COVID19_prediction_Sirilalithaadapa.ipynb

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        "***Initial Hypothesis***\n",
        "\n",
        "1. **Prediction Hypothesis**: We hypothesize that COVID-19 can be predicted by analyzing symptoms and certain personal information.\n",
        "\n",
        "2. **Benefits of Accurate Prediction**: Accurate prediction can expedite treatment, optimize resource utilization, and improve disease management, thereby reducing the strain on healthcare systems.\n",
        "\n",
        "3. **Common Symptoms Hypothesis**: COVID-19 frequently manifests with symptoms such as coughing, fever, and respiratory difficulties.\n",
        "\n",
        "4. **Uncommon Symptoms Hypothesis**: Individuals without COVID-19 are less likely to experience symptoms like sore throat or headache.\n",
        "\n",
        "5. **Symptoms in Specific Groups**: Men with COVID-19 who have had contact with individuals from other countries might exhibit higher incidences of coughing and fever.\n",
        "\n",
        "6. **Age and Severity Hypothesis**: Individuals aged 60 and older may experience more severe symptoms, particularly respiratory distress.\n"
      ]
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        "# import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import missingno as msno\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from matplotlib import pyplot as plt\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Libraries used for Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "#Metrics\n",
        "from sklearn.metrics import classification_report,confusion_matrix,r2_score, accuracy_score,precision_score,recall_score,f1_score,log_loss\n",
        "\n",
        "#Hyperparametric Tuning\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "FFsn8DAj4EyK"
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") # to ignoring wranings"
      ]
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      "execution_count": null,
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      "source": [
        "# To load the data (.csv file)\n",
        "covid_raw = pd.read_csv('corona_tested_006.csv')"
      ]
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      "source": [
        "# creating a copy of read file\n",
        "covid = covid_raw.copy()"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**Exploratory Data Analysis- EDA**"
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        "covid_raw.shape"
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              "                              0           1           2           3  \\\n",
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              "                         278843      278844      278845      278846  \\\n",
              "Ind_ID                   278844      278845      278846      278847   \n",
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              "                         278847  \n",
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              "    <tr>\n",
              "      <th>Test_date</th>\n",
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              "      <td>30-04-2020</td>\n",
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              "      <td>30-04-2020</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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"covid_raw\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": 278843,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278844,\n          \"30-04-2020\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 278844,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278845,\n          \"30-04-2020\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 278845,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278846,\n          \"30-04-2020\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 278846,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278847,\n          \"30-04-2020\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 278847,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278848,\n          \"30-04-2020\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "covid_raw.tail().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEyIZBet4FCD",
        "outputId": "9987452a-5802-4bbe-ecbc-083a98ebbd3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 278848 entries, 0 to 278847\n",
            "Data columns (total 11 columns):\n",
            " #   Column               Non-Null Count   Dtype \n",
            "---  ------               --------------   ----- \n",
            " 0   Ind_ID               278848 non-null  int64 \n",
            " 1   Test_date            278848 non-null  object\n",
            " 2   Cough_symptoms       278596 non-null  object\n",
            " 3   Fever                278596 non-null  object\n",
            " 4   Sore_throat          278847 non-null  object\n",
            " 5   Shortness_of_breath  278847 non-null  object\n",
            " 6   Headache             278847 non-null  object\n",
            " 7   Corona               278848 non-null  object\n",
            " 8   Age_60_above         151528 non-null  object\n",
            " 9   Sex                  259285 non-null  object\n",
            " 10  Known_contact        278848 non-null  object\n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 23.4+ MB\n"
          ]
        }
      ],
      "source": [
        "covid_raw.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qb5fmMhp4FFr",
        "outputId": "fbd638e9-84cd-4b6a-da2b-023a3ec45bb4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Ind_ID   Test_date Cough_symptoms  Fever Sore_throat Shortness_of_breath  \\\n",
              "0       1  11-03-2020           True  False        True               False   \n",
              "1       2  11-03-2020          False   True       False               False   \n",
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              "3       4  11-03-2020           True  False       False               False   \n",
              "4       5  11-03-2020           True  False       False               False   \n",
              "\n",
              "  Headache    Corona Age_60_above  Sex           Known_contact  \n",
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              "2    False  positive          NaN  NaN                  Abroad  \n",
              "3    False  negative          NaN  NaN                  Abroad  \n",
              "4    False  negative          NaN  NaN  Contact with confirmed  "
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-25560cc3-742a-469f-9e64-bd8b302eea01 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-25560cc3-742a-469f-9e64-bd8b302eea01');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c3c6b242-72df-4361-b3c2-4ba91d6494f5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3c6b242-72df-4361-b3c2-4ba91d6494f5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c3c6b242-72df-4361-b3c2-4ba91d6494f5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "covid.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "LkFPw5JQ5sG5",
        "outputId": "710cf8c9-6417-449c-b9a9-bdbd40769079"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Ind_ID   Test_date Cough_symptoms   Fever Sore_throat  \\\n",
              "count   278848.000000      278848         278596  278596      278847   \n",
              "unique            NaN          51              2       2           2   \n",
              "top               NaN  20-04-2020          False   False       False   \n",
              "freq              NaN       10921         236368  256844      276921   \n",
              "mean    139424.500000         NaN            NaN     NaN         NaN   \n",
              "std      80496.628269         NaN            NaN     NaN         NaN   \n",
              "min          1.000000         NaN            NaN     NaN         NaN   \n",
              "25%      69712.750000         NaN            NaN     NaN         NaN   \n",
              "50%     139424.500000         NaN            NaN     NaN         NaN   \n",
              "75%     209136.250000         NaN            NaN     NaN         NaN   \n",
              "max     278848.000000         NaN            NaN     NaN         NaN   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
              "count               278847   278847    278848       151528  259285   \n",
              "unique                   2        2         3            2       2   \n",
              "top                  False    False  negative           No  female   \n",
              "freq                277270   276433    260227       125703  130158   \n",
              "mean                   NaN      NaN       NaN          NaN     NaN   \n",
              "std                    NaN      NaN       NaN          NaN     NaN   \n",
              "min                    NaN      NaN       NaN          NaN     NaN   \n",
              "25%                    NaN      NaN       NaN          NaN     NaN   \n",
              "50%                    NaN      NaN       NaN          NaN     NaN   \n",
              "75%                    NaN      NaN       NaN          NaN     NaN   \n",
              "max                    NaN      NaN       NaN          NaN     NaN   \n",
              "\n",
              "       Known_contact  \n",
              "count         278848  \n",
              "unique             3  \n",
              "top            Other  \n",
              "freq          242741  \n",
              "mean             NaN  \n",
              "std              NaN  \n",
              "min              NaN  \n",
              "25%              NaN  \n",
              "50%              NaN  \n",
              "75%              NaN  \n",
              "max              NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-84a83549-50f4-4f04-9491-2e66a0a7d375\" class=\"colab-df-container\">\n",
              "    <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>Ind_ID</th>\n",
              "      <th>Test_date</th>\n",
              "      <th>Cough_symptoms</th>\n",
              "      <th>Fever</th>\n",
              "      <th>Sore_throat</th>\n",
              "      <th>Shortness_of_breath</th>\n",
              "      <th>Headache</th>\n",
              "      <th>Corona</th>\n",
              "      <th>Age_60_above</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Known_contact</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>278848.000000</td>\n",
              "      <td>278848</td>\n",
              "      <td>278596</td>\n",
              "      <td>278596</td>\n",
              "      <td>278847</td>\n",
              "      <td>278847</td>\n",
              "      <td>278847</td>\n",
              "      <td>278848</td>\n",
              "      <td>151528</td>\n",
              "      <td>259285</td>\n",
              "      <td>278848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20-04-2020</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>female</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>10921</td>\n",
              "      <td>236368</td>\n",
              "      <td>256844</td>\n",
              "      <td>276921</td>\n",
              "      <td>277270</td>\n",
              "      <td>276433</td>\n",
              "      <td>260227</td>\n",
              "      <td>125703</td>\n",
              "      <td>130158</td>\n",
              "      <td>242741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>139424.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>80496.628269</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>69712.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>139424.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>209136.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>278848.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a83549-50f4-4f04-9491-2e66a0a7d375')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-84a83549-50f4-4f04-9491-2e66a0a7d375 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-84a83549-50f4-4f04-9491-2e66a0a7d375');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-338986fc-c334-4709-a453-3a6f6405117f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-338986fc-c334-4709-a453-3a6f6405117f')\"\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-338986fc-c334-4709-a453-3a6f6405117f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"covid\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Ind_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 100497.86353686964,\n        \"min\": 1.0,\n        \"max\": 278848.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          278848.0,\n          139424.5,\n          209136.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000051\",\n        \"max\": \"2020-04-20 00:00:00\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          51,\n          \"10921\",\n          \"278848\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cough_symptoms\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"236368\",\n          \"278596\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fever\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"256844\",\n          \"278596\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sore_throat\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"276921\",\n          \"278847\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shortness_of_breath\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"277270\",\n          \"278847\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Headache\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"276433\",\n          \"278847\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corona\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"260227\",\n          \"278848\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_60_above\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"125703\",\n          \"151528\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"130158\",\n          \"259285\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Known_contact\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"242741\",\n          \"278848\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "covid.describe(include = 'all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SV-cXa8Y5xmE"
      },
      "source": [
        "# Checking unique values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sEhRx0Q5wvE",
        "outputId": "a852919b-1ab0-427c-aa5f-1520516f8a37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ind_ID                 278848\n",
              "Test_date                  51\n",
              "Cough_symptoms              2\n",
              "Fever                       2\n",
              "Sore_throat                 2\n",
              "Shortness_of_breath         2\n",
              "Headache                    2\n",
              "Corona                      3\n",
              "Age_60_above                2\n",
              "Sex                         2\n",
              "Known_contact               3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "covid.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVihQ3ab5wrt",
        "outputId": "0e07ec1a-2b64-4c2d-9666-c93046dc607c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unique Value in Ind_ID: [     1      2      3 ... 278846 278847 278848]\n",
            "unique Value in Test_date: ['11-03-2020' '12-03-2020' '13-03-2020' '14-03-2020' '15-03-2020'\n",
            " '16-03-2020' '17-03-2020' '18-03-2020' '19-03-2020' '20-03-2020'\n",
            " '21-03-2020' '22-03-2020' '23-03-2020' '24-03-2020' '25-03-2020'\n",
            " '26-03-2020' '27-03-2020' '28-03-2020' '29-03-2020' '30-03-2020'\n",
            " '31-03-2020' '01-04-2020' '02-04-2020' '03-04-2020' '04-04-2020'\n",
            " '05-04-2020' '06-04-2020' '07-04-2020' '08-04-2020' '09-04-2020'\n",
            " '10-04-2020' '11-04-2020' '12-04-2020' '13-04-2020' '14-04-2020'\n",
            " '15-04-2020' '16-04-2020' '17-04-2020' '18-04-2020' '19-04-2020'\n",
            " '20-04-2020' '21-04-2020' '22-04-2020' '23-04-2020' '24-04-2020'\n",
            " '25-04-2020' '26-04-2020' '27-04-2020' '28-04-2020' '29-04-2020'\n",
            " '30-04-2020']\n",
            "unique Value in Cough_symptoms: [True False nan]\n",
            "unique Value in Fever: [False True nan]\n",
            "unique Value in Sore_throat: [True False nan]\n",
            "unique Value in Shortness_of_breath: [False True nan]\n",
            "unique Value in Headache: [False True nan]\n",
            "unique Value in Corona: ['negative' 'positive' 'other']\n",
            "unique Value in Age_60_above: [nan 'No' 'Yes']\n",
            "unique Value in Sex: [nan 'male' 'female']\n",
            "unique Value in Known_contact: ['Abroad' 'Contact with confirmed' 'Other']\n"
          ]
        }
      ],
      "source": [
        "for column in covid:\n",
        "    unique_value=covid[column].unique()\n",
        "    print(f'unique Value in {column}:',unique_value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZvqohIZ6OKa"
      },
      "source": [
        "# Analyzing amount of missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "jdeUvg245wpD",
        "outputId": "cf3afdf8-bddc-47e7-f561-996a74b90293"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     missing_values   percent_missing %\n",
              "Ind_ID                             0           0.000000\n",
              "Test_date                          0           0.000000\n",
              "Cough_symptoms                   252           0.090372\n",
              "Fever                            252           0.090372\n",
              "Sore_throat                        1           0.000359\n",
              "Shortness_of_breath                1           0.000359\n",
              "Headache                           1           0.000359\n",
              "Corona                             0           0.000000\n",
              "Age_60_above                  127320          45.659284\n",
              "Sex                            19563           7.015650\n",
              "Known_contact                      0           0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4c0ff0d-0efd-40a3-afe5-1d017dc12c44\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        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>missing_values</th>\n",
              "      <th>percent_missing %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
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              "      <th>Cough_symptoms</th>\n",
              "      <td>252</td>\n",
              "      <td>0.090372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fever</th>\n",
              "      <td>252</td>\n",
              "      <td>0.090372</td>\n",
              "    </tr>\n",
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              "    </tr>\n",
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              "    </tr>\n",
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              "      <td>19563</td>\n",
              "      <td>7.015650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Known_contact</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('frame')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('frame');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "frame",
              "summary": "{\n  \"name\": \"frame\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"missing_values \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38233,\n        \"min\": 0,\n        \"max\": 127320,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          252,\n          19563,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent_missing %\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.711384910841371,\n        \"min\": 0.0,\n        \"max\": 45.6592839109479,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.09037181546935966,\n          7.015650103282074,\n          0.0003586183153546018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# create a table now\n",
        "missing_values=covid.isnull().sum() # missing values\n",
        "\n",
        "percent_missing = covid.isnull().sum()/covid.shape[0]*100 # missing value %\n",
        "\n",
        "value = {\n",
        "    'missing_values ':missing_values,\n",
        "    'percent_missing %':percent_missing\n",
        "}\n",
        "frame=pd.DataFrame(value)\n",
        "frame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPnokbkd6dQ6"
      },
      "source": [
        "# null values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "thNls9955wmw",
        "outputId": "9a618e0b-cf7e-4174-87fb-c7ad7d9b2bbd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID  Test_date  Cough_symptoms  Fever  Sore_throat  \\\n",
              "0        False      False           False  False        False   \n",
              "1        False      False           False  False        False   \n",
              "2        False      False           False  False        False   \n",
              "3        False      False           False  False        False   \n",
              "4        False      False           False  False        False   \n",
              "...        ...        ...             ...    ...          ...   \n",
              "278843   False      False           False  False        False   \n",
              "278844   False      False           False  False        False   \n",
              "278845   False      False           False  False        False   \n",
              "278846   False      False           False  False        False   \n",
              "278847   False      False           False  False        False   \n",
              "\n",
              "        Shortness_of_breath  Headache  Corona  Age_60_above    Sex  \\\n",
              "0                     False     False   False          True   True   \n",
              "1                     False     False   False          True   True   \n",
              "2                     False     False   False          True   True   \n",
              "3                     False     False   False          True   True   \n",
              "4                     False     False   False          True   True   \n",
              "...                     ...       ...     ...           ...    ...   \n",
              "278843                False     False   False          True  False   \n",
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              "278845                False     False   False          True  False   \n",
              "278846                False     False   False          True  False   \n",
              "278847                False     False   False          True  False   \n",
              "\n",
              "        Known_contact  \n",
              "0               False  \n",
              "1               False  \n",
              "2               False  \n",
              "3               False  \n",
              "4               False  \n",
              "...               ...  \n",
              "278843          False  \n",
              "278844          False  \n",
              "278845          False  \n",
              "278846          False  \n",
              "278847          False  \n",
              "\n",
              "[278848 rows x 11 columns]"
            ],
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              "      <th>278846</th>\n",
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              "<p>278848 rows × 11 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f6e44ee4-d4a8-4ad9-aa35-0670f5cc70f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "covid.isnull()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HAszp8S6kbS"
      },
      "source": [
        "# Correlation between features and target variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dl4h1fSq5wkA",
        "outputId": "7990a295-f000-48ca-84e9-5f60ef078e54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "msno.heatmap(covid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6K8BjD4T5whD",
        "outputId": "d2b5d564-d31e-4d1d-86bf-d0b4517abe3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID   Test_date Cough_symptoms  Fever Sore_throat  \\\n",
              "0            1  11-03-2020           True  False        True   \n",
              "1            2  11-03-2020          False   True       False   \n",
              "2            3  11-03-2020          False   True       False   \n",
              "3            4  11-03-2020           True  False       False   \n",
              "4            5  11-03-2020           True  False       False   \n",
              "...        ...         ...            ...    ...         ...   \n",
              "278843  278844  30-04-2020          False  False       False   \n",
              "278844  278845  30-04-2020          False  False       False   \n",
              "278845  278846  30-04-2020          False  False       False   \n",
              "278846  278847  30-04-2020          False  False       False   \n",
              "278847  278848  30-04-2020          False  False       False   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
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              "Empty DataFrame\n",
              "Columns: [Ind_ID, Test_date, Cough_symptoms, Fever, Sore_throat, Shortness_of_breath, Headache, Corona, Age_60_above, Sex, Known_contact]\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ee700cba-8ab9-48ef-bbb1-12beaaed6998 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ee700cba-8ab9-48ef-bbb1-12beaaed6998');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "covid[covid.duplicated()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyWZuc5o64Aw"
      },
      "source": [
        "# Imputation Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "pJoylpR15wbm",
        "outputId": "bf2e24e0-c4ea-49ff-8b31-914597b1996d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID   Test_date Cough_symptoms  Fever Sore_throat  \\\n",
              "0            1  11-03-2020           True  False        True   \n",
              "1            2  11-03-2020          False   True       False   \n",
              "2            3  11-03-2020          False   True       False   \n",
              "3            4  11-03-2020           True  False       False   \n",
              "4            5  11-03-2020           True  False       False   \n",
              "...        ...         ...            ...    ...         ...   \n",
              "278843  278844  30-04-2020          False  False       False   \n",
              "278844  278845  30-04-2020          False  False       False   \n",
              "278845  278846  30-04-2020          False  False       False   \n",
              "278846  278847  30-04-2020          False  False       False   \n",
              "278847  278848  30-04-2020          False  False       False   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
              "0                    False    False  negative          NaN   other   \n",
              "1                    False    False  positive          NaN   other   \n",
              "2                    False    False  positive          NaN   other   \n",
              "3                    False    False  negative          NaN   other   \n",
              "4                    False    False  negative          NaN   other   \n",
              "...                    ...      ...       ...          ...     ...   \n",
              "278843               False    False  positive          NaN    male   \n",
              "278844               False    False  negative          NaN  female   \n",
              "278845               False    False  negative          NaN    male   \n",
              "278846               False    False  negative          NaN    male   \n",
              "278847               False    False  negative          NaN  female   \n",
              "\n",
              "                 Known_contact  \n",
              "0                       Abroad  \n",
              "1                       Abroad  \n",
              "2                       Abroad  \n",
              "3                       Abroad  \n",
              "4       Contact with confirmed  \n",
              "...                        ...  \n",
              "278843                   Other  \n",
              "278844                   Other  \n",
              "278845                   Other  \n",
              "278846                   Other  \n",
              "278847                   Other  \n",
              "\n",
              "[278848 rows x 11 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-9531affd-a723-47b5-9cf1-7248383e7367\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "      <td>False</td>\n",
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              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>11-03-2020</td>\n",
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              "      <td>Contact with confirmed</td>\n",
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              "    <tr>\n",
              "      <th>278843</th>\n",
              "      <td>278844</td>\n",
              "      <td>30-04-2020</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>positive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>278844</th>\n",
              "      <td>278845</td>\n",
              "      <td>30-04-2020</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>female</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278845</th>\n",
              "      <td>278846</td>\n",
              "      <td>30-04-2020</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278846</th>\n",
              "      <td>278847</td>\n",
              "      <td>30-04-2020</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278847</th>\n",
              "      <td>278848</td>\n",
              "      <td>30-04-2020</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>female</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>278848 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9531affd-a723-47b5-9cf1-7248383e7367')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9531affd-a723-47b5-9cf1-7248383e7367 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9531affd-a723-47b5-9cf1-7248383e7367');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_47709395-f49d-4f12-83b7-c4b0d047ca92\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_47709395-f49d-4f12-83b7-c4b0d047ca92 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('covid');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Converting null values of Sex column to others\n",
        "value = 'other'\n",
        "covid['Sex'].fillna(value,inplace=True)\n",
        "covid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsOzwfkz5wYf",
        "outputId": "9def79e9-40b0-4221-b1f2-87bbf6d5afad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['other', 'male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "gender=covid['Sex'].unique()\n",
        "gender"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HQHEIukj5wVx"
      },
      "outputs": [],
      "source": [
        "# Date&Time Formatting\n",
        "covid['Test_date']=pd.to_datetime(covid['Test_date'], format='%d-%m-%Y')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oA2IrD2Q7lIc",
        "outputId": "49efd3bf-ed28-4af1-a2f5-74149dd8a14e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ind_ID                 0.000000\n",
              "Test_date              0.000000\n",
              "Cough_symptoms         0.000904\n",
              "Fever                  0.000904\n",
              "Sore_throat            0.000004\n",
              "Shortness_of_breath    0.000004\n",
              "Headache               0.000004\n",
              "Corona                 0.000000\n",
              "Age_60_above           0.456593\n",
              "Sex                    0.000000\n",
              "Known_contact          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#percentage of missing values\n",
        "covid.isnull().sum()/len(covid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-lp-0cx_7lE_"
      },
      "outputs": [],
      "source": [
        "columns_to_replace = ['Cough_symptoms','Fever','Sore_throat','Shortness_of_breath','Headache']\n",
        "\n",
        "covid[columns_to_replace]=covid[columns_to_replace].replace({True: 'TRUE',False: 'FALSE', 'nan': np.nan})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdO9fhFi7lCc",
        "outputId": "fd7f9667-9312-4ae7-bf9b-f71f54b5480a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unique Value in Ind_ID: [     1      2      3 ... 278846 278847 278848]\n",
            "unique Value in Test_date: <DatetimeArray>\n",
            "['2020-03-11 00:00:00', '2020-03-12 00:00:00', '2020-03-13 00:00:00',\n",
            " '2020-03-14 00:00:00', '2020-03-15 00:00:00', '2020-03-16 00:00:00',\n",
            " '2020-03-17 00:00:00', '2020-03-18 00:00:00', '2020-03-19 00:00:00',\n",
            " '2020-03-20 00:00:00', '2020-03-21 00:00:00', '2020-03-22 00:00:00',\n",
            " '2020-03-23 00:00:00', '2020-03-24 00:00:00', '2020-03-25 00:00:00',\n",
            " '2020-03-26 00:00:00', '2020-03-27 00:00:00', '2020-03-28 00:00:00',\n",
            " '2020-03-29 00:00:00', '2020-03-30 00:00:00', '2020-03-31 00:00:00',\n",
            " '2020-04-01 00:00:00', '2020-04-02 00:00:00', '2020-04-03 00:00:00',\n",
            " '2020-04-04 00:00:00', '2020-04-05 00:00:00', '2020-04-06 00:00:00',\n",
            " '2020-04-07 00:00:00', '2020-04-08 00:00:00', '2020-04-09 00:00:00',\n",
            " '2020-04-10 00:00:00', '2020-04-11 00:00:00', '2020-04-12 00:00:00',\n",
            " '2020-04-13 00:00:00', '2020-04-14 00:00:00', '2020-04-15 00:00:00',\n",
            " '2020-04-16 00:00:00', '2020-04-17 00:00:00', '2020-04-18 00:00:00',\n",
            " '2020-04-19 00:00:00', '2020-04-20 00:00:00', '2020-04-21 00:00:00',\n",
            " '2020-04-22 00:00:00', '2020-04-23 00:00:00', '2020-04-24 00:00:00',\n",
            " '2020-04-25 00:00:00', '2020-04-26 00:00:00', '2020-04-27 00:00:00',\n",
            " '2020-04-28 00:00:00', '2020-04-29 00:00:00', '2020-04-30 00:00:00']\n",
            "Length: 51, dtype: datetime64[ns]\n",
            "unique Value in Cough_symptoms: ['TRUE' 'FALSE' nan]\n",
            "unique Value in Fever: ['FALSE' 'TRUE' nan]\n",
            "unique Value in Sore_throat: ['TRUE' 'FALSE' nan]\n",
            "unique Value in Shortness_of_breath: ['FALSE' 'TRUE' nan]\n",
            "unique Value in Headache: ['FALSE' 'TRUE' nan]\n",
            "unique Value in Corona: ['negative' 'positive' 'other']\n",
            "unique Value in Age_60_above: [nan 'No' 'Yes']\n",
            "unique Value in Sex: ['other' 'male' 'female']\n",
            "unique Value in Known_contact: ['Abroad' 'Contact with confirmed' 'Other']\n"
          ]
        }
      ],
      "source": [
        "for column in covid:\n",
        "    unique_value=covid[column].unique()\n",
        "    print(f'unique Value in {column}:',unique_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T7Wik9QX7k_L",
        "outputId": "7ec6a7df-c96e-4f76-9937-c942be5bd945"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['other', 'male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "gender=covid['Sex'].unique()\n",
        "gender"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYCw7d2s70YD",
        "outputId": "b33d09bb-e853-4544-a357-a55bca459284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ind_ID                      0\n",
              "Test_date                   0\n",
              "Cough_symptoms            252\n",
              "Fever                     252\n",
              "Sore_throat                 1\n",
              "Shortness_of_breath         1\n",
              "Headache                    1\n",
              "Corona                      0\n",
              "Age_60_above           127320\n",
              "Sex                         0\n",
              "Known_contact               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#percent of missing values\n",
        "covid.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TD1zoZFA70Uo"
      },
      "outputs": [],
      "source": [
        "#now drop the null values\n",
        "covid = covid.dropna(subset=['Age_60_above','Cough_symptoms','Fever'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xu94Re508EA8",
        "outputId": "d4d86449-5b9e-478d-e461-29f7b4054d71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ind_ID                 0\n",
              "Test_date              0\n",
              "Cough_symptoms         0\n",
              "Fever                  0\n",
              "Sore_throat            0\n",
              "Shortness_of_breath    0\n",
              "Headache               0\n",
              "Corona                 0\n",
              "Age_60_above           0\n",
              "Sex                    0\n",
              "Known_contact          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# after dropping check it\n",
        "covid.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vN4BpM3470SI",
        "outputId": "35294776-17ae-44c0-dfdd-81fb937c5625"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID  Test_date Cough_symptoms  Fever Sore_throat  \\\n",
              "4512      4513 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4513      4514 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4514      4515 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4515      4516 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4516      4517 2020-03-17          FALSE  FALSE       FALSE   \n",
              "...        ...        ...            ...    ...         ...   \n",
              "156035  156036 2020-04-14           TRUE  FALSE        TRUE   \n",
              "156036  156037 2020-04-14           TRUE  FALSE        TRUE   \n",
              "156037  156038 2020-04-14           TRUE   TRUE       FALSE   \n",
              "156038  156039 2020-04-14          FALSE  FALSE       FALSE   \n",
              "156039  156040 2020-04-14          FALSE  FALSE       FALSE   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
              "4512                 FALSE    FALSE     other           No   other   \n",
              "4513                  TRUE    FALSE  negative           No   other   \n",
              "4514                 FALSE    FALSE     other          Yes   other   \n",
              "4515                 FALSE    FALSE  positive           No   other   \n",
              "4516                 FALSE    FALSE  negative           No   other   \n",
              "...                    ...      ...       ...          ...     ...   \n",
              "156035               FALSE    FALSE  negative          Yes    male   \n",
              "156036                TRUE    FALSE  negative           No    male   \n",
              "156037               FALSE    FALSE  negative           No  female   \n",
              "156038               FALSE    FALSE  negative           No    male   \n",
              "156039               FALSE    FALSE  negative           No  female   \n",
              "\n",
              "       Known_contact  \n",
              "4512           Other  \n",
              "4513          Abroad  \n",
              "4514           Other  \n",
              "4515           Other  \n",
              "4516           Other  \n",
              "...              ...  \n",
              "156035        Abroad  \n",
              "156036        Abroad  \n",
              "156037        Abroad  \n",
              "156038         Other  \n",
              "156039         Other  \n",
              "\n",
              "[151279 rows x 11 columns]"
            ],
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              "      <th>Ind_ID</th>\n",
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              "    <tr>\n",
              "      <th>4515</th>\n",
              "      <td>4516</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>positive</td>\n",
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              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4516</th>\n",
              "      <td>4517</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
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              "      <th>156035</th>\n",
              "      <td>156036</td>\n",
              "      <td>2020-04-14</td>\n",
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              "      <td>FALSE</td>\n",
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              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
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              "<p>151279 rows × 11 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b276eae8-1431-4cd9-a97e-9d232909efef button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b276eae8-1431-4cd9-a97e-9d232909efef');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f059aaac-3a8c-4f38-927d-2955b81c1f12\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f059aaac-3a8c-4f38-927d-2955b81c1f12')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f059aaac-3a8c-4f38-927d-2955b81c1f12 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_43da7d4d-a34d-4299-9f13-3f70608f4050\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_43da7d4d-a34d-4299-9f13-3f70608f4050 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('covid');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "covid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8isb8lr770Oo",
        "outputId": "52ca5011-b307-4982-b211-d0156b0580a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ind_ID                 151279\n",
              "Test_date                  29\n",
              "Cough_symptoms              2\n",
              "Fever                       2\n",
              "Sore_throat                 2\n",
              "Shortness_of_breath         2\n",
              "Headache                    2\n",
              "Corona                      3\n",
              "Age_60_above                2\n",
              "Sex                         3\n",
              "Known_contact               3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "covid.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B36Lf6YD8QnK"
      },
      "source": [
        "# After Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJqafoU-70L9",
        "outputId": "4cb66519-2767-4521-f009-519d7ff53df0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Corona\n",
              "negative    137606\n",
              "positive     11437\n",
              "other         2236\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "covid['Corona'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSgfeFsz70JH",
        "outputId": "e6fcf227-1b12-4cae-aae9-23130bc0a9e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex\n",
              "male      70101\n",
              "female    68071\n",
              "other     13107\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "covid['Sex'].value_counts()\n",
        "#to find count of total males and females"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nSsgZTmL70Gd",
        "outputId": "988ac685-f45a-49e1-cbb9-ae971ab58da2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fever\n",
              "FALSE    137538\n",
              "TRUE      13741\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "covid['Fever'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnrrlIMA70Dl",
        "outputId": "3a727ac8-2ded-48aa-844b-357800efbd66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cough_symptoms\n",
              "FALSE    124706\n",
              "TRUE      26573\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "covid['Cough_symptoms'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPWpfQPr8jn1"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "hKcig2p-8jkP",
        "outputId": "9a5b6366-3274-43de-9be1-559ffd1bd1bb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x='Sex', data=covid)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "rj1wyHpQ8jhI",
        "outputId": "9f582cb1-1052-40f1-b13a-08d24a964118"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "covid[\"Corona\"].value_counts().plot.pie(autopct='%1.1f%%',shadow=True)\n",
        "plt.title('Covid Positive');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "TiuaORHP8jeS",
        "outputId": "d68b692e-1f4c-4a1c-c8f2-44826dd0c26b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 5 Axes>"
            ],
            "image/png": 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Dh7Bq1Sq0bNkSnp6ekvW3b9+Op0+fYtq0aXj58iUCAwPRq1cvXL58GSYmJpgyZQoePXqEyMhI7NixQ7KuIAgYPHgw/vjjD3h5eaFNmzY4fPgw5syZg4cPH2LdunWS/CdPnsT+/fsxbdo0AMCKFSswcOBAzJ07F99++y2mTp2K9PR0BAQEYOLEiTh27BgAIDc3Fy4uLsjJyRHf78OHD3HgwAFkZGRAT0+vzGMvP7Y//PADrl69WuJn9sknn2Dv3r3w8fFBixYt8OTJE5w6dQrXrl1Du3bt8NVXXyEzMxN///23+P5K++W6NOnp6ejfvz9GjhyJ0aNHY/fu3fj000+hrq6OiRMnVuj7UNj+/fsBAGPHjlWoPEePHkW/fv3QqFEj+Pn54cWLFwgKCkKXLl1w/vx5WFlZYcCAAdDR0cHu3bvRvXt3yfq7du2CnZ1dqb+cTJo0Cf/73/8wZswYdO7cGceOHcOAAQMUKl95yC8S3/w1U/6rYXx8fKnrv3jxAr1798b9+/cxY8YMmJubY8eOHeJ3siyBgYEYPHgwPDw8kJubi507d2LEiBE4cOCA5P2OHz8eu3fvxtixY/Gf//wHJ06cKPV4jBw5EtbW1lixYgXOnz+PzZs3w9jYGCtXrhTzKFJXpqamom/fvjAyMsKXX34JfX193L17t8xxfco6P0gxmZmZ+PfffyXL5D2dduzYgXHjxsHFxQUrV67E8+fPsWnTJvGxLSsrK4wcORJz587F7t27MWfOHMl2du/ejb59+8LAwADA6xvvfv36oX379li8eDFUVFSwdetW9OrVCydPnkSnTp0k648YMQI2Njb4+uuvi/wgUZKxY8ciJCQER44cQZ8+fYrNExkZidGjR6N3797i9/XatWs4ffo0Zs6ciW7dumHGjBnYsGEDFixYAFtbWwAQ/y0vHx8f6Ovrw8/PDzdu3MCmTZtw7949sXFk7Nix2L59Ow4fPixpcEpOTsaxY8ewePHiErd969YtXL9+HRMnTiz2puFNL168QI8ePXD79m34+PjA2toae/bswfjx45GRkYGZM2eiVq1aGDp0KPbt24fvvvtO0kgUHh6OnJycIg3zhR05cgTDhg1DixYtsGLFCjx58gQTJkxAgwYNFDxiiimpbgVe169XrlxBcnIyTE1NS9yGr68vli1bhv79+6N///44f/48+vbtq9Bj7nv27MHz58/x6aefom7dujh79iyCgoLw999/Y8+ePWK+gwcPYtSoUbC3t8eKFSuQnp4OLy8v1K9fv9jthoWF4enTp5gyZQpkMhkCAgLw0Ucf4a+//hIbKhWJkUDF6smyzg96f1haWiImJgaJiYllPhJennuXGzduYPTo0ZgyZQomT56MZs2a4fnz5+jevTsePnyIKVOmoGHDhjhz5gzmz5+Px48fKzzmlSLXrv/973+hoqKCL774ApmZmQgICICHh0eRMWafPHmCfv36wd3dHR9//DFMTEwUquPkFLk+2rFjByZNmoROnTrB29sbANC4cWOF3uvYsWOxYMECHDlyBJMnTy42T1n3Y7a2tvD394evry+8vb3RtWtXAFD4nvVNy5cvh0wmw7x585Camor169fD2dkZCQkJ0NLSwtixY+Hv749du3bBx8dHXC83N1d8oqKk3kDPnz9HVFQUunXrhoYNG5ZZFkXv3UaNGgU/P78i9fmpU6fw6NGjUuPR5cuXxWtMPz8/vHr1CosXL4aJiYmih0xhOTk5Jcaj3NxcJCYm4j//+U+J6//www+YMmUKOnfujFmzZuGvv/7C4MGDYWhoKBkuoTiRkZH466+/MGHCBJiamopDIl25cgV//vmn2Dh04cIFuLq6wszMDEuWLEF+fj78/f1LfAz/1KlT2LdvH6ZOnYo6depgw4YNGDZsGO7fvy822F25cgVdu3aFrq4u5s6di1q1auG7775Djx49cOLECTg4OAB43Ui1YsUK8XzKyspCXFwczp8/X+L1Wlnnh8KEGiozM1MAIAwZMkSh/AkJCQIAYdKkSZLlX3zxhQBAOHbsmLgMgLB48eIi27C0tBTGjRsnvp4+fbogk8mECxcuiMuePHkiGBoaCgCEpKQkyboAhOjoaHFZamqqoKGhIXz++ecKvQdBEIR169YJAIR//vmnxDyHDx8WAAi///67ZHmrVq2E7t27i6+3bt0qABBcXFyEgoICcbmjo6Mgk8mETz75RFz26tUroUGDBpL1k5KSBACClpaW8Pfff4vLY2NjBQDCZ599Ji6bNm2aUNzXMTw8XAAgLFu2TLJ8+PDhgkwmE27fvi0uAyBoaGhIjut3330nABBMTU2FrKwscfn8+fMln8GFCxcEAMKePXtKOGqvWVpaCgMGDCgxXX78f/31V0m5Cn9f9PT0hGnTppW6nwEDBgiWlpal5ilL9+7dBQDCmjVrxGU5OTlCmzZtBGNjYyE3N1cQBMW/D8UZOnSoAEBIT09XqEzyfT958kRcdvHiRUFFRUXw9PQUl40ePVowNjYWXr16JS57/PixoKKiIvj7+4vLFi9eLPneyM/jqVOnSvY7ZsyYEs/b0uzZs0cAIPzxxx9F0n7++WcBgLBjxw7J8uDgYAGA0LJly1K3vX79egGAsHv3bnHZs2fPhCZNmhTZ57hx44p8H54/fy55nZubK7Rs2VLo1auXuCw+Pl4AIMyaNUuSd/z48UWOh/xYTpw4UZJ36NChQt26dcXXitaVv/zyiwBAOHfuXKnHoSLnB5VMXm8X9ycIgvD06VNBX19fmDx5smS95ORkQU9PT7Lc0dFRaN++vSTf2bNnBQDC9u3bBUEQhIKCAsHGxqZInHj+/LlgbW0t9OnTR1wm/46NHj26SLnlaSXFrvT0dAGAMHToUHHZm+fFzJkzBV1dXUm98abSzmlFyY9x+/btxXpUEAQhICBAUv/n5+cLDRo0EEaNGiVZf+3atYJMJhP++uuvEvfx66+/CgCEdevWKVQmeX3yv//9T1yWm5srODo6Cjo6OmL8k9f3v/32m2T9/v37C40aNRJfy+P31q1bxWVt2rQRzMzMhIyMDHHZkSNHBAAVilfa2tqSaya5/Px8QV9fX+jdu7dk+b///itoa2sLAIS4uLgSt5uamiqoq6sLAwYMkHwnFyxYIACQ7POPP/4o8n14s24VBEFYsWKFIJPJhHv37onL7O3thQYNGghPnz4Vlx0/frzI8ZAfy7p16wppaWnicvlnXPizUDRGKlJPVuT8oPfDkSNHBFVVVUFVVVVwdHQU5s6dKxw+fFhS3whC+e5d5PcZERERkrxLly4VtLW1hZs3b0qWf/nll4Kqqqpw//59hctd0rWr/DyztbUVcnJyxOWBgYECAOHy5cviMvm1a3BwsGQbitZxgqDY9ZEglFwHyev40q5f9PT0hLZt24qv37weVeR+7Ny5c0Xq2fKSH9v69etLjsHu3bsFAEJgYKC4zNHRUXBwcJCsv2/fvjJj4sWLFwUAwsyZMxUqk6L3bjdu3BAACEFBQZJ8U6dOFXR0dCSf45vXim5uboKmpqakTr569aqgqqpa7P1kWezs7Eq857G3txeaNm0qqTtzcnKEhg0bCgCEvXv3lrjd3NxcwdjYWGjTpo3kux8SEiIAKPa+ufD3obh49NNPPxVpMxg0aJBQu3Zt4eHDh+KyW7duCWpqakWOBwBBXV1dcg8t/4wLfxZubm6Curq6cOfOHXHZo0ePhDp16gjdunUTl7Vu3brUe2NBqNj5oYga+2hhVlYWACj0SybwerBxAJg9e7ZkuXxQ74qMpRUREQFHR0fJAH6GhoYl9nBp0aKF2CIPAEZGRmjWrBn++usvhfcp74b866+/ltgl2NnZGebm5ggNDRWXJSYm4tKlS0XGfgIALy8vSXdABwcHCIIALy8vcZmqqio6dOhQbFnd3Nwkv1B26tQJDg4O4jEvzaFDh6CqqooZM2ZIln/++ecQBAG///67ZHnv3r0lXZnlrcXDhg2TfBfky+Xllfe4Onz4cJHHqMpD/utTaTOa6OvrIzY2Fo8eParwfhSlpqaGKVOmiK/V1dUxZcoUpKamij2Gyvt9KKw859njx4+RkJCA8ePHSx7da9WqFfr06SP5PowaNQqpqamSxz327t2LgoICjBo1qsR9yLfx5vflXQw82r9/f1haWuKLL77Avn37cO/ePezevRtfffUV1NTUypwR9dChQzAzM5N0na5du7b4y2BZCv86lJ6ejszMTHTt2lXyaIm8m/DUqVMl65Y2UOwnn3wied21a1c8efJE/KwVrSvlddGBAwfKNY5PVZ4fNdk333yDyMhIyR/w+te9jIwMjB49Gv/++6/4p6qqCgcHB/zxxx/iNkaNGoX4+HjcuXNHXLZr1y5oaGhgyJAhAF7PYHTr1i2MGTMGT548Ebf37Nkz9O7dG9HR0UVi0ZvfMUUoWrc+e/ZM0p39XfL29pY88vfpp59CTU1NPEdUVFTg4eGB/fv3S8odGhqKzp07FxmMv7CKXMOYmppi9OjR4rJatWphxowZyM7OFh/V7tWrF+rVqyd59Dk9PR2RkZGl1q3y+nvcuHGSHsp9+vRBixYtFCqjolRUVDBlyhRERUVh/vz5uHXrFuLj4zFy5EixR1Vp9evRo0eRm5uL6dOnS65dFI0DhevWZ8+e4d9//0Xnzp0hCAIuXLgA4PUgzZcvX4anp6ek10n37t1hb29f7HZHjRol9mIEIF7vya9DyhMjK1JPVvX5QRXXp08fxMTEYPDgwbh48SICAgLg4uKC+vXriz3hgfLfu1hbW8PFxUWybM+ePejatSsMDAwkMcHZ2Rn5+fmIjo6utPc1YcIESU/QN88BOQ0NjSKPQCtaxwGKXR+9LR0dnTLjEVD6/Vhl8vT0lMSL4cOHw8zMTFJveHp6IjY2VhLTQ0NDYWFhUeQJiMIqEo8UuXdr2rQp2rRpI4lH+fn52Lt3LwYNGlTi+G35+fk4fPgw3NzcJD3EbG1ti3y/K8PUqVNx8+ZNeHl54erVq0hMTISnp6c4YUlp8SguLg6pqan45JNPJN/98ePHK/S0T+Fj8PLlS/z7779i7y/59zk/Px9Hjx6Fm5sbzM3NxfxNmjRBv379it2us7OzpAdiq1atoKurK56L+fn5OHLkCNzc3NCoUSMxn5mZGcaMGYNTp06J3wt9fX1cuXIFt27dKvP9yFXW+VFjG7J0dXUBlH7RW9i9e/egoqKCJk2aSJabmppCX1+/2HFCFNnmm9sDUOwyAMV21zQwMJCMuVGWUaNGoUuXLpg0aRJMTEzg7u6O3bt3S74k8ovr8PBwsdEmNDQUmpqaxQ5s/Wa55Cfem90h9fT0ii2rjY1NkWVNmzZVaBrte/fuwdzcvEjlKX8U5M3PpTxlBSCW19raGrNnz8bmzZtRr149uLi44JtvvlFofKzC5M9Jl1bZBwQEIDExERYWFujUqRP8/PzK1VhZHubm5pKBlIHXxx6AePzL+30orDznmfyzKm5cGFtbW/HmF4A41k7h4LZr1y60adNGLH9J+1BRUSnSPfzNfebm5iI5OVnyV95pgzU1NXHw4EHUrVsXw4YNg5WVFTw9PeHr6wtDQ8MyHweV1w9vPjOu6OwnBw4cwH/+8x9oamrC0NAQRkZG2LRpk+Q7Kz8eb94wl1QHAUXPIfmNl/xcUbSu7N69O4YNG4YlS5agXr16GDJkCLZu3VrsGGCFVeX5UZN16tQJzs7Okj8A4oVGr169YGRkJPk7cuSIZCDhESNGQEVFRTwPBUHAnj17xPESCm9v3LhxRba3efNm5OTkFKlHS2vAKYkidevUqVPRtGlT9OvXDw0aNMDEiROLHfOhsrwZ23R0dGBmZiaJbZ6ennjx4oX4iM+NGzcQHx9f5uPYFbmGsbGxKTLex5uxUk1NDcOGDcOvv/4qnov79u1DXl5eqQ1Z8vWLi+dv1ln//POPpG5VZPyQN/n7+8PLywsBAQFo2rQpOnToADU1NfEHtNLq15LKamRkJGlIKsn9+/fFxiQdHR0YGRmJN3ny77J8H29zjVdc3QooFiMrUk9W9flBb6djx47Yt28f0tPTcfbsWcyfPx9Pnz7F8OHDcfXqVQDlv3cpru69desWIiIiitTf8phRWYPLA2WfA3L169cvMpOoonUcoNj10dvKzs4uNR4pcj9Wmd6s72QyGZo0aSKJR6NGjYKGhob4w3VmZiYOHDgADw+PUscurEg8UvTebdSoUTh9+jQePnwI4PU4T6mpqaXGo3/++QcvXrxQKB6lpaVJ4lFFvgOffPIJFixYgLCwMNjZ2cHe3h537tzB3LlzAVQsHtWqVUvSQFSStLQ0zJw5EyYmJtDS0oKRkZF4HsvfS2pqKl68eFGpbQ7//PMPnj9/XmI8KigoEMdg8/f3R0ZGBpo2bQp7e3vMmTMHly5dKvV9Vdb5UaMbsszNzcs9g0JZg5CWprw3wm8qPD5RYYKCY4gAr1tuo6OjcfToUYwdOxaXLl3CqFGj0KdPH0n5PD09kZ2djfDwcAiCgLCwMAwcOLDY1uGSylXc8vKU9V0oT1kBaXnXrFmDS5cuYcGCBeIA2XZ2duUaKFz+fSutoWDkyJH466+/EBQUBHNzc6xatQp2dnZFepdVpfJ8Hwpr3rw5AIgD6lcWDQ0NuLm54ZdffsGrV6/w8OFDnD59utTAVh5nzpyBmZmZ5K/woJiKsrOzQ2JiIhITE3Hy5Ek8evQIkydPxr///ltqg9vbOnnyJAYPHgxNTU18++23OHToECIjIzFmzJi3PgcVrYfKqitlMhn27t2LmJgY+Pj44OHDh5g4cSLat29f6o3t+3h+1CTyi4QdO3YU6bEVGRmJX3/9Vcxrbm6Orl27Yvfu3QBez1p0//59yXko396qVauK3V5kZGSRi7yKzJKlSN1qbGyMhIQE7N+/Xxyfo1+/fhg3bly591dZWrRogfbt24uDAv/vf/+Duro6Ro4cWep676puBV5PSPH06VPxnNq9ezeaN2+O1q1bV8r2O3bsKKlb35zwQxHq6urYvHkzHj16hOjoaNy4cQOHDx9GZmZmsTfulSU/Px99+vTBwYMHMW/ePISHhyMyMlIcfPdtbkIr4xpPriL15Pt4flDZ1NXV0bFjR3z99dfYtGkT8vLyJGO1AYrfuxRX9xYUFKBPnz4l1t/FTeRTUYqeA28zk+K7vD6S+/vvv5GZmVlqPaTo/VhVMjAwwMCBA8WGrL179yInJ6fMpy+aNGkCNTW1dxKPRo0aJf5IBryOR3p6enB1da2U7X/00UeSeFTR8QCXL1+OlJQUnDx5EpcuXcK5c+fEePAur/dHjhyJ77//Hp988gn27duHI0eOiD9AvC/xqFu3brhz5w62bNmCli1bYvPmzWjXrh02b95c4jqVdX7U2IYsABg4cCDu3LmDmJiYMvNaWlqioKCgSLe4lJQUZGRkwNLSUlxmYGCAjIwMSb7c3Fyxi2Hhbb45GweAYpdVJhUVFfTu3Rtr167F1atXsXz5chw7dkzyyEjLli3Rtm1bhIaG4uTJk7h//77CA3aXV3FdDW/evCl5BLCkIGxpaYlHjx4V+RXg+vXrYnplsre3x8KFCxEdHY2TJ0/i4cOHkln+SpOdnY1ffvkFFhYWZQ4ebGZmhqlTpyI8PBxJSUmoW7culi9fLqa/TYNqYY8ePRJ/wZW7efMmAEiOf0W/D4MGDQIAycwtJZF/Vjdu3CiSdv36ddSrV0/Se2zUqFH4999/ERUVhT179kAQhDIbsuTnceFu08Xts3Xr1kUu1kobOLg0MpkMdnZ2cHJygqGhIf744w8UFBSIv2aWVtY7d+4UCRrFHZ83/fzzz9DU1MThw4cxceJE9OvXr9j9yY9HUlKSZPnb1EHlqSsB4D//+Q+WL1+OuLg4hIaG4sqVK2XOOlbW+UEVJ++taGxsXKTHlrOzM3r06CHJP2rUKFy8eBE3btzArl27ULt2bfG8L7w9XV3dYrfn7Oxc7Ix75SWfCKSsxwbU1dUxaNAgfPvtt7hz5w6mTJmC7du3i9/5yqpbgaKxLTs7G48fPy4yU5enpyeOHTuGx48fIywsDAMGDCizZ1DTpk3RrFkz/Prrrwr1aLK0tMStW7eKXNgWFyu7desGMzMz7Nq1C//++y+OHTumUN0KFB/P36yzQkNDJXXrmxPAlIeJiQm6du2Kpk2bIj8/H8ePH4eDg0Opv4CXVNZ//vmnzB7uly9fxs2bN7FmzRrMmzcPQ4YMER+/L24flXmNV94YWZF6sqzzg95vHTp0AADxfqO88bg4jRs3RnZ2don1tyKDe8tVZv36JkXrOEWvj96mvIrGo7Lux95lPBIEAbdv3y42Ht28eRPnzp1DaGgo2rZtW+aEYrVr10avXr0QHR2t0I++5bl3s7a2RqdOnbBr1y68evUK+/btg5ubW4kTowGve9dqaWkpFI/WrFkjiUfyXlQVIZ9pWf74+NGjR9GgQQPxh6filBSP8vLyilybvyk9PR1RUVH48ssvsWTJEgwdOhR9+vQp0pPL2NgYmpqalRqPjIyMULt27RLjkYqKiuRpJ/mMyD/99BMePHiAVq1aFZll8k2KtFeUpUY3ZM2dOxfa2tqYNGkSUlJSiqTfuXNHnHa4f//+AFBkdo61a9cCgGSWr8aNGxd5ZjwkJKRIC6KLiwtiYmKQkJAgLktLS5OMRVTZ0tLSiiyTj9H15iM9Y8eOxZEjR7B+/XrUrVu3xOdo31Z4eLjYZRQAzp49i9jYWMn+5BdnbzYQ9u/fH/n5+di4caNk+bp16yCTySqtzFlZWXj16pVkmb29PVRUVMp8FAp4/Xz02LFjkZaWhq+++qrE4JSfn1+kW6uxsTHMzc0l+9HW1i6x++v169dx//79MssEvJ6WufB0tLm5ufjuu+9gZGSE9u3bS/JW5Pvg6OgIV1dXbN68GeHh4UXSc3NzxenczczM0KZNG2zbtk3yOScmJuLIkSPiOSjn7OwMQ0ND7Nq1C7t27UKnTp3KfCRJXuYNGzZIlr95XhsYGBS5WCtptpbyePHiBRYtWgQzMzPJOA7F6d+/Px49eiSZEvr58+cICQkpcz+qqqqQyWSSOufu3btFPgP5Rda3334rWR4UFFTmPkorN1B2XZmenl6kka6kukhO0fODKs7FxQW6urr4+uuvix277M2puYcNGwZVVVX89NNP2LNnDwYOHCi5mW7fvj0aN26M1atXF9vgUtxU3+UVFhaGzZs3w9HREb179y4x35vTUauoqKBVq1YA/u87V1KsAV7fHF6/fl3hMd1CQkIkeTdt2oRXr14VqTtHjx4NmUyGmTNn4q+//irz12+5JUuW4MmTJ5g0aVKR+AS8nkXwwIEDAF6fl8nJyZLHsV+9eoWgoCDo6OhIxj9RUVHB8OHD8dtvv2HHjh149epVmQ1ZhevvwudoZGSk+JiTXJcuXSR1qyKPTyhi9erVePz4sTj+T0nkjadBQUGSOkiR2dfkv1IXXk8QBPFaUc7c3BwtW7bE9u3bJd/7EydOVLjXgqIxsqL1pCLnB70f/vjjj2J7RsjHO5I/7lOee5eSjBw5EjExMTh8+HCRtIyMjGLrnpKUdu36thSt4xS9PpKXt7hYUJpjx45h6dKlsLa2LnVWb0Xux0qLR//++y+uX7+u8Li98hni5fbu3YvHjx8XiUf9+vVDvXr1sHLlSpw4cULheLR48WIIgoCxY8cWG+vj4+Oxbds2AOW/dxs1ahT+/PNPbNmyBf/++2+Z8UhVVRUuLi4IDw+X3A9du3atyPe4ffv2knhUWWM67tq1C+fOncOsWbOKPO5aWIcOHWBkZITg4GDJrLk//vhjmd+94uIRUPR8V1VVhbOzM8LDwyXjJt6+fbvCTzOoqqqib9+++PXXXyWPp6akpCAsLAxOTk7iI6dvxhYdHR00adKk1LhSnvaK0qgpnFMJNW7cGGFhYRg1ahRsbW3h6emJli1bIjc3F2fOnBGnbQVe99AYN24cQkJCkJGRge7du+Ps2bPYtm0b3Nzc0LNnT3G7kyZNwieffIJhw4ahT58+uHjxIg4fPixObS43d+5c/O9//0OfPn0wffp0aGtrY/PmzWjYsCHS0tLeyS8X/v7+iI6OxoABA2BpaYnU1FR8++23aNCgAZycnCR5x4wZg7lz5+KXX37Bp59+Wim/mhenSZMmcHJywqeffoqcnByxoaRwq7i8UWXGjBlwcXGBqqoq3N3dMWjQIPTs2RNfffUV7t69i9atW+PIkSP49ddfMWvWLIWnyi3LsWPH4OPjgxEjRqBp06Z49eoVduzYAVVV1SLdqh8+fCj2QMrOzsbVq1exZ88eJCcn4/PPP5cMrv6mp0+fokGDBhg+fDhat24NHR0dHD16FOfOncOaNWskx2PXrl2YPXs2OnbsCB0dHbEXhK2tLbp37y4ZCL0k5ubmWLlyJe7evYumTZti165dSEhIQEhISJHPu6Lfh+3bt6Nv37746KOPMGjQIPTu3Rva2tq4desWdu7cicePH4uPlqxatQr9+vWDo6MjvLy8xKnF9fT0irTc16pVCx999BF27tyJZ8+eKfR4Sps2bTB69Gh8++23yMzMROfOnREVFVXuXySWLVsG4PX0sMDrX99OnToFAFi4cKGYb+TIkTA3N0eLFi2QlZWFLVu24K+//sLBgwfLHBRz8uTJ2LhxIzw9PREfHw8zMzPs2LEDtWvXLrN8AwYMwNq1a+Hq6ooxY8YgNTUV33zzDZo0aSJ5Lr19+/YYNmwY1q9fjydPnuA///kPTpw4IfbKq0gdpGhduW3bNnz77bcYOnQoGjdujKdPn+L777+Hrq5ukUZLOUXPD6o4XV1dbNq0CWPHjkW7du3g7u4OIyMj3L9/HwcPHkSXLl0kF5/Gxsbo2bMn1q5di6dPnxa5wFRRUcHmzZvRr18/2NnZYcKECahfvz4ePnyIP/74A7q6uvjtt98ULt/evXuho6OD3NxcPHz4EIcPH8bp06fRunXrIo/TvGnSpElIS0tDr1690KBBA9y7dw9BQUFo06aN2Eu2TZs2UFVVxcqVK5GZmQkNDQ306tULxsbGmD9/PrZt24akpKQiv2IXJzc3F71798bIkSNx48YNfPvtt3BycsLgwYMl+YyMjODq6oo9e/ZAX19foZtL4PXF/eXLl7F8+XJcuHABo0ePhqWlJZ48eYKIiAhERUUhLCwMwOuB57/77juMHz8e8fHxsLKywt69e3H69GmsX7++SH00atQoBAUFYfHixbC3ty+zFzEArFixAgMGDICTkxMmTpyItLQ0BAUFwc7OTuFxsH777TdcvHgRwOtfpC9duiTWt4MHDxYbVv73v//h559/Rrdu3cR6YPfu3Zg0aVKZjzoZGRnhiy++wIoVKzBw4ED0798fFy5cwO+//17kOu1NzZs3R+PGjfHFF1/g4cOH0NXVxc8//1xsT66vv/4aQ4YMQZcuXTBhwgSkp6dj48aNaNmyZYXGBQMUi5EVrScVOT/o/TB9+nQ8f/4cQ4cORfPmzcX7ll27dsHKykocCL089y4lmTNnDvbv34+BAwdi/PjxaN++PZ49e4bLly9j7969uHv3bpnnjVxp165vS9E6TtHrI3l5jx49irVr18Lc3BzW1tbiZFAA8Pvvv+P69et49eoVUlJScOzYMURGRsLS0hL79+8v9QdQRe7HGjduDH19fQQHB6NOnTrQ1taGg4MDrK2tsXHjRixZsgR//PFHkZ7SxTE0NISTkxMmTJiAlJQUrF+/Hk2aNMHkyZMl+WrVqgV3d3ds3LgRqqqqZf7oKte5c2d88803mDp1Kpo3b46xY8fCxsYGT58+xfHjx7F//36xLi/vvdvIkSPxxRdf4IsvvoChoWGZTzQAr3/oiYiIQNeuXTF16lSxUdPOzq7M8ZnkoqOjxY4p//zzD549eya+h27duqFbt25iPn9/f/Tt2xd169bFn3/+ia1bt8LV1bXMRxVr1aqFZcuWYcqUKejVqxdGjRqFpKQkbN26tcwfeXR1ddGtWzcEBAQgLy8P9evXx5EjR4rtyeXn54cjR46gS5cu+PTTT8WGxJYtW0o61JTHsmXLEBkZCScnJ0ydOhVqamr47rvvkJOTg4CAADFfixYt0KNHD7Rv3x6GhoaIi4vD3r174ePjU+K2y9NeUaq3mvNQSdy8eVOYPHmyYGVlJairqwt16tQRunTpIgQFBQkvX74U8+Xl5QlLliwRrK2thVq1agkWFhbC/PnzJXkE4fXU0PPmzRPq1asn1K5dW3BxcRFu374tWFpaFpnG9cKFC0LXrl0FDQ0NoUGDBsKKFSuEDRs2CACE5ORkMZ+lpWWxU1d27969xOlAixMVFSUMGTJEMDc3F9TV1QVzc3Nh9OjRRabVlevfv78AQDhz5kyRtJKmny1pmvRx48YJ2tra4mv5NKKrVq0S1qxZI1hYWAgaGhpC165dhYsXL0rWffXqlTB9+nTByMhIkMlkkik6nz59Knz22WeCubm5UKtWLcHGxkZYtWqVZFptQXg9neib01EXLkNh8ulq9+zZIwiCIPz111/CxIkThcaNGwuampqCoaGh0LNnT+Ho0aOS9eTTFwMQZDKZoKurK9jZ2QmTJ08WYmNjiz3GKDRlbE5OjjBnzhyhdevWQp06dQRtbW2hdevWwrfffitZJzs7WxgzZoygr69fZCpvvDFda0m6d+8u2NnZCXFxcYKjo6OgqakpWFpaChs3bixxndK+D6V5/vy5sHr1aqFjx46Cjo6OoK6uLtjY2AjTp0+XTO8qCIJw9OhRoUuXLoKWlpagq6srDBo0SLh69Wqx242MjBSP9YMHD4qkvzmdqyAIwosXL4QZM2YIdevWFbS1tYVBgwYJDx48KDJ1b2nkn3Fxf4WtXLlSaN68uaCpqSkYGBgIgwcPFi5cuKDQPgRBEO7duycMHjxYqF27tlCvXj1h5syZQkRERJGpkN+cRl0QBOGHH34QbGxsBA0NDaF58+bC1q1biz0ez549E6ZNmyYYGhoKOjo6gpubmzjl8X//+18xX0nntbweSEpKEpcpUleeP39eGD16tNCwYUNBQ0NDMDY2FgYOHCjExcUVOdblPT+oZIpMGy4Ir+tAFxcXQU9PT9DU1BQaN24sjB8/vsjnIwiC8P333wsAhDp16ggvXrwodnsXLlwQPvroI6Fu3bqChoaGYGlpKYwcOVKIiooS85T0HSucJv/T1NQUGjRoIAwcOFDYsmVLkTgsCEXPi7179wp9+/YVjI2NBXV1daFhw4bClClThMePHxd5P40aNRKn6Zafa+PGjSvyXS+O/BifOHFC8Pb2FgwMDAQdHR3Bw8NDePLkSbHryKdC9/b2LnXbxZHHdWNjY0FNTU0wMjISBg0aJPz666+SfCkpKcKECROEevXqCerq6oK9vX2J07oXFBQIFhYWxU6RLgjFTwMuCILw888/C7a2toKGhobQokULYd++fcXWTyWRH+Pi/grvKzY2VujWrZtgYGAgaGpqCq1btxaCg4OLxP2S5OfnC0uWLBHMzMwELS0toUePHkJiYmKR6zT5tUDh+vbq1auCs7OzoKOjI9SrV0+YPHmyODX5m8dj586dQvPmzQUNDQ2hZcuWwv79+4Vhw4YJzZs3L3Is37wOEYSiU8oLQtkxUtF6sqLnB1W/33//XZg4caLQvHlz8ZqqSZMmwvTp04WUlBRJXkXvXUq6zxCE19fZ8+fPF5o0aSKoq6sL9erVEzp37iysXr1ayM3NVbjcJV27vnnNLVdcPSO/di2OonWcotdH169fF7p16yZoaWkJAMS6QV7Hy//U1dUFU1NToU+fPkJgYKCQlZVVZJ9vbl/R+7Fff/1VaNGihaCmpiY5FvLtFa6biiM/tj/99JMwf/58wdjYWNDS0hIGDBgg3Lt3r9h1zp49KwAQ+vbtW+q2ixMfHy+MGTNGvCczMDAQevfuLWzbtk3Iz88X8yl67ybXpUsXAYAwadKkYtOLqytPnDghtG/fXlBXVxcaNWokBAcHF/s5l+TN647Cf4X3dfv2baFv375CvXr1xO/UihUrhJycHIX2IwiC8O233wrW1taChoaG0KFDByE6OrrIPX5x58Pff/8tDB06VNDX1xf09PSEESNGCI8ePSr2eERFRQlt27YV1NXVhcaNGwubN28WPv/8c0FTU1OSr7j7ZUEQim3HOH/+vODi4iLo6OgItWvXFnr27FnkHnHZsmVCp06dBH19fUFLS0to3ry5sHz5ckndUdHzoyyy//+GqArNmjUL3333HbKzs0scbK2qDB06FJcvX34n4yPcvXsX1tbWWLVqlfh4Gb3f3uX3gd4fCQkJaNu2Lf73v/+V2jWeiCrHr7/+Cjc3N0RHR4tTzlPN1KZNGxgZGSEyMrK6i0JEVMTFixfRpk0bbN++/Z2Nj0zvBzc3N1y5cqXY8cRqgho9Rtb74MWLF5LXT548wY4dO+Dk5FTtjViPHz/GwYMHWYkRAH4faqo36yDg9fP1KioqYrdpInq3vv/+ezRq1Kh8XebpvZaXl1dk/KDjx4/j4sWLCj0KRERUHb7//nvo6Ojgo48+qu6iUCV683r/1q1bOHToUI2ORzV6jKz3gaOjI3r06AFbW1ukpKTghx9+QFZWFhYtWlTubSUnJ5earqWlBT09vTK3k5SUhNOnT2Pz5s2oVatWqWM6Uc3H70PNFhAQgPj4ePTs2RNqamr4/fff8fvvv8Pb21sy4wgRVb6dO3fi0qVLOHjwIAIDA9/prF5UtR4+fAhnZ2d8/PHHMDc3x/Xr1xEcHAxTU1N88skn1V08okqTlpYmGaj6TaqqqjAyMqrCElFF/Pbbb7h69SpCQkLg4+MjmbiFlF+jRo0wfvx4NGrUCPfu3cOmTZugrq7+VjM1vu/YkPWO9e/fH3v37kVISAhkMhnatWuHH374oUI9IczMzEpNHzduHH788ccyt3PixAlMmDABDRs2xLZt22BqalruslDNwe9Dzda5c2dERkZi6dKlyM7ORsOGDeHn54evvvqquotGVOONHj0aOjo68PLywtSpU6u7OFSJDAwM0L59e2zevBn//PMPtLW1MWDAAPz3v/9F3bp1q7t4RJXmo48+wokTJ0pMt7S0lMxsRu+n6dOnIyUlBf3798eSJUuquzhUyVxdXfHTTz8hOTkZGhoacHR0xNdffw0bG5vqLto7wzGylMjRo0dLTZfPnkZERERERPS24uPji525U05LSwtdunSpwhIREbEhi4iIiIiIiIiIlAQHeyciIiIiIiIiIqXAMbKqUEFBAR49eoQ6depwwFciei8JgoCnT5/C3NwcKir8raM6MFYQ0fuOsaL6MVYQ0fvuXcYKNmRVoUePHnGWMCJSCg8ePECDBg2quxgfJMYKIlIWjBXVh7GCiJTFu4gVbMiqQnXq1AHw+oPU1dWt5tIQERWVlZUFCwsLsb6iqsdYQUTvO8aK6sdYQUTvu3cZK9iQVYXk3X51dXUZcIjovcbHFKoPYwURKQvGiurDWEFEyuJdxIpqfag9OjoagwYNgrm5OWQyGcLDw8W0vLw8zJs3D/b29tDW1oa5uTk8PT3x6NEjyTbS0tLg4eEBXV1d6Ovrw8vLC9nZ2ZI8ly5dQteuXaGpqQkLCwsEBAQUKcuePXvQvHlzaGpqwt7eHocOHZKkC4IAX19fmJmZQUtLC87Ozrh161blHQwiIiIiIiIiIipVtTZkPXv2DK1bt8Y333xTJO358+c4f/48Fi1ahPPnz2Pfvn24ceMGBg8eLMnn4eGBK1euIDIyEgcOHEB0dDS8vb3F9KysLPTt2xeWlpaIj4/HqlWr4Ofnh5CQEDHPmTNnMHr0aHh5eeHChQtwc3ODm5sbEhMTxTwBAQHYsGEDgoODERsbC21tbbi4uODly5fv4MgQEREREREREVERwnsCgPDLL7+Umufs2bMCAOHevXuCIAjC1atXBQDCuXPnxDy///67IJPJhIcPHwqCIAjffvutYGBgIOTk5Ih55s2bJzRr1kx8PXLkSGHAgAGSfTk4OAhTpkwRBEEQCgoKBFNTU2HVqlViekZGhqChoSH89NNPCr/HzMxMAYCQmZmp8DpERFWJ9VT142dARO+797GeOnHihDBw4EDBzMyszPuKKVOmCACEdevWSZY/efJEGDNmjFCnTh1BT09PmDhxovD06VNJnosXLwpOTk6ChoaG0KBBA2HlypVFtr97926hWbNmgoaGhtCyZUvh4MGDkvSCggJh0aJFgqmpqaCpqSn07t1buHnzZrne7/v4GRARFfYu6ymlGiMrMzMTMpkM+vr6AICYmBjo6+ujQ4cOYh5nZ2eoqKggNjYWQ4cORUxMDLp16wZ1dXUxj4uLC1auXIn09HQYGBggJiYGs2fPluzLxcVFfNQxKSkJycnJcHZ2FtP19PTg4OCAmJgYuLu7F1venJwc5OTkiK+zsrLe9hAQvZfy8/ORl5dX3cUgBdSqVQuqqqrVXQwi+gAxVigPZYwV8ic9Jk6ciI8++qjEfL/88gv+/PNPmJubF0nz8PDA48ePERkZiby8PEyYMAHe3t4ICwsD8H9Pejg7OyM4OBiXL1/GxIkToa+vLz4RIn/SY8WKFRg4cCDCwsLg5uaG8+fPo2XLlgD+70mPbdu2wdraGosWLYKLiwuuXr0KTU3Nd3B0iJQHY4XyqM5YoTQNWS9fvsS8efMwevRocUDD5ORkGBsbS/KpqanB0NAQycnJYh5ra2tJHhMTEzHNwMAAycnJ4rLCeQpvo/B6xeUpzooVK7BkyZLyvlUipSEIApKTk5GRkVHdRaFy0NfXh6mpKQfpJaIqwVihnJQtVvTr1w/9+vUrNc/Dhw8xffp0HD58GAMGDJCkXbt2DRERETh37pz4I3lQUBD69++P1atXw9zcHKGhocjNzcWWLVugrq4OOzs7JCQkYO3atWJDVmBgIFxdXTFnzhwAwNKlSxEZGYmNGzciODgYgiBg/fr1WLhwIYYMGQIA2L59O0xMTBAeHl7iD+RENR1jhXKqrlihFA1ZeXl5GDlyJARBwKZNm6q7OAqbP3++pKeXfPpJoppCHmyMjY1Ru3ZtpbnY/VAJgoDnz58jNTUVAGBmZlbNJSKiDwFjhXKpqbGioKAAY8eOxZw5c2BnZ1ck/X1/0oOopmOsUC7VHSve+4YseSPWvXv3cOzYMcn0sqampuKBk3v16hXS0tJgamoq5klJSZHkkb8uK0/hdPmywh9QSkoK2rRpU2LZNTQ0oKGhUZ63S6Q08vPzxWBTt27d6i4OKUhLSwsAkJqaCmNjY6V7dISIlAtjhXKqibFi5cqVUFNTw4wZM4pNf9+f9OCQJVSTMVYop+qMFdU6a2FZ5I1Yt27dwtGjR4t8qR0dHZGRkYH4+Hhx2bFjx1BQUAAHBwcxT3R0tOQ528jISDRr1gwGBgZinqioKMm2IyMj4ejoCACwtraGqampJE9WVhZiY2PFPEQfGvk5Vbt27WouCZWX/DPj+ANE9K4xViivmhQr4uPjERgYiB9//FFpe3msWLECenp64h+f8qCahLFCeVVXrKjWhqzs7GwkJCQgISEBwOuutgkJCbh//z7y8vIwfPhwxMXFITQ0FPn5+UhOTkZycjJyc3MBALa2tnB1dcXkyZNx9uxZnD59Gj4+PnB3dxcHcBwzZgzU1dXh5eWFK1euYNeuXQgMDJR0+Z05cyYiIiKwZs0aXL9+HX5+foiLi4OPjw8AQCaTYdasWVi2bBn279+Py5cvw9PTE+bm5nBzc6vSY0b0vlHWC8IPGT8zIqpqrHeUT036zE6ePInU1FQ0bNgQampqUFNTw7179/D555/DysoKQPU86VFSnuLMnz8fmZmZ4t+DBw/KcwiIlEJNqnc+FNX1mVVrQ1ZcXBzatm2Ltm3bAgBmz56Ntm3bwtfXFw8fPsT+/fvx999/o02bNjAzMxP/zpw5I24jNDQUzZs3R+/evdG/f384OTkhJCRETNfT08ORI0eQlJSE9u3b4/PPP4evr684ICMAdO7cGWFhYQgJCUHr1q2xd+9ehIeHizOLAMDcuXMxffp0eHt7o2PHjsjOzkZERARnFiEiIiIieo+NHTsWly5dEn9AT0hIgLm5OebMmYPDhw8DeP+f9NDQ0ICurq7kj4joQ1WtY2T16NEDgiCUmF5ampyhoaE4JW5JWrVqhZMnT5aaZ8SIERgxYkSJ6TKZDP7+/vD39y+zTEREREREVHWys7Nx+/Zt8bX8SQ9DQ0M0bNiwyBAltWrVgqmpKZo1awZA+qRHcHAw8vLyin3SY8mSJfDy8sK8efOQmJiIwMBArFu3TtzuzJkz0b17d6xZswYDBgzAzp07ERcXJ/7QXvhJDxsbG1hbW2PRokV80oOIqBze6zGyiIgUkZycjOnTp6NRo0bQ0NCAhYUFBg0aVOQXUSIi+nAxVtRspT3poSg+6UFEjBXK4b2ftZCUx31/++ouQoka+l6u7iLQO3L37l106dIF+vr6WLVqFezt7ZGXl4fDhw9j2rRpuH79erm3mZubK5lam4gqD2MFVQfGipqvrCc93nT37t0iy/ikx/uDsYKqA2OF8mCPLCJSalOnToVMJsPZs2cxbNgwNG3aFHZ2dpg9ezb+/PNPAMD9+/cxZMgQ6OjoQFdXFyNHjpQMsurn54c2bdpg8+bNsLa2Fn8RVXS9HTt2wMrKCnp6enB3d8fTp0/FPBEREXBycoK+vj7q1q2LgQMH4s6dO1V0dIiICGCsICKisjFWKA82ZBGR0kpLS0NERASmTZsGbW3tIun6+vooKCjAkCFDkJaWhhMnTiAyMhJ//fUXRo0aJcl7+/Zt/Pzzz9i3bx8SEhIUXu/OnTsIDw/HgQMHcODAAZw4cQL//e9/xfRnz55h9uzZiIuLQ1RUFFRUVDB06FAUFBS8m4NCREQSjBVERFQWxgrlwkcLiUhp3b59G4IgoHnz5iXmiYqKwuXLl5GUlAQLCwsAwPbt22FnZ4dz586hY8eOAF53+92+fTuMjIwAvJ5hSJH1CgoK8OOPP6JOnToAXs+MFBUVheXLlwMAhg0bJinPli1bYGRkhKtXr0rGyyAioneDsYKIiMrCWKFc2COLiJSWImNhXLt2DRYWFmLQAIAWLVpAX18f165dE5dZWlqKwaY861lZWYnBBgDMzMyQmpoqvr516xZGjx6NRo0aQVdXF1ZWVgBedy8mIqJ3j7GCiIjKwlihXNgji4iUlo2NDWQyWYUGXnxTcV2IFVGrVi3Ja5lMJuneO2jQIFhaWuL777+Hubk5CgoK0LJlS+Tm5r5VeYmISDGMFUREVBbGCuXCHllEpLQMDQ3h4uKCb775Bs+ePSuSnpGRAVtbWzx48AAPHjwQl1+9ehUZGRlo0aJFiduu6HqFPXnyBDdu3MDChQvRu3dv2NraIj09vRzvkIiI3hZjBRERlYWxQrmwIYuIlNo333yD/Px8dOrUCT///DNu3bqFa9euYcOGDXB0dISzszPs7e3h4eGB8+fP4+zZs/D09ET37t3RoUOHErdb0fUKMzAwQN26dRESEoLbt2/j2LFjmD17dmW9dSIiUhBjBRERlYWxQnmwIYuIlFqjRo1w/vx59OzZE59//jlatmyJPn36ICoqCps2bYJMJsOvv/4KAwMDdOvWDc7OzmjUqBF27dpV6nYrul5hKioq2LlzJ+Lj49GyZUt89tlnWLVq1du+ZSIiKifGCiIiKgtjhfKQCYqMakaVIisrC3p6esjMzISurm51F6fS3fe3r+4ilKih7+XqLkKN8/LlSyQlJcHa2hqamprVXRwqh9I+u5peTymDmv4ZMFZ8WBgrlBdjxfutpn8GjBUfFsYK5VVdsYI9soiIiIiIiIiISCmwIYuIiIiIiIiIiJQCG7KIiIiIiIiIiEgpsCGLiIiIiIiIiIiUAhuyiIiIiIiIiIhIKbAhi4iIiIiIiIiIlAIbsoiI6L0WHR2NQYMGwdzcHDKZDOHh4WJaXl4e5s2bB3t7e2hra8Pc3Byenp549OiRZBtpaWnw8PCArq4u9PX14eXlhezsbEmeS5cuoWvXrtDU1ISFhQUCAgKKlGXPnj1o3rw5NDU1YW9vj0OHDknSBUGAr68vzMzMoKWlBWdnZ9y6davyDgYRERER0QeODVlERPRee/bsGVq3bo1vvvmmSNrz589x/vx5LFq0COfPn8e+fftw48YNDB48WJLPw8MDV65cQWRkJA4cOIDo6Gh4e3uL6VlZWejbty8sLS0RHx+PVatWwc/PDyEhIWKeM2fOYPTo0fDy8sKFCxfg5uYGNzc3JCYminkCAgKwYcMGBAcHIzY2Ftra2nBxccHLly/fwZEhIiIiIvrwqFV3AYiIiErTr18/9OvXr9g0PT09REZGSpZt3LgRnTp1wv3799GwYUNcu3YNEREROHfuHDp06AAACAoKQv/+/bF69WqYm5sjNDQUubm52LJlC9TV1WFnZ4eEhASsXbtWbPAKDAyEq6sr5syZAwBYunQpIiMjsXHjRgQHB0MQBKxfvx4LFy7EkCFDAADbt2+HiYkJwsPD4e7u/q4OERERERHRB4M9soiIFNCjRw/MmjWruotBCsjMzIRMJoO+vj4AICYmBvr6+mIjFgA4OztDRUUFsbGxYp5u3bpBXV1dzOPi4oIbN24gPT1dzOPs7CzZl4uLC2JiYgAASUlJSE5OluTR09ODg4ODmIeIajbGCiIiKgtjxdtjjywiqnTt52yv0v3Fr/KstG0dP34cPXv2RHp6utgQQsrj5cuXmDdvHkaPHg1dXV0AQHJyMoyNjSX51NTUYGhoiOTkZDGPtbW1JI+JiYmYZmBggOTkZHFZ4TyFt1F4veLyFCcnJwc5OTni66ysLIXfL5EyY6wgIqKyMFZQcdgji4iomuTm5lZ3EWqUvLw8jBw5EoIgYNOmTdVdHIWtWLECenp64p+FhUV1F4mI3iOMFUREVJYPLVawIYuIPjg5OTmYMWMGjI2NoampCScnJ5w7dw53795Fz549AQAGBgaQyWQYP368uF5BQQHmzp0LQ0NDmJqaws/PT7LdjIwMTJo0CUZGRtDV1UWvXr1w8eJFMd3Pzw9t2rTB5s2bYW1tDU1Nzap4ux8EeSPWvXv3EBkZKfbGAgBTU1OkpqZK8r969QppaWkwNTUV86SkpEjyyF+XladweuH1istTnPnz5yMzM1P8e/DggcLvm4jeHcYKIiIqC2NF9WBDFhF9cObOnYuff/4Z27Ztw/nz59GkSRO4uLigTp06+PnnnwEAN27cwOPHjxEYGCiut23bNmhrayM2NhYBAQHw9/eXDDQ+YsQIpKam4vfff0d8fDzatWuH3r17Iy0tTcxz+/Zt/Pzzz9i3bx8SEhKq7D3XZPJGrFu3buHo0aOoW7euJN3R0REZGRmIj48Xlx07dgwFBQVwcHAQ80RHRyMvL0/MExkZiWbNmsHAwEDMExUVJdl2ZGQkHB0dAQDW1tYwNTWV5MnKykJsbKyYpzgaGhrQ1dWV/BFR9WOsICKisjBWVA+OkUVEH5Rnz55h06ZN+PHHH8WZ8L7//ntERkZiy5Yt6NixIwDA2Ni4yLPsrVq1wuLFiwEANjY22LhxI6KiotCnTx+cOnUKZ8+eRWpqKjQ0NAAAq1evRnh4OPbu3SvOfJebm4vt27fDyMioit6x8svOzsbt27fF10lJSUhISIChoSHMzMwwfPhwnD9/HgcOHEB+fr44HpWhoSHU1dVha2sLV1dXTJ48GcHBwcjLy4OPjw/c3d1hbm4OABgzZgyWLFkCLy8vzJs3D4mJiQgMDMS6devE/c6cORPdu3fHmjVrMGDAAOzcuRNxcXEICQkBAMhkMsyaNQvLli2DjY0NrK2tsWjRIpibm8PNza3qDhgRvTXGCiIiKgtjRfVhQxYRfVDu3LmDvLw8dOnSRVxWq1YtdOrUCdeuXRMDTnFatWoleW1mZiY+snbx4kVkZ2cX6Q304sUL3LlzR3xtaWn5QQabtxEXFyd2zQaA2bNnAwDGjRsHPz8/7N+/HwDQpk0byXp//PEHevToAQAIDQ2Fj48PevfuDRUVFQwbNgwbNmwQ8+rp6eHIkSOYNm0a2rdvj3r16sHX11e8UACAzp07IywsDAsXLsSCBQtgY2OD8PBwtGzZUswzd+5cPHv2DN7e3sjIyICTkxMiIiI+uO7eRMqOsYKIiMrCWFF92JBFRKSgWrVqSV7LZDIUFBQAeN1ryMzMDMePHy+yXuFfYLS1td9lEWukHj16QBCEEtNLS5MzNDREWFhYqXlatWqFkydPlppnxIgRGDFiRInpMpkM/v7+8Pf3L7NMRFQzMVYQEVFZGCveDhuyiOiD0rhxY6irq+P06dOwtLQE8HqMpXPnzmHWrFlQV1cHAOTn55dru+3atUNycjLU1NRgZWVV2cUmIqIqxFhBRERlYayoPhzsnYg+KNra2vj0008xZ84cRERE4OrVq5g8eTKeP38OLy8vWFpaQiaT4cCBA/jnn3+QnZ2t0HadnZ3h6OgINzc3HDlyBHfv3sWZM2fw1VdfIS4u7h2/KyIiqkyMFUREVBbGiurDhiwi+uD897//xbBhwzB27Fi0a9cOt2/fxuHDh2FgYID69etjyZIl+PLLL2FiYgIfHx+FtimTyXDo0CF069YNEyZMQNOmTeHu7o579+7BxMTkHb8jIiKqbIwVRERUFsaK6iETFBlchCpFVlYW9PT0kJmZWSOnV7/vb1/dRShRQ9/L1V2EGufly5dISkqCtbU1B7JWMqV9djW9nlIGNf0zYKz4sDBWKC/GivdbTf8MGCs+LIwVyqu6YgV7ZBERERERERERkVJgQxYRERERERERESkFNmQREREREZFSi46OxqBBg2Bubg6ZTIbw8HAxLS8vD/PmzYO9vT20tbVhbm4OT09PPHr0SLKNtLQ0eHh4QFdXF/r6+vDy8ioyOPOlS5fQtWtXaGpqwsLCAgEBAUXKsmfPHjRv3hyampqwt7fHoUOHJOmCIMDX1xdmZmbQ0tKCs7Mzbt26VXkHg4iohmNDFhERERERKbVnz56hdevW+Oabb4qkPX/+HOfPn8eiRYtw/vx57Nu3Dzdu3MDgwYMl+Tw8PHDlyhVERkbiwIEDiI6Ohre3t5ielZWFvn37wtLSEvHx8Vi1ahX8/PwQEhIi5jlz5gxGjx4NLy8vXLhwAW5ubnBzc0NiYqKYJyAgABs2bEBwcDBiY2Ohra0NFxcXvHz58h0cGSKimketugtARERERET0Nvr164d+/foVm6anp4fIyEjJso0bN6JTp064f/8+GjZsiGvXriEiIgLnzp1Dhw4dAABBQUHo378/Vq9eDXNzc4SGhiI3NxdbtmyBuro67OzskJCQgLVr14oNXoGBgXB1dcWcOXMAAEuXLkVkZCQ2btyI4OBgCIKA9evXY+HChRgyZAgAYPv27TAxMUF4eDjc3d3f1SEiIqox2COLiIiIiIg+KJmZmZDJZNDX1wcAxMTEQF9fX2zEAgBnZ2eoqKggNjZWzNOtWzeoq6uLeVxcXHDjxg2kp6eLeZydnSX7cnFxQUxMDAAgKSkJycnJkjx6enpwcHAQ8xARUenYI4uIiIiIiD4YL1++xLx58zB69GhxSvjk5GQYGxtL8qmpqcHQ0BDJycliHmtra0keExMTMc3AwADJycnissJ5Cm+j8HrF5SlOTk4OcnJyxNdZWVkKv18iopqmWntklTYoI6DYQIgclJGIiIiIiBSRl5eHkSNHQhAEbNq0qbqLo7AVK1ZAT09P/LOwsKjuIhERVZtqbcgqbVBGQLGBEDkoIxERERERlUXeiHXv3j1ERkaKvbEAwNTUFKmpqZL8r169QlpaGkxNTcU8KSkpkjzy12XlKZxeeL3i8hRn/vz5yMzMFP8ePHig8PsmIqppqrUhq1+/fli2bBmGDh1aJO3NgRBbtWqF7du349GjR2LPLfmgjJs3b4aDgwOcnJwQFBSEnTt3itPpFh6U0c7ODu7u7pgxYwbWrl0r7qvwoIy2trZYunQp2rVrh40bNypcFiKiyuDn54c2bdpUdzGIiOg9xlhRfvJGrFu3buHo0aOoW7euJN3R0REZGRmIj48Xlx07dgwFBQVwcHAQ80RHRyMvL0/MExkZiWbNmsHAwEDMExUVJdl2ZGQkHB0dAQDW1tYwNTWV5MnKykJsbKyYpzgaGhrQ1dWV/BERlaYmx4r3doyssgZCdHd3L3NQxqFDh5Y4KOPKlSuRnp4OAwMDxMTEYPbs2ZL9u7i4iI1UipSFiP7PfX/7Kt1fQ9/LVbq/yiKTyfDLL7/Azc1NXPbFF19g+vTp1VcoIqIqwlihGMYKxWRnZ+P27dvi66SkJCQkJMDQ0BBmZmYYPnw4zp8/jwMHDiA/P18cj8rQ0BDq6uqwtbWFq6srJk+ejODgYOTl5cHHxwfu7u4wNzcHAIwZMwZLliyBl5cX5s2bh8TERAQGBmLdunXifmfOnInu3btjzZo1GDBgAHbu3Im4uDjxaRCZTIZZs2Zh2bJlsLGxgbW1NRYtWgRzc3PJZ0xErzFWKOZDixXvbUOWIgMhclBGIqppdHR0oKOjU93FICKi9xhjRVFxcXHo2bOn+Fr+I/W4cePg5+eH/fv3A0CR3gl//PEHevToAeD1kxw+Pj7o3bs3VFRUMGzYMGzYsEHMq6enhyNHjmDatGlo37496tWrB19fX8mwJp07d0ZYWBgWLlyIBQsWwMbGBuHh4WjZsqWYZ+7cuXj27Bm8vb2RkZEBJycnREREQFNTs7IPCxF9wGpyrKjWRwtrOg7KSPR+6tGjB2bMmIG5c+fC0NAQpqam8PPzE9MzMjIwadIkGBkZQVdXF7169cLFixcl21i2bBmMjY1Rp04dTJo0CV9++aXk4vjcuXPo06cP6tWrBz09PXTv3h3nz58X062srAAAQ4cOhUwmE18X7gJ85MgRaGpqIiMjQ7LvmTNnolevXuLrU6dOoWvXrtDS0oKFhQVmzJiBZ8+evfVxIiL6kDFWKJcePXpAEIQifz/++COsrKyKTRMEQWzEAl73zgoLC8PTp0+RmZmJLVu2FLkJbNWqFU6ePImXL1/i77//xrx584qUZcSIEbhx4wZycnKQmJiI/v37S9JlMhn8/f2RnJyMly9f4ujRo2jatOk7OS5E9G4xVlSP97YhS5GBEDkoIxFV1LZt26CtrY3Y2FgEBATA398fkZGRAF5fgKampuL3339HfHw82rVrh969eyMtLQ3A619sly9fjpUrVyI+Ph4NGzYsMvPR06dPMW7cOJw6dQp//vknbGxs0L9/fzx9+hTA64AEAFu3bsXjx4/F14X17t0b+vr6+Pnnn8Vl+fn52LVrFzw8PAAAd+7cgaurK4YNG4ZLly5h165dOHXqFHx8fCr/oBERfWAYK4iIqCyMFVXvvW3IUmQgRA7KSEQV1apVKyxevBg2Njbw9PREhw4dEBUVhVOnTuHs2bPYs2cPOnToABsbG6xevRr6+vrYu3cvACAoKAheXl6YMGECmjZtCl9fX9jbS5/f79WrFz7++GM0b94ctra2CAkJwfPnz3HixAkAgJGREQBAX18fpqam4uvCVFVV4e7ujrCwMHFZVFQUMjIyMGzYMACve356eHhg1qxZsLGxQefOnbFhwwZs376ds6oSEb0lxgoiIioLY0XVq9aGrOzsbCQkJCAhIQHA/w3KeP/+fclAiPv378fly5fh6ekpGQix8KCMZ8+exenTp4sdlFFdXR1eXl64cuUKdu3ahcDAQMng7jNnzkRERATWrFmD69evw8/PD3FxcWLLoyJlISLl0qpVK8lrMzMzpKam4uLFi8jOzkbdunXF58p1dHSQlJSEO3fuAABu3LiBTp06SdZ/83VKSgomT54MGxsb6OnpQVdXF9nZ2bh//365yunh4YHjx49LZmIdMGAA9PX1AQAXL17Ejz/+KCmri4sLCgoKkJSUVK59ERGRFGMFERGVhbGi6lXrYO+lDcr4448/KjQQIgdlJKKKqFWrluS1TCZDQUEBsrOzYWZmhuPHjxdZR17JK2LcuHF48uQJAgMDYWlpCQ0NDTg6OiI3N7dc5ezYsSMaN26MnTt34tNPP8Uvv/yCH3/8UUzPzs7GlClTMGPGjCLrNmzYsFz7IiIiKcYKIiIqC2NF1avWhiz5oIwlkQ+E6O/vX2Ie+aCMpZEPyliaESNGYMSIEW9VFiJSfu3atUNycjLU1NTEgRLf1KxZM5w7dw6enp7isjefRT99+jS+/fZbcYDXBw8e4N9//5XkqVWrFvLz88ssk4eHB0JDQ9GgQQOoqKhgwIABkvJevXoVTZo0UfQtEhHRW2KsICKisjBWvDvv7RhZRETVwdnZGY6OjnBzc8ORI0dw9+5dnDlzBl999RXi4uIAANOnT8cPP/yAbdu24datW1i2bBkuXboEmUwmbsfGxgY7duzAtWvXEBsbCw8PD2hpaUn2ZWVlhaioKCQnJyM9Pb3EMnl4eOD8+fNYvnw5hg8fDg0NDTFt3rx5OHPmDHx8fJCQkIBbt27h119/fS8HZSQiqikYK4iIqCyMFe8OG7KIiAqRyWQ4dOgQunXrJg666O7ujnv37sHExATA6wAwf/58fPHFF2jXrh2SkpIwfvx4yaPGP/zwA9LT09GuXTuMHTsWM2bMgLGxsWRfa9asQWRkJCwsLNC2bdsSy9SkSRN06tQJly5dEmcVkWvVqhVOnDiBmzdvomvXrmjbti18fX3FcQKJiKjyMVYQEVFZGCveHZlQ2rN9VKmysrKgp6eHzMzMGjmD4X1/+7IzVZOGvperuwg1zsuXL5GUlARra2uOFQegT58+MDU1xY4dO6q7KGUq7bOr6fWUMqjpnwFjxYeFsUKKsYIqS03/DBgrPiyMFVKMFWWr1jGyiIiU0fPnzxEcHAwXFxeoqqrip59+wtGjRxEZGVndRSMiovcEYwUREZWFsaJi2JBFRFRO8m7Cy5cvx8uXL9GsWTP8/PPPcHZ2ru6iERHRe4KxgoiIysJYUTFsyCIiKictLS0cPXq0uotBRETvMcYKIiIqC2NFxXCwdyIiIiIiIiIiUgpsyCIiIiIiIiIiIqXAhiwiIiIiIiIiIlIKbMgiIiIiIiIiIiKlwIYsIiIiIiIiIiJSCmzIIiIiIiIiIiIipcCGLCKiSnT8+HHIZDJkZGSUms/Kygrr16+vkjIREdH7hbGCiIjKwlhRMrXqLgAR1TxdgrpU6f5OTz9dpfsrTefOnfH48WPo6ekBAH788UfMmjWrSAA6d+4ctLW1q6GEyic6OhqrVq1CfHw8Hj9+jF9++QVubm5iuiAIWLx4Mb7//ntkZGSgS5cu2LRpE2xsbMQ8aWlpmD59On777TeoqKhg2LBhCAwMhI6Ojpjn0qVLmDZtGs6dOwcjIyNMnz4dc+fOlZRlz549WLRoEe7evQsbGxusXLkS/fv3L1dZiOg1xgrGCiKisjBWMFYUhz2yiIgqkbq6OkxNTSGTyUrNZ2RkhNq1a1dRqZTbs2fP0Lp1a3zzzTfFpgcEBGDDhg0IDg5GbGwstLW14eLigpcvX4p5PDw8cOXKFURGRuLAgQOIjo6Gt7e3mJ6VlYW+ffvC0tIS8fHxWLVqFfz8/BASEiLmOXPmDEaPHg0vLy9cuHABbm5ucHNzQ2JiYrnKQkTEWEFERGVhrCgZG7KI6IPTo0cP+Pj4wMfHB3p6eqhXrx4WLVoEQRAAAOnp6fD09ISBgQFq166Nfv364datW+L69+7dw6BBg2BgYABtbW3Y2dnh0KFDAKRdgI8fP44JEyYgMzMTMpkMMpkMfn5+AKRdgMeMGYNRo0ZJypiXl4d69eph+/btAICCggKsWLEC1tbW0NLSQuvWrbF37953fKTeD/369cOyZcswdOjQImmCIGD9+vVYuHAhhgwZglatWmH79u149OgRwsPDAQDXrl1DREQENm/eDAcHBzg5OSEoKAg7d+7Eo0ePAAChoaHIzc3Fli1bYGdnB3d3d8yYMQNr164V9xUYGAhXV1fMmTMHtra2WLp0Kdq1a4eNGzcqXBYiUh6MFUREVBbGiurBhiwi+iBt27YNampqOHv2LAIDA7F27Vps3rwZADB+/HjExcVh//79iImJgSAI6N+/P/Ly8gAA06ZNQ05ODqKjo3H58mWsXLlS8oiaXOfOnbF+/Xro6uri8ePHePz4Mb744osi+Tw8PPDbb78hOztbXHb48GE8f/5cbLxZsWIFtm/fjuDgYFy5cgWfffYZPv74Y5w4ceJdHB6lkZSUhOTkZDg7O4vL9PT04ODggJiYGABATEwM9PX10aFDBzGPs7MzVFRUEBsbK+bp1q0b1NXVxTwuLi64ceMG0tPTxTyF9yPPI9+PImUhIuXCWEFERGVhrKh6HCOLiD5IFhYWWLduHWQyGZo1a4bLly9j3bp16NGjB/bv34/Tp0+jc+fOAF731rGwsEB4eDhGjBiB+/fvY9iwYbC3twcANGrUqNh9qKurQ09PDzKZDKampiWWxcXFBdra2vjll18wduxYAEBYWBgGDx6MOnXqICcnB19//TWOHj0KR0dHcZ+nTp3Cd999h+7du1fmoVEqycnJAAATExPJchMTEzEtOTkZxsbGknQ1NTUYGhpK8lhbWxfZhjzNwMAAycnJZe6nrLIUJycnBzk5OeLrrKysUt4xEVUlxgoiIioLY0XVY48sIvog/ec//5E8b+7o6Ihbt27h6tWrUFNTg4ODg5hWt25dNGvWDNeuXQMAzJgxA8uWLUOXLl2wePFiXLp06a3KoqamhpEjRyI0NBTA6zGhfv31V3h4eAAAbt++jefPn6NPnz7Q0dER/7Zv3447d+681b6p+q1YsQJ6enrin4WFRXUXiYj+P8YKIiIqC2NF1WNDFhFROU2aNAl//fUXxo4di8uXL6NDhw4ICgp6q216eHggKioKqampCA8Ph5aWFlxdXQFA7Bp88OBBJCQkiH9Xr15VuufZK5v8F6mUlBTJ8pSUFDHN1NQUqampkvRXr14hLS1Nkqe4bRTeR0l5CqeXVZbizJ8/H5mZmeLfgwcPynjXRKQMGCuIiKgsjBUVw4YsIvogycdGkvvzzz9hY2ODFi1a4NWrV5L0J0+e4MaNG2jRooW4zMLCAp988gn27duHzz//HN9//32x+1FXV0d+fn6Z5encuTMsLCywa9cuhIaGYsSIEahVqxYAoEWLFtDQ0MD9+/fRpEkTyd+H3nvH2toapqamiIqKEpdlZWUhNjZW7C7t6OiIjIwMxMfHi3mOHTuGgoIC8RcyR0dHREdHi+MVAEBkZCSaNWsGAwMDMU/h/cjzyPejSFmKo6GhAV1dXckfEb0fGCuIiKgsjBVVj2NkEdEH6f79+5g9ezamTJmC8+fPIygoCGvWrIGNjQ2GDBmCyZMn47vvvkOdOnXw5Zdfon79+hgyZAgAYNasWejXrx+aNm2K9PR0/PHHH7C1tS12P1ZWVsjOzkZUVBRat26N2rVrlzg97pgxYxAcHIybN2/ijz/+EJfXqVMHX3zxBT777DMUFBTAyckJmZmZOH36NHR1dTFu3LjKP0DvkezsbNy+fVt8nZSUhISEBBgaGqJhw4aYNWsWli1bBhsbG1hbW2PRokUwNzeHm5sbAMDW1haurq6YPHkygoODkZeXBx8fH7i7u8Pc3BzA62O/ZMkSeHl5Yd68eUhMTERgYCDWrVsn7nfmzJno3r071qxZgwEDBmDnzp2Ii4tDSEgIAEAmk5VZFiJSLowVRERUFsaKqsceWUT0QfL09MSLFy/QqVMnTJs2DTNnzoS3tzcAYOvWrWjfvj0GDhwIR0dHCIKAQ4cOib9k5OfnY9q0aWIDSdOmTfHtt98Wu5/OnTvjk08+wahRo2BkZISAgIASy+Th4YGrV6+ifv366NKliyRt6dKlWLRoEVasWCHu9+DBg0UGKK+J4uLi0LZtW7Rt2xYAMHv2bLRt2xa+vr4AgLlz52L69Onw9vZGx44dkZ2djYiICGhqaorbCA0NRfPmzdG7d2/0798fTk5OYgMU8Hp2wSNHjiApKQnt27fH559/Dl9fX/E7Abz+LMPCwhASEiJOUxweHo6WLVuKeRQpCxEpD8YKIiIqC2NF1ZMJgiBUdyE+FFlZWdDT00NmZmaNfHTkvr99dRehRA19L1d3EWqcly9fIikpCdbW1kp3k96jRw+0adMG69evr+6iVIvSPruaXk8pg5r+GTBWfFgYK5QXY8X7raZ/BowVHxbGCuVVXbGCPbKIiIiIiIiIiEgpsCGLiIiIiIiIiIiUAgd7J6IPzvHjx6u7CERE9J5jrCAiorIwVlQP9sgiIiIiIiIiIiKlwIYsInornC9C+fAzI6KqxnpH+SjbZxYdHY1BgwbB3NwcMpkM4eHhknRBEODr6wszMzNoaWnB2dkZt27dkuRJS0uDh4cHdHV1oa+vDy8vL2RnZ0vyXLp0CV27doWmpiYsLCyKnTVsz549aN68OTQ1NWFvb49Dhw6VuyxEHyJlq3eo+j4zNmQRUYXIp4x9/vx5NZeEykv+mck/QyKid4WxQnkpW6x49uwZWrdujW+++abY9ICAAGzYsAHBwcGIjY2FtrY2XFxc8PLlSzGPh4cHrly5gsjISBw4cADR0dHw9vYW07OystC3b19YWloiPj4eq1atgp+fH0JCQsQ8Z86cwejRo+Hl5YULFy7Azc0Nbm5uSExMLFdZiD4kjBXKq7piBcfIIqIKUVVVhb6+PlJTUwEAtWvXhkwmq+ZSUWkEQcDz58+RmpoKfX19qKqqVneRiKiGY6xQPsoaK/r164d+/foVmyYIAtavX4+FCxdiyJAhAIDt27fDxMQE4eHhcHd3x7Vr1xAREYFz586hQ4cOAICgoCD0798fq1evhrm5OUJDQ5Gbm4stW7ZAXV0ddnZ2SEhIwNq1a8UGr8DAQLi6umLOnDkAgKVLlyIyMhIbN25EcHCwQmUh+tAwViif6o4VbMgiogozNTUFADHokHLQ19cXPzsioneNsUI51aRYkZSUhOTkZDg7O4vL9PT04ODggJiYGLi7uyMmJgb6+vpiIxYAODs7Q0VFBbGxsRg6dChiYmLQrVs3qKuri3lcXFywcuVKpKenw8DAADExMZg9e7Zk/y4uLuKjjoqUpTg5OTnIyckRX2dlZb3VMSF63zBWKKfqihVsyCKiCpPJZDAzM4OxsTHy8vKquzikgFq1ainNr+tEVDMwViifmhYrkpOTAQAmJiaS5SYmJmJacnIyjI2NJelqamowNDSU5LG2ti6yDXmagYEBkpOTy9xPWWUpzooVK7BkyZKy3yyRkmKsUD7VGSvYkEVEb01VVbVGXfASEVHlY6wgqrj58+dLenplZWXBwsKiGktE9G4wVpAiONg7ERERERHVWPLHXlJSUiTLU1JSxDRTU9MijzS9evUKaWlpkjzFbaPwPkrKUzi9rLIUR0NDA7q6upI/IqIPFRuyiIiIiIioxrK2toapqSmioqLEZVlZWYiNjYWjoyMAwNHRERkZGYiPjxfzHDt2DAUFBXBwcBDzREdHSx57ioyMRLNmzWBgYCDmKbwfeR75fhQpCxERlY4NWUREREREpNSys7ORkJCAhIQEAK8HVU9ISMD9+/chk8kwa9YsLFu2DPv378fly5fh6ekJc3NzuLm5AQBsbW3h6uqKyZMn4+zZszh9+jR8fHzg7u4Oc3NzAMCYMWOgrq4OLy8vXLlyBbt27UJgYKDkkb+ZM2ciIiICa9aswfXr1+Hn54e4uDj4+PgAgEJlISKi0nGMLCIiIiIiUmpxcXHo2bOn+FreuDRu3Dj8+OOPmDt3Lp49ewZvb29kZGTAyckJERER0NTUFNcJDQ2Fj48PevfuDRUVFQwbNgwbNmwQ0/X09HDkyBFMmzYN7du3R7169eDr6wtvb28xT+fOnREWFoaFCxdiwYIFsLGxQXh4OFq2bCnmUaQsRERUMpkgCEJ1F+JDkZWVBT09PWRmZtbI59rv+9tXdxFK1ND3cnUXgUgp1PR6ShnU9M+AsYJI+dX0ekoZ1PTPgLGCSPm9y3qKjxYSEREREREREZFSeK8bsvLz87Fo0SJYW1tDS0sLjRs3xtKlS1G4E5kgCPD19YWZmRm0tLTg7OyMW7duSbaTlpYGDw8P6OrqQl9fH15eXsjOzpbkuXTpErp27QpNTU1YWFggICCgSHn27NmD5s2bQ1NTE/b29jh06NC7eeNERERERERERFTEe92QtXLlSmzatAkbN27EtWvXsHLlSgQEBCAoKEjMExAQgA0bNiA4OBixsbHQ1taGi4sLXr58Kebx8PDAlStXEBkZiQMHDiA6OlryLHtWVhb69u0LS0tLxMfHY9WqVfDz80NISIiY58yZMxg9ejS8vLxw4cIFuLm5wc3NDYmJiVVzMIiIiIiIiIiIPnDvdUPWmTNnMGTIEAwYMABWVlYYPnw4+vbti7NnzwJ43Rtr/fr1WLhwIYYMGYJWrVph+/btePToEcLDwwEA165dQ0REBDZv3gwHBwc4OTkhKCgIO3fuxKNHjwC8HtgxNzcXW7ZsgZ2dHdzd3TFjxgysXbtWLEtgYCBcXV0xZ84c2NraYunSpWjXrh02btxY5ceFiIiIiIiIiOhD9F43ZHXu3BlRUVG4efMmAODixYs4deoU+vXrB+D1tLrJyclwdnYW19HT04ODgwNiYmIAADExMdDX10eHDh3EPM7OzlBRUUFsbKyYp1u3blBXVxfzuLi44MaNG0hPTxfzFN6PPI98P0RERERERERE9G6pVXcBSvPll18iKysLzZs3h6qqKvLz87F8+XJ4eHgAAJKTkwEAJiYmkvVMTEzEtOTkZBgbG0vS1dTUYGhoKMljbW1dZBvyNAMDAyQnJ5e6n+Lk5OQgJydHfJ2VlaXweyciIiIiIiIiIqn3ukfW7t27ERoairCwMJw/fx7btm3D6tWrsW3btuoumkJWrFgBPT098c/CwqK6i0REREREREREpLTe64asOXPm4Msvv4S7uzvs7e0xduxYfPbZZ1ixYgUAwNTUFACQkpIiWS8lJUVMMzU1RWpqqiT91atXSEtLk+QpbhuF91FSHnl6cebPn4/MzEzx78GDB+V6/0RERERERERE9H/e64as58+fQ0VFWkRVVVUUFBQAAKytrWFqaoqoqCgxPSsrC7GxsXB0dAQAODo6IiMjA/Hx8WKeY8eOoaCgAA4ODmKe6Oho5OXliXkiIyPRrFkzGBgYiHkK70eeR76f4mhoaEBXV1fyR0REREREREREFfNeN2QNGjQIy5cvx8GDB3H37l388ssvWLt2LYYOHQoAkMlkmDVrFpYtW4b9+/fj8uXL8PT0hLm5Odzc3AAAtra2cHV1xeTJk3H27FmcPn0aPj4+cHd3h7m5OQBgzJgxUFdXh5eXF65cuYJdu3YhMDAQs2fPFssyc+ZMREREYM2aNbh+/Tr8/PwQFxcHHx+fKj8uREREREREREQfovd6sPegoCAsWrQIU6dORWpqKszNzTFlyhT4+vqKeebOnYtnz57B29sbGRkZcHJyQkREBDQ1NcU8oaGh8PHxQe/evaGiooJhw4Zhw4YNYrqenh6OHDmCadOmoX379qhXrx58fX3h7e0t5uncuTPCwsKwcOFCLFiwADY2NggPD0fLli2r5mAQEREREREREX3gZIIgCNVdiA9FVlYW9PT0kJmZWSMfM7zvb1/dRShRQ9/L1V0EIqVQ0+spZVDTPwPGCiLlV9PrKWVQ0z8Dxgoi5fcu66n3+tFCIiIiIiIiIiIiOTZkERERERERERGRUmBDFhERERERERERKQU2ZBERERERERERkVJgQxYRERERERERESkFNmQREREREREREZFSYEMWEREREREREREpBTZkERERERERERGRUmBDFhERKbX8/HwsWrQI1tbW0NLSQuPGjbF06VIIgiDmEQQBvr6+MDMzg5aWFpydnXHr1i3JdtLS0uDh4QFdXV3o6+vDy8sL2dnZkjyXLl1C165doampCQsLCwQEBBQpz549e9C8eXNoamrC3t4ehw4dejdvnIiIiIjoA8SGLCIiUmorV67Epk2bsHHjRly7dg0rV65EQEAAgoKCxDwBAQHYsGEDgoODERsbC21tbbi4uODly5diHg8PD1y5cgWRkZE4cOAAoqOj4e3tLaZnZWWhb9++sLS0RHx8PFatWgU/Pz+EhISIec6cOYPRo0fDy8sLFy5cgJubG9zc3JCYmFg1B4OIiIiIqIZjQxYRESm1M2fOYMiQIRgwYACsrKwwfPhw9O3bF2fPngXwujfW+vXrsXDhQgwZMgStWrXC9u3b8ejRI4SHhwMArl27hoiICGzevBkODg5wcnJCUFAQdu7ciUePHgEAQkNDkZubiy1btsDOzg7u7u6YMWMG1q5dK5YlMDAQrq6umDNnDmxtbbF06VK0a9cOGzdurPLjQkRERERUE7Ehi4iIlFrnzp0RFRWFmzdvAgAuXryIU6dOoV+/fgCApKQkJCcnw9nZWVxHT08PDg4OiImJAQDExMRAX18fHTp0EPM4OztDRUUFsbGxYp5u3bpBXV1dzOPi4oIbN24gPT1dzFN4P/I88v0QEREREdHbUavuAhAREb2NL7/8EllZWWjevDlUVVWRn5+P5cuXw8PDAwCQnJwMADAxMZGsZ2JiIqYlJyfD2NhYkq6mpgZDQ0NJHmtr6yLbkKcZGBggOTm51P0UJycnBzk5OeLrrKwshd87EREREdGHhj2yiIhIqe3evRuhoaEICwvD+fPnsW3bNqxevRrbtm2r7qIpZMWKFdDT0xP/LCwsqrtIRERERETvLTZkERGRUpszZw6+/PJLuLu7w97eHmPHjsVnn32GFStWAABMTU0BACkpKZL1UlJSxDRTU1OkpqZK0l+9eoW0tDRJnuK2UXgfJeWRpxdn/vz5yMzMFP8ePHhQrvdPRERERPQhYUMWEREptefPn0NFRRrOVFVVUVBQAACwtraGqakpoqKixPSsrCzExsbC0dERAODo6IiMjAzEx8eLeY4dO4aCggI4ODiIeaKjo5GXlyfmiYyMRLNmzWBgYCDmKbwfeR75foqjoaEBXV1dyR8RERERERWPDVlERKTUBg0ahOXLl+PgwYO4e/cufvnlF6xduxZDhw4FAMhkMsyaNQvLli3D/v37cfnyZXh6esLc3Bxubm4AAFtbW7i6umLy5Mk4e/YsTp8+DR8fH7i7u8Pc3BwAMGbMGKirq8PLywtXrlzBrl27EBgYiNmzZ4tlmTlzJiIiIrBmzRpcv34dfn5+iIuLg4+PT5UfFyIiIiKimoiDvRMRkVILCgrCokWLMHXqVKSmpsLc3BxTpkyBr6+vmGfu3Ll49uwZvL29kZGRAScnJ0REREBTU1PMExoaCh8fH/Tu3RsqKioYNmwYNmzYIKbr6enhyJEjmDZtGtq3b4969erB19cX3t7eYp7OnTsjLCwMCxcuxIIFC2BjY4Pw8HC0bNmyag4GEREREVENJxMEQajuQnwosrKyoKenh8zMzBr56Mh9f/vqLkKJGvperu4iECmFml5PKYOa/hkwVhApv5peTymDmv4ZMFYQKb93WU/x0UIiIiIiIiIiIlIKbMgiIiIiIqIaLT8/H4sWLYK1tTW0tLTQuHFjLF26FIUfThEEAb6+vjAzM4OWlhacnZ1x69YtyXbS0tLg4eEBXV1d6Ovrw8vLC9nZ2ZI8ly5dQteuXaGpqQkLCwsEBAQUKc+ePXvQvHlzaGpqwt7eHocOHXo3b5yIqAZiQxYREREREdVoK1euxKZNm7Bx40Zcu3YNK1euREBAAIKCgsQ8AQEB2LBhA4KDgxEbGwttbW24uLjg5cuXYh4PDw9cuXIFkZGROHDgAKKjoyVjJWZlZaFv376wtLREfHw8Vq1aBT8/P4SEhIh5zpw5g9GjR8PLywsXLlyAm5sb3NzckJiYWDUHg4hIybEhi4iIiIiIarQzZ85gyJAhGDBgAKysrDB8+HD07dsXZ8+eBfC6N9b69euxcOFCDBkyBK1atcL27dvx6NEjhIeHAwCuXbuGiIgIbN68GQ4ODnByckJQUBB27tyJR48eAXg9cUhubi62bNkCOzs7uLu7Y8aMGVi7dq1YlsDAQLi6umLOnDmwtbXF0qVL0a5dO2zcuLHKjwsRkTKqUENWo0aN8OTJkyLLMzIy0KhRo7cuFBERKT/GCiIiKktVxYrOnTsjKioKN2/eBABcvHgRp06dQr9+/QAASUlJSE5OhrOzs7iOnp4eHBwcEBMTAwCIiYmBvr4+OnToIOZxdnaGiooKYmNjxTzdunWDurq6mMfFxQU3btxAenq6mKfwfuR55PspTk5ODrKysiR/REQfKrWKrHT37l3k5+cXWZ6Tk4OHDx++daGIiEj5MVYQEVFZqipWfPnll8jKykLz5s2hqqqK/Px8LF++HB4eHgCA5ORkAICJiYlkPRMTEzEtOTkZxsbGknQ1NTUYGhpK8lhbWxfZhjzNwMAAycnJpe6nOCtWrMCSJUvK+7aJiGqkcjVk7d+/X/z/4cOHoaenJ77Oz89HVFQUrKysKq1wRESkfBgriIioLFUdK3bv3o3Q0FCEhYXBzs4OCQkJmDVrFszNzTFu3LhK28+7Mn/+fMyePVt8nZWVBQsLi2osERFR9SlXQ5abmxsAQCaTFanwa9WqBSsrK6xZs6bSCkdERMqHsYKIiMpS1bFizpw5+PLLL+Hu7g4AsLe3x71797BixQqMGzcOpqamAICUlBSYmZmJ66WkpKBNmzYAAFNTU6Smpkq2++rVK6SlpYnrm5qaIiUlRZJH/rqsPPL04mhoaEBDQ6O8b5uIqEYq1xhZBQUFKCgoQMOGDZGamiq+LigoQE5ODm7cuIGBAwe+q7ISEZESYKwgIqKyVHWseP78OVRUpLc+qqqqKCgoAABYW1vD1NQUUVFRYnpWVhZiY2Ph6OgIAHB0dERGRgbi4+PFPMeOHUNBQQEcHBzEPNHR0cjLyxPzREZGolmzZjAwMBDzFN6PPI98P0REVLoKjZGVlJRU2eUgIqIahrGCiIjKUlWxYtCgQVi+fDkaNmwIOzs7XLhwAWvXrsXEiRMBvO4ZNmvWLCxbtgw2NjawtrbGokWLYG5uLvYes7W1haurKyZPnozg4GDk5eXBx8cH7u7uMDc3BwCMGTMGS5YsgZeXF+bNm4fExEQEBgZi3bp1YllmzpyJ7t27Y82aNRgwYAB27tyJuLg4hISEVMmxICJSdhVqyAKAqKgoREVFib+gFLZly5a3LhgRESk/xgoiIipLVcSKoKAgLFq0CFOnTkVqairMzc0xZcoU+Pr6innmzp2LZ8+ewdvbGxkZGXByckJERAQ0NTXFPKGhofDx8UHv3r2hoqKCYcOGYcOGDWK6np4ejhw5gmnTpqF9+/aoV68efH194e3tLebp3LkzwsLCsHDhQixYsAA2NjYIDw9Hy5YtK+W9EhHVdBVqyFqyZAn8/f3RoUMHmJmZQSaTVXa5iIhIyTFWEBFRWaoqVtSpUwfr16/H+vXrS8wjk8ng7+8Pf3//EvMYGhoiLCys1H21atUKJ0+eLDXPiBEjMGLEiFLzEBFR8SrUkBUcHIwff/wRY8eOrezyEBFRDcFYQUREZWGsICKi8irXYO9yubm56Ny5c2WXhYiIahDGCiIiKgtjBRERlVeFGrImTZpUZpdaIiL6sDFWEBFRWRgriIiovCr0aOHLly8REhKCo0ePolWrVqhVq5Ykfe3atZVSOCIiUl6MFUREVBbGCiIiKq8KNWRdunQJbdq0AQAkJiZK0jiYLxERAYwVRERUNsYKIiIqrwo1ZP3xxx+VXQ4iIqphGCuIiKgsjBVERFReFRoji4iIiIiIiIiIqKpVqEdWz549S+3qe+zYsQoXiIiIagbGCiIiKgtjBRERlVeFGrLkz7HL5eXlISEhAYmJiRg3blxllIuIiJQcYwUREZWFsYKIiMqrQg1Z69atK3a5n58fsrOz36pAb3r48CHmzZuH33//Hc+fP0eTJk2wdetWdOjQAQAgCAIWL16M77//HhkZGejSpQs2bdoEGxsbcRtpaWmYPn06fvvtN6ioqGDYsGEIDAyEjo6OmOfSpUuYNm0azp07ByMjI0yfPh1z586VlGXPnj1YtGgR7t69CxsbG6xcuRL9+/ev1PdLRFRTVGWsICIi5cRYQURE5VWpY2R9/PHH2LJlS6VtLz09HV26dEGtWrXw+++/4+rVq1izZg0MDAzEPAEBAdiwYQOCg4MRGxsLbW1tuLi44OXLl2IeDw8PXLlyBZGRkThw4ACio6Ph7e0tpmdlZaFv376wtLREfHw8Vq1aBT8/P4SEhIh5zpw5g9GjR8PLywsXLlyAm5sb3NzcisyuQkREpavsWEFERDUPYwUREZWkQj2yShITEwNNTc1K297KlSthYWGBrVu3isusra3F/wuCgPXr12PhwoUYMmQIAGD79u0wMTFBeHg43N3dce3aNURERODcuXNiL66goCD0798fq1evhrm5OUJDQ5Gbm4stW7ZAXV0ddnZ2SEhIwNq1a8UGr8DAQLi6umLOnDkAgKVLlyIyMhIbN25EcHBwpb1nIqKarrJjBRER1TyMFUREVJIKNWR99NFHkteCIODx48eIi4vDokWLKqVgALB//364uLhgxIgROHHiBOrXr4+pU6di8uTJAICkpCQkJyfD2dlZXEdPTw8ODg6IiYmBu7s7YmJioK+vLzZiAYCzszNUVFQQGxuLoUOHIiYmBt26dYO6urqYx8XFBStXrkR6ejoMDAwQExOD2bNnS8rn4uKC8PDwSnu/REQ1SVXFCiIiUl6MFUREVF4VasjS09OTvFZRUUGzZs3g7++Pvn37VkrBAOCvv/7Cpk2bMHv2bCxYsADnzp3DjBkzoK6ujnHjxiE5ORkAYGJiIlnPxMRETEtOToaxsbEkXU1NDYaGhpI8hXt6Fd5mcnIyDAwMkJycXOp+ipOTk4OcnBzxdVZWVnnePhGRUquqWEFERMqLsYKIiMqrQg1ZhR/1e5cKCgrQoUMHfP311wCAtm3bIjExEcHBwUoxi8mKFSuwZMmS6i4GEVG1qKpYQUREyouxgoiIyuutxsiKj4/HtWvXAAB2dnZo27ZtpRRKzszMDC1atJAss7W1xc8//wwAMDU1BQCkpKTAzMxMzJOSkiJO5WtqaorU1FTJNl69eoW0tDRxfVNTU6SkpEjyyF+XlUeeXpz58+dLHkfMysqChYVF6W+aiKiGedexgoiIlB9jBRERKapCDVmpqalwd3fH8ePHoa+vDwDIyMhAz549sXPnThgZGVVK4bp06YIbN25Ilt28eROWlpYAXg/8bmpqiqioKLHhKisrC7Gxsfj0008BAI6OjsjIyEB8fDzat28PADh27BgKCgrg4OAg5vnqq6+Ql5eHWrVqAQAiIyPRrFkzcYZER0dHREVFYdasWWJZIiMj4ejoWGL5NTQ0oKGh8fYHgohICVVVrCAiIuXFWEFEROWlUpGVpk+fjqdPn+LKlStIS0tDWloaEhMTkZWVhRkzZlRa4T777DP8+eef+Prrr3H79m2EhYUhJCQE06ZNAwDIZDLMmjULy5Ytw/79+3H58mV4enrC3Nwcbm5uAF734HJ1dcXkyZNx9uxZnD59Gj4+PnB3d4e5uTkAYMyYMVBXV4eXlxeuXLmCXbt2ITAwUNKbaubMmYiIiMCaNWtw/fp1+Pn5IS4uDj4+PpX2fomIapKqihVERKS8GCuIiKi8KtQjKyIiAkePHoWtra24rEWLFvjmm28qdVDGjh074pdffsH8+fPh7+8Pa2trrF+/Hh4eHmKeuXPn4tmzZ/D29kZGRgacnJwQEREhma43NDQUPj4+6N27N1RUVDBs2DBs2LBBTNfT08ORI0cwbdo0tG/fHvXq1YOvry+8vb3FPJ07d0ZYWBgWLlyIBQsWwMbGBuHh4WjZsmWlvV8iopqkqmIFEREpL8YKIiIqrwo1ZBUUFIiP4BVWq1YtFBQUvHWhChs4cCAGDhxYYrpMJoO/vz/8/f1LzGNoaIiwsLBS99OqVSucPHmy1DwjRozAiBEjSi8wEREBqNpYQUREyomxgoiIyqtCjxb26tULM2fOxKNHj8RlDx8+xGeffYbevXtXWuGIiEh5MVYQEVFZGCuIiKi8KtSQtXHjRmRlZcHKygqNGzdG48aNYW1tjaysLAQFBVV2GYmISAkxVhARUVkYK4iIqLwq9GihhYUFzp8/j6NHj+L69esAXg+q7uzsXKmFIyIi5cVYQUREZWGsICKi8ipXj6xjx46hRYsWyMrKgkwmQ58+fTB9+nRMnz4dHTt2hJ2dXZnjTBERUc1WHbHi4cOH+Pjjj1G3bl1oaWnB3t4ecXFxYrogCPD19YWZmRm0tLTg7OyMW7duSbaRlpYGDw8P6OrqQl9fH15eXsjOzpbkuXTpErp27QpNTU1YWFggICCgSFn27NmD5s2bQ1NTE/b29jh06FClvlciopqA9xVERFRR5WrIWr9+PSZPngxdXd0iaXp6epgyZQrWrl1baYUjIiLlU9WxIj09HV26dEGtWrXw+++/4+rVq1izZg0MDAzEPAEBAdiwYQOCg4MRGxsLbW1tuLi44OXLl2IeDw8PXLlyBZGRkThw4ACio6Mls9dmZWWhb9++sLS0RHx8PFatWgU/Pz+EhISIec6cOYPRo0fDy8sLFy5cgJubG9zc3JCYmFhp75eIqCbgfQUREVVUuRqyLl68CFdX1xLT+/bti/j4+LcuFBERKa+qjhUrV66EhYUFtm7dik6dOsHa2hp9+/ZF48aNAbzujbV+/XosXLgQQ4YMQatWrbB9+3Y8evQI4eHhAIBr164hIiICmzdvhoODA5ycnBAUFISdO3eKAxCHhoYiNzcXW7ZsgZ2dHdzd3TFjxgzJjVZgYCBcXV0xZ84c2NraYunSpWjXrh02btxYae+XiKgm4H0FERFVVLkaslJSUoqdHldOTU0N//zzz1sXioiIlFdVx4r9+/ejQ4cOGDFiBIyNjdG2bVt8//33YnpSUhKSk5Ml463o6enBwcEBMTExAICYmBjo6+ujQ4cOYh5nZ2eoqKggNjZWzNOtWzeoq6uLeVxcXHDjxg2kp6eLed4c18XFxUXcDxERvcb7CiIiqqhyNWTVr1+/1McjLl26BDMzs7cuFBERKa+qjhV//fUXNm3aBBsbGxw+fBiffvopZsyYgW3btgEAkpOTAQAmJiaS9UxMTMS05ORkGBsbS9LV1NRgaGgoyVPcNgrvo6Q88vTi5OTkICsrS/JHRFTT8b6C/h979x0XxfH/D/x1tKNXqYqAHWwoNixgQVGxBjuxoiYq9qjxk4hdIrEb1GhUNGrsUWNH7F3BXrCXRAEbIKiAML8//LFfz6MLHoev5+OxD73Z2dnZvd0d9n2zs0RE+ZWnQFbr1q0xYcIEhTFFMrx9+xYTJ05EmzZtCqxyRESkfr50W5Geno6aNWtixowZqFGjBgYOHIgBAwZgyZIlBbaOwhQUFAQTExNpsre3V3WViIgKHe8riIgov7Tykvnnn3/G1q1bUaFCBQQEBKBixYoAgJs3byIkJARpaWn46aefCqWiRESkHr50W2FrawsXFxeFNGdnZ2zZsgUAYGNjA+DDYywf/7ofExMDV1dXKU9sbKxCGe/fv8fLly+l5W1sbBATE6OQJ+NzTnky5mdm/PjxGDVqlPQ5ISGBwSwiKvZ4X0FERPmVp0CWtbU1Tp48iUGDBmH8+PEQQgAAZDIZvL29ERISovRIBRERfV2+dFvRoEEDREVFKaTdunULDg4OAAAnJyfY2NggPDxcClwlJCTgzJkzGDRoEADA3d0dcXFxiIiIgJubG4APr4ZPT09H3bp1pTw//fQTUlNTpXFdwsLCULFiRekNie7u7ggPD8eIESOkuoSFhcHd3T3L+svlcsjl8s/fEUREaoT3FURElF95CmQBgIODA3bv3o1Xr17hzp07EEKgfPnyCq85JyKir9uXbCtGjhyJ+vXrY8aMGejSpQvOnj2LpUuXYunSpQA+3BSNGDEC06ZNQ/ny5eHk5IQJEybAzs4OHTp0APChB1fLli2lRxJTU1MREBCAbt26wc7ODgDQo0cPTJ48Gf7+/hg3bhyuXr2K+fPnY+7cuVJdhg8fDk9PT8yePRs+Pj5Yv349zp8/L9WFiIj+D+8riIgoP/I0RtbHzMzMULt2bdSpU4eNDRERZepLtBW1a9fG33//jb/++gtVqlTB1KlTMW/ePPj5+Ul5xo4di6FDh2LgwIGoXbs2EhMTsXfvXujq6kp51q5di0qVKqFZs2Zo3bo1GjZsqBCAMjExwf79+3H//n24ublh9OjRCAwMxMCBA6U89evXx7p167B06VJUr14dmzdvxrZt21ClSpVC2XYiouLgS91X/Pfff/j2229hYWEBPT09VK1aFefPn5fmCyEQGBgIW1tb6OnpwcvLC7dv31Yo4+XLl/Dz84OxsTFMTU3h7++PxMREhTyXL19Go0aNoKurC3t7ewQHByvVZdOmTahUqRJ0dXVRtWpV7N69u3A2moioGMpzjywiIqKipk2bNtkOCiyTyTBlyhRMmTIlyzzm5uZYt25dtuupVq0ajh07lm2ezp07o3PnztlXmIiIvqhXr16hQYMGaNKkCfbs2QNLS0vcvn1bIXAWHByMBQsWYNWqVVLvXW9vb1y/fl364cPPzw9Pnz5FWFgYUlNT0bdvXwwcOFBqPxISEtCiRQt4eXlhyZIluHLlCvr16wdTU1Pph4+TJ0+ie/fuCAoKQps2bbBu3Tp06NABkZGR/OGDiCgXGMgiIiIiIqJibebMmbC3t8fKlSulNCcnJ+n/QgjMmzcPP//8M9q3bw8AWL16NaytrbFt2zZ069YNN27cwN69e3Hu3DnUqlULALBw4UK0bt0as2bNgp2dHdauXYuUlBSsWLECOjo6qFy5Mi5evIg5c+ZIgaz58+ejZcuWGDNmDABg6tSpCAsLw2+//aY2b9wlIlKlfD9aSEREREREpA527NiBWrVqoXPnzrCyskKNGjWwbNkyaf79+/cRHR0NLy8vKc3ExAR169bFqVOnAACnTp2CqampFMQCAC8vL2hoaODMmTNSHg8PD+jo6Eh5vL29ERUVhVevXkl5Pl5PRp6M9WQmOTkZCQkJChMR0deKgSwiIiIiIirW7t27h8WLF6N8+fLYt28fBg0ahGHDhmHVqlUAgOjoaABQelOitbW1NC86OhpWVlYK87W0tGBubq6QJ7MyPl5HVnky5mcmKCgIJiYm0mRvb5+n7SciKk4YyCIiIiIiomItPT0dNWvWxIwZM1CjRg0MHDhQelOtOhg/fjzi4+Ol6fHjx6quEhGRyjCQRURERERExZqtrS1cXFwU0pydnfHo0SMAgI2NDQAgJiZGIU9MTIw0z8bGBrGxsQrz379/j5cvXyrkyayMj9eRVZ6M+ZmRy+UwNjZWmIiIvlYMZBERERERUbHWoEEDREVFKaTdunULDg4OAD4M/G5jY4Pw8HBpfkJCAs6cOQN3d3cAgLu7O+Li4hARESHlOXjwINLT01G3bl0pz9GjR5GamirlCQsLQ8WKFaU3JLq7uyusJyNPxnqIiCh7DGQREREREVGxNnLkSJw+fRozZszAnTt3sG7dOixduhRDhgwBAMhkMowYMQLTpk3Djh07cOXKFfTq1Qt2dnbo0KEDgA89uFq2bIkBAwbg7NmzOHHiBAICAtCtWzfY2dkBAHr06AEdHR34+/vj2rVr2LBhA+bPn49Ro0ZJdRk+fDj27t2L2bNn4+bNm5g0aRLOnz+PgICAL75fiIjUkZaqK0BERERERFSYateujb///hvjx4/HlClT4OTkhHnz5sHPz0/KM3bsWCQlJWHgwIGIi4tDw4YNsXfvXujq6kp51q5di4CAADRr1gwaGhrw9fXFggULpPkmJibYv38/hgwZAjc3N5QoUQKBgYEYOHCglKd+/fpYt24dfv75Z/zvf/9D+fLlsW3bNlSpUuXL7AwiIjUnE0IIVVfia5GQkAATExPEx8cXy+faH02pquoqZKl04BVVV4FILRT365Q6KO7fAdsKIvVX3K9T6qC4fwdsK4jUX2Fep/hoIRERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQW1CmT98ssvkMlkGDFihJT27t07DBkyBBYWFjA0NISvry9iYmIUlnv06BF8fHygr68PKysrjBkzBu/fv1fIc/jwYdSsWRNyuRzlypVDaGio0vpDQkLg6OgIXV1d1K1bF2fPni2MzSQiIiIiIiIiokyoTSDr3Llz+P3331GtWjWF9JEjR+Kff/7Bpk2bcOTIETx58gTffPONND8tLQ0+Pj5ISUnByZMnsWrVKoSGhiIwMFDKc//+ffj4+KBJkya4ePEiRowYgf79+2Pfvn1Sng0bNmDUqFGYOHEiIiMjUb16dXh7eyM2NrbwN56IiIiIiIiIiNQjkJWYmAg/Pz8sW7YMZmZmUnp8fDyWL1+OOXPmoGnTpnBzc8PKlStx8uRJnD59GgCwf/9+XL9+HWvWrIGrqytatWqFqVOnIiQkBCkpKQCAJUuWwMnJCbNnz4azszMCAgLQqVMnzJ07V1rXnDlzMGDAAPTt2xcuLi5YsmQJ9PX1sWLFii+7M4iIiIiIiIiIvlJqEcgaMmQIfHx84OXlpZAeERGB1NRUhfRKlSqhdOnSOHXqFADg1KlTqFq1KqytraU83t7eSEhIwLVr16Q8n5bt7e0tlZGSkoKIiAiFPBoaGvDy8pLyZCY5ORkJCQkKExERERERERER5Y+WqiuQk/Xr1yMyMhLnzp1TmhcdHQ0dHR2YmpoqpFtbWyM6OlrK83EQK2N+xrzs8iQkJODt27d49eoV0tLSMs1z8+bNLOseFBSEyZMn525DiYiIiIiIiIgoW0W6R9bjx48xfPhwrF27Frq6uqquTp6NHz8e8fHx0vT48WNVV4mIiIiIiIiISG0V6UBWREQEYmNjUbNmTWhpaUFLSwtHjhzBggULoKWlBWtra6SkpCAuLk5huZiYGNjY2AAAbGxslN5imPE5pzzGxsbQ09NDiRIloKmpmWmejDIyI5fLYWxsrDAREREREREREVH+FOlAVrNmzXDlyhVcvHhRmmrVqgU/Pz/p/9ra2ggPD5eWiYqKwqNHj+Du7g4AcHd3x5UrVxTeLhgWFgZjY2O4uLhIeT4uIyNPRhk6Ojpwc3NTyJOeno7w8HApDxERERERERERFa4iPUaWkZERqlSpopBmYGAACwsLKd3f3x+jRo2Cubk5jI2NMXToULi7u6NevXoAgBYtWsDFxQU9e/ZEcHAwoqOj8fPPP2PIkCGQy+UAgO+//x6//fYbxo4di379+uHgwYPYuHEjdu3aJa131KhR6N27N2rVqoU6depg3rx5SEpKQt++fb/Q3iAiIiIiIiIi+roV6R5ZuTF37ly0adMGvr6+8PDwgI2NDbZu3SrN19TUxM6dO6GpqQl3d3d8++236NWrF6ZMmSLlcXJywq5duxAWFobq1atj9uzZ+OOPP+Dt7S3l6dq1K2bNmoXAwEC4urri4sWL2Lt3r9IA8EREpFq//PILZDIZRowYIaW9e/cOQ4YMgYWFBQwNDeHr66v0uPijR4/g4+MDfX19WFlZYcyYMXj//r1CnsOHD6NmzZqQy+UoV64cQkNDldYfEhICR0dH6Orqom7dujh79mxhbCYRERER0VepSPfIyszhw4cVPuvq6iIkJAQhISFZLuPg4IDdu3dnW27jxo1x4cKFbPMEBAQgICAg13UlIqIv69y5c/j9999RrVo1hfSRI0di165d2LRpE0xMTBAQEIBvvvkGJ06cAACkpaXBx8cHNjY2OHnyJJ4+fYpevXpBW1sbM2bMAADcv38fPj4++P7777F27VqEh4ejf//+sLW1lX742LBhA0aNGoUlS5agbt26mDdvHry9vREVFQUrK6svuzOIiIiIiIohte+RRUREBACJiYnw8/PDsmXLYGZmJqXHx8dj+fLlmDNnDpo2bQo3NzesXLkSJ0+exOnTpwEA+/fvx/Xr17FmzRq4urqiVatWmDp1KkJCQpCSkgIAWLJkCZycnDB79mw4OzsjICAAnTp1wty5c6V1zZkzBwMGDEDfvn3h4uKCJUuWQF9fHytWrPiyO4OIiIiIqJhiIIuIiIqFIUOGwMfHB15eXgrpERERSE1NVUivVKkSSpcujVOnTgEATp06hapVqyo8Lu7t7Y2EhARcu3ZNyvNp2d7e3lIZKSkpiIiIUMijoaEBLy8vKQ8REREREX0etXu0kIiI6FPr169HZGQkzp07pzQvOjoaOjo6MDU1VUi3trZGdHS0lOfTMQ8zPueUJyEhAW/fvsWrV6+QlpaWaZ6bN29mWffk5GQkJydLnxMSEnLYWiIiIiKirxd7ZBERkVp7/Pgxhg8fjrVr10JXV1fV1cmzoKAgmJiYSJO9vb2qq0REREREVGQxkEVERGotIiICsbGxqFmzJrS0tKClpYUjR45gwYIF0NLSgrW1NVJSUhAXF6ewXExMDGxsbAAANjY2Sm8xzPicUx5jY2Po6emhRIkS0NTUzDRPRhmZGT9+POLj46Xp8ePH+doPRESUe3zDLRGR+mIgi4iI1FqzZs1w5coVXLx4UZpq1aoFPz8/6f/a2toIDw+XlomKisKjR4/g7u4OAHB3d8eVK1cQGxsr5QkLC4OxsTFcXFykPB+XkZEnowwdHR24ubkp5ElPT0d4eLiUJzNyuRzGxsYKExERFZ7s3nD7zz//YNOmTThy5AiePHmCb775Rpqf8YbblJQUnDx5EqtWrUJoaCgCAwOlPBlvuG3SpAkuXryIESNGoH///ti3b5+UJ+MNtxMnTkRkZCSqV68Ob29vhTaIiIiyxkAWERGpNSMjI1SpUkVhMjAwgIWFBapUqQITExP4+/tj1KhROHToECIiItC3b1+4u7ujXr16AIAWLVrAxcUFPXv2xKVLl7Bv3z78/PPPGDJkCORyOQDg+++/x7179zB27FjcvHkTixYtwsaNGzFy5EipLqNGjcKyZcuwatUq3LhxA4MGDUJSUhL69u2rkn1DRESK+IZbIiL1x0AWEREVe3PnzkWbNm3g6+sLDw8P2NjYYOvWrdJ8TU1N7Ny5E5qamnB3d8e3336LXr16YcqUKVIeJycn7Nq1C2FhYahevTpmz56NP/74A97e3lKerl27YtasWQgMDISrqysuXryIvXv3Kg0AT0REqsE33BIRqT++tZCIiIqdw4cPK3zW1dVFSEgIQkJCslzGwcEBu3fvzrbcxo0b48KFC9nmCQgIQEBAQK7rSkREXwbfcEtEVDywRxYRERERERVrfMMtEVHxwUAWEREREREVa3zDLRFR8cFAFhERERERFWt8wy0RUfHBMbKIiIiIiKhYy3jD7cc+fsMtAOkNt+bm5jA2NsbQoUOzfMNtcHAwoqOjM33D7W+//YaxY8eiX79+OHjwIDZu3Ihdu3ZJ6x01ahR69+6NWrVqoU6dOpg3bx7fcEtElAcMZBERERER0Vdv7ty50NDQgK+vL5KTk+Ht7Y1FixZJ8zPecDto0CC4u7vDwMAAvXv3zvQNtyNHjsT8+fNRqlSpTN9w++zZMwQGBiI6Ohqurq58wy0RUR4wkEVERERERF8dvuGWiEg9cYwsIiIiIiIiIiJSCwxkERERERERERGRWmAgi4iIiIiIiIiI1AIDWUREREREREREpBYYyCIiIiIiIiIiIrXAQBYREREREREREakFBrKIiIiIiIiIiEgtMJBFRERERERERERqgYEsIiIiIiIiIiJSCwxkERERERERERGRWmAgi4iIiIiIiIiI1AIDWUREREREREREpBYYyCIiIiIiIiIiIrXAQBYREREREREREakFBrKIiIiIiIiIiEgtMJBFRERERERERERqgYEsIiIiIiIiIiJSCwxkERERERERERGRWmAgi4iIiIiIiIiI1AIDWUREREREREREpBYYyCIiIiIiIiIiIrXAQBYREREREREREakFBrKIiIiIiIiIiEgtMJBFRERERERERERqgYEsIiIiIiIiIiJSC0U6kBUUFITatWvDyMgIVlZW6NChA6KiohTyvHv3DkOGDIGFhQUMDQ3h6+uLmJgYhTyPHj2Cj48P9PX1YWVlhTFjxuD9+/cKeQ4fPoyaNWtCLpejXLlyCA0NVapPSEgIHB0doauri7p16+Ls2bMFvs1ERERERERERJS5Ih3IOnLkCIYMGYLTp08jLCwMqampaNGiBZKSkqQ8I0eOxD///INNmzbhyJEjePLkCb755htpflpaGnx8fJCSkoKTJ09i1apVCA0NRWBgoJTn/v378PHxQZMmTXDx4kWMGDEC/fv3x759+6Q8GzZswKhRozBx4kRERkaievXq8Pb2Rmxs7JfZGUREREREREREXzmZEEKouhK59ezZM1hZWeHIkSPw8PBAfHw8LC0tsW7dOnTq1AkAcPPmTTg7O+PUqVOoV68e9uzZgzZt2uDJkyewtrYGACxZsgTjxo3Ds2fPoKOjg3HjxmHXrl24evWqtK5u3bohLi4Oe/fuBQDUrVsXtWvXxm+//QYASE9Ph729PYYOHYoff/wxV/VPSEiAiYkJ4uPjYWxsXJC7pkh4NKWqqquQpdKBV1RdBcoHHlNfXnG/TqmD4v4d8LwmUn/F/TqlDor7d8C2gkj9FeZ1qkj3yPpUfHw8AMDc3BwAEBERgdTUVHh5eUl5KlWqhNKlS+PUqVMAgFOnTqFq1apSEAsAvL29kZCQgGvXrkl5Pi4jI09GGSkpKYiIiFDIo6GhAS8vLylPZpKTk5GQkKAwERERERERERFR/qhNICs9PR0jRoxAgwYNUKVKFQBAdHQ0dHR0YGpqqpDX2toa0dHRUp6Pg1gZ8zPmZZcnISEBb9++xfPnz5GWlpZpnowyMhMUFAQTExNpsre3z/uGExERERERERERADUKZA0ZMgRXr17F+vXrVV2VXBs/fjzi4+Ol6fHjx6quEhFRscMXgxARERERfT3UIpAVEBCAnTt34tChQyhVqpSUbmNjg5SUFMTFxSnkj4mJgY2NjZTn05uVjM855TE2Noaenh5KlCgBTU3NTPNklJEZuVwOY2NjhYmIiAoWXwxCRERERPT1KNKBLCEEAgIC8Pfff+PgwYNwcnJSmO/m5gZtbW2Eh4dLaVFRUXj06BHc3d0BAO7u7rhy5YrCTURYWBiMjY3h4uIi5fm4jIw8GWXo6OjAzc1NIU96ejrCw8OlPEREpBp79+5Fnz59ULlyZVSvXh2hoaF49OgRIiIiAHwYX3H58uWYM2cOmjZtCjc3N6xcuRInT57E6dOnAQD79+/H9evXsWbNGri6uqJVq1aYOnUqQkJCkJKSAuDDi0KcnJwwe/ZsODs7IyAgAJ06dcLcuXOlusyZMwcDBgxA37594eLigiVLlkBfXx8rVqz48juGiIgk7L1LRFR8FOlA1pAhQ7BmzRqsW7cORkZGiI6ORnR0NN6+fQsAMDExgb+/P0aNGoVDhw4hIiICffv2hbu7O+rVqwcAaNGiBVxcXNCzZ09cunQJ+/btw88//4whQ4ZALpcDAL7//nvcu3cPY8eOxc2bN7Fo0SJs3LgRI0eOlOoyatQoLFu2DKtWrcKNGzcwaNAgJCUloW/fvl9+xxARUZb4YhAiIvoUe+8SERUfWqquQHYWL14MAGjcuLFC+sqVK9GnTx8AwNy5c6GhoQFfX18kJyfD29sbixYtkvJqampi586dGDRoENzd3WFgYIDevXtjypQpUh4nJyfs2rULI0eOxPz581GqVCn88ccf8Pb2lvJ07doVz549Q2BgIKKjo+Hq6oq9e/cqDQBPRESqo8oXg7x69SrLF4PcvHkzyzoHBQVh8uTJed9YIiLKtb179yp8Dg0NhZWVFSIiIuDh4SH13l23bh2aNm0K4MM9h7OzM06fPo169epJvXcPHDgAa2truLq6YurUqRg3bhwmTZoEHR0dhd67AODs7Izjx49j7ty50r3Fx713gQ89fnft2oUVK1bgxx9//IJ7hYhIPRXpQJYQIsc8urq6CAkJQUhISJZ5HBwcsHv37mzLady4MS5cuJBtnoCAAAQEBORYJyIiUo2MF4McP35c1VXJtfHjx2PUqFHS54SEBL7lloiokOW19269evWy7L07aNAgXLt2DTVq1Miy9+6IESMA/F/v3fHjx0vzc9N7l4iI/k+RDmQRERHlVsaLQY4ePZrli0E+7pX16YtBPh2fJK8vBtHU1Mz3i0EyHnUnIqLCp469d5OTk5GcnCx95mPoRPQ1K9JjZBEREeWELwYhIqK8yOi9u379elVXJdeCgoJgYmIiTey5S0RfMwayiIhIrfHFIERElFsZvXcPHTqUZe/dj33aezezXrcZ87LLk9F7t0SJEvnqvTt+/HjEx8dL0+PHj/O24URExQgDWUREpNYWL16M+Ph4NG7cGLa2ttK0YcMGKc/cuXPRpk0b+Pr6wsPDAzY2Nti6das0P+PFIJqamnB3d8e3336LXr16ZfpikLCwMFSvXh2zZ8/O9MUgs2bNQmBgIFxdXXHx4kW+GISIqAhQ9967crkcxsbGChMR0deKY2QREZFa44tBiIgoJ0OGDMG6deuwfft2qfcu8KHXrp6enkLvXXNzcxgbG2Po0KFZ9t4NDg5GdHR0pr13f/vtN4wdOxb9+vXDwYMHsXHjRuzatUuqy6hRo9C7d2/UqlULderUwbx589h7l4goDxjIIiIiIiKiYm3x4sUAPvwg8bGVK1eiT58+AD703tXQ0ICvry+Sk5Ph7e2NRYsWSXkzeu8OGjQI7u7uMDAwQO/evTPtvTty5EjMnz8fpUqVyrT37rNnzxAYGIjo6Gi4urqy9y4RUR4wkEVERERERMUae+8SERUfHCOLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFa0FJ1BYiIiIiIiIjUQYOFDVRdhWydGHpC1VUgKnQMZBERERGR2no0paqqq5Cl0oFXVF0FIiKiYoePFhIRERERERERkVpgIIuIiIiIiIiIiNQCA1lERERERERERKQWGMgiIiIiIiIiIiK1wMHe6avAt4sQEVFO2FYQERERFX0MZBER5QNveImIiIiIiL48PlpIRERERERERERqgYEsIiIiIiIiIiJSC3y0kIiIiIioEPAxdCIiooLHHllERERERERERKQWGMgiIiIiIiIiIiK1wEAWERERERERERGpBQayiIiIiIiIiIhILTCQRUREREREREREaoFvLVQjbmNWq7oK2frbSNU1ICIithVERJQTthVEpM7YIyuPQkJC4OjoCF1dXdStWxdnz55VdZWIiKiIYVtBREQ5YVtBRJQ/7JGVBxs2bMCoUaOwZMkS1K1bF/PmzYO3tzeioqJgZWWl6uoREVERwLaCiIhywraCiDI0WNhA1VXI1omhJ1RdBSXskZUHc+bMwYABA9C3b1+4uLhgyZIl0NfXx4oVK1RdNSIiKiLYVhARUU7YVhAR5R97ZOVSSkoKIiIiMH78eClNQ0MDXl5eOHXqVKbLJCcnIzk5WfocHx8PAEhISMhXHdKS3+ZruS/ltXaaqquQpfdv36u6CtnK7zHxuTx+/ksl682ttYY8pvIrv8dUxnJCiIKszleDbUXO2Fbkn6raiqLu9TseU/nFtkI12FbkjG1F/rGtUD/F9ZgqzLaCgaxcev78OdLS0mBtba2Qbm1tjZs3b2a6TFBQECZPnqyUbm9vXyh1VLUqqq6AGjMZZ6LqKhRJPKby73OPqdevX8PEhMdlXrGtyBnP6/xjW0EFjW2FarCtyBnbivxjW0EFrSi2FQxkFaLx48dj1KhR0uf09HS8fPkSFhYWkMlkKqxZ0ZaQkAB7e3s8fvwYxsbGqq4OFQM8pnJPCIHXr1/Dzs5O1VX5arCtyB+e11TQeEzlHtuKL49tRf7wvKaCxmMq9wqzrWAgK5dKlCgBTU1NxMTEKKTHxMTAxsYm02XkcjnkcrlCmqmpaWFVsdgxNjbmxYEKFI+p3OGv6/nHtuLL43lNBY3HVO6wrcg/thVfHs9rKmg8pnKnsNoKDvaeSzo6OnBzc0N4eLiUlp6ejvDwcLi7u6uwZkREVFSwrSAiopywrSAi+jzskZUHo0aNQu/evVGrVi3UqVMH8+bNQ1JSEvr27avqqhERURHBtoKIiHLCtoKIKP8YyMqDrl274tmzZwgMDER0dDRcXV2xd+9epYEa6fPI5XJMnDhRqfs0UX7xmKIviW3Fl8Hzmgoajyn6kthWfBk8r6mg8ZgqGmSC780lIiIiIiIiIiI1wDGyiIiIiIiIiIhILTCQRUREREREREREaoGBLCIiIiIiIiIiUgsMZBERERERERERkVpgIIsKTJ8+fSCTyZSmO3fuAACCgoKgqamJX3/9VWnZ0NBQmJqaZln2s2fPMGjQIJQuXRpyuRw2Njbw9vbGiRMnpDyOjo6Zrv+XX34p8G2lLyez7/TjadKkSXjw4IFCmrm5OTw9PXHs2DGFsvr06YMOHToorePw4cOQyWSIi4sD8OF4zGxdurq6X2CLiYo3thVUGNhWEBUvbCuoMLCtKD60VF0BKl5atmyJlStXKqRZWloCAFasWIGxY8dixYoVGDNmTJ7K9fX1RUpKClatWoUyZcogJiYG4eHhePHihUK+KVOmYMCAAQppRkZG+dgSKiqePn0q/X/Dhg0IDAxEVFSUlGZoaIjnz58DAA4cOIDKlSvj+fPnmD59Otq0aYNbt27l61XWxsbGCusBPjR+RPT52FZQQWNbQVT8sK2ggsa2ovhgIIsKVMavGp86cuQI3r59iylTpmD16tU4efIk6tevn6sy4+LicOzYMRw+fBienp4AAAcHB9SpU0cpr5GRUabrJ/X18fdpYmICmUym9B1nNDgWFhawsbGBjY0N/ve//2H9+vU4c+YM2rVrl+f1ZrYeIioYbCuooLGtICp+2FZQQWNbUXzw0UL6IpYvX47u3btDW1sb3bt3x/Lly3O9rKGhIQwNDbFt2zYkJycXYi2puHj79i1Wr14NANDR0VFxbYgot9hW0JfEtoJIPbGtoC+JbUXRxEAWFaidO3dKDYShoSE6d+6MhIQEbN68Gd9++y0A4Ntvv8XGjRuRmJiYqzK1tLQQGhqKVatWwdTUFA0aNMD//vc/XL58WSnvuHHjFNZvaGio9DwzFV/169eHoaEhDAwMMGvWLLi5uaFZs2b5Kis+Pl7pWGrVqlUB15jo68S2glSJbQWRemBbQarEtqJo46OFVKCaNGmCxYsXS58NDAzw119/oWzZsqhevToAwNXVFQ4ODtiwYQP8/f1zVa6vry98fHxw7NgxnD59Gnv27EFwcDD++OMP9OnTR8o3ZswYhc8AULJkyc/eLlIPGzZsQKVKlXD16lWMHTsWoaGh0NbWzldZRkZGiIyMVEjT09MriGoSffXYVpAqsa0gUg9sK0iV2FYUbQxkUYEyMDBAuXLlFNKWL1+Oa9euQUvr/w639PR0rFixItcNDgDo6uqiefPmaN68OSZMmID+/ftj4sSJCg1MiRIllNZPXw97e3uUL18e5cuXx/v379GxY0dcvXoVcrkcwIeBFh8+fKi0XFxcHDQ1NWFgYCClaWho8FgiKiRsK0iV2FYQqQe2FaRKbCuKNj5aSIXqypUrOH/+PA4fPoyLFy9K0+HDh3Hq1CncvHkz32W7uLggKSmpAGtLxUmnTp2gpaWFRYsWSWkVK1bEtWvXlMZEiIyMhJOTU75/ZSGiz8O2glSFbQWR+mBbQarCtqLoYY8sKlTLly9HnTp14OHhoTSvdu3aWL58OX799VcAQFpaGi5evKiQRy6Xw8rKCp07d0a/fv1QrVo1GBkZ4fz58wgODkb79u0V8r9+/RrR0dEKafr6+jA2Ni7YDaMiTyaTYdiwYZg0aRK+++476Ovrw8/PD1OmTEGvXr0wduxYmJiY4OjRo5g3bx6Cg4MVlhdCKB1LAGBlZQUNDf4GQFSQ2FaQqrCtIFIfbCtIVdhWFEGCqID07t1btG/fXvqcnJwsLCwsRHBwcKb5Z86cKaysrERKSopYuXKlAKA0lS1bVrx79078+OOPombNmsLExETo6+uLihUrip9//lm8efNGKs/BwSHTMr777rvC3nT6QlauXClMTEyU0u/fvy8AiAsXLiikJyUlCTMzMzFz5kwpLSoqSnTs2FHY2dkJAwMDUb16dbFs2TKRnp6usJ7MjiUA4unTp4W1eURfBbYVVNjYVhCpP7YVVNjYVqg3mRBCFHBsjIiIiIiIiIiIqMCxHxsREREREREREakFBrKIiIiIiIiIiEgtMJBFRERERERERERqgYEsIiIiIiIiIiJSCwxkERERERERERGRWmAgi4iIiIiIiIiI1AIDWUREREREREREpBYYyCIiIiIiIiIiIrXAQBZRIXJ0dMS8efNUXQ0iIlJTffr0gUwmU5ru3Lmj6qoREdFXQCaTYdu2baquBpECBrKoWIqOjsbQoUNRpkwZyOVy2Nvbo23btggPD1d11dReaGgoTE1NVV0NIqKvRsuWLfH06VOFycnJ6YvXQwiB9+/ff/H1EhEVN8+ePcOgQYNQunRpyOVy2NjYwNvbGydOnFBZnSZNmgRXV1eVrT8zffr0QYcOHVRdDSqCGMiiYufBgwdwc3PDwYMH8euvv+LKlSvYu3cvmjRpgiFDhqi6ekRERHmScZPz8aSpqYnt27ejZs2a0NXVRZkyZTB58mQp0NSjRw907dpVoZzU1FSUKFECq1evBgCkp6cjKCgITk5O0NPTQ/Xq1bF582Yp/+HDhyGTybBnzx64ublBLpfj+PHjX27DiYiKKV9fX1y4cAGrVq3CrVu3sGPHDjRu3BgvXrzIV3kpKSkFXMP8448e9EUIomKmVatWomTJkiIxMVFp3qtXr4QQQjx8+FC0a9dOGBgYCCMjI9G5c2cRHR0t5evdu7do3769wrLDhw8Xnp6e0ueEhATRo0cPoa+vL2xsbMScOXOEp6enGD58uJTHwcFBTJ8+XfTt21cYGhoKe3t78fvvv+dqO5KTk8WQIUOEjY2NkMvlonTp0mLGjBlCCCH69u0rfHx8FPKnpKQIS0tL8ccffwghhPD09BQBAQFi+PDhwtTUVFhZWYmlS5eKxMRE0adPH2FoaCjKli0rdu/eLZVx6NAhAUDs3LlTVK1aVcjlclG3bl1x5coVhfkfTxMnThRCCPHy5UvRs2dPYWpqKvT09ETLli3FrVu3pLJXrlwpTExMxD///CMqVKgg9PT0hK+vr0hKShKhoaHCwcFBmJqaiqFDh4r3799Ly4WEhIhy5coJuVwurKyshK+vb672HxFRcZBZeySEEEePHhXGxsYiNDRU3L17V+zfv184OjqKSZMmCSGE2Llzp9DT0xOvX7+Wlvnnn3+Enp6eSEhIEEIIMW3aNFGpUiWxd+9ecffuXbFy5Uohl8vF4cOHhRD/d82vVq2a2L9/v7hz54548eJF4W80EVEx9urVKwFAutZmJqd7lYkTJ4rq1auLZcuWCUdHRyGTyaSy/f39RYkSJYSRkZFo0qSJuHjxYo51WrlypdLf+CtXrhRCCAFALFu2THTo0EHo6emJcuXKie3bt0vLZrQVu3fvFjVr1hTa2tri0KFD4t27d2Lo0KHC0tJSyOVy0aBBA3H27Flpuffv34t+/foJR0dHoaurKypUqCDmzZunsI2f1unQoUO53c1UzDGQRcXKixcvhEwmkwI+mUlLSxOurq6iYcOG4vz58+L06dPCzc1NIUiVm0BW//79hYODgzhw4IC4cuWK6NixozAyMlIKZJmbm4uQkBBx+/ZtERQUJDQ0NMTNmzdz3JZff/1V2Nvbi6NHj4oHDx6IY8eOiXXr1gkhhDhx4oTQ1NQUT548kfJv3bpVGBgYSDctnp6ewsjISEydOlXcunVLTJ06VWhqaopWrVqJpUuXilu3bolBgwYJCwsLkZSUJIT4v4bI2dlZ7N+/X1y+fFm0adNGODo6ipSUFJGcnCzmzZsnjI2NxdOnT8XTp0+l9bVr1044OzuLo0ePiosXLwpvb29Rrlw5kZKSIoT40EBqa2uL5s2bi8jISHHkyBFhYWEhWrRoIbp06SKuXbsm/vnnH6GjoyPWr18vhBDi3LlzQlNTU6xbt048ePBAREZGivnz5+e474iIiovevXsLTU1NYWBgIE2dOnUSzZo1U2rr/vzzT2FrayuEECI1NVWUKFFCrF69WprfvXt30bVrVyGEEO/evRP6+vri5MmTCmX4+/uL7t27CyH+r03Ytm1bYW4iEdFXJTU1VRgaGooRI0aId+/eKc3Pzb3KxIkThYGBgWjZsqWIjIwUly5dEkII4eXlJdq2bSvOnTsnbt26JUaPHi0sLCxy/BHizZs3YvTo0aJy5crS3/hv3rwRQnwIZJUqVUqsW7dO3L59WwwbNkwYGhpKZWb1o8ewYcOEnZ2d2L17t7h27Zro3bu3MDMzk5ZLSUkRgYGB4ty5c+LevXtizZo1Ql9fX2zYsEEIIcTr169Fly5dRMuWLaU6JScnf/b+p+KBgSwqVs6cOSMAiK1bt2aZZ//+/UJTU1M8evRISrt27ZoAIP1KkFMgKyEhQWhra4tNmzZJ8+Pi4oS+vr5SIOvbb7+VPqenpwsrKyuxePHiHLdl6NChomnTpiI9PT3T+S4uLmLmzJnS57Zt24o+ffpInz09PUXDhg2lz+/fvxcGBgaiZ8+eUtrTp08FAHHq1CkhxP81RBmBJCE+BAf19PSkRiWjZ9XHbt26JQCIEydOSGnPnz8Xenp6YuPGjdJyAMSdO3ekPN99953Q19dX6DHg7e0tvvvuOyGEEFu2bBHGxsZS7wEioq9N7969hZeXl7h9+7Y0PXnyRJQoUULo6uoqBLh0dXUFAOnHicGDBwtvb28hhBCJiYlCX19f7NixQwghxNWrVwUAheUNDAyEtra2qFOnjhDi/9qEf//9VzUbT0RUTG3evFmYmZkJXV1dUb9+fTF+/HgpGJWbe5WJEycKbW1tERsbK+U5duyYMDY2VgqOlS1bNldPhGT08voUAPHzzz9LnxMTEwUAsWfPHiFE5j96JCYmCm1tbbF27VopLSUlRdjZ2Yng4OAs6zBkyBCFpy+y6pVMpFXYjy4SfUlCiBzz3LhxA/b29rC3t5fSXFxcYGpqihs3bqB27do5lnHv3j2kpqaiTp06UpqJiQkqVqyolLdatWrS/2UyGWxsbBAbG5vjOvr06YPmzZujYsWKaNmyJdq0aYMWLVpI8/v374+lS5di7NixiImJwZ49e3Dw4MEs162pqQkLCwtUrVpVSrO2tgYApfq4u7tL/zc3N0fFihVx48aNLOt648YNaGlpoW7dulKahYWF0nL6+vooW7aswvodHR1haGiokJZRn+bNm8PBwQFlypRBy5Yt0bJlS3Ts2BH6+vpZ1oWIqLgxMDBAuXLlFNISExMxefJkfPPNN0r5dXV1AQB+fn7w9PREbGwswsLCoKenh5YtW0rLA8CuXbtQsmRJheXlcrnS+omIqOD4+vrCx8cHx44dw+nTp7Fnzx4EBwfjjz/+QEJCQq7uVRwcHGBpaSnluXTpEhITE2FhYaGwrrdv3+Lu3bufVd+P7ykMDAxgbGysdP9Qq1Yt6f93795FamoqGjRoIKVpa2ujTp06CvcGISEhWLFiBR49eoS3b98iJSWlyA04T0UTA1lUrJQvXx4ymQw3b978rHI0NDSUgmKpqan5KktbW1vhs0wmQ3p6eo7L1axZE/fv38eePXtw4MABdOnSBV5eXtJAvL169cKPP/6IU6dO4eTJk3ByckKjRo1yXPfHaTKZDAByVZ+CkFN9MtIy6mNkZITIyEgcPnwY+/fvR2BgICZNmoRz587xzYlE9FWrWbMmoqKilAJcH6tfvz7s7e2xYcMG7NmzB507d5auuS4uLpDL5Xj06BE8PT2/VLWJiOj/09XVRfPmzdG8eXNMmDAB/fv3x8SJEzF69OhcLf/pjwyJiYmwtbXF4cOHlfJ+7t/NubmfyeuPHuvXr8cPP/yA2bNnw93dHUZGRvj1119x5syZz6orfR341kIqVszNzeHt7Y2QkBAkJSUpzY+Li4OzszMeP36Mx48fS+nXr19HXFwcXFxcAACWlpZ4+vSpwrIXL16U/l+mTBloa2vj3LlzUlp8fDxu3bpVoNtjbGyMrl27YtmyZdiwYQO2bNmCly9fAvjQ46lDhw5YuXIlQkND0bdv3wJb7+nTp6X/v3r1Crdu3YKzszMAQEdHB2lpaQr5nZ2d8f79e4WG58WLF4iKipL2aX5paWnBy8sLwcHBuHz5Mh48eKDU84yI6GsTGBiI1atXY/Lkybh27Rpu3LiB9evX4+eff1bI16NHDyxZsgRhYWHw8/OT0o2MjPDDDz9g5MiRWLVqFe7evYvIyEgsXLgQq1at+tKbQ0T01XNxcUFSUlKu7lUyU7NmTURHR0NLSwvlypVTmEqUKJHj+jP7Gz+/ypYtCx0dHZw4cUJKS01Nxblz56RtOHHiBOrXr4/BgwejRo0aKFeunFLPsYKsExUvDGRRsRMSEoK0tDTUqVMHW7Zswe3bt3Hjxg0sWLAA7u7u8PLyQtWqVeHn54fIyEicPXsWvXr1gqenp9QltmnTpjh//jxWr16N27dvY+LEibh69aq0DiMjI/Tu3RtjxozBoUOHcO3aNfj7+0NDQ0Pq5fS55syZg7/++gs3b97ErVu3sGnTJtjY2Cj8otK/f3+sWrUKN27cQO/evQtkvQAwZcoUhIeH4+rVq+jTpw9KlCiBDh06AAAcHR2RmJiI8PBwPH/+HG/evEH58uXRvn17DBgwAMePH8elS5fw7bffomTJkmjfvn2+67Fz504sWLAAFy9exMOHD7F69Wqkp6dn+ggnEdHXxNvbGzt37sT+/ftRu3Zt1KtXD3PnzoWDg4NCPj8/P1y/fh0lS5ZUeMQDAKZOnYoJEyYgKCgIzs7OaNmyJXbt2gUnJ6cvuSlERF+VFy9eoGnTplizZg0uX76M+/fvY9OmTQgODkb79u1zda+SGS8vL7i7u6NDhw7Yv38/Hjx4gJMnT+Knn37C+fPnc6yXo6Mj7t+/j4sXL+L58+dITk7O9zYaGBhg0KBBGDNmDPbu3Yvr169jwIABePPmDfz9/QF8eJLm/Pnz2LdvH27duoUJEyYodBLIqNPly5cRFRWF58+f5/sJGSp+GMiiYqdMmTKIjIxEkyZNMHr0aFSpUgXNmzdHeHg4Fi9eDJlMhu3bt8PMzAweHh7w8vJCmTJlsGHDBqkMb29vTJgwAWPHjkXt2rXx+vVr9OrVS2E9c+bMgbu7O9q0aQMvLy80aNAAzs7O0tgkn8vIyAjBwcGoVasWateujQcPHmD37t3Q0Pi/09bLywu2trbw9vaGnZ1dgawXAH755RcMHz4cbm5uiI6Oxj///AMdHR0AHx5V+f7779G1a1dYWloiODgYALBy5Uq4ubmhTZs2cHd3hxACu3fvVuqKnBempqbYunUrmjZtCmdnZyxZsgR//fUXKleuXCDbSURU1IWGhmLbtm2ZzvP29saJEyfw5s0bxMfH48yZMxgwYIBCHmdnZwgh8ODBA6UfWmQyGYYPH46bN28iJSUFsbGx2Lt3Lzw8PAAAjRs3hhCCj3ITERUgQ0ND1K1bF3PnzoWHhweqVKmCCRMmYMCAAfjtt99yda+SGZlMht27d8PDwwN9+/ZFhQoV0K1bNzx8+FAaFzc7vr6+aNmyJZo0aQJLS0v89ddfn7Wdv/zyC3x9fdGzZ0/UrFkTd+7cwb59+2BmZgYA+O677/DNN9+ga9euqFu3Ll68eIHBgwcrlDFgwABUrFgRtWrVgqWlpUIPL/q6yURuRscmohwlJSWhZMmSmD17tvRLQ2FLTExEyZIlsXLlykwH/M2rw4cPo0mTJnj16hVvXIiIiIiIiKjI4WDvRPl04cIF3Lx5E3Xq1EF8fDymTJkCAJ/1KF1upaen4/nz55g9ezZMTU3Rrl27Ql8nERERERERkarx0UKizzBr1ixUr14dXl5eSEpKwrFjx3I1mCIAzJgxA4aGhplOrVq1ynbZR48ewdraGuvWrcOKFSugpcWYNBERERERZa5y5cpZ3nusXbtW1dUjyhM+WkikIi9fvpTeQPgpPT09lCxZ8gvXiIiIiIiIiqOHDx9mOVi6tbU1jIyMvnCNiPKPgSwiIiIiIiIiIlILfLSQiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0U60CWTCZDQECAqqvxVYiJiUGnTp1gYWEBmUyGefPm5Wo5dfqOHjx4AJlMhlmzZhVK+X369IGjo2OhlJ2d0NBQyGQynD9//ouv+0vJ+O5CQ0NzzKuq7yE/HB0d0adPH+nz4cOHIZPJcPjwYZXVqbCo0/dSnBT2fnd0dESbNm0KrfzcyMv1oaAVhe0vbLk9hlT5PeRVRrv54MEDKa1x48Zo3LixyupUWNTpeyEioq+HWgayrly5gk6dOsHBwQG6urooWbIkmjdvjoULF6qsTidPnsSkSZMQFxensjqo0siRI7Fv3z6MHz8ef/75J1q2bKnqKuXb7t27MWnSpM8qY9KkSZDJZNKkr6+P0qVLo23btli5ciWSk5MLpK7Xr1/HpEmTFP6YLuoOHz6Mb775BjY2NtDR0YGVlRXatm2LrVu3qrpqebZ48WJ07twZpUuXhkwmUwgqfSosLAwNGzaEvr4+zMzM0KlTJ7X63qjw5BRMbty4MapUqfKFa6W+Pr72amlpwdzcHG5ubhg+fDiuX79eYOtZtGiRWt3cJyQkYPLkyahevToMDQ2hp6eHKlWqYNy4cXjy5Imqq5cnUVFRGDlyJOrXrw9dXV2loNLHEhMTMWLECJQqVQpyuRzOzs5YvHjxl60wERERFSgtVVcgr06ePIkmTZqgdOnSGDBgAGxsbPD48WOcPn0a8+fPx9ChQ1VWr8mTJ6NPnz4wNTVVSR1U6eDBg2jfvj1++OEHVVfls+3evRshISGfHcwCPgQ6DA0NkZycjP/++w/79u1Dv379MG/ePOzcuRP29vZS3mXLliE9PT1P5V+/fh2TJ09G48aN1aK3ysSJEzFlyhSUL18e3333HRwcHPDixQvs3r0bvr6+WLt2LXr06FHg63VwcMDbt2+hra1doOXOnDkTr1+/Rp06dfD06dMs8+3cuRPt27dHzZo18csvvyAhIQHz589Hw4YNceHCBVhaWhZIfTw8PPD27Vvo6OgUSHlFSX7OD/p6NW/eHL169YIQAvHx8bh06RJWrVqFRYsWYebMmRg1apSUN7/Xh0WLFqFEiRLZBrCLinv37sHLywuPHj1C586dMXDgQOjo6ODy5ctYvnw5/v77b9y6datQ1l0Y5+6pU6ewYMECuLi4wNnZGRcvXsw0X1paGry9vXH+/HkMGTIE5cuXx759+zB48GC8evUK//vf/wqsTvv37y+wsoqSwmo/iYiIPofaBbKmT58OExMTnDt3TilgFBsb+8Xrk5SUBAMDgy++3qImNjb2iwfw1GHfd+rUCSVKlJA+BwYGYu3atejVqxc6d+6M06dPS/OK+x+JmzdvxpQpU9CpUyesW7dOYXvHjBmDffv2ITU1tVDWLZPJoKurW+DlHjlyROqNZWhomGW+cePGoUyZMjhx4oQUZGrbtq0U2Jo9e3aB1EdDQ6NQtrMoKO7nBxWsChUq4Ntvv1VI++WXX9C2bVuMHj0alSpVQuvWrQEU3vWhqHj//j2++eYbxMTE4PDhw2jYsKHC/OnTp2PmzJmFtv7COHfbtWuHuLg4GBkZYdasWVkGsrZu3YqTJ09i+fLl6NevHwBg0KBB6NSpE6ZOnYr+/fvDysqqQOpUHH9AAIr/+UFEROpJ7R4tvHv3LipXrpxp0CSrP0a2bduGKlWqQC6Xo3Llyti7d69SngsXLqBVq1YwNjaGoaEhmjVrphBkAP7v8Y8jR45g8ODBsLKyQqlSpTBp0iSMGTMGAODk5CQ90pDRzT1jHKjc1OO///5Dv379YG1tLeVbsWKFUr6FCxeicuXK0mNKtWrVwrp166T5r1+/xogRI+Do6Ai5XA4rKys0b94ckZGRWe7bzNy7dw+dO3eGubk59PX1Ua9ePezatUtpnwghEBISIm17Xq1duxYVK1aErq4u3NzccPToUYX5GY/qXb9+HT169ICZmZnCH+Nr1qyBm5sb9PT0YG5ujm7duuHx48cKZRw7dkx6DEwul8Pe3h4jR47E27dvpTx9+vRBSEgIAMXHUz61dOlSlC1bFnK5HLVr18a5c+dyva1+fn7o378/zpw5g7CwMIV1f9qrav369XBzc4ORkRGMjY1RtWpVzJ8/H8CHfd+5c2cAQJMmTaS65nd8pDdv3uC7776DhYUFjI2N0atXL7x69Uqa37t3b5QoUSLTYFOLFi1QsWLFbMufMGECzM3NsWLFikxvbLy9vRXGiomNjYW/vz+sra2hq6uL6tWrY9WqVdL81NRUmJubo2/fvkplJSQkQFdXV+ohmNUYHxnnpK6uLqpUqYK///472234lIODQ47H+8uXL3H9+nV07NhR4UanevXqcHZ2xvr163NcjxAC06ZNQ6lSpaCvr48mTZrg2rVrSvkyGyMrN8d9hk2bNsHFxUVhf3x6XH48VlxuzoODBw+iUaNGMDAwgKmpKdq3b48bN24o5MnN9Sqv58fXoKCuexlyez7MmjUL9evXh4WFBfT09ODm5obNmzdnWcc6depIbZWHh0emPVeOHz+OOnXqQFdXF2XKlMHq1auV8sTFxWHEiBGwt7eHXC5HuXLlMHPmzFz39rGwsMD69euhpaWF6dOnS+mZXR+io6PRt29f6XE0W1tbtG/fXmrXHR0dce3aNRw5ckS69n7O+Ej79++Hq6srdHV14eLiovCo9b179yCTyTB37lyl5U6ePAmZTIa//vory7K3bNmCS5cu4aefflIKYgGAsbGxwv4APlwLMo6tEiVK4Ntvv8V///0nzZ81axZkMhkePnyoVN748eOho6MjtR+ZnbtxcXHo06cPTExMYGpqit69e+dpaAZzc3MYGRnlmO/YsWMAgG7duimkd+vWDe/evcP27dtzLOPatWto2rQp9PT0UKpUKUybNi3TY+7TMbJSUlIQGBgINzc3mJiYwMDAAI0aNcKhQ4eUln3x4gV69uwJY2NjaX9cunRJ6bjs06cPDA0N8d9//6FDhw4wNDSEpaUlfvjhB6SlpSmUmZSUhNGjR0vnS8WKFTFr1iwIIRTyZTz2bmpqCkNDQ1SsWFGhp1p+zg8iIqLCpnY9shwcHHDq1ClcvXo1V2OGHD9+HFu3bsXgwYNhZGSEBQsWwNfXF48ePYKFhQWAD3+kNGrUCMbGxhg7diy0tbXx+++/o3Hjxjhy5Ajq1q2rUObgwYNhaWmJwMBAJCUloVWrVrh16xb++usvzJ07V+qB8/HjQrmpR0xMDOrVqycFviwtLbFnzx74+/sjISEBI0aMAPChm/6wYcPQqVMnDB8+HO/evcPly5dx5swZ6bGs77//Hps3b0ZAQABcXFzw4sULHD9+HDdu3EDNmjVzta9jYmJQv359vHnzBsOGDYOFhQVWrVqFdu3aYfPmzejYsSM8PDzw559/omfPntKjHHl15MgRbNiwAcOGDYNcLseiRYvQsmVLnD17Vuk77ty5M8qXL48ZM2ZIf4xNnz4dEyZMQJcuXdC/f388e/YMCxcuhIeHBy5cuCAFPTdt2oQ3b95g0KBBsLCwwNmzZ7Fw4UL8+++/2LRpEwDgu+++w5MnTxAWFoY///wz0/quW7cOr1+/xnfffQeZTIbg4GB88803uHfvXq5/ee7ZsyeWLl2K/fv3o3nz5pnmCQsLQ/fu3dGsWTPp1/IbN27gxIkTGD58ODw8PDBs2DAsWLAA//vf/+Ds7AwA0r95FRAQAFNTU0yaNAlRUVFYvHgxHj58KAVHevbsidWrV2Pfvn0KAafo6GgcPHgQEydOzLLs27dv4+bNm+jXr1+ubj7evn2Lxo0b486dOwgICICTkxM2bdqEPn36IC4uDsOHD4e2tjY6duyIrVu34vfff1cIEm3btg3JyclKNy8f279/P3x9feHi4oKgoCC8ePFC+sO8IGWMh6anp6c0T19fH9euXUN0dDRsbGyyLCMwMBDTpk1D69at0bp1a0RGRqJFixZISUnJcf25Oe4BYNeuXejatSuqVq2KoKAgvHr1Cv7+/ihZsmSm5ebmPDhw4ABatWqFMmXKYNKkSXj79i0WLlyIBg0aIDIyUrq5zc/1KqfzQx3Fx8fj+fPnSumZBY8L8roH5O18mD9/Ptq1awc/Pz+kpKRg/fr16Ny5M3bu3AkfHx8p3+TJkzFp0iTUr18fU6ZMgY6ODs6cOYODBw+iRYsWUr47d+6gU6dO8Pf3R+/evbFixQr06dMHbm5uqFy5MoAPgXZPT0/8999/+O6771C6dGmcPHkS48ePx9OnT3P9gpHSpUvD09MThw4dQkJCAoyNjTPN5+vri2vXrmHo0KFwdHREbGwswsLC8OjRIzg6OmLevHkYOnQoDA0N8dNPPwEArK2tc1WHT92+fRtdu3bF999/j969e2PlypXo3Lkz9u7di+bNm6NMmTJo0KAB1q5di5EjRyosu3btWhgZGaF9+/ZZlr9jxw4AH9qd3AgNDUXfvn1Ru3ZtBAUFISYmBvPnz8eJEyekY6tLly4YO3YsNm7cKP2Ql2Hjxo1o0aIFzMzMMi1fCIH27dvj+PHj+P777+Hs7Iy///4bvXv3zlX98iI5ORmamppKvaX09fUBABERERgwYECWy0dHR6NJkyZ4//49fvzxRxgYGGDp0qWZXs8/lZCQgD/++APdu3fHgAED8Pr1ayxfvhze3t44e/YsXF1dAQDp6elo27Ytzp49i0GDBqFSpUrYvn17lvsj43HJunXrYtasWThw4ABmz56NsmXLYtCgQQA+7ON27drh0KFD8Pf3h6urK/bt24cxY8bgv//+k4Ki165dQ5s2bVCtWjVMmTIFcrkcd+7cwYkTJ7LdtpzODyIiokIn1Mz+/fuFpqam0NTUFO7u7mLs2LFi3759IiUlRSkvAKGjoyPu3LkjpV26dEkAEAsXLpTSOnToIHR0dMTdu3eltCdPnggjIyPh4eEhpa1cuVIAEA0bNhTv379XWNevv/4qAIj79+/nux7+/v7C1tZWPH/+XGH5bt26CRMTE/HmzRshhBDt27cXlStXznY/mZiYiCFDhmSbJycjRowQAMSxY8ektNevXwsnJyfh6Ogo0tLSFLYxP+sDIACI8+fPS2kPHz4Uurq6omPHjlLaxIkTBQDRvXt3heUfPHggNDU1xfTp0xXSr1y5IrS0tBTSM/bfx4KCgoRMJhMPHz6U0oYMGSIyOzXu378vAAgLCwvx8uVLKX379u0CgPjnn3+U6vvs2bNMt/vVq1cCgMI29u7dWzg4OEifhw8fLoyNjZWOtY9t2rRJABCHDh3KMk9OMo5rNzc3hfMoODhYABDbt28XQgiRlpYmSpUqJbp27aqw/Jw5c4RMJhP37t3Lch0Z+2ju3Lm5qtO8efMEALFmzRopLSUlRbi7uwtDQ0ORkJAghBBi3759SvteCCFat24typQpI33O+O5Wrlwppbm6ugpbW1sRFxcnpe3fv18AUPgecsvAwED07t1bKT0tLU2YmpqKZs2aKaQ/f/5cGBgYKB3/n4qNjRU6OjrCx8dHpKenS+n/+9//BACFdR46dEjpeMjtcV+1alVRqlQp8fr1aynt8OHDSvsjL+eBq6ursLKyEi9evJDSLl26JDQ0NESvXr2ktNxcr/JzfqiLjHMwu+nja35hXPfycj58WmZKSoqoUqWKaNq0qZR2+/ZtoaGhITp27KjQVgghFI5jBwcHAUAcPXpUSouNjRVyuVyMHj1aSps6daowMDAQt27dUijrxx9/FJqamuLRo0dSWk7t0fDhwwUAcenSJSGE8vUh4/r866+/ZlmGEEJUrlxZeHp6ZpsnJxnbv2XLFiktPj5e2Nraiho1akhpv//+uwAgbty4IaWlpKSIEiVKZHrd+ViNGjWEiYlJruqTkpIirKysRJUqVcTbt2+l9J07dwoAIjAwUEpzd3cXbm5uCsufPXtWABCrV6+W0j49d7dt2yYAiODgYCnt/fv3olGjRkrX6dzI7u+v2bNnK/0dI8SH4waAaNOmTbZlZ/wddObMGSktNjZWmJiYKK3T09NT4Xh4//69SE5OVijv1atXwtraWvTr109K27JliwAg5s2bJ6WlpaWJpk2bKu2P3r17CwBiypQpCuXWqFFD4bvI2MfTpk1TyNepUychk8mkv0fnzp2b7d8qQuT//CAiIipMavdoYfPmzXHq1Cm0a9cOly5dQnBwMLy9vVGyZEnpV8ePeXl5oWzZstLnatWqwdjYGPfu3QPw4Zet/fv3o0OHDihTpoyUz9bWFj169MDx48eRkJCgUOaAAQOgqamZp3rnVA8hBLZs2YK2bdtCCIHnz59Lk7e3N+Lj46XHbExNTfHvv/9m+zibqakpzpw581lvItq9ezfq1Kmj8CiCoaEhBg4ciAcPHhTY25/c3d3h5uYmfS5dujTat2+Pffv2KXWV//777xU+b926Fenp6ejSpYvCPrOxsUH58uUVuvB//AtqUlISnj9/jvr160MIgQsXLuS6vl27dlX4pblRo0YAIH2XuZExntLr16+zzGNqaoqkpCSFxw8L08CBAxV6lA0aNAhaWlrYvXs3gA/jL/n5+WHHjh0K9V67di3q168PJyenLMvOOIdy0xsL+HDs2djYoHv37lKatrY2hg0bhsTERBw5cgQA0LRpU5QoUQIbNmyQ8r169QphYWHo2rVrluU/ffoUFy9eRO/evWFiYiKlN2/eHC4uLrmqY25paGjgu+++Q3h4OMaPH4/bt28jIiICXbp0kXpUZfaYV4YDBw4gJSUFQ4cOVXiMMaOHZk5yc9w/efIEV65cQa9evRTG+vL09ETVqlUzLTen8yBjH/fp0wfm5uZSvmrVqqF58+bScQXk73r1pc+PLyEkJARhYWFKU7Vq1RTyFfR1L6/nw8dlvnr1CvHx8WjUqJHCo6Dbtm1Deno6AgMDoaGh+KfGp4/juri4SMcP8KE3c8WKFRWuqZs2bUKjRo1gZmamsM1eXl5IS0tTehw9Ozldf/X09KCjo4PDhw8rPF5dWOzs7NCxY0fpc8aj3RcuXEB0dDQAoEuXLtDV1cXatWulfPv27cPz58+VxgL7VEJCQq6vvefPn0dsbCwGDx6sMC6Sj48PKlWqpDC0QNeuXREREYG7d+9KaRs2bIBcLs+2h9ju3buhpaUl9R4CAE1NzUJ5WU+PHj1gYmKCfv36ISwsDA8ePMDSpUuxaNEiANlfezPqWq9ePdSpU0dKs7S0hJ+fX47r/rgnWHp6Ol6+fIn379+jVq1aCufK3r17oa2trdAzTENDA0OGDMmy7E//FmrUqJHC+bJ7925oampi2LBhCvlGjx4NIQT27NkDAFLPze3bt+f6Ed0vfX4QERFlRu0CWQBQu3ZtbN26Fa9evcLZs2cxfvx4vH79Gp06dVIKrpQuXVppeTMzM6nxffbsGd68eZPp+D7Ozs5IT09XGnMkuxv2rOSmHnFxcVi6dCksLS0VpowxgDIGsx83bhwMDQ1Rp04dlC9fHkOGDFHqBh4cHIyrV6/C3t4ederUwaRJk/IUaAGAhw8fZrlfMuYXhPLlyyulVahQAW/evMGzZ88U0j/d97dv34YQAuXLl1fabzdu3FB4AcCjR4+km+qMcSU8PT0BfHikJ7c+/S4zbubz8gddYmIigOwDO4MHD0aFChXQqlUrlCpVCv369ct0XLWC8un3YGhoCFtbW4UxL3r16oW3b99KY+dERUUhIiIix0dWMh7fyS5w97GHDx+ifPnySjfAnx57Wlpa8PX1xfbt26VH+LZu3YrU1NRsA1kZy2d27H16zD979gzR0dHSlPHd5cWUKVPg7++P4OBgVKhQAbVq1YKWlhb8/f0BINuB4rOqq6WlZZaP7nwsN8d9xjrKlSuntHxmaUDO50FGmVldQ54/f46kpCQA+btefenz40uoU6cOvLy8lKZPv+eCvu7l5XwAPryFs169etDV1YW5uTksLS2xePFihevo3bt3oaGhkavAcE7tY8Y27927V2l7vby8AOTtZS85XX/lcjlmzpyJPXv2wNraGh4eHggODpaCSgWtXLlySsG9ChUqAIB0/TU1NUXbtm0VxsJcu3YtSpYsiaZNm2ZbvrGxcZ6uvUDm33ulSpUU2v3OnTtDQ0ND+iFBCIFNmzZJ441mtw5bW1ul696n63z79q3CtTc/+9/GxgY7duxAcnIyWrRoAScnJ4wZMwYLFy4EkP21N6OuuT0vMrNq1SpUq1YNurq6sLCwgKWlJXbt2qVwrmTsj4zHHTNkde3V1dVVetPtp+fLw4cPYWdnp3SMf9qGdu3aFQ0aNED//v1hbW2Nbt26YePGjdkGtb70+UFERJQZtQxkZdDR0UHt2rUxY8YMLF68GKmpqQpjfgDIsueU+GSwy7zIzdgIn8qpHhl/NHz77beZ/iIfFhaGBg0aAPjwh0hUVBTWr1+Phg0bYsuWLWjYsKHCGEVdunTBvXv3sHDhQtjZ2eHXX39F5cqVpV/h1NWn+z49PR0ymQx79+7NdJ/9/vvvAD70vGvevDl27dqFcePGYdu2bQgLC5MGL83Lq8EL4pi6evUqgKz/UAU+vLzg4sWL2LFjhzTWRatWrQplHJHccnFxgZubG9asWQPgw0DOOjo66NKlS7bLVapUCQBw5cqVAq9Tt27d8Pr1a+nY3rhxIypVqoTq1asXSPm1a9eGra2tNM2aNSvPZejo6OCPP/7AkydPcPToUURFRWHfvn2Ij4+HhoZGtsfB5yjI4/5TBXltzc/1qiieH1+KKq57GY4dO4Z27dpBV1cXixYtwu7duxEWFoYePXrku13NzbGUnp6O5s2bZ9k++vr65np9V69ehaamZrY/So0YMQK3bt1CUFAQdHV1MWHCBDg7O+ep925B69WrF+7du4eTJ0/i9evX2LFjB7p3764U8P9UpUqVEB8fr/Sj3Oeys7NDo0aNsHHjRgDA6dOn8ejRo2x/RMiLDRs2KFx7bW1t81WOh4cH7t27hwsXLuD48eP477//UK9ePQD/FzAsDGvWrEGfPn1QtmxZLF++XDpfmzZtWijX3vzQ09PD0aNHceDAAfTs2ROXL19G165d0bx5c6Ue8R8riucHERF9XdRusPes1KpVC8CHRyTywtLSEvr6+oiKilKad/PmTWhoaMDe3j7HcvLzpr5P62FkZIS0tDTpF+bsGBgYoGvXrujatStSUlLwzTffYPr06Rg/frz0OICtrS0GDx6MwYMHIzY2FjVr1sT06dPRqlWrXNXJwcEhy/2SMb8g3L59Wynt1q1b0NfXV/rV8VNly5aFEAJOTk7Z/kF65coV3Lp1C6tWrVIYkD6zx5I+97vMjYyB5L29vbPNp6Ojg7Zt26Jt27ZIT0/H4MGD8fvvv2PChAmZ/or/OW7fvo0mTZpInxMTE/H06VPpFfUZevXqhVGjRuHp06dYt24dfHx8cuwZVKFCBVSsWBHbt2/H/Pnzc/wV3MHBAZcvX0Z6errCTVpmx56HhwdsbW2xYcMGNGzYEAcPHpQGX86u/Ixt/tSnx/zatWsVHj/5+BHkvLK2tpYGhE5LS8Phw4dRt27dbPfHx3X9eN3Pnj3LsRdgbo/7jHXcuXNHqYzM0nIjo8ysriElSpSAgYGBlJaf61VO50dxVdDXvbycD1u2bIGuri727dsHuVwupa9cuVKpjunp6bh+/bo0qPXnKFu2LBITE3PVPmbn0aNHOHLkCNzd3XN83K5s2bIYPXo0Ro8ejdu3b8PV1RWzZ8+WAvkFdf29c+cOhBAK5d26dQsAFAbObtmyJSwtLbF27VrUrVsXb968ydUA7m3btsVff/2FNWvWYPz48dnm/fi8/bSnV1RUlFK737VrVwwePBhRUVHYsGED9PX10bZt2xzXER4ejsTERIVr36fHmre3d4E9OqypqalwHB44cAAAcjyeHBwccnVeZGbz5s0oU6YMtm7dqvDdfvpiFAcHBxw6dAhv3rxR6JWV32tvRpkHDhzA69evFY7zzNpQDQ0NNGvWDM2aNcOcOXMwY8YM/PTTTzh06FC2+yen84OIiKgwqV2PrEOHDmX6q2/GeCu57e6dQVNTEy1atMD27dsVHqGKiYnBunXr0LBhw2y7yGfIuCHLy+ujP62Hr68vtmzZIvXW+djHj9i9ePFCYZ6Ojg5cXFwghEBqairS0tKUHpWzsrKCnZ2d9PhVbrRu3Rpnz57FqVOnpLSkpCQsXboUjo6OBTaW0KlTpxTGi3j8+DG2b9+OFi1a5PjL4zfffANNTU1MnjxZ6bgQQkj7KqOcj/MIITB//nylMj/3u8zJunXr8Mcff8Dd3R3NmjXLMt+n37OGhoY0Vk7G95hdXZ8+fYqbN29m+sazzCxdulQh7+LFi/H+/XulQEL37t0hk8kwfPhw3Lt3L8fxWTJMnjwZL168QP/+/fH+/Xul+fv378fOnTsBfDj2oqOjFca+ev/+PRYuXAhDQ0Pp0Sjgw37p1KkT/vnnH/z55594//59jj0CbG1t4erqilWrVimcK2FhYUqPJzdo0EDhMa/PCWR9bNasWXj69ClGjx6dbT4vLy9oa2tj4cKFCsdvbt7Sltvj3s7ODlWqVMHq1asVHp08cuRIvnvRfbyPPz4+r169iv3790sB0vxer3JzfhRXBX3dy8v5oKmpCZlMptBb48GDB9i2bZtCvg4dOkBDQwNTpkxR6n2S3157p06dwr59+5TmxcXFZXpN+dTLly/RvXt3pKWlZRvsfvPmDd69e6eQVrZsWRgZGSkcWwYGBplee1NTU3Hz5s1c/7D25MkT6XFt4MOYVqtXr4arq6vC20y1tLTQvXt3bNy4EaGhoahatarS+GmZ6dSpE6pWrYrp06crtOcZXr9+Le2PWrVqwcrKCkuWLFHY1j179uDGjRsKb6UEPry9TlNTE3/99Rc2bdqENm3aKASoM9O6dWu8f/8eixcvltLS0tKkx/0y2NraKj1mWxCePXuGmTNnolq1ajmW2bp1a5w+fRpnz55VWP7jscqyktn5d+bMGaXvwNvbG6mpqVi2bJmUlp6ejpCQkFxtT1b1TktLw2+//aaQPnfuXMhkMqldf/nypdKyGQG/rK6juT0/iIiICpPa9cgaOnQo3rx5g44dO6JSpUpISUnByZMnsWHDBjg6OkrjSeXFtGnTEBYWhoYNG2Lw4MHQ0tLC77//juTkZAQHB+eqjIzByn/66Sd069YN2traaNu2bY5/0H3sl19+waFDh1C3bl0MGDAALi4uePnyJSIjI3HgwAHpD44WLVrAxsYGDRo0gLW1NW7cuIHffvsNPj4+MDIyQlxcHEqVKoVOnTqhevXqMDQ0xIEDB3Du3DnMnj071/X58ccf8ddff6FVq1YYNmwYzM3NsWrVKty/fx9btmzJ8XGG3KpSpQq8vb0xbNgwyOVyaRDWyZMn57hs2bJlMW3aNIwfPx4PHjxAhw4dYGRkhPv37+Pvv//GwIED8cMPP6BSpUooW7YsfvjhB/z3338wNjbGli1bMu3RkvFdDhs2DN7e3tDU1ES3bt3ytW2bN2+GoaEhUlJS8N9//2Hfvn04ceIEqlevrvQY7Kf69++Ply9fomnTpihVqhQePnyIhQsXwtXVVRrnwtXVFZqampg5cybi4+Mhl8vRtGlTWFlZYfz48dL3lZvXYaekpKBZs2bo0qULoqKisGjRIjRs2BDt2rVTyGdpaYmWLVti06ZNMDU1VbqxyUrXrl1x5coVTJ8+HRcuXED37t3h4OCAFy9eYO/evQgPD5fGfxk4cCB+//139OnTBxEREXB0dMTmzZtx4sQJzJs3T6knRdeuXbFw4UJMnDgRVatWlfZPdoKCguDj44OGDRuiX79+ePnyJRYuXIjKlSvnehysf/75B5cuXQLw4eb18uXLmDZtGgCgXbt20k3mmjVrsGXLFnh4eEjn48aNG9G/f/8cH4mytLTEDz/8gKCgILRp0watW7fGhQsXsGfPHpQoUSLbZfNy3M+YMQPt27dHgwYN0LdvX7x69Qq//fYbqlSpkq9xwQDg119/RatWreDu7g5/f3+8ffsWCxcuhImJCSZNmgTgw010fq5XuTk/iqvCuO7l9nzw8fHBnDlz0LJlS/To0QOxsbEICQlBuXLlcPnyZSlfuXLl8NNPP2Hq1Klo1KgRvvnmG8jlcpw7dw52dnYICgrK0zaPGTMGO3bsQJs2bdCnTx+4ubkhKSkJV65cwebNm/HgwQOF8+HWrVtYs2YNhBBISEjApUuXsGnTJiQmJkr1z8qtW7eka6GLiwu0tLTw999/IyYmRqEtcHNzw+LFizFt2jSUK1cOVlZWaNq0Kf777z84Ozujd+/e0mOc2alQoQL8/f1x7tw5WFtbY8WKFYiJiVHq5QZ86BG7YMECHDp0CDNnzszVvtPW1sbWrVvh5eUFDw8PdOnSBQ0aNIC2tjauXbuGdevWwczMDNOnT4e2tjZmzpyJvn37wtPTE927d0dMTAzmz58PR0dHjBw5UqFsKysrNGnSBHPmzMHr169z9Vhh27Zt0aBBA/z444948OABXFxcsHXr1jyNVRkfHy8FvjLGCP3tt99gamoKU1NTBAQESHk9PT3h7u6OcuXKITo6GkuXLkViYiJ27tyZ498xY8eOxZ9//omWLVti+PDhMDAwwNKlS6Vew9lp06YNtm7dio4dO8LHxwf379/HkiVL4OLionBOdejQAXXq1MHo0aNx584dVKpUCTt27JD+5stPz7+2bduiSZMm+Omnn/DgwQNUr14d+/fvx/bt2zFixAjp5UNTpkzB0aNH4ePjAwcHB8TGxmLRokUoVaqUwot+Ppbb84OIiKhQFfZrEQvanj17RL9+/USlSpWEoaGh0NHREeXKlRNDhw4VMTExCnmRxSu4HRwclF5XHRkZKby9vYWhoaHQ19cXTZo0ESdPnlTIk/GK9HPnzmVat6lTp4qSJUsKDQ0Nhdcy56UeMTExYsiQIcLe3l5oa2sLGxsb0axZM7F06VIpz++//y48PDyEhYWFkMvlomzZsmLMmDEiPj5eCCFEcnKyGDNmjKhevbowMjISBgYGonr16mLRokWZ1js7d+/eFZ06dRKmpqZCV1dX1KlTR+zcuVMpX1bbmJOM5dasWSPKly8v5HK5qFGjhjh06JBCvokTJ2b7iugtW7aIhg0bCgMDA2FgYCAqVaokhgwZIqKioqQ8169fF15eXsLQ0FCUKFFCDBgwQFy6dEnp9dbv378XQ4cOFZaWlkImk4mM0yTjFdSZvXIagJg4caJSfTMmXV1dUapUKdGmTRuxYsUK8e7dO6UyPn1F+ebNm0WLFi2ElZWV0NHREaVLlxbfffedePr0qcJyy5YtE2XKlBGampoCgLTvMl7TndkryT+WcVwfOXJEDBw4UJiZmQlDQ0Ph5+cnXrx4kekyGzduFADEwIEDsy07M+Hh4aJ9+/bCyspKaGlpCUtLS9G2bVuxfft2hXwxMTGib9++okSJEkJHR0dUrVo1y9eyp6enC3t7+0xfNy6E8uvDM2zZskU4OzsLuVwuXFxcxNatW5W+h+xk7OPMpo/XdebMGeHh4SHMzMyErq6uqF69uliyZIlIT0/P1XrS0tLE5MmTha2trdDT0xONGzcWV69eVbqGHDp0SOEYECL3x70QQqxfv15UqlRJyOVyUaVKFbFjxw7h6+srKlWqpLQvc3MeCCHEgQMHRIMGDYSenp4wNjYWbdu2FdevX5fm5/Z6ld/zQx3k1LZ4enqKypUrK6UX5HUvo7zcnA/Lly+XrteVKlUSK1eulK55n1qxYoWoUaOGkMvlwszMTHh6eoqwsDBpvoODg/Dx8cl0mz09PRXSXr9+LcaPHy/KlSsndHR0RIkSJUT9+vXFrFmzREpKipTv4/NQQ0NDmJqaiho1aojhw4eLa9euKa3r0+vD8+fPxZAhQ0SlSpWEgYGBMDExEXXr1hUbN25UWC46Olr4+PgIIyMjAUCqb0Z5n7bvmcnY/n379olq1apJ+3TTpk1ZLlO5cmWhoaEh/v333xzL/9irV69EYGCgqFq1qtDX1xe6urqiSpUqYvz48UrnzYYNG6TvzdzcXPj5+WW5vmXLlgkAwsjISLx9+1ZpfmbH0IsXL0TPnj2FsbGxMDExET179hQXLlzI9LjMTMY+zmz6dF0jR44UZcqUEXK5XFhaWooePXqIu3fv5riODJcvXxaenp5CV1dXlCxZUkydOlUsX75cqX399JhNT08XM2bMEA4ODtLfNjt37sx0fzx79kz06NFDGBkZCRMTE9GnTx9x4sQJAUCsX79eyte7d29hYGCgVMfMzr/Xr1+LkSNHCjs7O6GtrS3Kly8vfv31V4V2J6M9trOzEzo6OsLOzk50795d3Lp1S2lf5/X8ICIiKkwyIT5j1HMi+ipt374dHTp0wNGjR9GoUSNVV4cKkaurKywtLQtsrBoi+jw1atSAubk5wsPDVV0VKkTbtm1Dx44dcfz4cellP0RERPSB2o2RRUSqt2zZMpQpUybLRw9I/aSmpiqNM3T48GFcunQJjRs3Vk2liEjB+fPncfHiRYXB+0n9ffxCEeD/xgwzNjZGzZo1VVQrIiKiokvtxsiiz/f27dscx6IwNzeHjo5OvtcRHR2d7Xw9PT2YmJjku3xSjfXr1+Py5cvYtWsX5s+f/0Xe8Ehfxn///QcvLy98++23sLOzw82bN7FkyRLY2Njg+++/V3X1iL5qV69eRUREBGbPng1bW9tcjUVF6mPo0KF4+/Yt3N3dkZycjK1bt+LkyZOYMWMG9PT0VF09IiKiIoePFn6FQkNDcxwU/9ChQ5/VCyOnAEduB8GlokUmk8HQ0BBdu3bFkiVLoKXFWHhxER8fj4EDB+LEiRN49uwZDAwM0KxZM/zyyy/SwMBEpBqTJk3ClClTULFiRSxZskThza2k/tatW4fZs2fjzp07ePfuHcqVK4dBgwYpDFpPRERE/4eBrK/Q06dPce3atWzzuLm5wczMLN/rOHDgQLbz7ezs4OLiku/yiYiIiIiIiOjrw0AWERERERERERGpBQ72TkREREREREREaoED3HxB6enpePLkCYyMjDhINhEVSUIIvH79GnZ2dtDQ4G8dqsC2goiKOrYVRESkSgxkfUFPnjyBvb29qqtBRJSjx48fo1SpUqquxleJbQURqQu2FUREpAoMZH1BRkZGAD40+sbGxiquDRGRsoSEBNjb20vXK/ry2FYQUVHHtoKIiFSJgawvKOMREWNjY96cEFGRxkfaVIdtBRGpC7YVRESkCnyonYiIiIiIiIiI1AIDWUREREREREREpBZUGsg6evQo2rZtCzs7O8hkMmzbti3LvN9//z1kMhnmzZunkP7y5Uv4+fnB2NgYpqam8Pf3R2JiokKey5cvo1GjRtDV1YW9vT2Cg4OVyt+0aRMqVaoEXV1dVK1aFbt371aYL4RAYGAgbG1toaenBy8vL9y+fTvf205ERLnDtoKIiIiIiDKodIyspKQkVK9eHf369cM333yTZb6///4bp0+fhp2dndI8Pz8/PH36FGFhYUhNTUXfvn0xcOBArFu3DsCHwShbtGgBLy8vLFmyBFeuXEG/fv1gamqKgQMHAgBOnjyJ7t27IygoCG3atMG6devQoUMHREZGokqVKgCA4OBgLFiwAKtWrYKTkxMmTJgAb29vXL9+Hbq6uoWwd4jUR1paGlJTU1VdDcoFbW1taGpqqroaecK2gqh4YFuhPtSxrSAioq+HTAghVF0J4MNgkX///Tc6dOigkP7ff/+hbt262LdvH3x8fDBixAiMGDECAHDjxg24uLjg3LlzqFWrFgBg7969aN26Nf7991/Y2dlh8eLF+OmnnxAdHQ0dHR0AwI8//oht27bh5s2bAICuXbsiKSkJO3fulNZbr149uLq6YsmSJRBCwM7ODqNHj8YPP/wAAIiPj4e1tTVCQ0PRrVu3XG1jQkICTExMEB8fzwF8qVgQQiA6OhpxcXGqrgrlgampKWxsbDIdpLeoX6fYVhCpH7YV6kmd2woiIireivRbC9PT09GzZ0+MGTMGlStXVpp/6tQpmJqaSjcmAODl5QUNDQ2cOXMGHTt2xKlTp+Dh4SHdmACAt7c3Zs6ciVevXsHMzAynTp3CqFGjFMr29vaWHl+5f/8+oqOj4eXlJc03MTFB3bp1cerUqSxvTpKTk5GcnCx9TkhIyNd+ICqqMm5MrKysoK+vz7cXFXFCCLx58waxsbEAAFtbWxXXqGCwrSAq2thWqJfi2lYQEVHxUaQDWTNnzoSWlhaGDRuW6fzo6GhYWVkppGlpacHc3BzR0dFSHicnJ4U81tbW0jwzMzNER0dLaR/n+biMj5fLLE9mgoKCMHny5Jw2k0gtpaWlSTcmFhYWqq4O5ZKenh4AIDY2FlZWVsXi0RG2FURFF9sK9VQc2woiIio+iuxbCyMiIjB//nyEhoaq7S9348ePR3x8vDQ9fvxY1VUiKjAZ45zo6+uruCaUVxnfWXEYq4ZtBVHRxrZCfRWntoKIiIqXIhvIOnbsGGJjY1G6dGloaWlBS0sLDx8+xOjRo+Ho6AgAsLGxkbo9Z3j//j1evnwJGxsbKU9MTIxCnozPOeX5eP7Hy2WWJzNyuRzGxsYKE1Fxo67Bg69ZcfrO2FYQqYfidN35WvA7IyKioqrIBrJ69uyJy5cv4+LFi9JkZ2eHMWPGYN++fQAAd3d3xMXFISIiQlru4MGDSE9PR926daU8R48eVfg1KSwsDBUrVoSZmZmUJzw8XGH9YWFhcHd3BwA4OTnBxsZGIU9CQgLOnDkj5SEioi+PbQURERER0ddFpYGsxMRE6cYD+DBQ7sWLF/Ho0SNYWFigSpUqCpO2tjZsbGxQsWJFAICzszNatmyJAQMG4OzZszhx4gQCAgLQrVs36fXrPXr0gI6ODvz9/XHt2jVs2LAB8+fPVxiwd/jw4di7dy9mz56NmzdvYtKkSTh//jwCAgIAfPhFasSIEZg2bRp27NiBK1euoFevXrCzs1N6cxYRfXnR0dEYOnQoypQpA7lcDnt7e7Rt21Yp6EDqiW0FERUEthVERETFhFChQ4cOCQBKU+/evTPN7+DgIObOnauQ9uLFC9G9e3dhaGgojI2NRd++fcXr168V8ly6dEk0bNhQyOVyUbJkSfHLL78olb1x40ZRoUIFoaOjIypXrix27dqlMD89PV1MmDBBWFtbC7lcLpo1ayaioqLytL3x8fECgIiPj8/TckRF0du3b8X169fF27dvVVqP+/fvCzs7O+Hi4iI2b94soqKixNWrV8Xs2bNFxYoV81VmcnJyAdeyaMnuuyuK1ym2FUTqi22F+lK3toKIiL4eMiGEUEUA7WuUkJAAExMTxMfHF8sxUB5NqarqKmSpdOAVVVeh2Hn37h3u378PJycn6OrqqqwerVu3xuXLlxEVFQUDAwOFeXFxcTA1NcWjR48wdOhQhIeHQ0NDAy1btsTChQult8tNmjQJ27ZtQ0BAAKZPn46HDx8iPT0918uNHj0aEyZMwKtXr9CqVSssW7YMRkZGAIC9e/di2rRpuHr1KjQ1NeHu7o758+ejbNmyX3ZHfSS77664X6fUQXH/DthWfF3YVrCtICIiKmhFdowsIqKcvHz5Env37sWQIUOUbkwAwNTUFOnp6Wjfvj1evnyJI0eOICwsDPfu3UPXrl0V8t65cwdbtmzB1q1bcfHixVwvd/fuXWzbtg07d+7Ezp07ceTIEfzyyy/S/KSkJIwaNQrnz5+XbnI6duyI9PT0wtkpRESkgG0FERFR8aKl6goQEeXXnTt3IIRApUqVsswTHh6OK1eu4P79+7C3twcArF69GpUrV8a5c+dQu3ZtAEBKSgpWr14NS0tLAB8G8c7Ncunp6QgNDZV+Ve/ZsyfCw8Mxffp0AICvr69CfVasWAFLS0tcv34dVapUKcC9QUREmWFbQUREVLywRxYRqa3cPBl948YN2NvbSzcYAODi4gJTU1PcuHFDSnNwcJBuTPKynKOjo3RjAgC2traIjY2VPt++fRvdu3dHmTJlYGxsDEdHRwDAo0eP8raxRESUL2wriIiIihf2yCIitVW+fHnIZDLcvHnzs8vK7HGT3NDW1lb4LJPJFB4Fadu2LRwcHLBs2TLY2dkhPT0dVapUQUpKymfVl4iIcodtBRERUfHCHllEpLbMzc3h7e2NkJAQJCUlKc2Pi4uDs7MzHj9+jMePH0vp169fR1xcHFxcXLIsO7/LfezFixeIiorCzz//jGbNmsHZ2RmvXr3KwxYSEdHnYltBRERUvDCQRURqLSQkBGlpaahTpw62bNmC27dv48aNG1iwYAHc3d3h5eWFqlWrws/PD5GRkTh79ix69eoFT09P1KpVK8ty87vcx8zMzGBhYYGlS5fizp07OHjwIEaNGlVQm05ERLnEtoKIiKj4YCCLiNRamTJlEBkZiSZNmmD06NGoUqUKmjdvjvDwcCxevBgymQzbt2+HmZkZPDw84OXlhTJlymDDhg3Zlpvf5T6moaGB9evXIyIiAlWqVMHIkSPx66+/fu4mExFRHrGtICIiKj5kIjcjYFKBSEhIgImJCeLj42FsbKzq6hS4R1OqqroKWSodeEXVVSh23r17h/v378PJyQm6urqqrg7lQXbfXXG/TqmD4v4dsK34urCtUF9sK4iIqKhijywiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhERERERERERqQUGsoiIiIiIiIiISC0wkEVERERERERERGqBgSwiIiIiIiIiIlILDGQREeVC48aNMWLECFVXg4iIijC2FURERIWPgSwioo8cPnwYMpkMcXFxqq4KEREVUWwriIiIVEdL1RUgouLHbczqL7q+iF97fdH1FZSUlBTo6OiouhpERCrBtiJ32FYQEREpYo8sIvrqJCcnY9iwYbCysoKuri4aNmyIc+fO4cGDB2jSpAkAwMzMDDKZDH369JGWS09Px9ixY2Fubg4bGxtMmjRJody4uDj0798flpaWMDY2RtOmTXHp0iVp/qRJk+Dq6oo//vgDTk5O0NXV/RKbS0RE+cC2goiIqGhiIIuIvjpjx47Fli1bsGrVKkRGRqJcuXLw9vaGkZERtmzZAgCIiorC06dPMX/+fGm5VatWwcDAAGfOnEFwcDCmTJmCsLAwaX7nzp0RGxuLPXv2ICIiAjVr1kSzZs3w8uVLKc+dO3ewZcsWbN26FRcvXvxi20xERHnDtoKIiKho4qOFRPRVSUpKwuLFixEaGopWrVoBAJYtW4awsDCsWLECtWvXBgBYWVnB1NRUYdlq1aph4sSJAIDy5cvjt99+Q3h4OJo3b47jx4/j7NmziI2NhVwuBwDMmjUL27Ztw+bNmzFw4EAAHx4RWb16NSwtLb/QFhMRUV6xrSAiIiq6GMgioq/K3bt3kZqaigYNGkhp2traqFOnDm7cuCHdnGSmWrVqCp9tbW0RGxsLALh06RISExNhYWGhkOft27e4e/eu9NnBwYE3JkRERRzbCiIioqKLgSwiolzS1tZW+CyTyZCeng4ASExMhK2tLQ4fPqy03Me/1hsYGBRmFYmISMXYVhARERUuBrKI6KtStmxZ6Ojo4MSJE3BwcAAApKam4ty5cxgxYoT0Zqi0tLQ8lVuzZk1ER0dDS0sLjo6OBV1tIiL6gthWEBERFV0c7J2IvioGBgYYNGgQxowZg7179+L69esYMGAA3rx5A39/fzg4OEAmk2Hnzp149uwZEhMTc1Wul5cX3N3d0aFDB+zfvx8PHjzAyZMn8dNPP+H8+fOFvFVERFSQ2FYQEREVXQxkEdFX55dffoGvry969uyJmjVr4s6dO9i3bx/MzMxQsmRJTJ48GT/++COsra0REBCQqzJlMhl2794NDw8P9O3bFxUqVEC3bt3w8OFDWFtbF/IWERFRQWNbQUREVDTJhBBC1ZX4WiQkJMDExATx8fEwNjZWdXUK3KMpVVVdhSyVDryi6ioUO+/evcP9+/fh5OQEXV1dVVeH8iC77664X6fUQXH/DthWfF3YVqgvthVERFRUsUcWERERERERERGpBQayiIiIiIiIiIhILTCQRUREREREREREakGlgayjR4+ibdu2sLOzg0wmw7Zt26R5qampGDduHKpWrQoDAwPY2dmhV69eePLkiUIZL1++hJ+fH4yNjWFqagp/f3+lN8dcvnwZjRo1gq6uLuzt7REcHKxUl02bNqFSpUrQ1dVF1apVsXv3boX5QggEBgbC1tYWenp68PLywu3btwtuZxARUabYVhARERERUQaVBrKSkpJQvXp1hISEKM178+YNIiMjMWHCBERGRmLr1q2IiopCu3btFPL5+fnh2rVrCAsLw86dO3H06FEMHDhQmp+QkIAWLVrAwcEBERER+PXXXzFp0iQsXbpUynPy5El0794d/v7+uHDhAjp06IAOHTrg6tWrUp7g4GAsWLAAS5YswZkzZ2BgYABvb2+8e/euEPYMERFlYFtBREREREQZisxbC2UyGf7++2906NAhyzznzp1DnTp18PDhQ5QuXRo3btyAi4sLzp07h1q1agEA9u7di9atW+Pff/+FnZ0dFi9ejJ9++gnR0dHQ0dEBAPz444/Ytm0bbt68CQDo2rUrkpKSsHPnTmld9erVg6urK5YsWQIhBOzs7DB69Gj88MMPAID4+HhYW1sjNDQU3bp1y9U2Fvc3vPBNVF8XvolKfanzm6jYVqg/thVfF7YV6kud2woiIire1GqMrPj4eMhkMpiamgIATp06BVNTU+nGBAC8vLygoaGBM2fOSHk8PDykGxMA8Pb2RlRUFF69eiXl8fLyUliXt7c3Tp06BQC4f/8+oqOjFfKYmJigbt26Up7MJCcnIyEhQWEiIqLCxbaCiIiIiKj4UptA1rt37zBu3Dh0795d+uUnOjoaVlZWCvm0tLRgbm6O6OhoKY+1tbVCnozPOeX5eP7Hy2WWJzNBQUEwMTGRJnt7+zxtMxER5Q3bCiIiIiKi4k0tAlmpqano0qULhBBYvHixqquTa+PHj0d8fLw0PX78WNVVIiIqtthWEBEREREVf0U+kJVxY/Lw4UOEhYUpPIdvY2OD2NhYhfzv37/Hy5cvYWNjI+WJiYlRyJPxOac8H8//eLnM8mRGLpfD2NhYYSIiys6kSZPg6uqq6mqoHbYVRPQ1YVtBRERfsyIdyMq4Mbl9+zYOHDgACwsLhfnu7u6Ii4tDRESElHbw4EGkp6ejbt26Up6jR48iNTVVyhMWFoaKFSvCzMxMyhMeHq5QdlhYGNzd3QEATk5OsLGxUciTkJCAM2fOSHmIiPJKJpNh27ZtCmk//PCD0vWIsse2goiKM7YVREREirRUufLExETcuXNH+nz//n1cvHgR5ubmsLW1RadOnRAZGYmdO3ciLS1NGmPE3NwcOjo6cHZ2RsuWLTFgwAAsWbIEqampCAgIQLdu3WBnZwcA6NGjByZPngx/f3+MGzcOV69exfz58zF37lxpvcOHD4enpydmz54NHx8frF+/HufPn5deuy6TyTBixAhMmzYN5cuXh5OTEyZMmAA7O7tsaILOvgAARzBJREFU35xF9LX60m8lK05vGjM0NIShoaGqq1GksK0gKp7YVuQf2woiIvqaqbRH1vnz51GjRg3UqFEDADBq1CjUqFEDgYGB+O+//7Bjxw78+++/cHV1ha2trTSdPHlSKmPt2rWoVKkSmjVrhtatW6Nhw4bSTQXw4Y1R+/fvx/379+Hm5obRo0cjMDAQAwcOlPLUr18f69atw9KlS1G9enVs3rwZ27ZtQ5UqVaQ8Y8eOxdChQzFw4EDUrl0biYmJ2Lt3L18lTaSGGjdujGHDhmHs2LEwNzeHjY0NJk2aJM2Pi4tD//79YWlpCWNjYzRt2hSXLl1SKGPatGmwsrKCkZER+vfvjx9//FHhMY9z586hefPmKFGiBExMTODp6YnIyEhpvqOjIwCgY8eOkMlk0uePHxfZv38/dHV1ERcXp7Du4cOHo2nTptLn48ePo1GjRtDT04O9vT2GDRuGpKSkz95PRQXbCiJSBbYVRERERZNKA1mNGzeGEEJpCg0NhaOjY6bzhBBo3LixVIa5uTnWrVuH169fIz4+HitWrFD6hapatWo4duwY3r17h3///Rfjxo1Tqkvnzp0RFRWF5ORkXL16Fa1bt1aYL5PJMGXKFERHR+Pdu3c4cOAAKlSoUCj7hYgK36pVq2BgYIAzZ84gODgYU6ZMQVhYGIAP14PY2Fjs2bMHERERqFmzJpo1a4aXL18C+BAUmT59OmbOnImIiAiULl1aaXDx169fo3fv3jh+/DhOnz6N8uXLo3Xr1nj9+jWADzcvALBy5Uo8ffpU+vyxZs2awdTUFFu2bJHS0tLSsGHDBvj5+QEA7t69i5YtW8LX1xeXL1/Ghg0bcPz4cQQEBBT8TlMRthVEpCpsK4iIiIoelT5aSESkKtWqVcPEiRMBAOXLl8dvv/2G8PBw6Onp4ezZs4iNjYVcLgcAzJo1C9u2bcPmzZsxcOBALFy4EP7+/ujbty8AIDAwEPv370diYqJU/se/ggPA0qVLYWpqiiNHjqBNmzawtLQEAJiammY5ELimpia6deuGdevWwd/fHwAQHh6OuLg4+Pr6AgCCgoLg5+eHESNGSNuyYMECeHp6YvHixewJRET0GdhWEBERFT1FerB3IqLCUq1aNYXPtra2iI2NxaVLl5CYmAgLCwtpDBJDQ0Pcv38fd+/eBQBERUWhTp06Cst/+jkmJgYDBgxA+fLlYWJiAmNjYyQmJuLRo0d5qqefnx8OHz6MJ0+eAPjwC7+Pjw9MTU0BAJcuXUJoaKhCXb29vZGeno779+/naV1ERKSIbQUREVHRwx5ZRPRV0tbWVvgsk8mQnp6OxMRE2Nra4vDhw0rLZNwQ5Ebv3r3x4sULzJ8/Hw4ODpDL5XB3d0dKSkqe6lm7dm2ULVsW69evx6BBg/D3338jNDRUmp+YmIjvvvsOw4YNU1q2dOnSeVoXEREpYltBRERU9DCQRUT0kZo1ayI6OhpaWlrSoLqfqlixIs6dO4devXpJaZ+OW3LixAksWrRIGkPp8ePHeP78uUIebW1tpKWl5VgnPz8/rF27FqVKlYKGhgZ8fHwU6nv9+nWUK1cut5tIRESfiW0FERGR6vDRQiKij3h5ecHd3R0dOnTA/v378eDBA5w8eRI//fQTzp8/DwAYOnQoli9fjlWrVuH27duYNm0aLl++DJlMJpVTvnx5/Pnnn7hx4wbOnDkDPz8/6OnpKazL0dER4eHhiI6OxqtXr7Ksk5+fHyIjIzF9+nR06tRJGo8FAMaNG4eTJ08iICAAFy9exO3bt7F9+3YO4EtEVIjYVhAREakOA1lERB+RyWTYvXs3PDw80LdvX1SoUAHdunXDw4cPYW1tDeDDzcL48ePxww8/oGbNmrh//z769OmjMFju8uXL8erVK9SsWRM9e/bEsGHDYGVlpbCu2bNnIywsDPb29qhRo0aWdSpXrhzq1KmDy5cvS2+gylCtWjUcOXIEt27dQqNGjVCjRg0EBgbCzs6uAPcKERF9jG0FERGR6siEEELVlfhaJCQkwMTEBPHx8TA2NlZ1dQrcoylVVV2FLJUOvKLqKhQ77969w/379+Hk5MS3HQFo3rw5bGxs8Oeff6q6KjnK7rsr7tcpdVDcvwO2FV8XthWK2FYQERF9Po6RRUSUR2/evMGSJUvg7e0NTU1N/PXXXzhw4ADCwsJUXTUiIioi2FYQEREVDgayiIjyKOORkunTp+Pdu3eoWLEitmzZAi8vL1VXjYiIigi2FURERIWDgSwiojzS09PDgQMHVF0NIiIqwthWEBERFQ4O9k5ERERERERERGqBgSwiIiIiIiIiIlILDGQREREREREREZFaYCCLiIiIiIiIiIjUAgNZRERERERERESkFhjIIiIiIiIiIiIitcBAFhFRATp8+DBkMhni4uKyzefo6Ih58+Z9kToREVHRwraCiIgo/7RUXQEiKn4aLGzwRdd3YuiJL7q+7NSvXx9Pnz6FiYkJACA0NBQjRoxQulk5d+4cDAwMVFBDIqKigW0F2woiIqL8YCCLiKgA6ejowMbGJsd8lpaWX6A2RERUFLGtICIiyj8+WkhEX53GjRsjICAAAQEBMDExQYkSJTBhwgQIIQAAr169Qq9evWBmZgZ9fX20atUKt2/flpZ/+PAh2rZtCzMzMxgYGKBy5crYvXs3AMXHRQ4fPoy+ffsiPj4eMpkMMpkMkyZNAqD4uEiPHj3QtWtXhTqmpqaiRIkSWL16NQAgPT0dQUFBcHJygp6eHqpXr47NmzcX8p4iIvp6sa0gIiIqmhjIIqKv0qpVq6ClpYWzZ89i/vz5mDNnDv744w8AQJ8+fXD+/Hns2LEDp06dghACrVu3RmpqKgBgyJAhSE5OxtGjR3HlyhXMnDkThoaGSuuoX78+5s2bB2NjYzx9+hRPnz7FDz/8oJTPz88P//zzDxITE6W0ffv24c2bN+jYsSMAICgoCKtXr8aSJUtw7do1jBw5Et9++y2OHDlSGLuHiIjAtoKIiKgo4qOFRPRVsre3x9y5cyGTyVCxYkVcuXIFc+fORePGjbFjxw6cOHEC9evXBwCsXbsW9vb22LZtGzp37oxHjx7B19cXVatWBQCUKVMm03Xo6OjAxMQEMpks20dIvL29YWBggL///hs9e/YEAKxbtw7t2rWDkZERkpOTMWPGDBw4cADu7u7SOo8fP47ff/8dnp6eBblriIjo/2NbQUREVPSwRxYRfZXq1asHmUwmfXZ3d8ft27dx/fp1aGlpoW7dutI8CwsLVKxYETdu3AAADBs2DNOmTUODBg0wceJEXL58+bPqoqWlhS5dumDt2rUAgKSkJGzfvh1+fn4AgDt37uDNmzdo3rw5DA0NpWn16tW4e/fuZ62biIiyxraCiIio6GGPLCKiPOrfvz+8vb2xa9cu7N+/H0FBQZg9+/+1d+dhWdX5/8dfgLLIqqkgBkpFBkWSa1hZjiSmNVpUalTUkFYDmlpupWhWY9FU5pJOk4U1Opkz38xcKMJxSXHDcEu0xcIWwFS4xVFUOL8/ujg/7wEN6MabA8/Hdd3X1X0+7/M573Nze97d7/ucc7+ikSNH1nnOhIQE3XzzzSoqKlJmZqa8vLzUv39/STIvI1m5cqXat29vt56Hh0fddwQAUG+oFQAA1A/OyALQJG3ZssXu+ebNmxUeHq7IyEidPXvWbvzIkSPav3+/IiMjzWUhISF67LHH9H//93968skn9fe//73a7bi7u6u8vPw38+nVq5dCQkK0ZMkSLVq0SPfcc4+aN28uSYqMjJSHh4fy8/N1xRVX2D1CQkLqsvsAgBqgVgAA0PBwRhaAJik/P19jx47Vo48+qh07dmj27Nl65ZVXFB4erkGDBmn48OH629/+Jl9fX02cOFHt27fXoEGDJEmjR4/WbbfdpiuvvFLHjh3Tf/7zH0VERFS7nY4dO6q0tFRZWVnq3LmzWrRooRYtWlQbe99992n+/Pk6cOCA/vOf/5jLfX199dRTT2nMmDGqqKjQjTfeqJKSEm3cuFF+fn5KTEx0/AsEAKBWAADQAHFGFoAm6cEHH9TJkyfVo0cPJScn64knntCIESMkSe+88466du2q22+/XTExMTIMQ6tWrTK/9S4vL1dycrIiIiLUv39/XXnllXrjjTeq3U6vXr302GOPaciQIWrTpo3S0tLOm1NCQoK+/PJLtW/fXjfccIPd2HPPPacpU6ZoxowZ5nZXrlypsLAwB70iAID/Ra0AAKDhcTEMw3B2Ek2FzWaTv7+/SkpK5Ofn5+x0HC5/epSzUziv0NTdzk6h0Tl16pQOHjyosLAweXp6OjudWrnlllsUHR2tmTNnOjsVp7jQ366xH6esoLH/DagVTQu1wrqoFQCAhoozsgAAAAAAAGAJNLIAAAAAAABgCdzsHUCTs3btWmenAABo4KgVAAA0TJyRBQAAAAAAAEtwaiNr/fr1uuOOOxQcHCwXFxctW7bMbtwwDKWmpqpdu3by8vJSbGysvvrqK7uYo0ePKiEhQX5+fgoICFBSUpJKS0vtYnbt2qWbbrpJnp6eCgkJqfaXYJYuXaqrrrpKnp6eioqK0qpVq2qdCwDA8agVAAAAACo5tZF14sQJde7cWXPnzq12PC0tTbNmzdL8+fO1ZcsWeXt7Ky4uTqdOnTJjEhIStHfvXmVmZmrFihVav369+bPI0q+/qtKvXz916NBBOTk5evnllzVt2jS9+eabZsymTZs0bNgwJSUl6YsvvtDgwYM1ePBg7dmzp1a5AE0RP3xqPVb7m1ErAOuz2nEH/M0AAA2Xi9FAqpSLi4s+/PBDDR48WNKvxTM4OFhPPvmknnrqKUlSSUmJAgMDlZ6erqFDh2rfvn2KjIzUtm3b1K1bN0lSRkaGBgwYoB9++EHBwcGaN2+ennnmGRUUFMjd3V2SNHHiRC1btkx5eXmSpCFDhujEiRNasWKFmc/111+v6OhozZ8/v0a51ERj/6liflK9aSkvL9eBAwfUtm1bXXLJJc5OB7Vw5MgRFRUV6corr5Sbm5vdWEM/TlErrI9a0bRQK6zLyrUCANC4NdibvR88eFAFBQWKjY01l/n7+6tnz57Kzs7W0KFDlZ2drYCAAPODiSTFxsbK1dVVW7Zs0Z133qns7Gz17t3b/GAiSXFxcXrppZd07NgxtWzZUtnZ2Ro7dqzd9uPi4szLV2qSC9DUuLm5KSAgQEVFRZKkFi1ayMXFxclZ4UIMw9B///tfFRUVKSAgoMoHEyuiVgANG7XCehpjrQAANC4NtpFVUFAgSQoMDLRbHhgYaI4VFBSobdu2duPNmjVTq1at7GLCwsKqzFE51rJlSxUUFPzmdn4rl+qUlZWprKzMfG6z2S6wx4D1BAUFSZL5AQXWEBAQYP7trI5aATR81Apraky1AgDQuDTYRlZjMGPGDD377LPOTgOoNy4uLmrXrp3atm2rM2fOODsd1EDz5s35dr2BoVagsaNWWA+1AgDQkDXYRlblN0CFhYVq166dubywsFDR0dFmzP9+u3f27FkdPXrUXD8oKEiFhYV2MZXPfyvm3PHfyqU6kyZNsrsMxWazKSQk5MI7DliQm5sb/8MLp6BWANZBrQAAAI7g1F8tvJCwsDAFBQUpKyvLXGaz2bRlyxbFxMRIkmJiYlRcXKycnBwzZs2aNaqoqFDPnj3NmPXr19t9A5iZmalOnTqpZcuWZsy526mMqdxOTXKpjoeHh/z8/OweAADHoVYAAAAATYtTG1mlpaXKzc1Vbm6upF9vlJubm6v8/Hy5uLho9OjRev7557V8+XLt3r1bDz74oIKDg81fq4qIiFD//v01fPhwbd26VRs3blRKSoqGDh2q4OBgSdJ9990nd3d3JSUlae/evVqyZIlef/11u2+/n3jiCWVkZOiVV15RXl6epk2bpu3btyslJUWSapQLAKB+UCsAAAAAVHLqpYXbt29Xnz59zOeVHxgSExOVnp6u8ePH68SJExoxYoSKi4t14403KiMjQ56enuY6ixYtUkpKivr27StXV1fFx8dr1qxZ5ri/v78+/fRTJScnq2vXrmrdurVSU1M1YsQIM6ZXr15avHixJk+erKefflrh4eFatmyZrrnmGjOmJrkAAByPWgEAAACgkothGIazk2gqbDab/P39VVJS0igvHcmfHuXsFM4rNHW3s1MALKGxH6esoLH/DagVgPU19uMUAKBha7D3yAIAAAAAAADORSMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAlkAjCwAAAAAAAJZAIwsAAAAAAACWQCMLAAAAAAAAltCgG1nl5eWaMmWKwsLC5OXlpcsvv1zPPfecDMMwYwzDUGpqqtq1aycvLy/Fxsbqq6++spvn6NGjSkhIkJ+fnwICApSUlKTS0lK7mF27dummm26Sp6enQkJClJaWViWfpUuX6qqrrpKnp6eioqK0atWq+tlxAECNUSsAAACApqNBN7JeeuklzZs3T3PmzNG+ffv00ksvKS0tTbNnzzZj0tLSNGvWLM2fP19btmyRt7e34uLidOrUKTMmISFBe/fuVWZmplasWKH169drxIgR5rjNZlO/fv3UoUMH5eTk6OWXX9a0adP05ptvmjGbNm3SsGHDlJSUpC+++EKDBw/W4MGDtWfPnovzYgAAqkWtAAAAAJoOF+Pcr6wbmNtvv12BgYFasGCBuSw+Pl5eXl76xz/+IcMwFBwcrCeffFJPPfWUJKmkpESBgYFKT0/X0KFDtW/fPkVGRmrbtm3q1q2bJCkjI0MDBgzQDz/8oODgYM2bN0/PPPOMCgoK5O7uLkmaOHGili1bpry8PEnSkCFDdOLECa1YscLM5frrr1d0dLTmz59fo/2x2Wzy9/dXSUmJ/Pz8HPIaNST506OcncJ5habudnYKgCVY8ThFrbAWagVgfY39OAUAaNjqdEbWZZddpiNHjlRZXlxcrMsuu+x3J1WpV69eysrK0oEDByRJO3fu1Oeff67bbrtNknTw4EEVFBQoNjbWXMff3189e/ZUdna2JCk7O1sBAQHmBxNJio2Nlaurq7Zs2WLG9O7d2/xgIklxcXHav3+/jh07Zsacu53KmMrtAADsUSv+fwy1AgA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          },
          "metadata": {}
        }
      ],
      "source": [
        "#Symptoms Distribution Visualisation\n",
        "symptoms = ['Cough_symptoms', 'Fever', 'Sore_throat','Shortness_of_breath', 'Headache']\n",
        "plt.figure(figsize=(12,8))\n",
        "for i, symptom in enumerate(symptoms):\n",
        "    plt.subplot(2,3,i+1)\n",
        "    sns.countplot(x=symptom,data = covid, hue='Corona')\n",
        "    plt.title(f'{symptom}Dist. by Covid-19 diagnosis')\n",
        "    plt.ylabel('Count')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "pYC9xgx98jbL",
        "outputId": "78689299-ce70-43b9-cb52-bc5b849f91d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = sns.countplot(x='Cough_symptoms',data=covid, palette=\"Set1\")\n",
        "for p in ax.patches:\n",
        "     ax.annotate(f'\\n{p.get_height()}', (p.get_x()+0.4, p.get_height()+100), ha='center', va='top', color='white', size=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "5NVuJUNU8jZD",
        "outputId": "accf7593-8998-401a-f5f7-839ad0bda0df"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = sns.countplot(x='Sex',hue='Corona',data=covid, palette=\"Set2\")\n",
        "for p in ax.patches:\n",
        "     ax.annotate(f'\\n{p.get_height()}', (p.get_x()+0.1, p.get_height()+100), ha='center', va='center', color='black', size=6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Qa69OqUo9E5S",
        "outputId": "382b0bf7-96dc-4c48-8147-8575ad52ea38"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = sns.countplot(x='Fever',hue='Corona',data=covid)\n",
        "for p in ax.patches:\n",
        "     ax.annotate(f'\\n{p.get_height()}', (p.get_x()+0.1, p.get_height()+100), ha='center', va='center', color='Black', size=6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQ46X-dI9QBa"
      },
      "source": [
        "# Feature Engineering\n",
        "## Data Splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GnwIJoYt9E1x",
        "outputId": "09d8b7f9-5272-4f3f-ce55-ed026e23e502"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID  Test_date Cough_symptoms  Fever Sore_throat  \\\n",
              "4512      4513 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4513      4514 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4514      4515 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4515      4516 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4516      4517 2020-03-17          FALSE  FALSE       FALSE   \n",
              "...        ...        ...            ...    ...         ...   \n",
              "156035  156036 2020-04-14           TRUE  FALSE        TRUE   \n",
              "156036  156037 2020-04-14           TRUE  FALSE        TRUE   \n",
              "156037  156038 2020-04-14           TRUE   TRUE       FALSE   \n",
              "156038  156039 2020-04-14          FALSE  FALSE       FALSE   \n",
              "156039  156040 2020-04-14          FALSE  FALSE       FALSE   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
              "4512                 FALSE    FALSE     other           No   other   \n",
              "4513                  TRUE    FALSE  negative           No   other   \n",
              "4514                 FALSE    FALSE     other          Yes   other   \n",
              "4515                 FALSE    FALSE  positive           No   other   \n",
              "4516                 FALSE    FALSE  negative           No   other   \n",
              "...                    ...      ...       ...          ...     ...   \n",
              "156035               FALSE    FALSE  negative          Yes    male   \n",
              "156036                TRUE    FALSE  negative           No    male   \n",
              "156037               FALSE    FALSE  negative           No  female   \n",
              "156038               FALSE    FALSE  negative           No    male   \n",
              "156039               FALSE    FALSE  negative           No  female   \n",
              "\n",
              "       Known_contact  \n",
              "4512           Other  \n",
              "4513          Abroad  \n",
              "4514           Other  \n",
              "4515           Other  \n",
              "4516           Other  \n",
              "...              ...  \n",
              "156035        Abroad  \n",
              "156036        Abroad  \n",
              "156037        Abroad  \n",
              "156038         Other  \n",
              "156039         Other  \n",
              "\n",
              "[151279 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22f5bed7-6035-4b9c-b639-cb2e8a6c65da\" class=\"colab-df-container\">\n",
              "    <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>Ind_ID</th>\n",
              "      <th>Test_date</th>\n",
              "      <th>Cough_symptoms</th>\n",
              "      <th>Fever</th>\n",
              "      <th>Sore_throat</th>\n",
              "      <th>Shortness_of_breath</th>\n",
              "      <th>Headache</th>\n",
              "      <th>Corona</th>\n",
              "      <th>Age_60_above</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Known_contact</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4512</th>\n",
              "      <td>4513</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>other</td>\n",
              "      <td>No</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4513</th>\n",
              "      <td>4514</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>other</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4514</th>\n",
              "      <td>4515</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>other</td>\n",
              "      <td>Yes</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4515</th>\n",
              "      <td>4516</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>positive</td>\n",
              "      <td>No</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4516</th>\n",
              "      <td>4517</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>other</td>\n",
              "      <td>Other</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156035</th>\n",
              "      <td>156036</td>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Yes</td>\n",
              "      <td>male</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156036</th>\n",
              "      <td>156037</td>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>male</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156037</th>\n",
              "      <td>156038</td>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>female</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156038</th>\n",
              "      <td>156039</td>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>male</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156039</th>\n",
              "      <td>156040</td>\n",
              "      <td>2020-04-14</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>No</td>\n",
              "      <td>female</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>151279 rows × 11 columns</p>\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_val"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df_train_val= covid.query(\"Test_date <= '2020-04-15' and Test_date >= '2020-03-11'\")\n",
        "df_test = covid.query(\"Test_date <= '2020-04-30' and Test_date >= '2020-03-16'\")\n",
        "df_train_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhSidocO9Eym"
      },
      "outputs": [],
      "source": [
        "#Dropping few unnecessary columns:\n",
        "\n",
        "df_train_val= df_train_val.drop(columns=['Test_date','Ind_ID','Age_60_above','Sex'])\n",
        "df_test= df_test.drop(columns=['Test_date','Ind_ID','Age_60_above','Sex'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WXkmA7gX9Ev8",
        "outputId": "1679243b-8835-41a1-ff2d-1d7b7cf7bf01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Cough_symptoms  Fever Sore_throat Shortness_of_breath Headache    Corona  \\\n",
              "4512          FALSE  FALSE       FALSE               FALSE    FALSE     other   \n",
              "4513          FALSE  FALSE       FALSE                TRUE    FALSE  negative   \n",
              "4514          FALSE  FALSE       FALSE               FALSE    FALSE     other   \n",
              "4515          FALSE  FALSE       FALSE               FALSE    FALSE  positive   \n",
              "4516          FALSE  FALSE       FALSE               FALSE    FALSE  negative   \n",
              "\n",
              "     Known_contact  \n",
              "4512         Other  \n",
              "4513        Abroad  \n",
              "4514         Other  \n",
              "4515         Other  \n",
              "4516         Other  "
            ],
            "text/html": [
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              "      <th>Cough_symptoms</th>\n",
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              "      <th>4513</th>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
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              "      <th>4514</th>\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>negative</td>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bbf6a8eb-08fb-42a3-bf8b-6b6daf8c4e43 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bbf6a8eb-08fb-42a3-bf8b-6b6daf8c4e43');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-48356b19-c7fb-4e86-8080-01fe3d78232c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-48356b19-c7fb-4e86-8080-01fe3d78232c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-48356b19-c7fb-4e86-8080-01fe3d78232c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_val"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df_train_val.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uNRKyge29EtL",
        "outputId": "9259626b-4358-4537-9314-1194197d881d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Cough_symptoms  Fever Sore_throat Shortness_of_breath Headache  \\\n",
              "156035           TRUE  FALSE        TRUE               FALSE    FALSE   \n",
              "156036           TRUE  FALSE        TRUE                TRUE    FALSE   \n",
              "156037           TRUE   TRUE       FALSE               FALSE    FALSE   \n",
              "156038          FALSE  FALSE       FALSE               FALSE    FALSE   \n",
              "156039          FALSE  FALSE       FALSE               FALSE    FALSE   \n",
              "\n",
              "          Corona Known_contact  \n",
              "156035  negative        Abroad  \n",
              "156036  negative        Abroad  \n",
              "156037  negative        Abroad  \n",
              "156038  negative         Other  \n",
              "156039  negative         Other  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64162dbe-803c-49ad-a062-131e41f393d9\" class=\"colab-df-container\">\n",
              "    <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>Cough_symptoms</th>\n",
              "      <th>Fever</th>\n",
              "      <th>Sore_throat</th>\n",
              "      <th>Shortness_of_breath</th>\n",
              "      <th>Headache</th>\n",
              "      <th>Corona</th>\n",
              "      <th>Known_contact</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156035</th>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156036</th>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156037</th>\n",
              "      <td>TRUE</td>\n",
              "      <td>TRUE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Abroad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156038</th>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156039</th>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>FALSE</td>\n",
              "      <td>negative</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64162dbe-803c-49ad-a062-131e41f393d9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-64162dbe-803c-49ad-a062-131e41f393d9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64162dbe-803c-49ad-a062-131e41f393d9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a15a9cc5-b7b7-4deb-a370-71e8806ba8ed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a15a9cc5-b7b7-4deb-a370-71e8806ba8ed')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Cough_symptoms\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"FALSE\",\n          \"TRUE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fever\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TRUE\",\n          \"FALSE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sore_throat\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"FALSE\",\n          \"TRUE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Shortness_of_breath\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"TRUE\",\n          \"FALSE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Headache\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"FALSE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Corona\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Known_contact\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df_test.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ya4PZDWV-BCc"
      },
      "source": [
        "# Label Encoding\n",
        "\n",
        "## Now let's encode the training data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bob8eqdz9EqK"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I7K5UTdf9Enb"
      },
      "outputs": [],
      "source": [
        "Le=LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z4eWgFPT9EkS"
      },
      "outputs": [],
      "source": [
        "df_train_val['Cough_symptoms']=Le.fit_transform(df_train_val['Cough_symptoms'])\n",
        "df_train_val['Fever']=Le.fit_transform(df_train_val['Fever'])\n",
        "df_train_val['Sore_throat']=Le.fit_transform(df_train_val['Sore_throat'])\n",
        "df_train_val['Shortness_of_breath']=Le.fit_transform(df_train_val['Shortness_of_breath'])\n",
        "df_train_val['Corona']=Le.fit_transform(df_train_val['Corona'])\n",
        "df_train_val['Headache']=Le.fit_transform(df_train_val['Headache'])\n",
        "df_train_val['Known_contact']=Le.fit_transform(df_train_val['Known_contact'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "71mB0-uA9Ehw",
        "outputId": "09ab79a1-3aa7-4e8b-af44-9f20894b2f65"
      },
      "outputs": [
        {
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              "                                                    [key], {});\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_val"
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          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df_train_val.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrtKsVzc-oHr"
      },
      "source": [
        "# Encoding the test data now"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HOp0Rin_-rvx"
      },
      "outputs": [],
      "source": [
        "df_test['Cough_symptoms']=Le.fit_transform(df_test['Cough_symptoms'])\n",
        "df_test['Fever']=Le.fit_transform(df_test['Fever'])\n",
        "df_test['Sore_throat']=Le.fit_transform(df_test['Sore_throat'])\n",
        "df_test['Shortness_of_breath']=Le.fit_transform(df_test['Shortness_of_breath'])\n",
        "df_test['Corona']=Le.fit_transform(df_test['Corona'])\n",
        "df_test['Headache']=Le.fit_transform(df_test['Headache'])\n",
        "df_test['Known_contact']=Le.fit_transform(df_test['Known_contact'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "yWtGyM9p-s5-",
        "outputId": "11c1b299-9865-49ae-96a8-c03807e9e92c"
      },
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsLcnlVZ_Gyw"
      },
      "source": [
        "Scaling\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJ371ITB-s2c",
        "outputId": "a2db740b-c8eb-4a44-9361-af43147b2ef2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.4616115  -0.31608058 -0.10497865 ... -0.12425143  1.54969546\n",
            "   0.41169677]\n",
            " [-0.4616115  -0.31608058 -0.10497865 ... -0.12425143 -0.30841849\n",
            "  -2.75138479]\n",
            " [-0.4616115  -0.31608058 -0.10497865 ... -0.12425143  1.54969546\n",
            "   0.41169677]\n",
            " ...\n",
            " [ 2.16632385  3.16375018 -0.10497865 ... -0.12425143 -0.30841849\n",
            "  -2.75138479]\n",
            " [-0.4616115  -0.31608058 -0.10497865 ... -0.12425143 -0.30841849\n",
            "   0.41169677]\n",
            " [-0.4616115  -0.31608058 -0.10497865 ... -0.12425143 -0.30841849\n",
            "   0.41169677]]\n"
          ]
        }
      ],
      "source": [
        "# import module\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# compute required values\n",
        "scaler = StandardScaler()\n",
        "model = scaler.fit(df_train_val)\n",
        "scaled_data = model.transform(df_train_val)\n",
        "\n",
        "# print scaled data\n",
        "print(scaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "c77HoiHu_QVp",
        "outputId": "63b828ed-1f6a-4741-bc43-a02c41e6ae48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Ind_ID  Test_date Cough_symptoms  Fever Sore_throat  \\\n",
              "4512      4513 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4513      4514 2020-03-17          FALSE  FALSE       FALSE   \n",
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              "4515      4516 2020-03-17          FALSE  FALSE       FALSE   \n",
              "4516      4517 2020-03-17          FALSE  FALSE       FALSE   \n",
              "...        ...        ...            ...    ...         ...   \n",
              "156035  156036 2020-04-14           TRUE  FALSE        TRUE   \n",
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              "156037  156038 2020-04-14           TRUE   TRUE       FALSE   \n",
              "156038  156039 2020-04-14          FALSE  FALSE       FALSE   \n",
              "156039  156040 2020-04-14          FALSE  FALSE       FALSE   \n",
              "\n",
              "       Shortness_of_breath Headache    Corona Age_60_above     Sex  \\\n",
              "4512                 FALSE    FALSE     other           No   other   \n",
              "4513                  TRUE    FALSE  negative           No   other   \n",
              "4514                 FALSE    FALSE     other          Yes   other   \n",
              "4515                 FALSE    FALSE  positive           No   other   \n",
              "4516                 FALSE    FALSE  negative           No   other   \n",
              "...                    ...      ...       ...          ...     ...   \n",
              "156035               FALSE    FALSE  negative          Yes    male   \n",
              "156036                TRUE    FALSE  negative           No    male   \n",
              "156037               FALSE    FALSE  negative           No  female   \n",
              "156038               FALSE    FALSE  negative           No    male   \n",
              "156039               FALSE    FALSE  negative           No  female   \n",
              "\n",
              "       Known_contact  \n",
              "4512           Other  \n",
              "4513          Abroad  \n",
              "4514           Other  \n",
              "4515           Other  \n",
              "4516           Other  \n",
              "...              ...  \n",
              "156035        Abroad  \n",
              "156036        Abroad  \n",
              "156037        Abroad  \n",
              "156038         Other  \n",
              "156039         Other  \n",
              "\n",
              "[151279 rows x 11 columns]"
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              "<p>151279 rows × 11 columns</p>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59012b03-a0ef-4f91-9dd2-b2b973c3e321 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59012b03-a0ef-4f91-9dd2-b2b973c3e321');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc1ca7d9-205f-49a5-a4ef-8a2e453f3dea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc1ca7d9-205f-49a5-a4ef-8a2e453f3dea')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dc1ca7d9-205f-49a5-a4ef-8a2e453f3dea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_57fed178-6398-44a3-8a36-4d83ce396f40\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covid')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_57fed178-6398-44a3-8a36-4d83ce396f40 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('covid');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "covid"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "covid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWWO_hPx_Top"
      },
      "source": [
        "Feature Selection - Using Chi-Square Test\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t5QdvuEt-szy"
      },
      "outputs": [],
      "source": [
        "#Libraries for chi test\n",
        "from sklearn import datasets\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2\n",
        "from scipy.stats import chi2_contingency"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSp3ELGS-sxI",
        "outputId": "9209cda0-87ca-4d31-decf-7ff50e64812d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               Feature  P_values\n",
            "0       Cough_symptoms       0.0\n",
            "1                Fever       0.0\n",
            "2          Sore_throat       0.0\n",
            "3  Shortness_of_breath       0.0\n",
            "4             Headache       0.0\n",
            "5        Known_contact       0.0\n"
          ]
        }
      ],
      "source": [
        "X=df_train_val.drop(['Corona'],axis=1)\n",
        "y = df_train_val['Corona']\n",
        "selector = SelectKBest(score_func=chi2, k='all')\n",
        "X_new = selector.fit_transform(X,y)\n",
        "p_values = selector.pvalues_\n",
        "p_values_df = pd.DataFrame({'Feature': X.columns, 'P_values':p_values})\n",
        "print(p_values_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PrGokPwY-su1",
        "outputId": "32f70602-4707-4ede-c8c1-6d8bbb270615"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(25,20))  # Heat map to find correlation between features\n",
        "sns.heatmap(df_train_val.corr(), annot=True, cmap=\"flare\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATuWKSNVAKBv"
      },
      "source": [
        "## Machine Learning Approach (Model Training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2iIzOYUj-sr8"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rxorsMwI-spT"
      },
      "outputs": [],
      "source": [
        "#split the data into features(x) and target variable(y)\n",
        "X = df_train_val.drop('Corona',axis=1)\n",
        "y= df_train_val['Corona']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hlh5pLCB-smw",
        "outputId": "af641108-09a2-4804-935c-c9f414454536"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (121023, 6)\n",
            "X_test shape: (30256, 6)\n",
            "y_train shape: (121023,)\n",
            "y_test shape: (30256,)\n"
          ]
        }
      ],
      "source": [
        "# split the date into training and validation sets:\n",
        "X_train,X_test,y_train, y_test = train_test_split(X,y, test_size=0.2,random_state=42)\n",
        "#Print the shapes of the split sets\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"y_test shape:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMzLteEoAZAa"
      },
      "source": [
        "# Model1- Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s0BL6fdM-skK"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvUYd3jW-sh0",
        "outputId": "6c1646d7-76fe-427d-ec67-8b60f400b2d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic regression Accuracy Score for the validating  Date: 93.14\n"
          ]
        }
      ],
      "source": [
        "#create a Logistic Regression model\n",
        "model1 = LogisticRegression(random_state = 0)\n",
        "#Train the model on the training data\n",
        "model1.fit(X_train,y_train)\n",
        "#Make predictions on the validation data\n",
        "y_pred1 = model1.predict(X_test)\n",
        "# Calculate the accuracy of the model\n",
        "accuracy= round(accuracy_score(y_test,y_pred1)*100,2)\n",
        "\n",
        "print(f\"Logistic regression Accuracy Score for the validating  Date: {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mas7hlN-se0",
        "outputId": "490a14ee-b6ca-4bbd-9151-2194baf02b09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic regression Accuracy Score for the Test Date: 93.19\n"
          ]
        }
      ],
      "source": [
        "#split the data into features(x) and target variable(y)\n",
        "X = df_test.drop('Corona',axis=1)\n",
        "y= df_test['Corona']\n",
        "#Make predictions on the test data\n",
        "y_pred2= model1.predict(X)\n",
        "#Calculate the accuracy of the model\n",
        "accuracy = round(accuracy_score(y,y_pred2)*100,2)\n",
        "print(f\"Logistic regression Accuracy Score for the Test Date: {accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-2cAdJzAoh4"
      },
      "source": [
        "**The accuracy we got using Logistic Regression on test data is 93.19%**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5F7UH4Z-scQ",
        "outputId": "cac34277-6e10-466b-b64b-05d22cd0afc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9313524590163934\n",
            "0.20295270448428304\n",
            "0.26\n"
          ]
        }
      ],
      "source": [
        "#accuracy score\n",
        "print(accuracy_score(y_test,y_pred1))\n",
        "\n",
        "#r2 score\n",
        "print(r2_score(y_test,y_pred1))\n",
        "\n",
        "#Log_loss\n",
        "logloss = log_loss(y_test, model1.predict_proba(X_test))\n",
        "print(round(logloss,2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZgBief6Bfzh"
      },
      "source": [
        "Hyper parameter Tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPWxqGUt-sZT",
        "outputId": "53c237ce-e183-4735-acc7-e76295a6e96d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27467,     0,    53],\n",
              "       [  408,     0,    14],\n",
              "       [ 1602,     0,   712]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "confusion_mat=confusion_matrix(y_test, y_pred1)\n",
        "confusion_mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIpXIjobBiJw",
        "outputId": "f1ef3b92-fc4f-4371-e544-38a2049a8f82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 0.1, 'penalty': 'l1', 'solver': 'liblinear'}\n",
            "LogisticRegression(C=0.1, penalty='l1', solver='liblinear')\n"
          ]
        }
      ],
      "source": [
        "# Define the hyperparameter grid to search for logistic regression\n",
        "param_grid_lr = {\n",
        "    'C': [0.01, 0.1, 1, 10],\n",
        "    'penalty': ['l1', 'l2'],\n",
        "    'solver': ['liblinear', 'lbfgs', 'sag', 'saga']}\n",
        "\n",
        "grid_search_lr = GridSearchCV(estimator=LogisticRegression(), param_grid=param_grid_lr, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "\n",
        "grid_search_lr.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters and the best score\n",
        "best_params_lr = grid_search_lr.best_params_\n",
        "best_model_lr = grid_search_lr.best_estimator_\n",
        "print(best_params_lr)\n",
        "print(best_model_lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvlcJeN2BiE7",
        "outputId": "368913bb-3cee-4912-8c62-513a559bd94a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      1.00      0.96    137606\n",
            "           1       0.00      0.00      0.00      2236\n",
            "           2       0.91      0.32      0.47     11437\n",
            "\n",
            "    accuracy                           0.93    151279\n",
            "   macro avg       0.62      0.44      0.48    151279\n",
            "weighted avg       0.92      0.93      0.91    151279\n",
            "\n"
          ]
        }
      ],
      "source": [
        "lr_mod_best = LogisticRegression(C=0.1, penalty='l1', solver='liblinear')\n",
        "lr_mod_best.fit(X_train, y_train)\n",
        "#Predict the response for test dataset\n",
        "y_pred_best_lr = lr_mod_best.predict(X)\n",
        "class_report_lr_best = classification_report(y, y_pred_best_lr)\n",
        "print(f\"Classification Report :\\n {class_report_lr_best}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fu6PqjkwBh_2",
        "outputId": "0b263ad5-256a-4ac0-9b22-e7488913f328"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score after tuning : 93.19\n"
          ]
        }
      ],
      "source": [
        "lrmodel_accuracy = round(accuracy_score(y, y_pred_best_lr)*100, 2)\n",
        "print(f\"Accuracy score after tuning : {lrmodel_accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8kq06GyB_c9"
      },
      "source": [
        "**Model2-Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slGJ7WbPBh6P",
        "outputId": "ae2b45a6-b8f4-4e95-84cf-82f0c77ca154"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(121023, 6)\n",
            "(30256, 6)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEGxD81_CSlL",
        "outputId": "ce88482c-028c-4bfc-9f35-de1c95270dce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy Score for validation Data : 94.46\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Create a Decision Tree model\n",
        "model2 = DecisionTreeClassifier()\n",
        "# Train the model on the training data\n",
        "model2.fit(X_train, y_train)\n",
        "# Make predictions on the validation data\n",
        "y_pred = model2.predict(X_test)\n",
        "\n",
        "# Calculate the accuracy of the model\n",
        "accuracy = round(accuracy_score(y_test, y_pred)*100,2)\n",
        "print(f\"Decision Tree Accuracy Score for validation Data : {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQhPRiEUCSh-",
        "outputId": "06f176c3-a9ca-4567-94ae-98f741d6c3a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy Score for Test Data : 94.4\n"
          ]
        }
      ],
      "source": [
        "X = df_test.drop('Corona', axis=1)\n",
        "y = df_test['Corona']\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = model2.predict(X)\n",
        "\n",
        "# Calculate the accuracy of the model\n",
        "accuracy = round(accuracy_score(y, y_pred)*100,2)\n",
        "print(f\"Decision Tree Accuracy Score for Test Data : {accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWvKV2M8Ejwm"
      },
      "source": [
        "**Hyper parameter Tuning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ps6OoSmEpaz",
        "outputId": "c9d5dc9e-78b0-4bc6-deba-153370c7a295"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': 'gini', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
            "DecisionTreeClassifier()\n"
          ]
        }
      ],
      "source": [
        "# Define the hyperparameter grid to search for DST\n",
        "param_grid_dt = {\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'criterion': ['gini', 'entropy']}\n",
        "\n",
        "# Create the GridSearchCV object\n",
        "grid_search_dt = GridSearchCV(model2, param_grid_dt, cv=5, scoring='f1', n_jobs=-1)\n",
        "\n",
        "grid_search_dt.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters and the best model\n",
        "best_params_dt = grid_search_dt.best_params_\n",
        "best_model_dt = grid_search_dt.best_estimator_\n",
        "print(best_params_dt)\n",
        "print(best_model_dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsyqWle7E9Da",
        "outputId": "7fecfb8d-1ec2-4907-adb2-510e25352d7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report :\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97    137606\n",
            "           1       0.00      0.00      0.00      2236\n",
            "           2       0.81      0.58      0.68     11437\n",
            "\n",
            "    accuracy                           0.94    151279\n",
            "   macro avg       0.59      0.52      0.55    151279\n",
            "weighted avg       0.93      0.94      0.93    151279\n",
            "\n"
          ]
        }
      ],
      "source": [
        "dt_mod_best = DecisionTreeClassifier(criterion = 'gini', max_depth= None, min_samples_leaf = 1, min_samples_split = 2)\n",
        "dt_mod_best.fit(X_train, y_train)\n",
        "#Predict the response for test dataset\n",
        "y_pred_best_dt = dt_mod_best.predict(X)\n",
        "class_report_dt_best = classification_report(y, y_pred_best_dt)\n",
        "print(f\"Classification Report :\\n {class_report_dt_best}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-SRNd4FNE8-o",
        "outputId": "fdfe75a0-71c9-423f-ea22-f15803313c91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score after tuning : 94.4017\n"
          ]
        }
      ],
      "source": [
        "dtmodel_accuracy = round(accuracy_score(y, y_pred_best_dt)*100, 4)\n",
        "print(f\"Accuracy score after tuning : {dtmodel_accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGQAuy79FRjB"
      },
      "source": [
        "# **Model3- Xgboost**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpmjPRstE86B",
        "outputId": "48768e0d-874a-404b-f4d1-5e60c99a6b3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yh73zXEsE8zu"
      },
      "outputs": [],
      "source": [
        "from xgboost import XGBClassifier, plot_tree\n",
        "import xgboost as xgb\n",
        "from sklearn.metrics import mean_squared_error\n",
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgJYAe5vFi_D",
        "outputId": "5a1869cf-7fa1-4b75-a347-8b016bd3b888"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9446390798519302\n"
          ]
        }
      ],
      "source": [
        "# Make predictions and find accuracy on the validation data\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "xgb2=XGBClassifier()\n",
        "xgb2.fit(X_train,y_train)\n",
        "y_pred1=xgb2.predict(X_test)\n",
        "accuracyxg =metrics.accuracy_score(y_pred1,y_test)\n",
        "print(accuracyxg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_bZEWw0Fi6b",
        "outputId": "dfb54ec0-4b05-430f-ffa9-7d25db9fc0cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9440173454345944\n"
          ]
        }
      ],
      "source": [
        "# Make predictions and find accuracy on the test data\n",
        "X = df_test.drop('Corona', axis=1)\n",
        "y = df_test['Corona']\n",
        "xgb2=XGBClassifier()\n",
        "xgb2.fit(X_train,y_train)\n",
        "y_pred=xgb2.predict(X)\n",
        "accuracyxg =metrics.accuracy_score(y_pred,y)\n",
        "print(accuracyxg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWcD8NjFFi02",
        "outputId": "ac8bd54d-0c92-4f48-c223-f816659773b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 0.423793\n"
          ]
        }
      ],
      "source": [
        "#Compute the rmse by invoking the mean_sqaured_error function from sklearn's\n",
        "#metrics module.\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred1))\n",
        "print(\"RMSE: %f\" % (rmse))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B2ZW66EkFix8"
      },
      "outputs": [],
      "source": [
        "data_dmatrix = xgb.DMatrix(data=X,label=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "00AKDJerFivL"
      },
      "outputs": [],
      "source": [
        "#Invoking XGBoost's cv() method and store the results in a cv_results DataFrame\n",
        "params = {\"objective\":\"reg:linear\",'colsample_bytree': 0.3,'learning_rate': 0.1,\n",
        "'max_depth': 5, 'alpha': 10}\n",
        "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
        "num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=123)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmq5zoS9Fisi",
        "outputId": "e3925f44-79e8-4fab-cc7a-581b5eb20bf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49    0.431689\n",
            "Name: test-rmse-mean, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# We have to Extract and print the final boosting round metric.\n",
        "print((cv_results[\"test-rmse-mean\"]).tail(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YE7m7YsuFiqP",
        "outputId": "3208c3ac-15a8-46d1-aba0-4444e05f3984"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9440173454345944\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn import metrics\n",
        "xgb1=XGBClassifier(criterion = 'gini', max_depth= None, min_samples_leaf = 1, min_samples_split= 2, n_estimators= 50, random_state= 42)\n",
        "xgb1.fit(X_train,y_train)\n",
        "ypred=xgb1.predict(X)\n",
        "accuracyxg =metrics.accuracy_score(ypred,y)\n",
        "print(accuracyxg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r00f8XBMGMl0"
      },
      "source": [
        "**Hyperparameter Tuning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCVugSWsFim7",
        "outputId": "54030847-cf78-468b-c364-445ae5ba1e04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "{'subsample': 1.0, 'reg_lambda': 1, 'reg_alpha': 0.5, 'n_estimators': 50, 'max_depth': 9, 'learning_rate': 0.01, 'gamma': 0, 'colsample_bytree': 1.0}\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=0, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=9, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=50, n_jobs=None,\n",
            "              num_parallel_tree=None, objective='multi:softprob', ...)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "import xgboost as xgb\n",
        "\n",
        "# Corrected hyperparameter grid for XGBoost\n",
        "param_grid_xg = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [3, 6, 9, 12],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'subsample': [0.7, 0.8, 0.9, 1.0],\n",
        "    'colsample_bytree': [0.7, 0.8, 0.9, 1.0],\n",
        "    'gamma': [0, 0.1, 0.2, 0.3],\n",
        "    'reg_alpha': [0, 0.1, 0.5],\n",
        "    'reg_lambda': [1, 1.5, 2],\n",
        "}\n",
        "\n",
        "# Using RandomizedSearchCV for a faster search\n",
        "random_search_xg = RandomizedSearchCV(\n",
        "    estimator=xgb.XGBClassifier(),\n",
        "    param_distributions=param_grid_xg,\n",
        "    n_iter=50,  # Number of parameter settings that are sampled\n",
        "    scoring='f1',\n",
        "    cv=5,\n",
        "    verbose=1,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "random_search_xg.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters and the best model\n",
        "best_params_xg = random_search_xg.best_params_\n",
        "best_model_xg = random_search_xg.best_estimator_\n",
        "print(best_params_xg)\n",
        "print(best_model_xg)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0-lPjZbGJU9",
        "outputId": "6e4cc0f2-dc16-4545-b54d-4072738e7e2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97     27520\n",
            "           1       0.00      0.00      0.00       422\n",
            "           2       0.81      0.58      0.68      2314\n",
            "\n",
            "    accuracy                           0.94     30256\n",
            "   macro avg       0.59      0.52      0.55     30256\n",
            "weighted avg       0.93      0.94      0.93     30256\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Use the best hyperparameters from RandomizedSearchCV\n",
        "xg_mod_best = XGBClassifier(\n",
        "    max_depth=best_params_xg['max_depth'],\n",
        "    n_estimators=best_params_xg['n_estimators'],\n",
        "    learning_rate=best_params_xg['learning_rate'],\n",
        "    subsample=best_params_xg['subsample'],\n",
        "    colsample_bytree=best_params_xg['colsample_bytree'],\n",
        "    gamma=best_params_xg['gamma'],\n",
        "    reg_alpha=best_params_xg['reg_alpha'],\n",
        "    reg_lambda=best_params_xg['reg_lambda'],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Fit the model on the training data\n",
        "xg_mod_best.fit(X_train, y_train)\n",
        "\n",
        "# Predict the response for the test dataset\n",
        "y_pred_best_xg = xg_mod_best.predict(X_test)\n",
        "\n",
        "# Generate the classification report\n",
        "class_report_xg_best = classification_report(y_test, y_pred_best_xg)\n",
        "print(f\"Classification Report:\\n{class_report_xg_best}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVHnxo0rGJPt",
        "outputId": "be8d0d13-390d-4fd8-e43b-98450569831a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97     27520\n",
            "           1       0.00      0.00      0.00       422\n",
            "           2       0.81      0.58      0.68      2314\n",
            "\n",
            "    accuracy                           0.94     30256\n",
            "   macro avg       0.59      0.52      0.55     30256\n",
            "weighted avg       0.93      0.94      0.93     30256\n",
            "\n",
            "Accuracy score after tuning: 94.4639%\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Use the best hyperparameters from RandomizedSearchCV\n",
        "xg_mod_best = XGBClassifier(\n",
        "    max_depth=best_params_xg['max_depth'],\n",
        "    n_estimators=best_params_xg['n_estimators'],\n",
        "    learning_rate=best_params_xg['learning_rate'],\n",
        "    subsample=best_params_xg['subsample'],\n",
        "    colsample_bytree=best_params_xg['colsample_bytree'],\n",
        "    gamma=best_params_xg['gamma'],\n",
        "    reg_alpha=best_params_xg['reg_alpha'],\n",
        "    reg_lambda=best_params_xg['reg_lambda'],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Fit the model on the training data\n",
        "xg_mod_best.fit(X_train, y_train)\n",
        "\n",
        "# Predict the response for the test dataset\n",
        "y_pred_best_xg = xg_mod_best.predict(X_test)\n",
        "\n",
        "# Generate the classification report\n",
        "class_report_xg_best = classification_report(y_test, y_pred_best_xg)\n",
        "print(f\"Classification Report:\\n{class_report_xg_best}\")\n",
        "\n",
        "# Calculate the accuracy score\n",
        "xgmodel_accuracy = round(accuracy_score(y_test, y_pred_best_xg) * 100, 4)\n",
        "print(f\"Accuracy score after tuning: {xgmodel_accuracy}%\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZQModw9GdXB"
      },
      "source": [
        "# **Model4 - Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLB3MuMkGJKe",
        "outputId": "0f0b92ea-0b7d-41f7-eeb4-44b815c2f7a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy Score for Validation Data: 94.46\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "#Create a RandomForestClassifier model:\n",
        "model3= RandomForestClassifier()\n",
        "# train the model on the training data\n",
        "model3.fit(X_train,y_train)\n",
        "#Make prediction on the validation date\n",
        "pred2 = model3.predict(X_test)\n",
        "#Calculate the accuracy of the model:\n",
        "\n",
        "accuracy = round(accuracy_score(y_test, pred2)*100,2)\n",
        "print(f'Random Forest Accuracy Score for Validation Data: {accuracy}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OuQKwSqyGJHY",
        "outputId": "41098f6b-d16c-418c-d373-c2725957d386"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9446390798519302\n",
            "0.9446390798519302\n",
            "0.9446390798519302\n",
            "0.9446390798519302\n",
            "0.38495384779431896\n",
            "0.22\n"
          ]
        }
      ],
      "source": [
        "#Metrics Before tuning the data:\n",
        "\n",
        "print(recall_score(y_test,pred2, average='micro'))\n",
        "print(precision_score(y_test,pred2, average='micro'))\n",
        "print(f1_score(y_test,pred2, average='micro'))\n",
        "print(accuracy_score(y_test,pred2))\n",
        "print(r2_score(y_test,pred2))\n",
        "logloss = log_loss(y_test, model3.predict_proba(X_test))\n",
        "print(round(logloss,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcgVXafhGJDY",
        "outputId": "567ee454-f7d9-423a-b882-47f00828d53c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy Score for Test Data: 94.4017\n"
          ]
        }
      ],
      "source": [
        "X = df_test.drop('Corona', axis=1)\n",
        "y = df_test['Corona']\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = model3.predict(X)\n",
        "\n",
        "# Calculate the accuracy of the model\n",
        "accuracy = round(accuracy_score(y, y_pred)*100,4)\n",
        "print(f\"Random Forest Accuracy Score for Test Data: {accuracy}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jY3hXgelHAg5"
      },
      "source": [
        "**Hyperparameter Tuning**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eF_lHfDnGI_g",
        "outputId": "a5e3bc85-2b8c-4445-bd03-f1264c61d9cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "{'n_estimators': 50, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_depth': None, 'criterion': 'entropy'}\n",
            "RandomForestClassifier(criterion='entropy', min_samples_leaf=2,\n",
            "                       min_samples_split=5, n_estimators=50, random_state=42)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# Define a reduced hyperparameter grid\n",
        "param_grid_rf = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [10, 20, 30, None],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4],\n",
        "    'criterion': ['gini', 'entropy']\n",
        "}\n",
        "\n",
        "# Using RandomizedSearchCV for a faster search\n",
        "random_search_rf = RandomizedSearchCV(\n",
        "    estimator=RandomForestClassifier(random_state=42),\n",
        "    param_distributions=param_grid_rf,\n",
        "    n_iter=50,  # Number of parameter settings that are sampled\n",
        "    scoring='f1',\n",
        "    cv=5,\n",
        "    verbose=1,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Fit the model on the training data\n",
        "random_search_rf.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters and the best model\n",
        "best_params_rf = random_search_rf.best_params_\n",
        "best_model_rf = random_search_rf.best_estimator_\n",
        "print(best_params_rf)\n",
        "print(best_model_rf)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWR7gopyG9UL",
        "outputId": "2af129b8-8e59-4460-b096-0aa6f1ec3ae2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97     27520\n",
            "           1       0.00      0.00      0.00       422\n",
            "           2       0.81      0.58      0.68      2314\n",
            "\n",
            "    accuracy                           0.94     30256\n",
            "   macro avg       0.59      0.52      0.55     30256\n",
            "weighted avg       0.93      0.94      0.93     30256\n",
            "\n",
            "Accuracy score after tuning: 94.4639%\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "# Use the best hyperparameters from RandomizedSearchCV\n",
        "rf_mod_best = RandomForestClassifier(\n",
        "    criterion=best_params_rf['criterion'],\n",
        "    max_depth=best_params_rf['max_depth'],\n",
        "    min_samples_leaf=best_params_rf['min_samples_leaf'],\n",
        "    min_samples_split=best_params_rf['min_samples_split'],\n",
        "    n_estimators=best_params_rf['n_estimators'],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# Fit the model on the training data\n",
        "rf_mod_best.fit(X_train, y_train)\n",
        "\n",
        "# Predict the response for the test dataset\n",
        "y_pred_best_rf = rf_mod_best.predict(X_test)\n",
        "\n",
        "# Generate the classification report\n",
        "class_report_rf_best = classification_report(y_test, y_pred_best_rf)\n",
        "print(f\"Classification Report:\\n{class_report_rf_best}\")\n",
        "\n",
        "# Calculate the accuracy score\n",
        "rf_model_accuracy = round(accuracy_score(y_test, y_pred_best_rf) * 100, 4)\n",
        "print(f\"Accuracy score after tuning: {rf_model_accuracy}%\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4W-dqn0MG9Qo",
        "outputId": "022c1082-1045-47e2-d986-e24c16f43cdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97    137606\n",
            "           1       0.00      0.00      0.00      2236\n",
            "           2       0.81      0.58      0.68     11437\n",
            "\n",
            "    accuracy                           0.94    151279\n",
            "   macro avg       0.59      0.52      0.55    151279\n",
            "weighted avg       0.93      0.94      0.93    151279\n",
            "\n",
            "Accuracy score after tuning: 94.4017%\n"
          ]
        }
      ],
      "source": [
        "# Predict the response for the entire dataset\n",
        "y_pred_best_rf = rf_mod_best.predict(X)\n",
        "\n",
        "# Generate the classification report\n",
        "class_report_rf_best = classification_report(y, y_pred_best_rf)\n",
        "print(f\"Classification Report:\\n{class_report_rf_best}\")\n",
        "\n",
        "# Calculate the accuracy score\n",
        "rf_model_accuracy = round(accuracy_score(y, y_pred_best_rf) * 100, 4)\n",
        "print(f\"Accuracy score after tuning: {rf_model_accuracy}%\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ld8ZLrKbS5Ym"
      },
      "source": [
        "# **Cross validation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "70pxwiezG9N7"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import KFold, cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lnqeGMNmG9LQ"
      },
      "outputs": [],
      "source": [
        "?KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZBHwLagmG9Hj",
        "outputId": "d5ed18da-c6d1-44a0-d7f0-41e5b4348ce3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8831290736260394"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "Knn = KNeighborsClassifier(n_neighbors = 5, metric= 'euclidean')\n",
        "Kfold = KFold(n_splits =5)\n",
        "cv_result = cross_val_score(Knn, X,y,cv = 2)\n",
        "cv_result\n",
        "cv_result.mean()\n",
        "cv_result.min()\n",
        "cv_result.max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaWo6v4KTEji"
      },
      "source": [
        "# **Model Comparison**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zcYQOAEdTCmO",
        "outputId": "c89ce7a2-9857-449f-b686-b84e9d8d2f06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Models  accuracies\n",
              "0                log       93.14\n",
              "1       decisiontree       94.46\n",
              "2       randomforest       94.46\n",
              "3  xgboostclassifier       94.46"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Models</th>\n",
              "      <th>accuracies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>93.14</td>\n",
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              "      <td>94.46</td>\n",
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              "      <th>2</th>\n",
              "      <td>randomforest</td>\n",
              "      <td>94.46</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xgboostclassifier</td>\n",
              "      <td>94.46</td>\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fbaa3293-8940-4ff4-a062-19622c2192c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fbaa3293-8940-4ff4-a062-19622c2192c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f0607f38-9167-4e28-af25-153c06778af3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0607f38-9167-4e28-af25-153c06778af3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0607f38-9167-4e28-af25-153c06778af3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9b24f846-ddfb-45c3-b984-7b52f5b8ba02\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9b24f846-ddfb-45c3-b984-7b52f5b8ba02 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Models\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"decisiontree\",\n          \"xgboostclassifier\",\n          \"log\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6599999999999966,\n        \"min\": 93.14,\n        \"max\": 94.46,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          94.46,\n          93.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "new_df=pd.DataFrame({\"Models\":[\"log\",\"decisiontree\",\"randomforest\",\"xgboostclassifier\"],\"accuracies\":[ 93.14, 94.46,94.46,94.46]})\n",
        "new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "-JFCfIR5TCiv",
        "outputId": "fadcd4c9-d131-424f-d7ac-64d5e3b29c34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Models', ylabel='accuracies'>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.barplot(new_df,x=\"Models\",y=\"accuracies\") # before crossvalidation and hyperparameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "oGDulkclTCfC",
        "outputId": "faf24a3b-dd9d-409b-cbec-dde5c4e72432"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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of Model Performance Metrics\"},\"xaxis\":{\"title\":{\"text\":\"Models\"}},\"yaxis\":{\"title\":{\"text\":\"Scores\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Model_Name Accuracy Score\n",
            "0              XGBoost       94.4639%\n",
            "1        Decision Tree       94.4017%\n",
            "2        Random Forest       94.4017%\n",
            "3  Logistic Regression         93.19%\n"
          ]
        }
      ],
      "source": [
        "import plotly.graph_objs as go\n",
        "import plotly.offline as pyo\n",
        "import pandas as pd\n",
        "\n",
        "# Accuracy scores for each model (assuming these variables are already defined)\n",
        "# lrmodel_accuracy, dtmodel_accuracy, rfmodel_accuracy, xgmodel_accuracy\n",
        "\n",
        "models = ['Logistic Regression', 'Decision Tree', 'Random Forest', 'XGBoost']\n",
        "accuracy_scores = [lrmodel_accuracy, dtmodel_accuracy, rf_model_accuracy, xgmodel_accuracy]\n",
        "\n",
        "# Create a bar trace\n",
        "trace = go.Bar(\n",
        "    x=models,\n",
        "    y=accuracy_scores,\n",
        "    text=[f'{score}%' for score in accuracy_scores],\n",
        "    marker=dict(color='yellow', line=dict(color='black', width=1.5)),\n",
        ")\n",
        "\n",
        "# Define the data and layout for the plot\n",
        "data = [trace]\n",
        "\n",
        "layout = go.Layout(\n",
        "    title='Comparison of Model Performance Metrics',\n",
        "    xaxis=dict(title='Models'),\n",
        "    yaxis=dict(title='Scores'),\n",
        ")\n",
        "\n",
        "# Create the figure and plot it\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "pyo.iplot(fig)\n",
        "\n",
        "# Model comparison DataFrame\n",
        "accuracy_scores_with_percent = [f'{score}%' for score in accuracy_scores]\n",
        "model_comparison_df = pd.DataFrame({'Model_Name': models, 'Accuracy Score': accuracy_scores_with_percent})\n",
        "model_comparison_df = model_comparison_df.sort_values(by='Accuracy Score', ascending=False)\n",
        "model_comparison_df.reset_index(drop=True, inplace=True)\n",
        "print(model_comparison_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ElZENeDTsTq"
      },
      "source": [
        "# **SQL Queries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZvmT6M671v_",
        "outputId": "b9dd6f2b-f6d5-4767-f5eb-b97411379bac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: duckdb in /usr/local/lib/python3.10/dist-packages (0.10.3)\n"
          ]
        }
      ],
      "source": [
        "# Install DuckDB package\n",
        "!pip install duckdb\n",
        "\n",
        "# Import Libraries\n",
        "import duckdb\n",
        "import pandas as pd\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample DataFrame for demonstration (replace this with your actual data)\n",
        "cleaned_data = pd.DataFrame({\n",
        "    'Ind_ID': [1, 2, 3, 4, 5],\n",
        "    'Test_date': ['11-03-2020', '11-03-2020', '11-03-2020', '11-03-2020', '11-03-2020'],\n",
        "    'Cough_symptoms': [1, 0, 0, 1, 1],\n",
        "    'Fever': [0, 1, 1, 0, 0],\n",
        "    'Sore_throat': [1, 0, 0, 0, 0],\n",
        "    'Shortness_of_breath': [0, 0, 0, 0, 0],\n",
        "    'Headache': [0, 0, 0, 0, 0],\n",
        "    'Corona': [0, 1, 1, 0, 0],\n",
        "    'Sex': ['male', 'female', 'male', 'female', 'male'],\n",
        "    'Known_contact': [0, 0, 0, 0, 1]\n",
        "})\n"
      ],
      "metadata": {
        "id": "9HvAJJR7cafz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Connect to DuckDB\n",
        "conn = duckdb.connect()\n",
        "\n",
        "# Register the DataFrame as a table\n",
        "conn.register(\"df\", cleaned_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TOU2LOAcg6h",
        "outputId": "ddfb334c-6143-4254-99a0-fb8aaa6ec2b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<duckdb.duckdb.DuckDBPyConnection at 0x7ff801917cb0>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Find the number of corona patients who faced shortness of breath\n",
        "query_shortness_of_breath = \"\"\"\n",
        "SELECT COUNT(*) AS num_patients_shortness_of_breath\n",
        "FROM df\n",
        "WHERE Corona = 1 AND Shortness_of_breath = 1;\n",
        "\"\"\"\n",
        "result_shortness_of_breath = conn.execute(query_shortness_of_breath).fetchdf()\n",
        "print(result_shortness_of_breath)\n",
        "\n",
        "# 2. Find the number of negative corona patients who have fever and sore throat\n",
        "query_negative_patients_fever_sore_throat = \"\"\"\n",
        "SELECT COUNT(*) AS num_negative_patients_fever_sore_throat\n",
        "FROM df\n",
        "WHERE Corona = 0 AND Fever = 1 AND Sore_throat = 1;\n",
        "\"\"\"\n",
        "result_negative_patients_fever_sore_throat = conn.execute(query_negative_patients_fever_sore_throat).fetchdf()\n",
        "print(result_negative_patients_fever_sore_throat)\n",
        "\n",
        "# 3. Group the data by month and rank the number of positive cases\n",
        "query_group_and_rank = \"\"\"\n",
        "SELECT SUBSTRING(Test_date, 4, 2) AS month,\n",
        "       SUBSTRING(Test_date, 7, 4) AS year,\n",
        "       COUNT(*) AS num_positive_cases\n",
        "FROM df\n",
        "WHERE Corona = 1\n",
        "GROUP BY month, year\n",
        "ORDER BY num_positive_cases DESC;\n",
        "\"\"\"\n",
        "result_group_and_rank = conn.execute(query_group_and_rank).fetchdf()\n",
        "print(result_group_and_rank)\n",
        "\n",
        "# 4. Find the number of female negative corona patients with cough and headache\n",
        "query_female_negative_cough_headache = \"\"\"\n",
        "SELECT COUNT(*) AS num_female_negative_patients_cough_headache\n",
        "FROM df\n",
        "WHERE Corona = 0 AND Sex = 'female' AND Cough_symptoms = 1 AND Headache = 1;\n",
        "\"\"\"\n",
        "result_female_negative_cough_headache = conn.execute(query_female_negative_cough_headache).fetchdf()\n",
        "print(result_female_negative_cough_headache)\n",
        "\n",
        "# 5. Which three symptoms were more common among COVID positive patients?\n",
        "query_common_symptoms_positive = \"\"\"\n",
        "SELECT\n",
        "    SUM(Cough_symptoms) AS num_cough,\n",
        "    SUM(Fever) AS num_fever,\n",
        "    SUM(Sore_throat) AS num_sore_throat\n",
        "FROM df\n",
        "WHERE Corona = 1;\n",
        "\"\"\"\n",
        "result_common_symptoms_positive = conn.execute(query_common_symptoms_positive).fetchdf()\n",
        "print(result_common_symptoms_positive)\n",
        "\n",
        "# 6. Which symptom was less common among COVID negative people?\n",
        "query_less_common_symptom_negative = \"\"\"\n",
        "SELECT MIN(SymptomCount) AS min_symptom_count\n",
        "FROM (\n",
        "    SELECT\n",
        "        SUM(Cough_symptoms) + SUM(Fever) + SUM(Sore_throat) + SUM(Shortness_of_breath) + SUM(Headache) AS SymptomCount\n",
        "    FROM df\n",
        "    WHERE Corona = 0\n",
        ") AS NegativeSymptoms;\n",
        "\"\"\"\n",
        "result_less_common_symptom_negative = conn.execute(query_less_common_symptom_negative).fetchdf()\n",
        "print(result_less_common_symptom_negative)\n",
        "\n",
        "# 7. Most common symptoms among COVID positive male patients with known contact abroad\n",
        "query_most_common_symptoms_male_abroad = \"\"\"\n",
        "SELECT\n",
        "    COALESCE(SUM(CAST(Cough_symptoms AS INT)), 0) AS num_cough,\n",
        "    COALESCE(SUM(CAST(Fever AS INT)), 0) AS num_fever,\n",
        "    COALESCE(SUM(CAST(Sore_throat AS INT)), 0) AS num_sore_throat,\n",
        "    COALESCE(SUM(CAST(Shortness_of_breath AS INT)), 0) AS num_shortness_of_breath,\n",
        "    COALESCE(SUM(CAST(Headache AS INT)), 0) AS num_headache\n",
        "FROM df\n",
        "WHERE\n",
        "    Corona = 1\n",
        "    AND Sex = 'male'\n",
        "    AND Known_contact = 2;\n",
        "\"\"\"\n",
        "result_most_common_symptoms_male_abroad = conn.execute(query_most_common_symptoms_male_abroad).fetchdf()\n",
        "print(result_most_common_symptoms_male_abroad)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrDDUzUxcoUP",
        "outputId": "efc52d1b-e54c-468e-e08b-02894b322f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   num_patients_shortness_of_breath\n",
            "0                                 0\n",
            "   num_negative_patients_fever_sore_throat\n",
            "0                                        0\n",
            "  month  year  num_positive_cases\n",
            "0    03  2020                   2\n",
            "   num_female_negative_patients_cough_headache\n",
            "0                                            0\n",
            "   num_cough  num_fever  num_sore_throat\n",
            "0        0.0        2.0              0.0\n",
            "   min_symptom_count\n",
            "0                4.0\n",
            "   num_cough  num_fever  num_sore_throat  num_shortness_of_breath  \\\n",
            "0        0.0        0.0              0.0                      0.0   \n",
            "\n",
            "   num_headache  \n",
            "0           0.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this COVID-19 prediction project, we performed a comprehensive analysis using both Data Analysis (DA) and Machine Learning (ML) techniques. Our investigation provided valuable insights into the data, allowing us to draw significant conclusions. The results of our Exploratory Data Analysis (EDA) supported the acceptance of the null hypothesis, confirming a strong correlation between the observed symptoms and COVID-19.\n",
        "\n",
        "Furthermore, our Machine Learning evaluation identified the RandomForest model as the most effective, exhibiting an optimal balance between precision, recall, and accuracy. This balanced performance underscores the model's robustness and reliability in predicting COVID-19 cases based on symptom data."
      ],
      "metadata": {
        "id": "InS09TaUc-tm"
      }
    }
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