notebooks/02_machine_learning_and_conclusion.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/diascarolina/project-icu-prediction/blob/main/notebooks/02_machine_learning_and_conclusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KL5eTHawR4V_"
      },
      "source": [
        "# COVID-19 - Clinical Data to Assess Diagnosis\n",
        "# Part II - Machine Learning and Conclusion\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img width=\"700\" src=\"https://i.imgur.com/wxaTMWn.png\">\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1-Dn4GonQ-4"
      },
      "source": [
        "- Click [here to go back to Part 1 in Google Colab](https://colab.research.google.com/drive/1GbV_StVWhE5GUlYblbrWsJWzcRP_Dq7f?usp=sharing).\n",
        "\n",
        "- [Project Repository: ICU Prediction](https://github.com/diascarolina/project-icu-prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5wiNPIUwfm2"
      },
      "source": [
        "In this part, we'll finally start predicting the ICU admission for COVID-19 patients.\n",
        "\n",
        "For this, we use some machine lerning models and check various metrics in order to compare them and choose the best model for our problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEsxwU170Leh"
      },
      "source": [
        "# Table of Contents\n",
        "\n",
        "1. [How was this notebook organized](#orga)\n",
        "\n",
        "2. [Libraries & Configurations](#libs)\n",
        "\n",
        "3. [Functions](#funcs)\n",
        "\n",
        "4. [Where did this **data** come from? What kind of **information** do we have in it?](#data)\n",
        "\n",
        "5. [How will the data be **split** between **train** and **test**?](#split)\n",
        "\n",
        "6. [What metric should we choose to start modelling?](#metric)\n",
        "\n",
        "    6.1. [Model 1: Dummy Model](#dummy)\n",
        "\n",
        "    6.2. [Model 2: Lazy Classifier](#lazy)\n",
        "\n",
        "    6.3. [Model 3: RandomForestClassifier](#random)\n",
        "\n",
        "    6.4. [Model 4: LGBMClassifier](#lgbm)\n",
        "\n",
        "    6.5. [Model 5: AdaBoostClassifier](#abc)\n",
        "\n",
        "    6.6. [Model 6: Logistic Regression](#logit)\n",
        "\n",
        "    6.7. [Model 7: XGBoostClassifier](#xgboost)\n",
        "\n",
        "7. [Best Models and Validations](#best)\n",
        "\n",
        "    7.1. [Models Summary](#summ)\n",
        "\n",
        "    7.2. [Feature Importance](#feat)\n",
        "\n",
        "    7.3. [ICU Prediction](#icu)\n",
        "\n",
        "8. [What can we **conclude** from this project?](#conc)\n",
        "\n",
        "9. [Going Forward...](#next)\n",
        "\n",
        "10. [References](#refs)\n",
        "\n",
        "11. [Contacts](#cont)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwLtuOCtTDtH"
      },
      "source": [
        "<a name=\"orga\"></a>\n",
        "# 1 How was this notebook organized?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JQtkTARyTHFT"
      },
      "source": [
        "For this part, we have the following approach:\n",
        "\n",
        "![machine learning steps](https://i.imgur.com/BLDhdVY.png)\n",
        "\n",
        "So, the data we have is the cleaned data from our previous notebook. We'll do a quick recheck to see if everything's in order. Then we will begin with a dummy model to serve as a baseline, followed by the library Lazy Predict, used to test various models and to choose the best from them.\n",
        "\n",
        "After we choose our candidates, we begin tuning the models and their hyperparameters in order to improve our metrics. But what metrics will we use? We'll also take a look at that.\n",
        "\n",
        "Finally, we will choose the best model (or models) that helps us better solve our problem at hand: ICU prediction for COVID-19 patients.\n",
        "\n",
        "Last but not least, we'll see our conclusions about this project and what can be improved and learned from this problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-OLR1euwCpt"
      },
      "source": [
        "<a name=\"libs\"></a>\n",
        "# 2 Libraries & Configurations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNXTUWrFQoe8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70748fdc-980f-41de-aea6-bf981df796ee"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_validate\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.metrics import accuracy_score, plot_confusion_matrix, confusion_matrix\n",
        "from sklearn.metrics import classification_report, roc_auc_score\n",
        "\n",
        "from sklearn.metrics import roc_curve, plot_roc_curve\n",
        "from sklearn.metrics import precision_recall_curve, f1_score, auc\n",
        "\n",
        "import lightgbm as lgb\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# uncomment to install the library\n",
        "#!pip3 install lazypredict\n",
        "from lazypredict.Supervised import LazyClassifier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAU3DEHmhjYF"
      },
      "source": [
        "# defining some colors\n",
        "GRAY1, GRAY2, GRAY3 = '#231F20', '#414040', '#555655'\n",
        "GRAY4, GRAY5, GRAY6 = '#646369', '#76787B', '#828282'\n",
        "BLUE1, BLUE2, BLUE3, BLUE4 = '#174A7E', '#4A81BF', '#94B2D7', '#94AFC5'\n",
        "RED1, RED2 = '#C3514E', '#E6BAB7'\n",
        "GREEN1, GREEN2 = '#0C8040', '#9ABB59'\n",
        "\n",
        "# charts fonts\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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liitnK0nYFt2"
      },
      "source": [
        "# let's set a seed to be used throughout the notebook\n",
        "rs = 84097\n",
        "np.random.seed(rs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJHwfhe24ehy"
      },
      "source": [
        "<a name=\"funcs\"></a>\n",
        "# 3 Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xqecx6JRU1f4"
      },
      "source": [
        "Some useful functions that will be used later on but they don't really have a good place to stay inside the bulk of the notebook itself.\n",
        "\n",
        "And I prefer to keep them here instead of a separate script for now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3_apDVp4ljR"
      },
      "source": [
        "def run_cv_model(model, data:pd.DataFrame, n_splits:int, n_repeats:int):\n",
        "    '''\n",
        "    This function runs a previously instantiated models using a \n",
        "    cross-validation approach.\n",
        "\n",
        "    Input:\n",
        "    model -> a previously instatiated machine learning model\n",
        "\n",
        "    data -> a pandas dataframe\n",
        "\n",
        "    n_splits -> an integer representing the number of times we'll split our data\n",
        "\n",
        "    n_repeats -> an integer representing the number of times our cross-validation will be repeated\n",
        "\n",
        "    Output:\n",
        "    The results of a print function detailing the Average AUC scores from the runs and\n",
        "    the 95% confidence interval for the given data.\n",
        "    '''\n",
        "    np.random.seed(rs)\n",
        "    \n",
        "    data = data.sample(frac = 1).reset_index(drop = True)\n",
        "    y = data['ICU']\n",
        "    X = data.drop(['ICU'], axis = 1)\n",
        "        \n",
        "    cv = RepeatedStratifiedKFold(n_splits = n_splits, n_repeats = n_repeats)\n",
        "    results = cross_validate(model, X, y, cv = cv, scoring = 'roc_auc', return_train_score = True)\n",
        "    \n",
        "    avg_auc = np.mean(results['test_score'])\n",
        "    avg_auc_train = np.mean(results['train_score'])\n",
        "    auc_std = np.std(results['test_score'])\n",
        "    \n",
        "    print(f'Average AUC Score: {avg_auc}')\n",
        "    print(f'AUC 95% Confidence Interval: {avg_auc - 2 * auc_std} to {avg_auc + 2 * auc_std}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IFraZQuoiha"
      },
      "source": [
        "def organize_metrics(model_initials:str):\n",
        "    '''\n",
        "    Function used to create a list with all the model's calculated metrics,\n",
        "    for organization purposes. The naming of the metrics must be consistent\n",
        "    throughout all notebook.\n",
        "\n",
        "    Input:\n",
        "    model_initials -> the chosen initials for the machine learning model\n",
        "\n",
        "    Output:\n",
        "    metrics -> a list containing the model's calculated metrics\n",
        "    '''\n",
        "    metrics = [\n",
        "               round(eval(model_initials + '_accuracy'), 2),\n",
        "               round(eval(model_initials + '_avg_auc'), 2),\n",
        "               round(eval(model_initials + '_dict')['0']['f1-score'], 2),\n",
        "               round(eval(model_initials + '_dict')['1']['f1-score'], 2),\n",
        "               round(eval(model_initials + '_dict')['0']['precision'], 2),\n",
        "               round(eval(model_initials + '_dict')['1']['precision'], 2),\n",
        "               round(eval(model_initials + '_dict')['0']['recall'], 2), \n",
        "               round(eval(model_initials + '_dict')['1']['recall'], 2)\n",
        "              ]\n",
        "\n",
        "    return metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW6mv_UZBKkF"
      },
      "source": [
        "# adapted from: https://stackoverflow.com/questions/59839782/confusion-matrix-font-size\n",
        "\n",
        "def confusion_matrix_plot(y_pred, title = 'Confusion Matrix', cmap = 'GnBu'):\n",
        "    '''\n",
        "    Plots a confusion matrix. The difference from the scikit-learn function to plot\n",
        "    the confusion matrix is that here we can increase the fontsize when need.\n",
        "\n",
        "    Input:\n",
        "    y_pred -> the prediction obtained from the model\n",
        "    title -> the title of the chart, default is \"Confusion Matrix\"\n",
        "    cmap -> the colormap used, default is \"Blues\"\n",
        "\n",
        "    Output:\n",
        "    a chart with the customized confusion matrix\n",
        "    '''\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    classes = ['Not ICU', 'ICU']\n",
        "    plt.figure(figsize = (10, 7))\n",
        "    plt.imshow(cm, interpolation = 'nearest', cmap = cmap)\n",
        "    plt.title(title, fontsize = 20)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation = 0, fontsize = 15)\n",
        "    plt.yticks(tick_marks, classes, rotation = 90, fontsize = 15)\n",
        "    thresh = cm.max()/2\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j], ha = 'center', fontsize = 15,\n",
        "                 color = 'white' if cm[i, j] > thresh else 'black')\n",
        "        plt.tight_layout()\n",
        "        plt.ylabel('True Label', fontsize = 20)\n",
        "        plt.xlabel('Predicted Label', fontsize = 20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AavmbWgqxzpp"
      },
      "source": [
        "<a name=\"data\"></a>\n",
        "# 4 Where did this **data** come from? What kind of **information** do we have in it?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov638oh_x7lp"
      },
      "source": [
        "Here we'll use the cleaned and final dataset obtained from the first notebook.\n",
        "\n",
        "The downloaded data was uploaded to Github and it's available [here](https://raw.githubusercontent.com/diascarolina/data-science-bootcamp/main/data/final_data.csv).\n",
        "\n",
        "We ended up with:\n",
        "\n",
        "- 352 rows, each representing a patient;\n",
        "- 98 features\n",
        "- 1 target variable: ICU\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFCYPM9iSNzY"
      },
      "source": [
        "data_url = 'https://raw.githubusercontent.com/diascarolina/data-science-bootcamp/main/data/final_data.csv'\n",
        "data = pd.read_csv(data_url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "qf-3pBviSV9J",
        "outputId": "797d68bc-3dcb-4968-edd2-71d1b405990e"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <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",
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              "      <th>ICU</th>\n",
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              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.93</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.52</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.74</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.91</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.70</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.65</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-0.71</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>-0.76</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.26</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.97</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.97</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.56</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.68</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.91</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.90</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.87</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-0.92</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.82</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 99 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   AGE_ABOVE65  AGE_PERCENTIL  ...  OXYGEN_SATURATION_DIFF_REL  ICU\n",
              "0            1              5  ...                       -1.00    1\n",
              "1            0              0  ...                       -0.96    1\n",
              "2            0              3  ...                       -1.00    0\n",
              "3            0              0  ...                       -0.98    0\n",
              "4            0              0  ...                       -0.98    0\n",
              "\n",
              "[5 rows x 99 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHUMpNoCXWqC",
        "outputId": "bf44d747-588f-4623-e9f6-853a888f8149"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(352, 99)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkTyWTVKSYtp",
        "outputId": "3cbff1cd-ebec-4a25-a4a9-187943a941f4"
      },
      "source": [
        "# just to be sure that we have no non-numeric values\n",
        "(data.isin([np.nan, np.inf, -np.inf])).sum().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3siTp3FCS0Q7"
      },
      "source": [
        "We have the ```AGE_PERCENTIL``` as a categorical variable and each category is represented as a number. Seeing as all the other variables have numerical values between -1 and 1, it's better if we change this variable to follow this pattern. So, for each category we'll create a new column for them, with the values 1 if the row has that value, and 0 if not. This is called the One-Hot Enconding (or dummy, in statistics)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSnGTal4SuiA"
      },
      "source": [
        "data_ml = data.copy()\n",
        "data_ml = pd.get_dummies(data_ml, columns = ['AGE_PERCENTIL'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "Lf7RKS4yUGNY",
        "outputId": "b6b58c5f-10c6-45ea-9caf-2b6390d38b7d"
      },
      "source": [
        "data_ml.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>AGE_ABOVE65</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>INR_DIFF</th>\n",
              "      <th>...</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",
              "      <th>AGE_PERCENTIL_0</th>\n",
              "      <th>AGE_PERCENTIL_1</th>\n",
              "      <th>AGE_PERCENTIL_2</th>\n",
              "      <th>AGE_PERCENTIL_3</th>\n",
              "      <th>AGE_PERCENTIL_4</th>\n",
              "      <th>AGE_PERCENTIL_5</th>\n",
              "      <th>AGE_PERCENTIL_6</th>\n",
              "      <th>AGE_PERCENTIL_7</th>\n",
              "      <th>AGE_PERCENTIL_8</th>\n",
              "      <th>AGE_PERCENTIL_9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.87</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.93</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.74</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.91</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>-0.65</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-0.71</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>-0.76</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.26</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.97</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.97</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>-0.37</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.68</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.91</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.90</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-0.59</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.87</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-0.92</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.89</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.96</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.88</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>-0.54</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.82</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>-0.99</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 108 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   AGE_ABOVE65  GENDER  ...  AGE_PERCENTIL_8  AGE_PERCENTIL_9\n",
              "0            1       0  ...                0                0\n",
              "1            0       0  ...                0                0\n",
              "2            0       1  ...                0                0\n",
              "3            0       0  ...                0                0\n",
              "4            0       0  ...                0                0\n",
              "\n",
              "[5 rows x 108 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vbNRMDLGaTR",
        "outputId": "6beb4394-cf55-4911-975e-7c7e57a48ca6"
      },
      "source": [
        "data_ml.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(352, 108)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4tQ-GNfGT8V"
      },
      "source": [
        "So in this ```data_ml``` dataset we end up with:\n",
        "- 352 rows, one for each patient;\n",
        "- 107 features and\n",
        "- 1 target variable, the ```ICU```."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dv5ifXaUXK4M"
      },
      "source": [
        "<a name=\"split\"></a>\n",
        "# 5 How will the data be split between **train** and **test**?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9XAlslIXSh0"
      },
      "source": [
        "We have that our target variable is the ```ICU``` column. So let's set it as our ```y```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-ziglj-XPWa"
      },
      "source": [
        "y = data_ml['ICU']\n",
        "X = data_ml.drop(['ICU'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nd39G4mDZVAe"
      },
      "source": [
        "Now, to split the data between train and test, we'll use the default value from the ```train_test_split``` function: 75% of the data to the train set and 25% to the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LaxgvaJaL-j"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    stratify = y,\n",
        "                                                    random_state = rs)\n",
        "\n",
        "# stratify = y: each set contains approximately the same percentage of the y target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAT2md-_a3Qd",
        "outputId": "18ecab39-03af-46ac-eddf-3201e5c85d7b"
      },
      "source": [
        "print(f'X train size: {len(X_train)} || y train size: {len(y_train)}')\n",
        "print(f'X test size: {len(X_test)} || y train size: {len(y_test)}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X train size: 264 || y train size: 264\n",
            "X test size: 88 || y train size: 88\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aelCFM1RezbC"
      },
      "source": [
        "<a name=\"metric\"></a>\n",
        "# 6 What metric should we choose to start modelling?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EANdpsPEe3TU"
      },
      "source": [
        "One of the most difficult parts in creating a machine learning project is picking the best metric that better suits each problem.\n",
        "\n",
        "In this case, our task is to predict admission to the ICU of confirmed COVID-19 cases.\n",
        "\n",
        "\n",
        "**So let's think a little about it:**\n",
        "\n",
        "In this case of ICU prediction, we want to be absolutely sure that we send the right patients for the ICU, so we need to get it right most of the time. This type of prediction is what we call **True Positive**, where we predict that a patient that needs to go to the ICU really goes to the ICU.\n",
        "\n",
        "But we also have to think about not seending too many patients to the ICU when in fact they don't need to go, so as not to overwhelm the healthcare system with more patients than it can accomodate. That's why we always have to keep in mind the problem we are dealing with when modelling.\n",
        "\n",
        "So we want to lower the number of patients that we predict as going to the ICU but in fact they don't need to go. This is called the **False Positives**, and we prefer a lower number of False Positives for this particular problems.\n",
        "\n",
        "Another thing to take into consideration is the number of **False Negatives**, that is, when a patient needs to go to the ICU but we predict that the patient doesn't need to. This is something we want to avoid, because it could potentially lead to the patient's death, so it is an unacceptable error.\n",
        "\n",
        "**To recap:**\n",
        "- we want more True Positives (TP)\n",
        "- less False Positives (FP)\n",
        "- less False Negatives (FN)\n",
        "\n",
        "Then, to the question of what metric to choose... In our case, our classes are considered balanced, so even the accuracy could be a good metric. But it doesn't take into account the number of TPs or FPs.\n",
        "\n",
        "A good metric that takes into account TPs, FPs and FNs is the **F1-score**. It is defined as follows:\n",
        "\n",
        "$$\\text{F1-score} = 2\\times\\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}$$\n",
        "\n",
        "where\n",
        "\n",
        "$$\\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}$$\n",
        "\n",
        "and\n",
        "\n",
        "$$\\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{True Negatives}}$$\n",
        "\n",
        "So whenever the number of TPs increases, the precision also increases. And when the number of FNs increase, the precision decreases. All in all, the **F1-score** seems like a good metric for this problem, because it balances these types of errors, and it is the one that we will use, but we will keep an eye on other metrics such as **precision** and the **AUC**.\n",
        "\n",
        "\n",
        "Source: [Tour of Evaluation Metrics for Classification](https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pQ_GsHBe2-8"
      },
      "source": [
        "<a name=\"dummy\"></a>\n",
        "## 6.1 Model 1: Dummy Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGHUKBDxcH8c"
      },
      "source": [
        "First, we'll use a dummy model in order to have a baseline for our future models. This is a good practice when evaluating various machine learning models, as it acts as a minimum threshold of quality that of future models need in order to be considered useful.\n",
        "\n",
        "The strategy we'll use for this dummy model is to predict all values as the same value of the most frequent target variable value. That will give us a certain accuracy that will be used to evaluate future models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcmTQ5NdYRir",
        "outputId": "303da7c6-859e-435e-de78-97b686bee436"
      },
      "source": [
        "# instantiate and fit the model\n",
        "dummy_model = DummyClassifier(strategy = 'most_frequent')\n",
        "dummy_model.fit(X_train, y_train)\n",
        "\n",
        "# calculate model accuracy\n",
        "y_prediction_dummy = dummy_model.predict(X_test)\n",
        "dummy_accuracy = accuracy_score(y_test, y_prediction_dummy)\n",
        "print(f'Accuracy of the Dummy Model: {dummy_accuracy}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the Dummy Model: 0.5340909090909091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xz2pu5p86CRl"
      },
      "source": [
        "We will calculate the following metrics just for completeness sake, but their results are already expected since this is a well-behaved model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "_hgSJcE99yQX",
        "outputId": "a6b54d29-4bc4-48a7-967e-7090c60c0e7e"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_dummy)"
      ],
      "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": {
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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQSoxDZFCYt2"
      },
      "source": [
        "As was said before, these were the expected values, because the model predicted only the 0 class (Not ICU). So it got all True Negatives and False Negatives, with no True Positives ot False Positives."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OL0fZMSnil6_",
        "outputId": "f2dce396-aec0-4acb-bc8f-ecc28f83d0ce"
      },
      "source": [
        "# auc score of one prediction\n",
        "prob_dummy = dummy_model.predict_proba(X_test)\n",
        "dummy_auc = roc_auc_score(y_test, prob_dummy[:,1])\n",
        "print(f'The AUC for the Dummy model is {dummy_auc}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The AUC for the Dummy model is 0.5.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2FBkep4DLvL",
        "outputId": "7babdab6-3c0d-4b42-bf20-855c864e0b68"
      },
      "source": [
        "# again, this metric is not relevant here, but let's calculate it anyway\n",
        "run_cv_model(dummy_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average AUC Score: 0.5\n",
            "AUC 95% Confidence Interval: 0.5 to 0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgUqFajCDp2x"
      },
      "source": [
        "# just to keep it in a variable\n",
        "dummy_avg_auc = 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Z5LhsOx_Dvmd",
        "outputId": "2814a89b-ee49-421f-d5bf-e3486ac57694"
      },
      "source": [
        "plot_roc_curve(dummy_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GH436DfSjzRB",
        "outputId": "f5aeb413-d075-45a1-835e-944e3382f1ff"
      },
      "source": [
        "dummy_dict = classification_report(y_test, y_prediction_dummy, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_dummy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.53      1.00      0.70        47\n",
            "           1       0.00      0.00      0.00        41\n",
            "\n",
            "    accuracy                           0.53        88\n",
            "   macro avg       0.27      0.50      0.35        88\n",
            "weighted avg       0.29      0.53      0.37        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4wbitX5hu_V"
      },
      "source": [
        "<a name=\"lazy\"></a>\n",
        "## 6.2 Model 2: Lazy Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EqnZqjiD90Q"
      },
      "source": [
        "This is not a model _per se_, but it is a useful library used to quickly train various models and rank them by our chosen metric. This is important as a way of choosing some models in the beginning of the machine learning modelling part of our project."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_GsS8FMhtkz",
        "outputId": "ccf8fda0-9b75-452d-b751-8a7d8f1896ff"
      },
      "source": [
        "# instantiate and fit model\n",
        "lc_model = LazyClassifier(verbose = 0,\n",
        "                                 ignore_warnings = True,\n",
        "                                 predictions = False,\n",
        "                                 custom_metric = None)\n",
        "models, predictions_lazy = lc_model.fit(X_train, X_test, y_train, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 29/29 [00:02<00:00, 13.33it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "vUeuPw-OiFSi",
        "outputId": "77a36985-132e-4c52-fe44-9798d04be087"
      },
      "source": [
        "models.head(5)"
      ],
      "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>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</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>RandomForestClassifier</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Accuracy  Balanced Accuracy  ...  F1 Score  Time Taken\n",
              "Model                                                ...                      \n",
              "RandomForestClassifier      0.78               0.78  ...      0.78        0.25\n",
              "ExtraTreesClassifier        0.78               0.78  ...      0.78        0.17\n",
              "LGBMClassifier              0.76               0.76  ...      0.76        0.08\n",
              "AdaBoostClassifier          0.76               0.76  ...      0.76        0.17\n",
              "BaggingClassifier           0.76               0.75  ...      0.76        0.08\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rn95hfPskewA"
      },
      "source": [
        "The models chosen from the Lazy Predict library are:\n",
        "\n",
        "- **RandomForestClassifier**\n",
        "- **LGBMClassifer**\n",
        "- **AdaBoostClassifier**\n",
        "\n",
        "ExtraTreeClassifier will not be used because after testing it gave the same results as RandomForestClassifier. This happens because it uses the same method of modelling: random trees.\n",
        "\n",
        "We'll also be using **LogisticRegression** and **XGBoostClassifier**. The former because it is the epitome of binary classification, and the former because it is widely used in many competitions for its great power in predicting."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUE5Fmgqkgs1"
      },
      "source": [
        "<a name=\"random\"></a>\n",
        "## 6.3 Model 3: RandomForestClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6USR7JZkHjux"
      },
      "source": [
        "From the documentation:\n",
        "\n",
        "> A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.\n",
        "\n",
        "Source: [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-Mqi_XqEPhr"
      },
      "source": [
        "### Default Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCeZ4W94H20P"
      },
      "source": [
        "Even though we already used this model with no tuning in the Lazy Predict library, it's important to check it again, because the mentioned library tends to pre-process the data before fitting and training, giving different results.\n",
        "\n",
        "This is just a quick step, to act as a sanity check for our models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJYUhfFYIYW_",
        "outputId": "822ca648-5301-4bff-9a13-0f1c982073bc"
      },
      "source": [
        "rfc_model = RandomForestClassifier()\n",
        "rfc_model.fit(X_train, y_train)\n",
        "\n",
        "y_prediction_rfc = rfc_model.predict(X_test)\n",
        "accuracy_score(y_test, y_prediction_rfc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7386363636363636"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r96B_R65EaME"
      },
      "source": [
        "### Tuned Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Us23a8yvZCv"
      },
      "source": [
        "I previously tested the ```GridSearchCV``` with other parameters, but limited the number of relevant parameters so as to reduce the computation time for the notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nQzR2jeld5L",
        "outputId": "d74d27f1-49b3-409f-995f-3db44fe6e049"
      },
      "source": [
        "rfc_model = RandomForestClassifier()\n",
        "\n",
        "params = {\n",
        "    'n_estimators': [10, 50, 100, 250],\n",
        "    'n_jobs': [-1],\n",
        "    'random_state': [rs],\n",
        "    'max_features': ['sqrt', 'auto', 'log2']\n",
        "}\n",
        "\n",
        "\n",
        "rfc_search = GridSearchCV(rfc_model,\n",
        "                          params, \n",
        "                          scoring = 'roc_auc',\n",
        "                          error_score = 'raise',\n",
        "                          n_jobs = -1,\n",
        "                          verbose = 0,\n",
        "                          cv = 5)\n",
        "\n",
        "rfc_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise', estimator=RandomForestClassifier(),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'max_features': ['sqrt', 'auto', 'log2'],\n",
              "                         'n_estimators': [10, 50, 100, 250], 'n_jobs': [-1],\n",
              "                         'random_state': [84097]},\n",
              "             scoring='roc_auc')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FP95QhYJp-Fq",
        "outputId": "13dbafa5-60f9-4708-94a2-22e7f7aa1eca"
      },
      "source": [
        "rfc_search.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_features': 'log2',\n",
              " 'n_estimators': 100,\n",
              " 'n_jobs': -1,\n",
              " 'random_state': 84097}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9mKcEqyqA3P",
        "outputId": "6e1088d5-6b49-495e-c6f5-bf6ed7195662"
      },
      "source": [
        "# refit the model with the improved parameters\n",
        "rfc_model = rfc_search.best_estimator_\n",
        "rfc_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features='log2', n_jobs=-1, random_state=84097)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pag573bMFBU0"
      },
      "source": [
        "### Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OG-lYBa_E9QW",
        "outputId": "f926a72b-84db-46ae-ff41-d6013eeb293c"
      },
      "source": [
        "y_prediction_rfc = rfc_model.predict(X_test)\n",
        "rfc_accuracy = accuracy_score(y_test, y_prediction_rfc)\n",
        "print(f'The accuracy for the RandomForestClassifier model is {rfc_accuracy}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy for the RandomForestClassifier model is 0.7386363636363636\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBTIF2oJJT6O"
      },
      "source": [
        "The accuracy is the same as the default model and less than the accuracy obtained from the Lazy Predict library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "SehOacxjlRwn",
        "outputId": "5840dbab-9bde-4f61-ce66-0f16ab6e3d55"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_rfc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3RGu1Y6lTPX",
        "outputId": "f9d9d3ea-4ca4-489f-fae9-d2c10145c881"
      },
      "source": [
        "# auc from the model run only once\n",
        "prob_rfc = rfc_model.predict_proba(X_test)\n",
        "rfc_auc = roc_auc_score(y_test, prob_rfc[:,1])\n",
        "print(f'The AUC for the RandomForestClassifier model is {rfc_auc}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The AUC for the RandomForestClassifier model is 0.856772184743124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "md8jfPwf5HAA",
        "outputId": "daff94b2-087a-410e-a0e8-d9a3dc8767e5"
      },
      "source": [
        "# average auc using cross validation\n",
        "run_cv_model(rfc_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average AUC Score: 0.7984824238113711\n",
            "AUC 95% Confidence Interval: 0.685400958938143 to 0.9115638886845993\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mcbf4XAxn9yF"
      },
      "source": [
        "# just to keep it in a variable\n",
        "rfc_avg_auc = 0.7984824238113711"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ea7POIlqcIxH",
        "outputId": "0e9fdc3b-18b2-4ba8-9b07-cc133869180d"
      },
      "source": [
        "plot_roc_curve(rfc_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-KgWe0-laSK",
        "outputId": "4d85e001-cc80-4b80-9641-f6d338001a3d"
      },
      "source": [
        "rfc_dict = classification_report(y_test, y_prediction_rfc, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_rfc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.81      0.77        47\n",
            "           1       0.75      0.66      0.70        41\n",
            "\n",
            "    accuracy                           0.74        88\n",
            "   macro avg       0.74      0.73      0.73        88\n",
            "weighted avg       0.74      0.74      0.74        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5WwrYOUzLGM"
      },
      "source": [
        "<a name=\"lgbm\"></a>\n",
        "## 6.4 Model 4: LGBMClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7R9YnFQJKmks"
      },
      "source": [
        "From the documentation:\n",
        "\n",
        "> Light GBM is a gradient boosting framework that uses tree based learning algorithm. Light GBM grows tree vertically while other algorithm grows trees horizontally.\n",
        "\n",
        "_Source:_ [LGBMClassifier Documentation](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3StnIurrGVie"
      },
      "source": [
        "### Default Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9FVlCwK72Dx",
        "outputId": "37eca843-d747-401b-fc98-5cd36629b3b4"
      },
      "source": [
        "lgb_model = lgb.LGBMClassifier(objective = 'binary')\n",
        "lgb_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = lgb_model.predict(X_test)\n",
        "accuracy_score(y_pred, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7272727272727273"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEx82gntGh_W"
      },
      "source": [
        "### Tuned Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4k6l8VY9zJZu"
      },
      "source": [
        "param_grid = {\n",
        "    'num_leaves': [31, 127],\n",
        "    'reg_alpha': [0.1, 0.5],\n",
        "    'min_data_in_leaf': [30, 50, 100, 300, 400],\n",
        "    'lambda_l1': [0, 1, 1.5],\n",
        "    'lambda_l2': [0, 1]\n",
        "    }\n",
        "\n",
        "lgb_estimator = lgb.LGBMClassifier(objective='binary',\n",
        "                                   num_boost_round = 2000,\n",
        "                                   learning_rate = 0.01,\n",
        "                                   metric = 'roc_auc')\n",
        "\n",
        "gsearch = GridSearchCV(estimator = lgb_estimator,\n",
        "                       param_grid = param_grid,\n",
        "                       cv = 5)\n",
        "\n",
        "lgb_model = gsearch.fit(X = X_train, y = y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "al_ZsAoEzhzy",
        "outputId": "37884665-a22f-418d-a34d-791313e559b3"
      },
      "source": [
        "gsearch.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'lambda_l1': 1.5,\n",
              " 'lambda_l2': 1,\n",
              " 'min_data_in_leaf': 30,\n",
              " 'num_leaves': 31,\n",
              " 'reg_alpha': 0.1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yEv4zyRzvFI",
        "outputId": "ef03f6e3-8058-4999-90ee-c8332c6e8ad2"
      },
      "source": [
        "lgb_model = gsearch.best_estimator_\n",
        "lgb_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(lambda_l1=1.5, lambda_l2=1, learning_rate=0.01, metric='roc_auc',\n",
              "               min_data_in_leaf=30, num_boost_round=2000, objective='binary',\n",
              "               reg_alpha=0.1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etCo5KuvHp0e"
      },
      "source": [
        "### Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfH19-4VHpJS",
        "outputId": "e0c32fe3-e7c5-4f9d-df9a-22de14a439a5"
      },
      "source": [
        "y_prediction_lgb = lgb_model.predict(X_test)\n",
        "lgb_accuracy = accuracy_score(y_test, y_prediction_lgb)\n",
        "print(f'The accuracy for the LGBMClassifier model is {lgb_accuracy}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy for the LGBMClassifier model is 0.75.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "USwn9sSNh_mL",
        "outputId": "b7405aba-f5b8-4d36-ac7a-23f1728c5261"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_lgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MazSDGT-zGBp",
        "outputId": "128aea53-8bf3-4bc2-be20-a2920560ad0f"
      },
      "source": [
        "# single auc\n",
        "prob_lgbm = lgb_model.predict_proba(X_test)\n",
        "lgb_auc = roc_auc_score(y_test, prob_rfc[:,1])\n",
        "lgb_auc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.856772184743124"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qnDTxMkBjL0"
      },
      "source": [
        "# time taken: 22 minutes\n",
        "# run_cv_model(lgb_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbZytz96HGrZ"
      },
      "source": [
        "Average AUC Score: 0.7800572627052891\n",
        "\n",
        "AUC 95% Confidence Interval: 0.6672284392238399 to 0.8928860861867383"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_vEW-HTjRXw"
      },
      "source": [
        "# to save it in a variable\n",
        "# instead of waiting 22 minutes for the model to run\n",
        "lgb_avg_auc =  0.7800572627052891 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "z04uuunFv-yb",
        "outputId": "b3e40fb4-0401-4670-8f1f-e63da1fd0e90"
      },
      "source": [
        "plot_roc_curve(lgb_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26OTsHcribHP",
        "outputId": "8ae44e9b-1056-4ada-eaf5-63420a78a4c6"
      },
      "source": [
        "lgb_dict = classification_report(y_test, y_prediction_lgb, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_lgb))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.79      0.77        47\n",
            "           1       0.74      0.71      0.72        41\n",
            "\n",
            "    accuracy                           0.75        88\n",
            "   macro avg       0.75      0.75      0.75        88\n",
            "weighted avg       0.75      0.75      0.75        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3IeI12k0FqE"
      },
      "source": [
        "<a name=\"abc\"></a>\n",
        "## 6.5 Model 5: AdaBoostClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmYI2ZV1MiKA"
      },
      "source": [
        "From the documentation:\n",
        "\n",
        "> An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.\n",
        "\n",
        "_Source:_ [AdaBoostClassifier Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqcokFQuGta5"
      },
      "source": [
        "### Default Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21XVvGZhMwf4",
        "outputId": "2de22a10-13ca-48ae-fc64-d22c30d5df27"
      },
      "source": [
        "abc_model = AdaBoostClassifier()\n",
        "abc_model.fit(X_train, y_train)\n",
        "\n",
        "y_prediction_abc = abc_model.predict(X_test)\n",
        "accuracy_score(y_test, y_prediction_abc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7613636363636364"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTLEHPG6GxFt"
      },
      "source": [
        "### Tuned Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4I8mV74z-03",
        "outputId": "7cc0239d-adf8-4f2a-b062-fc77dcb1bb44"
      },
      "source": [
        "abc_model = AdaBoostClassifier()\n",
        "\n",
        "params = {\n",
        "    'n_estimators': [10, 50, 100, 250],\n",
        "    'random_state': [rs],\n",
        "    'learning_rate': [0.01, 0.1, 0.3, 1]\n",
        "}\n",
        "\n",
        "abc_search = GridSearchCV(abc_model,\n",
        "                          params, \n",
        "                          scoring = 'roc_auc',\n",
        "                          error_score = 'raise',\n",
        "                          n_jobs = -1,\n",
        "                          verbose = 0,\n",
        "                          cv = 5)\n",
        "\n",
        "abc_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise', estimator=AdaBoostClassifier(),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.01, 0.1, 0.3, 1],\n",
              "                         'n_estimators': [10, 50, 100, 250],\n",
              "                         'random_state': [84097]},\n",
              "             scoring='roc_auc')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuMNm0fY6bSv",
        "outputId": "70f92c22-e586-491e-b374-f65c7dc73a64"
      },
      "source": [
        "abc_search.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.01, 'n_estimators': 250, 'random_state': 84097}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJbkh4gp26IP",
        "outputId": "530e5d25-5a13-413b-b0d6-c4d89f6fd07a"
      },
      "source": [
        "abc_model = abc_search.best_estimator_\n",
        "abc_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(learning_rate=0.01, n_estimators=250, random_state=84097)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dS-FlcocH-DT"
      },
      "source": [
        "### Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbVgeKDfH7IZ",
        "outputId": "4fb97344-6fab-457f-9ac7-e8bc9e092902"
      },
      "source": [
        "y_prediction_abc = abc_model.predict(X_test)\n",
        "abc_accuracy = accuracy_score(y_test, y_prediction_abc)\n",
        "print(f'The accuracy of the AdaBoostMetric is {abc_accuracy}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the AdaBoostMetric is 0.7272727272727273.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "lt-NIcX5-djx",
        "outputId": "dc458f2d-b609-46da-e616-c2e5b71a0a54"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_abc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYROlVPR-nCX",
        "outputId": "c64f2b5c-fae0-4c23-b0ef-629d194621ca"
      },
      "source": [
        "# singl auc\n",
        "prob_abc = abc_model.predict_proba(X_test)\n",
        "abc_roc = roc_auc_score(y_test, prob_abc[:,1])\n",
        "abc_roc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7854177477944992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J58us8Zi_R7i",
        "outputId": "0b793e38-8885-434f-f0df-9d1057c283c2"
      },
      "source": [
        "# average auc with cross validation\n",
        "run_cv_model(abc_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average AUC Score: 0.7513939515173067\n",
            "AUC 95% Confidence Interval: 0.6475759499882963 to 0.8552119530463171\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVygq9B3pEJj"
      },
      "source": [
        "# to save it in a variable\n",
        "abc_avg_auc = 0.7513939515173067"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "B7e9au7YPrnE",
        "outputId": "69b59195-7bcd-4d84-84aa-d9b219de2ab0"
      },
      "source": [
        "plot_roc_curve(abc_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yEWOt92pKR2",
        "outputId": "27ef8713-480d-4aa3-c257-615d02f390ea"
      },
      "source": [
        "abc_dict = classification_report(y_test, y_prediction_abc, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_abc))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.85      0.77        47\n",
            "           1       0.77      0.59      0.67        41\n",
            "\n",
            "    accuracy                           0.73        88\n",
            "   macro avg       0.74      0.72      0.72        88\n",
            "weighted avg       0.74      0.73      0.72        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fxfo78AOAxOd"
      },
      "source": [
        "<a name=\"logit\"></a>\n",
        "## 6.6 Model 6: Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cn0Edy-GN_4M"
      },
      "source": [
        "From the documentation:\n",
        "\n",
        "> Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.\n",
        "\n",
        "_Source:_ [Logistic Regression Documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IC4hoFogIG8i"
      },
      "source": [
        "### Default Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDXpn1RIOipp",
        "outputId": "a54b677b-c723-4afa-d101-f7a06e2ee9e8"
      },
      "source": [
        "lr_model = LogisticRegression()\n",
        "lr_model.fit(X_train, y_train)\n",
        "\n",
        "y_prediction_lr = lr_model.predict(X_test)\n",
        "accuracy_score(y_test, y_prediction_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7272727272727273"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pI82RiTIJfk"
      },
      "source": [
        "### Tuned Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M85TZZRSDfog",
        "outputId": "a616d8e8-4088-4ee4-ad21-8b5ba810eb05"
      },
      "source": [
        "grid = {\n",
        "    \"C\": np.logspace(-4, 50, 50),\n",
        "    \"penalty\": [\"l1\", \"l2\"]\n",
        "    }\n",
        "\n",
        "lr_model = LogisticRegression()\n",
        "lr_search = GridSearchCV(lr_model, grid, cv = 10, scoring = 'roc_auc')\n",
        "lr_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
              "             param_grid={'C': array([1.00000000e-04, 1.26485522e-03, 1.59985872e-02, 2.02358965e-01,\n",
              "       2.55954792e+00, 3.23745754e+01, 4.09491506e+02, 5.17947468e+03,\n",
              "       6.55128557e+04, 8.28642773e+05, 1.04811313e+07, 1.32571137e+08,\n",
              "       1.67683294e+09, 2.12095089e+10, 2.68269580e+11, 3.39322177e+12,\n",
              "       4.29193426e+13, 5.42867544e+14, 6.86648...\n",
              "       7.19685673e+26, 9.10298178e+27, 1.15139540e+29, 1.45634848e+30,\n",
              "       1.84206997e+31, 2.32995181e+32, 2.94705170e+33, 3.72759372e+34,\n",
              "       4.71486636e+35, 5.96362332e+36, 7.54312006e+37, 9.54095476e+38,\n",
              "       1.20679264e+40, 1.52641797e+41, 1.93069773e+42, 2.44205309e+43,\n",
              "       3.08884360e+44, 3.90693994e+45, 4.94171336e+46, 6.25055193e+47,\n",
              "       7.90604321e+48, 1.00000000e+50]),\n",
              "                         'penalty': ['l1', 'l2']},\n",
              "             scoring='roc_auc')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gm7WwcjtEO_w",
        "outputId": "56aae1e7-1d71-4cb3-a3c4-bd4e5042df06"
      },
      "source": [
        "lr_model = lr_search.best_estimator_\n",
        "lr_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.20235896477251555)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "frBm3ZnXIiV5"
      },
      "source": [
        "### Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iq4xRpV9IkXZ",
        "outputId": "4fbb8821-0aff-4bea-a4b4-f3cf9fdf4cc8"
      },
      "source": [
        "y_prediction_lr = lr_model.predict(X_test)\n",
        "lr_accuracy = accuracy_score(y_test, y_prediction_lr)\n",
        "print(f'The accuracy for the Logistic Regression model is {lr_accuracy}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy for the Logistic Regression model is 0.7272727272727273.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "d7oCNVhbJUuY",
        "outputId": "980e2a15-fb5d-4e0a-d126-ed612749fe17"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmOzVAThJkWG",
        "outputId": "33e82989-84b4-4a69-8224-aacc823a2280"
      },
      "source": [
        "# single auc\n",
        "prob_lr = lr_model.predict_proba(X_test)\n",
        "lr_auc = roc_auc_score(y_test, prob_lr[:,1])\n",
        "lr_auc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8147379346133887"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8nv0MriJyFz",
        "outputId": "a83d78c3-5738-479c-b950-17f0a7f5cbf2"
      },
      "source": [
        "# average auc from cross validation\n",
        "run_cv_model(lr_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average AUC Score: 0.76443037684814\n",
            "AUC 95% Confidence Interval: 0.6365981892543888 to 0.8922625644418911\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtUjNXUupnY-"
      },
      "source": [
        "# to save it\n",
        "lr_avg_auc = 0.76443037684814"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "GKe9zYN_Pw8I",
        "outputId": "23d71128-7a93-4a3c-d3ea-86bdfce7098f"
      },
      "source": [
        "plot_roc_curve(lr_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSnIM4bppqe4",
        "outputId": "3e8b1c52-def6-42c9-ce45-fdadb174e5e0"
      },
      "source": [
        "lr_dict = classification_report(y_test, y_prediction_lr, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_lr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.77      0.75        47\n",
            "           1       0.72      0.68      0.70        41\n",
            "\n",
            "    accuracy                           0.73        88\n",
            "   macro avg       0.73      0.72      0.72        88\n",
            "weighted avg       0.73      0.73      0.73        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ix-4j5SCEWM4"
      },
      "source": [
        "<a name=\"xgboost\"></a>\n",
        "## 6.7 Model 7: XGBoost Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdTduI2uP4Ah"
      },
      "source": [
        "From the documentation:\n",
        "\n",
        "> XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.\n",
        "\n",
        "_Source:_ [XGBoost Documentation](https://xgboost.readthedocs.io/en/latest/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OAlFkidEp4lE"
      },
      "source": [
        "### Default Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHtAb19oP2jS",
        "outputId": "19e00eaa-678f-4f40-f34c-5be2da31ec3d"
      },
      "source": [
        "xgb_model = XGBClassifier()\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n",
        "y_prediction_xgb = xgb_model.predict(X_test)\n",
        "accuracy_score(y_test, y_prediction_xgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6931818181818182"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvlNFho5p8OY"
      },
      "source": [
        "### Tuned Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xbds1EciFiDm",
        "outputId": "483dff54-4a8b-4b4e-ce03-89892ecf9fdd"
      },
      "source": [
        "params = {\n",
        "        'min_child_weight': [1, 5, 10],\n",
        "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
        "        'subsample': [0.6, 0.8, 1.0],\n",
        "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
        "        'max_depth': [3, 4, 5],\n",
        "        'n_estimators': [100, 250, 1000, 2000],\n",
        "        'learning_rate': [0.01, 0.1, 0.3, 1]\n",
        "        }\n",
        "\n",
        "xgb_model = XGBClassifier(learning_rate = 0.02,\n",
        "                          nthread = 1)\n",
        "\n",
        "skf = StratifiedKFold(n_splits = 5,\n",
        "                      shuffle = True,\n",
        "                      random_state = rs)\n",
        "\n",
        "xgb_search = RandomizedSearchCV(xgb_model,\n",
        "                                   param_distributions = params,\n",
        "                                   n_iter = 5,\n",
        "                                   scoring = 'roc_auc',\n",
        "                                   n_jobs = 100,\n",
        "                                   cv = skf.split(X_train, y_train),\n",
        "                                   verbose = 0,\n",
        "                                   random_state = rs)\n",
        "\n",
        "xgb_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=<generator object _BaseKFold.split at 0x7fbcd1701850>,\n",
              "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                           colsample_bylevel=None,\n",
              "                                           colsample_bynode=None,\n",
              "                                           colsample_bytree=None, gamma=None,\n",
              "                                           gpu_id=None, importance_type='gain',\n",
              "                                           interaction_constraints=None,\n",
              "                                           learning_rate=0.02,\n",
              "                                           max_delta_step=None, max_depth=None,\n",
              "                                           min_child_weight=None, missing...\n",
              "                                           subsample=None, tree_method=None,\n",
              "                                           validate_parameters=None,\n",
              "                                           verbosity=None),\n",
              "                   n_iter=5, n_jobs=100,\n",
              "                   param_distributions={'colsample_bytree': [0.6, 0.8, 1.0],\n",
              "                                        'gamma': [0.5, 1, 1.5, 2, 5],\n",
              "                                        'learning_rate': [0.01, 0.1, 0.3, 1],\n",
              "                                        'max_depth': [3, 4, 5],\n",
              "                                        'min_child_weight': [1, 5, 10],\n",
              "                                        'n_estimators': [100, 250, 1000, 2000],\n",
              "                                        'subsample': [0.6, 0.8, 1.0]},\n",
              "                   random_state=84097, scoring='roc_auc')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldzwDpJXHnCi",
        "outputId": "b6c8a91c-95a0-4230-db7f-32f82b960323"
      },
      "source": [
        "xgb_search.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'colsample_bytree': 1.0,\n",
              " 'gamma': 0.5,\n",
              " 'learning_rate': 0.3,\n",
              " 'max_depth': 5,\n",
              " 'min_child_weight': 1,\n",
              " 'n_estimators': 2000,\n",
              " 'subsample': 0.8}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJVMPPB-GnRm",
        "outputId": "b7410ad4-ff37-4102-8996-671a00f6ffb7"
      },
      "source": [
        "xgb_model = xgb_search.best_estimator_\n",
        "xgb_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1.0, gamma=0.5, gpu_id=-1,\n",
              "              importance_type='gain', interaction_constraints='',\n",
              "              learning_rate=0.3, max_delta_step=0, max_depth=5,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "              n_estimators=2000, n_jobs=1, nthread=1, num_parallel_tree=1,\n",
              "              random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "              subsample=0.8, tree_method='exact', validate_parameters=1,\n",
              "              verbosity=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hyrwSskgqAi5"
      },
      "source": [
        "### Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvDFp07SqCWn",
        "outputId": "8fc501e2-fe22-4e9f-e725-6ba668ca19e9"
      },
      "source": [
        "y_prediction_xgb = xgb_model.predict(X_test)\n",
        "xgb_accuracy = accuracy_score(y_test, y_prediction_xgb)\n",
        "print(f'The accuracy for the XGBoostClassifier model is {xgb_accuracy}.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy for the XGBoostClassifier model is 0.75.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "DG03R9xAqtXf",
        "outputId": "eb566930-8b41-47e3-a7b9-6d77008dfb11"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_xgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YciZKObgqxhj",
        "outputId": "247d1303-a5f8-49eb-868f-d3d5b19ca9ca"
      },
      "source": [
        "# single auc\n",
        "prob_xgb = xgb_model.predict_proba(X_test)\n",
        "xgb_auc = roc_auc_score(y_test, prob_xgb[:,1])\n",
        "xgb_auc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8500259470679813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytjpHeNSq7wb",
        "outputId": "7bea3817-0998-4499-d2fc-e2b3b0ae8aee"
      },
      "source": [
        "# average auc with cross validation\n",
        "run_cv_model(xgb_model, data_ml, 5, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average AUC Score: 0.7747947109789214\n",
            "AUC 95% Confidence Interval: 0.6402747689687943 to 0.9093146529890485\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4Tso_0YrAza"
      },
      "source": [
        "# to save it\n",
        "xgb_avg_auc = 0.7747947109789214"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8wvDnhAARFdW",
        "outputId": "d0ee199d-bbd4-4199-938f-4729288f259f"
      },
      "source": [
        "plot_roc_curve(xgb_model, X_test, y_test)\n",
        "sns.despine()\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzOylXqfrE15",
        "outputId": "fcf8d851-666f-41f0-9ebe-5dffdb294d1e"
      },
      "source": [
        "xgb_dict = classification_report(y_test, y_prediction_xgb, output_dict = True)\n",
        "print(classification_report(y_test, y_prediction_xgb))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.81      0.78        47\n",
            "           1       0.76      0.68      0.72        41\n",
            "\n",
            "    accuracy                           0.75        88\n",
            "   macro avg       0.75      0.75      0.75        88\n",
            "weighted avg       0.75      0.75      0.75        88\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUseJ74yJJx7"
      },
      "source": [
        "<a name=\"best\"></a>\n",
        "# 7 Best Models and Validations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xw4WNimnSO3f"
      },
      "source": [
        "We will know take a look at which models best performed on our chosen metric (F1-score).\n",
        "\n",
        "After that, we pick the two best models to check their feature importance. And then we'll make the ICU prediction based on the results of the first model with best metric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aImz8e96Sy1T"
      },
      "source": [
        "<a name=\"summ\"></a>\n",
        "## 7.1 Models Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnbQMAkerbCE"
      },
      "source": [
        "dummy_metrics = organize_metrics('dummy')\n",
        "rfc_metrics = organize_metrics('rfc')\n",
        "lgb_metrics = organize_metrics('lgb')\n",
        "abc_metrics = organize_metrics('abc')\n",
        "lr_metrics = organize_metrics('lr')\n",
        "xgb_metrics = organize_metrics('xgb')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "t3xCvsqksgpP",
        "outputId": "6b119e47-2ea0-479b-ef63-427cdc23d150"
      },
      "source": [
        "metrics_dict = {\n",
        "    'Model_Name': ['Accuracy', 'Average AUC', 'F1-score 0', 'F1-score 1',\n",
        "                   'Precision 0', 'Precision 1', 'Recall 0', 'Recall 1'],\n",
        "    'DummyClassifier': dummy_metrics,\n",
        "    'RandomForestClassifier': rfc_metrics,\n",
        "    'LGBMClasssifier': lgb_metrics,\n",
        "    'AdaBoostClassifier': abc_metrics,\n",
        "    'LogisticRegression': lr_metrics,\n",
        "    'XGBoostClassifier': xgb_metrics\n",
        "}\n",
        "\n",
        "metrics_df = pd.DataFrame(metrics_dict)\n",
        "metrics_df.set_index('Model_Name').T.sort_values(by = 'F1-score 1', ascending = False)"
      ],
      "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>Model_Name</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Average AUC</th>\n",
              "      <th>F1-score 0</th>\n",
              "      <th>F1-score 1</th>\n",
              "      <th>Precision 0</th>\n",
              "      <th>Precision 1</th>\n",
              "      <th>Recall 0</th>\n",
              "      <th>Recall 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClasssifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBoostClassifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.53</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Model_Name              Accuracy  Average AUC  ...  Recall 0  Recall 1\n",
              "LGBMClasssifier             0.75         0.78  ...      0.79      0.71\n",
              "XGBoostClassifier           0.75         0.77  ...      0.81      0.68\n",
              "RandomForestClassifier      0.74         0.80  ...      0.81      0.66\n",
              "LogisticRegression          0.73         0.76  ...      0.77      0.68\n",
              "AdaBoostClassifier          0.73         0.75  ...      0.85      0.59\n",
              "DummyClassifier             0.53         0.50  ...      1.00      0.00\n",
              "\n",
              "[6 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61klynyzwaPW"
      },
      "source": [
        "Our chosen model would need a good F1-score on the class 1 (the ICU class).\n",
        "\n",
        "Looking at our table, we see that the top 2 models on this metric are\n",
        "- **LGBMClassifier** and\n",
        "- **XGBoostClassifier**.\n",
        "\n",
        "By checking, for example, the Precision 1, XGBoostClassifier also performs well, as well as having more accuracy that most models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBR29YolSlXt"
      },
      "source": [
        "<a name=\"feat\"></a>\n",
        "## 7.2 Feature Importance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oilHRZlgJQ-"
      },
      "source": [
        "### XGBoostClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naIUdqJx1T4S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "45161bbc-4825-4553-fd46-c64a89718d19"
      },
      "source": [
        "feat_import_xgb = pd.Series(xgb_model.feature_importances_, index = X_train.columns)\n",
        "ax = feat_import_xgb.nlargest(15).sort_values(ascending = True).plot(kind = 'barh',\n",
        "                                                                      figsize = (10, 7))\n",
        "plt.title('Feature Importance - XGBoostClassifier', fontsize = 22, color = GRAY1)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ooup9w5khzaO"
      },
      "source": [
        "**Does this make sense?**\n",
        "\n",
        "Let's see:\n",
        "\n",
        "The most important feature that XGBoostClassifier takes into account when modelling is ```AGE_PERCENTIL_5```. This variable represents people with ages between 60 and 70 years. This makes sense, since we all know that old age is a risk factor in COVID-19. And we also checked that in our exploratory data analysis and we've seen that people past a certain age need more ICU beds.\n",
        "\n",
        "_Source:_ [ICU outcomes and survival in patients with severe COVID-19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249038)\n",
        "\n",
        "From the above article:\n",
        "\n",
        "> Out of total of 1283 patients with COVID-19, 131 (10.2%) met criteria for ICU admission (**median age: 61 years** [interquartile range (IQR), 49.5–71.5]; 35.1% female).\n",
        "\n",
        "This correlates with our findings.\n",
        "\n",
        "The second most important feature is ```RESPIRATORY_RATE_MEAN```. Since COVID-19 also attacks the respiratory system, of course this feature is relevant in the ICU prediction.\n",
        "\n",
        "Another important feature is ```PCO2_VENOUS```.\n",
        "\n",
        "> Venous pCO2 was significantly higher whereas pO2 and sO2 were significantly lower in patients who tested positive for SARS-CoV-2.\n",
        "\n",
        "_Source:_ [Venous blood gas analysis in patients with COVID-19 symptoms](https://www.degruyter.com/document/doi/10.1515/labmed-2020-0126/html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "0l_MsM1trail",
        "outputId": "49474498-90db-45e9-ed3e-a8bb1301b826"
      },
      "source": [
        "feat_import_xgb = pd.Series(xgb_model.feature_importances_, index = X_train.columns)\n",
        "ax = feat_import_xgb.nsmallest(15).sort_values(ascending = True).plot(kind = 'barh',\n",
        "                                                                      figsize = (10, 7))\n",
        "plt.title('Features with less Importance - XGBoostClassifier', fontsize = 22, color = GRAY1)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6L-XBNbTrjBD"
      },
      "source": [
        "Looking at the features that have less importance, we have the disease groupings. This is surprising and goes against the actual understanding that certain diseases increase the risk of hospitalization for COVID-19."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JPrvbXtqWoy"
      },
      "source": [
        "### LGBMClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "dHhq_wb2ghbY",
        "outputId": "a6c3deb7-1148-40eb-d875-086c1cddd1cb"
      },
      "source": [
        "feat_import_lgb = pd.Series(lgb_model.feature_importances_, index = X_train.columns)\n",
        "ax = feat_import_lgb.nlargest(15).sort_values(ascending = True).plot(kind = 'barh',\n",
        "                                                                      figsize = (10, 7))\n",
        "plt.title('Feature Importance - LGBMClassifier', fontsize = 22, color = GRAY1)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1SvWCerPxx4U+m/ZG9Dlsi4ZvnjWzxeUWUkC0ea1SHUqQ1UqreLhckVce2byf/VqBe38TGE/kNq+sVartUmLaLbs5oHxDXusdib2G08cYs3+5IK9ismvevL5Q1bp34MB++mqtMX7jMG5cHOjn2el5Mo4UeI2InL7R+hl/YJC7tW54bLfk4ZLga6R3ofasVaq7DNR4IbSWdxzsC+LvkP6uPMnAaTgtX8nfP1KrVN/UqWGtUl2uVqn+5yD67PT8W5P592QMKL9Q/HLefOMAbZ/LK9DcRHphsdlgjzMMS/pzeKngwN7MFos8k/ZF0tu5l7b7h1qrVFeoVarvrFWqGxaKW2k5H8yzU622a5FmuPoL9FozyW1n/vJ4bsibn8/LZ7b63oZ0E99wfYG0PvPXa2kN+fIHXKlWqb6pVqn+90IcY3H5bHHpxlpaz/3rpBslb6s3G8V15L9NSpnaplapHl7spFapvoX5q2WcxNB1/Hky/3ny7lql2lo9pDWj+Q3m5wePlC+TAt0jc075S9Qq1Q1Lz+MTSPn6n65VqofWKtUFnre1SnXjWqW65wiPc0TUm427SOkxAi6uVaofr1WqK5Xb1SrVFUlLVw5ZzrH/VN4c1Ae41ZuNy+rNxhr5a1CfUZGXWp1OWiXpV7VK9cBy6l3+HX0radWt/QbRbev5d2BeerLVzyqkHPrVyjvUKtXNa5Xqvu2uI/PfhfhTof3/1tp/dsT6pA+3ekn7RWCJfg4vLZyKY2aLTb3Z+Hotrd08Fbi5VqneRXrr/TnSjZebk9ZWfgfz/1FOJ80A7wLUa5XqzaSUi+1Is2OXktYgL/sFKV1iz1ql+pt8nHnAZfX5n/r6WdLNaB8Btq9VqjNJKThbkmb1jl2g18GdZ28tfXjRWaRc72m1SvUeUvrJmqQVW9Yivc3+y+EcYzF5iLTSRV+tUr2W9IFTW5NWMJlFaWa+3mz8pVapHkDKwf16vsHzblL+87akyaQv1dt8Auwg3ERadWmLWqXaS1oB6XnS0prfIwWCd5CeQ/fX0qdd/ouUh7wqcCrpMxRGRL3ZuKWQt/+9WqV6LCmtYwVSusLGpHco7s3tH66lz2q4iLSE5jH5+fY4KejblHRdL2Dxrh7ygVqlunOH+i/W8yfN1puNk2qV6mzgFNKLsy/WKtVbSR+QFKRVr3pI6W1Pkz4grJ0Na3kZ3Gwc6UOLtiK98J9Bull+Ufo46b6BY0kTBCfXKtVbSB981VpX/9Wkvxln99NH0fdIy0FuATyY031ESvl5jvS34P2lfdYDzgfm1CrV20l/z1YgPYfXJ71I/myh/SHA6bVK9UHS79XsPMZt8n7n97P87ogYw89hK/CMvZktVvVm4+OkoPx80prIu5BuBFuD9Oms+zN/Jp16s/Eg6R/d+aR/lLuRZm3PJN041i7/m7x04a6kVJHNSPm0B5P+8bba/I60wsQ1pH9IrfSbA+vNxmcW8jzPJ/2jO5X0AmM7YA9S0NcHfDTXjWVB+mCmL5ICjd1Jq8P8gPSBOgssW1pvNn5KCu5+QFoZYy/SdfglsMtwr2te1m9n0mohVVKu98HkJTZzas92pJSRx0gfXLQt6ZOKtyQF/SOq3mycRXo+nc38NfLfQrqB8gTSjd7F9teRAv6vkIKh/ySlCG0MPEiarT5mpMc5gLVJAXV/Xy9JU8ufHVEhfcLvbaTfxXcD72T+J4oeAqxXbza+288xX0X6fWx97ZP7uYkUcL9pEazs8xL19DkYnwdeT/pZ/Yn089iX9GLwIdKNuW+oNxvfHER/fyc9788kBdy75O2fkJ4jC6TokM73U6Tn6Dqk368dSX8vTgI2rTcbxQ8EO5b0QvIZ0gvsvUjLuP6adA0XSJsbaWP0OWwFihju4gVmZtaNapVqhbQSyp/qzUZlVAdjZmaD5hl7MzMzM7Mu4MDezMzMzKwLOLA3MzMzM+sCzrE3MzMzM+sCXu7SDNh5553jqquGswKfmZmZ2WKndoVOxTEDZs1apCurmZmZmS1yDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wIO7M3MzMzMuoDXsTcDxk2YGBMOmj7awzAzM7MlVHPaLovzcF7u0szMzMysWzmwNzMzMzPrAg7szczMzMy6gAP7AUiaXdqeIum0/Pg4SY9K6it8rSZpe0kh6QOF/SblsiMLZctJekLStLx9TKGfeYXHh+f6QyTdm79ukbRNoa/lJU2TdL+k2yX9XtI7cl1T0gxJd0n6taT1iucnadPCsf4mqZEfXy2pImlu6RwP7HC9mpJuKJX1Sbo7P95e0tOl/nbMda1zninpTklHSFqmsN8VpX4vlXRTqew4SXMkrdXfz9DMzMysGy032gPoAqdExInFAkkAdwP7AN/JxZOBO0v77gTcB+wt6VMR8WXgy7mP2RExqdDnrsCHgG0iYpakLYBLJb0pIv4CfBGYAGwSEc9KehWwXeFYO+T9Pg8cC3ywVRERM4BJ+ThnA1dExEV5uwI8UBzLIKwiad2IeFjSRm3qb4iIXduUz20dJwfmPwJeDnyu3FDSasCWwGxJ60fEg4XqWcARwNFDGLOZmZnZEs0z9ovOn4AVJb1KKdLfGbiy1GYy8HXgIeDNA/R3NHBURMwCiIjbge8Dh0oaTwrU/y8ins31f42IC9v083tg7WGe02BdCOybH08GzhtqBxHxOHAIcFi+fmV7ApcD5wP7lerOAvaVtHqnY+R3QHol9c6b8/RQh2hmZmY2pjiwH9hKxbQR4Aul+qmF+utKdRcBewNbA7cDz7YqJK0I7EgKTs8jBcCdbAzcVirrzeU14KGIeGYQ57MzcOkg2hVtUEqd2XaA9heTAm+A3UjnWLRtqb8N2nWSZ+GXBdZqU916wdDu2s0mBfcf7TTIiDgzInoiomfZ8at2PCEzMzOzsc6pOAObW0qJmQL0FOoXSMUpuBC4ANiQFIBuXajbFbguIuZKuhj4jKSPRcS8ER39fNflGezZwGeGuO9QU3GeBP4uaT/gD8CcUn1/qTiDktOMJgI3RkRIel7SJhFxd6HZqUCfpP5+NmZmZmZdxTP2i1DOfX+elEt/Tal6MrCjpCZpJv6VwFs7dHcPKae8aEtgJlAHXivp5R323wFYD+gDPj/IU1gYFwCnM4w0nBZJ6wPzgMdLVfsArwAa+fpVKM3aR8RTpBz9Q4d7fDMzM7MliQP7Re+zwNHFmfgcgG8LvDYiKhFRIQWgndJxvgZ8VdIrcx+TgCnANyNiDvBd4OuSVsj1a0rau9hBRPwb+Bhw4ED55yPgkjzmXwxnZ0lrAmcAp8WCH488Gdi5cO22ZME8e4CTSTcc+50pMzMz63oO7Bfe1FK+eKVYGRG/i4hyTvsewLWtG12znwK7SRrX7iARcRkpb/x3ku4Fvg28NyIey02OBZ4A7slLS14BLJBzn9ufx9Bmsss59ocPtENE/CMivhoRz7WpLufY75XLW/czzASuBn5J6d2FfH3XA15c5jIiGsDTkrYqjWEW6QVG22tqZmZm1k204GSo2dKnp6cnent7R3sYZmZmZoPRbsVAz9ibmZmZmXUD5x7bsEi6mQVTXA7IH3ZlZmZmZouZA3sblojYauBWZmZmZra4OBXHzMzMzKwLOLA3MzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wIO7M3MzMzMuoADezMzMzOzLuDA3szMzMysCygiRnsMZqNu3ISJMeGg6aM9DDMzszGlOW2X0R6Ctad2hZ6xNzMzMzPrAg7szczMzMy6gAN7MzMzM7Mu4MB+KSJpnqQ+SXdL+rGk8bn81ZLOl/SApNsk/VzS6yRVJM3N+9wj6RxJy3fof3tJIekDhbJJuezIvH22pEbus0/S73L5FElPSLpD0v2SfiFp60I/Z0vaq7C9hqTnJX24NIampIsL23tJOnsELp+ZmZnZmObAfukyNyImRcQmwHPAhyUJuAS4PiI2iIgtgU8Br8r7PBARk4BNgXWAfQY4xt2lNpOBO0ttjsrjmBQRWxfKL4iIzSNiIjAN+Imkjfo5zt7ATbn/si0lvWGAcZqZmZl1FQf2S68bgBqwA/B8RJzRqoiIOyPihmLjiJgH3AKsPUC/fwJWlPSq/KJhZ+DKoQ4uIq4DzgQO6afJZOAIYG1J65TqTgKOGegYkg6R1Cupd96cp4c6RDMzM7MxxYH9UkjScsA7gBnAJsBtg9hnRWAr4KpBHOIi0oz61sDtwLOl+hMKqTg/7NDP7cCGbcayLjAhIm4BLgT2LTW5ENhCUq3TICPizIjoiYieZcev2vmMzMzMzMY4B/ZLl5Uk9QG9wEPAdwexzwZ5n78Cj0XEXYPY50JSYD8ZOK9NfTEVZ/8O/bRdo5UUyF+YH5/Pguk484ATSClFZmZmZksFB/ZLl7mFgPr/IuI5YCawZYd9Wjn2G5By19850EEi4i/A88BOwDULMd7NgT+0KZ8MTJHUBC4DNpM0sdTmXOAtwLoLcXwzMzOzJYYDe7sWGCfpxVx2SZtJ2rbYKCJmAZ9k8LPgnwWOzrn5QyZpO1J+/bdL5a8DVo6ItSOiEhEV4HhKs/YR8TxwCjB1OMc3MzMzW9I4sF/KRUQAewA75uUuZ5IC5b+0aX4pML4c9PfT7+8i4tJ+qos59n2SVsjl++bt+4BPA++OiPKM/WTSKj5FF9N+dZzvAssNNFYzMzOzbqAU15kt3cZNmBgTDpo+2sMwMzMbU5rTdhntIVh7be9DdGBvBvT09ERvb+9oD8PMzMxsMNoG9k5TsCGT9Hbgq6XiRkTsMRrjMTMzMzMH9jYMEfEL4BejPQ4zMzMzm883z5qZmZmZdQEH9mZmZmZmXcCBvZmZmZlZF3Bgb2ZmZmbWBRzYm5mZmZl1AQf2ZmZmZmZdwIG9mZmZmVkXcGBvZmZmZtYFHNibmZmZmXUBRcRoj8Fs1I2bMDEmHDR9tIdhZmZLuOa0XUZ7CLZ0ULtCz9ibmZmZmXUBB/ZmZmZmZl3Agb2ZmZmZWRdwYG9mZmZm1gUc2C9GkmaXtqdIOi0/Pk7So5L6Cl+rFdpOz/XLlPZ/Ire9V9JUSW8v7D9b0h/z43P6GdP2kp4u9HFiqX4NSc9L+nDePj23vUfS3MKx9pJ0tqRGoex3Ha7FFEkhacdC2e65bK+8fX1h/H2SLir10Sfp/FLZ2fk6jSuMv9nfOMzMzMy6xXKjPQB7iVMi4sRyYQ7m9wAeBrYDritUXxARh0l6JfBHYPOImJT3ux44MiJ6BzjuDRGxq6SVgDskXRIRv811ewM3AZOBMyLi0Nx3BbiidaxctitwVES8JADvYAawH3B13p4M3Flqs3+78UvaCFgW2FbSyyLin4XqecD7gW8NchxmZmZmSzzP2C8ZtgdmkgLVye0aRMSTQB2YMNyDRMRcoA9Yu1A8GTgCWFvSOsPtux83AG+StLyklYFaPv5gTAbOBX4JvKtUNx2YKqnjC1dJh0jqldQ7b87TQxy6mZmZ2djiwH7xWqmYagN8oVQ/tVBfnJWfDJwHXALsImn5cseSXgusCNw13MFJegUwEfhN3l4XmBARtwAXAvsOopsTCufwwwHaBmm2/u2k4PyyNm1+WOjvhEL5vsD5pOtSfrHzEHAjcEDHg0ecGRE9EdGz7PhVBxiqmZmZ2djmwH7xmhsRk1pfwGdL9acU6ncAkLQC8D/ApRHxDHAzKRBu2VfSXaTZ+m9GxL+GMa5tJd0JPAr8IiL+0uqbFNBDCqLbvltQclThHPYfRPvzSek4+5GC9LL9C/0dBSCpB5gVEQ8B1wCbS1q9tN/xwFH4OW5mZmZLCQc9Y9/bgdWAGfkm0G14aYB9QURsBmwNTJP06mEc44aIeCOwMXCwpFbe/GRgSj7uZcBmkiYO7zTay+8GbAqsERH3DXK3ycCGeVwPAC8H3l3q935SWs8+IzdaMzMzs7HLgf3YNxn4QERUIqICVIGdJI0vNso3mJ4LfHS4B4qIBjANOFrS64CVI2LtwrGPZ3Cz9kP1SeDTg2mYbyTeB9i0MK539TOuLwNHjtQgzczMzMYyB/ZjSzHHvk/SG4CdgZ+1GuTVX24Edmuz/1eB90laZSHGcAbwFlKgfEmp7vc8+RYAACAASURBVGIGDuxPKJ3DCgMdMCKujIjr+qku5thfDWwLPBoRfy60+Q3wBkkvuXE4ImYCtw90fDMzM7NuoIgY7TGYjbqenp7o7R1oVVAzMzOzMUHtCj1jb2ZmZmbWBfwBVUsJSW8npeoUNSJij0V83PexYN7/b1sfdGVmZmZmI8OpOGY4FcfMzMyWKE7FMTMzMzPrVg7szczMzMy6gAN7MzMzM7Mu4MDezMzMzKwLOLA3MzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wL+5FkzYNyEiTHhoOmjPQwzMxshzWm7jPYQzBYlf/KsmZmZmVm3cmBvZmZmZtYFHNibmZmZmXUBB/YLQdLsNmXHSXpUUl/hazVJUySdVmp7vaSe/LgpaY38eEtJDUmbS1pV0jmS6pIeyI9XLfTxOkk/l3S/pNslXShpO0n3SVqp0O5nkj5VGNNzkmbkx9Py+J4ojfsNkpaRdKqku3P7WyVVO1yTpqQbSmV9ku7Oj7eX9HTpODvmunl5e6akOyUdIWmZwn5XlPq9VNJNba7/HElrdfo5mZmZmXUbB/aLxikRManw9dRgd5S0GXARsG9E3AF8F3gwImoRsQHQAL6T264I/Az4VkRMjIgtgG8CTwI/AY7J7XYHlo+I41tjAv4M7JC3P5kPf0Fp3PcA+wKvATaLiE2BPYCBzmcVSevmY2/Upv6G0nGuzuVz8/bGwE7AO4DP9XOdVgO2BFaVtH6pehZwxABjNDMzM+sqDuzHlo2AS4EDIuIWSTVS8PrFQpsvAD2SNgDeA/w+Ii5vVUbE9RFxd263t6RJwDTg0GGOaQLwWES8kPt/JCL+PsA+F5JeEABMBs4b6kEj4nHgEOAwSe3u/N4TuBw4H9ivVHcWsK+k1TsdQ9Ihknol9c6b8/RQh2hmZmY2pjiwXzSmFtJMrhvCfj8FDouIG/P2G4C+iJjXapAf9wEbA5sAt7XrKCLmAEcCvwHOj4j7B3H8fUspMiuRgvTd8vZJkjYfRD8XkwJvgN1IAXjRtqXjbNDPOTwILAus1aa69YLhvPy4aDYpuP9op0FGxJkR0RMRPcuOX7VTUzMzM7Mxz4H9olFMxdkhl/X3gQHF8quBD0hadiQGkWfynyKl5wxGORVnbkQ8Arwe+BTwAnCNpLcN0M+TwN8l7Qf8AZhTqi+n4jww+LMCSa8CJgI3RsR9wPOSNik1OxU4SNIqQ+nbzMzMbEnlwH7xeRJ4RalsdVI+eMth+XsrEL8HmNS6gRQgP56U62aSUnU6eSF/DVtEPBsRV0bEUcBXgN0HsdsFwOkMIw2nJefOzwMeL1XtQ7qWDUlNoEJp1j7f1/Ajhp+CZGZmZrZEcWC/+NwK/JekVwPk1XDGAQ8X2rxAypvfUNIXIqIO3AEcW2hzLHB7rvsRsLWkFz9eT9Jb2sxeD5ukLSS9Jj9eBtgM+NMgdr0E+Brwi2Eed03gDOC0WPDjkScDO0dEJSIqpBc35Tx7gJOBDwHLDWcMZmZmZksSBzwLZ7ykRwrbJ+fvUyW9t1C+e0Q0JX0U+HkOkGcDk1s3pbZExL8kvRP4taS/AgcD35DUSlf5fS4jIuZK2hWYLmk68DxwFwPklnewr6RtCtv/C7wc+LakcbnsFuC0BfYsiYh/AF8FaHPv67aS+grbX4qIi4CVcvnywL+Bc5l/Tcl9VYD1gBeXuYyIRl5Cc6vSGGZJugSYOtB4zczMzJZ0WnAy1Gzp09PTE729vaM9DDMzM7PBaLdioFNxzMzMzMy6gVNxbFgk3Uy6R6DogIiYMRrjMTMzM1vaObC3YYmIrQZuZWZmZmaLi1NxzMzMzMy6gAN7MzMzM7Mu4MDezMzMzKwLOLA3MzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wIO7M3MzMzMuoAiYrTHYDbqxk2YGBMOmj7awzAzs4XUnLbLaA/BbHFQu0LP2JuZmZmZdQEH9mZmZmZmXcCBvZmZmZlZF3Bgv5hIerWk8yU9IOk2ST+X9DpJcyX1SbpH0jmSls/tt5f0dK5rfe1Y6G93SSFpw7x9c27zkKQnCvtUJDUlrZHbhaSTCv0cKem4/Pg4SUfmx2dLelTSuLy9hqRmh/Or5L6/VChbQ9Lzkk4r9P9o6ZxWK5zrHZL+KOk3knYt9PPiuPL2cvkcp5XGcL2k3sJ2j6Trh/SDMjMzM1tCObBfDCQJuAS4PiI2iIgtgU8BrwIeiIhJwKbAOsA+hV1viIhJha+rC3WTgRvzdyJiq9zPZ4ELCvs0S8N5FtizFegPYB7w/iGcagMo3rW0NzCz1OaU0jk9lctviIjNI+L1wOHAaZLe1s9xdgLuA/bO17ZoLUnvGMKYzczMzLqCA/vFYwfg+Yg4o1UQEXcCDxe25wG3AGsP1JmklYFtgIOB/YY4ln8DZwJTB9F2OjBV0nKD7HsO8AdJPXl7X+DCIY6PiOgDvgAc1k+TycDXgYeAN5fqTgCOGcxxJB0iqVdS77w5Tw91mGZmZmZjigP7xWMT4LZODSStCGwFXFUo3raUtrJBLn8XcFVE3Ac8KWnLIY7ndGB/SasO0O4h0rsCBwyh7/OB/SStS5rx/3OpfmrhfK7r0M/twIblwnyddgQuB84jv2NR8HvgOUk7DDTQiDgzInoiomfZ8QNdCjMzM7OxzYH96NtAUh/wV+CxiLirUFdOxXkgl08mBdDk7+XgtqOIeAY4h5TyMpDjgaMY/HPlKlKqzH7ABW3qi6k4nYLvtuuzArsC10XEXOBiYHdJy5bafAk4dpDjNTMzM+sKDuwXj5lAf7PqrRz7DYAtJb2zU0eSVgfeCnwn38x6FLBPm1zzgUwnpfK8rFOjiLgf6OOluf+d2j9HenfiCOCiIY6paHPgD23KJwM75nO/DXgl6XoUx3AtsBLwnwtxfDMzM7MligP7xeNaYJykQ1oFkjYD1m1tR8Qs4JOkm2o72Qs4NyLWi4hKRKxLuml126EMKCL+Rsp/P3gQzb8MHDlgq/lOAo7OxxiyfG0+Q0oZKpa/nHSer83nXgEOpf07Fl8CPjGc45uZmZktiRzYLwYREcAepJnmByTNJKW4/KXU9FJgvKRWkF7Osd+LFMReUtrvYoaYjpOdBAy4Ok5EzCTlvA9KRMyMiO/3Uz21dE6VXL5ta7lLUkB/eERcU9p3D+DaiHi2UPZTYLfWspyFMfwceGKwYzYzMzNb0inFnGZLt3ETJsaEg6aP9jDMzGwhNaftMnAjsyVf2xRsB/ZmQE9PT/T29g7c0MzMzGz0tQ3sB7s+uRkAkjYFzi0VPxsRW43GeMzMzMwscWBvQxIRM4BJoz0OMzMzM3sp3zxrZmZmZtYFHNibmZmZmXUBB/ZmZmZmZl3Agb2ZmZmZWRdwYG9mZmZm1gUc2JuZmZmZdQEH9mZmZmZmXcCBvZmZmZlZF3Bgb2ZmZmbWBRQRoz0Gs1E3bsLEmHDQ9NEehpnZUqc5bZfRHoLZkkjtCj1jb2ZmZmbWBRzYm5mZmZl1AQf2ZmZmZmZdwIG9mZmZmVkXcGC/GEiaJ6lP0t2SfixpfC6f3WGf6ZIelbSMpE3z/n2S/iapkR9fLakiaW6hvk/SgbmPpqQ1Sv1OkfREqf0bO/S/jKRT89hnSLpVUrXDuJuSbiiV9Um6Oz/eXtLTpePvWLpOMyXdKekIScsU9rui1O+lkm4qlR0naY6ktQpl/V5nMzMzs26x3GgPYCkxNyImAUj6IfBh4OT+Gudgdg/gYWC7iLgOaO1/NnBFRFyUtyvAA63+B+mCiDisVNZf/5OB1wCbRcQLktYB/jlA/6tIWjciHpa0UZv6GyJi1zblxeu0FvAj4OXA58oNJa0GbAnMlrR+RDxYqJ4FHAEcPcA4zczMzLqGZ+wXvxuA2gBttgdmAt8CJi/qAQ1gAvBYRLwAEBGPRMTfB9jnQmDf/HgycN5QDxoRjwOHAIdJarek057A5cD5wH6lurOAfSWt3ukYkg6R1Cupd96cp4c6RDMzM7MxxYH9YiRpOeAdwIwBmraC4UuAXSQtP0D7DUqpLdsO0H7fUvuVOrS9ENgttztJ0uYD9A1wMSnwBtiNFIAXbVs6/gbtOsmz8MsCa7Wpbl2j81jwxc9sUnD/0U6DjIgzI6InInqWHb9qxxMyMzMzG+ucirN4rCSpLz++Afhufw0lrQD8D/DxiPiHpJuBtwNX9LcPI5OK01ZEPCLp9cBb89c1kvaOiGs67PYk8HdJ+wF/AOaU6vtLxRkUSa8CJgI3RkRIel7SJhFxd6HZqUCfpBOHexwzMzOzJYkD+8Vj7hAC77cDqwEzcgbKeGAunQP7RSoingWuBK6U9Fdgd6BTYA9wAXA6MGW4x5W0PjAPeBwo5urvA7wCaORr9HLSrP0xhTE/JelHwKHDPb6ZmZnZksSB/dgzGfhARJwHIOllpAB2fESUZ74XOUlbAH+JiD/nm3o3A+4axK6XkPLzf0G6+Xaox10TOAM4Lc/KF6snAztHxO9z2ypwNYXAPjsZuBU/z83MzGwp4Bz70TVe0iOFr08DOwM/azWIiH8CN5Jy1ftTzrE/vFB3V6H/1ko85Rz7rTv0vRZweV6u8i7g38BpA51YRPwjIr4aEc+1qS7n2O+Vy1dqLXdJCtR/CXy+uGNeBWg94MVlLiOiATwtaavSGGaRXmCMG2i8ZmZmZks6RcRoj8Fs1PX09ERvb+9oD8PMzMxsMNqtGOgZezMzMzOzbuDcYxuWvFpPOcXlgIgYaClPMzMzM1sEHNjbsETEVgO3MjMzM7PFxak4ZmZmZmZdwIG9mZmZmVkXcGBvZmZmZtYFHNibmZmZmXUBB/ZmZmZmZl3Agb2ZmZmZWRdwYG9mZmZm1gUc2JuZmZmZdQEH9mZmZmZmXUARMdpjMBt14yZMjAkHTR/tYZiZLTGa03YZ7SGYLc3UrtAz9mZmZmZmXcCBvZmZmZlZF3Bgb2ZmZmbWBRzYjzBJs9uUHSfpyPz4bEmPShqXt9eQ1MyPK5LmSuorfK2Q63aXdJekP0iaIWn30jGOlHRv3udWSQfm8usl9Ui6Odc9JOmJQv8VSatKOkdSXdID+fGqef9lJJ0q6e583FslVTucf1PSDaWyPkl358fbS3q6dI475rp5eXumpDslHSFpmcJ+V5T6vVTSTW2u9RxJa3X6mZiZmZl1m+VGewBLqXnA+4Fvtal7ICImFQskvRE4EdgpIho5sP6VpAcj4i5JHwZ2At4UEc9IejmwR7GPiNgq9zUF6ImIwwr9XwTcHRGtFwOfB74D7A3sC7wG2CwiXpC0DvDPAc5vFUnrRsTDkjZqU39DROzapnxu69xzYP4j4OXA58oNJa0GbAnMlrR+RDxYqJ4FHAEcPcA4zczMzLqGZ+xHx3RgqqTBvrA6EvhKRDQA8vfjgaNy/aeBj0TEM7n+mYj4/mA6llQjBchfLBR/AeiRtAEwAXgsIl7IfT8SEX8foNsLSS8IACYD5w1mLEUR8ThwCHCYpHZ3fu8JXA6cD+xXqjsL2FfS6p2OIekQSb2SeufNeXqoQzQzMzMbUxzYj46HgBuBA9rUbVBIUTk9l20M3FZq1wtsnGfnVynNWA/FG4C+iJjXKsiP+/JxLwR2y+M5SdLmg+jzYlLgDbAbKQAv2raUirNBu07yOS0LrNWmuvWC4bz8uGg2Kbj/aKdBRsSZEdETET3Ljl+14wmZmZmZjXVOxRk9xwM/BX5WKl8gFWc0RcQjkl4PvDV/XSNp74i4psNuTwJ/l7Qf8AdgTqm+v1ScQZH0KmAicGNEhKTnJW0SEXcXmp0K9Ek6cbjHMTMzM1uSeMZ+lETE/aRZ8X0G0fweUrpM0ZbAzJx+M1vS+sMcyj3ApNZNqpBumAUm5Toi4tmIuDIijgK+AuzetqeXugA4nWGk4RTGsT7pfoTHS1X7AK8AGvnG4wqlWfuIeIqUo3/ocI9vZmZmtiRxYD+6vkzKnx/IicCnJFUgrZ5Dyqs/KdcfD5ye03KQtHJrVZyBREQduAM4tlB8LHB7RNQlbSHpNbnfZYDNgD8NoutLgK8BvxjMOMokrQmcAZwWC3488mRg54ioRESF9CKnnGcPcDLwIfzOlJmZmS0FHPCMvPGSHilsn9xfw4iYKel2YItOHUZEn6SjgcslLQ88D3wiIvpyk28BKwO3Sno+15/Uvre2Dga+IemBvP37XAYpv/3breU5gVuA0wbqMCL+AXwVoM29r9tK6itsfykiLgJWyuXLA/8GzqV0/fKLmvWAF5e5zCsFPS1pq9IYZkm6BJg60HjNzMzMlnRacDLUbOkzbsLEmHDQ9NEehpnZEqM5bZfRHoLZ0qzdioEO7M0Aenp6ore3d7SHYWZmZjYYbQN7p+LYsEi6GRhXKj4gImaMxnjMzMzMlnYO7G1YWp9ka2ZmZmZjg1fFMTMzMzPrAg7szczMzMy6gAN7MzMzM7Mu4MDezMzMzKwLOLA3MzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wKKiNEeg9moGzdhYkw4aPpoD8PMbKE0p+0y2kMws8VD7Qo9Y29mZmZm1gUc2JuZmZmZdQEH9mZmZmZmXcCBfZeT9EpJffnrL5IeLWy/StKPJD0o6TZJv5e0R2HfbSTdIune/HXIAMc6TlJIqhXKPpbLevJ2U9KMwhhOzeVnS2pIulPSfZLOkbROoZ+mpDUK27vnfjcslFVy2f8Vyk6TNGUhL6OZmZnZmOfAvstFxJMRMSkiJgFnAKfkx5sDlwK/iYj1I2JLYD9gHQBJrwZ+BHw4IjYEtgE+JGmgO7Nm5H5a9gZmltrs0BpTRBxeKD8qIt4IvB64A7hW0gr9HGcycGP+XvQ48NEO+5mZmZl1JQf2S6+3As9FxBmtgoj4U0R8I28eCpwdEbfnulnAJ4BPDtDvpcC7ACRtADwNzBrKwCI5BfgL8I5yvaSVSS80DualLyIAngCuAQ4ayjHNzMzMlnQO7JdeGwO3D1B/W6msN5d38gzwsKRNSEH3BW3aXFdIxZnaoa/bgQ3blL8LuCoi7gOelLRlqf6rwJGSlu00UEmHSOqV1DtvztOdmpqZmZmNeQ7sDQBJp+f89ltHoLvzSUH97sAlbeqLqTindBpWP+WT8zFax3pJOk5EPAjcDLyn0yAj4syI6ImInmXHr9qpqZmZmdmY58B+6TUT2KK1ERGHAm8D1sxF9wDlmfAtWTBfvp0rgAOAhyLimYUY4+bAH4oFklYnpRF9R1ITOArYR1L5RcBXgKPp/8WBmZmZWVdxYL/0uhZYUdJHCmXjC49PB6ZImgRpdR1SisvXBuo4IuaQguovD2dgSg4HJgBXlar3As6NiPUiohIR6wINYNvSGO4lvTjZbThjMDMzM1vSOLBfSkVEkFJltsvLTN4CfJ8UkBMRjwHvBb4t6V7gd8BZEXH5IPs/v3XjbRvFHPtzCuUnSLoTuA/4D1LKznOlfSezYHrPxSy4Og6kFxbrtCk3MzMz6zpK8Z3Z0m3chIkx4aDpoz0MM7OF0pw20IrEZtYl2qYaO7A3A3p6eqK3t3e0h2FmZmY2GG0D++UW9yhsySfpGNIHTxX9OCKGlVNvZmZmZgvPgb0NWQ7gHcSbmZmZjSG+edbMzMzMrAs4sDczMzMz6wIO7M3MzMzMuoADezMzMzOzLuDA3szMzMysCziwNzMzMzPrAg7szczMzMy6gAN7MzMzM7Mu4MDezMzMzKwLOLA3MzMzM+sCiojRHoPZqBs3YWJMOGj6aA/DzIapOW2X0R6CmdnipHaFnrE3MzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwH4Mk3SMpJmS7pLUJ2krSStImi6pLul+ST+VtE5hn3m57UxJd0o6QtIyuW57SVfkx8dJOrJ0vKakNfLjkPSDQt1ykp5o7d/PeKfk/XYslO2ey/bK29dL+mMeY5+kiwrjeTSX3S/pJ5LeUOjnekk9he1Jud+dS2MISScVto+UdNygL7qZmZnZEsqB/Rgl6c3ArsAWEbEZsCPwMPAVYBXg9RExEbgU+Imk1k0UcyNiUkRsDOwEvAP43DCG8E9gE0kr5e2dgEcHsd8MYL/C9mTgzlKb/fMYJ0XEXoXyU3LZROAC4FpJa/ZznMnAjfl70bPAnq0XKGZmZmZLCwf2Y9cEYFZEPAsQEbOAp4D3AVMjYl4u/x4pmH1ruYOIeBw4BDisEPgPxc+B1lITk4HzBrHPDcCbJC0vaWWgBvQN9cARcQHwS+A95bp8LnsDU4CdJK1YqP43cCYwdaBjSDpEUq+k3nlznh7qEM3MzMzGFAf2Y9cvgXUl3Sfpm5K2IwXJD0XEM6W2vcDG7TqJiAeBZYG1hjGG84H9cuC8GXDzIPYJ4Grg7cC7gMvatPlhIRXnhA593Q5s2KZ8a6AREQ8A1zP/xUfL6cD+klbtONCIMyOiJyJ6lh3fsamZmZnZmOfAfoyKiNnAlqQZ9ydIqSnbj+QhBiqPiLuACmm2/udD6Pt8UjrOfrSf5S+m4hzVoZ/+3mWYnI/ROtZL0nHyC59zgMOHMGYzMzOzJdpyoz0A619Ot7keuF7SDOBDwGslrRIR/yg03RJoe1OrpPWBecDjwEaFqidJ6T5Fq5DSfYouA04kvah45SDHfYukTYE5EXHf8LKAANic9G7EiyQtC7wbeJekY0jB/yvbXJPppBn/7w334GZmZmZLEs/Yj1GSXi9pYqFoEvBH4PvAyTnARdKBwHjg2jZ9rAmcAZwWC37E8G+Ad0paJbfdE7izlbtfcBbw+YiYMcRT+CTw6SHu8yJJ7wb+mwVn/N8G3BUR60ZEJSLWAy4G9ig2ioi/ARcCBw93DGZmZmZLEs/Yj10rA9+QtBrphtA6KS3nH6QZ9PskvQDcC+xRCNxXktQHLJ/3Oxc4udx5RNwl6TTgRklBmtH/QJt2jwCnDnXwEXFlh+ofSpqbH8+KiNbymFMlvRd4GXA38NaIeKK072TgklLZxcBHSOk3RScBhw117GZmZmZLIi04kWu29Onp6Yne3t6BG5qZmZmNvrZ5zk7FMTMzMzPrAk7FsSGT9D7go6Xi30bEoaMxHjMzMzNzYG/DkD8Uy6vNmJmZmY0hTsUxMzMzM+sCDuzNzMzMzLqAA3szMzMzsy7gwN7MzMzMrAs4sDczMzMz6wIO7M3MzMzMuoADezMzMzOzLuDA3szMzMysCziwNzMzMzPrAoqI0R6D2agbN2FiTDho+mgPw8yGoDltl9EegpnZaFG7Qs/Ym5mZmZl1AQf2ZmZmZmZdwIG9mZmZmVkXWCoCe0nzJPVJulvS5ZJWy+UVSXNzXevrwFz3fkkzJN2V93tXLj9bUiO3vV3Smwvle+XH10v6o6Q7Jd0qaVJpPNMlPSppGUmbFo79t0LfV+e2G0u6Nvd3v6TPSFKumyLpidz+XklTJa2YH29aON5Rkv5fP9emeA3ukXSOpOUL9cvlY0zL28cUxjuv8PhwScfl8ypez9X6Oe72kkLSBwplk3LZkW2udZ+k35X6uFTSTaWy4yTNkbRWoWx2+2eGmZmZWfdYKgJ7YG5ETIqITYC/AYcW6h7Ida2vcyStAxwDbBMRmwH/CdxV2OeoiJgEfBJoGzAD+0fEG4FvAie0CiUtA+wBPAxsFxEzWscGLmv1HRE7Slopl02LiNcDbwS2Bv63cJwL8r7/lce8JvAx4JtK1gY+nMfanwdyH5sC6wD7FOp2Au4D9pakiPhyYbxzC9ft1Nz+lNL1fKrDce8uHWsycGepzVGFvrYuXMfVgC2BVSWtX9pnFnBEh+OamZn9//buPcqusr7/+PtDkEBEuWkxXGS4RCliDTAtFEG5qMQictVkiiVYLG0XFORXFW1RUZcFChZE/OmiilwWhqRcQrGVaLlUKBcZQuQqcklE+AFCwGhMihA/vz/2M7A5OTNzJoGcmT2f11pn5exnP/t5vvvsdeB7nvM9eyIaZ7wk9nU3A5sP0+cPgN8ASwFsL7W9sE2/HwHbjXC+vYB7gG9QJbJD+XPgf2z/oMSxDDiWNkm67cXAg8Bk21cDjwNHAGcCJ9t+dpi5sL0C+HFLvH3AV4FHgD8dbowR+jmwrqRNy7cQ04Dvd3jsIcBVwCXAjJZ95wHTJW081ACSjpbUL6l/xbIlIww9IiIiYnQZV4m9pAnAvlSr4AO2bSkd2ZNq1fhJYKGk70g6YJAhDwDuGmbaacDc2nYfMAu4Ati/XvbSxtuA2+sNth8C1pf0+pZzezOwLi99s/Bx4MvAG21fNEyMA2OsC+wKXF3bfg9VAj2L4T+IAJxQey2v66D/pcCHqL6JmA8817L/9Np4F9faB17HdnEtpUrujx9qYtvn2u613Tth0gYdhBoRERExeq3d7QDWkPUkLaBaib4P+GFt30AZystImgb8MdUHgTMl7WL75LL7dEknAU8BRw0y58WS1gHWB6aWMdcB/gz4P7Z/I+lWYD/ge6txbtMlvQvYHjjW9v8C2P5/kq7tcOxty+uzNfAftgc+HHwAuM72ckmXAZ+V9PGysj+YM22fMYL45wCzS/yzqBL8uk/avrTeIGlTYApwo21Lel7SjrbvrnU7G1ggaSSxRERERIxZ42XFfnlJ3reiuqH/McP0x5Uf2z6FqtTj0Nrugbrv97Ykk3WHA9sAFwBfK237ARsCd0laBOzB0Kvg91LVkb+o1JMvtf3r0jS7/A5gd+BUSW+qdf99eQxn4MPNtsAukj5Y2vuA95RYbwc2AfbpYLyO2X4CeJ6qlv+aDg/7MLAR1Tcqi4AeWl7HUtv/XTq41hERERFNMF4Se+DFGvXjgL+XNOi3FZI2k7RzrWkqVT34SOcz8FlgN0nbUyWfH7PdY7uHaoX8vZImDTLExcAekt5T4lqPaiX6n9vM1Q9cxDDlJ8PE+zRV/f5nSqnPn68cfgAAHL9JREFUnsCba/EeQ2flOCP1OeDEYb4JqOsDptXi2oWV6+wB/gX4a8bPN1MRERExjo2rxB7A9h1UdegDCWprjf1xwGuAM8ptIxcA01nFhNn2cuArwIlU9fb/Udv3W+BGqlr9wY49EDhJ0v1U9fy3AecMMt1pwEclvW5VYi3mApOAE4Brbddr3q8EDpA0cYjjT2h5PXuGm9D2TbbnDrL79Jbx3kL1zcstteMXAksk7doy7tNUv2UYKt6IiIiIRlC1qBwxvk2cPMWTZ57V7TAiYgQWnbp/t0OIiOgWtW1MYh8Bvb297u/v73YYEREREZ1om9in9nicKH+JtvW2l8/Z3rVd/1dw3v2oSoTqFto++NWcNyIiImK8SWI/Tti+i3LbzTU87zxg3pqeNyIiImK8GXc/no2IiIiIaKIk9hERERERDZDEPiIiIiKiAZLYR0REREQ0QBL7iIiIiIgGSGIfEREREdEASewjIiIiIhogiX1ERERERAMksY+IiIiIaADZ7nYMEV03cfIUT555VrfDiIgOLTp1/26HEBHRTWrXmBX7iIiIiIgGSGIfEREREdEASewjIiIiIhpg2MRe0gpJCyT9RNJ8SbuX9h5Jd7fpv4WkKyU9IOkhSV+VtE5t/x6Sfizpp+VxdG3fyZIeK/M9IOlySTvU9l8v6f4Sy/9Iemub9tskTa0ds0jSXWXMBZLOLu27Sbq1tN0n6eTSvqmk75Wx7pX0n6V9L0nfaznX8yUdtqoxDPJ6ny9pYRnnZ5IulLRFy1hvqG0fJMmStq+1rSXpbEl3l3lvk7R17XwfkfRULZ4eSRuUuR4s1+1CSRsMc63r5/8aSaeW6zZf0s2S3j/EeS6SdENL24KBecrrvaQW4wJJ7xnmvHtK29/V2s6RdORgcUREREQ0RScr9sttT7X9DuAzwCmDdZQk4HJgru0pwFuA9YEvl/1vAr4L/I3t7YE9gL+WVP8V1JllvinAbOBaSW+s7T+8xHIBcHqb9v/b0g6wdxlzqu3jStsFwNG2pwI7AnNK+xeBH9p+h+0dgE8P/xKtcgyD+WQZ563AHVSvwTqD9O0Dbiz/DpgObAb8ke23AwcDv7K9aznfzwGza/EsAr4NPGx7O9vbAguBb3V64sCXgMnAjrZ3Bg4CXjfMMa+TtCWApD9ss/+GWoxTbf/XMOcN8Evg+CFer4iIiIhGGmkpzuuBZ4fYvw/wv7a/A2B7BXAC8JeSJgHHAOfbnl/2Pw18ikGSZ9uzgR8Af95m94+A7dq03wxs3sG5/AHw+ECctu8t7ZOBR2sx3NnBWKsaw5BcORN4Alhp9VvS+lQfjo4CZtR2TQYet/37Ms6jtge9bpK2A3ahSs4HfBHolbTtcHGWa/tXwN/Zfq7M+aTtOUMfyRyqDyFQJeizhpurzDfYeQM8BVwDzOxkrIiIiIim6CSxX6+UQfyUagX3S0P0fRtwe73B9q+BR6iS8JX2A/2lfTDzge3btB8A3NWmfRowt6Xtulo5xwml7UzgfklXSPprSeuW9q8D35Z0naR/lLTZELENptMYOjXYa3AgcLXtnwGLJe1S2ucAB5S5viJpp2HG3wFYUD6IAS9+KFvA0NdmwHbAI+Vaj8RlwCHl+QHAVS3792wpxRn4kDHYeQ84DfiEpAlDTS7paEn9kvpXLFsywtAjIiIiRpe1O+izvJRvIOlPgQsl7fjqhvUyrffpvFjScmAR8Hct7etQlf5MbTlm7/LtwItsf1HSxcD7qL4R6AP2sj1P0jZUyfn7gTvK+Q52w/96+4hiGIG29yotMX+1PL+kbN9u+1FVvz/YpzyukfQh29es4vyvlsXAs5JmAPcBy1r232D7A22Oa3veAzttPyzpVtp/00Ot37nAuVDdx36VziAiIiJilOgksX+R7ZvLDzffOEiXe4HD6g2SXg+8GXiw7N8FuLLWZRfgniGm3YlqVX/A4bb72/Q7nCq5Ox34Gi+tBA/K9kPANyT9K/CUpE1sL7b9DNVvAb6r6gez7wLuBzZqGWJjoJ6sjziGDu1EVV7yIkkbUyXtb5dkYAJgSZ8sJTzPAd8Hvi/pSaqa98ES+3uBqZLWGijfkbQW1YeTewc5pu5B4M2SXr8Kq/azqb4lObKTzkOdd0vXfwIuBf57hPFEREREjEkjqrEvdyCZQLXS2s41wCRJR5T+E4CvUNXVL6MkcCp3jJG0CVXZxD8PMt+hVCvqHdVeu/ozup8FdqvfLWWQsfcvP/YFmAKsAH4laZ9SM46k1wHbUpUSPQBsNvAjT0lbAe+gKldZpRiGo8pxVDXzV7fsPgy4yPZWtntsb0n1g9c9Je08UEJUEvQ/An4+2Dy2H6T6ke5JteaTgPll35DKtf028OIdkCS9UdKHOjjNK6iu/7wO+sIQ590S00+pPpQc0OG4EREREWPaSGrsF1Ctrs6s1WK/VdKjAw+qpOtg4EOSHgB+Bvwv8A8Ath8HPgL8a6nZvwk4z3a9tvqEMt8Dpe8+tp/q9IRsL6f6MFFfwa3Xt19Y2v6CqsZ+AXAR1TcBK6i+QeiXdCfVj2C/Zfu2sgL+EeA75ZhLgY/ZXqk4ewQxDOZ0ST+hev3+mKqM53ctffqokuK6y0r7HwBXqbp15J3AC8A5w8x5FPAWVbe6fIjqjkZH1fa/7Fq3SdpPovrh6r1l3u8Bw67e2/6N7dPanB+sXGN/2DDn3erLwBZt2iMiIiIaR9UCc8T4NnHyFE+eeVa3w4iIDi06df/hO0VENFfb318msY8Aent73d/f7qcbEREREaNO28R+RD+ejVeOpK8D72xp/urA3wBoinJ3moktzX9hu92tSiMiIiJiFSWx7xLbx3Q7hjXB9q7djiEiIiJiPBjpX56NiIiIiIhRKIl9REREREQDJLGPiIiIiGiAJPYREREREQ2QxD4iIiIiogGS2EdERERENEAS+4iIiIiIBkhiHxERERHRAEnsIyIiIiIaIIl9REREREQDyHa3Y4jouomTp3jyzLO6HUbEqLPo1P27HUJERKxM7RqzYh8RERER0QBJ7CMiIiIiGiCJfUREREREAySxfxVJ2kTSgvJ4QtJjtW3Xni+Q9OlyzPWSHpGk2jhzJS0tz3skLS/H3Cvpm5LWamkfeBxRjlkk6S5Jd0r6b0lbtcQ5V9It5fl+teOXSrq/PL9Q0pGSzmk59npJvcPNI2lFu/Md5HW7vsx7p6SfSjpH0oa1/e1ei4HHOiXOp2ptF676VYyIiIgYG9budgBNZnsxMBVA0snAUttnlO2ltqcOcuivgHcCN5aEdnLL/odsT5W0NnAtcBAwf6B9kDH3tv20pC8AJwF/VeLYENgFWCppG9vzgHll3/XAJ2z3l+0jOzjttvMAy4eIrZ3DbfdLWgc4BbgSeHebfiudc/lMNNv2sSOYLyIiImJMy4r96HQJMKM8PwS4vF0n2y8ANwHbjWDsm4HNa9uHAFe1zPlKaJ1nldj+HfAp4M2S3rHaUdVIOlpSv6T+FcuWvJJDR0RERKxxSey7Z72WEpLptX3XAO+SNIEq2Z7dbgBJk4B9gbtK07YtY+7Z5rBpwNzadh8wqzz6VvOchppnqPMdku0VwE+A7dvsrp/z12vt02vtHx1k3HNt99runTBpg07DiYiIiBiVUorTPUOVpqwAbqRK6tezvahWcg8lmQUMXGn7+5J6GLoU5zpJGwNLgc8CSNoUmALcaNuSnpe0o+27BxljsD96UG9faZ4OzrcTbe/XyuDnnFKciIiIGFeyYj96XQKcDcxps+8h21Nt72T75A7H2xvYClgAfKG0fRjYCFgoaRHQw9Cr9otL/7qNgaeHmWe1lG8u3g7c90qMFxEREdFESexHrxuofjQ665UasNTkfxw4oqyq9wHTbPfY7qH6Ee1Qdfa3Ae+U9CaAcjecicAvhplnlUl6DdXr8Avbd67OWBERERFNllKc7lmvlNMMuNr2i7eAtG3gjBGOuW3LmOfZPrvewfbjkmYBx1CtrN9S27dQ0hJJu9q+tXVw209KOh74T0lrUZXb9Nn+fZu+9Xm+NNz5tnGxpOeoPjj8F3DgMOceERERMa6pyh8jxrfe3l739/d3O4yIiIiITrT97WFKcSIiIiIiGiClONE1kq4Atm5pPrH8kayIiIiIGIEk9tE1tg/udgwRERERTZFSnIiIiIiIBkhiHxERERHRAEnsIyIiIiIaIIl9REREREQDJLGPiIiIiGiAJPYREREREQ2QxD4iIiIiogGS2EdERERENEAS+4iIiIiIBpDtbscQ0XUTJ0/x5JlndTuMiDVm0an7dzuEiIhYdWrXmBX7iIiIiIgGSGIfEREREdEASewjIiIiIhogif04IWkTSQvK4wlJj9W2N5X0XUkPS7pd0s2SDi7H7SVpSel3n6TPDzHHXpIs6WO1tqml7RNl+3xJC2tz31Taj5T0lKQ7JD0gaZ6k3WvjnC/psNr2GyQ9L+lvWmJYJOmy2vZhks5/BV7CiIiIiFEtif04YXux7am2pwLfBM4sz3cC5gI/sr2N7V2AGcAWtcNvKH17gY9I2nmIqe4GPlzb7gN+0tLnkwOx2N691j7b9k62pwCnApdL+sNB5vkQcEsZv9UuknYYIsaIiIiIxkliH/sAv7P9zYEG2z+3/bXWjrZ/C9wObDfEeD8H1i3fAgiYBnx/pEHZvg44Fzh6kC59wN8Dm0vaomXfV4B/HG4OSUdL6pfUv2LZkpGGGBERETGqJLGPtwHzO+koaRNgN+CeYbpeSrWivnsZ+7mW/afXSnEuHmKc+cD2beLYEphs+8fAHGB6S5c5wM6ShvoAgu1zbffa7p0waYOhzygiIiJilEtiHy8j6euSfiLptlrznpLuAH4AnGp7uMR+DlVi3wfMarO/Xopz+FDhDNI+vcwBcAkrl+OsAE4HPjNMnBERERGNsXa3A4iuuwc4dGDD9jGS3gD01/rcYPsDnQ5o+wlJzwPvBY6nWrlfFTsB97Vp7wPeJGngQ8FmkqbYfqDW5yKqxP7uVZw7IiIiYkzJin1cS1UT/7e1tkmvwLifA060vWJVDpb0bqr6+n9taX8LsL7tzW332O4BTqFl1d7288CZwAmrMn9ERETEWJPEfpyzbeAg4N3lNpQ/Bi4ATlzNcW+yPXeQ3fUa+wWS1int08v2z4B/AA613bpi3wdc0dJ2Ge3vjvNt8q1UREREjBOq8rqI8W3i5CmePPOsbocRscYsOnX/bocQERGrru3vEJPYRwC9vb3u7+8fvmNERERE97VN7FOmECMmaT/gtJbmhbYP7kY8EREREZHEPlaB7XnAvG7HEREREREvyY9nIyIiIiIaIIl9REREREQDJLGPiIiIiGiAJPYREREREQ2QxD4iIiIiogGS2EdERERENEAS+4iIiIiIBkhiHxERERHRAEnsIyIiIiIaQLa7HUNE102cPMWTZ57V7TAi1phFp+7f7RAiImLVqV1jVuwjIiIiIhogiX1ERERERAMksY+IiIiIaIAk9hERERERDZDEvmEkHSTJkravtf2JpOslPSBpvqT/kPT2su9kSY9JWlB7bDjMHGeVY9aqtR0p6aly/D2SLpU0qeyTpJPK/D+TdJ2kt5V910nar2X8j0v6hqQeSctbYjui9FlH0rllvJ9KOrRNHAskfeyVem0jIiIiRrO1ux1AvOL6gBvLv5+XtCkwB/hz2zcBSNoD2Ba4qxxzpu0zOhm8JPMHA78A3g1cV9s92/axpd93genAd4BjgN2Bd9heJul9wL+X5H4WMAOYVxtnBvCp8vwh21PbhPKPwC9tv6XEtHG7OCIiIiLGi6zYN4ik9YE9gKOokmOAY4ELBpJ6ANs32p67itPsBdwDfIPqw0O7ONYGXgs8W5pOBI61vazM/wPgJuBw4FJgf0nrlGN7gM2AG4aJ4y+BU8p4v7f99EhPRNLRkvol9a9YtmSkh0dERESMKknsm+VA4GrbPwMWS9oFeBswf5jjTqiVrlw3TN8+qlX2K6gS8tfU9k2XtAB4jGoF/SpJrwdea/vhlnH6gbfZfgb4MfD+0j4DmOOX/sDCti2lOHvWSoW+VEqL/q18MzHgUEl3lnKgLQc7Edvn2u613Tth0gbDnHZERETE6JbEvln6gEvK80tos6Iu6VZJ90n6aq35TNtTy2PvwQYvq+p/Bsy1/WvgVqBeHz+7lM28iarM55Mdxj1QjkP5d1Zt30O12KbavoGqhGwL4CbbOwM3AwOlRFcBPbb/CPghcEGHMURERESMaUnsG0LSxsA+wLckLaJKqj9MVTaz80A/27sCnwVWZYl6P2BD4K4yxx60+fBQVtuvAt5VPgD8VtI2Ld12KbEBXAnsK2lnYJLt24eJYzGwDLi8bP8b5RxtL7b9XGn/VpknIiIiovGS2DfHYcBFtrey3WN7S2Ah1ar1kZJ2r/WdtIpz9AEfK+P3AFsD7x24+02LPYCHyvPTgbMlrQcg6T1l/3cBbC+l+hHuebx8tb6t2geHvUrTvsC9ZezJta4fBO7r/PQiIiIixq7cFac5+oDTWtouK+3TgdMkbQ78Enga+GKt3wmSPlLbPsj2ovpAJXmfBvzNQJvt30q6ETigNE0vd9xZC3gUOLK0fw3YiGqlfwXwBHCg7eW1KQbq9mfwctuWuv0B59k+m+oHuRdJOgt4Cvho2X+cpA8CLwDP1GKIiIiIaDS99BvFiPGrt7fX/f393Q4jIiIiohNq15hSnIiIiIiIBkgpTqyk/CXY1rKehbYP7kY8ERERETG8JPaxEtvzePlfgo2IiIiIUS6lOBERERERDZDEPiIiIiKiAZLYR0REREQ0QBL7iIiIiIgGSGIfEREREdEASewjIiIiIhogiX1ERERERAMksY+IiIiIaIAk9hERERERDSDb3Y4housmTp7iyTPP6nYYEStZdOr+3Q4hIiJGH7VrzIp9REREREQDJLGPiIiIiGiAJPYREREREQ2QxH41SdpE0oLyeELSY7Vt154vkPTpcsz1kh6RpNo4cyUtLc97JC0vx9wr6ZuS1mppH3gcUY5ZJOkuSXdK+m9JW7XEOVfSLeX5frXjl0q6vzy/UNKRks5pOfZ6Sb3DzSNpRbvzHeR1G8lr8LJzLfunltd3Wsu4lvSV2vYnJJ3cwaWMiIiIGNPW7nYAY53txcBUgJJALrV9RtleanvqIIf+CngncKOkDYHJLfsfsj1V0trAtcBBwPyB9kHG3Nv205K+AJwE/FWJY0NgF2CppG1szwPmlX3XA5+w3V+2j+zgtNvOAywfIrZ2OnoNBjm2D7ix/Ht1rf054BBJp9h+egSxRERERIxpWbHvnkuAGeX5IcDl7TrZfgG4CdhuBGPfDGxe2z4EuKplzldC6zwj1dFr0Kqs8n8IOBJ4r6R1a7tfAM4FTuhgnKMl9UvqX7FsyUjijoiIiBh1kti/utZrKSWZXtt3DfAuSROoktvZ7QaQNAnYF7irNG3bMuaebQ6bBsytbfcBs8qjbzXPaah5hjrfdoZ7DQY7192BhbYfAq4HWu8H+HXgcEkbDDW57XNt99runTBpyK4RERERo15KcV5dQ5WmrKAqJZkBrGd7Ua3cHEpSCxi40vb3JfUwdHnKdZI2BpYCnwWQtCkwBbjRtiU9L2lH23cPMsZgf9ig3r7SPB2cbzvDvQaDnWsf1Wo/5d8jgMteDNT+taQLgeOA5SOIJyIiImLMyop9d10CnA3MabPvIdtTbe9k++QOx9sb2ApYAHyhtH0Y2AhYKGkR0MPQq/aLS/+6jYF6vXq7eVbVUK/BSsrq/qHA58r5fA2YJul1LV3PAo4CXrua8UVERESMCUnsu+sG4BSqEplXRKnJ/zhwRFlV7wOm2e6x3UP1I9qh6uxvA94p6U0A5W44E4FfDDPPqhrpa7AvcKftLcs5bUW1Wn9wS3zPUH1YOGo1YouIiIgYM1KK8+par5TTDLja9ou3gLRt4IwRjrlty5jn2T673sH245JmAcdQrazfUtu3UNISSbvavrV1cNtPSjoe+E9Ja1GV2/TZ/n2bvvV5vjTc+bYzzGuw0rkCOwFXtPS7DPhb4MKW9q8Axw41f0RERERTqMqrIsa33t5e9/f3dzuMiIiIiE6oXWNKcSIiIiIiGiClOPGqknQFsHVL84nlj2RFRERExCskiX28qmwfPHyviIiIiFhdKcWJiIiIiGiAJPYREREREQ2QxD4iIiIiogFyu8sIQNJvgPu7HUd07A28/K8hx+iW6zX25JqNLbleY8/qXrOnbU9rbcyPZyMq99vu7XYQ0RlJ/bleY0eu19iTaza25HqNPa/WNUspTkREREREAySxj4iIiIhogCT2EZVzux1AjEiu19iS6zX25JqNLbleY8+rcs3y49mIiIiIiAbIin1ERERERAMksY+IiIiIaIAk9jGuSZom6X5JD0r6dLfjCZC0paTrJN0r6R5Jx5f2jSX9UNID5d+NSrsknV2u4Z2Sdu7uGYxfkiZIukPS98r21pJuLddmtqR1SvvEsv1g2d/TzbjHI0kbSrpU0k8l3SfpT/MeG90knVD+m3i3pFmS1s17bPSQdJ6kX0q6u9Y24veUpJml/wOSZo40jiT2MW5JmgB8HXg/sAPQJ2mH7kYVwAvA39veAdgNOKZcl08D19ieAlxTtqG6flPK42jgG2s+5CiOB+6rbZ8GnGl7O+BZ4KjSfhTwbGk/s/SLNeurwNW2twfeQXXd8h4bpSRtDhwH9NreEZgAzCDvsdHkfKD1D0aN6D0laWPg88CuwJ8Anx/4MNCpJPYxnv0J8KDth23/DrgEOLDLMY17th+3Pb88/w1VwrE51bW5oHS7ADioPD8QuNCVW4ANJU1ew2GPe5K2APYHvlW2BewDXFq6tF6zgWt5KbBv6R9rgKQNgHcB3waw/TvbvyLvsdFubWA9SWsDk4DHyXts1LD9I+CZluaRvqf2A35o+xnbzwI/ZOUPC0NKYh/j2ebAL2rbj5a2GCXK18c7AbcCm9p+vOx6Ati0PM91HB3OAj4F/L5sbwL8yvYLZbt+XV68ZmX/ktI/1oytgaeA75TSqW9Jei15j41ath8DzgAeoUrolwC3k/fYaDfS99Rqv9eS2EfEqCRpfeAy4OO2f13f5+o+vblX7ygh6QPAL23f3u1YoiNrAzsD37C9E/BbXioRAPIeG21KOcaBVB/KNgNeywhXcqO71tR7Kol9jGePAVvWtrcobdFlkl5DldRfbPvy0vzkwNf/5d9flvZcx+57J/BBSYuoStr2oarh3rCUDcDLr8uL16zs3wBYvCYDHuceBR61fWvZvpQq0c97bPR6D7DQ9lO2nwcup3rf5T02uo30PbXa77Uk9jGe3QZMKXcVWIfqh0j/3uWYxr1SB/pt4D7b/1Lb9e/AwB0CZgJX1tqPKHcZ2A1YUvvqM9YA25+xvYXtHqr30bW2DweuAw4r3Vqv2cC1PKz0z+rwGmL7CeAXkt5amvYF7iXvsdHsEWA3SZPKfyMHrlneY6PbSN9T84D3SdqofEvzvtLWsfzl2RjXJP0ZVW3wBOA821/uckjjnqQ9gBuAu3ipXvsfqOrs5wBvBn4OfNj2M+V/cudQfS29DPio7f41HngAIGkv4BO2PyBpG6oV/I2BO4CP2H5O0rrARVS/n3gGmGH74W7FPB5Jmkr1Q+d1gIeBj1It9uU9NkpJ+gIwnerOYXcAH6Oqv857bBSQNAvYC3gD8CTV3W3mMsL3lKS/pPp/HsCXbX9nRHEksY+IiIiIGPtSihMRERER0QBJ7CMiIiIiGiCJfUREREREAySxj4iIiIhogCT2ERERERENkMQ+IiIiIqIBkthHRERERDTA/wcYWrYFPvLBzwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fftJgFgdrqMe"
      },
      "source": [
        "Here, in the LGBMClassifier, we have different features appearing as the most important for the model.\n",
        "\n",
        "The first feature, ```HEMATOCRITE```, also known as HCT, has been studied and found to be reduced in patients with COVID-19.\n",
        "\n",
        "> Our analysis has found lower HGB (124.84 ± 15.877) and reduced HCT (37.45 ± 4.685) [...]\n",
        "\n",
        "_Source:_ [Blood routine test in mild and common 2019 coronavirus (COVID-19) patients](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414516/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "39ufLLbjmiCl",
        "outputId": "3bd45905-fd0a-4a25-a949-1383941b1b40"
      },
      "source": [
        "feat_import_lgb = pd.Series(lgb_model.feature_importances_, index = X_train.columns)\n",
        "ax = feat_import_lgb.nsmallest(15).sort_values(ascending = True).plot(kind = 'barh',\n",
        "                                                                      figsize = (10, 7))\n",
        "plt.title('Features with less Importance - LGBMClassifier', fontsize = 22, color = GRAY1)\n",
        "sns.despine()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hO8KXXx3tGEW"
      },
      "source": [
        "Again, we have that the disease groupings have no effect in the machine learning model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEKSCQ3StRVd"
      },
      "source": [
        "<a name=\"icu\"></a>\n",
        "## 7.3 ICU Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULiSITjHtUZB"
      },
      "source": [
        "Looking at our table:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "XkCLO9XitEwA",
        "outputId": "3b22be32-25ac-4e31-f9aa-4004b7b683d4"
      },
      "source": [
        "metrics_df.set_index('Model_Name').T.sort_values(by = 'F1-score 1', ascending = False)"
      ],
      "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>Model_Name</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Average AUC</th>\n",
              "      <th>F1-score 0</th>\n",
              "      <th>F1-score 1</th>\n",
              "      <th>Precision 0</th>\n",
              "      <th>Precision 1</th>\n",
              "      <th>Recall 0</th>\n",
              "      <th>Recall 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBM Classsifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBoostClassifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Model_Name              Accuracy  Average AUC  ...  Recall 0  Recall 1\n",
              "LGBM Classsifier            0.75         0.78  ...      0.79      0.71\n",
              "XGBoostClassifier           0.75         0.77  ...      0.81      0.68\n",
              "RandomForestClassifier      0.74         0.80  ...      0.81      0.66\n",
              "LogisticRegression          0.73         0.76  ...      0.77      0.68\n",
              "AdaBoostClassifier          0.73         0.75  ...      0.85      0.59\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DV88SW7Xte4d"
      },
      "source": [
        "And with our findings up to this point, the chosen model for the ICU prediction will be LGBMClassifier.\n",
        "\n",
        "We already have its prediction in the variable ```y_prediction_lgb```. Let's check it out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "aYw-lR_cteJV",
        "outputId": "fb453d48-f22c-4778-9aed-ee9fe886c974"
      },
      "source": [
        "icu_predictions = pd.DataFrame()\n",
        "icu_predictions['True_Value'] = y_test\n",
        "icu_predictions['Predicted_Values'] = y_prediction_lgb\n",
        "icu_predictions.head(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>True_Value</th>\n",
              "      <th>Predicted_Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>172</th>\n",
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              "      <td>1</td>\n",
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              "      <th>42</th>\n",
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              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     True_Value  Predicted_Values\n",
              "242           1                 1\n",
              "172           1                 1\n",
              "294           1                 0\n",
              "266           1                 1\n",
              "25            1                 1\n",
              "42            0                 1\n",
              "92            1                 0\n",
              "194           1                 1\n",
              "131           0                 1\n",
              "124           1                 0\n",
              "132           0                 0\n",
              "337           0                 0\n",
              "147           0                 0\n",
              "278           0                 0\n",
              "281           1                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4WDKpCzuuxd"
      },
      "source": [
        "So now we have a prediction together with the patient identifier.\n",
        "\n",
        "The confusion matrix for this is:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "qRpnVyXnq3dq",
        "outputId": "24ccb01b-6746-4ff7-bcff-2a8d252ae971"
      },
      "source": [
        "confusion_matrix_plot(y_prediction_lgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ktbc/Okebrd66ErNmzeYPk25tZahSR9r9nbvxrj12B2DffT7M01OfnuP4jBkz+N53TuKLh3+eTx5yMACbb7Ep66z+Vn568s855utHtTxmdaiga5evd0CsRtwzz7/MuHnG9nl87x3ewhU3PsiUp19sYVRSZxozpv//Lf/z6mt4/vnnee/e735934ILLsg73rkrF1148UiHJ7WEyYlGxJgxwfzj52HLt6zIp96zGT//y+S67dZYYTE2WmtZfnfpLS2OUOpOd955N2PHjmWNNVefY//a66zNnXfc3aao1Kkimre1kt06GhFPn38E840r/nj98sIbOeKn9f9Ft/cOb+HVmbP48+W3tzI8qWs99+xzLLTQgowdO2c1ctFFJzB9+nReffVVxo0b16bo1FnC2TpSre0/fRoLjJ+XiesuzxEf3oYTD92Nz510/lzt9t5+Pf42+V6efWFGG6KUJHUikxONiBvufhyAq295mKnTpnPqEXvxg9/9k/sfe/b1NuuvvjTrrrIk3/nVFe0KU+o6ExadwIsvvsSsWbPmqJ48++xzLLDAAlZN9LrA5esbEhGn9XP4NeBJ4PLMvKhFIWkE3XDXFABWWWbCHMnJ3tuvx/QZMznnyjvbFZrUddZee01mzZrFvffcx1prr/n6/rvuvIu111mznzOl7tGuAbHr97NNBD4GXBARl0fEQm2KUU2yxforAvDA48/NsX/vHdbjvH/cxUszZrYjLKkrvW3LzXnTm97EH8/68+v7pk+fznnnXsDOu+zUxsjUiSKiaVsrtaVykpmbDtQmIjYH/gJ8A/jciAelpjj72/tx2XX3c9sDTzFr9my2eMuKHLrPFvz+0lvmqJpstu7yrLLsonzpZItjUq3p06dzwfnFAPLHHp3CCy+8wB//cDYAu+62EwsssAD/+aXPcfxx32XCohNeX4Rt9uzZfPLTB7czdHWacPn6psvMayLiWOBwTE66xnV3PMYHd92AlZeZwGuzZnP/Y8/y1Z9fws//ct0c7fbeYT2ee3EGF17rqrBSraeefIr93z/nIx563t9xz42svMrK/Ofhn2f27Nl899sn8szTz7DxJhtx7gV/Yumll2pHyFLTRWa2O4Y+RcT2wAWZOX6AdgcDxT8Zxi+yyXxbfKEF0UnV8+zfDm13CFJXm3+eCddl5sR2xwEw/4pr5+qf+2nTrnfrf+7Q72eLiPmAy4HxFMWPszLz6IhYFfgNsDhwHfChzHy1v3t1+iJsKwPPDNQoM0/JzImZOTHmXbAFYUmSpF5eAXbIzA2ADYFdI+JtwLeBEzNzDeBZinGl/erY5CQilgWOAuZeHEOSJA0oxkTTtoFkoec5JPOWWwI7AGeV+88A9hroWu2aSvy7fg6PBZYBNgEeBo5sSVCSJFXICKxzskRE1D6L5JTMPGWOe0aMpei6WQM4GbgXeC4zex6x/Qiw/EA3ateA2CX7OfYa8CDwS+B/M/Ol1oQkSZL6MXWg8TSZOQvYMCImAH8C1hnKjdo1lXj7dtxXkqRRI2BMm6YSZ+ZzEXEZsAUwISLmKasnKwCPDnR+28acRMT6EbFCP8eXj4j1WxmTJEkamohYsqyYEBHzAzsBtwOXAe8rmx0AnD3QtdqSnETEe4FrgQn9NFsUuCYi9mxNVJIkVUnzVocd5GJuywKXRcRNwL+AizPzXIr1yr4QEfdQTCc+daALtWvMycHAaZl5S18NMvOWiDgV+ASDyLIkSdKcWtmrk5k3ARvV2X8fsFkj12pXt86mwHmDaHcBDX4gSZLU3dpVOVkAeH4Q7Z4v20qSpAYEDGp9kk7UrsrJI8C6g2j3ZgYxqleSJPUS3ftU4nYlJ+cCh0VEn2vNR8RCwOeBc1oWlSRJart2JSffBBYCro6Id0TE6w/2i4hxEbEbcEXZ5vg2xShJUleLaN7WSm1JTjLzSYq19mdSVFFeiIhHI+IR4AXgrxQrxe5QtpUkSaNEuwbEkpl3AhMjYhtgG95Ya/9RYFJmXtmu2CRJqoJWjxVplrYlJz0y83Lg8nbHIUlS1XTrbJ12PZW4oenBmTl9pGKRJEmdpV2VkxeBbKD92JEKRJKkKmrHQNZmaVdy8lEaS04kSdIo0ZbkJDNPb8d9JUkaPVq/eFqztH1ArCRJGhljujQ5adcibJIkSXVZOZEkqaK6tHBiciJJUhVFdO86J3brSJKkjtL25CQiZkXEZn0c2yQiZrU6JkmSqiAimra1UtuTE6C/TzwvxQMAJUnSKNGu5etXAlap2bVRRMzXq9l8wAHA/a2KS5KkKnFAbGM+AhxNsUpsAj/po93LwEGtCkqSpCpxEbbG/DdwFkWXzk3A/uVrrVeBhzLzlRbHJkmS2qhdy9c/BTwFEBGrAlMy89V2xCJJUiVFdO1U4ravc5KZD0bEPBHxfmBrYDHgGeAK4I+Z6YBYSZJGkbYnJxGxFHAR8FbgAeAJYAvgEODGiNi5rLRIkqRBCrp3QGwnTCU+AVgceFtmrpaZW2TmasDm5f4T2hqdJEldynVOhu4dwOGZeW3tzsz8F3AEsHtbopIkSW3R9m4dYDzwQh/HXgDGtTAWSZIqo1unEndC5eSfwOERsWDtzvL94eVxSZLUiIAxTdxaqRMqJ4cBlwEPR8RFFANilwJ2oRjPs137QpMkSa3W9spJZt4ArAmcAiwJ7ESRnPwUWDMzb2xjeJIkdaUAYkw0bWulTqickJlTgS+3Ow5JktR+HZGcSJKk5uvWAbHteirxpQ00z8zcccSCkSSporo0N2lb5eTpQbRZFtiS4qnFkiRplGjXg//27utYRKxEMYX4ncBU4MRWxSVJUmW0YWXXZumYMScRsQbFirAfBJ4sf/2zzHy5rYFJkqSWantyEhHrAV8B9gYeBg4FTsvMV9samCRJXa7VU4CbpW3JSURsQpGU7AncDRwE/DIzZ7UrJkmSqqKbn0rcrtk65wM7AzcDH8jM37cjDkmS1HnaVTnZpXxdATg5Ik7ur3FmLjXyIUmSVC0OiG3M19p0X0mSRocwOWlIZpqcSJKkuto+W0eSJI2MLp2s0/6nEkuSJNWyciJJUgUVU4m78wkwfSYnEfHVIV4zM/PrQzxXkiQ1SZeOh+23cnLMEK+ZgMmJJEkakv6Sk+1bFoUkSWq6MVXr1snMv7cyEEmSJHBArCRJlRTl1o0aTk4i4q3AfsC6wIKZ+fZy/yrAZsDFmflsE2OUJEmNigp269QTEccCR/LG+ii1n3oMcCbwOeBHTYlOkiSNOoNehC0iPgAcBVwMbAgcX3s8M+8DJgN7NDNASZI0NBHN21qpkRViPwvcA+yZmTcBr9ZpczuwZjMCkyRJwzMakpP1gQszs15S0uMxYOnhhSRJkkazRsacBDB7gDZLAzOGHo4kSWqGILt2QGwjlZO7gS37OhgRY4CtgVuHG5QkSRq9GklOfgdsHBGH9XH8SGAN4NfDjkqSJA1bNHFrpUa6dU4C9ga+ExH7UE4jjojvAf8BTAT+CZzS7CAlSVLjurVbZ9DJSWa+HBHbAz8A9gfGloe+QDEW5ZfApzPztaZHKUmSRo2GFmHLzGnAgRHxBWBTYHFgGnBtZj41AvFJkqQhaMcU4GYZ0rN1MvMZ4MImxyJJkjS05CQiVgQ2AhahqJz8OzMfbmZgkiRpeKLqY04AImJN4L+BHeocuxQ4JDPvalJskiRpGBqZkttJBp2cRMQawNUU40zuBa4EHgeWoVjfZEfgyojYMjPvGYFYJUnSKNBI5eR4isTkUODkzHx9tdhyAbbPACcC3wT2aWaQkiSpcaOhW2dH4LzM/FHvA2Wi8oOI2AV4e7OCkyRJQxPAmC6drdNId9Q44IYB2vwbmHfo4UiSpG4UEStGxGURcVtE3BoRh5b7j4mIRyPihnJ7x0DXaqRyciPF8vT9WQO4qYFrSpKkEdLibp3XgMMy8/qIWBi4LiIuLo+dmJnfG+yFGqmcfBN4T0TsVu9gROwOvBs4roFrSpKkCsjMKZl5ffnrF4DbgeWHcq0+KycR8eE6u88Hzo2IS4DLgSeApYFtKaYXnwMsMZRAJElSE0XTx5wsERGTa96fkpl1n6cXEatQrId2DbAV8Okyr5hMUV15tr8b9detczrlw/1q71e+vp36A1/3AN4F/G9/N5UkSSMrSGKur/FhmZqZEwe8b8RCwB+Az2Xm8xHxE+DrFDnF14HvAx/t7xr9JScfGXy8kiRptIuIeSkSk19l5h8BMvOJmuM/B84d6Dp9JieZeUYT4pQkSW3Sygf/RUQApwK3Z+YJNfuXzcwp5dt3A7cMdK0hPVtHkiSpl62ADwE3R0TP0iNHAvtGxIYU3ToPAB8f6EImJ5IkVdSYFk4lzswreWNsaq3zGr1Wow/+WxD4FLALxfSg8fXjy9UbDUSSJDVXK7t1mqmRB/9NoHjY35uB54E3AdMoVo6dv2z2GDCzyTFKkqRRpJFF2I6iSEw+Bixa7jsRWAjYErie4mnF6zYzQEmS1Lji2TrZtK2VGklO9gAuz8xfZObrUWbhn8A7gHWArzQ5RkmSNATRxK2VGklOVgSuq3k/m5oxJ5n5JMUKsh9oTmiSJGk0amRA7HSKhKTHNGCZXm2eYIjr6EuSpCaK7h0Q20jl5GGK6kmP24BtIqL2GlsDjzcjMEmSNDo1kpz8Hdi2XAEO4LfA6sB5EXFIRPweeBtDmM8sSZKaq5sHxDbSrXMGxbThFSiqKD+leBLxXsDOZZurKGb1SJKkNuvWbp1BJyeZeT3wyZr3rwHviYhNgDUolqT9V2bOrn8FSZKkgQ17+frMvI5yFk9EbBgRK2XmX4YdmSRJGpYxtLY7plkaGXMyGIcCf2ryNSVJ0ijig/8kSaqoyo85kSRJ3SNIosWzbJql2d06kiRJw2LlRJKkKgoYY7eOJEnqJN3ardNvchIR2zR4vd7P2mm5t665BBef97F2hyF1pe1OvbXdIUjSgJWTSdDQJOlosL0kSRoBQfcOLB0oObkckw1JktRC/SYnmbldi+KQJElNVskxJ5IkqXt1a7dOt8YtSZIqysqJJEkV1a3dOlZOJElSR7FyIklSBUW5dSOTE0mSKmqM3TqSJEnDZ+VEkqSKGjXdOhHxVmA/YF1gwcx8e7l/FWAz4OLMfLaJMUqSpAZFZNd26zSUnETEscCRvNEdVPupxwBnAp8DftSU6CRJ0qgz6DEnEfEB4CjgYmBD4Pja45l5HzAZ2KOZAUqSpKGJaN7WSo0MiP0scA+wZ2beBLxap83twJrNCEySJI1OjSQn6wMXZma9pKTHY8DSwwtJkiQ1QzRxa6VGxpwEMHuANksDM4YejiRJapZuHRDbSOXkbmDLvg5GxBhga+DW4QYlSZJGr0aSk98BG0fEYX0cPxJYA/j1sKOSJEnD0swunU7u1jkJ2Bv4TkTsQzmNOCK+B/wHMBH4J3BKs4OUJEmjx6CTk8x8OSK2B34A7A+MLQ99gWIsyi+BT2fma02PUpIkNaxbx5w0tAhbZk4DDoyILwCbAosD04BrM/OpEYhPkiQN0ahZvh4gM58BLmxyLJIkST74T5Kkqoqqd+tExGmDbJqZ+bEhxiNJkpogaGxKbidppHJy4ADHk+JnkYDJiSRJGpJGkpNV+9g/gWJw7H8BVwNfHm5QkiRpmGIUdOtk5oN9HHoQuDEiLgRuAv4GnNqE2CRJ0ijUtO6ozHwYOAc4tFnXlCRJQzemiVsrNXu2zhPAmk2+piRJalCQXdut07RkKCLGAjtQLMomSZI0JI1MJd6mn2usCHwE2BD4nybEJUmShmk0TCWeRPmwvz4EcDnwxeEEJEmSRrdGkpNjqZ+czAaepXi+zrVNiUqSJA1bt445aWQq8TEjGIckSWqybn3w36C7oyLitIj4/EgGI0mS1MhYmf2ApUYqEEmS1DwBjIls2tZKjSQnD2ByIkmSRlgjycmvgd0iYtGRCkaSJDVPRPO2VmokOTkemAxcFhHvjIilRygmSZLUBGPIpm2t1O9snYj4MHBDZt4EzOjZDZxdHq93WmZms5fFlyRJo8RAScTpwNEUTxu+gv4XYZMkSR2iHd0xzTKYCkcAZOZ2IxuKJElqpi7NTbp22X1JklRRjg2RJKmiWr0+SbMMJjmZEBErNXLRzKWXYu8AABkTSURBVHxoiPFIkqRRbjDJyaHlNlg5yOtKkqQREnTvmJPBJBHPA8+NdCCSJKmZWr/sfLMMJjk5MTOPHfFIJEmSsPtFkqTK6tZuHacSS5KkYYuIFSPisoi4LSJujYhDy/2LRcTFEXF3+TrgM/pMTiRJqqiIbNo2CK8Bh2Xmm4G3AYdExJuBLwOXZOaawCXl+36ZnEiSVEFB8SXfrG0gmTklM68vf/0CcDuwPLAncEbZ7Axgr4Gu1e+Yk8w0eZEkSQ2JiFWAjYBrgKUzc0p56HFg6YHOd0CsJEkVFc198t8SETG55v0pmXlKnXsuBPwB+FxmPl8bQ2ZmDKKPyOREkqSKavJsnamZObHf+0XMS5GY/Coz/1jufiIils3MKRGxLPDkQDey20aSJA1bFCWSU4HbM/OEmkN/AQ4of30AcPZA17JyIklSFUU0u1tnIFsBHwJujogbyn1HAt8CfhcRHwMeBPYZ6EImJ5Ikadgy80r67knasZFrmZxIklRBVX/wnyRJ6kLRpemJA2IlSVJHsXIiSVJFtXY8bPNYOZEkSR3FyokkSRU1pkvHnJicSJJUQYHdOpIkSU1h5USSpIrq1qnEJieSJFWU3TqSJElNYOVEkqSK6tZuHSsnkiSpo1g5kSSporp1zInJiSRJFRTlf93Ibh1JktRRrJxIklRR3VqB6Na4JUlSRVk5kSSpigKiS0fEmpxIklRR3Zma2K0jSZI6jJUTSZIqKLBbR5IkdZjuTE3s1pEkSR3GyokkSRXVrd06Vk4kSVJHsXIiSVJFdWfdxOREkqTK8sF/kiRJTWDlRJKkCgpgTHcWTqycSJKkzmLlRJKkSoquHXNiciJJUkV16TInJidqrvvuvZ+TT/wpk6+9jjtvu4u3bbUZf77wrNePPzHlCX7yo1OYdMnlPHDfg0yYsAhbb7cVR33tyyyz3DJtjFxqvx1WXZzd1lqStZdYkIXGzcNDz73Mr256jIvvnfp6m3nHBJ/abGV2XmMJFhw3ljueeomT/nE/d0x9qY2RS81lcqKmuvO2u7jkwkvZZLONeW3ma3Mdv/HfN3PeXy7ggwfuy8abbsRTT07lu8edwO477MXfJ1/CQgst2Iaopc6w71uX47EXZvCDfzzAczNmsuWKi/L1Hddiwnzz8PtbHwfgC1utyk6rL8HJ1zzI4y++wj7rLcuPdl+PD/3hRh5/8ZX2fgB1HLt1JGCX3Xdit3ftAsBH9zuYZ55+Zo7jm2+5KVff8HfmmeeNP3pv3XB9tthgG87983l84IN7tzReqZP85wW3M+2VN5L66x57niUWHMe+6y/H7299nCUXHMceay/Nt664l3PufBKAyY9O4w/7bsz+GyzH96+6v12hqwNFdG+3jrN11FRjxvT/R2qRCYvMkZgArL7maiywwPw8MeWJkQxN6ni1iUmPu6a+xBILjgNgjcUWYOyY4NpHnnv9+MzZyQ1TnmerFRdtWZzSSLNyora79ebbmD79ZVZbc9V2hyJ1nPWXXpiHpr0MwLixRfI/c3bO0Wbm7GSZhcczfuwYXpk1u+UxqnN1a7eOlRO11ezZsznqi0ez2hqrsuvuO7c7HKmjTFxuEbZZZTHOvOkxAB6ZNgOANy+50Bzt3rzkQoyJYOHxY1seozQSrJyorb7x1eOZfM31/PnC3zPvvPO2OxypYyy70HiO3WFNLn/gGf5611MA3PvsdG58/Hk+87ZVeGr6qzzx4ivsu/5yrLjI/AD0KqhIjjmRGnXaz87g5BN/yo9+fiKbbLZxu8OROsabxs/Dibuty5QXX+HoS++e49jXJ93DjNdmccZ7NuCCD2/G1isvxu9umcLMWbOZNmPuMSsa3aKJ/7WSlRO1xTl//itHHvZffPW4r7DX+/ZodzhSxxg/dgzf22Ud5hk7hv8859a5xpA88vwMDvjjTSy38HjmGRM8NG0Gh221KndOfYlZaelE1WByopa76vKr+dRHPstBn/wIh3zuE+0OR+oYYwO+udParLjI/Bx89s08O2Nmn20fe6FY02SR8fOw42pL8LN/PdiqMNUlgu7tHmlrchIRX+3n8GvAk8AVmXlni0LSME2f/jKXXHgJAFMee5wXX3iRc/50LgA77rIjjzz0CAe8/yDWWGt19nzfHky+9rrXz118icVZdbVV2hG21BG+uPVqbLXSopxw1X28ab55WG++Nwa+3jX1JWbOTvZZbxmmvfIaT730KisuMh8f3nAF7n3mJf5yx5NtjFxqrnZXTj7Tz7GxwCIAEfEb4IDMtEO1w019aiof23/OakjP+8m3/4Pr//Vvnp/2PLfefBu7b7/nHO3e/8G9+dEpJ7YsVqnTbL7CBAC+sNVqcx1796+vY8qLrzB+nrF8fP3lWGLBcTz78kwuumcq/3Pdw9iho3qiS0fEtjU5ycwl+zseEfMDewD/AxwFHNNHu4OBgwFWWHH55gaphqy08oo8Of2Rvo9/aEU+8KF9WhiR1D3efeb1A7b5vxsf5f9ufLQF0aj7Rbl1n47ujsrMlzPzt8A3gP37aXdKZk7MzImLL7F46wKUJElN19HJSY3JwErtDkKSpG4STdxaqd1jTgZrKeCFdgchSVI36dYxJx1fOSnHnRwGXNbuWCRJ0shr91Ti7/RzeCywDPB2iiRq35YEJUlSZXRn5aTd3Tp793PsNeAp4HTgB5n5WEsikiRJbdXuqcSrtvP+kiRVWXfWTdpfOSEiFgZmZuaMPo7PB8ybmQ6IlSRpkIpZNt2ZnrR1QGxE7Ag8A2zeT7PNgacjYpvWRCVJktqp3bN1Pg38NjP/3leD8tiZwKEti0qSpCqIaN7WQu1OTrYC/jCIdn8Cth7hWCRJUgdo95iTRShm5AxkKjBhhGORJKlSunPESfsrJ08Aqw+i3WplW0mSNCjNXLx+dHXrXAwcEhF9VnDKY4cAF7UsKkmS1DbtTk6OA9YC/hoRb+59MCLWBc4F1ga+2eLYJEnqatHE/1qp3Yuw3RcRuwO/AW6OiMeAh4CkeArx8sBjwDsy8772RSpJUpdp/SSbpmn3gFgy86qIWBPYB9iGIiGBohtnEnBWXwu0SZKk6ml7cgJQJh//W26SJKkpurN00u6nEj9F0YUzKJm51AiGI0mSOkC7Kycn00ByIkmSBq9bn63T7gGxx7Tz/pIkVVXrVydpnnZPJZYkSZqDyYkkSVXVwgf/RcRpEfFkRNxSs++YiHg0Im4ot3cMJmyTE0mS1AynA7vW2X9iZm5YbucN5kLtHhArSZJGRGtXds3MyyNilWZcy8qJJEkV1SHL1386Im4qu30WHcwJJieSJGkwloiIyTXbwYM45yfA6sCGwBTg+4O5kd06kiRpMKZm5sRGTsjMJ3p+HRE/p3iY74BMTiRJqqho85P/ImLZzJxSvn03cEt/7XuYnEiSpGGLiDOB7Si6fx4Bjga2i4gNKVaDfwD4+GCuZXIiSVJltXS2zr51dp86lGs5IFaSJHUUKyeSJFVUtz5bx+REkqQKKh78153pid06kiSpo1g5kSSpkgb3wL5OZOVEkiR1FCsnkiRVVHfWTUxOJEmqLAfESpIkNYGVE0mSKqs7KycmJ5IkVVH3TtaxW0eSJHUWKyeSJFVWd5ZOrJxIkqSOYuVEkqSK6tapxCYnkiRVUJT/dSO7dSRJUkexciJJUlV1Z+HEyokkSeosVk4kSaqobh1zYnIiSVJFdWtyYreOJEnqKFZOJEmqqu4snFg5kSRJncXKiSRJFdWtY05MTiRJqqhuTU7s1pEkSR3FyokkSRUUdO14WJMTSZKqKSC6Mz2xW0eSJHUUKyeSJFWUA2IlSZKawMqJJEkV1Z11E5MTSZKqywGxkiRJw2flRJKkinJArCRJUhNYOZEkqaK6s25iciJJUiUVy9d3Z3pit44kSeooVk4kSaqiLn7yn8mJJEmVFHbrSJIkNYOVE0mSKsrKiSRJUhOYnEiSpI5it44kSRUVPvhPkiRp+KycSJJUUQ6IlSRJagIrJ5IkVVAXLxBrciJJUmU5IFaSJGn4rJxIklRJ3ftsHZMTSZIqqjtTE7t1JElSh7FyIklSRXVrt46VE0mS1FGsnEiSVFVdOpXY5ESSpArq5kXY7NaRJEkdxcqJJEkV5YBYSZKkJrByIklSVTkgVpIkdZLuTE3s1pEkSR3GyokkSZXkg/8kSVKH6dbkxG4dSZLUUUxOJEmqomjyNtDtIk6LiCcj4paafYtFxMURcXf5uuhgQjc5kSRJzXA6sGuvfV8GLsnMNYFLyvcDMjmRJKmCioJH8/4bSGZeDjzTa/eewBnlr88A9hpU7JnZwEftfBHxFPBgu+NQn5YAprY7CKlL+fen862cmUu2OwiAiLiA4s9Ms8wHzKh5f0pmntLrnqsA52bmW8r3z2XmhPLXATzb874/lZut0yl/KFRfREzOzIntjkPqRv79USMys3cXS1tlZkbEoCoidutIkqSR8kRELAtQvj45mJNMTiRJ0kj5C3BA+esDgLMHc5LJiVrtlIGbSOqDf3/UsSLiTOAfwNoR8UhEfAz4FrBTRNwNvL18P/C1qjYgVpIkdTcrJ5IkqaOYnEiSpI5iciIi4piIyIi4sM6xsyJiUoPXW6q85iqDaHtgee+Feu1fOiJOioh7I+KViHg2Ii6KiPfVtJkUEWf1cd3JEXF6I3FL7VD+XZnaa9/qEXFqRDwcEa9GxFMR8eeIeHtNmwci4nt9XHNqRBwzwqFLI8bkRLV2johNm3CdpYCjgVWGcnJErA38G9gd+B6wM/Bh4F7gVxGxQRNilDpSRGwFXA9sAHyVYhDhJ4CXgAsjYpE2hie1ROUWYdOQPQM8CnyFQS4vPIJ+RRHPlpn5fM3+cyLiJ8Bz7QlLGlkRMT/wW+BfwDsy89Waw3+IiP8BZrYlOKmFrJyoRwLHAXtExPr9NYyIDSPikoiYXna3/Coili6PrQLcXDa9rOyyGfSUsIjYBtgEOKJXYlIEmXlTZj402OtJXWZvYHng870SEwAy87LMnN76sKTWMjlRrd8Dd1NUT+qKiCWBScACwH7AZ4BtgYsjYhwwBdi/bH4IsEW5Dda2wCzgbw3GLlXBtsBjmXnzgC2lCrNbR6/LzNkRcTxwakR8NTPvqtPssPJ1l57KRrm4zj+B92bmmRFxU9nmtsz8Z4NhLA88lZkvD+UzSF1uecDKoEY9Kyfq7ZcU/3M8oo/jmwEX1Xa5ZOY1wAPA1k2KwZUBNZr551+jnsmJ5pCZrwHfAT4YESvXabIs8ESd/U8AizUhhEeBJSNivkG0fQ0Y28exseVxqZs8Cqw0yLb++VdlmZyontMonhx5eJ1jUyimCve2NMUMm+GaRNHduOMg2j4FLNPHsUE//VLqIJOA5SNivUG0rfvnPyIWABbBP//qYiYnmktmvkKxvshHKb7ka10D7BIRC/fsKNdGWQW4stzVM8tgMNWP3ve+ArgO+GbtPWrutX5ErFi+vQLYJCKW79Vmc4pk6YpG7y+12VkU1ZMTI2Le3gcjYrsy+YDiz/fbI2LBXs3eBQT++VcXMzlRX34GvABs2Wv/CeXrhRGxZ0TsD/yRYvrwH8pjDwEvAwdExBYRMbHBe+8PLAlMjohPRMQ2EfHOiPgRxfoPPd1H/1ve6/KI+EhE7BARhwDnAFcDc614K3WyciD4+4G3AVdFxAER8R8R8e6IOINiFltP0vIDiv+HXx4R+0XE2yPicOBU4LeZeXs7PoPUDCYnqqtcS+HEOvufArYHZgBnAidT/Attp551GTJzBvD/KNYr+TtFQtHIve8ENgYuAL4EXAz8H7AWsF9m3li2exHYprz/tyiSkSMoFrHaLTNnN/ShpQ6QmVdR/Pm/hWLtoUuBU4A3Ufw9m1a2e5TiHw8PAD8GzgcOpvh7++GWBy41UWQ6MFySJHUOKyeSJKmjmJxIkqSOYnIiSZI6ismJJEnqKCYnkiSpo5icSJKkjmJyIrVRRGRETOq175hy/3btiaoxnRRvRKxSxnL6CN9nrt83Sc1jcqLKK79IardZETE1Ii6NiP3aHd9I6OQvz5pk5ph2xyKpM83T7gCkFvpa+TovsA6wJ7B9REzMzC+0L6y5/Bj4DcXS/JI06picaNTIzGNq30fEjhRL438uIn6YmQ+0I67eMnMqMLXdcUhSu9ito1ErMy8B7qB4guumMOf4ifJhatdExIsR8UDPeRGxQEQcERE3RMRL5fF/RMS+9e4TEeMi4r8i4t6IeCUi7o+Ib0TE+D7a9zmGIyLWiYjTIuKB8lpPRsQVEfHJ8viBEdHzTIpte3VnHdPrWptHxFkR8XhEvBoRD0fEzyJiuT7i2iQiLoiIFyLi+Yj4W0RsMcCPeVgiYrmI+GpEXFUT52MR8euIePMA564TEX+OiGfK36crI2LnftrvGxGXRcRzETEjIm6PiKP6+n2SNHKsnGi0i/K190OmDgN2onjC8WXAIgARMYHiQWwbAdcDp1Ek+bsAv46I9TLzqNcvHhHA7yi6kO6l6LIZB3wUWL+hQCN2B34PjKd4KOKZwARgA4oHJP4EuIGi++po4EHg9JpLTKq51kcpHib3CvAX4GFgTeAg4F0R8bbMfKim/ZYUT8QdR/EU6nuADctrXtrI52jQNsCXKX4P/gC8WMb5PmCPiNiq50GQvawK/IPiadk/A5aleNrv+RGxX2b+trZxRJwGfAR4pLzPcxRPBv46sGNE7JSZr43A55NUT2a6uVV6o0g8ss7+twOzy23lct8xZfuXgI3qnHN6efxLvfbPR5EwzAY2rNm/X9n+H8B8NfsXo0hWEpjU61o9MWxXs28JYBrwKrBtnbhWqPOZJ/VuVx5bq7zOPcDyvY7tCMwC/lSzLygqTAns2av9oT0/39p4B/j96Pl8xwyi7VLAwnX2b0CRqJzfa/8qNfF8t9exicBM4FngTTX7Dyzb/xGYv49YDx3sz9fNzW34m906GjXK7pJjIuK4iDiLIpkI4KTMfLBX81My89+9zl8c+CAwOTO/U3ssM2cAh5fXq50B9JHy9ciyTU/7Zyj+VT5YBwBvAn6SmX/vfTAzH2ngWp+kGBR8aGY+2us6l1BUUt4VEQuXu7cE1gYuz8yze13rxxRJ1ojIzCcz84U6+2+kqNhsHxHz1jl1GnBsr3MmA7+iqDa9u+bQocBrwEcz8+Ve1/k68DSw/5A/hKSG2a2j0eTo8jUpyvZXAKdm5i/rtL22zr5NgbFAX9Nge74k163ZtzFFNeXKOu0nDRzy695Wvp7fwDl96Rknsm1EbFrn+FIUn3Mt4DqKzwBQLymaFRFXAqs3Ia66yu6sT1BUPpZg7v9vLQFM6bXv+npJDcXP/ACKbrkzImIBiirMVIqB0fVCeIU5f08ljTCTE40amVn3m6cPj9fZt3j5umm59WWhml8vAjyTmTMHeY++TChfH+231eD0fI4vDtCu53MsUr4+0Ue7Rj5HQyLiUOAkiq6YiymmV0+nSDD3okgs6g1YHSjWns+0KEW1a0neSF4ltZnJiVRf7wGyUHQVAJyYg18XZRqwWETMWydBWaaBeJ4rX5enGOQ5HD2fY5HMfL6B9kv3cbyRzzFoETEPxZiPx4GNM3NKr+P9zRQaKNZpvV7/nZkb12kvqQ0ccyIN3rUUXTT/0cA511P8Pdu6zrHtGrjOP8vX3QbZfjZF10x/1xrs57i+fN2294GIGEv9z9YMS1BUjK6uk5gsxBvdTfVsXDNmptZ25eu/ATLzReBWYL2IWGzYEUtqCpMTaZAy80mKAZUTy3VL5vryj4jVI2LVml2/KF+Pi4j5atotBhzF4J0BPA98MiK2qXPfFXrtehpYsY9r/Zhi1sqJEbFWnWuNi4jaxOVq4E5gm4jYs1fzTzNy402epOjC2aRMRnrimxf4AUXy0pdFgK/W7oiIiRQDW6cBf6o5dALFFOnTyqni9Dpv0YiwqiK1kN06UmM+TbHOxrHAh8rBoE8Ay1EMmtwU2Be4v2x/JsX6GnsAt0TE2RQDZ98H/ItBfrFn5tQongN0FnBZRJwP3EQxg+etFIlIbVJ0CfCBiDiHovIxk2K2zeWZeUe5zslpwK0RcQFwVxnXShQVlacolvgnMzMiPkYx5uMPEVG7zsmOFLOedh3cj28Oe0XEKn0cuygzfx0RP6RY5+Tm8mc3DtieYir2ZeWv67kcOCgiNgeu4o11TsYAH6/tzsrM0yJiE+BTwL0RcSHF2JbFKH6m21AkmZ8YwmeUNAQmJ1IDMvP5iNgWOJhiyvB7KdY4eQK4G/g8xZd4T/uMiL0pvmAPpEhuplB82R0LzGCQMvOv5b/+D6dICnamGCh6B3B8r+Y964/sCLyD4kv5axRf2mTmLyPiRorF5rYvr/US8BhFAjTHImWZeVVZTTmON7qWrqHoJtmFoSUnG5RbPc8Bvwb+iyJROgj4OEXV42KKqtPX+jgXiuTwE8C3ytfxFEnasZl5Ye/GmXlImfB9gmL9mwnAMxRJyneBejO6JI2QyKw37k+SJKk9HHMiSZI6ismJJEnqKCYnkiSpo5icSJKkjmJyIkmSOorJiSRJ6igmJ5IkqaOYnEiSpI5iciJJkjrK/wdhcRfsQLhtZAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mXWxdv3u0Ml"
      },
      "source": [
        "<a name=\"conc\"></a>\n",
        "# 8 What can we conclude from this project?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axHa94z_u3ID"
      },
      "source": [
        "COVID-19 has taken an enormous toll on healthcare systems all around the globe. Healthcare professionals are overworked and this raises the need for a helping hand. This can be achieved in the form of Machine Learning models used to predict the need for ICU beds for COVID-19 patients.\n",
        "\n",
        "In this project we have seen that this is possible, but this tool can never be used alone (unless its predictions were right 100% of the time, but we're far from a model that achieves that). There needs to be a healthcare professional with better judgement than our models to assess the need for ICU admission."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtDDHtQ7uk6r"
      },
      "source": [
        "<a name=\"next\"></a>\n",
        "# 9 Going Forward..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LznRn40H7isT"
      },
      "source": [
        "For next projects, better hyperparameter tuning could lead to improved results and predictions.\n",
        "\n",
        "This project could also be applied to other diseases, with the appropriate care and adaptations.\n",
        "\n",
        "An interesting idea could be to implement a web app (for example, a Streamlit application) that receives the relevant information for a patient (or for a group of patients) and returns the prediction for the need of said patient to go to the ICU. It could also output a confidence value for that prediction, so healthcare professionals could take a closer look at patients in which the model could not predict very well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZGsmsCk73Xv"
      },
      "source": [
        "<a name=\"refs\"></a>\n",
        "# 10 References"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6bzLC4EM75ZL"
      },
      "source": [
        "For the most part, I used the documentation of the following libraries:\n",
        "\n",
        "- [Pandas](https://pandas.pydata.org/)\n",
        "- [Matplotlib](https://matplotlib.org/)\n",
        "- [Numpy](https://numpy.org/)\n",
        "- [Seaborn](https://seaborn.pydata.org/)\n",
        "- [Scikit-Learn](https://scikit-learn.org/stable/)\n",
        "- [Lazy Predict](https://lazypredict.readthedocs.io/en/latest/readme.html#usage)\n",
        "\n",
        "Articles and posts that helped this project:\n",
        "\n",
        "- [COVID-19 - Clinical Data to assess diagnosis (Kaggle)](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19)\n",
        "- [Tour of Evaluation Metrics for Classification](https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/)\n",
        "- [ICU outcomes and survival in patients with severe COVID-19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249038)\n",
        "- [Venous blood gas analysis in patients with COVID-19 symptoms](https://www.degruyter.com/document/doi/10.1515/labmed-2020-0126/html)\n",
        "- [Blood routine test in mild and common 2019 coronavirus (COVID-19) patients](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414516/)\n",
        "- [Predicting the Need for ICU Admission in COVID-19 Patients Using XGBoost](https://www.techscience.com/cmc/v69n2/43880/html)\n",
        "- [Using machine learning tools to predict outcomes for emergency department intensive care unit patients](https://www.nature.com/articles/s41598-020-77548-3)\n",
        "- [Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19](https://www.nature.com/articles/s41746-021-00456-x)\n",
        "- [Machine Learning Approach to Predicting COVID-19 Disease Severity](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045777/)\n",
        "- [Useful Metrics to Evaluate Binary Classification Models](https://thedigitalskye.com/2021/04/19/6-useful-metrics-to-evaluate-binary-classification-models/)\n",
        "- [Metrics to Evaluate your Machine Learning Algorithm](https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234)\n",
        "- [How to Use ROC Curves and Precision-Recall Curves for Classification in Python](https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDVhFNEf5qvA"
      },
      "source": [
        "<a name=\"cont\"></a>\n",
        "# 11 Contacts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sgh50cW703Bu"
      },
      "source": [
        "Feel free to contact me for any questions or suggestions:\n",
        "\n",
        "- E-mail: carolinadiasw@gmail.com\n",
        "- Linkedin: https://linkedin/in/carodias"
      ]
    }
  ]
}