYz7LiEgY0ALIBYYBfxWRrcDPgftF5C4P22rMqfJ3wLH8ui2QK691D2df60O5/jumMR7xcprrQiBVRFJwAsFk4NpyZaYDNwHzgInA1+qspBlVWkBEHgAOqurTHrbVmFNluaue/X0FAU43U8yZ/juuMR7w7ArCHVO4C/gMWAO8paqrROQhESnd4fsFnDGHjcAvgVOmwhpTb3avBARal598Vwet3ZlMNtXVnAY8XSinqh8DH5d77A9lfj4KXFXFMR7wpHHGVCVrObTq7N99pJt3cGZF5Wz03zGN8UiwDlIbU//8lWKjLBGI7+qszjYmyFmAMMaXYwdg3xZo48fxh1Lx3SBnQ9XljKlnFiCM8WW3uz7T31cQAHGpzgypgsP+P7YxfmQBwhhf/Jlio7z4VEBh7yb/H9sYP7IAYYwvu1dCVKwzqOxv8d2cexuHMEHOAoQxvmStdK4enNRg/hXXBRCbyWSCngUIY8orKXbGIPy5grqs8GiITbIrCBP0LEAYU97ezVB0xJvxh1Lx3SxAmKBnAcKY8o4PUHt0BQFOgMjdCCUl3tVhTB1ZgDCmvKwVEBJ2YoMfL8R1hcLDcGCXd3UYU0cWIIwpb/dK5xt+WKR3ddhMJnMasABhTHmlM5i8dDxA2EwmE7wsQBhT1qFcp9vHqxlMpZq2hsjmdgVhgpoFCGPK2h2AAWpwk/alWoAwQc0ChDFllW4S5EWSvvJKZzIZE6QsQBhT1u6V0LQtNA3AHudxXWH/TidzrDFByAKEMWVlrfC+e6lU6UC1XUWYIGUBwphSRQWQvc77GUyljs9ksr0hTHCyAGFMqey1UFIYuADRKgUk1AKECVoWIIwplbXcuW/bLzD1hUVCy2SbyWSClgUIY0plrYDwGGjVOXB1WtI+E8QsQBhTKmsFtOkNIaGBqzM+1U3aVxy4Oo2pJgsQxgCoujOYAjT+UCq+GxQXQN62wNZrTDVYgDAGnA/oY/sDHyASujv3NlBtgpAFCGPgxB4Q7QI0QF0qrqtzb+MQJghZgDAGIHM5SAi07hXYepu0gpgECxAmKIXVdwOMqakjBcVsyj7I/iOFtGoaQdeEpoSF1vG7TtYKZzwgPNo/jayJ+G7WxWSCkgUIc9rYsfcwT36xnk9WZnGk8MSsn2aRYYzv344fjEyhW5tmtTt41groNMJPLa2h+FRY82H91G1MJSxAmNPCW4t28Lv3VxIqwhUDO3Bm13haxkSQlX+U2RtyeHfxTqYu3ME1Qzvy67E9aNEkvPoHP7wX9mcEboFcefHd4HCusxdFTFz9tMEYHyxAmKD3z6828MQX6xnZNY4nrkqjbYuok56/bEAHfndxT576agOvzt/Gt+uyeWpyGoOTW1WvgtIB6kDPYCp1PGnfBgsQJqjYILUJaq/O28oTX6znigEdePkHQ08JDqVaxkTwwITevPvjMwgNESY9P5/nZm5CVauupN4DRKpzbwPVJshYgDBBK33bXh78cDXn9WjNXyf2q9ZAdP+kWD762ZmM7d2Gv3yylnunLedYURWrlHctgeYdICbeTy2voRZJEBblZJI1Joh4GiBEZJyIrBORjSJyn4/nI0Vkqvv8AhFJdh8fKiJL3dsyEbncy3aa4HPoWBE/e3Mp7WOj+duktBrNUmoWFc4z1w7k7vNSmZaewQ3//Z69hwoqfsGuxdB+gB9aXUshoRCXajOZTNDxLECISCjwDHAh0Au4RkTKTzK/Fdinql2BJ4HH3MdXAoNVNQ0YBzwnIjZe0og8+cV6duYd4W9X96dFdA0GnF0iwi8u6MY/rhnAsow8Ln1mDuuyfOzcdmQf7N0MHQb6odV1YPtTmyDk5RXEUGCjqm5W1QJgCnBpuTKXAi+7P08DzhMRUdXDqlrkPh4FVKMj2TQUq3bl8+LcLVw7rGP1B5orMKF/e6bePoJjhSVc8a+5fLYq6+QCu5Y69+3rO0B0c9J9FB6t33YYU4aXAaIDsKPM7xnuYz7LuAEhH4gDEJFhIrIKWAHcUSZgHCcit4nIIhFZlJ2d7cEpmPrw2KfraB4dzm/G9fDL8dKSYvnwp2fStU0zbn81nae+3EBJifudY9di574+u5jAuYLQEudqxpggEbSD1Kq6QFV7A0OA34rIKdNXVPV5VR2sqoMTEgKwybzx3PzNucxan82do7vUqmupIm2aRzH1tuFcMaADT365npv+9z1Z+Udh52Jo1QWiY/1WV60c337UuplM8PAyQOwEksr8nug+5rOMO8bQAsgtW0BV1wAHgQDtJG/q0xOfr6NN80huHJHs92NHhYfyxNX9eeTyvizauo+xf5/Foa0LKanv7iUok7TPBqpN8PAyQCwEUkUkRUQigMnA9HJlpgM3uT9PBL5WVXVfEwYgIp2AHsBWD9tqgkD6tn0s3LqPH5/dhahwbzbtERGuHdaRj+8exdD4QmKO7ub5jS34dGUmxSX1ONQV0QRadLQrCBNUPJsZpKpFInIX8BkQCryoqqtE5CFgkapOB14AXhWRjcBenCACcCZwn4gUAiXAnaqa41VbTXB4Yc5mmkeFcdXgpKoL11FKfAzPnycwBZZrFx59bTEdYqO5NK09Y3q3pV+HFoSEiOftOInNZDJBxtOpo6r6MfBxucf+UObno8BVPl73KvCql20zwWXH3sN8ujKL28/uQkxkYGY0y64lICH84+c3MGHTQV6bv53nZm3mX99uollkGP2TYhnQ0bmlJbWkVUyEtw2K7waLX3F2t5MABydjfLC1BSYovDh3CyEi3OTB2EOFdqZDQk/Copsxrk8zxvVpR97hAr5Zt4dFW/exZHse//p20/Gup+S4JgxLieOS/u0Z0SWOUH9fYSR0g8JDkJ8Bsd5fRRlTFQsQpt4dLSzmnfQMLuzbrsJcS35XUgwZC6HPlSc9HNskgssHJHL5gEQADhcUsSIjnyU78li8bR8frchk6qIdJLWK5qfnpnLlwET/BYrSzYr2rLEAYYKCBQhT7z5blcX+o0VMHhLAD8U9a5w9qDsOr7RYk4gwhnWOY1hnJ8vq0cJivlyzm+dmbubX05bz1sIdPHF1fzrFxdS9TQnuuo89q6DbmLofz5g6qtYsJhF5V0QuFpGgXTdhTl9TFzrfyEd0DmCq6x3znfukYTV6WVR4KOP7tWf6XSN54qr+rNt9gAlPz+X7LXvr3qboWGie6AQvY4JAdT/w/wVcC2wQkUdFpLuHbTKNyPbcw3y3KZerBiUFdtbQ9gXQtA20TK7Vy0WEKwcl8vHPRhHXNILrX1jArPV+WM3fuifsXl334xjjB9UKEKr6papeBwzEWY/wpYh8JyI/EBH/LXc1jc60xRmIwMRBiYGteMd85+qhjrOFklo14Z07zqBLQlPueC2d5Rl5dWtXm16Qsw6KC+t2HGP8oNpdRiISB9wM/BBYAjyFEzC+8KRlpsFTVT5ctoszusTRPjY6cBXvz4S87VWOP1RXy5gIXv7BEFo2ieCHLy8i9+Cx2h+sdW8oLrCcTCYoVHcM4j1gNtAEuERVJ6jqVFX9KdDUywaahmvVrv1syTnE+H7tA1vx8fEH/wQIgNbNo/jPjYPJO1LIPW8vO5EMsMYH6unc717lt7YZU1vVvYL4j6r2UtW/qGomOJv9AKjqYM9aZxq0D5fvIixEGNe7bWAr3j4fwqKhXT+/HrZX++b8/uKefLsum9cXbKvdQeK7gYTaQLUJCtUNEH/28dg8fzbENC6qyoxlmZyZGk9Lr1col7dlNnQcBqH+Hz67fngnRqXG89dP17F7fy32dgiPgrgusMcGqk39qzRAiEhbERkERIvIABEZ6N5G43Q3GVMrS3bksTPvCJcEunvpYLazziDlLE8OLyL86dI+HCsu4aEZtfyQb93LuphMUKhqodxYnIHpROBvZR4/ANzvUZtMI/Dhsl1EhIVwQe82ga1462znPmW0Z1Ukx8dw5+gu/P3LDdx65j4GdmxZswO07gWrP4CCQxDhhwV4xtRSpVcQqvqyqp4D3Kyq55S5TVDVdwPURtPAqCqfr9rNWanxNI8K8CzpLbMgsjm06+9pNT8a1Zn4ppE8+slaVGs4YN2mF6CwZ60nbTOmuqrqYrre/TFZRH5Z/haA9pkGaHXmfnbmHeGCXgG+egDYMhM6jYRQb7PMxESGcfd5Xfl+y16+XVfDBXRt+zr3Wcv93zBjaqCqQerS69umQDMfN2Nq7MvVexCBc3sEOEDk7XDWF3g0/lDe5KEd6RTXhMc/X1ezq4jYThAVC5lLvWqaMdVS6dcoVX3OvX8wMM0xjcEXa7IY2LElCc0iA1vx8fGHwASI8NAQ7hzdhd+8s4JZG3I4u1s1900XcbrAMpd520BjqlDdhXJ/FZHmIhIuIl+JSHaZ7idjqm1X3hFW7tzP+T3roXtpwxcQ0/pEWu0AuHxAIm2bR/GvbzbW7IXt+jszmYoKvGmYMdVQ3XUQY1R1PzAeJxdTV+BerxplGq4v1+wGCPz4Q3ERbPoKUsdASOCSEkeEhfDDUSks2LKX9G01yPjaPs1JuZFtA9Wm/lT3/5TSrqiLgbdVNd+j9pgG7ovVu+kcH0PX1gHO0JLxPRzNh9QLAlsvcO2wjsQ2Cec/s7ZU/0Xt0px7G4cw9ai6AWKGiKwFBgFfiUgCUItloqYxO3C0kPmbc+tn9tL6zyAkDLqcE/Cqm0SEMWlIEp+vzmJn3pHqvahlCkQ0s3EIU6+qm+77PuAMYLCqFgKHgEu9bJhpeOZtyqWwWBndvXXgK9/wBXQcAVEtAl83cMPwTgC8Pr+aOZpCQmyg2tS7mnTG9gAmiciNwETA9kQ0NTJ7Qw5NIkIZ1KmGK4vrKm+Hk14jtf7+ySa2bML5PdswZeEOjhYWV+9F7fpD1kpn/MSYelDdWUyvAo8DZwJD3JtlcTU1MntDNiM6xxERFuCda9d/6tx3GxvYesu5+Yxk9h4q4MNlu6r3gvZpUHQEsi2zq6kf1V1OOhjopTXOGWCMY8few2zNPcxNZyQHvvLVH0B8d0io351yR3SJo1ubprw8bysTByUiVe1mlzjEud/x/YnV1cYEUHW/yq0EApy03zQkszfkADAqtZqLxfzl4B7YNhd6XxbYen0QEW4YkczKnftZsbMaEwFbJjvrNnZ873nbjPGlugEiHlgtIp+JyPTSm5cNMw3L7A3ZtG8RRZeEAGcnXfMhaAn0Co45FZemtScqPIQpC3dUXVgEkoY6U3SNqQfV7WJ6wMtGmIatuESZuzGHC/u0q7pbxd9Wvw9xqQFdPV2Z5lHhXNy3PdOX7uL/LupJTGQV/wsmDYW1M5x9LJoG+OrLNHrVneY6E2cFdbj780JgsYftMg3I8ow89h8t4szU+MBWfCgHts5xupcCHZgqcc3QJA4eK+KjFZlVF04a5txnLPS2Ucb4UN1ZTD8CpgHPuQ91AN73qE2mgZm9IQcRGNk1wAFizfSg6l4qNahTS7okxDDl++1VF26XBiHhsGOB5+0yprzqjkH8BBgJ7AdQ1Q1APax2Mqej2Ruy6duhBa0Cvff00jcgoQe06RPYeqsgIkwe0pHF2/NYv/tA5YXDo5z1ENvnB6ZxxpRR3QBxTFWPp5UUkTDApryaKh04Wsji7XmMCnT30p41TrfMgBuCqnup1BUDOxAeKkz5vhqD1SmjYOciZwtSYwKougFipojcD0SLyAXA28CH3jXLNBTzN++luEQ5s2uAB1gXv+p0zfSfHNh6qymuaSRjerXl3SUZHCuqYmV1yllQUgTb5wWmcca4qhsg7gOygRXA7cDHwO+qepGIjBORdSKyUUTu8/F8pIhMdZ9fICLJ7uMXiEi6iKxw78+t9hmZoDJ7QzZNIkIZ2Ck2cJUWHYPlU6DHRRAT4CuXGpg8NIm8w4V8tmp35QWThjvBbsuswDTMGFe1prmqaomIvA+8r6rV2mBXREKBZ4ALgAxgoYhMV9XVZYrdCuxT1a4iMhl4DJgE5ACXqOouEekDfIYzMG5OM7M35DC8cxyRYaGBq3Tdx3A4FwbeGLg6a2Fkl3gSW0Yz5fvtTOjfvuKCEU2c6a4WIEyAVXoFIY4HRCQHWAesc3eT+0M1jj0U2Kiqm93xiymcmgH2UuBl9+dpwHkiIqq6RFVLE9aswunaCvD+lKauduw9zJacQ4Eff1j8KjRPhM6BT+1dEyEhwqTBSXy3KZdtuVWML6ScBbuWwpF9AWmbMVB1F9MvcGYvDVHVVqraChgGjBSRX1Tx2g5A2RG4DE69CjheRlWLgHwgrlyZK4HFqnqsfAUicpuILBKRRdnZ1bqwMQE0Z2Npeo0ABoi87bDpaxhwPYQE8Kqllq4anESIwNSqVlannAUobJkdkHYZA1UHiBuAa1T1+FZYqroZuB7w/PpdRHrjdDvd7ut5VX1eVQer6uCEBFtlGmxmb8imXYsouiQEcPe4pW849wOuC1ydddC2RRTn9mjN2+kZFBaXVFwwcQhEtoANnwWucabRqypAhKtqTvkH3XGI8CpeuxNIKvN7ovuYzzLu1NkWQK77eyLwHnCjqm6qoi4TZIpLlDkbchiVGh+49BolxbDkNWfXuNiOganTDyYP6Uj2gWN8vXZPxYVCw6HrebD+cyipJJAY40dVBYiCWj4HTjqOVBFJEZEIYDJQPsHfdOAm9+eJwNeqqiISC3wE3Keqc6uoxwSh0vQaAc3euvlbyN/hrH04jYzunkCb5pFVr6zuNg4O7YFdluXGBEZVAaK/iOz3cTsAVJqg3h1TuAtnBtIa4C1VXSUiD4nIBLfYC0CciGwEfokznRb3dV2BP4jIUvdmK7dPI3PqI73GklchuhX0uDhwdfpBWGgIVw1KYub6bHZVtmd16gUgIbDuk8A1zjRqlQYIVQ1V1eY+bs1UtaouJlT1Y1XtpqpdVPVh97E/qOp09+ejqnqVqnZV1aHu+Aaq+mdVjVHVtDK3Sq6/TbCZvSGHPu0DmF7jUC6smeEsjAs7/Sa8TRqSRInCW4sqGaxu0spZE1G6Q54xHgvw3o+mMXDSa+wL7Oyl5VOhpPC0614qldSqCaNS43lr4Q6KSyrJYtPjYti9EnI2Bq5xptGyAGH8bv7mvRSVaODGH1Rh8SvQYTC0CY59H2pj8pCO7Mo/yqwNlUzZ7nMFILByWsDaZRovCxDG7+ZsyCY6PIDpNXamQ/YaGHh6Xj2UuqBXG+JiIiofrG7eHpLPhBVvO4HRGA9ZgDB+56TXaBW49BqLX4bwGOhzZWDq80hEWAhXDkrkqzV72HPgaMUF+06E3I2QuTRgbTONkwUI41c79h5mc86hwHUvHTsIK9+F3pdDZLPA1OmhSUOSKCpR3l6UUXGhnhOc5H3L3wpcw0yjZAHC+FVpeo2zugVogHrVe1BwMOgT81VXl4SmjOwax2vzt1W8srpJK+g53lk1XljJtFhj6sgChPGrORtyaNs8gOk1lrwK8d2cbKcNxA/OSCEz/yifrcqquNDgW+BonhMgjfGIBQjjN8UlypyNAUyvkb3O2at54I1BuWtcbZ3bozWd4prwv7lbKy6UPAriUmHhCwFrl2l8LEAYv1mxM5/8I4WM6hag8Yclr0FIGPQLzl3jaiskRLhpRDLp2/axPCPPdyER5ypi5yLYaak3jDcsQBi/mb0+GxE4MxDpNYoLYdkUJz9R04aXyfeqwYk0jQyr/CpiwPVOhte5fw9Us0wjYwHC+M3sjTn0bt88MOk1Nn7pJK5LOz3SetdUs6hwJg5KZMbyXezZX8GU16jmMORWWD0dcjYEtoGmUbAAYfzi4LEiFm/bF7jprUteg5gEJ4FdA/WDkckUlygvzNlScaHhdzq5p+wqwnjAAoTxi3mbcp30GoHoXjqU4ySs6z/Z2SehgeoUF8P4fu15bf429h2qILt+0wQn/9SyqbBvW2Ab6Ev2evjyAfjoHifrrK32Pq1ZgDB+MWt9Nk0iQhmU3NL7ypZPhZIiSLve+7rq2U/O6cqhgmL+993Wigud+Qtne9VvHglYu3xa/Ao8OwK+e9pZxPfmZHjnViiqausYE6wsQBi/mLUhmxGd47xPr6EKS16HDoOgdQ9v6woC3ds2Y0yvNrw0dwsHjhb6LtSiAwy73QmcWSsD28BSy6bA9J9CytnwyzXw6y1w7u9h5Tvwya/rp02mzixAmDrblnuIbbmHOSsQ01szl8KeVQ12cNqXu87tyv6jRbw6v5IupDN/4Qxaf/Vg4BpWas8a+PDnztqMa6Y43V6hYXDWr2Dk3ZD+P9jwReDbZerMAoSps1nrnfTUAQkQS9+EsKjTPjFfTfRLjGV09wSem7mZ/CMVXEVEt3SCxIbPYfPMwDWupAQ+vBsimsCVL0BYuRls5/yfs6Dv419B0bHAtcv4hQUIU2cz12eT1Cqa5Lgm3lZUXASr3nXWPkTHeltXkLl3bHfyjxTy75mbKi407A6I7Qif3uf8rQJh2RvOavYLHoJmbU59PiwSLnwU9m2Fpa8Hpk3GbyxAmDopKCph3qZczkpN8D69xpaZcCgb+l7lbT1BqHf7FlyW1p7/zd1CVn4F6yLCo2HsI7BnNSwKQAqOwiPw1Z8gcSj0v7bicl3Oc8aM5vw9cIHL+IUFCFMn6dv2caigODDdSyumOSuHG/Dah8rcM6Y7xSXKk1+sr7hQj/HQ+Rz45mFnOrCXFr8KB7PgvN9DSCUfJSIw6h7I2wZrZ3jbJuNXFiBMnczakE1YiHBGlzhvKyo8Ams+hF6XON0WjVBSqybcOCKZt9J3VJ6j6cLHoOAQfPWQd40pOuYszus4whmcrkq3cdC8g7O5kzltWIAwdTJrfTYDO7WkWZTHC9bWfwYFBxpl91JZd5+fSnzTSH7//kqKSypYhJbQ3RmPWPyKd4n8lr4O+3fC2b+uXibdkFAn6+6mb5zxCHNasABhai37wDFW7drP2QHpXnobmrap3rfVBqx5VDj/d1FPlmXkM3XhjooLnv0baNoaPvollBT7txHFhTD7Segw2OnOqq4B1zvBZMlr/m2P8YwFCFNrsze401u9zr90JM+ZvtnnSuebaCN3aVp7hqW04rFP11aeyG/cX2DXElj4X/82YNkUyN/uBKGaTExokQgpZzmL5ywFx2nBAoSptZnrs4mLiaB3++beVrR2BhQXQN+J3tZzmhARHrmiL0cLi/ntuyvQij5se18BXc93Zhrt3+WfyouLYPbj0C6tdpMFel8BezdD5jL/tMd4ygKEqZWi4hK+XZfN6O6tCQnxeHrrirehVWdoP9Dbek4jXRKa8ptxPfhq7R7eTs/wXUgELnocSgrhk9/4p+KV05wxhJpePZTqeYmzydOqd/3THuMpCxCmVtK37SP/SCHn92ztbUUHsmDLLGdwugFtK+oPN5+RzLCUVjz04Woy9h32XahVivNhvma6k121LoqLYNb/gzZ9ofuFtTtGk1bOuMWq96yb6TRgAcLUyldr9xAeKpyZ6nF671XvgZZAH+teKi8kRHj8qv4A/PTNJRQUlfgueMZPoXUv+PheZ/prba14G3I3wuhaXj2U6nMF5G2HXbZVarCzAGFq5as1uxneOc776a0r3oa2/SChm7f1nKaSWjXhrxP7sWR7Hn/5ZI3vQqHhMP7vkL+j9inBi4tg5mPOe9FjfK3bCzhrIiQE1n1at+MYz1mAMDW2NecQm7IPcV4Pj7uXcjfBzvRGv/ahKhf1bcfNZyTzv7lb+XhFpu9CHYfB4Ftg3jOw7buaV7J8CuzbAufcX/euviatIGl43bu8jOcsQJga+3LNbgDO6+kjOZs/rXwHkEaVubW27r+oJ2lJsfzq7WWs3rXfd6EL/gQtk+G92+FoBWV8OXYQvn7YmSTQbZxf2kv3C2H3CsirZC2HqXcWIEyNfb12D93aNCWplYfZW1Wd7qVOI50NcUylIsJCeO6GQTSPCufWlxf6Xh8R2RSueB7yM2o2q2nO3+DALhj3qP8mCnS/yLlfb91MwczTACEi40RknYhsFJH7fDwfKSJT3ecXiEiy+3iciHwjIgdF5Gkv22hqZv/RQr7fstf7q4es5ZCzHvra1UN1tWkexX9vGkze4UJ++MoijhT4WEGdNBRG/cpJ012dBXS7V8F3/4R+k51uKn+J7wpxXa2bKch5FiBEJBR4BrgQ6AVcIyK9yhW7Fdinql2BJ4HH3MePAr8HfuVV+0ztfLsum6IS9X78YcXbEBIOvS7ztp4Gpk+HFvzjmgGs2JnPT99cQlGxj5lNo++D1LHOVcTmbys+WMEheOdHEBULYx/2f2O7XwhbZ8OxA/4/tvELL68ghgIbVXWzqhYAU4BLy5W5FChN7zgNOE9ERFUPqeocnEBhgsinKzNJaBbJgI4tvaukpARWvOOsAm7Syrt6GqgLerXhwQm9+XLNbn79znJKyif1CwmFK//j7PT2xmTY9PWpBykqgGm3QPYauOxZiPFgOnP3i5wV8hu/8v+xjV94GSA6AGVHoDLcx3yWUdUiIB/wOG+0qa3DBUV8szabcb3bEurl6unt3zl93pZao9ZuHJHMPRd0493FO3loxupT03FEtYCbPnRWqL820UnHcWSf81z2enhlgjM+cOFfIfV8bxqZONS5OtnwuTfHN3UWVt8NqAsRuQ24DaBjx4713JqG79t12RwpLObCvm29rWjF2xAec2Ig09TKXed2Je9IIS/M2ULTyDDuGdPt5F3/mibALZ/AR79y8ivNedK5YjuU7fz9r3zB2yAdGgapY5xU7iXFlogxCHkZIHYCSWV+T3Qf81UmQ0TCgBZAbnUrUNXngecBBg8ebOv2PfbxikziYiIYluLhRV5RAax6H3qOhwiP97hu4ESE/7uoJ4eOFfH0NxspVuXXY7ufHCSiWjjdTWf8FFZ/4ASH+G7Qb5ITQLzWbSyseMvZtyJpiPf1mRrxMkAsBFJFJAUnEEwGym9cOx24CZgHTAS+1gpTU5r6dLSwmK/X7uGyAR287V7a+CUczbPFcX4SEiI8cnlfQkKEZ7/dRFFxCfdf1PPU/cPb9XNugdb1PJBQpzvLAkTQ8SxAqGqRiNwFfAaEAi+q6ioReQhYpKrTgReAV0VkI7AXJ4gAICJbgeZAhIhcBoxR1dVetddU7tt12RwuKOaiPu28rWjF29AkDjqP9raeRiQkRHj4sj6Ehwj/mb2FwmLlD+N7eZ+FtzqiWzrblq7/zNnb2gQVT8cgVPVj4ONyj/2hzM9HAZ9fFVU12cu2mZr5ZGUmLZuEM6yzh7OKjh1w5sUPuM7JH2T8RkR4YEJvwkND+O+cLeQcPMbjV/UnKjwI+v27jYUvfu+sqo5Nqrq8CRhbSW2qdOhYEZ+v2s24Pm0JD/Xwn8zaj6DoiGVu9YiI8H8X9+S3F/ZgxvJMbnzxe/IOF9R3s06k79jwWf22w5zCAoSp0mersjhSWMzlAxK9rWjJa9AyBToO97aeRkxEuP3sLvzjmgEs3Z7Hlc9+x8Y9B+u3UfGpznTb9RYggo0FCFOl95bsJKlVNIM7ebg4bu8WZ1Vt2nW2MVAATOjfnlduHUre4UIufXoOHy2vIAtsIIg4VxGbZ9ZtvwrjdxYgTKWy8o8yd2MOl6d18HZQc+kbgEDaNd7VYU4yvHMcM352Jt3bNuMnbyzmjx+s5HBBUf00pttYKD7mBAkTNCxAmEp9sHQnJQqXD/Swe6mk2AkQXc6BFh53Y5mTtGsRzZTbRnDLyBRenreNcX+fzXebcgLfkI5nQEQzy+4aZCxAmAqpKu8t2UlaUiwp8THeVbRlJuzPgAHXe1eHqVBEWAh/uKQXU28bTojAtf9ZwJ2vp7M5O4BjE2ER0PVcZxzClkIFDQsQpkKLt+exNusAVw0OwOB0VAvofrG39ZhKDescxyd3n8XPz09l5rpsLnhyFve8tYwVGfmBaUC3C+FgFmQuC0x9pkqndS4m463X52+jaWQYl6V5uGHPwT2werqzHWZ4lHf1mGqJjgjl5+d347phnXjmm428tWgH7yzOoH9SLJf0a8fY3m292ygq9QJAnG6m9mne1GFqxAKE8WnfoQJmrMhk0uAkYiI9/GeS/hKUFMLQH3lXh6mxhGaRPDChN78c041pizJ4Oz2DP3+0hj9/tIbO8TEMSW7F0JRWDEluRVKr6FNTd9RGTDwkDnECxOhT9hcz9cAChPFpWnoGBUUlXDfcwyy5xYWw6EXocq4zF94EneZR4dxyZgq3nJnC9tzDfL46i/mbc/lkZSZTFznZ/OObRjKoUyyDOrVkUKeW9OnQgsiwWq7Q7jYWvv4THMiCZh5nDTZVsgBhTlFSorzx/XYGd2pJj7bNvatozYdwIBPG/927OozfdIxrwg9HdeaHozpTUqKs232A9G37WLxtH+nb9/HZqt0ARISG0C+xBRf0asPF/dqR2LIGXVLdxjkBYv2nMOhmb07EVJsFCHOKr9fuYUvOIX5+vsff6r//D8R2cvuezekkJETo2a45Pds15/rhnQDYc+Aoi7flsXj7Pr7blMNfPlnLXz5Zy9CUVtwyMoULerWpOhNwm97OqupV71mACAIWIMwpnpu1iQ6x0Vzc18PMrTu+d3aOG/uIbRTTQLRuFsW4Pm0Z18fpGtqee5gPl+/ijQXbueO1dDq2asI9Y7pxSb/2FS+6FIE+V8LsJ5wJDE093vvcVMqmuZqTpG/by8Kt+/jRqBTCvEzMN+txiG5l3xIbsI5xTfjJOV2Zee9o/n39QJpFhXH3lKVMeGYOi7burfiFfa4ELXE2MDL1ygKEOcm/Z24mtkk4Vw/xMO1y5nInc+fwOyHCwwV4JiiEhYYwrk87PrzrTP52dX9yDxZw1XPzeGD6Kt+pPVr3hNa9YOU7gW+sOYkFCHPciox8vli9m5vPSKZJhIe9j7OfgMjmNrW1kQkJEa4YmMgXvzybG4Z34qXvtnLhU7N9L8TrcwVsnwf5GYFvqDnOAoQ57vHP1xHbJJxbz0zxrpKdi2H1+zDsdoiO9a4eE7SaRobx0KV9mHrbcAqLSrjy2e94Zd5WTtptuM+Vzv2yKfXTSANYgDCuBZtzmbk+mx+f3YVmUR7t5qYKn/8eYhJg5N3e1GFOG8M6x/HRz0Yxsmscf/hgFXe9ueREl1OrzpA8Cpa8CiUl9dvQRswChKGkRPnLJ2tp3SySG0cke1fRuk9g2xxnlWxkM+/qMaeNljERvHDTEH49rjufrMhk4rPz2JV3xHly4E2wbytsnVWvbWzMLEAYpqVnsHRHHr8e14PoCI+mnBYchs/uh7hU5398Y1whIcKdo7vyws1D2LH3MBOensvi7fug5yUQFQuLX6nvJjZaFiAaufzDhTz66VoGd2rJFQM8TMo381HYtwXG/w1CPerCMqe1c7q35t07z6BJRCiTn5/PeytzIO1aJ5nj/l313bxGyQJEI/eXT9aQd7iAhy7t492OcTsXw3dPw4AbIOUsb+owDUJqm2Z88JORDOwYyy+mLuPZIxegWgzzn63vpjVKFiAasS9X72bKwh3cfnYXerX3KOfS0f0w7RYn8dqYP3lTh2lQWsZE8Motw7h2WEceW3CE75ucjab/D44GaF8Kc5wFiEYq5+Ax7nt3OT3bNecX53fzphJVmPFzyNsGV/4Xolt6U49pcCLCQnjk8r786bI+PJJ3AXLsAHu/tauIQLMA0QgVFpfwk9cXs/9oEU9O6k9EmEf/DGY/7qyGPef/oNMZ3tRhGrQbhnfivlsmM4uBhM1/iu9WrK/vJjUqFiAaoT/NWM2CLXt57Mq+3qXzXvomfP1n6DcZRt3jTR2mURjRJY7U6/5GDEdZN/X3PDdzEyUltm91IFiAaGSe/XYTr8zbxm1ndebyAR7tNb30DXj/x5ByNkz4h5Oh05g6aJc6gJK067kx7AumffolP3hpIdkHjtV3sxo8CxCNyItztvDYp2uZ0L89vxnXw/8VqDqzTd6/EzqPhmumQFik/+sxjVL4+X8gpEksbya8zMLNe7jwqdl8s25PfTerQbMA0QioKo9/to6HZqxmXO+2/O3q/lVv3FJThUfhg7vg0/ugx8VwzZsQ4dHm9qZxapqAXPQ48ftXMWvY97SKCecH/1vIL6YuJfegXU14wTYMauDyjxRy/7sr+GhFJpOHJPGny/r4f5+Hnenw3o8hZx2c/Rs4+z4Ise8exgN9roCNXxGf/hQzrhzA05m9eHbmJr5Zt4d7xnRn8pAkwr3cx6SRkZMyKJ7GBg8erIsWLarvZgSV7zbmcO+05WTtP8q9Y7tz+1mdEX+OBxzMhm8fgfSXoWkbuPSf0PV8/x3fGF+KjsFLFzv7ikx+gw3Nh/G791eyYMteOsU14ZcXdGN8v/b+v0puoEQkXVUH+3zOAkTDsyn7II9/to5PVmbRKa4Jf5+UxoCOflyDkL8Tvn8OFr4IhYdhyK3OVFZL320C5fBeeOVSyF4L459E067j23XZPPbpWtZmHSCpVTQ3Dk/m6sFJtGhiqV0qYwGiESgqLuG7Tbm8On8bX67ZTVRYKHeO7sKPzupMVLgfEvAVHoENnzvrGtZ+5GwJ2etSJzDEp9b9+MbU1JF98NZNsGUm9L4Cxj5MSdN2fLYqi//N3cr3W/cSGRbCuT1ac3G/dpzbo7W3G2GdpuotQIjIOOApIBT4r6o+Wu75SOAVYBCQC0xS1a3uc78FbgWKgZ+p6meV1dUYA8Tu/UeZvzmXeZty+XLNbnIOFhDbJJwbh3fixjOSiW9ahxlExYWwexVsmwtb58CWWVBw0NnLod8kGHobtOzkv5MxpjaKi5wdCmc/ASGhTnK/gTdB276sytzP1IU7+HhFFjkHjxEeKgxIasmILnGkdYyld7vmJDSL9G+362moXgKEiIQC64ELgAxgIXCNqq4uU+ZOoJ+q3iEik4HLVXWSiPQC3gSGAu2BL4FuqlpcUX0NJUAUlyiHC4o4UlDM4YJiDh4rIufgMfYcOEb2gWNk5h9hw+6DbNhzkL2HCgBoFhXGWakJXNK/PaO7J1R+xVBcBIWHoMC9Hc2HA5lwIMu537sF9qyB3I1QUui8plVnZ01D78ug05kQat/CTJDZuxlm/j9YOQ2KC6B5IiSPhLZ9KW6VysoD0czcFcqs7cdYvOsIJeoEhbiYCFLiY+jQMpoOsdG0j42mZZMIYpuE0yLauTWNDCMiLISIsBDCQqTBBZT6ChAjgAdUdaz7+28BVPUvZcp85paZJyJhQBaQANxXtmzZchXVV9sAsTZrPz95fTEA6v5Hnbop/cuogqLOvZY+psdfowoDS5bzq6L/IijivlJOHNV97MTjpWVQ5cQ/Nz2pfOnjZY8ZIhAWIu4NwkOE0BBxntcTrz/5Z/c/xQVQdLTiP4aEQotEZ8P41j2gTR/oOAJaeJgG3Bh/OpTjbEy1/lMni/CBU9OEK0JJWDQFEkmBhlJYAkUqFJZAiTr/t5Uc/7+u7P+JDhH35j5+/NkyxU4JIUL50gCsC+3KX6N/UYcTdozulsDvxveq1WsrCxBefhXsAOwo83sGMKyiMqpaJCL5QJz7+Pxyrz3lU0pEbgNuA+jYsWOtGhkdHuqkm3DfN3GO696feDtLH8P9h3Hyc5B4pAMHc0r74p1/VCJlQoKc+Lg/8a/FLeceREQIDQkhLDSkzL0QFRFKdHgYUeGh7hRVOVFx+WNX9nNoBETElLk1dXZ2a9oGmreHJnHOZboxp6uYeBh4g3MDJ2Ds23riKvnYAaTwMKGFR4guOES0FjvjaaqUlBRzrKCIgqIiCouKKCwuoaiomKKSEkrU2XmxRPX4z8V64gvY8S+TZR446bEKvogXRSTRPa7uuyu2i42u8zF8Oa37ClT1eeB5cK4ganOMTnExPHPdQD+0pj9wtR+OY4zxm5h451YNIUC0ewukiwJcX014uaJkJ5BU5vdE9zGfZdwuphY4g9XVea0xxhgPeRkgFgKpIpIiIhHAZGB6uTLTgdINiicCX6vTuT8dmCwikSKSAqQC33vYVmOMMeV41sXkjincBXyGM831RVVdJSIPAYtUdTrwAvCqiGwE9uIEEdxybwGrgSLgJ5XNYDLGGON/tlDOGGMascpmMVlWK2OMMT5ZgDDGGOOTBQhjjDE+WYAwxhjjU4MZpBaRbGBbfbfDD+KBnPpuRAA1pvNtTOcKdr6ni06qmuDriQYTIBoKEVlU0YyChqgxnW9jOlew820IrIvJGGOMTxYgjDHG+GQBIvg8X98NCLDGdL6N6VzBzve0Z2MQxhhjfLIrCGOMMT5ZgDDGGOOTBYggISLjRGSdiGwUkfvquz3+JiJJIvKNiKwWkVUicrf7eCsR+UJENrj3Leu7rf4iIqEiskREZri/p4jIAvc9nuqmwW8QRCRWRKaJyFoRWSMiIxr4e/sL99/xShF5U0SiGuL7awEiCIhIKPAMcCHQC7hGRGq3wWzwKgLuUdVewHDgJ+453gd8paqpwFfu7w3F3cCaMr8/Bjypql2BfcCt9dIqbzwFfKqqPXC2V1xDA31vRaQD8DNgsKr2wdnOYDIN8P21ABEchgIbVXWzqhYAU4BL67lNfqWqmaq62P35AM4HSAec83zZLfYycFm9NNDPRCQRuBj4r/u7AOcC09wiDelcWwBn4ezvgqoWqGoeDfS9dYUB0e5OmE2ATBrg+2sBIjh0AHaU+T3DfaxBEpFkYACwAGijqpnuU1lAm/pql5/9Hfg1UOL+HgfkqWqR+3tDeo9TgGzgf26X2n9FJIYG+t6q6k7gcWA7TmDIB9JpgO+vBQgTUCLSFHgH+Lmq7i/7nLvd7Gk/71pExgN7VDW9vtsSIGHAQOBZVR0AHKJcd1JDeW8B3LGUS3ECY3sgBhhXr43yiAWI4LATSCrze6L7WIMiIuE4weF1VX3XfXi3iLRzn28H7Kmv9vnRSGCCiGzF6S48F6ePPtbtkoCG9R5nABmqusD9fRpOwGiI7y3A+cAWVc1W1ULgXZz3vMG9vxYggsNCINWdBRGBM+A1vZ7b5FduH/wLwBpV/VuZp6YDN7k/3wR8EOi2+Zuq/lZVE1U1Gee9/FpVrwO+ASa6xRrEuQKoahawQ0S6uw+dh7OffIN7b13bgeEi0sT9d116vg3u/bWV1EFCRC7C6bcOBV5U1Yfrt0X+JSJnArOBFZzol78fZxziLaAjTrr2q1V1b7000gMiMhr4laqOF5HOOFcUrYAlwPWqeqwem+c3IpKGMyAfAWwGfoDzBbRBvrci8iAwCWd23hLghzhjDg3q/bUAYYwxxifrYjLGGOOTBQhjjDE+WYAwxhjjkwUIY4wxPlmAMMYY45MFCGOMMT5ZgDDGGOPT/wd4JZEDxOTajgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data[\"DEATH_EVENT\"] == True][\"ejection_fraction\"].plot.kde(label=\"Death event\").set_title(\"Ejection Fraction Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"ejection_fraction\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "14466a8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:18.638472Z",
     "iopub.status.busy": "2022-03-28T14:16:18.634919Z",
     "iopub.status.idle": "2022-03-28T14:16:18.873015Z",
     "shell.execute_reply": "2022-03-28T14:16:18.872446Z",
     "shell.execute_reply.started": "2022-03-28T14:14:52.145108Z"
    },
    "papermill": {
     "duration": 0.484465,
     "end_time": "2022-03-28T14:16:18.873172",
     "exception": false,
     "start_time": "2022-03-28T14:16:18.388707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2686d0abd0>"
      ]
     },
     "execution_count": 30,
     "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": [
    "data[data[\"DEATH_EVENT\"] == True][\"platelets\"].plot.kde(label=\"Death event\").set_title(\"Platelets Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"platelets\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1cdfdf42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:19.051052Z",
     "iopub.status.busy": "2022-03-28T14:16:19.050259Z",
     "iopub.status.idle": "2022-03-28T14:16:19.315725Z",
     "shell.execute_reply": "2022-03-28T14:16:19.316253Z",
     "shell.execute_reply.started": "2022-03-28T14:14:52.522300Z"
    },
    "papermill": {
     "duration": 0.360032,
     "end_time": "2022-03-28T14:16:19.316433",
     "exception": false,
     "start_time": "2022-03-28T14:16:18.956401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2686c98390>"
      ]
     },
     "execution_count": 31,
     "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": [
    "data[data[\"DEATH_EVENT\"] == True][\"serum_sodium\"].plot.kde(label=\"Death event\").set_title(\"Serum Sodium Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"serum_sodium\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd11a3c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:19.516003Z",
     "iopub.status.busy": "2022-03-28T14:16:19.512532Z",
     "iopub.status.idle": "2022-03-28T14:16:19.772805Z",
     "shell.execute_reply": "2022-03-28T14:16:19.772224Z",
     "shell.execute_reply.started": "2022-03-28T14:14:52.787325Z"
    },
    "papermill": {
     "duration": 0.372744,
     "end_time": "2022-03-28T14:16:19.772964",
     "exception": false,
     "start_time": "2022-03-28T14:16:19.400220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2686bc7c50>"
      ]
     },
     "execution_count": 32,
     "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": [
    "data[data[\"DEATH_EVENT\"] == True][\"creatinine_phosphokinase\"].plot.kde(label=\"Death event\").set_title(\"Creatinine Phosphokinase Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"creatinine_phosphokinase\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "82894156",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:19.960513Z",
     "iopub.status.busy": "2022-03-28T14:16:19.959771Z",
     "iopub.status.idle": "2022-03-28T14:16:20.229337Z",
     "shell.execute_reply": "2022-03-28T14:16:20.228737Z",
     "shell.execute_reply.started": "2022-03-28T14:14:53.048368Z"
    },
    "papermill": {
     "duration": 0.366866,
     "end_time": "2022-03-28T14:16:20.229488",
     "exception": false,
     "start_time": "2022-03-28T14:16:19.862622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2686adeb50>"
      ]
     },
     "execution_count": 33,
     "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": [
    "data[data[\"DEATH_EVENT\"] == True][\"serum_creatinine\"].plot.kde(label=\"Death event\").set_title(\"Creatinine Creatinine Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"serum_creatinine\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "66c0d145",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:20.414951Z",
     "iopub.status.busy": "2022-03-28T14:16:20.414172Z",
     "iopub.status.idle": "2022-03-28T14:16:20.678561Z",
     "shell.execute_reply": "2022-03-28T14:16:20.679168Z",
     "shell.execute_reply.started": "2022-03-28T14:14:53.295730Z"
    },
    "papermill": {
     "duration": 0.361616,
     "end_time": "2022-03-28T14:16:20.679348",
     "exception": false,
     "start_time": "2022-03-28T14:16:20.317732",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2686aa4190>"
      ]
     },
     "execution_count": 34,
     "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": [
    "data[data[\"DEATH_EVENT\"] == True][\"time\"].plot.kde(label=\"Death event\").set_title(\"Time Creatinine Density Graph\")\n",
    "data[data[\"DEATH_EVENT\"] == False][\"time\"].plot.kde(label=\"Survived\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea55a23",
   "metadata": {
    "papermill": {
     "duration": 0.088152,
     "end_time": "2022-03-28T14:16:20.858255",
     "exception": false,
     "start_time": "2022-03-28T14:16:20.770103",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Classification using Random Forest model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f01afb",
   "metadata": {
    "papermill": {
     "duration": 0.091013,
     "end_time": "2022-03-28T14:16:21.038341",
     "exception": false,
     "start_time": "2022-03-28T14:16:20.947328",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Because the dataset is not large enough KFold would be a good choice 0 it would allow the model to train on enough examples and would provide food evaluation for the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "337c61c0",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:21.219433Z",
     "iopub.status.busy": "2022-03-28T14:16:21.218629Z",
     "iopub.status.idle": "2022-03-28T14:16:21.434206Z",
     "shell.execute_reply": "2022-03-28T14:16:21.432877Z",
     "shell.execute_reply.started": "2022-03-28T14:14:53.555253Z"
    },
    "papermill": {
     "duration": 0.307416,
     "end_time": "2022-03-28T14:16:21.434362",
     "exception": false,
     "start_time": "2022-03-28T14:16:21.126946",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "features = [x for x in data.columns if x!='DEATH_EVENT']\n",
    "X = pd.get_dummies(data[features])\n",
    "y = data.DEATH_EVENT\n",
    "\n",
    "rkf = RepeatedKFold(n_splits=5, random_state=12345)\n",
    "train_index, test_index = list(rkf.split(X, y))[0]\n",
    "train, test = X.iloc[train_index], X.iloc[test_index]\n",
    "y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc173681",
   "metadata": {
    "papermill": {
     "duration": 0.089091,
     "end_time": "2022-03-28T14:16:21.613075",
     "exception": false,
     "start_time": "2022-03-28T14:16:21.523984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "I will use the Random Forest model, adjusting its parameters using GridSearchCV: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f3df7066",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:21.806279Z",
     "iopub.status.busy": "2022-03-28T14:16:21.805196Z",
     "iopub.status.idle": "2022-03-28T14:16:21.807851Z",
     "shell.execute_reply": "2022-03-28T14:16:21.808356Z",
     "shell.execute_reply.started": "2022-03-28T14:14:53.623078Z"
    },
    "papermill": {
     "duration": 0.107099,
     "end_time": "2022-03-28T14:16:21.808553",
     "exception": false,
     "start_time": "2022-03-28T14:16:21.701454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, roc_curve\n",
    "\n",
    "def find_best_threshold(thresholds, fpr, tpr):\n",
    "    \"\"\"\n",
    "    find the best threshold from the roc curve.\n",
    "    \"\"\"\n",
    "    fpr_tpr = pd.DataFrame({'thresholds': thresholds, 'fpr': fpr, 'tpr': tpr})\n",
    "    fpr_tpr['dist'] = (fpr_tpr['fpr'])**2 + (fpr_tpr['tpr']-1)**2\n",
    "    return fpr_tpr.loc[fpr_tpr.dist.argmin(), 'thresholds']\n",
    "\n",
    "\n",
    "def get_model_results(model, train, test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Get the results by printing ROC curve and other metrics.\n",
    "    \"\"\"\n",
    "    probabilities = model.predict_proba(test)[:,1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, probabilities)\n",
    "    threshold = find_best_threshold(thresholds, fpr, tpr)\n",
    "    predictions = probabilities > threshold\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='test')\n",
    "    probabilities = model.predict_proba(train)[:,1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_train, probabilities)\n",
    "    plt.plot(fpr, tpr, label='train')\n",
    "    plt.plot([0, 1], [0, 1], 'r--', label='random guess')\n",
    "    print(classification_report(y_test, predictions))\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "94aa7752",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T14:16:22.015162Z",
     "iopub.status.busy": "2022-03-28T14:16:22.013574Z",
     "iopub.status.idle": "2022-03-28T14:16:22.685357Z",
     "shell.execute_reply": "2022-03-28T14:16:22.685924Z",
     "shell.execute_reply.started": "2022-03-28T14:14:53.633663Z"
    },
    "papermill": {
     "duration": 0.779778,
     "end_time": "2022-03-28T14:16:22.686125",
     "exception": false,
     "start_time": "2022-03-28T14:16:21.906347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.94      0.81      0.87        37\n",
      "        True       0.74      0.91      0.82        22\n",
      "\n",
      "    accuracy                           0.85        59\n",
      "   macro avg       0.84      0.86      0.84        59\n",
      "weighted avg       0.86      0.85      0.85        59\n",
      "\n"
     ]
    },
    {
     "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": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "pipeline = make_pipeline(StandardScaler(), RandomForestClassifier(n_estimators=150, min_samples_split=10, min_samples_leaf=5, max_depth=10, bootstrap=False))\n",
    "pipeline.fit(train, y_train)\n",
    "get_model_results(pipeline, train, test, y_train, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 28.557929,
   "end_time": "2022-03-28T14:16:23.687487",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-03-28T14:15:55.129558",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}