Heart_Failure_Machine_Learning_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Heart Failure Machine Learning Prediction.ipynb",
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      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-P9kBxZBQwrI"
      },
      "source": [
        "# Data Description\n",
        "\n",
        "Age: Age in years.\n",
        "\n",
        "Anaemia: 0 = False, 1 = True\n",
        "\n",
        "Creatinine_phosphokinase: Level of CPK enzyme in the blood.\n",
        "\n",
        "Diabetes: 0 = False, 1 = True\n",
        "\n",
        "Ejection fraction: Percentage of how much blood the left ventricle pumps out with each contraction.\n",
        "\n",
        "High blood pressure: 0 = False, 1 = True\n",
        "\n",
        "Platelets: Platelet count in the blood.\n",
        "\n",
        "Serum creatinine: Measure of creatinine in the blood.\n",
        "\n",
        "Serum sodium: The measure of sodium in the blood.\n",
        "\n",
        "Sex: 0 = Woman, 1 = Man\n",
        "\n",
        "Smoking: 0 = False, 1 = True\n",
        "\n",
        "Time: Number of days\n",
        "\n",
        "Death event: 0 = False, 1 = True"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Axdefqqn3gp"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jddr1IZjMa9z"
      },
      "source": [
        "#necessary imports\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import plotly as py\n",
        "import plotly.graph_objs as go\n",
        "import plotly.express as px\n",
        "from plotly.offline import download_plotlyjs, plot, init_notebook_mode, iplot"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmwbfEjC7B-V",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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            }
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          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "b821d84a-1301-4b0e-d6be-3fb69c9745c7"
      },
      "source": [
        "HF=files.upload()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "     <input type=\"file\" id=\"files-789624c8-384f-4887-8d66-332b87c38907\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-789624c8-384f-4887-8d66-332b87c38907\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving heart_failure_clinical_records_dataset.csv to heart_failure_clinical_records_dataset (4).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gcyNojUQMyQ"
      },
      "source": [
        "import io\n",
        "HF = pd.read_csv(io.BytesIO(HF[\"heart_failure_clinical_records_dataset.csv\"]))"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_XOBc4JMd_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb7334c5-4474-4592-c22f-5ff1c303f903"
      },
      "source": [
        "pip install plotly"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (4.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly) (1.3.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUt40I6nMvrZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e447bf07-58ed-489f-f787-993101d5145c"
      },
      "source": [
        "pip install -U seaborn"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.1)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.1.5)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AbxOATMVMyMq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93790a4a-6170-4f12-e82f-86e137677164"
      },
      "source": [
        "pip install xgboost"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zV-wl0U9vH10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "ea7391db-6df7-4022-ed6f-8d1cc2930681"
      },
      "source": [
        "HF.head()"
      ],
      "execution_count": 118,
      "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>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>265000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>130</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>162000.00</td>\n",
              "      <td>1.3</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>210000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>327000.00</td>\n",
              "      <td>2.7</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  anaemia  creatinine_phosphokinase  ...  smoking  time  DEATH_EVENT\n",
              "0  75.0        0                       582  ...        0     4            1\n",
              "1  55.0        0                      7861  ...        0     6            1\n",
              "2  65.0        0                       146  ...        1     7            1\n",
              "3  50.0        1                       111  ...        0     7            1\n",
              "4  65.0        1                       160  ...        0     8            1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RRgENoSNrbp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec3bdf32-95fd-4662-972e-0d1bbc6b96c3"
      },
      "source": [
        "HF.dtypes"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                         float64\n",
              "anaemia                       int64\n",
              "creatinine_phosphokinase      int64\n",
              "diabetes                      int64\n",
              "ejection_fraction             int64\n",
              "high_blood_pressure           int64\n",
              "platelets                   float64\n",
              "serum_creatinine            float64\n",
              "serum_sodium                  int64\n",
              "sex                           int64\n",
              "smoking                       int64\n",
              "time                          int64\n",
              "DEATH_EVENT                   int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yX-NR-xNwQL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ebb8017-32a4-4930-a3f4-86e33385504e"
      },
      "source": [
        "HF.columns"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
              "       'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
              "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
              "       'DEATH_EVENT'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDtBKnKJNwym",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "708b552d-0219-4ed4-80f9-41cd62ce2b19"
      },
      "source": [
        "HF.shape"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(299, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AGt6-ELNw8R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "995d0bea-880f-4b2b-cd41-03b17026e5c9"
      },
      "source": [
        "HF.info"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of       age  anaemia  creatinine_phosphokinase  ...  smoking  time  DEATH_EVENT\n",
              "0    75.0        0                       582  ...        0     4            1\n",
              "1    55.0        0                      7861  ...        0     6            1\n",
              "2    65.0        0                       146  ...        1     7            1\n",
              "3    50.0        1                       111  ...        0     7            1\n",
              "4    65.0        1                       160  ...        0     8            1\n",
              "..    ...      ...                       ...  ...      ...   ...          ...\n",
              "294  62.0        0                        61  ...        1   270            0\n",
              "295  55.0        0                      1820  ...        0   271            0\n",
              "296  45.0        0                      2060  ...        0   278            0\n",
              "297  45.0        0                      2413  ...        1   280            0\n",
              "298  50.0        0                       196  ...        1   285            0\n",
              "\n",
              "[299 rows x 13 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmGaNXIuNxBu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "0fabb8ae-c6e7-4805-b9f4-1f549da216b2"
      },
      "source": [
        "HF.describe()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.00000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.00000</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>299.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>60.833893</td>\n",
              "      <td>0.431438</td>\n",
              "      <td>581.839465</td>\n",
              "      <td>0.418060</td>\n",
              "      <td>38.083612</td>\n",
              "      <td>0.351171</td>\n",
              "      <td>263358.029264</td>\n",
              "      <td>1.39388</td>\n",
              "      <td>136.625418</td>\n",
              "      <td>0.648829</td>\n",
              "      <td>0.32107</td>\n",
              "      <td>130.260870</td>\n",
              "      <td>0.32107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.894809</td>\n",
              "      <td>0.496107</td>\n",
              "      <td>970.287881</td>\n",
              "      <td>0.494067</td>\n",
              "      <td>11.834841</td>\n",
              "      <td>0.478136</td>\n",
              "      <td>97804.236869</td>\n",
              "      <td>1.03451</td>\n",
              "      <td>4.412477</td>\n",
              "      <td>0.478136</td>\n",
              "      <td>0.46767</td>\n",
              "      <td>77.614208</td>\n",
              "      <td>0.46767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25100.000000</td>\n",
              "      <td>0.50000</td>\n",
              "      <td>113.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>116.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>212500.000000</td>\n",
              "      <td>0.90000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>262000.000000</td>\n",
              "      <td>1.10000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>582.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>303500.000000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>203.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7861.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>850000.000000</td>\n",
              "      <td>9.40000</td>\n",
              "      <td>148.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>285.000000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age     anaemia  ...        time  DEATH_EVENT\n",
              "count  299.000000  299.000000  ...  299.000000    299.00000\n",
              "mean    60.833893    0.431438  ...  130.260870      0.32107\n",
              "std     11.894809    0.496107  ...   77.614208      0.46767\n",
              "min     40.000000    0.000000  ...    4.000000      0.00000\n",
              "25%     51.000000    0.000000  ...   73.000000      0.00000\n",
              "50%     60.000000    0.000000  ...  115.000000      0.00000\n",
              "75%     70.000000    1.000000  ...  203.000000      1.00000\n",
              "max     95.000000    1.000000  ...  285.000000      1.00000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8PgSdkrNxCs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "outputId": "48b3f2d2-3485-4668-b668-e47026418608"
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(HF.corr(method='pearson'), annot=True)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb833573fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haPoeJItRnHA"
      },
      "source": [
        "## Renaming Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_Q8KWCANxHb"
      },
      "source": [
        "HF.rename(columns={'creatinine_phosphokinase':'CPK','high_blood_pressure':'HBP', 'ejection_fraction':'EF', 'DEATH_EVENT':'death_event'},inplace=True);\n",
        "HF.rename(columns=lambda x:x.strip().replace(' ','_'),inplace=True)"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUe8HiTbODhU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc16022e-fa90-488b-8727-350a7ec35290"
      },
      "source": [
        "HF.columns"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'anaemia', 'CPK', 'diabetes', 'EF', 'HBP', 'platelets',\n",
              "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time',\n",
              "       'death_event'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y89VJ5qxODiW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "950c20e5-db43-4650-b381-a20f73d5a88c"
      },
      "source": [
        "#check for null values\n",
        "\n",
        "HF.isnull()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>anaemia</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>294</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <th>298</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>299 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  anaemia    CPK  diabetes  ...    sex  smoking   time  death_event\n",
              "0    False    False  False     False  ...  False    False  False        False\n",
              "1    False    False  False     False  ...  False    False  False        False\n",
              "2    False    False  False     False  ...  False    False  False        False\n",
              "3    False    False  False     False  ...  False    False  False        False\n",
              "4    False    False  False     False  ...  False    False  False        False\n",
              "..     ...      ...    ...       ...  ...    ...      ...    ...          ...\n",
              "294  False    False  False     False  ...  False    False  False        False\n",
              "295  False    False  False     False  ...  False    False  False        False\n",
              "296  False    False  False     False  ...  False    False  False        False\n",
              "297  False    False  False     False  ...  False    False  False        False\n",
              "298  False    False  False     False  ...  False    False  False        False\n",
              "\n",
              "[299 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QcHUL2WODm0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "429fc752-34ea-4a57-dfa5-fc4531d48a0a"
      },
      "source": [
        "HF.isnull().sum()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0\n",
              "anaemia             0\n",
              "CPK                 0\n",
              "diabetes            0\n",
              "EF                  0\n",
              "HBP                 0\n",
              "platelets           0\n",
              "serum_creatinine    0\n",
              "serum_sodium        0\n",
              "sex                 0\n",
              "smoking             0\n",
              "time                0\n",
              "death_event         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIP3qYavODvE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b2d5397-ca80-462a-8d4b-84e89c5731e8"
      },
      "source": [
        "#Check for duplicated values\n",
        "\n",
        "HF.duplicated()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "294    False\n",
              "295    False\n",
              "296    False\n",
              "297    False\n",
              "298    False\n",
              "Length: 299, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwWrjsKuODwA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "679f15be-d22f-4347-ab99-b62dd93c8acd"
      },
      "source": [
        "HF.duplicated().sum()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQD1HzysODz-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "92199a62-e853-4ed5-af67-1041e0c96684"
      },
      "source": [
        "HF.drop_duplicates(subset=None, keep='first', inplace=False)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>anaemia</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
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              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
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              "      <td>20</td>\n",
              "      <td>0</td>\n",
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              "      <td>1.3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>210000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>137</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>327000.00</td>\n",
              "      <td>2.7</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>155000.00</td>\n",
              "      <td>1.1</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1820</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.00</td>\n",
              "      <td>1.2</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>271</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>742000.00</td>\n",
              "      <td>0.8</td>\n",
              "      <td>138</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>278</td>\n",
              "      <td>0</td>\n",
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              "      <td>45.0</td>\n",
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              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "      <td>140000.00</td>\n",
              "      <td>1.4</td>\n",
              "      <td>140</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>196</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>395000.00</td>\n",
              "      <td>1.6</td>\n",
              "      <td>136</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>285</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>299 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      age  anaemia   CPK  diabetes  ...  sex  smoking  time  death_event\n",
              "0    75.0        0   582         0  ...    1        0     4            1\n",
              "1    55.0        0  7861         0  ...    1        0     6            1\n",
              "2    65.0        0   146         0  ...    1        1     7            1\n",
              "3    50.0        1   111         0  ...    1        0     7            1\n",
              "4    65.0        1   160         1  ...    0        0     8            1\n",
              "..    ...      ...   ...       ...  ...  ...      ...   ...          ...\n",
              "294  62.0        0    61         1  ...    1        1   270            0\n",
              "295  55.0        0  1820         0  ...    0        0   271            0\n",
              "296  45.0        0  2060         1  ...    0        0   278            0\n",
              "297  45.0        0  2413         0  ...    1        1   280            0\n",
              "298  50.0        0   196         0  ...    1        1   285            0\n",
              "\n",
              "[299 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUDH6eWvOD1C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "090dedc3-7ced-4265-c410-dc97f1d48f7d"
      },
      "source": [
        "HF.nunique()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  47\n",
              "anaemia               2\n",
              "CPK                 208\n",
              "diabetes              2\n",
              "EF                   17\n",
              "HBP                   2\n",
              "platelets           176\n",
              "serum_creatinine     40\n",
              "serum_sodium         27\n",
              "sex                   2\n",
              "smoking               2\n",
              "time                148\n",
              "death_event           2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xo3C7XvGQ5jG"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0PRWL03RJOJ"
      },
      "source": [
        "**The Target Column Is The** **Death** **Event** **Column**\n",
        "\n",
        "\n",
        "Death Event Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xF9F2pCLOD4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "7e3ba2a7-5c26-4d69-96da-005095d19c6a"
      },
      "source": [
        "plt.figure(figsize = (12, 7))\n",
        "\n",
        "sns.countplot(y = 'death_event', data = HF)\n",
        "plt.show()\n",
        "\n",
        "values = HF['death_event'].value_counts()\n",
        "labels = ['Survived', 'Dead']\n",
        "\n",
        "fig, ax = plt.subplots(figsize = (5, 5), dpi = 100)\n",
        "explode = (0, 0.06)\n",
        "\n",
        "patches, texts, autotexts = ax.pie(values, labels = labels, autopct = '%1.2f%%', shadow = True,\n",
        "                                   startangle = 90, explode = explode)\n",
        "\n",
        "plt.setp(texts, color = 'grey')\n",
        "plt.setp(autotexts, size = 12, color = 'white')\n",
        "autotexts[1].set_color('black')\n",
        "plt.show()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yZMkSbrnlFrRaLStWrECvv/CYkKDpZof37GLHmk+5rvwgemmUKUTK+O3KAsbmpdPHmXbBr1VUVMQzzzyDVqvFbDaTm5vLTTfd1OaCzfvuu481a9awcOFCvF4vNpuNAQMGYLVaAZg1axYLFy7k5ZdfxmKxcNVVVxEMtj1XPHv2bBYtWsRrr73Wurz51FVp50ujyB6l3aalqYn5z/yOS2rL6HesWO1yRDez3/kgzkd/AMDBwhJeevsjcrJOXsS3JpBPk3LhJ1ZF4pjUz8nf7p2YEFfvdyc5R9NNopEIi155kT4+l4SMEClqe3kzC/ZUdn5gkpOg6SbrFy8kXFPJuOLdapcihFDRXz4rps6T2pczSNB0g9KDB9i7ZROXVRzCIHvLCJHSPMEIv19VoHYZqpKgucg8rmY+eesNRgddOF2y5asQAtYV1bOusE7tMlQjQXMRRaNRFr36MjYlypDS/WqXI4SII39YfRh/KDXbTknQXESbly+lqa6WsUdlKbMQoq0ad5AXNpeqXYYqJGgukoqSYnZuXM+QoJvsxmq1yxFCxKF3tpdTVOdRu4weJ0FzEYRDIT55ez6ZVgvDimSVmRCiY9GYwjNrkn/759NJ0FwEm5cvJRT0M6J0H0bpyiyEOIttR5vYVNKgdhk9SoLmAtVVHmPP1k30j/jJrenZvSiEEInpfz8rIpZCTVkkaC5ANBpl2fw3sFssDD+8U+1yhBAJoqjey9L9qXMuV4LmAmxftwaPq5khlSWkBXxqlyOESCDPbywhGEmN5c4SNOfJ1dDAl6tXkWUy0K+i+/eeEEIklxp3kHe3V6hdRo+QoDkPiqKwfP7rmCwWhpbuRxeLqV2SECIBvf75EVz+5G9TJUFzHvZ9vpX6mmqyAx5618qOmUKI8+MORnhlS5naZXQ7CZpzFPT72bhsMXank6HFe9QuRwiR4D7YVUGN+8K3S45nEjTnaNOyJaCBnPpK0lsa1S5HCJHgwlGFt79M7pkRCZpz4Gpo4MD2L7BabQyWpplCiItkwZ5KWgLJe65GguYcrP7ofUxpFvKqyrD63GqXI4RIEr5wlA92Ju8KNAmaLjpWWsKxkmLSDAYGlh1UuxwhRJJ5b0cFgXByXlcjQdMFiqKw5qMPsKWnk1tdhjnkV7skIUSSafKFWbS3Su0yuoUETRcU79tLc0M9Op2O/uWp13lVCNEz3tp2lGgs+XqgSdB0IhqNsn7xAuxOJ71qK0gLeNUuSQiRpCpdAT4tqFG7jItOgqYTB778Ap/Hg1arpX95gdrlCCGS3BtfJF8XeAmas4hGo2xd9Ql2p5PMhmrsHpfaJQkhktzhWg87ypvULuOikqA5i6K9uwn4fGg0GvofldGMEKJnLNyTXIsCJGjOQFEUNn+yDLvTiaOlkQxXvdolCSFSxOrDtbiT6AJOCZozOHK4AI+r+fi5GRnNCCF6UDASY/mB5FkUoFe7gHikKAqbly/B5kjH4m0hu75S7ZKEUI2/uozKVa/jPVZIxN2I1mDC3HsAuTPm4Bx9JQBKLEbDjpU07d2Ir7KIqM+NMTOXzEuuJXf6HLQGY6fv4zq8jabda/EcPUSg9ihGZw7j/+3tDo+tXP0W3qMH8ZYfJOJpJu+Gh+gz8+F2x7nL9lG+4K8E6o9h6TucAXf+kLRe/dscc3Th/xKoK2f4Y0+fx3en+yzcW8mcS/uqXcZFISOaDtSUH6WhpgadXk+fyhI0ahckhIqCTTVEg36yJ82k3+1PknfDAwAUvfZz6rYuASAWDlL2/v8Q8TaTM/U2+t3+Haz9RlK58nUKX/4pitL5tSGNO1fTsHMNOrMVgyPrrMdWrngFX0UBlvxhZzwm4vdQ/NrPMTiy6HvL4yiREMVvPoUSO3n1vb+6jLrPl9Lvtn/qyreiRx2u9XCwukXtMi4KGdF0YMPSxVhsVjSxKL1rkrurqhCdcY6agnPUlDb39bryDg48+0/UbPiQnKm3otHpGfmdv2AbOKb1mJwpt2DK6E3lqtdxF+3AMWzSWd+nz02PMuDun6DV6Sl85Wf4a8rOeOy4n76FKTOXsNfF7l/d2eEx3qMHiIVDDHnwKbQGI+kjLmfv7+4nWF+JuVc/AI4u/j9yptxCWu+BXftm9LCFe6oYletQu4wLJiOa0zTV1lJz9AgGo4mcukoMkZDaJQkRdzRaHUZnDhG/BwCt3tAmZL7iHHs1AP7azq8NMaZno9V17XdfU2Zup8fEwiE0BkPrtJ3eYj9x//G9X5r2bcR/rIj8G9tPucWLFYdqkqL/mQTNaTYuW4IpLQ2AvKpSlasRIn5EQ37CXheBhkqq13+Iq+ALHEMvPetzwu7jezbpLek9UWIblvyhRANeqj97n2BTDcdWvo7ObMWU049YJETFkr+TP/Ph1gCKR55ghE8LatUu44LJ1Nkp/F4v5UWHsaWnY/Z7yWiuU7skIeJGxeK/U/f58XMyaLRkjL2a/nd876zPqf7sPXRmK+kjJ/dAhW2ZMnPp+7VvUbH8RSqWPo/WYGLA3f+Mzmimas1baI1mcqbe1uN1nasVB2u4dWye2mVcEAmaUxzc/iWKEgMgr7pMFgEIcYpe0+4iY/x0Qi0NNO1eh6LEUKJnvtajas1buAt30P/rP0CfZuvBSk/KnTGHrEtvINhYjTmnH3qLnZCrnqo17zD04f9EiUUpX/QczQc2o7dn0O+272AfOFaVWs9k29EmPMEINlPiflzL1NkJiqKwe9MGbOlOUBRyq4+oXZIQcSWtV38cwyaRPWkmwx75DbGgn8LX/qPDFWWNu9ZybMWrZF/+NXpdcbsK1Z5ksGdiGzC6dYqsYvmLOIZNxDHsUqo+nU9L0Q4G3/8fZIy5iqJXftZ63ileRGIK64sS+4JxCZoT6o5V4GlpQaPRkNlYjTkoe84IcTYZ46bjKy8gWNd2Z0jX4W2Uvvc06SOnMODOH6lUXcc8Rw7QtGc9fW89vpy5cdcacq+5F9uAMeRddz86sxXXwS0qV9ne2sLEnsaXoDlh29o1mE8sAsivKlO1FiESQSwSBCByytYZnqMHKX7jl1j6DmfIA79Ao9OpVV47iqJwdNH/0vvqOzFn5QMQamnA4MhuPcbgyCIUh+2mtpY1JPTqMwkaIBQMcqSwAFNaGvpwiKyG5GpoJ8SFCHvadxKORSM0bF+F1mAirfcAAPw1Ryh65WcYM3IZNu/XaA2mM76mv/YowaaebbHSsG0F4eY68q67v/U+gz2DwIml17FohGBDJQZ7Zo/W1RWBcIzNpQ1ql3HeEvfs0kVUuGcX0XAEgKyGKrRduIpZiFRx5KM/EQ36sA8aj8GRRdjTROPO1QRqj9L31ifQmdKIBnwUvvxTIn4PvWfMwXVoa5vXMGXlYxtw8jqb/X+Yh23wBEY+8cfW+3xVxTQfOD5tFWyoJBrwUrl6PgCWvMGt7W4AGravIthcQyx0/JoYT+me1mOzLr0RU0bvNu8fDfg49snL9LnpUXRmS+v9GeOmU/Xpm6AoeMr2EQuHSB/Z9uLUeLH2cB3XDe+ldhnnRYIG2PHZOmzpx6++lb5mQrSVOeEa6r5cTu2WRUR9LWhNFix9h9H3a9/COeb4h3/E10Ko+fj1HseWv9TuNbImzWwTNB3xHSukcsWrbe776nbWpJltgqbuy+V4Sna33nYX78JdvAsA28Cx7YKmcvWbGNJzyLpsVpv782c+TMTbTOWnb2CwZzLkwV9isDnPWqdaNpY0EInG0OsSbyJKo3SlCVESa6qtZf6f/of0zEy00ShXb1qMLpa4c6FCPfY7H8T56A8AOFhYwktvf0ROVkbr42sC+TQpZrXKE0ng2bsncOWgs/eBi0eJF40X2e7NGzEYj7eoyGiqkZARQsStraWNapdwXlI6aBRFoXjfXtKsVgByZNpMCBHHth1NzC2eUzpomuvq8Pu8aDQaUBRZbSaEiGtFdR5c/sTbeTOlg6Zg9w602uPfAqerHmNYOjULIeKXAuwob1a7jHOW2kGzcydWx4nVZnUybSaEiH/byxNv+ixlg8bjasbtajo+bQZkN0jQCCHi3/YEPE+TskFTvG9fa3fmNJ+HtIBP1XqEEKIriuu9NPsSa5o/ZYPmwLbPsTqOb8aUHoe9jYQQoiOJeJ4mJYMm4PPRVFd3ciFAswSNECJxbEuw8zQpGTRlBQeJRiKtt52uxG7BLYRILXuOudQu4ZykZNAc2rmjdbWZKeCT8zNCiIRS2uAjEo2pXUaXpVzQKIpC/bFj6A0GQM7PCCESTygao6wxcX5BTrmg8bhcBAMnd8+U8zNCiER0uNatdgldlnJBU1lWQuyUxplOGdEIIRJQQa1H7RK6LOWCpnDPbiw2GwCGUBCrL3F+KxBCiK8UStDEr7qKCgzG41vMyvkZIUSiKqyToIlLPo8bn/fkX47Nk1hLBIUQ4ivN/jA17oDaZXRJSgVN1ZEjRKMnz89YvS0qViOEEBcmUabPUipoivbubt3kDMDmlRGNECJxFdVL0MSd6iNlGE3Hz89oo1HMfq/KFQkhxPmrbJaps7gSjUTwejyt2wJYfG60KCpXJYQQ56+qRYImrrgaG1BiJ1s2WGXaTAiR4CRo4kxDTU2bhQA2WQgghEhw1RI08aWypBiT2dx6W0Y0QohEF4zEaPTG/yZoKRM01eVHMaWltd6Wpc1CiGSQCNNnKRM0Hldz60ZnukgEc9DfyTOEECL+JcL0WUoETSgYJOg7GSymYOK01xZCiLOpdEnQxIXm+ro2HZtNofj/ixFCiK6QEU2cqKusbHPbKNNmQogk0eiTxQBxoaqspM1CAJMEjRAiSbj8YbVL6FRKBE1TXR2GE61nQIJGCJE8WgISNHEh4PO1rjgDOUcjhEge7mBE7RI6lRJBEwy2DRYZ0QghkkVLQIJGdbFYjHBAgkYIkZx8IQka1fm9HmLKyWaaKAqGUFC9goQQ4iKKKRAIRzs/UEXJHzQeD0rs5HYAxlBQtgcQQiQVX0iCRlVet5tY5OTQUheN/2GmEEKcC5+MaNTlamhAq9e33tbG4vsvRAghzpVMnamsua4Wg9HYeltGNEII0bOSPmhcTY1tgkZGNEKIZKM9sUV9vEr6oAl4vWh1utbbOgkaIUSSifOcSf6giUTCbboCaKMSNEIdeTofOmKdHyjEOYrznEHf+SGJLRZt+4MtIxqhllGGZgbrWyiKpFMccRBG1/mThOgCTZwPaZI+aKKRtif/ZUQj1GTSxBhjaGK4vpniSDpFkXSCEjjiAsV5ziR/0MSiUbS6k1NnupisOhPqM2gURhqaGap3URq1Uxhx4leS/sdRdJM4z5kUCJpYrE3QaGMyRy66n9FgIByJEAqHMRoMZzxOr1EYpm9hiK6FI1E7BREnXuXMxwvRkXifOkv6xQCx06bKlLjPfpGo/Ns24f9yIwBDBvbjn5+YS6YznYZmF4Hg2fvraTUwSO9mlqmcyYYaHJr43zVRxI94/1RL+hFNNBoBTm56pmjj/a9EJKrI0RLqn/ohhsHDccx5hH5XXcd3591HdV09C1espbisHLPJiCXNfMbX0Gign95LX52XqpiFQ2EnTcqZjxcCwKSP7zFD0gdNuxFNnA8xReILlxym4Xc/Rd93AI6759L7uq/x+AP3UN/YxJJPP+NAYQlGgx6bxXLG19BoIF/nI1/nozZq5lAkg7pY2hmPF6ktPS2+p1uTOmii0SiK0rZTc0wT38kvkkek4giNf/4VrrdfwH7Xg2TNnM3cOXfQ7Grhk3Wb2LX/EDqtFrvNetbX6aUL0EtXRUPUxKGIk+rY2Y8XqcVs0GLQxffnWnxXd4E6OkGmSNCIHhatraL5b7+n8pHbafnwdRwGHd+Y/TX+/Qff5rJLxuJye3C5Pe1+KTpdli7IVaYarjdV0FfnAdnuQgAOc3yPZiAFgub0rIlp5ZoFoY5YUwOuV/9K1bzbcM1/Hksswtdvup6f//AJZkydhMfnp7nF3WngOLUhphhrmWkqZ6CuBY0ETkpLl6BRl0ajgdNGMFGdBI1QV8zTQss7L1I17zaaX/ozJr+Hm66dxi9+9AQzr7kSXyBAY3NLp4Fj10aYZKznJtNRhuhc0t4mRdnN8X8GJP4rvECndzWVEY2IF4rfh/sf83EveR/rjbfhuKvEBg4AACAASURBVPthrrtyCtMun8QXu/ayav0WvH4/GQ57m359p7Noo1xibGCk0kRhxElJxEEkuX+HFKdIhKmzpA+a0+fOorrk/5JFggmH8C77CO+KBVhmzMJxzzyuunwiUy8dz679h1i2ZgNNLjdOhw3dWUbkZk2McYZGRuibKY44KIqkE5L2NknPISMa9WlP+8GUqTMRt6JRfGuW4Vv3CWlXXINjziNMGj+GiWNHsb+gmCWrP6Oh2UW6zYpef+YfXaMmxihDM8P0LkoiDgoj6QSS/0c9ZcmIJg7oThvBhA2mMxwpRJyIxfBvWoN/0xrMk67Ece8jjBtzCWNHDqWw9AiLV66jpq4Bm83SaXub4QYXQ/Su1vY2Pmlvk3SccX4NDaRA0Jw+tx0ymlGI/5YNQgAEtm8msH0zpjETsc+Zx/DLruTHjz/M0WNVLFyxhvLKGmyWNEwm4xlfQ6eBwXo3g3RuyqM2DkWcuJUzHy8SS54j/jtHJH/Q6HScer2BotUS1hsxRqSXlEgcwf07Cf5yJ4aho3DMmUf/K6/l+48+QGVNHYtWrqXkSDlpZjNp5jOP2DUa6K/30E/noTJm4VA4g2ZFRviJro8z/jtGaJTO1lAmuLf+9AeikXCb+y7/chU2b4tKFQlx4fT9B+O452EsM2ah0empa2hkyarPOFRcitFgwGrp2odPdTSNgoiTemlvk7BWPXk1Tkt8j1CTPmg+eO6veN0tbabQJuzeQGZTrYpVCXFx6Hr3wXH3g1hvvB2NwUhTcwvL1m5gz8EC9Do9duuZ+6mdqj5q4lAkg5pY144X8cFq1LHuBzPULqNTSR80K955i/KiwxjNJ+cxRx38ktyaoypWJcTFpcvKwXbH/dhuvgutOY0Wj5eVn21m+579aDQa7FZLl/YsaYoZKQg7ORazImcy49+wHBtvz52sdhmdSvqrutKzsgiH256PCRrj/+SZEOci2lCH6+U/UzXvVlxvv4hNiXL3LTfyHz94nKsum4jb6+tSe5sMbYipplpuNFXQX+eW9jZxLhHOz0AKBI0zO5tIqO05mpAEjUhSsRYXLW89T+W822h+5S+Ygz5uuWE6v/jRE9ww7Qp8/gCNrs7b2zi0YS431jHLVM5gnQuttLeJS33SE+OzLOlXndnSne3uk6ARyU7xe3F/9AaeJe9hvXE29rse4oZpU5kx9TK27tzDp+u34A8EcHbS3saqjTDR2MAopZnDkXRKpb1NXMlPT4wRTdIHjdlqRXPaD1LQJEEjUoMSDOJZ8j6eTz7Geu3XsN8zl2mTL+XKSRPYvvcAn6zdiLvFjdNuR3eWPU3MmijjDY2M1DdTFEmnOOKQ9jZxIFGmzpI+aCw2W7vf2PxpNpWqEUIlkQjeVYvxrl5K2lXX45gzj8mXjOOy8WPYd6jonNrbjDY0MfyUfmrS3kY9Q7ITYxO8pP8XYrZY2zXWDJnSCOsNGE67vkaIpBeL4d+wCv+GVZgvvxrHvY8wfvR4xo0aRkFxGYtXraO2vhGHzYrBcOaPB71GYYTBxVB9C2VRO4cj6dLepodlWAzkJkBXAEiBoNFqtRiM7S9m8lkcpLc0qFCREPEh8OVGAl9uxDRuEo57H2HkxCmMGDKQ0qMVLFr1GceqT7S36eDn5ys6jcIQfQuDdC2t7W080t6mR4zoZVe7hC5L+qABMHZwTsZrtUvQCAEE926nbu92jMPH4Jgzj0FTZ/DDxx6goqqGRSvXUlZ+DIvZjPks7W20Ghig99Bf5+FYzMqhsBOXtLfpViN7S9DEFavdjsfV3GbLAJ8lcf6ShOgJocP7qf/vf8YwcCj2e+bSZ9qNfOfhb1Bb38jiVesoKC7FZDSetb2NRgN9dV766rxURdMoiGTQEEuM6Z1Ek0hBk/SdAQA2LFnM/i+3YrGdXASQ2VDNhL2bVKxKiPimz++H/a6HsF5/KxqDgcZmF0tXr2dfQRF6na7L7W3qomYORZzUSnubi2rBt65ImFVnKRE0xfv3sfTN13BmZbXeZwr4uHLrchWrEiIx6LJ7Y7/zAayzvo7WbMbl9rBy3SZ27DsIgMNm7XJ7m0PhDCpjFqS9zYVxmPWs/t50tcvospQImsbaGt7+8zM4MjLa3D9tw0L00YhKVQmRWLTpGdhnfxPbrXPQWm14fX7WbPqcrdt3E1OULgdOS8zAoYiTiqgNRQLnvFzWP4O/3TtR7TK6LCUu8XVkZHZ49bOcpxGi62KuJlxvPEflvFtpfv3/MIcD3HbjNfzHD5/g2qsm4/UHaOpie5vJxjpmmsoZpGuR9jbnYVQCnZ+BFBnRALzym/9Ef9q2tyMPbSOv+ohKFQmR2DQmM9abvo79zgfRZ/ciFAqzZftuVm/aSiAQwulof7F0R/yKjsPhdEqjDqKp8bvvBXv69rFcN6KX2mV0WcoEzfv/9xd8Hnebf/h5lSWMPLxTxaqESAJ6A9brb8Fxz1z0eX2JRCJs33OAFZ9twuPzkW47e3ubrwQVbWt7m7C0tzkjrQZWPjmN9LTEuUA2ZYLms4X/4NDO7aRZT7ZssHhbmPLlKhWrEiKJaHVYpt2Afc48jAOHEovF2H3gMMvWrKfZ5SbdYUOv6zxAwoqGkoiDwkg6wdS4AuOcjOxt582HLle7jHOSMn+L+QMHsXvLpjZB47PYCeuNGCKhszxTCNElsSi+z1bgW78S8+RpOO59hIljx3LJmBEcLCxm8aef0dDYjN169vY2hlPa25RG7RyOOPErKfNR1anJAzI6PyjOpMzfXkbv3u3nizUaXOmZZDdUq1OUEMlIUQh8vp7A5+sxXTIZx5xHGD3hMkYNG0LJkQoWrlxLVU0dNqsFk/HM0z86jcJQfQuDdS0cOdFPTdrbwOX9Ey9oUmbqLBqJ8MJ//gKrve1qjf5HChhSuk+lqoRIDcaR43HcO4+0ydMAKD9WxcJV6zhSUYk1zYzZ1Hm7GkWBiqiVgkjqtrcx6rSs/t40zIbEOoeVMkEDMP+PvycaibRZ65/eXM+luz5TsSohUodh0PDjgXPV9Wi0Wqrr6lm8ch2FpUcwm0xY0jpvV6MoUBWzUBB20qikVnubSf2c/P0bl6pdxjlLqaA5viBgG2nWk61oNLEo0zYsQqfIWn4heoq+T3/s98zFeu3NaPR66hubWLp6PfsPF2PU67F1sb1NbdTMoUgGdbHEaMVyoZ64ehCPXjFI7TLOWUoFTXlRIQtefJ707Kw290/csQ6ndHIWosfpcnKx3/Ugtll3oDGacLW4Wb5uI7v3FaDRanDYuraxV2PMxKGwk6okb2/zyv2TGJefrnYZ5yylgibo9/PSfz+F3elsc//g4r0MKD+sTlFCCLTOLOx3fBPbLfegtVhxe72s3vg5X+zcSywWI91u61J7G1fMwKFIBhVRK8kWOA6znpVPTkOnTbyvK6WCBuDV3/03utPW8ksnZyHig9bmwHbrHGyzv4HO4cQfCPLZli/Z8OUOopEI6XZ7lwLHHdNzOOLkSNSeNP3Ubhubxy++NkrtMs5LygXN8rfe4FhpCcZTVrloo1Gu3rQYXSyqYmVCiK9ozGlYv3YnjjsfRJeZTTAUYtO2nazb9CWBYIiM9K4Fji+m43DESVnUnvDtbf5813iuGpytdhnnJeWCpnD3Lj55dz7pmW3P04zdu5mchiqVquoaw5ARpN//OMbRE9AYTESrj+H55GM8i99D1yuP/FcXn/G5nk/+QdNff33W19dYrDjufZS0K65Bl92LmKuJwK4vaHn7BaJ1NW2OTZs+E8ddD2HoP4iY34f/8/W4Xv0LsRbXyYP0BpyP/RDL9Jko4RDeZR/T8t7LbV5Hl9WL3Oc/pO7n3yN0cPe5f1NEcjMYsd54G467HkKf24dIJMKXu/ezYt0mfH4/Toe9S/3UgoqWwkg6xZF0IgkYOHaTnhVPXo2hC6184lHKXLD5ldwBA9Fq269Br8/Oj+ugMU2cQs4v/0SouICWd19G8fvQ5/VFl90bON5Zt+EPP2/3PPOkK7BeezOBnVvP/gYaDTn//RyG/oPwLP2QyLEj6PP7YbvlbsyXTqX6iXtQ/D4ArDffReaT/0Zg1+c0v/QndFm9sM3+Jsaho6j58VwIH++04LjrIazX3ULLe6+gSbPg+OZjRKor8H22ovVt0x/9Af7P10vIiI6FQ3iXfYR3xQIsM2bhuGceV0yawORLxrJr/yGWrdlIU4sLp93Wbkr8VCZNjLGGJobrXRRHHBRF0gklUD+16UOzEzZkIAWDxpaejsncfu19Q1YuCvF5+lCTZiXrJ7/C/+VGGn7z/45fSHAaJRjAt7b9Rm7WG24l5vXg/3zDWd/DOHIcphFjaHruaTxLP2i9P1JxhMwf/RLzJZPxb1kHej3Oh58ksHc7df/+ZOtxwYN7yHnqz9hu+jqexe8BYJ58Ne5/zMf90RsA6HN6Y54yvTVojKMnkDZ5GtWP333O3xORYqJRfGuW4Vu7nLQrrsFx76NMGj+GiWNHcbCwhMWrPqOhuRmHzYpBf+aPNaMmxihDM8P0LkqjDgrD6fgT4GPwhgTq1NyRxI3I86TRaMgfNJhQINDm/rDRTIsjU6Wqzs5yzU3oMrJxvfEcKAoak/n45uyd0GZkYRp3Gf7Na1pHGWc81nJ8GWm0ubHN/dGmegCUUBAAw4AhaG0O/BvaNiMNfLmRmM+LZfrM1vs0RhMxj7v1dszdgtZ0IuQ1GjIe/2fcH71BtKG2069FCAAUBf/mtdT84AHqfv49wgd2M2bEUP7fk4/wrfvuwpJmpqHZRSgcPuvL6DUKw/QubjIfZaKhDqvm7MeryWbSM2VgfH42dVX8R3k3GDN5KsX79mI8bWRTn5VPekvjGZ6lHvMlk4l5PeiyepH9H89g6DuAmN+Hb80yml784xlDxDJjFhqdDu+6Tzp9j1DhAWJ+H+kPPkHM7To+dZbXj/R53ydYsJ/Azi8A0BiO95pSgsF2r6GEghgGjzgegopCqPAA1pu+TnDPNjRpFiwzZuFe8j4A1pmz0TqcuD9683y/LSLFBXZsIbBjC8Yxl+CYM4/hl13Fj7/9MOWV1Sz4ZA3lldXYLGmYTGfuj6bVwGC9m0E6N+VRGwURJy1x1k9tRoJPm0EKjmgA+gwa3G4TNID67DwVqumcPr8f6HRk//wZAju2UP/rf8G7ahG2W+4m80e/POPzrNfcRLShjuDuLzt9j1iLi4an/w2txUav3/6d/DeW0+vpF4g21lH3syfgxIq8SOVRlFgM4+gJbWvsMwCdMxOt2YzW5gCg5a0X0KAh97n36P3Mq4Qrj+JZ+A4ai5X0h75D8yt/aR0pCXG+Qvt3Uf/LH1D9/Qfwb15Dv/xcvv/o/fz48YfI7Z1DY3ML/tNmME6n0UB/vYcbTBVMNVaToTn78T3p+gSfNoMUHdHoDQay8/LxuJrRnTKf67M68KVZsfi9KlbXnjbNgtachmfphzQ//wcA/JvXotEbsN18Fy3z/06ksrzNc/T5/TEOG437H291eE6nIzFXM6GSAoJL3id8tATjoOHY736IzB/9kobf/vT4MS0u/Bs+xXr9rUTKS/FtWYcuK4eMJ/4FJRxGYzCgMR5fOh5tqKX6+/dhGDAEJRwmUlEGioLzsR8RqTiCf8MqjKMn4HzsR+gys/FvWUfzy3+GSOTiffNEyggXH6LhN/8Pfb9BOO55mNxrbuLxB+6hrqGRJZ+u52BRCUaDHpvlzO1tNBroo/PRR+ejJprGoYiTehXb2zjMeqYm+LQZpOiIBo5Pn3laWtrd35AVf6Oar6apTl2tBeA7MSVmHDm+3XMs194EgHdd+wUCHdHl9iHnt3/Hu3IR7vdfJbD1M1reeZGm536H5eobME+6svXYxv/9NYFtm3A+9iPyX15I79+/RLisGP8XxxccxAK+ky8cjRIuOUykvBQUBX3fAdhuuZumF/6A1uYg56ln8W9ZR8Nvf4p54hQc9z7S9W+MEB2IlJfS+MenqPrWnXiWfki2w8a8e+/gZ999jNHDh9Lc4sbt6fyXyd46PzNMVcwwHiNX6+v0+O5wy5i8hJ82gxQOmkGjRnW4/r6mVz8Vqjm7aGPd8f+efqLedfy21mZv9xzLjJsIl5cRLjrUpfew3nArGoOxNSy+4v98PUCbqTLF56X+v35C5dxbqP3Xb1E591Yan/kFuswsos2NKF7PGd/H+a0f4127nHBxAebLrybmduH+4DVCBftwf/gGlmu+1qV6hehMtKaSpud+R+Ujt9Py0Zs4jHruu+Nmfvb9bzNp/Ghcbg8ut4fOLiXM1gW5ylTN9aYK+ug8QM9denjnhPwee6/ulLJBk2a14cjIIBZr27XZ7cjEa2n/wa2mUNFBAHRZOW3u12Uevx1zNbW53zhiDIY+/fF1cTQDoHNmgUaD5rTfnjQ6/Yn/tr/mIFpXQ3D/TqJ11WisNoxDRxHY9cUZ38N8+dWYRk3A9fr/tX49X61qg+OBevrXKMSFijXW43rlWarm3Ybr7RewKVHuvPlGfv7DJ5g2+VLcXh9NLnengePUhphqrGWmqYIBOjeabg6ciX2dDMzqWlPReJeyQQMwYuIkfG53u/urcgeoUM2Z+TZ8ChxfqXUq66w7UCIRAnu3t7nfMuOrabOOV5tpTCb0fQegdZzsAhs5dgSNVkvatBtPe61ZAISKC85ao/Ph74JWh2fB2x0foNfj/NaPaHn35dZgjDY3oM/rBycuoNX3G0SsSbpoi+4Rc7toeesFKufdRvMrz2IKeLn5+un88sf/xMwZV+ILBGhsbuk0cOzaMJcZ65hlKmewzoWW7tliJFlGMwC6p5566im1i1CLxe5gz5ZNmNLanuwLmG30PVYUNxdvxpoa0OX0xnbDrRj6DUTnzMB+14NYZ8zC/cFrBLaesnGbVkvmD39BuKwIz8fzO3w90+hLyH32TZRgkOCJkIocO4r1uluwTLsBrcOJLicX26yv45gzl8jRUppffAZOjP7s9zyM7ea70GX1wjB0JOkPfgfrjFm45v8d//qVHb6n/Y77MQ4dSeMff9n6OtGGOux3PYRx0FB0Wb1wfONRfGuXEzzLqEiICxYJEzq4B8+SD4g21WMePIKho0YybfIkbFYLhaVltHi8mIzGs/ZTM2pi5On8DNK70QCumJHYRfrUcKYZ+I9ZoxKyU3NHUjpozBYL+7/4HI1W0+YfVFSvx+FuxOI/87mGnhbYtgmiMdImT8Ny9Q1ojCZa3n0J9/uvtjnOPHEKtpvvxv3RG4QO7+/wtfS987HecBvBvdtbg0YJBfGtX4HW4SRt0lQs02eiz8nFt2EVjc/8AsXvb32+1moj7crrsEy/EfOkK1G8bppf+iPe5R93+H5aZybZP3uapv/9LZHystb7lWCAcPEhrNffQtrkqwl8vgHXK89CVJqbih4QjRI6fADP0veJVB/DNGAwg0aNYtqUSWRlOikoPkJzixuT0Yj2LB/4eo1Cb52fwfoWdMRoiRkvuIHn3RP7ctXgrM4PTBAp11TzdF+sWcW21Z9iO22Pmuy6Y4zb30l/MCFE8tBqSbvyOhxz5mEcMoJYLMa+Q0UsXf0ZTa4WHDYr+rO0t/lKRNFQEnFQGEkncB5XkGiADx+bSv+Mru0ymghSPmi87hZe+92v222GponFuHLLUoydtG4RQiQf82VX4bj3EUyjJ6AoCgXFZSz+dB21dQ3YbVaMHVzwfbqoAmVRO4cjTnxK58d/5bL+Gfzt3okXUn7cSfmgAXjvf5/F525pc/EmwNDC3fQ7VqRSVUIItZnGXYrj3kcwT5yKoiiUlR9j0ap1VFTVHG9vY+y8XU1MobW9jbsL7W2enj2W64YnfjeAU0nQAMX79rJs/hukZ7W9AtfqaWbyttUqVSWEiBfG4aOxz3mEtKkz0Gg0VFTVsGjVWsqOHiPNbCbNbOr0NRQFKmNWDoWdNCsdH98/w8IHj05B24WmuYlEggaIRiK8+F+/xGKztXts0vY1ONxNHTxLCJFqDAOGYL9nLpbpM9HodNTWN7Lk03UUFJdhNBiwWrrWrqb6RHubhtPa2/z7rJHcMT55ljV/RYLmhJXvvUPZoQOYT+uD1Ku2nDEHZLmtEOIkfV5f7Hc9hPWG29AYDDQ2u1i2egP7CgrR63TYrF07kV8XNbM/aKdBYyfbamTht6/EqE++yxslaE6or6rknWf/SHrWaUsKFYWpn39CWkCdXkdCiPily+qF/c4HsN50J1qzGZfbw8rPNrNj735Ag8NmPeu1OF6fn1HDhzBx6tXUeoLMGJqcnTEkaE5QFIXXnv41Go2mXQ+0PhVFDC+SrYaFEB3TOpzYZ38T2233orXa8Pr8rNn8OVu27SYWi5Fut3UYOI3NLn763cfIdKZ38KrJQ4LmFDs3fMbmT5Zid2a0uV8bjXDlluUYIrLUWQhxZhqLFdutc7DfcR+69AwCwSDrP9/O+q3bCYfDOB321sAJBkPk9s7h8QfuUbnq7idBc4pQIMDLv/4VVoej3WODSvcz8EjXOiELIVKbxmTCOuvr2O96EH12b0KhMFt37uHT9VvxB4NkOGw0tbj50bceIr93ck6XnUqC5jRrF3zMoR1fYrW3DRtDKMAVW5eji3VPAz0hRBLSG7BedzP2e+ZiyO9HJBJh+94DrFi3iYz0dL73yH1qV9gjJGhO421x8drvfoM9w9nuseEFO+hTVapCVUKIhKbVkT73uzjuehCAWCxGNBrDYEiNTY6Tbx3dBbI60uk/fATBU5pIfqW837Ae3PJICJE0YlFMYy5pvanValMmZECCpkNX3XxLh0Hjt9ipy06+i6mEEN3LNHEKppHj1C5DNRI0Hcjs1ZucPn0Jh9qvMisdNOai7TkhhEgN6fd9S+0SVCVBcwZX3XwrPk/7/Wh8VgfVcbYDpxAifpnGX4Zp9CWdH5jEJGjOoM+gwTgyMol1sAlX6aDRRE9sPyyEEGek0ZD+8HfVrkJ1EjRnoNFouGLWTbhdrnaPhUxpVPQZokJVQohEYpk+E9PIsWqXoToJmrMYOm4CtvR0oh2Mao72H0FY3/neEkKI1KQxmkif+z21y4gLEjRnodVqufaOu/A0N7d7LGIwcmTACBWqEkIkAtsd96Hvlat2GXFBgqYTA0aMJCs3t8MVaBV9hhAwJc++3kKIi0PrzMJxz1y1y4gbEjSd0Gg0XH/XHHxud7vHFK2O0kGjVahKCBHP0h98Aq3FqnYZcUOCpgt69+tP3yFDCQYC7R6r7t0ft619uxohRGoyDBqGdeZstcuIKxI0XXTt1+8m6Otg8zONhsPDJ0prGiEEAM5Hf4hGKx+tp5LvRhc5s7MZNuGSDi/ibHFkUpk3SIWqhBDxxDx5GuaJU9QuI+5I0JyDq2+5jVik/VJngJLBYwkZTD1ckRAiXmjSLGT807+qXUZckqA5B1a7g/FXXoWnpf1FnBGDkaIhqds0T4hUlz73u+h75aldRlySoDlHk2+YicFgItbBBmg1uQNozOilQlVCCDWZxl6K7Zbk35L5fCVM0JSVlfGrX/2KQAcrvy6mBQsW8O67757xcaPJxPV3z8HdwUWcAAXDLyUifdCESB1GExk/+DkajXR1P5Nz3nnH6/Wydu1aCgsL8Xq9mM1mcnNzmT59Ov379++OGgHo168fP/nJTzCZ1D8PMmjUaPoMGkxDdRWmtLQ2jwXSrJQOGsOw4j0qVSeE6EnpDzyBIb+f2mXEtXMOmvfff59oNModd9xBRkYGXq+XkpIS/B1sFNYViqKgKAraTpYD6nQ6bDbbeb3HxabRaLhxzjd54/e/wWg2t/tNpqLvUHrVVZDe0qhShUKInmAcMRb7HfepXUbc0z311FNPdfXgQCDAsmXLuPPOOxk0aBBmsxmHw8GAAQPIzs6mubmZp59+mpEjR7aGQiAQ4Ne//jUDBw7E6XRSVlbGs88+S58+ffjoo49Yvnw5drudF198kbFjx2KxnGzpsmXLFv7xj38wZcqU1udNnTqVaDTK008/TV5eHllZWa3HHzx4kJdeeompU6ei0+lwuVwsXryYxYsXs2nTJioqKujbty9msxk4vm/3ypUr+fDDD9m8eTM+n49wOEwkEmHs2LN3XDWZzWi0GkoO7G83qkGjoTk9m9yqMrSKXGEjRFLSG8j5z7+gy8jq/NgUd07naIxGI0ajkUOHDhGJRC7ojVevXs3111/Pk08+yejRo8nPz2fPnrbTTXv37u3wA99kMjF8+HD27dvX7viRI0diMBiIRqPMnz8fo9HIvHnzeOSRRzAajcyfP7+1G/OWLVvYtWsXs2fPZt68efj9fg4ePNjlr2HSjOtIz8zqsA+a32KncFhqb3YkRDJzfPNRDP0Hq11GQjinoNFqtcyePZvdu3fz9NNP88orr7B69WpqamrO+Y2vueYahgwZQmZmJmlpaYwbN65NcDQ0NFBVVcX48eM7fP64ceM4dOgQ4XAYgGAwSGFhIePGHV9ivH//fhRF4fbbb6d3797k5OQwe/ZsXC4XZWVlAGzdupWrr76aUaNGkZOTw6233to62unq9+PmBx/usA8aQHXeQKp7ydytEMnGMGKsNM08B+d8jmb06NEMHz6cI0eOUFFRQVFREZs2beL2229n4MCBXX6d/Pz8NrfHjh3LypUrW6e39uzZQ15eHtnZ2R0+f9iwYWi1WgoKChg7diwHDhzAZDIxePDx3zCqq6tpbGzkt7/9bZvnRSIRGhsb6dOnDx6Ph759+7Y+ptVqyc/PRzmH6a6s3rlccvV09mzZhN3ZvufZ4eETSW9pJC3g7fJrCiHimNVO9k9/h0Z3zh+fKeu8vlN6vZ4hQ4YwZMgQZsyYwaJFi1i3bh3z5s0DaPNB3dGmYXB8Gu5UNpuNQYMGsXfvXvr27cu+ffu47LLLzliDTqdj9OjRrdNr+/btY8yYMa2LCkKhEPn5+dx5553tnnvqeaCL4YpZX6N4317CoRCG076uqN7A/tGTXXdxVwAAEjZJREFUuXTnOjlfI0SCU9CQ88//JfvMnKOLch1NTk4OoVCo9QPcc0o/sOrq6i6/zrhx49i/fz/l5eU0NTV1ekJ+3LhxFBUVUVtbS2lpaeu0GUBeXh4NDQ1YrVYyMzPb/DGbzZjNZmw2GxUVFa3PicViVFZWdrner+gNBm5/5DF8bneHoyG3I5PSQWPO+XWFEPHFfucDpE2+Wu0yEs45BY3P5+P1119nz5491NTU0NTUxP79+9m0aRMjRozAYDDQt29fNm7cSF1dHWVlZaxdu7bLrz9q1CiCwSBLly5l4MCB2O32sx4/YMAAbDYbH3/8MU6ns8002Pjx47FYLLz77rscOXKEpqYmysrKWL58OS0tLQBMmTKFTZs2cejQIerr61m6dOl5XxCa2as3M27/Oi1NTR0+frTfcOkaIEQC0w4bjXPuk2qXkZDOaerMaDTSp08ftm7dSmNjI7FYDIfDwaWXXsq0adMAuP3221m0aBEvvPAC2dnZ3HDDDcyfP79Lr28ymRgxYgT79+/n9ttv7/R4jUbD2LFj2bx5M9OnT2/zmMFgYN68eXz66ae8//77BINBHA4HgwYNar3o88orr8Tj8bBgwQI0Gg2XXHIJo0aNOu+wGXfFlZQe3E91+RHSrKdd86PRcHDk5fz/9u49tq3zPuP4c87hnRRFUhIpiZIl6+KLZFt2YseX2Ens2EkaJ2nWLFvWoHPmBVs3bAM6DNsf7YB0GzAsGIamXVGsu3Rz1g7YmiZN21y2ZLnHS+I4TnyTbUmWrLtokSLFO8857/6Q47ixKcu2DkmRzwcwHMSM8Asi5Uue877v2XToFVhymev6+kRUHLrDhYZv/C3vy1wnSVzLnW+6qkwqhQNP/jVMFjMU0+XflJ7IFHo+eZv3a4iWCAGg7pvfhn3jtmKPsmQtmbPOlgqr3Y69j+1HPHr5Cc8AMOP140wH99cQLRWuh36TkblBDI0BGltacfMdu/IevDkWbMNIsL3AUxHRtZLX3ATvPt6XuVEMjUG23HUPagL1yOQ5A+5MRw+mvYECT0VEC6UGgmh44luQFJ7GfqMYGoPIsoz7H9sPVVWvvJdIknC8ezMSjvlX1hFR4WXtTjT/zfch2xd3z12lYmgM5HRX44u//TuIz8xccX+NZjLj6JptyJksV/iniagYVMWE+r/6Lkx1vOKwWBgagzW2tGLXQw8jFr7yIwNSDheOdW+BzocmERWdDgnOP/pzOFfNv1mcrg1DUwDdmzZj3bbteRcHzHjrcLpzQ4GnIqLPE7/6GPy79xZ7jLLD0BTIjvseQOPyNiQvOZ7nUuONyzHQ2lXgqYjoU6ktO9H6W1xhZgSGpkBkWcberzwGm8OJbJ6TB4ZaV2OoeUWBJyOi2eWr0PmNJ4s9RtliaArIYrXiod/9fai5HLQ8D44baF/LPTZEBTRTvwwr/+5fLnskOy0ehqbAqjwePPj4VxGPRvM+9+ZMRw/G61sKPBlR5ZmubcDKbz8NxcKVn0ZiaIqgftky3P3Io4hOT185NpKE3pU3Y9LfdPmfEdGiCHnqsOKpf4PF6Sz2KGWPoSmSFes3YNeXHkY0nD82J1dtQqimofDDEZW5qSofWp/8Jzg8vmKPUhEYmiJas3kLdux9IO8nGyHLON69mc+xIVpEUy4vAn/5HfiCwWKPUjEYmiLbsON2bNlzd94NnUJWcHTNVkz7+OhYohsVcnoQ+IvvoKlzZbFHqSgMTQnYdOcerN9xe96nc+qKCUfXbMWEv7nAkxGVj5DTg7pvPoWmlauKPUrFYWhKgCRJ2H7vfejaeEve0wOELOPk6k0YDnYUeDqipW/MXYu6J55C8+ruYo9SkRiaEiFJEnb+ykPoWNuD+MyVH5oGSUJfZw8GlvOHhWihBmoa0fjEt9DcxZ+bYmFoSogkSdjza4+gtasLsTyfbABgqGUVeldsAB8GTZSfAHC8vg2tX38SzbxcVlSSyLdrkIpGCIHXnn0GJw69B7c3//LLutAoVp94H4rQCzgdUenTJRmHgyux4Y+/jlZGpugYmhIlhMB7r7yMD179H7h9NXmPx/BEprD22EGYtCsfaUNUaVTFhP9b1o2tf/AnaF21utjjEBiakvfJu+/g9Z8+i+oaX97YOBIxrD12EI7UlU+GJqoUaYsN77Wtx56v/RkCTVylWSoYmiXgzCcf46UfPY0qjwdynueXm3JZdJ38ADXhiQJPR1QaYvYqfLR6Mx742p/C7eOO/1LC0CwRI/19eO6fvw9nVRUUk+nKLxICy8+eQMu5XvAcWqokwzWNGFq3FQ/+3h/C7nQVexz6HIZmCQmNjeLH3/t7WGxWmC3WvK+rC41iVe8h3rehsqdJMo4FVyC3cTvu27cfZp7CXJIYmiUmOj2NZ/7hu8hlMrC78r9zm7tv8y4cqUQBpyMqnJTNgfeauxDYeht2P/wIZJm7NUoVQ7MEZdNp/PzADzA2NAi3x5P3dXP3bd5HTXiygNMRGe+8L4CD/nbcdM+92Lz7bj60rMQxNEuUEALvvvQCPnz9f+H2+fK/mxMCLedOoXXwBGT+p6YlTgDob16B494g7vqNR7Fi3fpij0QLwNAscf3Hj+HFHx6A3emc9/q0azaCrpMfwJmcLeB0RIsna7bicGs3EvXL8ODjX4WntrbYI9ECMTRlIBIK4dl//N5V79vImob2gaMIjvZzVRotKaGaBhzytyG4bj32/PqXYbHmXwxDpYehKROZVAq/ePpfMT40iKp57tsAgDc8idW9h2DNpgs0HdH1URUTTrWtwWlbNW67/4vo2baD92OWIIamjOi6joMvv4jDb7wGV3V1/v02mFsosPL0YfhDowWckGjhIp46HGnpRsbqwP37H0d987Jij0TXiaEpQ2NDg3jhwA+QU3NwuqrmfW1gYgidZz6GWcsVaDqi+amKCX1ta3Da4YO/qRn37dsPu9NZ7LHoBjA0ZSqbTuPVZ/4LfUc/RpXXO+8eA0smhfb+o6ifGi7ghESXm/YFcLxtHcKZHDbduRu33HkX98eUAYamzPUfO4r//s//gKIosDkc877WMxNC5+kjcCVjBZqOaE7GYkN/2xr02z1wuFy4b99+1DY0FnssWiQMTQVIxmfx4r8fwMS5Ibg8nnlvpkq6jqaRPrQOneQRNmQ4TZIx0tSBgWAHookk1m7Zhu1774fJbC72aLSIGJoKIYTAJwffwVs/fx42h+Oqy0MtmRQ6+j5BIDRSoAmp0oRqGtDXsQ7TWR1Whx1feHQfGlpaij0WGYChqTDR6Wm8+MMDOD8xBle156rXv72RKXSeOcKNnrRo4g43+jrWIVTlQyIWw7pt23HrF/byU0wZY2gqkBACpz/+CK8/9xPougZnlXve10u6jsaxAbQO9cKSyxRoSio3OZMFZ1tXY6SxDbPRKFzuatz7lX3wB5uKPRoZjKGpYNl0Gm//4mc4fuh9OFzOeR89AACypqJ5+Ayah89wOTQtmCYrGGtcjsGW1YhlMhC6wJa778G6rduh5HmQH5UXhoYQnprESz96GuGpKVRdZbEAMLfZc9m5UwiO9sOkawWakpYaVVYwFmzDueYVSEJCMh7Hyg03Y8feB7gvpsIwNARg7nJa7+EP8ebzz0EXOpxV82/0BABzNoNlw6cQHB2AwuDQBapiwmiwHcNNnciYzIjPzKC2oRF3PfJl+PyBYo9HRcDQ0C/JpFJ4+4Wf4eSHH8BitV117w0AmLNpLBs+jcaxs1wSXcFyihmjTXOByZnMiEdjsNqs2Pmlh9HW1c0zyioYQ0NXFItE8Obzz+LsyZOwu5wLOi1XUXNoGB9E02g/7Gk+2bNS5ExmjDR1YCTYAdVsQSIWgxACG+/YhZtu38nVZMTQ0PzCU5N47Sc/xtjQ4IIWDAAAhEDN9DiaR/rgnQkZPyQVRczlwWiwHVP+JmiygsTsLACBNbdsxaZdu3kfhi5iaGhBJkeG8cZPn8XkyDAcLte8D1m7lDMeRdNoHwKTw7yPUwY0WcGUvwmjjW2YdfsghEAiFoMkSVi7ZRs27rxzQZdbqbIwNHRNLg2O3bmwS2rA3Eq1xvGzaBwbgD2dNHhKWmxJuwtjjcsxXt8K1Wy5JDAyem7djptv3wmr3V7sMalEMTR0XSZHhnHw5ZcwOtAHRVFgd7kWdrNXCFTHphGYHEbd1Agsatb4Yem6aJKM6Zp6jDW2IeL1A5IEIQTi0SgURcH6Hbfhph13wGKzFXtUKnEMDd2QRCyKw2+8geOH3oeazcDlufqxNp+SdB2+8AQCk8OonR7npbUSoEsywj4/puqacL62EZpp7kZ+LptFcnYWdpcLG7bfhrVbb+XjlGnBGBpaFGouh1NHDuODV1/BbHTmmu7jAHMr1mrPj6F+8hw8kRBk8NuyUHRJQsQbwJS/CaFL4iKEQDI+C13TEWhehs2770KwrZ3Ph6FrxtDQohJCYHxoCAdffgHjQ4NQFAWOqqpr2kNhzqZRMz0BX3gSvsgkzCqPu1lsuiQh4vFjyj/3yUU1f/amQFNVxKMxWGxWdG/ajPXbd8BV7SnitLTUMTRkmFgkgqMH30Hv4Q+RSsRhsS1sA+gvEQLuWBi+8CRqwhOomo2A2/6unQAQd1Yj4vUj4vUjWl0LzWT67M8vfHrRchp8AT827dqDtq5uKJe8huh6MTRkOCEEQmOjOPL2WxjsPYFMOn1NK9YuZc5m4I1MoiY8CW9kCtZs2oCJy0PK5rgYlojHj9zn9kAJIZBKxKHmVNidTqzo2YA1m7fCW1dXpImpXDE0VFCapmF0oB9H3n4To2cHoGazcFVXX/c7Z2s6CXcsDPdsGO5YBK7ZSEUe9KlLMhJON2ZdHsTcPkS8dUjbXZe9bi4uCai5LOwOFzp71qP7ls3w+QM8IoYMw9BQ0eSyWQyePIEj77yF8+PjUHPZi5fXrvt/ekLAmYhdjE9VLAJnMga5jL7NNVlB3FWNWZcH8SoPZl1eJJxuiDw36T/95JLL5mB3OtG5tgfdm7egJlDPuFBBMDRUEjRVxcTwOZz66EMMnepFIj4LoQu43O4bvk8g6Tps6QTsqQTsqTgcqTjsqTjsyThs6WRJrnDTJQkZqx1pmxNpmwNpmwNJuwtxlwdJRxUwTyCEEMim00gnk1BMJrg8XrSuXIWujZtQU9/AuFDBMTRUkuLRGQydPo3ew4cQGhuFms1CMZtgd7oWdXntZxGKw5ZOwaxmYc5lYM5d+nsWplx2UR72pksSciYLVLMFqsmCnMmMnMV6ISZzUUnZHMhYHfPG5PNy2SyS8VnIsgyHqwrBtnas6NmAhpZW7tinomNoqORpmobQ6AgGTpzA8JlTiIbDyGUz0DUNVrsdVru9IO/SJV2HSc3CpKoABKQLPzqS+PSv536XAEAIQJKgyQo0xXTxl74IT5TUdR3pZALZTBaKosBqs8Hf1IyVG25CU3vHVR/NTVRoDA0tOZ8egzI5MozBUycxMTiIeHQGmqZBCB02uwNmq7UsLhFpmoZ0IgE1l4OsKLDYbHB7fQgub0NzRydq6uvhdFeXxb8rlS+GhsqCpmmYOR/CxNAQRs/2Izw5iVQijnQyBV3XoGsa5Avv/kstQpqqIptJI5vOAJIERVYAWYLVZoOntg5N7e0ItnWgJhCA3Xn5SjKiUsfQUFnTNA2JaBSxSBih8TGERkcwPTmBVDyObDoNXdchhPjsl65BVkwwmU0wmcxQzOaL94QWEqe5r6FD0zToug5dU6HmVGiqCkCCLEuQJBmSLM2tsLM74PJ4UBOoR21DIzw1tXBWV8NZVcXNklQ2GBqqWEIIqLkcMqkkMuk0MqkU0skEZiMzSMzGEI/OIBGLIZvJABdCBAACAkIXF78GhIDAXIiUC5e3rDb7haXaTriq3XC6q2F3OmG98Peci7CajmipYGiIiMhQPIaViIgMxdAQEZGhGBoiIjIUQ0NERIZiaIiIyFAMDRERGYqhISIiQzE0RERkKIaGiIgMxdAQEZGhGBoiIjIUQ0NERIZiaIiIyFAMDRERGYqhISIiQzE0RERkKIaGiIgMxdAQEZGhGBoiIjIUQ0NERIZiaIiIyFAMDRERGYqhISIiQzE0RERkKIaGiIgMxdAQEZGhGBoiIjIUQ0NERIZiaIiIyFAMDRERGYqhISIiQzE0RERkKIaGiIgMxdAQEZGhGBoiIjIUQ0NERIZiaIiIyFAMDRERGYqhISIiQzE0RERkKIaGiIgMxdAQEZGh/h9cBo7N1ZJmoAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21RetFGsSc-o"
      },
      "source": [
        "**ALL CONTINOUS VALUE**S"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjozAY70SZF2"
      },
      "source": [
        "Age Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XECnCvEaOD-t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "1425a855-7280-41ad-d22b-4fec059be8bc"
      },
      "source": [
        "plt.figure(figsize = (15, 6))\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "sns.distplot(HF['age'])\n",
        "plt.show()\n",
        "\n",
        "#Check for outliers\n",
        "sns.boxplot(HF['age'])\n",
        "\n"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb83332fcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEJCAYAAACqmv3eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANbklEQVR4nO3df2jV9R7H8dc5Z7/zuh/nTGWbqUuLqBBCzUpauflHMxEaCHkjRkS0lVFSMAjEymhEtTGbTKKyzIpouFCEYGaDMmvpiWSG5KVEt9p23Bxn7vf2uX/IRuMG1x87571993z8ZcftfD5vjt+n53xy5/icc04AgLjzW28AAGYrAgwARggwABghwABghAADgBECDABGEq72G9ra2q5poVAopEgkck3fO915eTbJ2/Mx28w1k+bLycn5x9t5BgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGDkqj8TDlemvr5era2t1tu4bp2dnfL7/QoGg9ZbiYmlS5equLjYehuYpQhwjLS2turcmd803zdmvZXrMuAuv0j618WZ8eGHV6Pd+ZWYmGi9DcxiBDiG5vvG9O+kQettXJd9Q8mSNOPn+CfjswFWOAMGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwnxWKS+vl6pqakqLi6Ox3IAMGXq6+slSSUlJVN+33EJcGtrqxITE+OxFABMqdbW1pjdN0cQAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYCQhHot0dnZqeHhYNTU18Vgu7hITEzU8PDzptvPnzyvR+Yx2hCvR7XyKnD07q/5cekm85jt//rySk5Njct//N8CNjY1qbGyUJFVWVsZkEwAwG/3fABcVFamoqOi6FsnOzlZiYqLKysqu636mq1AopEgkMum2mpoaDf3ntNGOcCUyfU43LFo0q/5cekm85ovlKyTOgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwnxWCQ3N1epqanxWAoAplRubm7M7jsuAS4pKVEoFFIkEonHcgAwZUpKSmJ23xxBAIARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABghAADgBECDABGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4ARAgwARggwABghwABgJMF6A17W7vzaN5RsvY3r0u4u/x090+f4J+3Or3zrTWBWI8Axkpuba72FKZHS2Sm/36+kYNB6K1NuoaRFixZZbwOzGAGOkZKSEustTJlQKKRIJGK9jZjw8myY/jgDBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcAIAQYAIwQYAIwQYAAwQoABwAgBBgAjBBgAjBBgADBCgAHACAEGACMEGACMEGAAMEKAAcCIzznnrDcBALNR3J4BV1RUxGupuPPybJK352O2mcsL83EEAQBGCDAAGAls3759e7wWy8/Pj9dScefl2SRvz8dsM9dMn4//CQcARjiCAAAjBBgAjCTE6o7HxsZUUVGhrKwsVVRUqKOjQ9XV1YpGo8rPz9eWLVuUkBCz5WPq6aefVkpKivx+vwKBgCorK9Xb26uqqip1dnYqOztbzz//vObMmWO91at26dIl1dXV6dy5c/L5fCorK1NOTs6Mn62trU1VVVUT/93R0aFNmzapoKBgxs827uDBg/r666/l8/m0cOFClZeX6+LFi5647g4dOqTDhw/LOafCwkKtX7/eG9eci5EDBw646upq9/rrrzvnnHvrrbfct99+65xzbvfu3e6rr76K1dIxV15e7np6eibdtnfvXrd//37nnHP79+93e/futdjaddu5c6drbGx0zjk3PDzsent7PTPbuNHRUffEE0+4jo4Oz8x24cIFV15e7gYHB51zl6+3I0eOeOK6O3v2rNu6dasbGBhwIyMj7pVXXnF//vmnJx67mBxBXLhwQSdOnFBhYeF45NXS0qLVq1dLku6//341NzfHYmkzzc3NKigokCQVFBTMyPn6+vr066+/au3atZKkhIQE3XDDDZ6Y7e9OnjypBQsWKDs721OzjY2NaWhoSKOjoxoaGlJGRoYnrrvW1lYtXbpUycnJCgQCuvXWW/XDDz944rGLyWuRPXv26NFHH1V/f78kKRqNKi0tTYFAQJKUlZWlrq6uWCwdN6+99pokad26dSoqKlJPT48yMzMlSRkZGerp6bHc3jXp6OjQ3LlztWvXLp09e1b5+fkqLS31xGx/99133+nee++VJM/MlpWVpQ0bNqisrExJSUlavny58vPzPXHdLVy4UJ999pmi0aiSkpIUDod10003eeKxm/IAHz9+XOnp6crPz1dLS8tU3/208OqrryorK0s9PT3asWOHcnJyJv2+z+eTz+cz2t21Gx0d1e+//67HH39cy5Yt0wcffKCGhoZJXzNTZxs3MjKi48ePa/Pmzf/zezN5tt7eXjU3N6u2tlZpaWl6++239fPPP1tva0rk5eVp48aN2rFjh1JSUrR48WL5/ZNfvM/Ux27KA3z69Gn99NNPCofDGhoaUn9/v/bs2aO+vj6Njo4qEAioq6tLWVlZU7103IzvPT09XStXrtSZM2eUnp6u7u5uZWZmqru7W3PnzjXe5dULBoMKBoNatmyZJGn16tVqaGjwxGzjwuGwlixZooyMDEnyzGwnT57UvHnzJvZ/11136fTp05657tauXTtxNPbJJ58oGAx64rGb8jPgzZs3q66uTrW1tXruued0++2369lnn9Vtt92mY8eOSZK++eYbrVixYqqXjouBgYGJo5WBgQH98ssvuvHGG7VixQo1NTVJkpqamrRy5UrLbV6TjIwMBYNBtbW1Sbp8Uefl5XlitnF/P36Q5JnZQqGQfvvtNw0ODso5N/HYeeW6Gz9eiEQi+vHHH7VmzRpPPHYx/Um4lpYWHThwQBUVFWpvb1d1dbV6e3u1ZMkSbdmyRYmJibFaOmba29v15ptvSrr8kn3NmjV6+OGHFY1GVVVVpUgkMnP/SYykP/74Q3V1dRoZGdG8efNUXl4u55wnZhsYGFB5ebneeecdpaWlSZJnHjdJ+vzzz3X06FEFAgEtXrxYTz31lLq6ujxx3W3btk3RaFQJCQl67LHHdMcdd3jiseNHkQHACD8JBwBGCDAAGCHAAGCEAAOAEQIMAEYIMAAYIcAAYIQAA4CRmffOzJh1GhoadPjwYfX09CgYDOqRRx7RqlWrNDY2po8//lhNTU1KSUnRhg0b9P777+vTTz9VIBBQX1+fPvzwQ4XDYfl8Pj3wwAPatGnT/7yRC2CFAGPamz9/vl5++WVlZGTo2LFj2rlzp2pqatTc3KxwOKw33nhDycnJkz7xQpJqa2uVnp6umpoaDQ4OqrKyUsFgUOvWrTOaBJiMpwKY9u6++25lZWXJ7/frnnvu0YIFC3TmzBl9//33Ki4uVjAY1Jw5c7Rx48aJ77l48aLC4bBKS0uVkpKi9PR0rV+/XkePHjWcBJiMZ8CY9pqamnTw4EF1dnZKuvymOtFoVN3d3QoGgxNfFwqFJn4diUQ0OjqqJ598cuI259ykrwesEWBMa52dndq9e7e2bdumm2++WX6/Xy+++KKcc8rMzJz0CQ+RSGTi18FgUAkJCXrvvfcmPhECmG44gsC0Njg4KJ/PN/Fm20eOHNG5c+ckXT6aOHTokLq6unTp0iV9+eWXE9+XmZmp5cuX66OPPlJfX5/Gxsb0119/6dSpUyZzAP+EZ8CY1vLy8vTQQw/ppZdekt/v13333adbbrlFklRYWKi2tja98MILSk1N1YMPPqhTp05N/CuHZ555Rvv27dPWrVvV39+v+fPnTzonBqzxfsDwjHA4rHfffVe7du2y3gpwRTiCwIw1NDSkEydOaHR0VF1dXfriiy+0atUq620BV4xnwJixBgcHtX37drW2tiopKUl33nmnSktLJz5uCJjuCDAAGOEIAgCMEGAAMEKAAcAIAQYAIwQYAIz8FwZR54GLC/yFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NCZyNCtYbkud"
      },
      "source": [
        "CPK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcD9bkuVNxIk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "db52590c-d156-4b33-9780-3a5257aef5c5"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"CPK\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"rocket\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")\n"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb83351c610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUWNMDDAS4_W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "1f9a9c46-909e-4ff0-c2c5-d6f9edae565b"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['CPK'])"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb833510f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7DRPZQebGw2"
      },
      "source": [
        "Ejection Fraction (EF)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R_9_MBvax_H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "e8685145-fbd1-43e9-edf9-2be1c1e39137"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"EF\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"viridis\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84177f4d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaSQdgHUa7uc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "4d92d997-4276-49c0-99c0-14d0df885cd6"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['EF'])"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8418188d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHVYkHtubL78"
      },
      "source": [
        "Platelets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHu0BAbATLUM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "6af96b5e-3afe-4a14-bbf9-dbc519a22118"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"platelets\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"magma\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84316f0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgaEAPjPT3fK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "8dccfcda-4341-4d44-86fb-42aa8c20fd0e"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['platelets'])"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84c9b4190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-89jcT1bRD_"
      },
      "source": [
        "Serum Sodium"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaSbtX2BUK9K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "24c2dc7e-e369-454e-f864-bbc99b83716d"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"serum_sodium\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"rocket\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8446f5610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRoBsKJCUfwS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "700e8a6a-aa3f-4fcf-b6ae-e5bb2be71364"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['serum_sodium'])"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84182d710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Z42aeekbXq-"
      },
      "source": [
        "Serum Creatinine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iVmXR_pUXZH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "f9368108-558b-46e8-d038-ee27f63cd879"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"serum_creatinine\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"mako\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84177fb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DhuCmm27CF198gVdeeQVvvPEGhg0bhoqKClxyySUwGtlNkUqsKRBRKsW989rx48exdetWbN26FQaDAdOmTUNRURHWr1+PHTt24I9//GMq4qSTEh1SypoCEfUk5qSwfv16bN26FfX19Rg7dizmzp2LoUOHho9fdNFFuO2221ISJP2ANQUiSqWYk8KePXswadIklJeXIysrq8txi8XCWoIKEp6nwJoCEfUg5g6A4cOH4+KLL+6SENauXRv+edSoUcmLjLrF5iMiSqWYk8Jbb70V1/uUGpzRTESpFLX5aN++fQAASZLCP7c7ceIEcnJyUhMZdYtDUokolaImhWeeeQYAEAqFwj8DgMFgQN++fXHrrbemLjrqItGHOjuaiagnUZPC8uXLAQDLli3D3LlzUx4QRSZJMmQpsaWvJUmGJMkwmTifhIi6ivnJwISQfsla9ppNSEQUSY81hbvvvhtLliwBAMyaNSvieR2blSh1kjWcNBSSkJPddVgxEVGPSWHmzJnhn++4446EbrRnzx6sWrUKsixj/PjxmDJlSpdztm/fjjfffBMGgwE//vGP8Yc//CGhe2aaZCWFAPsViCiCHpPCsGHDwj8PHz681zeRZRmVlZV46KGHYLPZ8MADD6C8vBxnnHFG+Jz6+nqsWbMGjzzyCPLy8tDS0tLr+2WqREceteOwVCKKJOY+hbVr1+Lw4cMAgIMHD2LWrFmYM2cODh48GPWzNTU1KC4uxsCBAyEIAsaOHYvq6upO53z44Ye4+uqrkZeXBwAoKCiIoxinh2T1BbBPgYgiiTkprFu3LrxE9uuvv45JkybhV7/6FV588cWon3U6nbDZbOHXNpsNTqez0znHjh1DfX095s+fjwcffBB79uyJNbTTRrKajzgslYgiiXntI6/XC6vVCp/Ph8OHD2P+/PkwGo14+eWXkxKILMuor6/Hn//8ZzidTvz5z3/G448/jtzc3E7nVVVVoaqqCgCwaNEiFBUVJeX+ahAEIaF47Y0irNbmhOPIycntdRyJlkErMqEcLIN2ZEo5gDiSgs1mw9dff42jR4/ivPPOg9FohNfrjWn/hMLCQjgcjvBrh8OBwsLCLuecc845EAQBAwYMwI9+9CPU19fj7LPP7nReRUUFKioqwq8bGxtjLULaFRUVJRRvQ6MDXq834TgaGpvQ2Ni7meiJlkErMqEcLIN26K0cgwYNings5uajm266CU888QT+8Y9/4Ne//jUAYPfu3V0e2t0pLS1FfX097HY7RFHE9u3bUV5e3umcCy+8EPv37wcAtLa2or6+HgMHDow1vNNCKJSkjmb2KRBRBDHXFMrKyvDss892em/MmDEYM2ZM1M+aTCbceuutWLhwIWRZxrhx41BSUoLVq1ejtLQU5eXlGDVqFPbu3Yu7774bRqMRN910E/r06RN/iTKYKCZpSGpATMp1iCjzxLXzmtfrxbFjx+D3+zu9P3LkyKifLSsrQ1lZWaf3pk2bFv7ZYDDglltuwS233BJPSKeVZNUUOPqIiCKJOSn885//RGVlJbKzs2E2m8PvGwwGLFu2LCXBUWfJephzngIRRRJzUnj99ddxzz334Kc//Wkq46EeJKv5iDUFIook5o5mWZa5s1qaJav5SBRlyHJyrkVEmSXmpDB58mS89dZbfJikUTJHDXH9IyLqTszNR+vWrUNzczPeeeed8FIU7bhKqjqStXQ2AAQDXCmViLqKOSkkukoqJUYUJShyYhvsdMR+BSLqTsxJIZFVUilxwST1J7QLBDlXgYi6ijkphEIh/P3vf8e2bdvgcrnw0ksvYe/evaivr8c111yTyhgJyZ+FzEXxiKg7MXc0v/TSSzh69CjuvPNOGAwGAEBJSQk++OCDlAVHP0h2UmBNgYi6E3NN4dNPP8XTTz+N7OzscFIoLCzssgQ2pUayhqOGr8eaAhF1I+aagiAIXYajtra2cn0ilSS/psCkQERdxZwUxowZg2XLlsFutwMAmpqaUFlZibFjx6YsOPpB8vsU2HxERF3FnBRuvPFGDBw4EPfeey+8Xi/uvPNO9OvXD9dff30q46OTgkla4iJ8PdYUiKgbMfcpHD9+HIMGDcIvf/lLyLKMCy+8EGeeeWYqY6MOxKQPSWVSIKKuoiYFRVHwzDPPYPPmzbDZbOjXrx+cTif+/ve/4/LLL8esWbPCHc+UOhySSkRqiJoUqqqqcODAASxcuLDTLms1NTV46qmnsHHjRkycODGlQVJyl7gA2pKMLMsxbadKRKePqE+ELVu24He/+12XbTfPPvtsTJ8+HVu3bk1ZcPSDVCxLwSYkIjpV1KRQV1cXcYmL4cOHo66uLulBUVep2FeZ23IS0amiJgVZlpGTk9PtsZycHC6lrZJUJAX2KxDRqaL2KUiShH379kU8zqSgjmTPaAYAP2sKRHSKqEmhoKCgx/0S8vPzkxoQdS/RPgVJUeCVRCgKYDEZYTGa2HxERF1ETQrLly9XIw7qQSJ7KchQ0BgIoDEQhKz8cI0+WQLO9AaSFSIRZQiOR9SB3rb9y4qC7zxe2P2BTgkBAFwhEZ/aHTju8ycjRCLKEEwKOtCb/gQFCr7zeuEWIzcRBYIS/tXYCLufiYGI2jAp6EBv1j1qCAR6TAgAIAUlSIqCfzU60BIK9TY8IsogTAo6EO/eB15JRIM/en9B+3pKIVnGx40OSEry9oAmIn1iUtCBeEYeKVBwzOdHLI93qUOyaQmFsKepuRfREVEmYVLQgXgmrjmDQfil2M6XRBmy9EN/xTduNxoCHJFEdDpTLSns2bMHf/jDH3DHHXdgzZo1Ec/75JNPMHXqVHzzzTdqhaZ5sdYUJEWJqdmo02c6dGIrAHY6m9iMRHQaUyUpyLKMyspKzJs3D0uWLMG2bdu6XTPJ5/Ph/fffxznnnKNGWLoR6+gjZzAIMc4HunhKf0VrKIQalzuuaxBR5lAlKdTU1KC4uBgDBw6EIAgYO3Ysqquru5y3evVqTJ48GVlZWWqEpRuxNB9JigJHL5p+pGDXhLO/tRWBGJugiCizxLzzWiKcTidsNlv4tc1mw6FDhzqd8+2336KxsRFlZWV45513Il6rqqoKVVVVAIBFixahqKgoNUGngCAIvYrXYnHCag32eI7d54NBEBBvOhWMWbBarV3ePwpgTDex9rYMWpMJ5WAZtCNTygGolBSikWUZL7/8MmbPnh313IqKClRUVIRfNzY2pjK0pCoqKupVvA5HE7xeX8TjChQcc7kR6sXihO5WD7K9Xf8afObz4UeKglyh87HelkFrMqEcLIN26K0cgwYNinhMleajwsJCOByO8GuHw4HCwsLwa7/fj6NHj+K//uu/MGfOHBw6dAh/+ctf2Nl8UrSOZldIRLCXq9VKEforZEXBgZbWXl2TiPRLlZpCaWkp6uvrYbfbUVhYiO3bt+POO+8MH7daraisrAy/XrBgAW6++WaUlpaqEZ7mRVv7yBHsuWmpJ6d2NHdU6/FgWH4f9GEfD9FpQ5WkYDKZcOutt2LhwoWQZRnjxo1DSUkJVq9ejdLSUpSXl6sRhi4pitLj/swBWYInynIWPekpKSgAvmp14ee2wojnEFFmUa1PoaysDGVlZZ3emzZtWrfnLliwQIWI9CEkyj0um90UTGzNIjHQcy3ksNeL4QX5XfoWiCgzcUazxvW07pEMBc0JNB0BPdcUgLa+ha9aXQndg4j0g0lB43rqZHaFxLgnq51KkRWIUTqyaz2emJfOICJ9Y1LQuJ46mZtDidUS2kVrQpIUBYc4y5notMCkoHHBUPedyKIiwx3hWLyiNSEBQI27d/MgiEhfmBQ0LlJNoSUUiml57FhIUWoKABCUZdR6PEm6IxFpFZOCxkXqU2hOcNRRR7HUFADgoMvdZa9nIsosTAoa193oo4AswZfEjt9QDDUFAPCIIr5j3wJRRmNS0LhANw/sZO+nHK2juaN9Tc6k3puItIVJQeMCwa6dyS1J6mBuF2tNAQAafH40cnc2oozFpKBxp3Y0+yQp6XsdiP74ksxBNiERZSwmBY07taaQ7KYjoG2vZqmH9ZVOVef1JrTeEhFpF5OCxp1aU2hNQVIA4utXUMDaAlGmYlLQMEmSIXb4Bu+TpF7vmxBNKBDfN/9aj4eT2YgyEJOChgVOqSWkoumoneiPr58ixMlsRBmJSUHDgqf0J6Sq6QgAQnF2NgOczEaUiZgUNKxjf0Iqm46A3iUFjyjie1/kvaOJSH+YFDSs48ijVDYdAUDQ17vRRF+7uNcCUSZhUtCwYIcRQalsOgKAUJx9Cu0cgSAcnMxGlDGYFDTMf7KmkOqmIwCQQlJccxU6+oq1BaKMwaSgYYGT7fypriW0602/AgB87/XBzclsRBmBSUHD2oekqpYUetmvoAD4mvs4E2UEJgUNCwRE+CUJAZUmiQW9vU8+tR5P0tdkIiL1MSlomD8gpnzUUUe9HYEEnNzH2c2lL4j0jklBwwIBEa2iiknBm1i/QI2L+zgT6R2TgkaJogx3IISApN5DNuhLLAEFuPQFke4xKWhUQOWmIwCQQnLM+zVH8lWrCxKXviDSLSYFjfIHRNVGHXWUSGcz0Dan4jBrC0S6xaSgUXaXV7VRRx0FPIknoi9bXVwoj0inBLVutGfPHqxatQqyLGP8+PGYMmVKp+Nr167Fhx9+CJPJhPz8fMyaNQv9+/dXKzzNOdKSnm/byUgKHlHEYY8XZ+XlJiEiIlKTKjUFWZZRWVmJefPmYcmSJdi2bRvq6uo6nTNkyBAsWrQIjz/+OMaMGYNXX31VjdA0qy5NSSGYhKQAAAdaW1lbINIhVZJCTU0NiouLMXDgQAiCgLFjx6K6urrTOSNHjoTFYgEAnHPOOXA6nWqEpkmOQABuXzAt9w54QlCS8DBvqy2wb4FIb1RpPnI6nbDZbOHXNpsNhw4dinj+pk2bMHr06G6PVVVVoaqqCgCwaNEiFBUVJTfYFBIEIaZ4a06cgBECsrKyVIiqK8FghsXa/b2NRiOsVmtM1zksSSgrLIRg1F7XVaz/L7SMZdCOTCkHoGKfQqy2bNmCb7/9FgsWLOj2eEVFBSoqKsKvGxsbVYoscUVFRVHjlRUF+4+fgNflRSgNo48AoMnegvwB3T/4rVYrvF5vTNfxAvj0yBEM7dMnidElRyz/L7SOZdAOvZVj0KBBEY+p8hWusLAQDocj/NrhcKCwsLDLeZ9//jn+8Y9/4L777kvbt+R0s/sD8EtSwvMFEhFwJ6/p6kBLK2c5E+mIKkmhtLQU9fX1sNvtEEUR27dvR3l5eadzamtr8dxzz+G+++5DQUGBGmFp0mGvB7KsQAql70HqT2JSCMgyvuIKqkS6oUrzkclkwq233oqFCxdClmWMGzcOJSUlWL16NUpLS1FeXo5XX30Vfr8fTzzxBIC26tif/vQnNcLTjJAs43uvD2IgvauN+luDUBQFBoMhKdf72uVCaV4urILmWiuJ6BSq/SstKytDWVlZp/emTZsW/nn+/PlqhaJZ3/t8EBUFYiC9G9ZIooygNwRLrjk511MUfN7SgjEdBhsQkTZpb1jIaax9Mbne7pecTL6W5A6JPeLxwu7nXs5EWsekoBFuUUTDyYdmb7fFTCZfa/If4J81NXFCG5HGMSloRK3bg/bHZbr7FADA25z8pNAcCuGQixvxEGkZk4IGKIrSaR8CLdQUQn4x4f0VurOvpQUeMf3lI6LuMSlowDGfH74O+xuHNFBTAABvU/JrC6KiYKezKenXJaLkYFLQgJoOexsrigJRAzUFAPA0+VNy3eN+P2rYjESkSUwKaeYKhXDC/8PDVwxIkGVtdMZ6m/xQUhTL3uZmuNK0jAcRRcakkGY1HTqYASDo00YtAWibr+BtSc0wUlFR8LHDya07iTSGSSGNgt1sdB/SUFIAAHdDbIvf9UZTMIi9zc0puz4RxY9JIY2+dbu7LBaX6B7JyeZq9CVlf4VIDrnc3HeBSEOYFNJEUhQc7KazVUvNRwAgBqWUzFnoaKezCc5AejYVIqLOmBTS5IjH02kYajutJQUAaDme2m/ykqJga2Mj3Jy/QJR2TAppICkKDnSznLQsyZrrUwAAd6MPkpjapbz9koQt9oZuEyURqYdJIQ2OeDzdzuoNeincka0AABEuSURBVMWUtt/3lizJaKlP/bwClyhis70BASYGorRhUlCZKMvY39La7bFk7niWbE3fu1VJWC2hED6yN8AnMjEQpQOTgsoOutzwRvgmHPBoa+RRRyG/mPK+hXYtoRA+tNvRysltRKpjUlCRVxTxlSvy1pR+DScFAHAcaYUsqbNNqEcUUXXCjnqfT5X7EVEbJgUVfWq3R9zEXlEU+Fu123wEtNUW7N+qt5hdSJaxtaERXzS3cB8GIpUwKajkmM+H2h42sA94Qqp9C09E45GWlC190R0FwIHWVmyy27lWEpEKmBRUEJAk7IqyXLRPxQdtIhRFwbEDjarv+eAIBLHh+Ansa2mBGKG2RUSJY1JQwc6mpoidy+30khSAtpVcv9tjR8CjbnOXpCjY39KK9+qP41u3m01KRCnApJBiB10u1Hl77ixVFCVlexekSsgv4shuOxzftaq+1LdPklDtbMJ79cdx0OWK2E9DRPET0h1AJqv3+bCnKfoqoL7WIKSQ/h5ssiSj4dtmNNW5UPCjPOQPtMJizVLt/h5RxGdNzdjX0ooSaw7Oys2FzWJR7f5EmYhJIUUcgQA+djgRy3dod6O+h12KQQmOIy1wHGmBJTcLffpb0ad/Diy5ZlXuH5JlfOv24Fu3B7mCgDNycjDYmgOb2QyjwaBKDESZgkkhBRyBADY3NMbUrKEoClpPZM7S0QFPCAFPCxoPtyWI/AFW9BmQC3OOOn/VPKKIr10ufO1ywWw0YoDFgoHZ2eifbUG+IMDAJEHUIyaFJKvzevFJHDuKuR0+iMHMXNIh4AmhobYFDbUtyMm3nEwQVghmkyr3D8oy6nw+1J2cAGcxGmGzmGEzW3B2Tg4gyzAb2a1G1BGTQpJIioLPm5txyOWOqcmoneNI9+sgZRpfawC+1gDs3zTD2s+C/P5W5PW3wiTE/1BWFAViUAr3w5gEIwSLKWotICDLOObz45jPj29CIfi8XuQJAvqZzSf/y0LfrCxYTOokLSItYlJIguM+P3Y3N8EVim/sfssJD/wubc9iTjZFUeBx+uFx+mE81ARrYTb62HJgLcxGlqX7v44hvwhfS6DtP1cQQU+oy4gng8EAs1VAdh8zcgossPbNjtpkpaBtZVaXKOI77w/bjuaYTOhnzkJBlhkFWQL6ZpmRlyXAxKYnOg2olhT27NmDVatWQZZljB8/HlOmTOl0PBQKYdmyZfj222/Rp08f3HXXXRgwYIBa4cVNURQc9/vxZasLDYH45xgEfSHYa9RbMkKLZFmBu9EX7mjPyhZgzhFgyjJCUQApKCHoE2NqXlMU5WR/Rii8cJ85R0Buv2zkFubA2s8Coym2WolPkuDzSTjm+2GYsAFAXpaAfCELfbIE9BEE5AkCcgUBOSYTO7Q1TFYUiIoCSVEgn/yvndFggBEGGA2AYDQy8UOlpCDLMiorK/HQQw/BZrPhgQceQHl5Oc4444zwOZs2bUJubi6WLl2Kbdu24bXXXsPdd9+tRngxkxQFjkAQx3w+HPV6o05Ii8TvCuL7/Y26HIaaSiG/mNSZ0kGfiKDPjaZjbhiNBuQUWMIJIicnJ65rKQBcIbGtNnjKYDEDgGyTCdkGI0xBBZBkCLIBWUYjLGYTci1ZyLWakWUytj2EDG2f6XhtBW2JTVYAGQqUk3/KJ99TcMoxWUaWL4SGE06IkgRZBkwmI7IsJmRnC50SoCF8v/Z7/xCD0WCAyWBo+xMGCEYDhJPvCUYjsk7+qaWHZUiW4Zck+GUZAUmCX5LhkyQETr4OyDICsoygLCMkyzH37wFoK7fBALPJCIvRBIvRCIvR2Pb/12SExWRCjsmEbKMROSYThAzsk1IlKdTU1KC4uBgDBw4EAIwdOxbV1dWdksLOnTtx/fXXAwDGjBmDF154AYqiqD5aRFKU8F8sryjBI7U9CJpCQTQHQ3H9BetIlhW4nT7YjzjRcsIDReUJX6c7WW6bINg+SfBEbjOEHCOy+5hhycuCOUeAkC3AaOz575uiKJBCMsSAhKA3hIBXRPBkDSXkj7xJksFgQFaOAEtuFizWLJitAszWLAgWE0xZxm7/niuKAkmUIfrbakxBb6jtnp4Qgj4RgklAqJv1oAwGAwSLqe1euVkwn7xfVrYQ8V7RGA2GcIIwG41tD06jEVlGAwSDEYLRgKyTfwoGYzixmNqToMEAI9oSEtCW5ABA9HrR6PeHv82LsgJRaXuYB2UFQVlGUJYQkGT4TyaD3v4bjIV0skYRkGW4EP0LStbJpFHk8UL0emExnUwkprbfU9vvqC25Zp38vWktyZ5KlaTgdDphs9nCr202Gw4dOhTxHJPJBKvVCpfLhfz8/E7nVVVVoaqqCgCwaNEiDBo0KMXRE1Eqndm3b7pDoA50V/epqKjAokWLsGjRonSHErf7778/3SEkLBPKAGRGOVgG7ciUcgAqJYXCwkI4HI7wa4fDgcLCwojnSJIEr9eLPn36qBEeERGdpEpSKC0tRX19Pex2O0RRxPbt21FeXt7pnJ/97Gf45z//CQD45JNPMGLECM4+JSJSmWnBggULUn0To9GI4uJiLF26FOvXr8dll12GMWPGYPXq1fD7/Rg0aBDOPPNM/Otf/8L//u//4vDhw5gxYwby8vJSHZrqzjrrrHSHkLBMKAOQGeVgGbQjU8phUCINlyAiotOO7jqaiYgodZgUiIgojGsfqSTaMh9a19jYiOXLl6O5uRkGgwEVFRX4xS9+ke6wekWWZdx///0oLCzU7VBCj8eDlStX4ujRozAYDJg1axaGDh2a7rDisnbtWmzatAkGgwElJSWYPXs2zGZ19uBIxIoVK7B7924UFBRg8eLFAAC3240lS5agoaEB/fv3x913363bPlHWFFTQvszHvHnzsGTJEmzbtg11dXXpDisuJpMJN998M5YsWYKFCxdiw4YNuitDu/feew+DBw9OdxgJWbVqFUaPHo0nn3wSf/3rX3VXHqfTiffffx+LFi3C4sWLIcsytm/fnu6wYnLllVdi3rx5nd5bs2YNzj//fDz99NM4//zzsWbNmjRFlzgmBRV0XOZDEITwMh960q9fv/DoipycHAwePBhOpzPNUcXP4XBg9+7dGD9+fLpD6TWv14svv/wSV111FQBAEATk5uamOar4ybKMYDAISZIQDAbRr1+/dIcUk+HDh3epBVRXV+OKK64AAFxxxRW6+/fdEZuPVBDLMh96YrfbUVtbi7PPPjvdocTtxRdfxE033QSfT79boNrtduTn52PFihU4cuQIzjrrLEyfPh3Z2dnpDi1mhYWFuPbaazFr1iyYzWaMGjUKo0aNSndYvdbS0hJOan379kVLS0uaI+o91hQoLn6/H4sXL8b06dNhtVrTHU5cdu3ahYKCAt2PJ5ckCbW1tZg4cSL+8pe/wGKx6K65wu12o7q6GsuXL8ezzz4Lv9+PLVu2pDuspDAYDLqeeMukoIJYlvnQA1EUsXjxYlx22WW46KKL0h1O3L7++mvs3LkTc+bMwZNPPol9+/bh6aefTndYcbPZbLDZbDjnnHMAtK0qXFtbm+ao4vPFF19gwIAByM/PhyAIuOiii3Dw4MF0h9VrBQUFaGpq2x+lqampy0KeesKkoIJYlvnQOkVRsHLlSgwePBiTJk1Kdzi9cuONN2LlypVYvnw57rrrLowcORJ33nlnusOKW9++fWGz2XDs2DEAbQ/YjsvQ60FRUREOHTqEQCAARVHwxRdf6K6zvKPy8nJs3rwZALB582b8/Oc/T3NEvccZzSrZvXs3XnrpJciyjHHjxuHf//3f0x1SXL766is8/PDDOPPMM8NV4xtuuAFlZWVpjqx39u/fj3fffVe3Q1IPHz6MlStXQhRFDBgwALNnz9bdEMg33ngD27dvh8lkwpAhQ3D77bcjKysr3WFF9eSTT+LAgQNwuVwoKCjA1KlT8fOf/xxLlixBY2Oj7oekMikQEVEYm4+IiCiMSYGIiMKYFIiIKIxJgYiIwpgUiIgojEmBiIjCmBSIdOiee+7B/v37k34uEecpEGnc8uXLYbPZ8Jvf/CbdodBpgDUFyiiSJKU7hC60GBNRJKwpkKasWbMG77//Pnw+H/r164fbbrsNI0aMwDvvvIMPP/wQHo8HI0eOxIwZM5CXlwe73Y65c+fi9ttvx5tvvokBAwZg6tSpWLp0KVauXBm+7pw5czBz5kxccMEFeOONN1BXVwdBELBz5070798f9957L3bs2IF169YhKysLt99+e9SlnN1uN15++WXs3bsXwWAQ5513Hu677z7s378fS5cuxTXXXIN169bhggsuwJw5cyKWAQCeeOIJfPnllwgGgxgyZAhuu+02lJSUoKqqCpWVlQDa9k0YMWIE7r///m7LYzab8emnn6KoqAhz5sxBaWlpxLJHOtfpdOKFF17Al19+iezsbPzbv/2bbnfYo95hTYE049ixY9iwYQMee+wxvPzyy3jwwQfRv39/rF+/HtXV1ViwYAGeffZZ5OXl4fnnn+/02QMHDmDJkiV48MEHY7rXrl27cPnll2PVqlX4yU9+goULF4YX/fvVr36Fv/3tb1GvsXTpUgQCASxevBjPPfdcp4UCm5ub4Xa7sWLFCsycOTNqGUaPHo2nn34azz//PH7yk5+EV2+tqKjApZdeismTJ+OVV16JuFbTrl27MHbsWLz44osoLy/HCy+80GPZuztXlmX8z//8D4YMGYJnn30WDz/8MN577z3s2bMnpt8pZQYmBdIMo9GIUCiEurq68EJvxcXF2LhxI37zm9/AZrMhKysL119/PXbs2NGpWeb6669HdnZ2zHv8Dhs2DKNHj4bJZMKYMWPQ2tqKKVOmQBAEXHLJJWhoaIDH44n4+aamJuzZswe///3vkZeXB0EQMHz48PBxg8GAqVOnIisrC2azOWoZrrrqKuTk5ISPHTlyBF6vN+bf3bBhw1BWVgaj0YjLL78chw8fjvvcb775Bq2trfj1r38NQRAwcOBAjB8/XjfbZFJycOc10ozi4mJMnz4db775Jurq6jBq1Cj89re/RUNDAx5//PFOG5cYjcZOu1t13NkuFgUFBeGfzWYz8vPzYTQaw6+Btg2FIm1z6XA4kJeXF3ElzPz8/E4Jqqcy9O3bF6+//jo++eQTtLa2hs9pbW2NeSOjU8sTCoUgSRJMJlPM5zY0NKCpqQnTp08PH5dlGeedd15MMVBmYFIgTbn00ktx6aWXwuv14m9/+xtee+012Gw2zJo1C8OGDetyvt1uB4BOD1uLxYJAIBB+LcsyWltbkxqnzWaD2+2Gx+PpNnGcuvNWT2XYsmULdu7cifnz56N///7wer343e9+F/FaqVJUVIQBAwbocuMhSh42H5FmHDt2DPv27UMoFILZbIbZbIbBYMCECRPwf//3f2hoaADQ9g26p43RBw0ahFAohN27d0MURbz11lsIhUJJjbVfv34YPXo0nn/+ebjdboiiiAMHDkQ8v6cy+Hw+CIKAvLw8BAIBvP76650+W1BQgBMnTiQ1/u6cffbZyMnJwZo1axAMBiHLMr777jvU1NSk/N6kHawpkGaEQiG89tpr+P7772EymXDuuedixowZ6Nu3LwDg0UcfRVNTEwoKCnDxxRdH3N3KarXitttuw8qVKyHLMq677rq4m5dicccdd+DFF1/E3XffDVEUMWLEiE79Ch21j+DprgxXXHEF9u7di9tvvx15eXmYNm0aPvjgg/Bnr7rqKjzxxBOYPn06hg8fjvvuuy/pZQHamrP+9Kc/4eWXX8acOXMgiiIGDRqEadOmpeR+pE0ckkpERGFsPiIiojA2HxH14Oabb+72/Xnz5nFUDmUkNh8REVEYm4+IiCiMSYGIiMKYFIiIKIxJgYiIwv4fTVJ8GVQjOqUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzDTBUa6Umol",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "ea5abdf5-1993-48ac-bdf5-f683feef32df"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['serum_creatinine'])"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb84c9f9b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjfmoYUXbdWE"
      },
      "source": [
        "Time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00XkamqWUzRE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "d0e7fb71-b4b9-4b45-80c4-3c4301173d6b"
      },
      "source": [
        "sns.kdeplot(\n",
        "   data=HF, x=\"time\", hue=\"death_event\",\n",
        "   fill=True, common_norm=False, palette=\"flare\",\n",
        "   alpha=.5, linewidth=0,\n",
        ")"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8330ffcd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcvKojTZU8Ij",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "5bf3868c-75d6-4246-c539-dcfa320d20a8"
      },
      "source": [
        "#Check for outliers\n",
        "\n",
        "sns.boxplot(HF['time'])"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb833065f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-Bk7lzsVOPP"
      },
      "source": [
        "**ALL BOOLEAN VALUES**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mLlmgUcVOnL"
      },
      "source": [
        "Anaemia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-8CDIwjXN09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb64a9ac-7826-4ee2-acc4-44e552a3d2cd"
      },
      "source": [
        "(HF['anaemia'].value_counts()/sum(HF['anaemia'].value_counts()))*100"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    56.856187\n",
              "1    43.143813\n",
              "Name: anaemia, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfuZHCUwXJzU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf2f8a1c-1e2b-4b1f-f48f-e7e1d05be776"
      },
      "source": [
        "HF.anaemia.value_counts()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    170\n",
              "1    129\n",
              "Name: anaemia, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zv4WoUtvV0b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "517c71cd-1759-448b-b304-3cdae83dfddf"
      },
      "source": [
        "HF.groupby(['anaemia', 'death_event'])['death_event'].count()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anaemia  death_event\n",
              "0        0              120\n",
              "         1               50\n",
              "1        0               83\n",
              "         1               46\n",
              "Name: death_event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zM6Z07GdVLDJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "de81d490-73a8-4ece-aab9-cbaf94143661"
      },
      "source": [
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "sns.countplot('anaemia', hue = 'death_event', data = HF)\n",
        "plt.show()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrocwQYCXXK5"
      },
      "source": [
        "Diabetes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-5YEOX-XWJH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ddb55c4-5923-4829-d3c4-352afae2cd3f"
      },
      "source": [
        "(HF['diabetes'].value_counts()/sum(HF['diabetes'].value_counts()))*100"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    58.19398\n",
              "1    41.80602\n",
              "Name: diabetes, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGW66gj-XepY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ac72db2-c074-4532-f032-5430137a5f64"
      },
      "source": [
        "HF.diabetes.value_counts()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    174\n",
              "1    125\n",
              "Name: diabetes, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr9hwFSYXhiR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbd33017-329c-4822-8a0e-2197b42c3224"
      },
      "source": [
        "HF.groupby(['diabetes', 'death_event'])['death_event'].count()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "diabetes  death_event\n",
              "0         0              118\n",
              "          1               56\n",
              "1         0               85\n",
              "          1               40\n",
              "Name: death_event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6niEfqO3Xj_7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "fa415a41-5ce1-487a-a87c-14d8f432deba"
      },
      "source": [
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "sns.countplot('diabetes', hue = 'death_event', data = HF)\n",
        "plt.show()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zZ0Vb26YuWw"
      },
      "source": [
        "Hgh Blood Pressure (HBP)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6QKJWmFYlo_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca7b1ba3-9439-4d28-882f-632fa25ecaef"
      },
      "source": [
        "(HF['HBP'].value_counts()/sum(HF['HBP'].value_counts()))*100"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    64.882943\n",
              "1    35.117057\n",
              "Name: HBP, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09pEV3CcY63Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a50f08b-91da-4386-b3bc-f0ebe6fef25c"
      },
      "source": [
        "HF.HBP.value_counts()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    194\n",
              "1    105\n",
              "Name: HBP, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3Vog9_HY_wP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6370e134-36f8-481f-d075-d33dafe82703"
      },
      "source": [
        "HF.groupby(['HBP', 'death_event'])['death_event'].count()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HBP  death_event\n",
              "0    0              137\n",
              "     1               57\n",
              "1    0               66\n",
              "     1               39\n",
              "Name: death_event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_C71DHeoZCpK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "0c7b3e86-3f32-4e0b-bbcc-3bb84c9d653d"
      },
      "source": [
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "sns.countplot('HBP', hue = 'death_event', data = HF)\n",
        "plt.show()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8k6yMSEZe4r"
      },
      "source": [
        "Sex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T6QUA5uZCqy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e59ab0a3-9e8a-4c64-c927-aaa9bd3a8ba1"
      },
      "source": [
        "(HF['sex'].value_counts()/sum(HF['sex'].value_counts()))*100"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    64.882943\n",
              "0    35.117057\n",
              "Name: sex, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNmabWbnZCt4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9ce67fe-cea3-45bf-fdfb-48b67c8069b6"
      },
      "source": [
        "HF.sex.value_counts()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    194\n",
              "0    105\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KD1tla9ZZCv0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a0ee48a-2298-4f9d-9586-271961a7c949"
      },
      "source": [
        "HF.groupby(['sex', 'death_event'])['death_event'].count()"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex  death_event\n",
              "0    0               71\n",
              "     1               34\n",
              "1    0              132\n",
              "     1               62\n",
              "Name: death_event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FKeuwf3ZCyr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "c8c51d77-15c1-47f7-a06b-536aebba3764"
      },
      "source": [
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "sns.countplot('sex', hue = 'death_event', data = HF)\n",
        "plt.show()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oS2VRt6Zb4sG"
      },
      "source": [
        "Smoking"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyCDGL-Ub8eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "275e52ca-2981-46ad-c3cb-92d37e97360a"
      },
      "source": [
        "(HF['smoking'].value_counts()/sum(HF['smoking'].value_counts()))*100"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    67.892977\n",
              "1    32.107023\n",
              "Name: smoking, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbsNvnypb_Oq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41501f40-9688-4d56-ae8b-b0bdff32c8ec"
      },
      "source": [
        "HF.smoking.value_counts()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    203\n",
              "1     96\n",
              "Name: smoking, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ojcy20LGcFxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f930e4e0-c04d-4ee8-ee7b-c07f2da6e117"
      },
      "source": [
        "HF.groupby(['smoking', 'death_event'])['death_event'].count()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "smoking  death_event\n",
              "0        0              137\n",
              "         1               66\n",
              "1        0               66\n",
              "         1               30\n",
              "Name: death_event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVMw7LOZcKn4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "bbd7eddf-713e-418e-fe56-7b33126418f9"
      },
      "source": [
        "plt.figure(figsize = (16, 8))\n",
        "\n",
        "sns.countplot('smoking', hue = 'death_event', data = HF)\n",
        "plt.show()"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxhKRyyTaIxK"
      },
      "source": [
        "# **Model Building**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtSJ6DmWhZmK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6f05b81-8482-4111-eeed-7fb5bbf92824"
      },
      "source": [
        "outliers = ['CPK','Platelets','serum_sodium','serum_creatinine']\n",
        "def outlier_removal(HF,column):\n",
        "    q1 = HF[column].quantile(0.25)\n",
        "    q3 = HF[column].quantile(0.75)\n",
        "    iqr = q3 - q1\n",
        "    point_low = q1 - 1.5 * iqr\n",
        "    point_high = q3 + 1.5 * iqr\n",
        "    clean_HF = HF.loc[(HF[column] >  point_low) & (HF[column] <  point_high)]\n",
        "    return clean_HF\n",
        "\n",
        "# clean the dataset by removing outliers\n",
        "HF_cleaned = outlier_removal(outlier_removal(outlier_removal(HF,'CPK'),'platelets'),'serum_sodium')\n",
        "print(HF.shape)\n",
        "print(HF_cleaned.shape)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(299, 13)\n",
            "(247, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HneYo8YWZC0U"
      },
      "source": [
        "X = HF.drop('death_event',axis=1)\n",
        "y = HF['death_event']"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0op2Jur8ZC4j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a30ab82-476b-4d6f-cd87-9d0fc8430e54"
      },
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "rs = RandomOverSampler(random_state=42)\n",
        "X, y = rs.fit_resample(X,y)\n",
        "X.shape,y.shape"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((406, 12), (406,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gspw_92jgkwp"
      },
      "source": [
        "features = ['age', 'anaemia', 'CPK', 'diabetes',\n",
        "       'EF', 'HBP', 'platelets',\n",
        "       'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time']\n",
        "label = ['death_event']\n",
        "X=HF[features]\n",
        "y=HF[label]"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iz1gKA86ZC7Z"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test, y_train,y_test=train_test_split(X,y,test_size=0.33, shuffle =True,random_state=42)"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGw-qAhdgAQI"
      },
      "source": [
        "F1 = ['time','EF','serum_creatinine','age','serum_sodium','HBP']\n",
        "predictors = HF[F1]\n",
        "target = HF[\"death_event\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size = 0.22, random_state = 0)"
      ],
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3uQNIxxgAZK"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "\n",
        "X_train = sc.fit_transform(X_train[F1])\n",
        "X_test = sc.transform(X_test[F1])"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9XUGvjoyaYL"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWZfuw_Tzh8U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f21dfb24-929e-4bf4-f41b-24ff53ae9801"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train,y_train)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmqD46Dlzloh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35e65873-1c2d-40d3-d4b0-f92efebe6289"
      },
      "source": [
        "y_pred_log_reg = log_reg.predict(X_test)\n",
        "y_pred_log_reg"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_vHuCWxzvaL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee623530-89ed-4346-bdc9-268f56118d94"
      },
      "source": [
        "from sklearn.metrics import f1_score, roc_auc_score,accuracy_score,confusion_matrix, precision_recall_curve, auc, roc_curve, recall_score, classification_report \n",
        "classification_report = classification_report(y_test, y_pred_log_reg)\n",
        "print(classification_report)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXL4qTrI0Roz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bc40ed8-554c-45db-d6f1-5ffb5c95ce59"
      },
      "source": [
        "print(accuracy_score(y_test,y_pred_log_reg))"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7878787878787878\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAohjCeSSCnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ba09418-c537-41f4-ea0a-d959bb46389f"
      },
      "source": [
        "auc = roc_auc_score(y_test, y_pred_log_reg)\n",
        "auc"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.726190476190476"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p30LVDuaysBe"
      },
      "source": [
        "Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp9OV9jfZDCi"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RFC = RandomForestClassifier()"
      ],
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIoonAnO9Yns",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "110822f7-aed7-4db7-a894-63e8b2caab38"
      },
      "source": [
        "#random forest\n",
        "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "acc_randomforest = round(accuracy_score(y_pred, y_test) * 100, 2)\n",
        "print(\"Random Forest Model Accuracy : \",acc_randomforest)\n",
        "print(classification_report)"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Model Accuracy :  84.85\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZbts7FpS8ro",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7e9596b-14d5-400d-82c1-c217e48c833a"
      },
      "source": [
        "auc = roc_auc_score(y_pred, y_test)\n",
        "auc"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.87875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvvfNP_Ln0Cl"
      },
      "source": [
        "MLPC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exDcBIcmZDGe"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "M = MLPClassifier(hidden_layer_sizes=(128,128))"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BD_h9FQWZDIK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f4c8b4f-3ffd-4ffe-dbb0-d071bc25628a"
      },
      "source": [
        "M.fit(X_train,y_train)\n",
        "y_predM = M.predict(X_test)\n",
        "print(f1_score(y_test,y_predM))\n",
        "print(confusion_matrix(y_test,y_predM))\n",
        "print(classification_report)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7555555555555556\n",
            "[[38  4]\n",
            " [ 7 17]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsoGuGlkTIkm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "562849ec-b58e-4d45-9ddc-620874766c47"
      },
      "source": [
        "auc = roc_auc_score(y_test, y_predM)\n",
        "auc"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8065476190476191"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQfrY_kWoN15"
      },
      "source": [
        "XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mU5vUmlVZDMr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2d381a6-322e-4377-dbc8-969316c408e3"
      },
      "source": [
        "import xgboost as xgb\n",
        "model = xgb.XGBClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "y_pred1 = model.predict(X_test)\n",
        "roc_auc_score(y_test, y_pred1)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.818452380952381"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuGjB1_Aogfb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdadc2bb-e64b-43d8-ffbf-7c002ff6fc49"
      },
      "source": [
        "print(classification_report)"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3mhIfMNoR7j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47d3b9e6-c78d-465e-a2ad-f6da0e551978"
      },
      "source": [
        "accuracy = accuracy_score(y_test, y_pred1)\n",
        "print(accuracy)"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8484848484848485\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbERU7A3pbW3"
      },
      "source": [
        "Gaussian Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMdhKghEpe1N"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PG2eBhRYpORv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88b19421-d441-4473-8d4d-37169aab34f0"
      },
      "source": [
        "#gaussian naive bayes\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, y_train)\n",
        "  \n",
        "# making predictions on the testing set\n",
        "y_pred = gnb.predict(X_test)\n",
        "  \n",
        "# comparing actual response values (y_test) with predicted response values (y_pred)\n",
        "acc_naivebayes = round(accuracy_score(y_test, y_pred)*100,2)\n",
        "print(\"Gaussian Naive Bayes model accuracy(in %):\",acc_naivebayes )\n",
        "print(classification_report)"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gaussian Naive Bayes model accuracy(in %): 78.79\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrAxF2jnTZ0n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "688584f9-f014-4bb4-bbda-921f0427d65c"
      },
      "source": [
        "auc = roc_auc_score(y_test, y_pred)\n",
        "auc"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.726190476190476"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTeJj1-DqzJD"
      },
      "source": [
        "SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0h907KKDq0WT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b8c4727-6449-4074-9210-a855d9ae75cd"
      },
      "source": [
        "#SVM Classifier\n",
        "\n",
        "from sklearn.svm import SVC  \n",
        "clf = SVC(kernel='linear') \n",
        "  \n",
        "# fitting x samples and y classes \n",
        "clf.fit(X_train, y_train) \n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "acc_SVM = round(accuracy_score(y_pred, y_test) * 100, 2)\n",
        "print(\"SVM Model Accuracy : \",acc_SVM)\n",
        "print(classification_report)"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM Model Accuracy :  78.79\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0UUIZ3WTg3h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2479c602-8122-4905-902a-ca961ac4a39e"
      },
      "source": [
        "auc = roc_auc_score(y_pred, y_test)\n",
        "auc"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8131868131868132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7d4IZ06hq_rr"
      },
      "source": [
        "DTC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbSQA2ATrCqb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3f822b2-fd28-477a-bd3d-b5ad6983f868"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "modeldt = DecisionTreeClassifier()\n",
        "\n",
        "# fit the model with the training data\n",
        "modeldt.fit(X_train,y_train)\n",
        "\n",
        "# depth of the decision tree\n",
        "print('Depth of the Decision Tree :', modeldt.get_depth())\n",
        "\n",
        "# predict the target on the train dataset\n",
        "predict_train = modeldt.predict(X_train)\n",
        "\n",
        "# Accuray Score on train dataset\n",
        "accuracy_train = accuracy_score(y_train,predict_train)\n",
        "print('accuracy_score on train dataset : ', accuracy_train)\n",
        "\n",
        "# predict the target on the test dataset\n",
        "predict_test = model.predict(X_test)\n",
        "\n",
        "# Accuracy Score on test dataset\n",
        "accuracy_test = accuracy_score(y_test,predict_test)\n",
        "print('accuracy_score on test dataset : ', accuracy_test*100)\n",
        "print(classification_report)"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Depth of the Decision Tree : 10\n",
            "accuracy_score on train dataset :  1.0\n",
            "accuracy_score on test dataset :  84.84848484848484\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGOkMvKWTrle",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8878cada-29fc-4c2a-a6a8-8059c6d38236"
      },
      "source": [
        "auc = roc_auc_score(y_test, predict_test)\n",
        "auc"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.818452380952381"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ce2qPaucEFoO"
      },
      "source": [
        "Gradient Boostingg Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4ePVgtHDQaU"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "import lightgbm"
      ],
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BY8rsCUC-J9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fccff61c-9711-4e85-cae3-f7f41c01bc19"
      },
      "source": [
        "gradientboost_clf = GradientBoostingClassifier(max_depth=2, random_state=1)\n",
        "gradientboost_clf.fit(X_train,y_train)\n",
        "gradientboost_pred = gradientboost_clf.predict(X_test)\n",
        "gradientboost_f1 = f1_score(y_test, gradientboost_pred)\n",
        "gradientboost_f1\n",
        "\n",
        "acc_GBC= round(accuracy_score(y_pred, y_test) * 100, 2)\n",
        "print(acc_GBC)\n",
        "\n",
        "print(classification_report)"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.79\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        42\n",
            "           1       0.86      0.50      0.63        24\n",
            "\n",
            "    accuracy                           0.79        66\n",
            "   macro avg       0.81      0.73      0.74        66\n",
            "weighted avg       0.80      0.79      0.77        66\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DJK9ThfTzzm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfc842ec-4975-4697-c8c1-94528d033b83"
      },
      "source": [
        "auc = roc_auc_score(y_pred, y_test)\n",
        "auc"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8131868131868132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZcfxjLLFI9F"
      },
      "source": [
        "LGBMC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgP3FrtZEazT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a38d6582-d495-4c94-f708-ce9d485d5081"
      },
      "source": [
        "from sklearn.metrics import precision_score\n",
        "lgb_clf = lightgbm.LGBMClassifier(max_depth=2, random_state=4)\n",
        "lgb_clf.fit(X_train,y_train)\n",
        "lgb_pred = lgb_clf.predict(X_test)\n",
        "lgb_f1 = f1_score(y_test, lgb_pred)\n",
        "lgb_precision = precision_score(y_test, lgb_pred)\n",
        "lgb_f1"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7999999999999999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLpgiZqNUwvt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8cdbba8-8f00-4100-9a14-d57f3f66d6db"
      },
      "source": [
        "acc_lgb = round(accuracy_score(y_test, lgb_pred) * 100, 2)\n",
        "acc_lgb"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86.36"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBcroDZnT7xO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5939f63b-1b1e-49b0-f3b3-17e17a7ea480"
      },
      "source": [
        "auc = roc_auc_score(y_test, lgb_pred)\n",
        "auc"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8392857142857143"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqFFO1NQVOS-"
      },
      "source": [
        ""
      ],
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZMywD0OVObI"
      },
      "source": [
        "# saving the model \n",
        "import pickle \n",
        "pickle_out = open(\"classifier.pkl\", mode = \"wb\") \n",
        "pickle.dump(lgb_clf, pickle_out) \n",
        "pickle_out.close()"
      ],
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXJZnvrQVk39"
      },
      "source": [
        "!pip install -q pyngrok\n",
        "\n",
        "!pip install -q streamlit\n",
        "\n",
        "!pip install -q streamlit_ace"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye-Yr3PcVk_b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e7f9ff9-2187-4c85-b1fb-0766023dd7a2"
      },
      "source": [
        "%%writefile app.py\n",
        " \n",
        "import pickle\n",
        "import streamlit as st\n",
        " \n",
        "# loading the trained model\n",
        "pickle_in = open('classifier.pkl', 'rb') \n",
        "classifier = pickle.load(pickle_in)\n",
        " \n",
        "@st.cache()\n",
        "  \n",
        "# defining the function which will make the prediction using the data which the user inputs \n",
        "def prediction(time,EF,serum_creatinine,age,serum_sodium,HBP):   \n",
        " \n",
        "    # Pre-processing user input    \n",
        "    if HBP == \"Yes\":\n",
        "        HBP = 1\n",
        "    else:\n",
        "        HBP = 0\n",
        " \n",
        "  \n",
        "    # Making predictions \n",
        "    prediction = classifier.predict( \n",
        "        [[time,EF,serum_creatinine,age,serum_sodium,HBP]])\n",
        "     \n",
        "    if prediction == 0:\n",
        "        pred = 'Will Survive'\n",
        "    else:\n",
        "        pred = 'Will Die'\n",
        "    return pred\n",
        "      \n",
        "  \n",
        "# this is the main function in which we define our webpage  \n",
        "def main():       \n",
        "    # front end elements of the web page \n",
        "    html_temp = \"\"\" \n",
        "    <div style =\"background-color:yellow;padding:13px\"> \n",
        "    <h1 style =\"color:black;text-align:center;\">Streamlit Heart Failure Survival Prediction App</h1> \n",
        "    </div> \n",
        "    \"\"\"\n",
        "      \n",
        "    # display the front end aspect\n",
        "    st.markdown(html_temp, unsafe_allow_html = True) \n",
        "      \n",
        "    # following lines create boxes in which user can enter data required to make prediction \n",
        "    age = st.number_input(\"What Is Patient's Age?\") \n",
        "    HBP = st.selectbox('Does Patient Have High Blood Pressure?',(\"Yes\",\"No\"))\n",
        "    time = st.number_input(\"How Long Has Patient Had It?\") \n",
        "    EF = st.number_input(\"Ejection Fraction In Percentage\")\n",
        "    serum_creatinine = st.number_input(\"How much creatinine is in the patient's blood?\")\n",
        "    serum_sodium = st.number_input(\"How much sodium is in the patient's blood?\")\n",
        "    result =\"\"\n",
        "      \n",
        "    # when 'Predict' is clicked, make the prediction and store it \n",
        "    if st.button(\"Predict\"): \n",
        "        result = prediction(time,EF,serum_creatinine,age,serum_sodium,HBP) \n",
        "        st.success('Patient {}'.format(result))\n",
        "        \n",
        "     \n",
        "if __name__=='__main__': \n",
        "    main()"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting app.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-w7qQr8zcpak"
      },
      "source": [
        "!streamlit run app.py &>/dev/null&"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yrwho1t2wBYK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3dc7ee9-24ca-4f08-bba5-3c2b52fc4ba2"
      },
      "source": [
        "!git init"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reinitialized existing Git repository in /content/.git/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h259c2mHx6sl"
      },
      "source": [
        "!mkdir -p ~/.streamlit/"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G12HSzxrylch"
      },
      "source": [
        "!echo \"\\\n",
        "[server]\\n\\\n",
        "headless = true\\n\\\n",
        "port = $PORT\\n\\\n",
        "enableCORS = false\\n\\\n",
        "\\n\\\n",
        "\" > ~/.streamlit/config.toml"
      ],
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBMMYrj2zHd_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f9823a1-588f-44fb-bbed-b9a577f3a9c1"
      },
      "source": [
        "%%writefile requirements.txt\n",
        "\n",
        "streamlit"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting requirements.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Rtlz1UOypGP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfc52a68-7a29-4bb7-e7e9-fa3d02d27b3f"
      },
      "source": [
        "%%writefile Procfile\n",
        "\n",
        "web: sh setup.sh && streamlit run app.py"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting Procfile\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6_uc1aT0_KM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f958419-8757-4c59-fe7b-431a5f38ea52"
      },
      "source": [
        "!curl https://cli-assets.heroku.com/install-ubuntu.sh | sh"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  1232  100  1232    0     0   5625      0 --:--:-- --:--:-- --:--:--  5625\n",
            "+ dpkg -s apt-transport-https\n",
            "+ echo deb https://cli-assets.heroku.com/apt ./\n",
            "+ dpkg -s heroku-toolbelt\n",
            "+ true\n",
            "+ curl https://cli-assets.heroku.com/apt/release.key\n",
            "+ apt-key add -\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  5393  100  5393    0     0  53396      0 --:--:-- --:--:-- --:--:-- 53396\n",
            "OK\n",
            "+ apt-get update\n",
            "Hit:1 https://cli-assets.heroku.com/apt ./ InRelease\n",
            "Hit:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:4 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Get:5 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n",
            "Hit:6 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Ign:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Hit:9 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n",
            "Hit:10 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:11 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:13 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:16 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Get:17 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,767 kB]\n",
            "Get:18 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [904 kB]\n",
            "Fetched 2,939 kB in 6s (483 kB/s)\n",
            "Reading package lists... Done\n",
            "+ apt-get install -y heroku\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "heroku is already the newest version (7.54.0-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 72 not upgraded.\n",
            "heroku installed to /usr/bin/heroku\n",
            "heroku/7.54.0 linux-x64 node-v12.21.0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}