WiDS_Starter_Notebook_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "celltoolbar": "Slideshow",
    "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": "3.6.7"
    },
    "colab": {
      "name": "WiDS_Starter_Notebook_1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anarosner/testing/blob/master/WiDS_Starter_Notebook_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtNPjJP7yipK",
        "colab_type": "text"
      },
      "source": [
        "# WiDS CAMBRIGE DATATHON WORKSHOP\n",
        "## Data Exploration\n",
        "### February 15th, 2020\n",
        "### Instructors: Weiwei Pan (Harvard IACS), Karren Dai Yang (MIT IDSS)\n",
        "---\n",
        "\n",
        "<img src=\"https://github.com/onefishy/wids_datathon_2020/raw/master/workshop_materials/images/inst_logos.png\" alt=\"Harvard IACS\" style=\"height: 80px;\" align=\"left\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "qc0tjJ14yipN",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "\n",
        "### Task: Predicting Patient Survival\n",
        " \n",
        "The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival.\n",
        "\n",
        "### Part I. Data exploration and preprocessing\n",
        "* Exploration: understand the data in order to use it optimally\n",
        "* Preprocessing: cleaning/engineer data for model development\n",
        "\n",
        "\n",
        "### Part II. ML and classification algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WhIA-VsKyipO",
        "colab_type": "text"
      },
      "source": [
        "### Look at the data\n",
        "* Use [Pandas](https://pandas.pydata.org/pandas-docs/stable/) package to read and display data\n",
        "* Identify the columns (e.g. targets, irrelevant features, useful features) based on prior knowledge\n",
        "* Identify numerical and categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6cV044AyipP",
        "colab_type": "code",
        "outputId": "1fc4ebbf-84b7-444f-9925-67006331bb8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/onefishy/wids_datathon_2020/master/workshop_materials/data/training_v2.csv') # read in the data as a DataFrame\n",
        "data.head(3) # show the first 3 rows of the dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>encounter_id</th>\n",
              "      <th>patient_id</th>\n",
              "      <th>hospital_id</th>\n",
              "      <th>hospital_death</th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>elective_surgery</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>hospital_admit_source</th>\n",
              "      <th>icu_admit_source</th>\n",
              "      <th>icu_id</th>\n",
              "      <th>icu_stay_type</th>\n",
              "      <th>icu_type</th>\n",
              "      <th>pre_icu_los_days</th>\n",
              "      <th>readmission_status</th>\n",
              "      <th>weight</th>\n",
              "      <th>albumin_apache</th>\n",
              "      <th>apache_2_diagnosis</th>\n",
              "      <th>apache_3j_diagnosis</th>\n",
              "      <th>apache_post_operative</th>\n",
              "      <th>arf_apache</th>\n",
              "      <th>bilirubin_apache</th>\n",
              "      <th>bun_apache</th>\n",
              "      <th>creatinine_apache</th>\n",
              "      <th>fio2_apache</th>\n",
              "      <th>gcs_eyes_apache</th>\n",
              "      <th>gcs_motor_apache</th>\n",
              "      <th>gcs_unable_apache</th>\n",
              "      <th>gcs_verbal_apache</th>\n",
              "      <th>glucose_apache</th>\n",
              "      <th>heart_rate_apache</th>\n",
              "      <th>hematocrit_apache</th>\n",
              "      <th>intubated_apache</th>\n",
              "      <th>map_apache</th>\n",
              "      <th>paco2_apache</th>\n",
              "      <th>paco2_for_ph_apache</th>\n",
              "      <th>pao2_apache</th>\n",
              "      <th>ph_apache</th>\n",
              "      <th>...</th>\n",
              "      <th>h1_inr_max</th>\n",
              "      <th>h1_inr_min</th>\n",
              "      <th>h1_lactate_max</th>\n",
              "      <th>h1_lactate_min</th>\n",
              "      <th>h1_platelets_max</th>\n",
              "      <th>h1_platelets_min</th>\n",
              "      <th>h1_potassium_max</th>\n",
              "      <th>h1_potassium_min</th>\n",
              "      <th>h1_sodium_max</th>\n",
              "      <th>h1_sodium_min</th>\n",
              "      <th>h1_wbc_max</th>\n",
              "      <th>h1_wbc_min</th>\n",
              "      <th>d1_arterial_pco2_max</th>\n",
              "      <th>d1_arterial_pco2_min</th>\n",
              "      <th>d1_arterial_ph_max</th>\n",
              "      <th>d1_arterial_ph_min</th>\n",
              "      <th>d1_arterial_po2_max</th>\n",
              "      <th>d1_arterial_po2_min</th>\n",
              "      <th>d1_pao2fio2ratio_max</th>\n",
              "      <th>d1_pao2fio2ratio_min</th>\n",
              "      <th>h1_arterial_pco2_max</th>\n",
              "      <th>h1_arterial_pco2_min</th>\n",
              "      <th>h1_arterial_ph_max</th>\n",
              "      <th>h1_arterial_ph_min</th>\n",
              "      <th>h1_arterial_po2_max</th>\n",
              "      <th>h1_arterial_po2_min</th>\n",
              "      <th>h1_pao2fio2ratio_max</th>\n",
              "      <th>h1_pao2fio2ratio_min</th>\n",
              "      <th>apache_4a_hospital_death_prob</th>\n",
              "      <th>apache_4a_icu_death_prob</th>\n",
              "      <th>aids</th>\n",
              "      <th>cirrhosis</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>hepatic_failure</th>\n",
              "      <th>immunosuppression</th>\n",
              "      <th>leukemia</th>\n",
              "      <th>lymphoma</th>\n",
              "      <th>solid_tumor_with_metastasis</th>\n",
              "      <th>apache_3j_bodysystem</th>\n",
              "      <th>apache_2_bodysystem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66154</td>\n",
              "      <td>25312</td>\n",
              "      <td>118</td>\n",
              "      <td>0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>M</td>\n",
              "      <td>180.3</td>\n",
              "      <td>Floor</td>\n",
              "      <td>Floor</td>\n",
              "      <td>92</td>\n",
              "      <td>admit</td>\n",
              "      <td>CTICU</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>0</td>\n",
              "      <td>73.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>113.0</td>\n",
              "      <td>502.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Sepsis</td>\n",
              "      <td>Cardiovascular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>114252</td>\n",
              "      <td>59342</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>27.42</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>F</td>\n",
              "      <td>160.0</td>\n",
              "      <td>Floor</td>\n",
              "      <td>Floor</td>\n",
              "      <td>90</td>\n",
              "      <td>admit</td>\n",
              "      <td>Med-Surg ICU</td>\n",
              "      <td>0.927778</td>\n",
              "      <td>0</td>\n",
              "      <td>70.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>108.0</td>\n",
              "      <td>203.01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>36.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>...</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>557.0</td>\n",
              "      <td>557.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.2</td>\n",
              "      <td>145.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>12.7</td>\n",
              "      <td>12.7</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>7.45</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>54.8</td>\n",
              "      <td>51.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>7.45</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Respiratory</td>\n",
              "      <td>Respiratory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>119783</td>\n",
              "      <td>50777</td>\n",
              "      <td>118</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>31.95</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>F</td>\n",
              "      <td>172.7</td>\n",
              "      <td>Emergency Department</td>\n",
              "      <td>Accident &amp; Emergency</td>\n",
              "      <td>93</td>\n",
              "      <td>admit</td>\n",
              "      <td>Med-Surg ICU</td>\n",
              "      <td>0.000694</td>\n",
              "      <td>0</td>\n",
              "      <td>95.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>122.0</td>\n",
              "      <td>703.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Metabolic</td>\n",
              "      <td>Metabolic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 186 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   encounter_id  patient_id  ...  apache_3j_bodysystem  apache_2_bodysystem\n",
              "0         66154       25312  ...                Sepsis       Cardiovascular\n",
              "1        114252       59342  ...           Respiratory          Respiratory\n",
              "2        119783       50777  ...             Metabolic            Metabolic\n",
              "\n",
              "[3 rows x 186 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qEL3GgPyipU",
        "colab_type": "text"
      },
      "source": [
        "### Look out for features that may need to be reformatted\n",
        "* Diagnosis is numerical but actually represents a categorical code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "RTpG1Ai2yipU",
        "colab_type": "code",
        "outputId": "2315a35f-d4eb-42ba-a155-e5602e4d16d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "subset = data[['apache_2_diagnosis', 'apache_3j_diagnosis']] # get subset of data\n",
        "print(subset.dtypes) # get data types\n",
        "subset.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "apache_2_diagnosis     float64\n",
            "apache_3j_diagnosis    float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apache_2_diagnosis</th>\n",
              "      <th>apache_3j_diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>113.0</td>\n",
              "      <td>502.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>108.0</td>\n",
              "      <td>203.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>122.0</td>\n",
              "      <td>703.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>203.0</td>\n",
              "      <td>1206.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>119.0</td>\n",
              "      <td>601.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   apache_2_diagnosis  apache_3j_diagnosis\n",
              "0               113.0               502.01\n",
              "1               108.0               203.01\n",
              "2               122.0               703.03\n",
              "3               203.0              1206.03\n",
              "4               119.0               601.01"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Qr4JDpYyipX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# recast types\n",
        "data = data.astype({'apache_2_diagnosis': 'object',\n",
        "                    'apache_3j_diagnosis': 'object'\n",
        "                           })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y1DRh5R2yipa",
        "colab_type": "text"
      },
      "source": [
        "### Compute basic statistics of dataset and features\n",
        "* Identify sources of missing data, ranges of feature values, outliers, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "lPKvitmMyipb",
        "colab_type": "code",
        "outputId": "f2407866-df28-4d6e-c7b4-3d8620359545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "subset = data[['age', 'bmi', 'ethnicity', 'h1_bilirubin_max', 'h1_bilirubin_min']] # get subset of data\n",
        "subset.describe(include = 'all') # show summary statistics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>h1_bilirubin_max</th>\n",
              "      <th>h1_bilirubin_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>87485.000000</td>\n",
              "      <td>88284.000000</td>\n",
              "      <td>90318</td>\n",
              "      <td>7094.000000</td>\n",
              "      <td>7094.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70684</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>62.309516</td>\n",
              "      <td>29.185818</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>1.097752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.775119</td>\n",
              "      <td>8.275142</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.033590</td>\n",
              "      <td>2.032605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.844926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>23.641975</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>65.000000</td>\n",
              "      <td>27.654655</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>32.930206</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>1.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>67.814990</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.400000</td>\n",
              "      <td>40.400000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 age           bmi  ... h1_bilirubin_max  h1_bilirubin_min\n",
              "count   87485.000000  88284.000000  ...      7094.000000       7094.000000\n",
              "unique           NaN           NaN  ...              NaN               NaN\n",
              "top              NaN           NaN  ...              NaN               NaN\n",
              "freq             NaN           NaN  ...              NaN               NaN\n",
              "mean       62.309516     29.185818  ...         1.098612          1.097752\n",
              "std        16.775119      8.275142  ...         2.033590          2.032605\n",
              "min        16.000000     14.844926  ...         0.200000          0.200000\n",
              "25%        52.000000     23.641975  ...         0.400000          0.400000\n",
              "50%        65.000000     27.654655  ...         0.600000          0.600000\n",
              "75%        75.000000     32.930206  ...         1.100000          1.100000\n",
              "max        89.000000     67.814990  ...        40.400000         40.400000\n",
              "\n",
              "[11 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdjyMy2syipe",
        "colab_type": "text"
      },
      "source": [
        "\n",
        " \n",
        "### Visualizing the distributions of different features\n",
        "* Look for biases in the data that may affect model training or deployment\n",
        "* Use [matplotlib](https://matplotlib.org) package to plot data\n",
        "* Example: Make bar plot of distribution of patient survival\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1PfO894yipf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import scipy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QXJRr-5syipi",
        "colab_type": "code",
        "outputId": "bc3b80e0-34e4-4d36-d81d-99d396a4aad2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "# make bar plot of counts\n",
        "data['hospital_death'].value_counts().plot.bar() \n",
        "plt.ylabel('counts')\n",
        "plt.xlabel('hospital_death')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEECAYAAADpigmnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffRzJbyNyipk",
        "colab_type": "text"
      },
      "source": [
        "* Example: Make bar plot of distribution of ethnicity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQ2MeuSGyipl",
        "colab_type": "code",
        "outputId": "7e460a4a-f92a-46fe-d955-ac55dc6ea8b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "data['ethnicity'].value_counts().plot.bar() \n",
        "plt.ylabel('counts')\n",
        "plt.xlabel('ethnicity')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAFTCAYAAAD4ATHoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdAu6ITLyipo",
        "colab_type": "text"
      },
      "source": [
        "* Example: Make histogram of distribution of age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRSLoMVGyipp",
        "colab_type": "code",
        "outputId": "6d714051-3b7f-4002-8d7b-ceaea15945dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "data['age'].hist()\n",
        "plt.ylabel('frequency')\n",
        "plt.xlabel('age')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QrcSrYGGyips",
        "colab_type": "text"
      },
      "source": [
        "* Example: make boxplot of age vs. survival\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "wJAkAXNmyipt",
        "colab_type": "code",
        "outputId": "4fb39a04-ec7f-407b-bcb7-bd5a34e5a784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "data.boxplot(column=['age'], by=['hospital_death'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEdCAYAAADtk8dMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLAMOezOzDDjszcwy8P8Bmz310bPYj9UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysmbrAUMyipw",
        "colab_type": "text"
      },
      "source": [
        "* Example: make scatterplot of diastolic vs. systolic BP\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "aXp1Jub1yipx",
        "colab_type": "code",
        "outputId": "5674c902-8aab-44fb-90f3-469883a25749",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "subset = data[['h1_diasbp_max', 'h1_sysbp_max']].loc[0:1000, :].dropna() \n",
        "#print(np.corrcoef(subset['h1_diasbp_max'], subset['h1_sysbp_max'])) # compute correlation coefficient\n",
        "\n",
        "data.loc[0:1000, :].plot.scatter(x='h1_diasbp_max', y='h1_sysbp_max')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEHCAYAAACqbOGYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBueccxn+H97W+QVDjImoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "iAxOvnqkyip0",
        "colab_type": "text"
      },
      "source": [
        "### Part I. Data exploration and preprocessing\n",
        "* Exploration: understand the data in order to use it optimally\n",
        "\n",
        "\n",
        "* Preprocessing: cleaning/engineer data for model development\n",
        "    * Selecting features that are potentially useful for classification\n",
        "    * Encoding categorical features to numerical values\n",
        "    * Normalizing continuous features\n",
        "    * Handling missing values (so many missing values)\n",
        "\n",
        "### Part II. ML and classification algorithms"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHG2H8iMyip0",
        "colab_type": "text"
      },
      "source": [
        "### Select features that are potentially useful for classification based on prior knowledge"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJAUDPtWyip1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_you_want = ['hospital_death', 'age', 'bmi', 'elective_surgery', 'ethnicity', 'gender', 'height', 'pre_icu_los_days', 'readmission_status', 'weight', 'albumin_apache', 'apache_2_diagnosis', 'apache_post_operative', 'arf_apache', 'bilirubin_apache', 'bun_apache', 'creatinine_apache', 'fio2_apache', 'gcs_eyes_apache', 'gcs_motor_apache', 'gcs_unable_apache', 'gcs_verbal_apache', 'glucose_apache', 'heart_rate_apache', 'hematocrit_apache', 'intubated_apache', 'map_apache', 'paco2_apache', 'paco2_for_ph_apache', 'pao2_apache', 'ph_apache', 'resprate_apache', 'sodium_apache', 'temp_apache', 'urineoutput_apache', 'ventilated_apache', 'wbc_apache', 'd1_diasbp_invasive_max', 'd1_diasbp_invasive_min', 'd1_diasbp_max', 'd1_diasbp_min', 'd1_diasbp_noninvasive_max', 'd1_diasbp_noninvasive_min', 'd1_heartrate_max', 'd1_heartrate_min', 'd1_mbp_invasive_max', 'd1_mbp_invasive_min', 'd1_mbp_max', 'd1_mbp_min', 'd1_mbp_noninvasive_max', 'd1_mbp_noninvasive_min', 'd1_resprate_max', 'd1_resprate_min', 'd1_spo2_max', 'd1_spo2_min', 'd1_sysbp_invasive_max', 'd1_sysbp_invasive_min', 'd1_sysbp_max', 'd1_sysbp_min', 'd1_sysbp_noninvasive_max', 'd1_sysbp_noninvasive_min', 'd1_temp_max', 'd1_temp_min', 'h1_diasbp_invasive_max', 'h1_diasbp_invasive_min', 'h1_diasbp_max', 'h1_diasbp_min', 'h1_diasbp_noninvasive_max', 'h1_diasbp_noninvasive_min', 'h1_heartrate_max', 'h1_heartrate_min', 'h1_mbp_invasive_max', 'h1_mbp_invasive_min', 'h1_mbp_max', 'h1_mbp_min', 'h1_mbp_noninvasive_max', 'h1_mbp_noninvasive_min', 'h1_resprate_max', 'h1_resprate_min', 'h1_spo2_max', 'h1_spo2_min', 'h1_sysbp_invasive_max', 'h1_sysbp_invasive_min', 'h1_sysbp_max', 'h1_sysbp_min', 'h1_sysbp_noninvasive_max', 'h1_sysbp_noninvasive_min', 'h1_temp_max', 'h1_temp_min', 'd1_albumin_max', 'd1_albumin_min', 'd1_bilirubin_max', 'd1_bilirubin_min', 'd1_bun_max', 'd1_bun_min', 'd1_calcium_max', 'd1_calcium_min', 'd1_creatinine_max', 'd1_creatinine_min', 'd1_glucose_max', 'd1_glucose_min', 'd1_hco3_max', 'd1_hco3_min', 'd1_hemaglobin_max', 'd1_hemaglobin_min', 'd1_hematocrit_max', 'd1_hematocrit_min', 'd1_inr_max', 'd1_inr_min', 'd1_lactate_max', 'd1_lactate_min', 'd1_platelets_max', 'd1_platelets_min', 'd1_potassium_max', 'd1_potassium_min', 'd1_sodium_max', 'd1_sodium_min', 'd1_wbc_max', 'd1_wbc_min', 'h1_albumin_max', 'h1_albumin_min', 'h1_bilirubin_max', 'h1_bilirubin_min', 'h1_bun_max', 'h1_bun_min', 'h1_calcium_max', 'h1_calcium_min', 'h1_creatinine_max', 'h1_creatinine_min', 'h1_glucose_max', 'h1_glucose_min', 'h1_hco3_max', 'h1_hco3_min', 'h1_hemaglobin_max', 'h1_hemaglobin_min', 'h1_hematocrit_max', 'h1_hematocrit_min', 'h1_inr_max', 'h1_inr_min', 'h1_lactate_max', 'h1_lactate_min', 'h1_platelets_max', 'h1_platelets_min', 'h1_potassium_max', 'h1_potassium_min', 'h1_sodium_max', 'h1_sodium_min', 'h1_wbc_max', 'h1_wbc_min', 'd1_arterial_pco2_max', 'd1_arterial_pco2_min', 'd1_arterial_ph_max', 'd1_arterial_ph_min', 'd1_arterial_po2_max', 'd1_arterial_po2_min', 'd1_pao2fio2ratio_max', 'd1_pao2fio2ratio_min', 'h1_arterial_pco2_max', 'h1_arterial_pco2_min', 'h1_arterial_ph_max', 'h1_arterial_ph_min', 'h1_arterial_po2_max', 'h1_arterial_po2_min', 'h1_pao2fio2ratio_max', 'h1_pao2fio2ratio_min', 'apache_4a_hospital_death_prob', 'apache_4a_icu_death_prob', 'aids', 'cirrhosis', 'diabetes_mellitus', 'hepatic_failure', 'immunosuppression', 'leukemia', 'lymphoma', 'solid_tumor_with_metastasis', 'apache_3j_bodysystem', 'apache_2_bodysystem']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpoyhEkqyip5",
        "colab_type": "code",
        "outputId": "bef66d4f-687b-45c6-bb24-23ea6ec9d757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "# select desired features\n",
        "features = data[features_you_want] # select these columns of data\n",
        "features.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hospital_death</th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>elective_surgery</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>gender</th>\n",
              "      <th>height</th>\n",
              "      <th>pre_icu_los_days</th>\n",
              "      <th>readmission_status</th>\n",
              "      <th>weight</th>\n",
              "      <th>albumin_apache</th>\n",
              "      <th>apache_2_diagnosis</th>\n",
              "      <th>apache_post_operative</th>\n",
              "      <th>arf_apache</th>\n",
              "      <th>bilirubin_apache</th>\n",
              "      <th>bun_apache</th>\n",
              "      <th>creatinine_apache</th>\n",
              "      <th>fio2_apache</th>\n",
              "      <th>gcs_eyes_apache</th>\n",
              "      <th>gcs_motor_apache</th>\n",
              "      <th>gcs_unable_apache</th>\n",
              "      <th>gcs_verbal_apache</th>\n",
              "      <th>glucose_apache</th>\n",
              "      <th>heart_rate_apache</th>\n",
              "      <th>hematocrit_apache</th>\n",
              "      <th>intubated_apache</th>\n",
              "      <th>map_apache</th>\n",
              "      <th>paco2_apache</th>\n",
              "      <th>paco2_for_ph_apache</th>\n",
              "      <th>pao2_apache</th>\n",
              "      <th>ph_apache</th>\n",
              "      <th>resprate_apache</th>\n",
              "      <th>sodium_apache</th>\n",
              "      <th>temp_apache</th>\n",
              "      <th>urineoutput_apache</th>\n",
              "      <th>ventilated_apache</th>\n",
              "      <th>wbc_apache</th>\n",
              "      <th>d1_diasbp_invasive_max</th>\n",
              "      <th>d1_diasbp_invasive_min</th>\n",
              "      <th>d1_diasbp_max</th>\n",
              "      <th>...</th>\n",
              "      <th>h1_inr_max</th>\n",
              "      <th>h1_inr_min</th>\n",
              "      <th>h1_lactate_max</th>\n",
              "      <th>h1_lactate_min</th>\n",
              "      <th>h1_platelets_max</th>\n",
              "      <th>h1_platelets_min</th>\n",
              "      <th>h1_potassium_max</th>\n",
              "      <th>h1_potassium_min</th>\n",
              "      <th>h1_sodium_max</th>\n",
              "      <th>h1_sodium_min</th>\n",
              "      <th>h1_wbc_max</th>\n",
              "      <th>h1_wbc_min</th>\n",
              "      <th>d1_arterial_pco2_max</th>\n",
              "      <th>d1_arterial_pco2_min</th>\n",
              "      <th>d1_arterial_ph_max</th>\n",
              "      <th>d1_arterial_ph_min</th>\n",
              "      <th>d1_arterial_po2_max</th>\n",
              "      <th>d1_arterial_po2_min</th>\n",
              "      <th>d1_pao2fio2ratio_max</th>\n",
              "      <th>d1_pao2fio2ratio_min</th>\n",
              "      <th>h1_arterial_pco2_max</th>\n",
              "      <th>h1_arterial_pco2_min</th>\n",
              "      <th>h1_arterial_ph_max</th>\n",
              "      <th>h1_arterial_ph_min</th>\n",
              "      <th>h1_arterial_po2_max</th>\n",
              "      <th>h1_arterial_po2_min</th>\n",
              "      <th>h1_pao2fio2ratio_max</th>\n",
              "      <th>h1_pao2fio2ratio_min</th>\n",
              "      <th>apache_4a_hospital_death_prob</th>\n",
              "      <th>apache_4a_icu_death_prob</th>\n",
              "      <th>aids</th>\n",
              "      <th>cirrhosis</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>hepatic_failure</th>\n",
              "      <th>immunosuppression</th>\n",
              "      <th>leukemia</th>\n",
              "      <th>lymphoma</th>\n",
              "      <th>solid_tumor_with_metastasis</th>\n",
              "      <th>apache_3j_bodysystem</th>\n",
              "      <th>apache_2_bodysystem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>22.73</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>M</td>\n",
              "      <td>180.3</td>\n",
              "      <td>0.541667</td>\n",
              "      <td>0</td>\n",
              "      <td>73.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>113</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2.51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>39.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.1</td>\n",
              "      <td>46.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Sepsis</td>\n",
              "      <td>Cardiovascular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>27.42</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>F</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.927778</td>\n",
              "      <td>0</td>\n",
              "      <td>70.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>108</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>36.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>33.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>35.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.5</td>\n",
              "      <td>557.0</td>\n",
              "      <td>557.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.2</td>\n",
              "      <td>145.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>12.7</td>\n",
              "      <td>12.7</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>7.45</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>54.8</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>7.45</td>\n",
              "      <td>7.45</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Respiratory</td>\n",
              "      <td>Respiratory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>31.95</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>F</td>\n",
              "      <td>172.7</td>\n",
              "      <td>0.000694</td>\n",
              "      <td>0</td>\n",
              "      <td>95.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>102.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Metabolic</td>\n",
              "      <td>Metabolic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>22.64</td>\n",
              "      <td>1</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>F</td>\n",
              "      <td>165.1</td>\n",
              "      <td>0.000694</td>\n",
              "      <td>0</td>\n",
              "      <td>61.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>203</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>7.39</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.8</td>\n",
              "      <td>8.8</td>\n",
              "      <td>37.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>7.34</td>\n",
              "      <td>337.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>342.5</td>\n",
              "      <td>236.666667</td>\n",
              "      <td>36.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>7.37</td>\n",
              "      <td>7.34</td>\n",
              "      <td>337.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Cardiovascular</td>\n",
              "      <td>Cardiovascular</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>M</td>\n",
              "      <td>188.0</td>\n",
              "      <td>0.073611</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>119</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>99.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Trauma</td>\n",
              "      <td>Trauma</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 177 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   hospital_death   age  ...  apache_3j_bodysystem  apache_2_bodysystem\n",
              "0               0  68.0  ...                Sepsis       Cardiovascular\n",
              "1               0  77.0  ...           Respiratory          Respiratory\n",
              "2               0  25.0  ...             Metabolic            Metabolic\n",
              "3               0  81.0  ...        Cardiovascular       Cardiovascular\n",
              "4               0  19.0  ...                Trauma               Trauma\n",
              "\n",
              "[5 rows x 177 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71QJtDrbyip8",
        "colab_type": "text"
      },
      "source": [
        "### Handling missing data\n",
        "(Slide credit: based on slides by Usha Rengaraju)\n",
        "\n",
        "Missing values are particularly common when working with patient data, in particular due to incomplete measurements. Handling the missing values is one of the greatest challenges faced by analysts, because the choices made can greatly affect the performance of machine learning algorithms. Here we explore some common methods for handling missing data.\n",
        "\n",
        "* Drop missing values\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmZCzr4Yyip9",
        "colab_type": "code",
        "outputId": "1dc0f13e-8163-4be8-f3ef-97b88a0df39a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(\"Original shape before dropna()\" , features.shape)\n",
        "drop = features.dropna()\n",
        "print(\"Shape after dropna()\", drop.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original shape before dropna() (91713, 177)\n",
            "Shape after dropna() (27, 177)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyWHOQDeyiqA",
        "colab_type": "text"
      },
      "source": [
        "* Drop the values above a certain threshold"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ahL4kuPyiqD",
        "colab_type": "code",
        "outputId": "251d8573-fe6d-4e4e-dd2b-8df420a6c856",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Drop columns based on threshold limit\n",
        "threshold = len(features) * 0.90\n",
        "features = features.dropna(axis=1, thresh=threshold)\n",
        "\n",
        "# New number of columns in the dataset\n",
        "features.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(91713, 75)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQDxHhUNyiqG",
        "colab_type": "text"
      },
      "source": [
        "* Impute (complete) missing values using  e.g. [scikit-learn](https://scikit-learn.org/stable/modules/impute.html) or [fancyimpute](https://pypi.org/project/fancyimpute/)\n",
        "    * Mean/median/mode imputation\n",
        "    * K-nearest neighbors imputation\n",
        "\n",
        "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/220px-KnnClassification.svg.png\" style=\"height: 300px;\" align=\"center\"/>\n",
        "\n",
        "* Discussion: What are some pros and cons of these different approaches for handling missing data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO33S4ZeyiqG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import impute\n",
        "\n",
        "# impute numerical values using mean\n",
        "nume_features = features.select_dtypes(include=['float64'])\n",
        "category_names = list(nume_features.columns)\n",
        "imp = impute.SimpleImputer(strategy='median') # specify the imputer\n",
        "nume_features = imp.fit_transform(nume_features)\n",
        "nume_features = pd.DataFrame(nume_features, columns=category_names)\n",
        "#nume_features.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZM9BACeyiqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# impute categorical values using most frequent\n",
        "cat_features = features.select_dtypes(include=['int64','object'])\n",
        "category_names = list(cat_features.columns)\n",
        "imp = impute.SimpleImputer(strategy='most_frequent') # specify the imputer\n",
        "cat_features = imp.fit_transform(cat_features)\n",
        "cat_features = pd.DataFrame(cat_features, columns=category_names)\n",
        "#cat_features.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXUv6CAjyiqM",
        "colab_type": "text"
      },
      "source": [
        "### Encoding categorical features to numerical features\n",
        "\n",
        "* Many quantitative models only accept numerical inputs, so it is typical to convert categorical features to numerical features\n",
        "* Option 1: Represent categorical features as ordinal values (0 to num_categories-1)\n",
        "* Option 2: Represent categorical features as binary vectors\n",
        "\n",
        "<img src=\"images/onehot.png\" style=\"height: 200px;\" align=\"center\"/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "CYw4rq2lyiqN",
        "colab_type": "code",
        "outputId": "a86b878e-b678-44c0-d4d3-23588ee34f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "# get one-hot encodings of categorical variables\n",
        "\n",
        "one_hot_encoded = features.select_dtypes(include=['object', 'int64'])\n",
        "one_hot_encoded = pd.get_dummies(one_hot_encoded)\n",
        "one_hot_encoded.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hospital_death</th>\n",
              "      <th>elective_surgery</th>\n",
              "      <th>readmission_status</th>\n",
              "      <th>apache_post_operative</th>\n",
              "      <th>ethnicity_African American</th>\n",
              "      <th>ethnicity_Asian</th>\n",
              "      <th>ethnicity_Caucasian</th>\n",
              "      <th>ethnicity_Hispanic</th>\n",
              "      <th>ethnicity_Native American</th>\n",
              "      <th>ethnicity_Other/Unknown</th>\n",
              "      <th>gender_F</th>\n",
              "      <th>gender_M</th>\n",
              "      <th>apache_2_diagnosis_101.0</th>\n",
              "      <th>apache_2_diagnosis_102.0</th>\n",
              "      <th>apache_2_diagnosis_103.0</th>\n",
              "      <th>apache_2_diagnosis_104.0</th>\n",
              "      <th>apache_2_diagnosis_105.0</th>\n",
              "      <th>apache_2_diagnosis_106.0</th>\n",
              "      <th>apache_2_diagnosis_107.0</th>\n",
              "      <th>apache_2_diagnosis_108.0</th>\n",
              "      <th>apache_2_diagnosis_109.0</th>\n",
              "      <th>apache_2_diagnosis_110.0</th>\n",
              "      <th>apache_2_diagnosis_112.0</th>\n",
              "      <th>apache_2_diagnosis_113.0</th>\n",
              "      <th>apache_2_diagnosis_114.0</th>\n",
              "      <th>apache_2_diagnosis_115.0</th>\n",
              "      <th>apache_2_diagnosis_116.0</th>\n",
              "      <th>apache_2_diagnosis_117.0</th>\n",
              "      <th>apache_2_diagnosis_118.0</th>\n",
              "      <th>apache_2_diagnosis_119.0</th>\n",
              "      <th>apache_2_diagnosis_120.0</th>\n",
              "      <th>apache_2_diagnosis_121.0</th>\n",
              "      <th>apache_2_diagnosis_122.0</th>\n",
              "      <th>apache_2_diagnosis_123.0</th>\n",
              "      <th>apache_2_diagnosis_124.0</th>\n",
              "      <th>apache_2_diagnosis_202.0</th>\n",
              "      <th>apache_2_diagnosis_203.0</th>\n",
              "      <th>apache_2_diagnosis_207.0</th>\n",
              "      <th>apache_2_diagnosis_208.0</th>\n",
              "      <th>apache_2_diagnosis_209.0</th>\n",
              "      <th>apache_2_diagnosis_212.0</th>\n",
              "      <th>apache_2_diagnosis_213.0</th>\n",
              "      <th>apache_2_diagnosis_214.0</th>\n",
              "      <th>apache_2_diagnosis_215.0</th>\n",
              "      <th>apache_2_diagnosis_216.0</th>\n",
              "      <th>apache_2_diagnosis_217.0</th>\n",
              "      <th>apache_2_diagnosis_218.0</th>\n",
              "      <th>apache_2_diagnosis_219.0</th>\n",
              "      <th>apache_2_diagnosis_301.0</th>\n",
              "      <th>apache_2_diagnosis_302.0</th>\n",
              "      <th>apache_2_diagnosis_303.0</th>\n",
              "      <th>apache_2_diagnosis_304.0</th>\n",
              "      <th>apache_2_diagnosis_305.0</th>\n",
              "      <th>apache_2_diagnosis_306.0</th>\n",
              "      <th>apache_2_diagnosis_307.0</th>\n",
              "      <th>apache_2_diagnosis_308.0</th>\n",
              "      <th>apache_3j_bodysystem_Cardiovascular</th>\n",
              "      <th>apache_3j_bodysystem_Gastrointestinal</th>\n",
              "      <th>apache_3j_bodysystem_Genitourinary</th>\n",
              "      <th>apache_3j_bodysystem_Gynecological</th>\n",
              "      <th>apache_3j_bodysystem_Hematological</th>\n",
              "      <th>apache_3j_bodysystem_Metabolic</th>\n",
              "      <th>apache_3j_bodysystem_Musculoskeletal/Skin</th>\n",
              "      <th>apache_3j_bodysystem_Neurological</th>\n",
              "      <th>apache_3j_bodysystem_Respiratory</th>\n",
              "      <th>apache_3j_bodysystem_Sepsis</th>\n",
              "      <th>apache_3j_bodysystem_Trauma</th>\n",
              "      <th>apache_2_bodysystem_Cardiovascular</th>\n",
              "      <th>apache_2_bodysystem_Gastrointestinal</th>\n",
              "      <th>apache_2_bodysystem_Haematologic</th>\n",
              "      <th>apache_2_bodysystem_Metabolic</th>\n",
              "      <th>apache_2_bodysystem_Neurologic</th>\n",
              "      <th>apache_2_bodysystem_Renal/Genitourinary</th>\n",
              "      <th>apache_2_bodysystem_Respiratory</th>\n",
              "      <th>apache_2_bodysystem_Trauma</th>\n",
              "      <th>apache_2_bodysystem_Undefined Diagnoses</th>\n",
              "      <th>apache_2_bodysystem_Undefined diagnoses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   hospital_death  ...  apache_2_bodysystem_Undefined diagnoses\n",
              "0               0  ...                                        0\n",
              "1               0  ...                                        0\n",
              "2               0  ...                                        0\n",
              "3               0  ...                                        0\n",
              "4               0  ...                                        0\n",
              "\n",
              "[5 rows x 77 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODshv-hJyiqY",
        "colab_type": "text"
      },
      "source": [
        "### Standardization of Numerical Features\n",
        "* Many models are sensitive to the ranges of features, i.e. features with greater variability by default have greater influence over the outcome\n",
        "* Option 1: normalize data to have zero-mean and unit-variance\n",
        "* Option 2: rescale range of data to [0,1]\n",
        "\n",
        "<img src=\"images/rescaling.png\" style=\"height: 300px;\" align=\"center\"/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_XgpFGtyiqZ",
        "colab_type": "code",
        "outputId": "8e0be220-4392-4bb1-a3ad-cfb49810ab6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "# display summary statistics\n",
        "nume_features.describe(include = 'all')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>height</th>\n",
              "      <th>pre_icu_los_days</th>\n",
              "      <th>weight</th>\n",
              "      <th>arf_apache</th>\n",
              "      <th>gcs_eyes_apache</th>\n",
              "      <th>gcs_motor_apache</th>\n",
              "      <th>gcs_unable_apache</th>\n",
              "      <th>gcs_verbal_apache</th>\n",
              "      <th>heart_rate_apache</th>\n",
              "      <th>intubated_apache</th>\n",
              "      <th>map_apache</th>\n",
              "      <th>resprate_apache</th>\n",
              "      <th>temp_apache</th>\n",
              "      <th>ventilated_apache</th>\n",
              "      <th>d1_diasbp_max</th>\n",
              "      <th>d1_diasbp_min</th>\n",
              "      <th>d1_diasbp_noninvasive_max</th>\n",
              "      <th>d1_diasbp_noninvasive_min</th>\n",
              "      <th>d1_heartrate_max</th>\n",
              "      <th>d1_heartrate_min</th>\n",
              "      <th>d1_mbp_max</th>\n",
              "      <th>d1_mbp_min</th>\n",
              "      <th>d1_mbp_noninvasive_max</th>\n",
              "      <th>d1_mbp_noninvasive_min</th>\n",
              "      <th>d1_resprate_max</th>\n",
              "      <th>d1_resprate_min</th>\n",
              "      <th>d1_spo2_max</th>\n",
              "      <th>d1_spo2_min</th>\n",
              "      <th>d1_sysbp_max</th>\n",
              "      <th>d1_sysbp_min</th>\n",
              "      <th>d1_sysbp_noninvasive_max</th>\n",
              "      <th>d1_sysbp_noninvasive_min</th>\n",
              "      <th>d1_temp_max</th>\n",
              "      <th>d1_temp_min</th>\n",
              "      <th>h1_diasbp_max</th>\n",
              "      <th>h1_diasbp_min</th>\n",
              "      <th>h1_diasbp_noninvasive_max</th>\n",
              "      <th>h1_diasbp_noninvasive_min</th>\n",
              "      <th>h1_heartrate_max</th>\n",
              "      <th>h1_heartrate_min</th>\n",
              "      <th>h1_mbp_max</th>\n",
              "      <th>h1_mbp_min</th>\n",
              "      <th>h1_mbp_noninvasive_max</th>\n",
              "      <th>h1_mbp_noninvasive_min</th>\n",
              "      <th>h1_resprate_max</th>\n",
              "      <th>h1_resprate_min</th>\n",
              "      <th>h1_spo2_max</th>\n",
              "      <th>h1_spo2_min</th>\n",
              "      <th>h1_sysbp_max</th>\n",
              "      <th>h1_sysbp_min</th>\n",
              "      <th>h1_sysbp_noninvasive_max</th>\n",
              "      <th>h1_sysbp_noninvasive_min</th>\n",
              "      <th>d1_glucose_max</th>\n",
              "      <th>d1_glucose_min</th>\n",
              "      <th>apache_4a_hospital_death_prob</th>\n",
              "      <th>apache_4a_icu_death_prob</th>\n",
              "      <th>aids</th>\n",
              "      <th>cirrhosis</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>hepatic_failure</th>\n",
              "      <th>immunosuppression</th>\n",
              "      <th>leukemia</th>\n",
              "      <th>lymphoma</th>\n",
              "      <th>solid_tumor_with_metastasis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "      <td>91713.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>62.433548</td>\n",
              "      <td>29.128570</td>\n",
              "      <td>169.648256</td>\n",
              "      <td>0.835766</td>\n",
              "      <td>83.917765</td>\n",
              "      <td>0.027761</td>\n",
              "      <td>3.476138</td>\n",
              "      <td>5.482156</td>\n",
              "      <td>0.009421</td>\n",
              "      <td>4.015614</td>\n",
              "      <td>99.749021</td>\n",
              "      <td>0.150044</td>\n",
              "      <td>87.788100</td>\n",
              "      <td>25.840460</td>\n",
              "      <td>36.418303</td>\n",
              "      <td>0.323182</td>\n",
              "      <td>88.487390</td>\n",
              "      <td>50.161024</td>\n",
              "      <td>88.592250</td>\n",
              "      <td>50.239846</td>\n",
              "      <td>102.997405</td>\n",
              "      <td>70.319758</td>\n",
              "      <td>104.644979</td>\n",
              "      <td>64.869768</td>\n",
              "      <td>104.548679</td>\n",
              "      <td>64.926357</td>\n",
              "      <td>28.870673</td>\n",
              "      <td>12.846925</td>\n",
              "      <td>99.244589</td>\n",
              "      <td>90.460436</td>\n",
              "      <td>148.335689</td>\n",
              "      <td>96.922268</td>\n",
              "      <td>148.210515</td>\n",
              "      <td>96.982190</td>\n",
              "      <td>37.279786</td>\n",
              "      <td>36.271726</td>\n",
              "      <td>75.301059</td>\n",
              "      <td>62.805077</td>\n",
              "      <td>75.661204</td>\n",
              "      <td>63.168787</td>\n",
              "      <td>92.161384</td>\n",
              "      <td>83.613108</td>\n",
              "      <td>91.531364</td>\n",
              "      <td>79.329212</td>\n",
              "      <td>91.436230</td>\n",
              "      <td>79.639059</td>\n",
              "      <td>22.556006</td>\n",
              "      <td>17.153969</td>\n",
              "      <td>98.088232</td>\n",
              "      <td>95.211987</td>\n",
              "      <td>133.158909</td>\n",
              "      <td>116.308659</td>\n",
              "      <td>132.810180</td>\n",
              "      <td>116.425589</td>\n",
              "      <td>173.078015</td>\n",
              "      <td>113.913600</td>\n",
              "      <td>0.083599</td>\n",
              "      <td>0.041879</td>\n",
              "      <td>0.000850</td>\n",
              "      <td>0.015570</td>\n",
              "      <td>0.223436</td>\n",
              "      <td>0.012888</td>\n",
              "      <td>0.025961</td>\n",
              "      <td>0.007011</td>\n",
              "      <td>0.004100</td>\n",
              "      <td>0.020477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.393595</td>\n",
              "      <td>8.124165</td>\n",
              "      <td>10.716719</td>\n",
              "      <td>2.487756</td>\n",
              "      <td>24.645926</td>\n",
              "      <td>0.164287</td>\n",
              "      <td>0.944878</td>\n",
              "      <td>1.277178</td>\n",
              "      <td>0.096603</td>\n",
              "      <td>1.550540</td>\n",
              "      <td>30.725221</td>\n",
              "      <td>0.357117</td>\n",
              "      <td>41.860607</td>\n",
              "      <td>15.006459</td>\n",
              "      <td>0.814807</td>\n",
              "      <td>0.467694</td>\n",
              "      <td>19.780844</td>\n",
              "      <td>13.305602</td>\n",
              "      <td>19.681933</td>\n",
              "      <td>13.265685</td>\n",
              "      <td>22.000078</td>\n",
              "      <td>17.102447</td>\n",
              "      <td>20.783790</td>\n",
              "      <td>15.660920</td>\n",
              "      <td>20.536165</td>\n",
              "      <td>15.574637</td>\n",
              "      <td>10.681113</td>\n",
              "      <td>5.054311</td>\n",
              "      <td>1.791501</td>\n",
              "      <td>10.012275</td>\n",
              "      <td>25.711127</td>\n",
              "      <td>20.660033</td>\n",
              "      <td>25.648712</td>\n",
              "      <td>20.589027</td>\n",
              "      <td>0.684994</td>\n",
              "      <td>0.735936</td>\n",
              "      <td>18.044243</td>\n",
              "      <td>16.037959</td>\n",
              "      <td>17.732555</td>\n",
              "      <td>15.754051</td>\n",
              "      <td>21.492597</td>\n",
              "      <td>19.971061</td>\n",
              "      <td>20.010250</td>\n",
              "      <td>18.643002</td>\n",
              "      <td>19.513464</td>\n",
              "      <td>18.260219</td>\n",
              "      <td>7.342588</td>\n",
              "      <td>5.932187</td>\n",
              "      <td>3.145098</td>\n",
              "      <td>6.474594</td>\n",
              "      <td>27.012570</td>\n",
              "      <td>25.984842</td>\n",
              "      <td>26.561786</td>\n",
              "      <td>25.539242</td>\n",
              "      <td>84.112920</td>\n",
              "      <td>37.085108</td>\n",
              "      <td>0.236826</td>\n",
              "      <td>0.207821</td>\n",
              "      <td>0.029151</td>\n",
              "      <td>0.123806</td>\n",
              "      <td>0.416551</td>\n",
              "      <td>0.112792</td>\n",
              "      <td>0.159021</td>\n",
              "      <td>0.083438</td>\n",
              "      <td>0.063898</td>\n",
              "      <td>0.141626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.844926</td>\n",
              "      <td>137.200000</td>\n",
              "      <td>-24.947222</td>\n",
              "      <td>38.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>32.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>41.030000</td>\n",
              "      <td>35.100000</td>\n",
              "      <td>31.889000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>23.787760</td>\n",
              "      <td>162.560000</td>\n",
              "      <td>0.035417</td>\n",
              "      <td>67.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>36.900000</td>\n",
              "      <td>36.100000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>65.000000</td>\n",
              "      <td>27.654655</td>\n",
              "      <td>170.100000</td>\n",
              "      <td>0.138889</td>\n",
              "      <td>80.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>36.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>37.110000</td>\n",
              "      <td>36.400000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>131.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75.000000</td>\n",
              "      <td>32.653061</td>\n",
              "      <td>177.800000</td>\n",
              "      <td>0.409028</td>\n",
              "      <td>96.300000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>36.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>110.000000</td>\n",
              "      <td>37.600000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>196.000000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>89.000000</td>\n",
              "      <td>67.814990</td>\n",
              "      <td>195.590000</td>\n",
              "      <td>159.090972</td>\n",
              "      <td>186.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>39.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>181.000000</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>232.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>39.900000</td>\n",
              "      <td>37.800000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>164.000000</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>611.000000</td>\n",
              "      <td>288.000000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.970000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age           bmi  ...      lymphoma  solid_tumor_with_metastasis\n",
              "count  91713.000000  91713.000000  ...  91713.000000                 91713.000000\n",
              "mean      62.433548     29.128570  ...      0.004100                     0.020477\n",
              "std       16.393595      8.124165  ...      0.063898                     0.141626\n",
              "min       16.000000     14.844926  ...      0.000000                     0.000000\n",
              "25%       53.000000     23.787760  ...      0.000000                     0.000000\n",
              "50%       65.000000     27.654655  ...      0.000000                     0.000000\n",
              "75%       75.000000     32.653061  ...      0.000000                     0.000000\n",
              "max       89.000000     67.814990  ...      1.000000                     1.000000\n",
              "\n",
              "[8 rows x 66 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Z4WNupdyyiqd",
        "colab_type": "code",
        "outputId": "25854334-f1d5-4cc5-d39c-ae92ebd4d916",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "# scale features\n",
        "category_names = list(nume_features.columns)\n",
        "nume_features = preprocessing.scale(nume_features) # rescale features\n",
        "nume_features = pd.DataFrame(nume_features, columns=category_names)\n",
        "nume_features.describe(include = 'all')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>height</th>\n",
              "      <th>pre_icu_los_days</th>\n",
              "      <th>weight</th>\n",
              "      <th>arf_apache</th>\n",
              "      <th>gcs_eyes_apache</th>\n",
              "      <th>gcs_motor_apache</th>\n",
              "      <th>gcs_unable_apache</th>\n",
              "      <th>gcs_verbal_apache</th>\n",
              "      <th>heart_rate_apache</th>\n",
              "      <th>intubated_apache</th>\n",
              "      <th>map_apache</th>\n",
              "      <th>resprate_apache</th>\n",
              "      <th>temp_apache</th>\n",
              "      <th>ventilated_apache</th>\n",
              "      <th>d1_diasbp_max</th>\n",
              "      <th>d1_diasbp_min</th>\n",
              "      <th>d1_diasbp_noninvasive_max</th>\n",
              "      <th>d1_diasbp_noninvasive_min</th>\n",
              "      <th>d1_heartrate_max</th>\n",
              "      <th>d1_heartrate_min</th>\n",
              "      <th>d1_mbp_max</th>\n",
              "      <th>d1_mbp_min</th>\n",
              "      <th>d1_mbp_noninvasive_max</th>\n",
              "      <th>d1_mbp_noninvasive_min</th>\n",
              "      <th>d1_resprate_max</th>\n",
              "      <th>d1_resprate_min</th>\n",
              "      <th>d1_spo2_max</th>\n",
              "      <th>d1_spo2_min</th>\n",
              "      <th>d1_sysbp_max</th>\n",
              "      <th>d1_sysbp_min</th>\n",
              "      <th>d1_sysbp_noninvasive_max</th>\n",
              "      <th>d1_sysbp_noninvasive_min</th>\n",
              "      <th>d1_temp_max</th>\n",
              "      <th>d1_temp_min</th>\n",
              "      <th>h1_diasbp_max</th>\n",
              "      <th>h1_diasbp_min</th>\n",
              "      <th>h1_diasbp_noninvasive_max</th>\n",
              "      <th>h1_diasbp_noninvasive_min</th>\n",
              "      <th>h1_heartrate_max</th>\n",
              "      <th>h1_heartrate_min</th>\n",
              "      <th>h1_mbp_max</th>\n",
              "      <th>h1_mbp_min</th>\n",
              "      <th>h1_mbp_noninvasive_max</th>\n",
              "      <th>h1_mbp_noninvasive_min</th>\n",
              "      <th>h1_resprate_max</th>\n",
              "      <th>h1_resprate_min</th>\n",
              "      <th>h1_spo2_max</th>\n",
              "      <th>h1_spo2_min</th>\n",
              "      <th>h1_sysbp_max</th>\n",
              "      <th>h1_sysbp_min</th>\n",
              "      <th>h1_sysbp_noninvasive_max</th>\n",
              "      <th>h1_sysbp_noninvasive_min</th>\n",
              "      <th>d1_glucose_max</th>\n",
              "      <th>d1_glucose_min</th>\n",
              "      <th>apache_4a_hospital_death_prob</th>\n",
              "      <th>apache_4a_icu_death_prob</th>\n",
              "      <th>aids</th>\n",
              "      <th>cirrhosis</th>\n",
              "      <th>diabetes_mellitus</th>\n",
              "      <th>hepatic_failure</th>\n",
              "      <th>immunosuppression</th>\n",
              "      <th>leukemia</th>\n",
              "      <th>lymphoma</th>\n",
              "      <th>solid_tumor_with_metastasis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "      <td>9.171300e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.708470e-16</td>\n",
              "      <td>-7.963190e-16</td>\n",
              "      <td>8.263997e-17</td>\n",
              "      <td>9.498361e-16</td>\n",
              "      <td>-7.839194e-16</td>\n",
              "      <td>1.052900e-14</td>\n",
              "      <td>1.364853e-14</td>\n",
              "      <td>-2.271305e-14</td>\n",
              "      <td>2.812668e-14</td>\n",
              "      <td>4.803335e-15</td>\n",
              "      <td>-8.717948e-16</td>\n",
              "      <td>6.066186e-14</td>\n",
              "      <td>1.275716e-16</td>\n",
              "      <td>6.325820e-15</td>\n",
              "      <td>-1.393847e-15</td>\n",
              "      <td>-2.312099e-14</td>\n",
              "      <td>1.930231e-16</td>\n",
              "      <td>2.182726e-15</td>\n",
              "      <td>-1.288398e-15</td>\n",
              "      <td>-1.666309e-15</td>\n",
              "      <td>1.434856e-14</td>\n",
              "      <td>-5.607541e-16</td>\n",
              "      <td>6.825027e-18</td>\n",
              "      <td>2.061357e-16</td>\n",
              "      <td>-1.428803e-15</td>\n",
              "      <td>2.393178e-15</td>\n",
              "      <td>3.132429e-14</td>\n",
              "      <td>5.718448e-16</td>\n",
              "      <td>6.906347e-15</td>\n",
              "      <td>-7.147210e-16</td>\n",
              "      <td>-4.171910e-16</td>\n",
              "      <td>-1.028210e-15</td>\n",
              "      <td>1.821307e-15</td>\n",
              "      <td>1.840845e-15</td>\n",
              "      <td>-2.541899e-15</td>\n",
              "      <td>-2.967446e-15</td>\n",
              "      <td>-3.901596e-16</td>\n",
              "      <td>-1.303262e-14</td>\n",
              "      <td>4.825892e-16</td>\n",
              "      <td>9.720432e-15</td>\n",
              "      <td>1.184514e-16</td>\n",
              "      <td>-2.819246e-16</td>\n",
              "      <td>-7.295250e-16</td>\n",
              "      <td>4.430368e-15</td>\n",
              "      <td>-1.364248e-15</td>\n",
              "      <td>-2.653275e-15</td>\n",
              "      <td>-8.012217e-16</td>\n",
              "      <td>3.339300e-16</td>\n",
              "      <td>3.262506e-15</td>\n",
              "      <td>3.620261e-15</td>\n",
              "      <td>3.709716e-15</td>\n",
              "      <td>8.920449e-16</td>\n",
              "      <td>-5.087168e-15</td>\n",
              "      <td>1.194946e-15</td>\n",
              "      <td>9.193087e-16</td>\n",
              "      <td>-5.446004e-16</td>\n",
              "      <td>-7.005459e-17</td>\n",
              "      <td>2.922468e-15</td>\n",
              "      <td>2.727162e-15</td>\n",
              "      <td>-2.971552e-14</td>\n",
              "      <td>-6.914314e-15</td>\n",
              "      <td>9.631456e-15</td>\n",
              "      <td>-4.293059e-14</td>\n",
              "      <td>1.950584e-15</td>\n",
              "      <td>-4.033110e-15</td>\n",
              "      <td>-3.439167e-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "      <td>1.000005e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.832436e+00</td>\n",
              "      <td>-1.758177e+00</td>\n",
              "      <td>-3.027833e+00</td>\n",
              "      <td>-1.036401e+01</td>\n",
              "      <td>-1.838763e+00</td>\n",
              "      <td>-1.689768e-01</td>\n",
              "      <td>-2.620603e+00</td>\n",
              "      <td>-3.509442e+00</td>\n",
              "      <td>-9.752070e-02</td>\n",
              "      <td>-1.944890e+00</td>\n",
              "      <td>-2.270103e+00</td>\n",
              "      <td>-4.201568e-01</td>\n",
              "      <td>-1.141607e+00</td>\n",
              "      <td>-1.455412e+00</td>\n",
              "      <td>-5.299813e+00</td>\n",
              "      <td>-6.910154e-01</td>\n",
              "      <td>-2.147918e+00</td>\n",
              "      <td>-2.792901e+00</td>\n",
              "      <td>-2.164040e+00</td>\n",
              "      <td>-2.807247e+00</td>\n",
              "      <td>-2.045341e+00</td>\n",
              "      <td>-4.111701e+00</td>\n",
              "      <td>-2.148079e+00</td>\n",
              "      <td>-2.737387e+00</td>\n",
              "      <td>-2.169291e+00</td>\n",
              "      <td>-2.756186e+00</td>\n",
              "      <td>-1.392248e+00</td>\n",
              "      <td>-2.541790e+00</td>\n",
              "      <td>-5.539776e+01</td>\n",
              "      <td>-9.035003e+00</td>\n",
              "      <td>-2.268901e+00</td>\n",
              "      <td>-2.706800e+00</td>\n",
              "      <td>-2.269542e+00</td>\n",
              "      <td>-2.717588e+00</td>\n",
              "      <td>-3.182216e+00</td>\n",
              "      <td>-5.955337e+00</td>\n",
              "      <td>-2.122631e+00</td>\n",
              "      <td>-2.544295e+00</td>\n",
              "      <td>-2.180251e+00</td>\n",
              "      <td>-2.613233e+00</td>\n",
              "      <td>-2.147792e+00</td>\n",
              "      <td>-2.384118e+00</td>\n",
              "      <td>-2.125490e+00</td>\n",
              "      <td>-2.538726e+00</td>\n",
              "      <td>-2.174727e+00</td>\n",
              "      <td>-2.608913e+00</td>\n",
              "      <td>-1.710034e+00</td>\n",
              "      <td>-2.891693e+00</td>\n",
              "      <td>-3.118783e+01</td>\n",
              "      <td>-1.470556e+01</td>\n",
              "      <td>-2.153043e+00</td>\n",
              "      <td>-2.436382e+00</td>\n",
              "      <td>-2.176454e+00</td>\n",
              "      <td>-2.483470e+00</td>\n",
              "      <td>-1.189812e+00</td>\n",
              "      <td>-2.181847e+00</td>\n",
              "      <td>-4.575540e+00</td>\n",
              "      <td>-5.013380e+00</td>\n",
              "      <td>-2.917539e-02</td>\n",
              "      <td>-1.257640e-01</td>\n",
              "      <td>-5.363992e-01</td>\n",
              "      <td>-1.142642e-01</td>\n",
              "      <td>-1.632586e-01</td>\n",
              "      <td>-8.402680e-02</td>\n",
              "      <td>-6.416092e-02</td>\n",
              "      <td>-1.445856e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-5.754442e-01</td>\n",
              "      <td>-6.574017e-01</td>\n",
              "      <td>-6.614239e-01</td>\n",
              "      <td>-3.217171e-01</td>\n",
              "      <td>-6.742638e-01</td>\n",
              "      <td>-1.689768e-01</td>\n",
              "      <td>-5.039168e-01</td>\n",
              "      <td>4.054616e-01</td>\n",
              "      <td>-9.752070e-02</td>\n",
              "      <td>-1.007005e-02</td>\n",
              "      <td>-4.149390e-01</td>\n",
              "      <td>-4.201568e-01</td>\n",
              "      <td>-8.071618e-01</td>\n",
              "      <td>-9.889436e-01</td>\n",
              "      <td>-2.679217e-01</td>\n",
              "      <td>-6.910154e-01</td>\n",
              "      <td>-6.818447e-01</td>\n",
              "      <td>-6.133558e-01</td>\n",
              "      <td>-6.905990e-01</td>\n",
              "      <td>-6.211433e-01</td>\n",
              "      <td>-7.271562e-01</td>\n",
              "      <td>-6.034116e-01</td>\n",
              "      <td>-7.046385e-01</td>\n",
              "      <td>-6.302198e-01</td>\n",
              "      <td>-7.084457e-01</td>\n",
              "      <td>-6.373446e-01</td>\n",
              "      <td>-6.432579e-01</td>\n",
              "      <td>-5.632697e-01</td>\n",
              "      <td>-1.365282e-01</td>\n",
              "      <td>-1.458654e-01</td>\n",
              "      <td>-7.131460e-01</td>\n",
              "      <td>-6.254752e-01</td>\n",
              "      <td>-7.100011e-01</td>\n",
              "      <td>-6.305427e-01</td>\n",
              "      <td>-5.544408e-01</td>\n",
              "      <td>-2.333442e-01</td>\n",
              "      <td>-6.817202e-01</td>\n",
              "      <td>-6.737226e-01</td>\n",
              "      <td>-6.576191e-01</td>\n",
              "      <td>-6.454748e-01</td>\n",
              "      <td>-7.054274e-01</td>\n",
              "      <td>-6.816454e-01</td>\n",
              "      <td>-6.762253e-01</td>\n",
              "      <td>-6.613355e-01</td>\n",
              "      <td>-6.885658e-01</td>\n",
              "      <td>-6.374032e-01</td>\n",
              "      <td>-6.204939e-01</td>\n",
              "      <td>-5.316735e-01</td>\n",
              "      <td>-3.460108e-01</td>\n",
              "      <td>-1.871923e-01</td>\n",
              "      <td>-7.092629e-01</td>\n",
              "      <td>-7.045938e-01</td>\n",
              "      <td>-6.705226e-01</td>\n",
              "      <td>-6.823101e-01</td>\n",
              "      <td>-6.429251e-01</td>\n",
              "      <td>-5.909035e-01</td>\n",
              "      <td>-2.685504e-01</td>\n",
              "      <td>-1.533980e-01</td>\n",
              "      <td>-2.917539e-02</td>\n",
              "      <td>-1.257640e-01</td>\n",
              "      <td>-5.363992e-01</td>\n",
              "      <td>-1.142642e-01</td>\n",
              "      <td>-1.632586e-01</td>\n",
              "      <td>-8.402680e-02</td>\n",
              "      <td>-6.416092e-02</td>\n",
              "      <td>-1.445856e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.565530e-01</td>\n",
              "      <td>-1.814246e-01</td>\n",
              "      <td>4.215346e-02</td>\n",
              "      <td>-2.801243e-01</td>\n",
              "      <td>-1.467904e-01</td>\n",
              "      <td>-1.689768e-01</td>\n",
              "      <td>5.544262e-01</td>\n",
              "      <td>4.054616e-01</td>\n",
              "      <td>-9.752070e-02</td>\n",
              "      <td>6.348701e-01</td>\n",
              "      <td>1.383555e-01</td>\n",
              "      <td>-4.201568e-01</td>\n",
              "      <td>-4.966056e-01</td>\n",
              "      <td>1.439082e-01</td>\n",
              "      <td>1.002655e-01</td>\n",
              "      <td>-6.910154e-01</td>\n",
              "      <td>-1.257481e-01</td>\n",
              "      <td>-1.210204e-02</td>\n",
              "      <td>-8.089949e-02</td>\n",
              "      <td>-1.808028e-02</td>\n",
              "      <td>-9.079131e-02</td>\n",
              "      <td>-7.716823e-02</td>\n",
              "      <td>-1.272623e-01</td>\n",
              "      <td>-5.553776e-02</td>\n",
              "      <td>-1.241075e-01</td>\n",
              "      <td>-5.947890e-02</td>\n",
              "      <td>-2.687630e-01</td>\n",
              "      <td>3.028626e-02</td>\n",
              "      <td>4.216660e-01</td>\n",
              "      <td>1.537685e-01</td>\n",
              "      <td>-9.084399e-02</td>\n",
              "      <td>-4.464046e-02</td>\n",
              "      <td>-8.618474e-02</td>\n",
              "      <td>-4.770479e-02</td>\n",
              "      <td>-2.478670e-01</td>\n",
              "      <td>1.743020e-01</td>\n",
              "      <td>-7.210421e-02</td>\n",
              "      <td>-5.019847e-02</td>\n",
              "      <td>-9.368154e-02</td>\n",
              "      <td>-7.419005e-02</td>\n",
              "      <td>-1.005647e-01</td>\n",
              "      <td>-8.077273e-02</td>\n",
              "      <td>-7.652940e-02</td>\n",
              "      <td>-7.129855e-02</td>\n",
              "      <td>-7.360242e-02</td>\n",
              "      <td>-3.499751e-02</td>\n",
              "      <td>-2.119164e-01</td>\n",
              "      <td>-1.945279e-01</td>\n",
              "      <td>2.899030e-01</td>\n",
              "      <td>1.217091e-01</td>\n",
              "      <td>-7.992281e-02</td>\n",
              "      <td>-5.036266e-02</td>\n",
              "      <td>-1.057984e-01</td>\n",
              "      <td>-5.581984e-02</td>\n",
              "      <td>-2.743709e-01</td>\n",
              "      <td>-1.864263e-01</td>\n",
              "      <td>-1.418742e-01</td>\n",
              "      <td>-1.052793e-01</td>\n",
              "      <td>-2.917539e-02</td>\n",
              "      <td>-1.257640e-01</td>\n",
              "      <td>-5.363992e-01</td>\n",
              "      <td>-1.142642e-01</td>\n",
              "      <td>-1.632586e-01</td>\n",
              "      <td>-8.402680e-02</td>\n",
              "      <td>-6.416092e-02</td>\n",
              "      <td>-1.445856e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.665506e-01</td>\n",
              "      <td>4.338305e-01</td>\n",
              "      <td>7.606609e-01</td>\n",
              "      <td>-1.715363e-01</td>\n",
              "      <td>5.024077e-01</td>\n",
              "      <td>-1.689768e-01</td>\n",
              "      <td>5.544262e-01</td>\n",
              "      <td>4.054616e-01</td>\n",
              "      <td>-9.752070e-02</td>\n",
              "      <td>6.348701e-01</td>\n",
              "      <td>6.591031e-01</td>\n",
              "      <td>-4.201568e-01</td>\n",
              "      <td>8.650638e-01</td>\n",
              "      <td>6.770149e-01</td>\n",
              "      <td>3.457235e-01</td>\n",
              "      <td>1.447146e+00</td>\n",
              "      <td>5.314570e-01</td>\n",
              "      <td>5.891517e-01</td>\n",
              "      <td>5.288000e-01</td>\n",
              "      <td>5.849827e-01</td>\n",
              "      <td>5.910282e-01</td>\n",
              "      <td>6.244896e-01</td>\n",
              "      <td>5.463432e-01</td>\n",
              "      <td>6.468514e-01</td>\n",
              "      <td>5.576203e-01</td>\n",
              "      <td>5.825941e-01</td>\n",
              "      <td>2.929792e-01</td>\n",
              "      <td>6.238422e-01</td>\n",
              "      <td>4.216660e-01</td>\n",
              "      <td>4.534023e-01</td>\n",
              "      <td>6.092458e-01</td>\n",
              "      <td>6.330001e-01</td>\n",
              "      <td>6.156087e-01</td>\n",
              "      <td>6.322728e-01</td>\n",
              "      <td>4.674719e-01</td>\n",
              "      <td>4.460661e-01</td>\n",
              "      <td>5.929315e-01</td>\n",
              "      <td>6.356781e-01</td>\n",
              "      <td>5.266498e-01</td>\n",
              "      <td>6.240469e-01</td>\n",
              "      <td>5.973539e-01</td>\n",
              "      <td>6.202454e-01</td>\n",
              "      <td>5.731412e-01</td>\n",
              "      <td>6.260178e-01</td>\n",
              "      <td>5.413609e-01</td>\n",
              "      <td>5.674082e-01</td>\n",
              "      <td>4.690461e-01</td>\n",
              "      <td>4.797634e-01</td>\n",
              "      <td>6.078599e-01</td>\n",
              "      <td>5.850611e-01</td>\n",
              "      <td>5.864373e-01</td>\n",
              "      <td>6.423527e-01</td>\n",
              "      <td>5.718705e-01</td>\n",
              "      <td>6.098261e-01</td>\n",
              "      <td>2.725159e-01</td>\n",
              "      <td>4.068070e-01</td>\n",
              "      <td>1.537035e-01</td>\n",
              "      <td>8.719518e-02</td>\n",
              "      <td>-2.917539e-02</td>\n",
              "      <td>-1.257640e-01</td>\n",
              "      <td>-5.363992e-01</td>\n",
              "      <td>-1.142642e-01</td>\n",
              "      <td>-1.632586e-01</td>\n",
              "      <td>-8.402680e-02</td>\n",
              "      <td>-6.416092e-02</td>\n",
              "      <td>-1.445856e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.620547e+00</td>\n",
              "      <td>4.761921e+00</td>\n",
              "      <td>2.420693e+00</td>\n",
              "      <td>6.361398e+01</td>\n",
              "      <td>4.141974e+00</td>\n",
              "      <td>5.917972e+00</td>\n",
              "      <td>5.544262e-01</td>\n",
              "      <td>4.054616e-01</td>\n",
              "      <td>1.025423e+01</td>\n",
              "      <td>6.348701e-01</td>\n",
              "      <td>2.546813e+00</td>\n",
              "      <td>2.380064e+00</td>\n",
              "      <td>2.680623e+00</td>\n",
              "      <td>2.276335e+00</td>\n",
              "      <td>4.027595e+00</td>\n",
              "      <td>1.447146e+00</td>\n",
              "      <td>3.868037e+00</td>\n",
              "      <td>2.994167e+00</td>\n",
              "      <td>3.882147e+00</td>\n",
              "      <td>2.997235e+00</td>\n",
              "      <td>3.363761e+00</td>\n",
              "      <td>6.120809e+00</td>\n",
              "      <td>3.818142e+00</td>\n",
              "      <td>3.009433e+00</td>\n",
              "      <td>3.722785e+00</td>\n",
              "      <td>3.022472e+00</td>\n",
              "      <td>5.910402e+00</td>\n",
              "      <td>1.724341e+01</td>\n",
              "      <td>4.216660e-01</td>\n",
              "      <td>9.527920e-01</td>\n",
              "      <td>3.254030e+00</td>\n",
              "      <td>3.053145e+00</td>\n",
              "      <td>3.266828e+00</td>\n",
              "      <td>3.060764e+00</td>\n",
              "      <td>3.825185e+00</td>\n",
              "      <td>2.076651e+00</td>\n",
              "      <td>3.751851e+00</td>\n",
              "      <td>3.129775e+00</td>\n",
              "      <td>3.853882e+00</td>\n",
              "      <td>3.226566e+00</td>\n",
              "      <td>3.342500e+00</td>\n",
              "      <td>3.023736e+00</td>\n",
              "      <td>3.671570e+00</td>\n",
              "      <td>3.147085e+00</td>\n",
              "      <td>3.667425e+00</td>\n",
              "      <td>3.196088e+00</td>\n",
              "      <td>4.963398e+00</td>\n",
              "      <td>2.896857e+01</td>\n",
              "      <td>6.078599e-01</td>\n",
              "      <td>7.395118e-01</td>\n",
              "      <td>3.325918e+00</td>\n",
              "      <td>2.989888e+00</td>\n",
              "      <td>3.395491e+00</td>\n",
              "      <td>3.076632e+00</td>\n",
              "      <td>5.206387e+00</td>\n",
              "      <td>4.694266e+00</td>\n",
              "      <td>3.827312e+00</td>\n",
              "      <td>4.465991e+00</td>\n",
              "      <td>3.427547e+01</td>\n",
              "      <td>7.951402e+00</td>\n",
              "      <td>1.864283e+00</td>\n",
              "      <td>8.751650e+00</td>\n",
              "      <td>6.125250e+00</td>\n",
              "      <td>1.190097e+01</td>\n",
              "      <td>1.558581e+01</td>\n",
              "      <td>6.916319e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age           bmi  ...      lymphoma  solid_tumor_with_metastasis\n",
              "count  9.171300e+04  9.171300e+04  ...  9.171300e+04                 9.171300e+04\n",
              "mean   4.708470e-16 -7.963190e-16  ... -4.033110e-15                -3.439167e-15\n",
              "std    1.000005e+00  1.000005e+00  ...  1.000005e+00                 1.000005e+00\n",
              "min   -2.832436e+00 -1.758177e+00  ... -6.416092e-02                -1.445856e-01\n",
              "25%   -5.754442e-01 -6.574017e-01  ... -6.416092e-02                -1.445856e-01\n",
              "50%    1.565530e-01 -1.814246e-01  ... -6.416092e-02                -1.445856e-01\n",
              "75%    7.665506e-01  4.338305e-01  ... -6.416092e-02                -1.445856e-01\n",
              "max    1.620547e+00  4.761921e+00  ...  1.558581e+01                 6.916319e+00\n",
              "\n",
              "[8 rows x 66 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR-X7pC3yiqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# combine one-hot encoded categorical features with normalized numerical features\n",
        "features = one_hot_encoded.join(nume_features)\n",
        "\n",
        "# use the preprocessed data in the next stages!\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}