Thesis.ipynb

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      "name": "Thesis",
      "provenance": []
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      "cell_type": "code",
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      "metadata": {
        "id": "9XZhYTqDatiW"
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      "source": [
        "import fastai\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.api.types import is_string_dtype, is_numeric_dtype, is_categorical_dtype\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n"
      ],
      "metadata": {
        "id": "9X292hw6auuH"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastai --upgrade"
      ],
      "metadata": {
        "id": "aFiPCsK1efYd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.tabular.all import *\n"
      ],
      "metadata": {
        "id": "UpfEBxk7eQTA"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Goal of the below models is to build a classifier that can accurately predict the \"Group the patient belongs to given the features\"\n",
        "# The following reads the CSV file, classifier has to find the group to which the patient data belongs to\n",
        "# \"GROUP\" is the column to be predicted, also called dependent variable"
      ],
      "metadata": {
        "id": "SBWcCDkJe6pg"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "tf-r4fSmfMRo"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('temp_train_data.csv',header=0)"
      ],
      "metadata": {
        "id": "t-Ly4GUVgm1P"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
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          "height": 322
        },
        "id": "HOySkuTKgrRP",
        "outputId": "51842d01-6ee8-4bc5-8733-f22b2056b11f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>LA</th>\n",
              "      <th>AO</th>\n",
              "      <th>LVIDd</th>\n",
              "      <th>LVIDs</th>\n",
              "      <th>LVPWd</th>\n",
              "      <th>LVPWs</th>\n",
              "      <th>IVSDd</th>\n",
              "      <th>IVSDs</th>\n",
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              "      <th>ESV</th>\n",
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              "      <th>CONCENTRIC</th>\n",
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              "      <th>PAH</th>\n",
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              "      <th>PE</th>\n",
              "      <th>ASO</th>\n",
              "      <th>ESR</th>\n",
              "      <th>CRP</th>\n",
              "      <th>HEMOGLOBIN</th>\n",
              "      <th>RED BLOOD CELL DISTRIBUTION WIDTH</th>\n",
              "      <th>PLATELETS</th>\n",
              "      <th>NEUTROPHIL</th>\n",
              "      <th>EOSINOPHIL</th>\n",
              "      <th>BASOPHIL</th>\n",
              "      <th>LYMPHOCYTE</th>\n",
              "      <th>MONOCYTE</th>\n",
              "      <th>SODIUM</th>\n",
              "      <th>POTASSIUM</th>\n",
              "      <th>SBP</th>\n",
              "      <th>DBP</th>\n",
              "      <th>HR</th>\n",
              "      <th>DOE I-II</th>\n",
              "      <th>DOE III - IV</th>\n",
              "      <th>FEVER</th>\n",
              "      <th>COLD &amp; COUGH</th>\n",
              "      <th>SORE THROAT</th>\n",
              "      <th>CHEST PAIN</th>\n",
              "      <th>JOINT PAIN</th>\n",
              "      <th>PALPITATION</th>\n",
              "      <th>GIDDINESS</th>\n",
              "      <th>PEDAL EDEMA</th>\n",
              "      <th>sST2</th>\n",
              "      <th>GROUP</th>\n",
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              "      <td>NIL</td>\n",
              "      <td>400</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>12.3</td>\n",
              "      <td>12.5</td>\n",
              "      <td>256000</td>\n",
              "      <td>53.6</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.6</td>\n",
              "      <td>7.1</td>\n",
              "      <td>141</td>\n",
              "      <td>4.1</td>\n",
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              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>14.0</td>\n",
              "      <td>PHARYNGITIS</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>M</td>\n",
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              "      <td>4.4</td>\n",
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              "      <td>63</td>\n",
              "      <td>33</td>\n",
              "      <td>67</td>\n",
              "      <td>89</td>\n",
              "      <td>21</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.45</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>TRIVIAL</td>\n",
              "      <td>TRIVIAL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
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              "      <td>0</td>\n",
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              "      <td>265000</td>\n",
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              "      <td>1.3</td>\n",
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              "      <td>NO</td>\n",
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              "      <td>NO</td>\n",
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              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
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              "      <td>NO</td>\n",
              "      <td>6.5</td>\n",
              "      <td>PHARYNGITIS</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>M</td>\n",
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              "      <td>19</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.47</td>\n",
              "      <td>NIL</td>\n",
              "      <td>TRIVIAL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
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              "      <td>13.5</td>\n",
              "      <td>12.0</td>\n",
              "      <td>365000</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>21.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>140</td>\n",
              "      <td>3.5</td>\n",
              "      <td>130</td>\n",
              "      <td>70</td>\n",
              "      <td>61</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>3.6</td>\n",
              "      <td>PHARYNGITIS</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11</td>\n",
              "      <td>M</td>\n",
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              "      <td>19</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.57</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>TRIVIAL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>800</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>4</td>\n",
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              "      <td>58</td>\n",
              "      <td>NO</td>\n",
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              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
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              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>15.2</td>\n",
              "      <td>PHARYNGITIS</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27</td>\n",
              "      <td>M</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>58</td>\n",
              "      <td>29</td>\n",
              "      <td>60</td>\n",
              "      <td>106</td>\n",
              "      <td>46</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.52</td>\n",
              "      <td>NIL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>12.9</td>\n",
              "      <td>13.4</td>\n",
              "      <td>296000</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>41.6</td>\n",
              "      <td>4.3</td>\n",
              "      <td>140</td>\n",
              "      <td>3.5</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>109</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>14.6</td>\n",
              "      <td>PHARYNGITIS</td>\n",
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            "text/plain": [
              "   AGE GENDER   LA   AO  ...  GIDDINESS  PEDAL EDEMA  sST2        GROUP\n",
              "0    1      M  3.2  2.5  ...         NO           NO  14.0  PHARYNGITIS\n",
              "1   10      M  3.0  2.5  ...         NO           NO   6.5  PHARYNGITIS\n",
              "2    4      M  3.4  2.7  ...         NO           NO   3.6  PHARYNGITIS\n",
              "3   11      M  3.1  2.4  ...         NO           NO  15.2  PHARYNGITIS\n",
              "4   27      M  3.6  2.9  ...         NO           NO  14.6  PHARYNGITIS\n",
              "\n",
              "[5 rows x 52 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
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    },
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        "df.columns"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OeKLPi2gsPQ",
        "outputId": "b200b52a-cc6e-48a0-f834-dc6de23e36b1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['AGE', 'GENDER', 'LA', 'AO', 'LVIDd', 'LVIDs', 'LVPWd', 'LVPWs',\n",
              "       'IVSDd', 'IVSDs', 'EF', 'FS', 'SV', 'EDV', 'ESV', 'PASP', 'RWT',\n",
              "       'CONCENTRIC', 'MR', 'AR', 'TR', 'PAH', 'PR', 'PE', 'ASO', 'ESR', 'CRP',\n",
              "       'HEMOGLOBIN', 'RED BLOOD CELL DISTRIBUTION WIDTH', 'PLATELETS',\n",
              "       'NEUTROPHIL', 'EOSINOPHIL', 'BASOPHIL', 'LYMPHOCYTE', 'MONOCYTE',\n",
              "       'SODIUM', 'POTASSIUM', 'SBP', 'DBP', 'HR', 'DOE I-II', 'DOE III - IV',\n",
              "       'FEVER', 'COLD & COUGH', 'SORE THROAT', 'CHEST PAIN', 'JOINT PAIN',\n",
              "       'PALPITATION', 'GIDDINESS', 'PEDAL EDEMA', 'sST2', 'GROUP'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dependant_variable = 'GROUP'"
      ],
      "metadata": {
        "id": "R4V6K2fSg5cg"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#We have to shuffle rows to generate train and test data\n",
        "df = df.reindex(np.random.permutation(df.index))"
      ],
      "metadata": {
        "id": "WIE6O1RbhsZv"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "3ClUxrWtiClw",
        "outputId": "c399fea3-d996-4718-8fe9-48175cfc4dd3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-638d51c2-0ffd-4bf6-a5be-1621bf9382ef\">\n",
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              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>LA</th>\n",
              "      <th>AO</th>\n",
              "      <th>LVIDd</th>\n",
              "      <th>LVIDs</th>\n",
              "      <th>LVPWd</th>\n",
              "      <th>LVPWs</th>\n",
              "      <th>IVSDd</th>\n",
              "      <th>IVSDs</th>\n",
              "      <th>EF</th>\n",
              "      <th>FS</th>\n",
              "      <th>SV</th>\n",
              "      <th>EDV</th>\n",
              "      <th>ESV</th>\n",
              "      <th>PASP</th>\n",
              "      <th>RWT</th>\n",
              "      <th>CONCENTRIC</th>\n",
              "      <th>MR</th>\n",
              "      <th>AR</th>\n",
              "      <th>TR</th>\n",
              "      <th>PAH</th>\n",
              "      <th>PR</th>\n",
              "      <th>PE</th>\n",
              "      <th>ASO</th>\n",
              "      <th>ESR</th>\n",
              "      <th>CRP</th>\n",
              "      <th>HEMOGLOBIN</th>\n",
              "      <th>RED BLOOD CELL DISTRIBUTION WIDTH</th>\n",
              "      <th>PLATELETS</th>\n",
              "      <th>NEUTROPHIL</th>\n",
              "      <th>EOSINOPHIL</th>\n",
              "      <th>BASOPHIL</th>\n",
              "      <th>LYMPHOCYTE</th>\n",
              "      <th>MONOCYTE</th>\n",
              "      <th>SODIUM</th>\n",
              "      <th>POTASSIUM</th>\n",
              "      <th>SBP</th>\n",
              "      <th>DBP</th>\n",
              "      <th>HR</th>\n",
              "      <th>DOE I-II</th>\n",
              "      <th>DOE III - IV</th>\n",
              "      <th>FEVER</th>\n",
              "      <th>COLD &amp; COUGH</th>\n",
              "      <th>SORE THROAT</th>\n",
              "      <th>CHEST PAIN</th>\n",
              "      <th>JOINT PAIN</th>\n",
              "      <th>PALPITATION</th>\n",
              "      <th>GIDDINESS</th>\n",
              "      <th>PEDAL EDEMA</th>\n",
              "      <th>sST2</th>\n",
              "      <th>GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>57</td>\n",
              "      <td>M</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>63</td>\n",
              "      <td>33</td>\n",
              "      <td>55</td>\n",
              "      <td>96</td>\n",
              "      <td>41</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.49</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>510000</td>\n",
              "      <td>78.1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.1</td>\n",
              "      <td>2.9</td>\n",
              "      <td>136</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>75</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>5.0</td>\n",
              "      <td>PHARYNGITIS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>68</td>\n",
              "      <td>F</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>58</td>\n",
              "      <td>29</td>\n",
              "      <td>53</td>\n",
              "      <td>104</td>\n",
              "      <td>51</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.39</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45.7</td>\n",
              "      <td>10.3</td>\n",
              "      <td>10.9</td>\n",
              "      <td>3,99,000</td>\n",
              "      <td>86.8</td>\n",
              "      <td>9.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>49.7</td>\n",
              "      <td>5.3</td>\n",
              "      <td>135</td>\n",
              "      <td>3.7</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>62</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>30.9</td>\n",
              "      <td>RHD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>38</td>\n",
              "      <td>F</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>48</td>\n",
              "      <td>74</td>\n",
              "      <td>26</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.41</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>SEVERE</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1</td>\n",
              "      <td>11.9</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2,89,000</td>\n",
              "      <td>66.8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>141</td>\n",
              "      <td>4.5</td>\n",
              "      <td>100</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>33.1</td>\n",
              "      <td>RHD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>44</td>\n",
              "      <td>F</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>62</td>\n",
              "      <td>31</td>\n",
              "      <td>55</td>\n",
              "      <td>96</td>\n",
              "      <td>41</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.49</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>TRIVIAL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.9</td>\n",
              "      <td>13.5</td>\n",
              "      <td>325000</td>\n",
              "      <td>57.3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.6</td>\n",
              "      <td>2.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3.6</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>67</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>4.4</td>\n",
              "      <td>CONTROL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>11</td>\n",
              "      <td>F</td>\n",
              "      <td>4.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>65</td>\n",
              "      <td>32</td>\n",
              "      <td>44</td>\n",
              "      <td>102</td>\n",
              "      <td>58</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.39</td>\n",
              "      <td>NIL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>1200</td>\n",
              "      <td>57</td>\n",
              "      <td>53</td>\n",
              "      <td>10.3</td>\n",
              "      <td>10.9</td>\n",
              "      <td>510000</td>\n",
              "      <td>75.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>137</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>70</td>\n",
              "      <td>100</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>20.1</td>\n",
              "      <td>RF</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-638d51c2-0ffd-4bf6-a5be-1621bf9382ef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-638d51c2-0ffd-4bf6-a5be-1621bf9382ef button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-638d51c2-0ffd-4bf6-a5be-1621bf9382ef');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     AGE GENDER   LA   AO  ...  GIDDINESS  PEDAL EDEMA  sST2        GROUP\n",
              "57    57      M  3.5  3.1  ...         NO           NO   5.0  PHARYNGITIS\n",
              "151   68      F  4.7  2.7  ...        NIL           NO  30.9          RHD\n",
              "156   38      F  6.3  2.9  ...        YES           NO  33.1          RHD\n",
              "200   44      F  3.2  2.4  ...         NO           NO   4.4      CONTROL\n",
              "109   11      F  4.3  2.9  ...         NO           NO  20.1           RF\n",
              "\n",
              "[5 rows x 52 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "procs = [Categorify, FillMissing]"
      ],
      "metadata": {
        "id": "QQl36pM7iDu3"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets do a 70-30 split (357 rows, 249 will be used for training, 108 for testing)\n",
        "N = len(df)\n",
        "train_n = int( 0.70 * N )\n",
        "test_n = N - train_n"
      ],
      "metadata": {
        "id": "IAxod0p-iTwH"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_n,test_n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cpm4wH9oihFv",
        "outputId": "d18e4594-d648-49c0-c6fa-7b4b02f74894"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(249, 108)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "splits= (list(range(train_n)),list(range(train_n,N)))"
      ],
      "metadata": {
        "id": "1hZ40AIqisyn"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cont,cat = cont_cat_split(df, 1, dep_var=dependant_variable) # this preprocess the dataframe to find category/continous variables\n"
      ],
      "metadata": {
        "id": "x0FnnBJJjeMX"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cont"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvfjkZ5Ojmi_",
        "outputId": "0c7e4911-2dd9-4023-ca13-108de4886b15"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['AGE',\n",
              " 'LA',\n",
              " 'AO',\n",
              " 'LVIDd',\n",
              " 'LVIDs',\n",
              " 'LVPWd',\n",
              " 'LVPWs',\n",
              " 'IVSDd',\n",
              " 'IVSDs',\n",
              " 'EF',\n",
              " 'FS',\n",
              " 'SV',\n",
              " 'EDV',\n",
              " 'ESV',\n",
              " 'PASP',\n",
              " 'RWT',\n",
              " 'ASO',\n",
              " 'ESR',\n",
              " 'HEMOGLOBIN',\n",
              " 'RED BLOOD CELL DISTRIBUTION WIDTH',\n",
              " 'NEUTROPHIL',\n",
              " 'EOSINOPHIL',\n",
              " 'BASOPHIL',\n",
              " 'LYMPHOCYTE',\n",
              " 'MONOCYTE',\n",
              " 'SBP',\n",
              " 'DBP',\n",
              " 'HR',\n",
              " 'sST2']"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FmN6p3hNjpSX",
        "outputId": "b11d86ee-cf0d-4649-9078-f2b4149c2d6f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['GENDER',\n",
              " 'CONCENTRIC',\n",
              " 'MR',\n",
              " 'AR',\n",
              " 'TR',\n",
              " 'PAH',\n",
              " 'PR',\n",
              " 'PE',\n",
              " 'CRP',\n",
              " 'PLATELETS',\n",
              " 'SODIUM',\n",
              " 'POTASSIUM',\n",
              " 'DOE I-II',\n",
              " 'DOE III - IV',\n",
              " 'FEVER',\n",
              " 'COLD & COUGH',\n",
              " 'SORE THROAT',\n",
              " 'CHEST PAIN',\n",
              " 'JOINT PAIN',\n",
              " 'PALPITATION',\n",
              " 'GIDDINESS',\n",
              " 'PEDAL EDEMA']"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(0)"
      ],
      "metadata": {
        "id": "mV2foE-YkMI3"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "to = TabularPandas(df, procs, cat, cont, y_names=dependant_variable,y_block=CategoryBlock(), splits=splits)\n"
      ],
      "metadata": {
        "id": "Ut_mq3cajqmn"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(to.train),len(to.valid)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hM9TdT0Wj2uH",
        "outputId": "f5e73f6e-c4b4-454f-a68a-1393bf6c2189"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(249, 108)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "to.show(3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        },
        "id": "C2E6F8-3kPp3",
        "outputId": "66c330c6-6c75-411c-d193-b146be3160b2"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GENDER</th>\n",
              "      <th>CONCENTRIC</th>\n",
              "      <th>MR</th>\n",
              "      <th>AR</th>\n",
              "      <th>TR</th>\n",
              "      <th>PAH</th>\n",
              "      <th>PR</th>\n",
              "      <th>PE</th>\n",
              "      <th>CRP</th>\n",
              "      <th>PLATELETS</th>\n",
              "      <th>SODIUM</th>\n",
              "      <th>POTASSIUM</th>\n",
              "      <th>DOE I-II</th>\n",
              "      <th>DOE III - IV</th>\n",
              "      <th>FEVER</th>\n",
              "      <th>COLD &amp; COUGH</th>\n",
              "      <th>SORE THROAT</th>\n",
              "      <th>CHEST PAIN</th>\n",
              "      <th>JOINT PAIN</th>\n",
              "      <th>PALPITATION</th>\n",
              "      <th>GIDDINESS</th>\n",
              "      <th>PEDAL EDEMA</th>\n",
              "      <th>AGE</th>\n",
              "      <th>LA</th>\n",
              "      <th>AO</th>\n",
              "      <th>LVIDd</th>\n",
              "      <th>LVIDs</th>\n",
              "      <th>LVPWd</th>\n",
              "      <th>LVPWs</th>\n",
              "      <th>IVSDd</th>\n",
              "      <th>IVSDs</th>\n",
              "      <th>EF</th>\n",
              "      <th>FS</th>\n",
              "      <th>SV</th>\n",
              "      <th>EDV</th>\n",
              "      <th>ESV</th>\n",
              "      <th>PASP</th>\n",
              "      <th>RWT</th>\n",
              "      <th>ASO</th>\n",
              "      <th>ESR</th>\n",
              "      <th>HEMOGLOBIN</th>\n",
              "      <th>RED BLOOD CELL DISTRIBUTION WIDTH</th>\n",
              "      <th>NEUTROPHIL</th>\n",
              "      <th>EOSINOPHIL</th>\n",
              "      <th>BASOPHIL</th>\n",
              "      <th>LYMPHOCYTE</th>\n",
              "      <th>MONOCYTE</th>\n",
              "      <th>SBP</th>\n",
              "      <th>DBP</th>\n",
              "      <th>HR</th>\n",
              "      <th>sST2</th>\n",
              "      <th>GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>M</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>0</td>\n",
              "      <td>510000</td>\n",
              "      <td>136</td>\n",
              "      <td>4</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>NO</td>\n",
              "      <td>57</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>63</td>\n",
              "      <td>33</td>\n",
              "      <td>55</td>\n",
              "      <td>96</td>\n",
              "      <td>41</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>78.099998</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.099998</td>\n",
              "      <td>2.9</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>75</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>PHARYNGITIS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>45.7</td>\n",
              "      <td>3,99,000</td>\n",
              "      <td>135</td>\n",
              "      <td>3.7</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>68</td>\n",
              "      <td>4.7</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>58</td>\n",
              "      <td>29</td>\n",
              "      <td>53</td>\n",
              "      <td>104</td>\n",
              "      <td>51</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10.3</td>\n",
              "      <td>10.9</td>\n",
              "      <td>86.800003</td>\n",
              "      <td>9.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>49.700001</td>\n",
              "      <td>5.3</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>62</td>\n",
              "      <td>30.900000</td>\n",
              "      <td>RHD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>SEVERE</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>MILD</td>\n",
              "      <td>NIL</td>\n",
              "      <td>53.1</td>\n",
              "      <td>2,89,000</td>\n",
              "      <td>141</td>\n",
              "      <td>4.5</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NO</td>\n",
              "      <td>YES</td>\n",
              "      <td>NIL</td>\n",
              "      <td>NIL</td>\n",
              "      <td>YES</td>\n",
              "      <td>NO</td>\n",
              "      <td>38</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>48</td>\n",
              "      <td>74</td>\n",
              "      <td>26</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11.9</td>\n",
              "      <td>11.0</td>\n",
              "      <td>66.800003</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>9.1</td>\n",
              "      <td>100</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
              "      <td>33.099998</td>\n",
              "      <td>RHD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets create a decision tree\n",
        "xs,y = to.train.xs,to.train.y\n",
        "valid_xs,valid_y = to.valid.xs,to.valid.y"
      ],
      "metadata": {
        "id": "UH1OAYVEkRBH"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m = DecisionTreeRegressor(max_leaf_nodes=4)\n",
        "m.fit(xs, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AD0QBiG1kfWP",
        "outputId": "44dd7c65-2f06-49fd-9ef9-a25036c97d15"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_leaf_nodes=4)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -Uqq fastbook kaggle waterfallcharts treeinterpreter dtreeviz\n",
        "import fastbook\n",
        "fastbook.setup_book()\n",
        "\n"
      ],
      "metadata": {
        "id": "wpLXQXPwklhH"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from dtreeviz.trees import *\n",
        "from IPython.display import Image, display_svg, SVG"
      ],
      "metadata": {
        "id": "AzTRWfJekuB4"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from fastbook import *\n",
        "\n",
        "draw_tree(m, xs, size=10, leaves_parallel=True, precision=2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "oBuz2FB5l0JH",
        "outputId": "ddf4b394-7d5e-4b2a-c671-b30527a48c9b"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f9b01d4f9d0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"654pt\" height=\"396pt\"\n viewBox=\"0.00 0.00 654.00 396.32\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 392.3231)\">\n<title>Tree</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-392.3231 650,-392.3231 650,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#f5cfb3\" stroke=\"#000000\" d=\"M387,-373C387,-373 259,-373 259,-373 253,-373 247,-367 247,-361 247,-361 247,-317 247,-317 247,-311 253,-305 259,-305 259,-305 387,-305 387,-305 393,-305 399,-311 399,-317 399,-317 399,-361 399,-361 399,-367 393,-373 387,-373\"/>\n<text text-anchor=\"start\" x=\"283.5\" y=\"-357.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">sST2 ≤ 15.8</text>\n<text text-anchor=\"start\" x=\"255\" y=\"-342.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">squared_error = 1.52</text>\n<text text-anchor=\"start\" x=\"275\" y=\"-327.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 249</text>\n<text text-anchor=\"start\" x=\"283\" y=\"-312.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 1.14</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#fcf2eb\" stroke=\"#000000\" d=\"M302,-223C302,-223 174,-223 174,-223 168,-223 162,-217 162,-211 162,-211 162,-167 162,-167 162,-161 168,-155 174,-155 174,-155 302,-155 302,-155 308,-155 314,-161 314,-167 314,-167 314,-211 314,-211 314,-217 308,-223 302,-223\"/>\n<text text-anchor=\"start\" x=\"201\" y=\"-207.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">ESR ≤ 12.5</text>\n<text text-anchor=\"start\" x=\"170\" y=\"-192.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">squared_error = 0.26</text>\n<text text-anchor=\"start\" x=\"190\" y=\"-177.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 161</text>\n<text text-anchor=\"start\" x=\"198\" y=\"-162.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.31</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M303.7122,-304.9627C291.4748,-283.3673 275.5337,-255.236 262.4172,-232.0892\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"265.3337,-230.1366 257.3584,-223.1619 259.2435,-233.5877 265.3337,-230.1366\"/>\n<text text-anchor=\"middle\" x=\"250.6938\" y=\"-243.5572\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 2 -->\n<g id=\"node5\" class=\"node\">\n<title>2</title>\n<path fill=\"#e88f4f\" stroke=\"#000000\" d=\"M472,-223C472,-223 344,-223 344,-223 338,-223 332,-217 332,-211 332,-211 332,-167 332,-167 332,-161 338,-155 344,-155 344,-155 472,-155 472,-155 478,-155 484,-161 484,-167 484,-167 484,-211 484,-211 484,-217 478,-223 472,-223\"/>\n<text text-anchor=\"start\" x=\"341.5\" y=\"-207.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">CONCENTRIC ≤ 2.5</text>\n<text text-anchor=\"start\" x=\"340\" y=\"-192.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">squared_error = 0.22</text>\n<text text-anchor=\"start\" x=\"364\" y=\"-177.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 88</text>\n<text text-anchor=\"start\" x=\"368\" y=\"-162.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = 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          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rf(xs, y, n_estimators=40, max_samples=249,\n",
        "       max_features=0.5, min_samples_leaf=5, **kwargs):\n",
        "    return RandomForestRegressor(n_jobs=-1, n_estimators=n_estimators,\n",
        "        max_samples=max_samples, max_features=max_features,\n",
        "        min_samples_leaf=min_samples_leaf, oob_score=True).fit(xs, y)"
      ],
      "metadata": {
        "id": "CPiPhLp5l1QH"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m = rf(xs, y);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGQi9fpZmi2n",
        "outputId": "4f79ebee-23cd-4cf5-829a-bf87ed217555"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rf_feat_importance(m, df):\n",
        "    return pd.DataFrame({'cols':df.columns, 'imp':m.feature_importances_}\n",
        "                       ).sort_values('imp', ascending=False)"
      ],
      "metadata": {
        "id": "IpZKz-DimkKv"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fi = rf_feat_importance(m, xs)\n",
        "fi[:10]"
      ],
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        "colab": {
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          "height": 363
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        "id": "zmgTI4ydm3s_",
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      },
      "execution_count": 75,
      "outputs": [
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              "      <th>cols</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
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              "      <td>0.444459</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "    <tr>\n",
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            "text/plain": [
              "           cols       imp\n",
              "50         sST2  0.444459\n",
              "8           CRP  0.228155\n",
              "23           LA  0.100668\n",
              "39          ESR  0.069760\n",
              "1    CONCENTRIC  0.054083\n",
              "18   JOINT PAIN  0.038524\n",
              "19  PALPITATION  0.020487\n",
              "38          ASO  0.012486\n",
              "16  SORE THROAT  0.011201\n",
              "20    GIDDINESS  0.007262"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
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        "def plot_fi(fi):\n",
        "    return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)\n",
        "\n",
        "plot_fi(fi[:30]);"
      ],
      "metadata": {
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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9qCSZdAem9-3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}