notebooks/01_data_cleaning_and_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/diascarolina/project-icu-prediction/blob/main/notebooks/01_data_cleaning_and_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# COVID-19 - Clinical Data to Assess Diagnosis\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img width=\"700\" src=\"https://i.imgur.com/wxaTMWn.png\">\n",
        "</p>"
      ]
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    {
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      "source": [
        "# Table of Contents\n",
        "\n",
        "1. [What **problem** do we have here? What can we do to **help**?](#intro)\n",
        "\n",
        "2. [How was this notebook **organized**?](#orga)\n",
        "\n",
        "3. [Where did we get this **data**? What kind of **information** do we have in it?](#data)\n",
        "\n",
        "4. [Libraries & Configurations](#libs)\n",
        "\n",
        "5. [Data Checking: is everything in its right place?](#check)\n",
        "\n",
        "6. [Missing Values & Data Preparation](#prep)\n",
        "\n",
        "7. [Data Analysis](#eda)\n",
        "    \n",
        "    7.1. [Patient Demographic Information](#pdi)\n",
        "    \n",
        "    7.2. [Patient Previous Grouped Diseases](#ppgd)\n",
        "\n",
        "    7.3. [Vital Signs](#vitals)\n",
        "\n",
        "    7.4. [Blood Results](#blood)\n",
        "\n",
        "8. [Conclusions](#conc)\n",
        "\n",
        "[Part 2 - Machine Learning](#partwo)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4El8A3YZ2wd"
      },
      "source": [
        "<a name=\"intro\"></a>\n",
        "# 1 What **problem** do we have here? What can we do to **help**?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZwcZMpB8nTi"
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      "source": [
        "The COVID-19 pandemic. Unfortunately we are all aware of it by now. We all know the sad and high number of deaths and the hopeful increasing number of vaccinated people (in some countries).\n",
        "\n",
        "Seeing as the number of people infected by the virus keeps growing, we need more and more ways of better allocating these patients so as not to overwhelm our healthcare systems.\n",
        "\n",
        "In this context, brazilian hospital Sírio-Libânes [published a dataset at Kaggle](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19) urging people to take action in predicting the need for ICU beds.\n",
        "\n",
        "The tasks at hand are:\n",
        "\n",
        "**Task 01:** Predict admission to the ICU of confirmed COVID-19 cases.\n",
        "\n",
        "Based on the data available, is it feasible to predict which patients will need intensive care unit support?\n",
        "The aim is to provide tertiary and quarternary hospitals with the most accurate answer, so ICU resources can be arranged or patient transfer can be scheduled.\n",
        "\n",
        "**Task 02:** Predict NOT admission to the ICU of confirmed COVID-19 cases.\n",
        "\n",
        "Based on the subsample of widely available data, is it feasible to predict which patients will need intensive care unit support?\n",
        "The aim is to provide local and temporary hospitals a good enough answer, so frontline physicians can safely discharge and remotely follow up with these patients."
      ]
    },
    {
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      "source": [
        "<a name=\"orga\"></a>\n",
        "# 2 How was this notebook organized?"
      ]
    },
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      "metadata": {
        "id": "GlwtXlryALHl"
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      "source": [
        "In this **Part 1** we'll begin by **checking** and **cleaning** our data. For this, we'll follow these steps:\n",
        "\n",
        "![data cleaning steps](https://i.imgur.com/1AJyC0O.png)\n",
        "\n",
        "After that, we'll **analyse** the data as the following image:\n",
        "\n",
        "![data analysis steps](https://i.imgur.com/MakbgMD.png)"
      ]
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      "source": [
        "<a name=\"data\"></a>\n",
        "# 3 Where did we get this **data**? What kind of **information** do we have in it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FA9FFt3iJT4X"
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      "source": [
        "The data was taken directly from the [Kaggle problem](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19) and uploaded to [Github](https://github.com/diascarolina/data-science-bootcamp/blob/main/data/Kaggle_Sirio_Libanes_ICU_Prediction.xlsx?raw=true) for easy access.\n",
        "\n",
        "**From Kaggle:**\n",
        "\n",
        "> The dataset contains **anonymized data** from Hospital Sírio-Libanês, São Paulo and Brasília. All data were anonymized following the best international practices and recommendations.\n",
        "\n",
        "> Data has been cleaned and **scaled** by column according to MinMaxScaler to fit between -1 and 1.\n",
        "\n",
        "_Disclaimer._ It is highly recommended that we apply a scaler to the data only **after** splitting between train and test data (_source:_ [Data Normalization Before or After Splitting a Data Set?](https://www.baeldung.com/cs/data-normalization-before-after-splitting-set)).  But since our data is already scaled, we won't go in too much detail in this aspect.\n",
        "\n",
        "**From our dataset we have:**\n",
        "\n",
        "1. Patient demographic information (03 columns)\n",
        "2. Patient previous grouped diseases (09 columns)\n",
        "3. Blood results (36 columns)\n",
        "4. Vital signs (06 columns)\n",
        "\n",
        "In total there are **54 features**, expanded when pertinent to the _mean, median, max, min, diff and relative diff_, where\n",
        "\n",
        "- _diff = max - min_\n",
        "- _relative diff = diff/median_\n",
        "\n",
        "Furthermore, **our target variable is the ICU** column, in which we have **0** (zero) if that patient did not go to the ICU and **1** (one) if that corresponding patient did go to the ICU.\n",
        "\n",
        "We also have a column called **Window**. What does this variable mean?\n",
        "\n",
        "Again, from Kaggle:\n",
        "\n",
        "> We were careful to include real life cenarios of window of events and available data. Data was obtain and grouped as follows:\n",
        "\n",
        "- patient\n",
        "    - patient encounter\n",
        "    - aggregated by windows in chronological order\n",
        "\n",
        "\n",
        "Window | Description\n",
        ":---: | :---:\n",
        "**0-2**\t| From 0 to 2 hours of the admission\n",
        "**2-4**\t| From 2 to 4 hours of the admission\n",
        "**4-6**\t| From 4 to 6 hours of the admission\n",
        "**6-12** | From 6 to 12 hours of the admission\n",
        "**Above-12** | Above 12 hours from admission\n",
        "\n",
        "> Beware **NOT to use the data when the target variable is present**, as it is unknown the order of the event (maybe the target event happened before the results were obtained). They were kept there so we can grow this dataset in other outcomes latter on.\n",
        "\n",
        "**Examples obtained from Kaggle description:**\n",
        "\n",
        "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1591620%2Fb1bc424df771a4d2d3b3088606d083e6%2FTimeline%20Example%20Best.png?generation=1594740856017996&alt=media)\n",
        "\n",
        "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1591620%2F77ca2b4635bc4dd7800e1c777fed9de1%2FTimeline%20Example%20No.png?generation=1594740873237462&alt=media)\n",
        "\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "<a name=\"libs\"></a>\n",
        "# 4 Libraries & Configurations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ltLnlRiIEs5"
      },
      "source": [
        "Remember that this notebook is only used for data cleaning and analysis, so we'll only use libraries related to this task."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pq0RvhkaJ2uY"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJJOSt1VdjSS"
      },
      "source": [
        "# colors definition\n",
        "GRAY1, GRAY2, GRAY3 = '#231F20', '#414040', '#555655'\n",
        "GRAY4, GRAY5, GRAY6 = '#646369', '#76787B', '#828282'\n",
        "GRAY7, GRAY8, GRAY9 = '#929497', '#A6A6A5', '#BFBEBE'\n",
        "BLUE1, BLUE2, BLUE3, BLUE4 = '#174A7E', '#4A81BF', '#94B2D7', '#94AFC5'\n",
        "RED1, RED2 = '#C3514E', '#E6BAB7'\n",
        "GREEN1, GREEN2 = '#0C8040', '#9ABB59'\n",
        "ORANGE1 = '#F79747'\n",
        "\n",
        "# charts configs\n",
        "plt.rcParams['font.family'] = 'Arial'\n",
        "plt.rcParams['mathtext.fontset'] = 'custom'\n",
        "plt.rcParams['mathtext.bf'] = 'Arial:bold'\n",
        "plt.rcParams['mathtext.it'] = 'Arial:italic'"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONHiLAdlBSuL"
      },
      "source": [
        "<a name=\"check\"></a>\n",
        "# 5 Data Checking: is everything in its right place?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17yWoLa6I0Rs"
      },
      "source": [
        "data_url = 'https://github.com/diascarolina/data-science-bootcamp/blob/main/data/Kaggle_Sirio_Libanes_ICU_Prediction.xlsx?raw=true'\n",
        "\n",
        "raw_data = pd.read_excel(data_url)"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "outputId": "40be4776-01e0-4c3a-ee72-2a56ba2854e8"
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      "source": [
        "raw_data.head(15)"
      ],
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              "      <td>-0.586207</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.578947</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</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>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>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>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>...</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>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>0.033779</td>\n",
              "      <td>0.665932</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.098901</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.362319</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.339130</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-0.238095</td>\n",
              "      <td>-0.818182</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-0.242282</td>\n",
              "      <td>-0.814433</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>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>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.830508</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.862069</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.072165</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.264957</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.504274</td>\n",
              "      <td>-0.329730</td>\n",
              "      <td>-0.059701</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>-0.275362</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",
              "      <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>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.892308</td>\n",
              "      <td>-0.226415</td>\n",
              "      <td>-0.389831</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.892308</td>\n",
              "      <td>-0.226415</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-0.175258</td>\n",
              "      <td>0.9125</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>-0.589744</td>\n",
              "      <td>0.329730</td>\n",
              "      <td>-0.388060</td>\n",
              "      <td>-0.454545</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.894737</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>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>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.448276</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.175258</td>\n",
              "      <td>0.6750</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>0.362637</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.589744</td>\n",
              "      <td>0.124324</td>\n",
              "      <td>-0.492537</td>\n",
              "      <td>-0.515152</td>\n",
              "      <td>-0.217391</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",
              "      <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>4-6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.943723</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.456790</td>\n",
              "      <td>0.532308</td>\n",
              "      <td>-0.384906</td>\n",
              "      <td>-0.145763</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.506173</td>\n",
              "      <td>0.630769</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.103448</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.257732</td>\n",
              "      <td>0.4750</td>\n",
              "      <td>-0.299145</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.406593</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>0.167568</td>\n",
              "      <td>-0.477612</td>\n",
              "      <td>-0.090909</td>\n",
              "      <td>-0.014493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.913043</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-0.880952</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.906832</td>\n",
              "      <td>-0.831132</td>\n",
              "      <td>-0.940967</td>\n",
              "      <td>-0.817204</td>\n",
              "      <td>-0.882574</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.178122</td>\n",
              "      <td>0.212601</td>\n",
              "      <td>-0.141163</td>\n",
              "      <td>-0.380216</td>\n",
              "      <td>0.010915</td>\n",
              "      <td>0.841977</td>\n",
              "      <td>-0.185185</td>\n",
              "      <td>0.184615</td>\n",
              "      <td>-0.169811</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.587629</td>\n",
              "      <td>-0.3250</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.010989</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.556757</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.757576</td>\n",
              "      <td>0.710145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.513043</td>\n",
              "      <td>0.472393</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>-0.797980</td>\n",
              "      <td>0.315690</td>\n",
              "      <td>0.200359</td>\n",
              "      <td>-0.239515</td>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.139709</td>\n",
              "      <td>-0.802317</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</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>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>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</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>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>...</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>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>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</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>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>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.489712</td>\n",
              "      <td>-0.685470</td>\n",
              "      <td>-0.048218</td>\n",
              "      <td>-0.645951</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.935673</td>\n",
              "      <td>-0.506173</td>\n",
              "      <td>-0.815385</td>\n",
              "      <td>-0.056604</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.525773</td>\n",
              "      <td>-0.5125</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.714286</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>-0.435897</td>\n",
              "      <td>-0.491892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.533742</td>\n",
              "      <td>-0.603053</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.959596</td>\n",
              "      <td>-0.515528</td>\n",
              "      <td>-0.351328</td>\n",
              "      <td>-0.747001</td>\n",
              "      <td>-0.756272</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.961262</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</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>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>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.612654</td>\n",
              "      <td>-0.828846</td>\n",
              "      <td>0.037736</td>\n",
              "      <td>-0.720339</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.799342</td>\n",
              "      <td>-0.604938</td>\n",
              "      <td>-0.846154</td>\n",
              "      <td>0.028302</td>\n",
              "      <td>-0.724138</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>-0.505155</td>\n",
              "      <td>-0.6625</td>\n",
              "      <td>-0.179487</td>\n",
              "      <td>-0.642857</td>\n",
              "      <td>0.604396</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.762162</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.696970</td>\n",
              "      <td>0.101449</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.704348</td>\n",
              "      <td>-0.693252</td>\n",
              "      <td>-0.541985</td>\n",
              "      <td>-0.941176</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.797980</td>\n",
              "      <td>-0.658863</td>\n",
              "      <td>-0.563758</td>\n",
              "      <td>-0.721834</td>\n",
              "      <td>-0.926882</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.801293</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.181070</td>\n",
              "      <td>-0.551603</td>\n",
              "      <td>-0.280660</td>\n",
              "      <td>-0.543785</td>\n",
              "      <td>0.057292</td>\n",
              "      <td>0.797149</td>\n",
              "      <td>-0.160494</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.273585</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>0.107143</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>-0.298969</td>\n",
              "      <td>-0.4500</td>\n",
              "      <td>-0.487179</td>\n",
              "      <td>-0.642857</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.351351</td>\n",
              "      <td>-0.149254</td>\n",
              "      <td>-0.454545</td>\n",
              "      <td>0.101449</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.435583</td>\n",
              "      <td>-0.419847</td>\n",
              "      <td>-0.705882</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.898990</td>\n",
              "      <td>-0.612422</td>\n",
              "      <td>-0.343258</td>\n",
              "      <td>-0.576744</td>\n",
              "      <td>-0.695341</td>\n",
              "      <td>-0.505464</td>\n",
              "      <td>-0.900129</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  ...    WINDOW  ICU\n",
              "0                          0            1  ...       0-2    0\n",
              "1                          0            1  ...       2-4    0\n",
              "2                          0            1  ...       4-6    0\n",
              "3                          0            1  ...      6-12    0\n",
              "4                          0            1  ...  ABOVE_12    1\n",
              "5                          1            1  ...       0-2    1\n",
              "6                          1            1  ...       2-4    1\n",
              "7                          1            1  ...       4-6    1\n",
              "8                          1            1  ...      6-12    1\n",
              "9                          1            1  ...  ABOVE_12    1\n",
              "10                         2            0  ...       0-2    0\n",
              "11                         2            0  ...       2-4    0\n",
              "12                         2            0  ...       4-6    0\n",
              "13                         2            0  ...      6-12    0\n",
              "14                         2            0  ...  ABOVE_12    1\n",
              "\n",
              "[15 rows x 231 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95xpTQWALegD"
      },
      "source": [
        "Let's check this data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ktw70FmLKX_Y"
      },
      "source": [
        "def print_data_shape(dataset):\n",
        "    '''\n",
        "    Prints the number of rows and columns in a pandas dataframe.\n",
        "\n",
        "    Input:\n",
        "    dataset -> a pandas dataframe\n",
        "    '''\n",
        "    \n",
        "    print(f'Number of rows in the dataset: {dataset.shape[0]}')\n",
        "    print(f'Number of columns in the dataset: {dataset.shape[1]}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azviKp7ugynv",
        "outputId": "b50c333e-b907-43a7-d36b-5806dd73bff2"
      },
      "source": [
        "print_data_shape(raw_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows in the dataset: 1925\n",
            "Number of columns in the dataset: 231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yonTT55N4Ka"
      },
      "source": [
        "We have 1925 rows in our dataset. But does this mean that each row represents a patient? Let's check the column ```PATIENT_VISIT_IDENTIFIER```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpQxei-IOO0o",
        "outputId": "07f25af6-81bb-4fe5-f884-c576d6199353"
      },
      "source": [
        "num_unique = len(raw_data['PATIENT_VISIT_IDENTIFIER'].unique())\n",
        "\n",
        "print(f'Number of unique values in the column PATIENT_VISIT_IDENTIFIER: {num_unique}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of unique values in the column PATIENT_VISIT_IDENTIFIER: 385\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16JrtrcnOw1z"
      },
      "source": [
        "With the cell above, we see that we have 385 unique values in the ```PATIENT_VISIT_IDENTIFIER``` columns. This is exactly the number of rows, 1925, divided by 5. This means we only have 385 patients in this dataset, each one represented by 5 rows. Each of these 5 rows for a patient correspond with the ```WINDOW``` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0x78QkoQHO0",
        "outputId": "5b2d5014-50bd-4776-8aac-652cfd1ad1e0"
      },
      "source": [
        "raw_data['WINDOW'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0-2', '2-4', '4-6', '6-12', 'ABOVE_12'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzTVW_D3QO9I"
      },
      "source": [
        "We can see that the ```WINDOW``` column is divided into the following categories:\n",
        "\n",
        "- 0-2\n",
        "- 2-4\n",
        "- 4-6\n",
        "- 6-12\n",
        "- ABOVE_12\n",
        "\n",
        "Each of these categories represent a time window with how many hours did it take for the patient to go the ICU. For example, if a patient went to the ICU between 2 and 6 of being admited, then we would have the following data:\n",
        "\n",
        "**WINDOW** | **ICU**\n",
        ":---:|:---:\n",
        "0-2 | 0\n",
        "2-4 | 1\n",
        "4-6 | 1\n",
        "6-12 | 1\n",
        "ABOVE_12 | 1\n",
        "\n",
        "**Obs.** All of this information is also present in the [Kaggle problem page](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEnRHH7jRHjR"
      },
      "source": [
        "<a name=\"prep\"></a>\n",
        "# 6 Missing Values & Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCMZek7fApfK"
      },
      "source": [
        "Just from taking a quick look at the data we can see we have some missing values, specially on the columns where we have continuous values, ranging from the column ```ALBUMIN_MEDIAN``` to ```OXYGEN_SATURATION_DIFF_REL```. These represent the measurements from the patient and said measurements are not taken constantly, as explained bellow (also found at the [Kaggle problem page](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19)).\n",
        "\n",
        "> **Problem:** One of the major challenges of working with health care data is that the sampling rate varies across different type of measurements. For instance, vital signs are sampled more frequently (usually hourly) than blood labs (usually daily).\n",
        "\n",
        "> **Tips & Tricks:** It is reasonable to assume that a patient who does not have a measurement recorded in a time window is clinically stable, potentially presenting vital signs and blood labs similar to neighboring windows. Therefore, one may fill the missing values using the next or previous entry. Attention to multicollinearity and zero variance issues in this data when choosing your algorithm.\n",
        "\n",
        "With this in mind, we'll use this approach of filling the missing values using the next or the previous entry. For this, we'll create a function that fills the missing values for the continuous variables in the dataset. These variables are already aggregated for us in the columns ```[13:-2]```.\n",
        "\n",
        "The methods used will be the \"forward fill\"and the \"backfill\".\n",
        "\n",
        "But first let's check how many missing values we have in the whole dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waPpvGP9DhXQ"
      },
      "source": [
        "def check_missing_values(dataset):\n",
        "    '''\n",
        "    Function that prints the number of missing values in a dataframe, together\n",
        "    with its percentage against the total number of values in the dataframe.\n",
        "\n",
        "    Input:\n",
        "    dataset -> a pandas dataframe\n",
        "    \n",
        "    Output:\n",
        "    A print statement.\n",
        "    '''\n",
        "\n",
        "    number_of_null_values = dataset.isnull().values.sum()\n",
        "\n",
        "    # this is just the number of rows times the number of columns\n",
        "    total_values_in_dataset = dataset.shape[0] * dataset.shape[1]\n",
        "\n",
        "    percentage = number_of_null_values / total_values_in_dataset * 100\n",
        "\n",
        "    print(f'We have {number_of_null_values} null values in this dataset.')\n",
        "    print(f'This represents {round(percentage, 2)}% of the total number of values in the dataset')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oO1LmXjNJ2CK",
        "outputId": "2055b433-141d-4874-a610-547aec5bfa3c"
      },
      "source": [
        "check_missing_values(raw_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 223863 null values in this dataset.\n",
            "This represents 50.34% of the total number of values in the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_4VpRzlSBVG"
      },
      "source": [
        "We could try and visualize graphically the missing values and its distribution in the dataset, using a library like [missingno](https://github.com/ResidentMario/missingno). But since we have so many columns to visualize, it would not be a very good visualization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z17shPSaGZ1i"
      },
      "source": [
        "Bellow we define the function to fill the values in the continous variables using the previous or the next observation in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buYS5xMHNRKG"
      },
      "source": [
        "def fill_dataset(data):\n",
        "    '''\n",
        "    Function that fills the missing values in the dataset with the methods 'bfill'\n",
        "    and 'ffill'. This substitution is only applied in the continuous variables.\n",
        "    \n",
        "    Input:\n",
        "    data -> a pandas dataframe\n",
        "    \n",
        "    Output:\n",
        "    filled_data -> a pandas dataframe without missing values in the continuous variables\n",
        "    '''\n",
        "    \n",
        "    # select the columns with the continuous variables\n",
        "    columns_continuous_features = data.iloc[:, 13:-2].columns\n",
        "    \n",
        "    # group the dataset per patient and we substitute the missing values\n",
        "    # using the 'bfill' and 'ffill' methods for each patient\n",
        "    continuous_features = data.groupby('PATIENT_VISIT_IDENTIFIER',\n",
        "                                       as_index = False)[columns_continuous_features].fillna(method = 'bfill').fillna(method = 'ffill')\n",
        "    \n",
        "    categorical_features = data.iloc[:, :13]\n",
        "    output_data = data.iloc[:, -2:]\n",
        "    \n",
        "    filled_data = pd.concat([categorical_features, continuous_features, output_data],\n",
        "                            ignore_index = True,\n",
        "                            axis = 1)\n",
        "    \n",
        "    filled_data.columns = data.columns\n",
        "    \n",
        "    return filled_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "a94CSzTXHOID",
        "outputId": "f11f75e3-e632-4cb6-d0c3-86bf5633cafc"
      },
      "source": [
        "data = raw_data.copy()\n",
        "filled_data = fill_dataset(data)\n",
        "filled_data.head(10)"
      ],
      "execution_count": null,
      "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>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>ALBUMIN_MEDIAN</th>\n",
              "      <th>ALBUMIN_MEAN</th>\n",
              "      <th>ALBUMIN_MIN</th>\n",
              "      <th>ALBUMIN_MAX</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_MEAN</th>\n",
              "      <th>BE_ARTERIAL_MIN</th>\n",
              "      <th>BE_ARTERIAL_MAX</th>\n",
              "      <th>BE_ARTERIAL_DIFF</th>\n",
              "      <th>BE_VENOUS_MEDIAN</th>\n",
              "      <th>BE_VENOUS_MEAN</th>\n",
              "      <th>BE_VENOUS_MIN</th>\n",
              "      <th>BE_VENOUS_MAX</th>\n",
              "      <th>BE_VENOUS_DIFF</th>\n",
              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_MEAN</th>\n",
              "      <th>BIC_ARTERIAL_MIN</th>\n",
              "      <th>BIC_ARTERIAL_MAX</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_MEAN</th>\n",
              "      <th>BIC_VENOUS_MIN</th>\n",
              "      <th>BIC_VENOUS_MAX</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_MEAN</th>\n",
              "      <th>...</th>\n",
              "      <th>DIMER_MAX</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
              "      <th>HEART_RATE_MEDIAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEDIAN</th>\n",
              "      <th>TEMPERATURE_MEDIAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
              "      <th>HEART_RATE_MIN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>TEMPERATURE_MIN</th>\n",
              "      <th>OXYGEN_SATURATION_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
              "      <th>HEART_RATE_MAX</th>\n",
              "      <th>RESPIRATORY_RATE_MAX</th>\n",
              "      <th>TEMPERATURE_MAX</th>\n",
              "      <th>OXYGEN_SATURATION_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
              "      <th>TEMPERATURE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>WINDOW</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.432836</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.736842</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.578947</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.339130</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.339130</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>0.033779</td>\n",
              "      <td>0.665932</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.098901</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.362319</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.339130</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-0.238095</td>\n",
              "      <td>-0.818182</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-0.242282</td>\n",
              "      <td>-0.814433</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.830508</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.862069</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.072165</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.264957</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.504274</td>\n",
              "      <td>-0.329730</td>\n",
              "      <td>-0.059701</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>-0.275362</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",
              "      <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>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.892308</td>\n",
              "      <td>-0.226415</td>\n",
              "      <td>-0.389831</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.892308</td>\n",
              "      <td>-0.226415</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-0.175258</td>\n",
              "      <td>0.9125</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>-0.589744</td>\n",
              "      <td>0.329730</td>\n",
              "      <td>-0.388060</td>\n",
              "      <td>-0.454545</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.894737</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.943723</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.407407</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.448276</td>\n",
              "      <td>-0.035714</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.175258</td>\n",
              "      <td>0.6750</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>0.362637</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.589744</td>\n",
              "      <td>0.124324</td>\n",
              "      <td>-0.492537</td>\n",
              "      <td>-0.515152</td>\n",
              "      <td>-0.217391</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",
              "      <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>4-6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-0.839572</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.943723</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.456790</td>\n",
              "      <td>0.532308</td>\n",
              "      <td>-0.384906</td>\n",
              "      <td>-0.145763</td>\n",
              "      <td>0.114286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.506173</td>\n",
              "      <td>0.630769</td>\n",
              "      <td>-0.358491</td>\n",
              "      <td>-0.103448</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.257732</td>\n",
              "      <td>0.4750</td>\n",
              "      <td>-0.299145</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.406593</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>0.167568</td>\n",
              "      <td>-0.477612</td>\n",
              "      <td>-0.090909</td>\n",
              "      <td>-0.014493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.913043</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-0.880952</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.906832</td>\n",
              "      <td>-0.831132</td>\n",
              "      <td>-0.940967</td>\n",
              "      <td>-0.817204</td>\n",
              "      <td>-0.882574</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-0.914439</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-0.916230</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-0.268293</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.178122</td>\n",
              "      <td>0.212601</td>\n",
              "      <td>-0.141163</td>\n",
              "      <td>-0.380216</td>\n",
              "      <td>0.010915</td>\n",
              "      <td>0.841977</td>\n",
              "      <td>-0.185185</td>\n",
              "      <td>0.184615</td>\n",
              "      <td>-0.169811</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.587629</td>\n",
              "      <td>-0.3250</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.010989</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.556757</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.757576</td>\n",
              "      <td>0.710145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.513043</td>\n",
              "      <td>0.472393</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>-0.797980</td>\n",
              "      <td>0.315690</td>\n",
              "      <td>0.200359</td>\n",
              "      <td>-0.239515</td>\n",
              "      <td>0.645161</td>\n",
              "      <td>0.139709</td>\n",
              "      <td>-0.802317</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  ...    WINDOW  ICU\n",
              "0                         0            1  ...       0-2    0\n",
              "1                         0            1  ...       2-4    0\n",
              "2                         0            1  ...       4-6    0\n",
              "3                         0            1  ...      6-12    0\n",
              "4                         0            1  ...  ABOVE_12    1\n",
              "5                         1            1  ...       0-2    1\n",
              "6                         1            1  ...       2-4    1\n",
              "7                         1            1  ...       4-6    1\n",
              "8                         1            1  ...      6-12    1\n",
              "9                         1            1  ...  ABOVE_12    1\n",
              "\n",
              "[10 rows x 231 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMWF6jq1H5lC"
      },
      "source": [
        "So now, by just taking a look at the data above, we see that the missing values in the continuous variables were filled.\n",
        "\n",
        "Let's check if we have any other null values in the other columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGVLGk6AHwMH",
        "outputId": "a800f02b-1b7f-42ef-e682-60d5887a251d"
      },
      "source": [
        "check_missing_values(filled_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 45 null values in this dataset.\n",
            "This represents 0.01% of the total number of values in the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7rUOJtCL7PV"
      },
      "source": [
        "We can see we still have some missing values, but significantly less than before. We can simply go ahead and remove these ones, since they represent only a smal percentage of the dataset and are probably from errors in the data.\n",
        "\n",
        "We can also go ahead and remove the observations in which the admitted patients went to the ICU at window 0-2. This is also a recommendation from the hospital itself, regarding machine learning model and the usefulness of the data:\n",
        "\n",
        "**The earlier, the better!**\n",
        "\n",
        "> **Problem:** Early identification of those patients who will develop an adverse course of illness (and need intensive care) is a key for an appropriate treatment (saving lives) and to managing beds and resources.\n",
        "\n",
        "> **Tips & Tricks:** _Whereas a predictive model using all time windows will probably yield a greater accuracy, a nice model using only the first (0-2) is likely to be more clinically relevant._\n",
        "\n",
        "We also have:\n",
        "\n",
        "> [...] patients that went to the ICU on 0-2 window have no available data for predicting, therefore should also be excluded from the sample.\n",
        "\n",
        "So let's do that.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "qMu6bwHMGYai",
        "outputId": "ed215a13-cad1-4e2b-dc73-1fe599bef059"
      },
      "source": [
        "# check the number of 1's in the 0-2 window\n",
        "pd.crosstab(filled_data['WINDOW'], filled_data['ICU'])"
      ],
      "execution_count": null,
      "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>ICU</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WINDOW</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0-2</th>\n",
              "      <td>353</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-4</th>\n",
              "      <td>326</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4-6</th>\n",
              "      <td>286</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6-12</th>\n",
              "      <td>255</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABOVE_12</th>\n",
              "      <td>190</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "ICU         0    1\n",
              "WINDOW            \n",
              "0-2       353   32\n",
              "2-4       326   59\n",
              "4-6       286   99\n",
              "6-12      255  130\n",
              "ABOVE_12  190  195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATKArbctHYBY"
      },
      "source": [
        "As seen above, we have **32 observations in which the patient went to the ICU at the 0-2 window**. So we'll discard these 32 patients from our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "CUQk4IhNIboL",
        "outputId": "f924aac6-a50c-46d3-e4d4-78c463874784"
      },
      "source": [
        "# let's get only the patients which did not went to the ICU in the first two hours of admission\n",
        "to_be_removed = filled_data.query(\"WINDOW=='0-2' and ICU==1\")['PATIENT_VISIT_IDENTIFIER'].values\n",
        "filled_data = filled_data.query(\"PATIENT_VISIT_IDENTIFIER not in @to_be_removed\")\n",
        "# drop the remaining missing values\n",
        "filled_data = filled_data.dropna()\n",
        "\n",
        "filled_data.head()"
      ],
      "execution_count": null,
      "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>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>ALBUMIN_MEDIAN</th>\n",
              "      <th>ALBUMIN_MEAN</th>\n",
              "      <th>ALBUMIN_MIN</th>\n",
              "      <th>ALBUMIN_MAX</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_MEAN</th>\n",
              "      <th>BE_ARTERIAL_MIN</th>\n",
              "      <th>BE_ARTERIAL_MAX</th>\n",
              "      <th>BE_ARTERIAL_DIFF</th>\n",
              "      <th>BE_VENOUS_MEDIAN</th>\n",
              "      <th>BE_VENOUS_MEAN</th>\n",
              "      <th>BE_VENOUS_MIN</th>\n",
              "      <th>BE_VENOUS_MAX</th>\n",
              "      <th>BE_VENOUS_DIFF</th>\n",
              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_MEAN</th>\n",
              "      <th>BIC_ARTERIAL_MIN</th>\n",
              "      <th>BIC_ARTERIAL_MAX</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_MEAN</th>\n",
              "      <th>BIC_VENOUS_MIN</th>\n",
              "      <th>BIC_VENOUS_MAX</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_MEAN</th>\n",
              "      <th>...</th>\n",
              "      <th>DIMER_MAX</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
              "      <th>HEART_RATE_MEDIAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEDIAN</th>\n",
              "      <th>TEMPERATURE_MEDIAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
              "      <th>HEART_RATE_MIN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>TEMPERATURE_MIN</th>\n",
              "      <th>OXYGEN_SATURATION_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
              "      <th>HEART_RATE_MAX</th>\n",
              "      <th>RESPIRATORY_RATE_MAX</th>\n",
              "      <th>TEMPERATURE_MAX</th>\n",
              "      <th>OXYGEN_SATURATION_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
              "      <th>TEMPERATURE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>WINDOW</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.432836</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-1.00000</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>-1.00000</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.33913</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.33913</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>0.033779</td>\n",
              "      <td>0.665932</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.098901</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.362319</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.33913</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-0.238095</td>\n",
              "      <td>-0.818182</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-0.242282</td>\n",
              "      <td>-0.814433</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  ...    WINDOW  ICU\n",
              "0                         0            1  ...       0-2    0\n",
              "1                         0            1  ...       2-4    0\n",
              "2                         0            1  ...       4-6    0\n",
              "3                         0            1  ...      6-12    0\n",
              "4                         0            1  ...  ABOVE_12    1\n",
              "\n",
              "[5 rows x 231 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujqbVqmXNMpt",
        "outputId": "5d25571e-c314-461b-df02-e22ef846fdae"
      },
      "source": [
        "check_missing_values(filled_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 0 null values in this dataset.\n",
            "This represents 0.0% of the total number of values in the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6BtFDvycg6x",
        "outputId": "17af8e98-b85a-4598-8129-2a22ff0ad2ab"
      },
      "source": [
        "print_data_shape(filled_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows in the dataset: 1760\n",
            "Number of columns in the dataset: 231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8j9MCMsc_2G"
      },
      "source": [
        "Now, we'll group the rows by patient to obtain a dataset where each row represents only one patient, instead of 5 rows per patient."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "4OeDvqn7c5aZ",
        "outputId": "dc6ff900-08d0-46f2-f3d2-ff89088a5432"
      },
      "source": [
        "def prepare_window(rows):\n",
        "    '''\n",
        "    Clean the \"WINDOW\" row in the dataframe.\n",
        "    '''\n",
        "\n",
        "    if(np.any(rows['ICU'])):\n",
        "       rows.loc[rows['WINDOW'] == '0-2', 'ICU'] = 1\n",
        "    # return the WINDOW column for the first two hours\n",
        "    return rows.loc[rows['WINDOW'] == '0-2']\n",
        "\n",
        "\n",
        "# group the data by patient and apply the function above\n",
        "clean_data = filled_data.groupby('PATIENT_VISIT_IDENTIFIER').apply(prepare_window)\n",
        "\n",
        "# change the AGE_PERCENTIL column to a 'category' type\n",
        "clean_data.AGE_PERCENTIL = clean_data.AGE_PERCENTIL.astype('category').cat.codes\n",
        "\n",
        "clean_data.head()"
      ],
      "execution_count": null,
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>ALBUMIN_MEDIAN</th>\n",
              "      <th>ALBUMIN_MEAN</th>\n",
              "      <th>ALBUMIN_MIN</th>\n",
              "      <th>ALBUMIN_MAX</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_MEAN</th>\n",
              "      <th>BE_ARTERIAL_MIN</th>\n",
              "      <th>BE_ARTERIAL_MAX</th>\n",
              "      <th>BE_ARTERIAL_DIFF</th>\n",
              "      <th>BE_VENOUS_MEDIAN</th>\n",
              "      <th>BE_VENOUS_MEAN</th>\n",
              "      <th>BE_VENOUS_MIN</th>\n",
              "      <th>BE_VENOUS_MAX</th>\n",
              "      <th>BE_VENOUS_DIFF</th>\n",
              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_MEAN</th>\n",
              "      <th>BIC_ARTERIAL_MIN</th>\n",
              "      <th>BIC_ARTERIAL_MAX</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_MEAN</th>\n",
              "      <th>BIC_VENOUS_MIN</th>\n",
              "      <th>BIC_VENOUS_MAX</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_MEAN</th>\n",
              "      <th>...</th>\n",
              "      <th>DIMER_MAX</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
              "      <th>HEART_RATE_MEDIAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEDIAN</th>\n",
              "      <th>TEMPERATURE_MEDIAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
              "      <th>HEART_RATE_MIN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>TEMPERATURE_MIN</th>\n",
              "      <th>OXYGEN_SATURATION_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
              "      <th>HEART_RATE_MAX</th>\n",
              "      <th>RESPIRATORY_RATE_MAX</th>\n",
              "      <th>TEMPERATURE_MAX</th>\n",
              "      <th>OXYGEN_SATURATION_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
              "      <th>TEMPERATURE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>WINDOW</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
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              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.432836</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.736842</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>10</th>\n",
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              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.489712</td>\n",
              "      <td>-0.685470</td>\n",
              "      <td>-0.048218</td>\n",
              "      <td>-0.645951</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.935673</td>\n",
              "      <td>-0.506173</td>\n",
              "      <td>-0.815385</td>\n",
              "      <td>-0.056604</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.525773</td>\n",
              "      <td>-0.5125</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.714286</td>\n",
              "      <td>0.604396</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>-0.435897</td>\n",
              "      <td>-0.491892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>0.101449</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.533742</td>\n",
              "      <td>-0.603053</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959596</td>\n",
              "      <td>-0.515528</td>\n",
              "      <td>-0.351328</td>\n",
              "      <td>-0.747001</td>\n",
              "      <td>-0.756272</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.961262</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.012346</td>\n",
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              "      <td>-0.528302</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>-0.528302</td>\n",
              "      <td>-0.448276</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.175258</td>\n",
              "      <td>-0.1125</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>-0.299145</td>\n",
              "      <td>-0.556757</td>\n",
              "      <td>-0.626866</td>\n",
              "      <td>-0.515152</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.684211</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>20</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.196581</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.351351</td>\n",
              "      <td>-0.044776</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>0.072464</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.877301</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.882353</td>\n",
              "      <td>-0.952381</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.883669</td>\n",
              "      <td>-0.956805</td>\n",
              "      <td>-0.870968</td>\n",
              "      <td>-0.953536</td>\n",
              "      <td>-0.980333</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>25</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>0.030928</td>\n",
              "      <td>-0.3750</td>\n",
              "      <td>-0.401709</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>0.252747</td>\n",
              "      <td>0.919192</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.567568</td>\n",
              "      <td>-0.626866</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.826087</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.984733</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976190</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.860870</td>\n",
              "      <td>-0.714460</td>\n",
              "      <td>-0.986481</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.975891</td>\n",
              "      <td>-0.980129</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             PATIENT_VISIT_IDENTIFIER  AGE_ABOVE65  ...  WINDOW  ICU\n",
              "PATIENT_VISIT_IDENTIFIER                                            ...             \n",
              "0                        0                          0            1  ...     0-2    1\n",
              "2                        10                         2            0  ...     0-2    1\n",
              "3                        15                         3            0  ...     0-2    0\n",
              "4                        20                         4            0  ...     0-2    0\n",
              "5                        25                         5            0  ...     0-2    0\n",
              "\n",
              "[5 rows x 231 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhWAvZYjr33u"
      },
      "source": [
        "We can go ahead and drop the column ```WINDOW``` since we now have only one value in it: ```0-2```.  We'll also drop the ```PATIENT_VISIT_IDENTIFIER``` since we don't need this information in order to predict the ICU admission."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MHGuEmWr0kb"
      },
      "source": [
        "clean_data = clean_data.drop(['WINDOW', 'PATIENT_VISIT_IDENTIFIER'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YHxLxKCejUKy",
        "outputId": "7afa0d12-e8b0-4b68-8ff1-efcf3f134927"
      },
      "source": [
        "check_missing_values(clean_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 0 null values in this dataset.\n",
            "This represents 0.0% of the total number of values in the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ivys_ExckMlH",
        "outputId": "eef601f4-21c0-44f8-98c0-fe5ba7e7bd45"
      },
      "source": [
        "print_data_shape(clean_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows in the dataset: 352\n",
            "Number of columns in the dataset: 229\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6Mx-UApnFH8"
      },
      "source": [
        "Comparing to the previous dataframe, we now reduced our information from 1760 rows to only 352!\n",
        "\n",
        "With this new and clean dataset, we'll take a closer look at the variables in order to reduce the large number of columns and explore these features. Furthermore, seeing as we have many features that were created from a single variable, we have to address them and check their correlation against each other. This helps us in reducing the risk of overfitting our machine learning model.\n",
        "\n",
        "For the variables pertaining to the blood results and vital signs, they were expanded by the mean, median, max, min, diff and relative diff. Where ```diff = max - min``` and ```relative diff = diff/median```.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "IWsmTRCX9aBL",
        "outputId": "eb2221bc-d8ee-4575-b931-eb09dac10806"
      },
      "source": [
        "# let's check our data again\n",
        "clean_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
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              "      <th>ALBUMIN_MEAN</th>\n",
              "      <th>ALBUMIN_MIN</th>\n",
              "      <th>ALBUMIN_MAX</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_MEAN</th>\n",
              "      <th>BE_ARTERIAL_MIN</th>\n",
              "      <th>BE_ARTERIAL_MAX</th>\n",
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              "      <th>BE_VENOUS_MAX</th>\n",
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              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_MEAN</th>\n",
              "      <th>BIC_ARTERIAL_MIN</th>\n",
              "      <th>BIC_ARTERIAL_MAX</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_MEAN</th>\n",
              "      <th>BIC_VENOUS_MIN</th>\n",
              "      <th>BIC_VENOUS_MAX</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_MEAN</th>\n",
              "      <th>BILLIRUBIN_MIN</th>\n",
              "      <th>...</th>\n",
              "      <th>DIMER_MIN</th>\n",
              "      <th>DIMER_MAX</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEDIAN</th>\n",
              "      <th>HEART_RATE_MEDIAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEDIAN</th>\n",
              "      <th>TEMPERATURE_MEDIAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEDIAN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MIN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MIN</th>\n",
              "      <th>HEART_RATE_MIN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>TEMPERATURE_MIN</th>\n",
              "      <th>OXYGEN_SATURATION_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MAX</th>\n",
              "      <th>HEART_RATE_MAX</th>\n",
              "      <th>RESPIRATORY_RATE_MAX</th>\n",
              "      <th>TEMPERATURE_MAX</th>\n",
              "      <th>OXYGEN_SATURATION_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF_REL</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF_REL</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF_REL</th>\n",
              "      <th>TEMPERATURE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <td>1.0</td>\n",
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              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.432836</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.736842</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.489712</td>\n",
              "      <td>-0.685470</td>\n",
              "      <td>-0.048218</td>\n",
              "      <td>-0.645951</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.935673</td>\n",
              "      <td>-0.506173</td>\n",
              "      <td>-0.815385</td>\n",
              "      <td>-0.056604</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.525773</td>\n",
              "      <td>-0.5125</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.714286</td>\n",
              "      <td>0.604396</td>\n",
              "      <td>0.959596</td>\n",
              "      <td>-0.435897</td>\n",
              "      <td>-0.491892</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>0.101449</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.533742</td>\n",
              "      <td>-0.603053</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959596</td>\n",
              "      <td>-0.515528</td>\n",
              "      <td>-0.351328</td>\n",
              "      <td>-0.747001</td>\n",
              "      <td>-0.756272</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.961262</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.972789</td>\n",
              "      <td>-0.972789</td>\n",
              "      <td>-0.972789</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>-0.528302</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>-0.528302</td>\n",
              "      <td>-0.448276</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.175258</td>\n",
              "      <td>-0.1125</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>-0.299145</td>\n",
              "      <td>-0.556757</td>\n",
              "      <td>-0.626866</td>\n",
              "      <td>-0.515152</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.684211</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",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.196581</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>0.538462</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.351351</td>\n",
              "      <td>-0.044776</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>0.072464</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.877301</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.882353</td>\n",
              "      <td>-0.952381</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.883669</td>\n",
              "      <td>-0.956805</td>\n",
              "      <td>-0.870968</td>\n",
              "      <td>-0.953536</td>\n",
              "      <td>-0.980333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>0.030928</td>\n",
              "      <td>-0.3750</td>\n",
              "      <td>-0.401709</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>0.252747</td>\n",
              "      <td>0.919192</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.567568</td>\n",
              "      <td>-0.626866</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.826087</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.984733</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976190</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.860870</td>\n",
              "      <td>-0.714460</td>\n",
              "      <td>-0.986481</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.975891</td>\n",
              "      <td>-0.980129</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 229 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             AGE_ABOVE65  ...  ICU\n",
              "PATIENT_VISIT_IDENTIFIER                  ...     \n",
              "0                        0             1  ...    1\n",
              "2                        10            0  ...    1\n",
              "3                        15            0  ...    0\n",
              "4                        20            0  ...    0\n",
              "5                        25            0  ...    0\n",
              "\n",
              "[5 rows x 229 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uK9vM6pTQlEC"
      },
      "source": [
        "To check the highly correlated columns, we first declare a function that checks the correlation between each variable in the whole dataset and then it removes the ones that pass a certain threshold."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKc3D3UBOcrz"
      },
      "source": [
        "def remove_corr_var(data, threshold = 0.95):\n",
        "    '''\n",
        "    Given a dataset, this function calculates the correlation between the\n",
        "    variable and return a new dataset with only the variable with a correlation\n",
        "    below a certain threshold.\n",
        "\n",
        "    Input:\n",
        "    data -> a pandas dataframe\n",
        "    threshold -> a number (usually between 0 and 1) used as a cutoff for the \n",
        "                 correlation, default = 0.95\n",
        "    '''\n",
        "\n",
        "    matrix_corr = data.iloc[:,4:-2].corr().abs()\n",
        "    matrix_upper = matrix_corr.where(np.triu(np.ones(matrix_corr.shape), k = 1).astype(np.bool))\n",
        "    to_remove = [coluna for coluna in matrix_upper.columns if any(matrix_upper[coluna] > threshold)]\n",
        "    return data.drop(to_remove, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuwrPBo1QfK5"
      },
      "source": [
        "final_data = remove_corr_var(clean_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "xX_7pPXNR5Kx",
        "outputId": "9824a8b7-509f-434f-fc47-334af825af75"
      },
      "source": [
        "final_data.head()"
      ],
      "execution_count": null,
      "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",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>ALBUMIN_MEDIAN</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_DIFF</th>\n",
              "      <th>BE_VENOUS_MEDIAN</th>\n",
              "      <th>BE_VENOUS_DIFF</th>\n",
              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_DIFF</th>\n",
              "      <th>BLAST_MEDIAN</th>\n",
              "      <th>BLAST_DIFF</th>\n",
              "      <th>CALCIUM_MEDIAN</th>\n",
              "      <th>CALCIUM_DIFF</th>\n",
              "      <th>CREATININ_MEDIAN</th>\n",
              "      <th>CREATININ_DIFF</th>\n",
              "      <th>FFA_MEDIAN</th>\n",
              "      <th>FFA_DIFF</th>\n",
              "      <th>GGT_MEDIAN</th>\n",
              "      <th>GGT_DIFF</th>\n",
              "      <th>GLUCOSE_MEDIAN</th>\n",
              "      <th>GLUCOSE_DIFF</th>\n",
              "      <th>HEMATOCRITE_MEDIAN</th>\n",
              "      <th>HEMATOCRITE_DIFF</th>\n",
              "      <th>HEMOGLOBIN_DIFF</th>\n",
              "      <th>INR_MEDIAN</th>\n",
              "      <th>...</th>\n",
              "      <th>PH_ARTERIAL_DIFF</th>\n",
              "      <th>PH_VENOUS_MEDIAN</th>\n",
              "      <th>PH_VENOUS_DIFF</th>\n",
              "      <th>PLATELETS_MEDIAN</th>\n",
              "      <th>PLATELETS_DIFF</th>\n",
              "      <th>POTASSIUM_MEDIAN</th>\n",
              "      <th>POTASSIUM_DIFF</th>\n",
              "      <th>SAT02_ARTERIAL_MEDIAN</th>\n",
              "      <th>SAT02_ARTERIAL_DIFF</th>\n",
              "      <th>SAT02_VENOUS_MEDIAN</th>\n",
              "      <th>SAT02_VENOUS_DIFF</th>\n",
              "      <th>SODIUM_MEDIAN</th>\n",
              "      <th>SODIUM_DIFF</th>\n",
              "      <th>TGO_MEDIAN</th>\n",
              "      <th>TGO_DIFF</th>\n",
              "      <th>TGP_MEDIAN</th>\n",
              "      <th>TGP_DIFF</th>\n",
              "      <th>TTPA_MEDIAN</th>\n",
              "      <th>TTPA_DIFF</th>\n",
              "      <th>UREA_MEDIAN</th>\n",
              "      <th>UREA_DIFF</th>\n",
              "      <th>DIMER_MEDIAN</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.183673</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.868365</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.945093</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.090147</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.932246</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.540721</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.518519</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.028571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.997201</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.990854</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.825613</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.836145</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.247863</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.912243</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.958528</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.780261</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.144654</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.399199</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.703704</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.085714</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.995428</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.986662</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.836145</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.489712</td>\n",
              "      <td>-0.685470</td>\n",
              "      <td>-0.048218</td>\n",
              "      <td>-0.645951</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.935673</td>\n",
              "      <td>-0.714286</td>\n",
              "      <td>-0.435897</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.533742</td>\n",
              "      <td>-0.603053</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959596</td>\n",
              "      <td>-0.747001</td>\n",
              "      <td>-0.961262</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.972789</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.326531</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.968861</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.194030</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.316589</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.203354</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.564753</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.580247</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.989549</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.956555</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.937349</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>-0.528302</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>-0.299145</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.913659</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.829424</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938084</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.851024</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.358491</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.457944</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.592593</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.998507</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.991235</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.903614</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.877301</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.882353</td>\n",
              "      <td>-0.952381</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.956805</td>\n",
              "      <td>-0.980333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891012</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.958528</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.291405</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.292390</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.666667</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.085714</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.997947</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.988948</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.884337</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.826087</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.984733</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976190</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.986481</td>\n",
              "      <td>-0.980129</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 99 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             AGE_ABOVE65  ...  ICU\n",
              "PATIENT_VISIT_IDENTIFIER                  ...     \n",
              "0                        0             1  ...    1\n",
              "2                        10            0  ...    1\n",
              "3                        15            0  ...    0\n",
              "4                        20            0  ...    0\n",
              "5                        25            0  ...    0\n",
              "\n",
              "[5 rows x 99 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfzyT46KR6yA",
        "outputId": "ea0d8028-c2cd-48a4-d0d0-a95cce9804a4"
      },
      "source": [
        "print_data_shape(final_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of rows in the dataset: 352\n",
            "Number of columns in the dataset: 99\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bf6CVC0Sn8Y"
      },
      "source": [
        "So now we have a dataset with 352 rows, one for each patient, and 99 columns. From these 99 columns, 98 are variables and 1 is the target, the ICU column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3MlmeUfnxdC"
      },
      "source": [
        "# uncomment the following line to download the final data\n",
        "#final_data.to_csv('final_data.csv', index = False)\n",
        "#from google.colab import files\n",
        "#files.download('final_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOF9E7KbS9Xf"
      },
      "source": [
        "<a name=\"eda\"></a>\n",
        "# 7 Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJEzFVsnTR7r"
      },
      "source": [
        "Now we'll take a closer look at some features to check their distribution and whether or not they are balanced in order to be used in a machine learning model. If they are highly unbalanced, it could create a problem for the modelling part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "2SSUREjTSGNl",
        "outputId": "b224aa06-32ac-4c17-f326-c3f2dac8d2bf"
      },
      "source": [
        "# remembering our dataframe\n",
        "final_data.head()"
      ],
      "execution_count": null,
      "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></th>\n",
              "      <th>AGE_ABOVE65</th>\n",
              "      <th>AGE_PERCENTIL</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISEASE GROUPING 1</th>\n",
              "      <th>DISEASE GROUPING 2</th>\n",
              "      <th>DISEASE GROUPING 3</th>\n",
              "      <th>DISEASE GROUPING 4</th>\n",
              "      <th>DISEASE GROUPING 5</th>\n",
              "      <th>DISEASE GROUPING 6</th>\n",
              "      <th>HTN</th>\n",
              "      <th>IMMUNOCOMPROMISED</th>\n",
              "      <th>OTHER</th>\n",
              "      <th>ALBUMIN_MEDIAN</th>\n",
              "      <th>ALBUMIN_DIFF</th>\n",
              "      <th>BE_ARTERIAL_MEDIAN</th>\n",
              "      <th>BE_ARTERIAL_DIFF</th>\n",
              "      <th>BE_VENOUS_MEDIAN</th>\n",
              "      <th>BE_VENOUS_DIFF</th>\n",
              "      <th>BIC_ARTERIAL_MEDIAN</th>\n",
              "      <th>BIC_ARTERIAL_DIFF</th>\n",
              "      <th>BIC_VENOUS_MEDIAN</th>\n",
              "      <th>BIC_VENOUS_DIFF</th>\n",
              "      <th>BILLIRUBIN_MEDIAN</th>\n",
              "      <th>BILLIRUBIN_DIFF</th>\n",
              "      <th>BLAST_MEDIAN</th>\n",
              "      <th>BLAST_DIFF</th>\n",
              "      <th>CALCIUM_MEDIAN</th>\n",
              "      <th>CALCIUM_DIFF</th>\n",
              "      <th>CREATININ_MEDIAN</th>\n",
              "      <th>CREATININ_DIFF</th>\n",
              "      <th>FFA_MEDIAN</th>\n",
              "      <th>FFA_DIFF</th>\n",
              "      <th>GGT_MEDIAN</th>\n",
              "      <th>GGT_DIFF</th>\n",
              "      <th>GLUCOSE_MEDIAN</th>\n",
              "      <th>GLUCOSE_DIFF</th>\n",
              "      <th>HEMATOCRITE_MEDIAN</th>\n",
              "      <th>HEMATOCRITE_DIFF</th>\n",
              "      <th>HEMOGLOBIN_DIFF</th>\n",
              "      <th>INR_MEDIAN</th>\n",
              "      <th>...</th>\n",
              "      <th>PH_ARTERIAL_DIFF</th>\n",
              "      <th>PH_VENOUS_MEDIAN</th>\n",
              "      <th>PH_VENOUS_DIFF</th>\n",
              "      <th>PLATELETS_MEDIAN</th>\n",
              "      <th>PLATELETS_DIFF</th>\n",
              "      <th>POTASSIUM_MEDIAN</th>\n",
              "      <th>POTASSIUM_DIFF</th>\n",
              "      <th>SAT02_ARTERIAL_MEDIAN</th>\n",
              "      <th>SAT02_ARTERIAL_DIFF</th>\n",
              "      <th>SAT02_VENOUS_MEDIAN</th>\n",
              "      <th>SAT02_VENOUS_DIFF</th>\n",
              "      <th>SODIUM_MEDIAN</th>\n",
              "      <th>SODIUM_DIFF</th>\n",
              "      <th>TGO_MEDIAN</th>\n",
              "      <th>TGO_DIFF</th>\n",
              "      <th>TGP_MEDIAN</th>\n",
              "      <th>TGP_DIFF</th>\n",
              "      <th>TTPA_MEDIAN</th>\n",
              "      <th>TTPA_DIFF</th>\n",
              "      <th>UREA_MEDIAN</th>\n",
              "      <th>UREA_DIFF</th>\n",
              "      <th>DIMER_MEDIAN</th>\n",
              "      <th>DIMER_DIFF</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MEAN</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_MEAN</th>\n",
              "      <th>HEART_RATE_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MEAN</th>\n",
              "      <th>TEMPERATURE_MEAN</th>\n",
              "      <th>OXYGEN_SATURATION_MEAN</th>\n",
              "      <th>RESPIRATORY_RATE_MIN</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_MAX</th>\n",
              "      <th>BLOODPRESSURE_DIASTOLIC_DIFF</th>\n",
              "      <th>BLOODPRESSURE_SISTOLIC_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF</th>\n",
              "      <th>RESPIRATORY_RATE_DIFF</th>\n",
              "      <th>TEMPERATURE_DIFF</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF</th>\n",
              "      <th>HEART_RATE_DIFF_REL</th>\n",
              "      <th>OXYGEN_SATURATION_DIFF_REL</th>\n",
              "      <th>ICU</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PATIENT_VISIT_IDENTIFIER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.183673</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.868365</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.945093</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.090147</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.932246</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.540721</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.518519</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.028571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.997201</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.990854</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.825613</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.836145</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.247863</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.912243</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.958528</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.780261</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.144654</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.399199</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.703704</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.085714</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.995428</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.986662</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.836145</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.489712</td>\n",
              "      <td>-0.685470</td>\n",
              "      <td>-0.048218</td>\n",
              "      <td>-0.645951</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.935673</td>\n",
              "      <td>-0.714286</td>\n",
              "      <td>-0.435897</td>\n",
              "      <td>-0.547826</td>\n",
              "      <td>-0.533742</td>\n",
              "      <td>-0.603053</td>\n",
              "      <td>-0.764706</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.959596</td>\n",
              "      <td>-0.747001</td>\n",
              "      <td>-0.961262</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>15</th>\n",
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              "      <td>3</td>\n",
              "      <td>1</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>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.263158</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.972789</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.326531</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.968861</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.194030</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.316589</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.203354</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.564753</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.580247</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.989549</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.956555</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.937349</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>-0.528302</td>\n",
              "      <td>-0.457627</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>-0.357143</td>\n",
              "      <td>-0.299145</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.935113</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.913659</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.829424</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938084</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.851024</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.358491</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.457944</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.592593</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.998507</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.991235</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.903614</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.153846</td>\n",
              "      <td>0.160377</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.868421</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.877301</td>\n",
              "      <td>-0.923664</td>\n",
              "      <td>-0.882353</td>\n",
              "      <td>-0.952381</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.956805</td>\n",
              "      <td>-0.980333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>25</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891012</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.742004</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.958528</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.891993</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.291405</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.959849</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.292390</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.666667</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.345679</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.085714</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.997947</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.988948</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.846633</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.884337</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.037037</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.537736</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>-0.196429</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.826087</td>\n",
              "      <td>-0.754601</td>\n",
              "      <td>-0.984733</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.976190</td>\n",
              "      <td>-0.979798</td>\n",
              "      <td>-0.986481</td>\n",
              "      <td>-0.980129</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 99 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             AGE_ABOVE65  ...  ICU\n",
              "PATIENT_VISIT_IDENTIFIER                  ...     \n",
              "0                        0             1  ...    1\n",
              "2                        10            0  ...    1\n",
              "3                        15            0  ...    0\n",
              "4                        20            0  ...    0\n",
              "5                        25            0  ...    0\n",
              "\n",
              "[5 rows x 99 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOZtC0FqWMLh"
      },
      "source": [
        "We have 03 patient demographic information variables: ```AGE_ABOVE65```, ```AGE_PERCENTIL``` and ```GENDER```. Let's take a look at them together with the ```ICU``` column, our target variable. For reference, we can also take a look at the other continuous variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-QIthnoK2BD"
      },
      "source": [
        "<a name=\"pdi\"></a>\n",
        "## 7.1 Patient Demographic Information"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q36PEEV05H5G"
      },
      "source": [
        "Let's check graphically how many patiets are in each category of demographic information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "M9MOe4L_Tkwo",
        "outputId": "3ce27bd8-0e05-42f1-d9fe-33e5f8c4fd84"
      },
      "source": [
        "color = [GREEN2, RED1]\n",
        "ax = final_data['ICU'].value_counts().plot(kind = 'bar',\n",
        "                                           color = color,\n",
        "                                           figsize = (10, 6),\n",
        "                                           width = 0.9)\n",
        "\n",
        "ax.tick_params(color = 'darkgrey', bottom = 'off')\n",
        "\n",
        "ax.tick_params(axis = 'both',\n",
        "               which = 'both',\n",
        "               bottom = 'off',\n",
        "               top = 'off',\n",
        "               labelbottom = 'off',\n",
        "               right = 'off',\n",
        "               left = 'off',\n",
        "               labelleft = 'off')\n",
        "\n",
        "sns.despine(left = True, bottom = True)\n",
        "ax.set_xticklabels(['Patient did not\\n go to the ICU', 'Patient went\\n to the ICU'],\n",
        "                   fontsize = 18, color = GRAY4)\n",
        "\n",
        "ax.get_yaxis().set_visible(False)\n",
        "plt.xticks(rotation = 0)\n",
        "plt.title('ICU Admission', fontsize = 25, loc = 'left', color = GRAY2)\n",
        "\n",
        "perc_not_icu = round(len(final_data[final_data['ICU'] != 1]) /  len(final_data) * 100, 1)\n",
        "perc_icu = round(len(final_data[final_data['ICU'] == 1]) /  len(final_data) * 100, 1)\n",
        "labels = [str(len(final_data[final_data['ICU'] == 0])) + ' patients\\n(' + str(perc_not_icu) + ')%',\n",
        "          str(len(final_data[final_data['ICU'] == 1])) + ' patients\\n(' + str(perc_icu) + ')%']\n",
        "          \n",
        "rects = ax.patches\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    ax.text(rect.get_x() + rect.get_width() / 2,\n",
        "            height-30,\n",
        "            label,\n",
        "            ha = 'center',\n",
        "            va = 'bottom',\n",
        "            color = 'white',\n",
        "            fontsize = 22,\n",
        "            fontweight = 'bold')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdQDFtD_NCET"
      },
      "source": [
        "We don't have a hard threshold for when to consider our data unbalanced, but [some sources](https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data) indicate that we can begin to consider our data **unbalanced** when the difference between the classes is **above 20%**. Here in our case, the difference is only **7.4%**, so we'll consider our **target variable balanced**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1qmaADdOz8M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "c719abf4-eb80-4b01-898a-46b3844e6e06"
      },
      "source": [
        "color = [GREEN2, RED1]\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, sharey = True, figsize = (16, 7))\n",
        "\n",
        "final_data[final_data.GENDER == 0].ICU.value_counts().sort_values(ascending = True).plot(kind = 'bar',\n",
        "                                                           ax = axes[0],\n",
        "                                                           color = color,\n",
        "                                                           width = 0.9)\n",
        "axes[0].set_title('Gender 0: Man', loc = 'left', fontsize = 19, color = GRAY2)\n",
        "\n",
        "final_data[final_data.GENDER == 1].ICU.value_counts().plot(kind = 'bar',\n",
        "                                                           ax = axes[1],\n",
        "                                                           color = color,\n",
        "                                                           width = 0.9)\n",
        "axes[1].set_title('Gender 1: Woman', loc = 'left', fontsize = 19, color = GRAY2)\n",
        "\n",
        "sns.despine(left = True, bottom = True)\n",
        "\n",
        "for i in [0, 1]:\n",
        "    axes[i].tick_params(color = 'darkgrey', bottom = 'off')\n",
        "    axes[i].tick_params(axis = 'both',\n",
        "                        which = 'both',\n",
        "                        bottom = 'off',\n",
        "                        top = 'off',\n",
        "                        labelbottom = 'off',\n",
        "                        right = 'off',\n",
        "                        left = 'off',\n",
        "                        labelleft = 'off')\n",
        "    axes[i].set_xticklabels(['Patient did not\\n go to the ICU', 'Patient went\\n to the ICU'],\n",
        "                        fontsize = 18, color = GRAY4, rotation = 0)\n",
        "    axes[i].get_yaxis().set_visible(False)\n",
        "\n",
        "plt.suptitle('ICU Admission by Gender', fontsize = 24, color = GRAY1, y = 1)\n",
        "\n",
        "# Code for chart annotation\n",
        "\n",
        "perc_not_icu_0 = round(final_data[final_data.GENDER == 0].ICU.value_counts()[0] /  len(final_data[final_data.GENDER == 0]) * 100, 1)\n",
        "perc_icu_0 = round(final_data[final_data.GENDER == 0].ICU.value_counts()[1] /  len(final_data[final_data.GENDER == 0]) * 100, 1)\n",
        "labels = [str(final_data[final_data.GENDER == 0].ICU.value_counts()[0]) + ' patients\\n(' + str(perc_not_icu_0) + ')%',\n",
        "          str(final_data[final_data.GENDER == 0].ICU.value_counts()[1]) + ' patients\\n(' + str(perc_icu_0) + ')%']\n",
        "          \n",
        "rects = axes[0].patches\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    axes[0].text(rect.get_x() + rect.get_width() / 2,\n",
        "            height-15,\n",
        "            label,\n",
        "            ha = 'center',\n",
        "            va = 'bottom',\n",
        "            color = 'white',\n",
        "            fontsize = 18,\n",
        "            fontweight = 'bold')\n",
        "    \n",
        "perc_not_icu_1 = round(final_data[final_data.GENDER == 1].ICU.value_counts()[0] /  len(final_data[final_data.GENDER == 1]) * 100, 1)\n",
        "perc_icu_1 = round(final_data[final_data.GENDER == 1].ICU.value_counts()[1] /  len(final_data[final_data.GENDER == 1]) * 100, 1)\n",
        "labels = [str(final_data[final_data.GENDER == 1].ICU.value_counts()[0]) + ' patients\\n(' + str(perc_not_icu_1) + ')%',\n",
        "          str(final_data[final_data.GENDER == 1].ICU.value_counts()[1]) + ' patients\\n(' + str(perc_icu_1) + ')%']\n",
        "          \n",
        "rects = axes[1].patches\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    axes[1].text(rect.get_x() + rect.get_width() / 2,\n",
        "            height-15,\n",
        "            label,\n",
        "            ha = 'center',\n",
        "            va = 'bottom',\n",
        "            color = 'white',\n",
        "            fontsize = 18,\n",
        "            fontweight = 'bold')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uy2eCXOdw7C6"
      },
      "source": [
        "Here we can immediately see some interesting facts about how many man go to the ICU in contrast to the women.\n",
        "\n",
        "This phenomenon has already been well documented in this study: [Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission](nature.com/articles/s41467-020-19741-6)\n",
        "\n",
        "So gender really plays an important role in the ICU prediction we'll be making."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "fjVs9BrZidgn",
        "outputId": "af00523a-60b2-495b-c777-b6872b2e0fb8"
      },
      "source": [
        "color = [GREEN2, RED1]\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, sharey = True, figsize = (16, 7))\n",
        "\n",
        "final_data[final_data.AGE_ABOVE65 == 0].ICU.value_counts().plot(kind = 'bar',\n",
        "                                                           ax = axes[0],\n",
        "                                                           color = color,\n",
        "                                                           width = 0.9)\n",
        "axes[0].set_title('Patient is Younger than 65 Years', loc = 'left', fontsize = 19, color = GRAY2)\n",
        "\n",
        "final_data[final_data.AGE_ABOVE65 == 1].ICU.value_counts().sort_values(ascending = True).plot(kind = 'bar',\n",
        "                                                           ax = axes[1],\n",
        "                                                           color = color,\n",
        "                                                           width = 0.9)\n",
        "axes[1].set_title('Patient is Older than 65 Years', loc = 'left', fontsize = 19, color = GRAY2)\n",
        "\n",
        "sns.despine(left = True, bottom = True)\n",
        "\n",
        "for i in [0, 1]:\n",
        "    axes[i].tick_params(color = 'darkgrey', bottom = 'off')\n",
        "    axes[i].tick_params(axis = 'both',\n",
        "                        which = 'both',\n",
        "                        bottom = 'off',\n",
        "                        top = 'off',\n",
        "                        labelbottom = 'off',\n",
        "                        right = 'off',\n",
        "                        left = 'off',\n",
        "                        labelleft = 'off')\n",
        "    axes[i].set_xticklabels(['Patient did not\\n go to the ICU', 'Patient went\\n to the ICU'],\n",
        "                        fontsize = 18, color = GRAY4, rotation = 0)\n",
        "    axes[i].get_yaxis().set_visible(False)\n",
        "\n",
        "plt.suptitle('ICU Admission by Age', fontsize = 24, color = GRAY1, y = 1)\n",
        "\n",
        "# Code for chart annotation\n",
        "\n",
        "per_not_icu_0 = round(final_data[final_data.AGE_ABOVE65 == 0].ICU.value_counts()[0] /  len(final_data[final_data.AGE_ABOVE65 == 0]) * 100, 1)\n",
        "per_icu_0 = round(final_data[final_data.AGE_ABOVE65 == 0].ICU.value_counts()[1] /  len(final_data[final_data.AGE_ABOVE65 == 0]) * 100, 1)\n",
        "labels = [str(final_data[final_data.AGE_ABOVE65 == 0].ICU.value_counts()[0]) + ' patients\\n(' + str(per_not_icu_0) + ')%',\n",
        "          str(final_data[final_data.AGE_ABOVE65 == 0].ICU.value_counts()[1]) + ' patients\\n(' + str(per_icu_0) + ')%']\n",
        "          \n",
        "rects = axes[0].patches\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    axes[0].text(rect.get_x() + rect.get_width() / 2,\n",
        "            height-18,\n",
        "            label,\n",
        "            ha = 'center',\n",
        "            va = 'bottom',\n",
        "            color = 'white',\n",
        "            fontsize = 18,\n",
        "            fontweight = 'bold')\n",
        "    \n",
        "per_not_icu_1 = round(final_data[final_data.AGE_ABOVE65 == 1].ICU.value_counts()[0] /  len(final_data[final_data.AGE_ABOVE65 == 1]) * 100, 1)\n",
        "per_icu_1 = round(final_data[final_data.AGE_ABOVE65 == 1].ICU.value_counts()[1] /  len(final_data[final_data.AGE_ABOVE65 == 1]) * 100, 1)\n",
        "labels = [str(final_data[final_data.AGE_ABOVE65 == 1].ICU.value_counts()[0]) + ' patients\\n(' + str(per_not_icu_1) + ')%',\n",
        "          str(final_data[final_data.AGE_ABOVE65 == 1].ICU.value_counts()[1]) + ' patients\\n(' + str(per_icu_1) + ')%']\n",
        "          \n",
        "rects = axes[1].patches\n",
        "for rect, label in zip(rects, labels):\n",
        "    height = rect.get_height()\n",
        "    axes[1].text(rect.get_x() + rect.get_width() / 2,\n",
        "            height-18,\n",
        "            label,\n",
        "            ha = 'center',\n",
        "            va = 'bottom',\n",
        "            color = 'white',\n",
        "            fontsize = 18,\n",
        "            fontweight = 'bold')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM-l0HtF6kNh"
      },
      "source": [
        "It's also noticeable how age affects ICU admission. Let's see a more detailed breakdown below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "iNYf5blHuYFw",
        "outputId": "0ae9bd02-cb0a-4eb3-dada-a917e71552d5"
      },
      "source": [
        "label_names = ['10th', '20th', '30th', '40th', '50th', '60th', '70th', '80th', '90th', '>90th']\n",
        "label_codes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
        "\n",
        "not_icu = []\n",
        "icu = []\n",
        "for code in label_codes:\n",
        "    not_icu.append(final_data[final_data.AGE_PERCENTIL == code].ICU.value_counts()[0])\n",
        "    icu.append(final_data[final_data.AGE_PERCENTIL == code].ICU.value_counts()[1])\n",
        "\n",
        "x = np.arange(len(label_names))\n",
        "width = 0.35\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (15, 8))\n",
        "rects1 = ax.bar(x - width/2, not_icu, width, label = 'No ICU', color = GREEN2)\n",
        "rects2 = ax.bar(x + width/2, icu, width, label = 'ICU', color = RED1)\n",
        "\n",
        "ax.set_ylabel('Number of Patients', fontsize = 18)\n",
        "ax.set_xlabel('Age Percentil', fontsize = 18, color = GRAY4)\n",
        "ax.set_title('ICU Admission by Age Group', loc = 'left', fontsize = 24, color = GRAY2)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(label_names, fontsize = 18, color = GRAY4, rotation = 0)\n",
        "ax.yaxis.label.set_color(GRAY4)\n",
        "ax.tick_params(axis = 'both', colors = GRAY4)\n",
        "ax.tick_params(axis = 'y', labelsize = 18)\n",
        "ax.set_xticklabels(label_names)\n",
        "plt.legend(fontsize = 18, frameon = False)\n",
        "sns.despine()\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lD5Veni-6tUA"
      },
      "source": [
        "Here, it is imediate to see that people above 70 years of age are highly more susceptible in needing intensive care than younger people.\n",
        "\n",
        "This also has been largely studied and we all know by know how age is a risk factor for Covid-19.\n",
        "\n",
        "_Source:_ [The impact of frailty on survival in elderly intensive care patients with COVID-19](https://ccforum.biomedcentral.com/articles/10.1186/s13054-021-03551-3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJMhCECKK7U2"
      },
      "source": [
        "<a name=\"ppgd\"></a>\n",
        "## 7.2 Patient Previous Grouped Diseases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "8s2JxeT8N1Vb",
        "outputId": "f4a3ce5a-0ee2-42c5-9058-fe32f3b4d63b"
      },
      "source": [
        "diseases = {\n",
        "    'DISEASE GROUPING 1': {'not_icu': [], 'icu': []},\n",
        "    'DISEASE GROUPING 2': {'not_icu': [], 'icu': []},\n",
        "    'DISEASE GROUPING 3': {'not_icu': [], 'icu': []},\n",
        "    'DISEASE GROUPING 4': {'not_icu': [], 'icu': []},\n",
        "    'DISEASE GROUPING 5': {'not_icu': [], 'icu': []},\n",
        "    'DISEASE GROUPING 6': {'not_icu': [], 'icu': []},\n",
        "    'HTN': {'not_icu': [], 'icu': []},\n",
        "    'IMMUNOCOMPROMISED': {'not_icu': [], 'icu': []},\n",
        "    'OTHER': {'not_icu': [], 'icu': []}\n",
        "    }\n",
        "color = [GREEN2, RED1]\n",
        "\n",
        "label_names = ['Patient doesn\\'t have\\nGroup Disease', 'Patient has\\nGroup Disease']\n",
        "\n",
        "for disease in diseases.keys():\n",
        "    for num in [0, 1]:\n",
        "        diseases[disease]['not_icu'].append(final_data[final_data[disease] == num].ICU.value_counts()[0])\n",
        "        diseases[disease]['icu'].append(final_data[final_data[disease] == num].ICU.value_counts()[1])\n",
        "\n",
        "x = np.arange(len(label_names))\n",
        "width = 0.40\n",
        "\n",
        "ax = [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
        "fig, ((ax[0], ax[1], ax[2]), (ax[3], ax[4], ax[5]), (ax[6],ax[7], ax[8])) = plt.subplots(3, 3, sharey = True, figsize=(16, 10))\n",
        "\n",
        "for i in [0, 1, 2, 3, 4, 5, 6, 7, 8]:\n",
        "    disease = list(diseases.keys())[i]\n",
        "    ax[i].bar(x - width/2, diseases[disease]['not_icu'], width, label = 'No ICU', color = GREEN2)\n",
        "    ax[i].bar(x + width/2, diseases[disease]['icu'], width, label = 'ICU', color = RED1)\n",
        "    #ax[i].set_ylabel('Number of Patients', fontsize = 16)\n",
        "    ax[i].set_title(disease.title(), loc = 'left', fontsize = 18, color = GRAY2)\n",
        "    ax[i].set_xticks(x)\n",
        "    ax[i].set_xticklabels(label_names, fontsize = 12, color = GRAY4, rotation = 0)\n",
        "    ax[i].yaxis.label.set_color(GRAY4)\n",
        "    ax[i].tick_params(axis = 'both', colors = GRAY4)\n",
        "    ax[i].tick_params(axis = 'y', labelsize = 14)\n",
        "    ax[i].set_xticklabels(label_names)\n",
        "\n",
        "ax[2].legend(fontsize = 16, frameon = False, loc = 'upper right', bbox_to_anchor = (1.3, 1.0))\n",
        "sns.despine()\n",
        "plt.suptitle('ICU Admission by Disease Grouping', color = GRAY1, fontsize = 22, y = 1.05)\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZ-UVqhp5dnG"
      },
      "source": [
        "Here we can see how some disease groups directly impact in the ICU need for a patient. In almost all cases, patients with previous diseases are more prone to need intensive care for Covid-19 than those without previous diseases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Au8eQaor7p4D"
      },
      "source": [
        "<a name=\"vitals\"></a>\n",
        "## 7.3 Vital Signs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxIW_72AOMrs"
      },
      "source": [
        "We have different measurement that are expanded for various metrics. Here we'll only use the mean of the vital signs columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "bdCafaoy2wjP",
        "outputId": "8a438559-742a-45af-e2ce-d58cc5fcf58c"
      },
      "source": [
        "columns = ['BLOODPRESSURE_DIASTOLIC_MEAN', 'BLOODPRESSURE_SISTOLIC_MEAN',\n",
        "            'HEART_RATE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
        "            'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'ICU']\n",
        "\n",
        "ax = [0, 1, 2, 3, 4, 5]\n",
        "fig, ((ax[0], ax[1], ax[2]), (ax[3], ax[4], ax[5])) = plt.subplots(nrows = 2,\n",
        "                                                                   ncols = 3,\n",
        "                                                                   sharex = True,\n",
        "                                                                   figsize = (16, 10))\n",
        "\n",
        "for i in [0, 1, 2, 3, 4, 5]:\n",
        "    column = columns[i]\n",
        "    temp = final_data.reset_index()[[column, 'ICU']]\n",
        "    mdf = pd.melt(temp, id_vars = ['ICU'], var_name = [column[:-5]])\n",
        "    sns.boxplot(x = 'value', y = column[:-5], hue = 'ICU', data = mdf, palette = color, ax = ax[i])\n",
        "    ax[i].set_yticklabels([])\n",
        "    ax[i].set_ylabel(column[:-5], fontsize = 15)\n",
        "    if i != 2:\n",
        "        ax[i].legend([],[], frameon = False)\n",
        "    for j in ax[i].get_xticklabels():\n",
        "        j.set_fontsize(13)\n",
        "        j.set_color(GRAY1)\n",
        "sns.despine()\n",
        "plt.suptitle('ICU Admission by Vital Signs Results', fontsize = 20, color = GRAY1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t_YXSAJEcz2"
      },
      "source": [
        "**What information can we gather from this charts?**\n",
        "\n",
        "First of all, the data, as seen before, has been scaled to fit between -1 and 1 in order to anonymize it, so we cannot be sure what exactly are these values. But we can make some assumptions from this data as is.\n",
        "\n",
        "First, ```BLOODPRESSURE_DIASTOLIC``` in patients that needed to go to the ICU is a bit lower than in those patients that didn't go to the ICU. But the ```BLOODPRESSURE_SISTOLIC``` is just a bit above in those patients in the ICU, as oposed to those out of the ICU.\n",
        "\n",
        "The ```HEART_RATE``` is a bit lower on ICU patients, but with outliers in both groups.\n",
        "\n",
        "One of the most relevant features, ```OXYGEN_SATURATION``` has many outliers in the group that needed the ICU. A low oxygen saturation is a cause for concern in respiratory and COVID-19 patients. The ```RESPIRATORY_RATE``` is also higher in patients with admission to the ICU, as seen in the chart above.\n",
        "\n",
        "_Source:_ [Oxygenation and Ventilation](https://www.covid19treatmentguidelines.nih.gov/management/critical-care/oxygenation-and-ventilation/)\n",
        "\n",
        "The average ```TEMPERATURE``` is also higher in those patients that needed to go to the ICU, which could represent fever as a symptom.\n",
        "\n",
        "_Source:_ [Symptoms of COVID-19](https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtUOORN-KXz3"
      },
      "source": [
        "<a name=\"blood\"></a>\n",
        "## 7.4 Blood Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFLkgpsF700B"
      },
      "source": [
        "Here we will use the median of the relevant columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rztds_aX9Xa9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0b07df1-8416-4e70-bd9d-29545d02f1f1"
      },
      "source": [
        "# all other columns represent the blood results\n",
        "columns_to_drop = ['BLOODPRESSURE_DIASTOLIC_MEAN', 'BLOODPRESSURE_SISTOLIC_MEAN',\n",
        "            'HEART_RATE_MEAN', 'OXYGEN_SATURATION_MEAN',\n",
        "            'RESPIRATORY_RATE_MEAN', 'TEMPERATURE_MEAN', 'GENDER',\n",
        "            'AGE_ABOVE65', 'AGE_PERCENTIL', 'DISEASE GROUPING 1', 'DISEASE GROUPING 2',\n",
        "            'DISEASE GROUPING 3', 'DISEASE GROUPING 4', 'DISEASE GROUPING 5',\n",
        "            'DISEASE GROUPING 6', 'HTN', 'IMMUNOCOMPROMISED', 'OTHER']\n",
        "\n",
        "# we only want the columns that end with '_MEDIAN'\n",
        "blood_results = final_data.drop(columns_to_drop, axis = 1).columns[final_data.drop(columns_to_drop, axis = 1).columns.str.endswith('_MEDIAN')]\n",
        "blood_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ALBUMIN_MEDIAN', 'BE_ARTERIAL_MEDIAN', 'BE_VENOUS_MEDIAN',\n",
              "       'BIC_ARTERIAL_MEDIAN', 'BIC_VENOUS_MEDIAN', 'BILLIRUBIN_MEDIAN',\n",
              "       'BLAST_MEDIAN', 'CALCIUM_MEDIAN', 'CREATININ_MEDIAN', 'FFA_MEDIAN',\n",
              "       'GGT_MEDIAN', 'GLUCOSE_MEDIAN', 'HEMATOCRITE_MEDIAN', 'INR_MEDIAN',\n",
              "       'LACTATE_MEDIAN', 'LEUKOCYTES_MEDIAN', 'LINFOCITOS_MEDIAN',\n",
              "       'P02_ARTERIAL_MEDIAN', 'P02_VENOUS_MEDIAN', 'PC02_ARTERIAL_MEDIAN',\n",
              "       'PC02_VENOUS_MEDIAN', 'PCR_MEDIAN', 'PH_ARTERIAL_MEDIAN',\n",
              "       'PH_VENOUS_MEDIAN', 'PLATELETS_MEDIAN', 'POTASSIUM_MEDIAN',\n",
              "       'SAT02_ARTERIAL_MEDIAN', 'SAT02_VENOUS_MEDIAN', 'SODIUM_MEDIAN',\n",
              "       'TGO_MEDIAN', 'TGP_MEDIAN', 'TTPA_MEDIAN', 'UREA_MEDIAN',\n",
              "       'DIMER_MEDIAN'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJC1nUX8VO4s"
      },
      "source": [
        "From the above columns let's pick some relevant ones so as not to plot them all and overwhelm our charts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOxja10iVYCn"
      },
      "source": [
        "blood_results = ['CALCIUM_MEDIAN', 'HEMATOCRITE_MEDIAN', 'LACTATE_MEDIAN',\n",
        "                 'LEUKOCYTES_MEDIAN', 'LINFOCITOS_MEDIAN', 'PC02_ARTERIAL_MEDIAN',\n",
        "                 'PC02_VENOUS_MEDIAN', 'PCR_MEDIAN', 'PH_ARTERIAL_MEDIAN',\n",
        "                 'PH_VENOUS_MEDIAN', 'PLATELETS_MEDIAN', 'POTASSIUM_MEDIAN',\n",
        "                 'SAT02_ARTERIAL_MEDIAN', 'SAT02_VENOUS_MEDIAN', 'UREA_MEDIAN']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "sZc4AyoDLDjG",
        "outputId": "9c74db4c-7b3f-43d0-c606-b19a385808ca"
      },
      "source": [
        "columns = list(blood_results)\n",
        "columns.append('ICU')\n",
        "\n",
        "ax = list(range(0, 15))\n",
        "fig, ((ax[0], ax[1], ax[2], ax[3], ax[4]),\n",
        "      (ax[5], ax[6], ax[7], ax[8], ax[9]),\n",
        "      (ax[10], ax[11], ax[12], ax[13], ax[14])) = plt.subplots(ncols = 5, nrows = 3, sharex = True, figsize = (20, 10))\n",
        "\n",
        "for i in range(0, 15):\n",
        "    column = columns[i]\n",
        "    temp = final_data.reset_index()[[column, 'ICU']]\n",
        "    mdf = pd.melt(temp, id_vars = ['ICU'], var_name = [column[:-7]])\n",
        "    sns.boxplot(x = 'value', y = column[:-7], hue = 'ICU', data = mdf, palette = color, ax = ax[i])\n",
        "    ax[i].set_yticklabels([])\n",
        "    ax[i].set_ylabel(column[:-7], fontsize = 15)\n",
        "    if i != 4:\n",
        "        ax[i].legend([],[], frameon = False)\n",
        "    for j in ax[i].get_xticklabels():\n",
        "        j.set_fontsize(13)\n",
        "        j.set_color(GRAY1)\n",
        "sns.despine()\n",
        "plt.suptitle('ICU Admission by Blood Results', fontsize = 25, color = GRAY1, y = 0.95)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3XkKqPnJTUfn"
      },
      "source": [
        "We can see that there is a variation in the results from people who did not go to the ICU against those who had to be admitted to the ICU.\n",
        "\n",
        "We have lower ```HEMATOCRITE```, ```LINFOCITOS``` and ```PLATELETS``` in people who went to the ICU. And higher ```LEUKOCYTES```, ```PCR```, ```POTASSIUM``` and ```UREA``` for the patients who went to the ICU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgsItKVQZGGi"
      },
      "source": [
        "<a name=\"conc\"></a>\n",
        "# 8 Conclusions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqRDsBRIZFjY"
      },
      "source": [
        "From the cleaning part, we see that some data preparation was need in order to get an accurate sense of the data and to obtain the right kind information for analysis.\n",
        "\n",
        "From the data analysis part, we can clearly see that many variables correlate with the need for a patient to go to the ICU. Age, gender, vital signs and blood results can be (and will be) very indicative and useful for our future machine learning modelling in predicting ICU admission."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6mpSIEwaIS6"
      },
      "source": [
        "<a name=\"partwo\"></a>\n",
        "# Part 2 - Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDlP4EDdlq8W"
      },
      "source": [
        "- Click [here to access part 2 in Google Colab](https://colab.research.google.com/drive/1bLzbafHgRTXe2T4fFutOzRASrsSSs2or?usp=sharing). In this next part we'll begin the ICU prediction for our problem.\n",
        "\n",
        "- [Project Repository: ICU Prediction](https://github.com/diascarolina/project-icu-prediction)"
      ]
    }
  ]
}