Apollo.ipynb

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        "# Objective\n",
        "### Apollo wants to know, \n",
        "*   which variables are significant in predicting the reason for hospitalization for different regions\n",
        "*   how well some variables like viral load, smoking, severity level describe the hospitalization charges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYmInO19EO1z"
      },
      "source": [
        "#### Import Packages"
      ]
    },
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      "source": [
        "# Import required packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import datetime as dt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import shapiro\n",
        "from scipy.stats import levene\n",
        "from scipy.stats import f_oneway\n",
        "from scipy.stats import chi2_contingency # For Chi-Sq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwDHQYH5Eoks"
      },
      "source": [
        "#### Import & Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LGi2oRqIEiLw"
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      "source": [
        "df = pd.read_csv(\"https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/681/original/scaler_apollo_hospitals.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zrmpfi_xE3q-"
      },
      "source": [
        "# Statistical Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rejhDVLbE_8G"
      },
      "source": [
        "### Meta-data of dataset"
      ]
    },
    {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 8 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Unnamed: 0               1338 non-null   int64  \n",
            " 1   age                      1338 non-null   int64  \n",
            " 2   sex                      1338 non-null   object \n",
            " 3   smoker                   1338 non-null   object \n",
            " 4   region                   1338 non-null   object \n",
            " 5   viral load               1338 non-null   float64\n",
            " 6   severity level           1338 non-null   int64  \n",
            " 7   hospitalization charges  1338 non-null   int64  \n",
            "dtypes: float64(1), int64(4), object(3)\n",
            "memory usage: 83.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVG9g2R4FLSf"
      },
      "source": [
        "### Description of the dataframe"
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              "        Unnamed: 0          age   viral load  severity level  \\\n",
              "count  1338.000000  1338.000000  1338.000000     1338.000000   \n",
              "mean    668.500000    39.207025    10.221233        1.094918   \n",
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              "min       0.000000    18.000000     5.320000        0.000000   \n",
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              "max    1337.000000    64.000000    17.710000        5.000000   \n",
              "\n",
              "       hospitalization charges  \n",
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              "    }\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-2f67fa9f-eca8-4479-8e80-52c1b99c8bb4 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-2f67fa9f-eca8-4479-8e80-52c1b99c8bb4');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Unnamed: 0          age   sex smoker     region   viral load  \\\n",
              "count   1338.000000  1338.000000  1338   1338       1338  1338.000000   \n",
              "unique          NaN          NaN     2      2          4          NaN   \n",
              "top             NaN          NaN  male     no  southeast          NaN   \n",
              "freq            NaN          NaN   676   1064        364          NaN   \n",
              "mean     668.500000    39.207025   NaN    NaN        NaN    10.221233   \n",
              "std      386.391641    14.049960   NaN    NaN        NaN     2.032796   \n",
              "min        0.000000    18.000000   NaN    NaN        NaN     5.320000   \n",
              "25%      334.250000    27.000000   NaN    NaN        NaN     8.762500   \n",
              "50%      668.500000    39.000000   NaN    NaN        NaN    10.130000   \n",
              "75%     1002.750000    51.000000   NaN    NaN        NaN    11.567500   \n",
              "max     1337.000000    64.000000   NaN    NaN        NaN    17.710000   \n",
              "\n",
              "        severity level  hospitalization charges  \n",
              "count      1338.000000              1338.000000  \n",
              "unique             NaN                      NaN  \n",
              "top                NaN                      NaN  \n",
              "freq               NaN                      NaN  \n",
              "mean          1.094918             33176.058296  \n",
              "std           1.205493             30275.029296  \n",
              "min           0.000000              2805.000000  \n",
              "25%           0.000000             11851.000000  \n",
              "50%           1.000000             23455.000000  \n",
              "75%           2.000000             41599.500000  \n",
              "max           5.000000            159426.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe(include='all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "LHfQFo_9FaPp",
        "outputId": "dbd5ea8f-57f4-47d6-fe33-dc85a7b1d9b6"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-2ed9296a-d544-4f3f-8a18-d50b3441abae\">\n",
              "    <div 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>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338</td>\n",
              "      <td>1338</td>\n",
              "      <td>1338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>male</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>676</td>\n",
              "      <td>1064</td>\n",
              "      <td>364</td>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-2ed9296a-d544-4f3f-8a18-d50b3441abae 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-2ed9296a-d544-4f3f-8a18-d50b3441abae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         sex smoker     region\n",
              "count   1338   1338       1338\n",
              "unique     2      2          4\n",
              "top     male     no  southeast\n",
              "freq     676   1064        364"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# only object type columns\n",
        "df.describe(include='object')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Whor3lhsGRqq"
      },
      "source": [
        "### Shape of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qJloq5hFjDe",
        "outputId": "62df87a9-f402-47c3-ba79-7d17c6bc1156"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Apollo dataset has 1338 rows and 8 columns.\n"
          ]
        }
      ],
      "source": [
        "### Data shape\n",
        "print(\"Apollo dataset has {} rows and {} columns.\".format(df.shape[0], df.shape[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufraIb4VGxuU"
      },
      "source": [
        "### Size & Count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFnAf4wGGv5P",
        "outputId": "78344e49-0974-4f16-df5e-2c9f0a2a3075"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10704"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# total number of elements/records \n",
        "df.size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMfq2jNgGMjy"
      },
      "source": [
        "### Column Names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNQDpxNNGIr1",
        "outputId": "e26d7574-2b78-4739-925c-e35ebcc1c055"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'age', 'sex', 'smoker', 'region', 'viral load',\n",
              "       'severity level', 'hospitalization charges'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Columna names\n",
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_0Hps-9Gh5r"
      },
      "source": [
        "### Column data typess"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnxXC6KGGYMk",
        "outputId": "0b28ebcd-7bd5-4ceb-fa77-e52d0ed349d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Unnamed: 0                   int64\n",
              "age                          int64\n",
              "sex                         object\n",
              "smoker                      object\n",
              "region                      object\n",
              "viral load                 float64\n",
              "severity level               int64\n",
              "hospitalization charges      int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "### Data type of all attributes\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZBZ7rXSHBZ0"
      },
      "source": [
        "# Data Pre-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M48_qj38M8HO"
      },
      "source": [
        "### Check for Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOvJrivwGtnD",
        "outputId": "b2f59751-20d5-43ac-9c82-a6be15f78c33"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Unnamed: 0                 0\n",
              "age                        0\n",
              "sex                        0\n",
              "smoker                     0\n",
              "region                     0\n",
              "viral load                 0\n",
              "severity level             0\n",
              "hospitalization charges    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Missing values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMKjvuMkNI-B",
        "outputId": "98f29bd2-41ff-40cb-f0b1-6c031dc3f282"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "752jOWbRNZzJ"
      },
      "source": [
        "#### **Observation**: *There is no missing value in the given dataset for any attribute*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUqmst_CN_IC"
      },
      "source": [
        "### Check for duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qsli-DzdNNhl",
        "outputId": "f2f0b24a-0349-4b37-8406-44cfa7022662"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# duplicates\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzfnF3p9OEoL"
      },
      "source": [
        "#### **Observation**: *There is no duplicate value in the given dataset*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dVfgwKLUOXRo",
        "outputId": "a4e5a103-abe8-457b-8663-3f4fbdd3dfca"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "      <td>9667</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-482352f2-a667-4909-aca6-be14764ecc25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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",
              "    [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-482352f2-a667-4909-aca6-be14764ecc25 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-482352f2-a667-4909-aca6-be14764ecc25');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Unnamed: 0  age     sex smoker     region  viral load  severity level  \\\n",
              "0           0   19  female    yes  southwest        9.30               0   \n",
              "1           1   18    male     no  southeast       11.26               1   \n",
              "2           2   28    male     no  southeast       11.00               3   \n",
              "3           3   33    male     no  northwest        7.57               0   \n",
              "4           4   32    male     no  northwest        9.63               0   \n",
              "\n",
              "   hospitalization charges  \n",
              "0                    42212  \n",
              "1                     4314  \n",
              "2                    11124  \n",
              "3                    54961  \n",
              "4                     9667  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeOYccD1Qy90"
      },
      "source": [
        "### Dataset Copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vFTiQxDgPahJ"
      },
      "outputs": [],
      "source": [
        "# Take a working copy of the given dataset\n",
        "work_df = df.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqWXJxt6R3-a"
      },
      "source": [
        "### Delete irrelevant columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7SBVdiUkOdTR"
      },
      "outputs": [],
      "source": [
        "# Delete the columns that are not required/significant\n",
        "work_df.drop(['Unnamed: 0'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4h3DOjMJQSET",
        "outputId": "a41b8e1d-a026-46a7-cf74-4b3dea5babe1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1338, 7)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "work_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZ6GoE-TSssF"
      },
      "source": [
        "### Data categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v64H7FcbQvmj",
        "outputId": "5d81ab30-0bad-4b76-c104-1b38902199c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['sex', 'smoker', 'region']"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# categorical_features=list(work_df.select_dtypes('object').columns)\n",
        "cat_features = [x for x in work_df.columns if work_df[x].dtype == 'object']\n",
        "cat_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L16CTTyqRHtC",
        "outputId": "c40cf6e2-0376-4dae-acce-89d6d43ddf4b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['age', 'viral load', 'severity level', 'hospitalization charges']"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_features = [y for y in work_df.columns if work_df[y].dtype not in ('object', 'datetime64[ns')]\n",
        "num_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5fQuF7gRZdO",
        "outputId": "dfde0a00-890a-440e-fd46-9350589dd762"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "date_features = [z for z in work_df.columns if work_df[z].dtype == 'datetime']\n",
        "date_features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xw0bIptqlyVU"
      },
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8dDv3Sr5l_nd"
      },
      "source": [
        "### Non-Graphical Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7sCxmVvmPVz"
      },
      "source": [
        "#### Unique value counts of all attributes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPRfHmrdfiYW",
        "outputId": "9c6ccea8-d3c6-49f9-8a44-e88107c8acf5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    age Count\n",
            "18         69\n",
            "19         68\n",
            "50         29\n",
            "51         29\n",
            "47         29\n",
            "46         29\n",
            "45         29\n",
            "20         29\n",
            "48         29\n",
            "52         29\n",
            "22         28\n",
            "49         28\n",
            "54         28\n",
            "53         28\n",
            "21         28\n",
            "26         28\n",
            "24         28\n",
            "25         28\n",
            "28         28\n",
            "27         28\n",
            "23         28\n",
            "43         27\n",
            "29         27\n",
            "30         27\n",
            "41         27\n",
            "42         27\n",
            "44         27\n",
            "31         27\n",
            "40         27\n",
            "32         26\n",
            "33         26\n",
            "56         26\n",
            "34         26\n",
            "55         26\n",
            "57         26\n",
            "37         25\n",
            "59         25\n",
            "58         25\n",
            "36         25\n",
            "38         25\n",
            "35         25\n",
            "39         25\n",
            "61         23\n",
            "60         23\n",
            "63         23\n",
            "62         23\n",
            "64         22\n",
            "        sex Count\n",
            "male          676\n",
            "female        662\n",
            "     smoker Count\n",
            "no           1064\n",
            "yes           274\n",
            "           region Count\n",
            "southeast           364\n",
            "southwest           325\n",
            "northwest           325\n",
            "northeast           324\n",
            "       viral load Count\n",
            "9.63                 13\n",
            "10.77                13\n",
            "11.37                12\n",
            "10.17                12\n",
            "8.80                 10\n",
            "...                 ...\n",
            "7.93                  1\n",
            "10.71                 1\n",
            "9.70                  1\n",
            "14.37                 1\n",
            "10.32                 1\n",
            "\n",
            "[462 rows x 1 columns]\n",
            "   severity level Count\n",
            "0                   574\n",
            "1                   324\n",
            "2                   240\n",
            "3                   157\n",
            "4                    25\n",
            "5                    18\n",
            "       hospitalization charges Count\n",
            "4593                               2\n",
            "35986                              2\n",
            "4099                               2\n",
            "23618                              2\n",
            "35003                              2\n",
            "...                              ...\n",
            "70720                              1\n",
            "2873                               1\n",
            "92698                              1\n",
            "11846                              1\n",
            "72853                              1\n",
            "\n",
            "[1320 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "for col in work_df.columns:\n",
        "  attribute_cnt = {f'{col} Count' : work_df[col].value_counts()}\n",
        "  attribute_cnt = pd.DataFrame(attribute_cnt)\n",
        "  print(attribute_cnt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJfd_M24tmmN",
        "outputId": "25c03156-b669-4e4c-958c-1a2011db5619"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age                          47\n",
              "sex                           2\n",
              "smoker                        2\n",
              "region                        4\n",
              "viral load                  462\n",
              "severity level                6\n",
              "hospitalization charges    1320\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Number of unique records for each columns\n",
        "work_df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qCXIBuSSHn7y",
        "outputId": "16f1921d-4e79-4a3e-a30a-60ee4e5b4660"
      },
      "outputs": [
        {
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "                                                     [key], {});\n",
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              "\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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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      age     sex smoker     region  severity level\n",
              "0      19  female    yes  southwest               0\n",
              "1      18    male     no  southeast               1\n",
              "2      28    male     no  southeast               3\n",
              "3      33    male     no  northwest               0\n",
              "4      32    male     no  northwest               0\n",
              "...   ...     ...    ...        ...             ...\n",
              "1333   50    male     no  northwest               3\n",
              "1334   18  female     no  northeast               0\n",
              "1335   18  female     no  southeast               0\n",
              "1336   21  female     no  southwest               0\n",
              "1337   61  female    yes  northwest               0\n",
              "\n",
              "[1338 rows x 5 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "subset_df = work_df.iloc[:,[0,1,2,3,5]]\n",
        "subset_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsvfvcYYmLpO"
      },
      "source": [
        "#### Value Counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tw_j8HZ3oZ7o",
        "outputId": "b942bee6-896f-425c-f9d8-080c9810c275"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    age Count\n",
            "18         69\n",
            "19         68\n",
            "50         29\n",
            "51         29\n",
            "47         29\n",
            "46         29\n",
            "45         29\n",
            "20         29\n",
            "48         29\n",
            "52         29\n",
            "22         28\n",
            "49         28\n",
            "54         28\n",
            "53         28\n",
            "21         28\n",
            "26         28\n",
            "24         28\n",
            "25         28\n",
            "28         28\n",
            "27         28\n",
            "23         28\n",
            "43         27\n",
            "29         27\n",
            "30         27\n",
            "41         27\n",
            "42         27\n",
            "44         27\n",
            "31         27\n",
            "40         27\n",
            "32         26\n",
            "33         26\n",
            "56         26\n",
            "34         26\n",
            "55         26\n",
            "57         26\n",
            "37         25\n",
            "59         25\n",
            "58         25\n",
            "36         25\n",
            "38         25\n",
            "35         25\n",
            "39         25\n",
            "61         23\n",
            "60         23\n",
            "63         23\n",
            "62         23\n",
            "64         22\n",
            "        sex Count\n",
            "male          676\n",
            "female        662\n",
            "     smoker Count\n",
            "no           1064\n",
            "yes           274\n",
            "           region Count\n",
            "southeast           364\n",
            "southwest           325\n",
            "northwest           325\n",
            "northeast           324\n",
            "   severity level Count\n",
            "0                   574\n",
            "1                   324\n",
            "2                   240\n",
            "3                   157\n",
            "4                    25\n",
            "5                    18\n"
          ]
        }
      ],
      "source": [
        "# Value counts of subset of dataframe\n",
        "for col in subset_df.columns:\n",
        "  attribute_cnt = {f'{col} Count' : subset_df[col].value_counts()}\n",
        "  attribute_cnt = pd.DataFrame(attribute_cnt)\n",
        "  print(attribute_cnt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgAzjMi0diT4",
        "outputId": "62547816-d14b-4883-8934-09682d6a959a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Column is: sex and Values are:\n",
            "['female' 'male']\n",
            "\n",
            "Column is: smoker and Values are:\n",
            "['yes' 'no']\n",
            "\n",
            "Column is: region and Values are:\n",
            "['southwest' 'southeast' 'northwest' 'northeast']\n",
            "\n",
            "Column is: severity level and Values are:\n",
            "[0 1 3 2 5 4]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Value counts of certain attributes\n",
        "for col in work_df.columns:\n",
        "  if len(work_df[col].unique())<10:\n",
        "    print(f\"Column is: {col} and Values are:\")\n",
        "    print(work_df[col].unique())\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRPnRmtOyNSa"
      },
      "source": [
        "### Graphical Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "KGqwGEURszRu",
        "outputId": "d75dd8e1-b036-432f-945a-7cb510a0ffad"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f60d7117910>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(work_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbSMMe7F-kqZ"
      },
      "source": [
        "#### Univariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "Oi-3sIEpBKcZ",
        "outputId": "0b8c25e2-738a-4d5f-b2f6-e58ac0fb20c8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.pie(work_df['sex'].value_counts(),labels=['Male','Female'], autopct='%.0f%%')\n",
        "plt.title('Gender Distribution')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6P1lJXZnMku"
      },
      "source": [
        "#### **Observation**: *Gender distribution amongst patients is almost same*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SYZn0EpRmrcb",
        "outputId": "58bb2240-2115-49c7-a5e6-c8fb03d6a863"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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i1mhmZgWKtqUQES9GxNw0/DrwDDC4lVnGAzdHxNsRsQRYDIwsVn1mZrapkhxTkFQJfAx4IjV9XdJ8SddJ2im1DQaWF8y2gtZDxMzM2lnRQ0FSH+C3wJSI+AdwJfAhoAp4EbhkC5c3WVKNpJq6urp2r9fMrDsraihI2oYsEH4VEb8DiIiXIqIhIjYAV7NxF9FKYGjB7ENS27tExFURUR0R1QMGDChm+WZm3U4xzz4ScC3wTERcWtA+qGCyo4EFafhOYJKkXpL2AIYBs4pVn5mZbaqYZx8dApwAPCWpNrV9DzhWUhUQwFLgqwARsVDSrcDTZGcunekzj8zMSqtooRARjwJqZtTdrcxzIXBhsWoyM7PW+RvNZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWa1MoSLqvLW1mZta5tfpznJJ6A+8D+kvaiY0/r7kDMLjItZmZWYlt7jeavwpMAXYF5rAxFP4B/FcR6zIzszJodfdRRPy/iNgD+HZEfDAi9ki3ERHRaihIGirpAUlPS1oo6ZupvZ+kmZKeS393Su2SdJmkxZLmS9q/3Z6lmZm1yea2FACIiMslHQxUFs4TETe0Mts7wLciYq6k7YE5kmYCXwHui4ifSDoHOAc4GxgLDEu3A4Ar018zMyuRNoWCpBuBDwG1QENqDqDFUIiIF4EX0/Drkp4hOw4xHhiVJpsGPEgWCuOBGyIigMcl9ZU0KC3HzMxKoE2hAFQD+6YP7C0mqRL4GPAEsEvBB/0qYJc0PBhYXjDbitT2rlCQNBmYDLDbbrttTTlmZtaCtn5PYQHwga15AEl9gN8CUyLiH4XjUshsUdBExFURUR0R1QMGDNiakszMrAVt3VLoDzwtaRbwdmNjRHy+tZkkbUMWCL+KiN+l5pcadwtJGgSsTu0rgaEFsw9JbWZmViJtDYXzt3TBkgRcCzwTEZcWjLoTOAn4Sfp7R0H71yXdTHaA+TUfTzAzK622nn300FYs+xDgBOApSbWp7XtkYXCrpFOBZcCX0ri7gXHAYuAt4OSteEwzM3sP2nr20ets3Pe/LbAN8GZE7NDSPBHxKBu/7NbUkc1MH8CZbanHzMyKo61bCts3DqfdQuOBA4tVlJmZlccWXyU1MrcDnylCPWZmVkZt3X30hYK7Pci+t7C+KBWZmVnZtPXso88VDL8DLCXbhWRmZl1IW48p+EwgM7NuoK0/sjNE0nRJq9Ptt5KGFLs4MzMrrbYeaP4F2ZfLdk2336c2MzPrQtoaCgMi4hcR8U66XQ/4wkNmZl1MW0PhZUnHS6pIt+OBl4tZmJmZlV5bQ+EUsstRrCK7lPVEsh/LMTOzLqStp6ReAJwUEa9C9pOawH+ShYWZmXURbd1S+GhjIABExCtkP5pjZmZdSFtDoYeknRrvpC2Ftm5lmJlZJ9HWD/ZLgMck/Sbd/yJwYXFKMjOzcmnrN5pvkFQDHJGavhARTxevLDMzK4c27wJKIeAgMDPrwrb40tlmZtZ1ORTMzCznUDAzs1zRQkHSdemKqgsK2s6XtFJSbbqNKxj3XUmLJT0ryb/qZmZWBsXcUrgeGNNM+08joird7gaQtC8wCdgvzXOFpIoi1mZmZs0oWihExMPAK22cfDxwc0S8HRFLgMXAyGLVZmZmzSvHMYWvS5qfdi81fkt6MLC8YJoVqW0TkiZLqpFUU1dXV+xazcy6lVKHwpXAh4AqsqutXrKlC4iIqyKiOiKqBwzwTzqYmbWnkoZCRLwUEQ0RsQG4mo27iFYCQwsmHZLazMyshEoaCpIGFdw9Gmg8M+lOYJKkXpL2AIYBs0pZm5mZFfFKp5JuAkYB/SWtAM4DRkmqAgJYCnwVICIWSrqV7DIa7wBnRkRDsWozM7PmFS0UIuLYZpqvbWX6C/GVV83MysrfaDYzs5xDwczMcg4FMzPLORTMzCznUDAzs5xDwczMcg4FMzPLORTMzCznUDAzs5xDwczMcg4FMzPLORTMzCznUDAzs5xDwczMcg4FMzPLORTMzCznUDAzs5xDwczMcg4FMzPLORTMzCxXtFCQdJ2k1ZIWFLT1kzRT0nPp706pXZIuk7RY0nxJ+xerLjMza1kxtxSuB8Y0aTsHuC8ihgH3pfsAY4Fh6TYZuLKIdZmZWQuKFgoR8TDwSpPm8cC0NDwNmFDQfkNkHgf6ShpUrNrMzKx5pT6msEtEvJiGVwG7pOHBwPKC6Vaktk1ImiypRlJNXV1d8So1M+uGynagOSICiK2Y76qIqI6I6gEDBhShMjOz7qvUofBS426h9Hd1al8JDC2YbkhqMzOzEip1KNwJnJSGTwLuKGg/MZ2FdCDwWsFuJjMzK5GexVqwpJuAUUB/SSuA84CfALdKOhVYBnwpTX43MA5YDLwFnFysuszMrGVFC4WIOLaFUUc2M20AZxarFjMzaxt/o9nMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMwsV7TfaG6NpKXA60AD8E5EVEvqB9wCVAJLgS9FxKvlqM/MrLsq55bC4RFRFRHV6f45wH0RMQy4L903M7MS6ki7j8YD09LwNGBCGWsxM+uWyhUKAfxJ0hxJk1PbLhHxYhpeBezS3IySJkuqkVRTV1dXilrNzLqNshxTAD4ZESslDQRmSlpUODIiQlI0N2NEXAVcBVBdXd3sNGZmtnXKsqUQESvT39XAdGAk8JKkQQDp7+py1GZm1p2VPBQkvV/S9o3DwGhgAXAncFKa7CTgjlLXZmbW3ZVj99EuwHRJjY//64iYIWk2cKukU4FlwJfKUJuZWbdW8lCIiOeBEc20vwwcWep6zMxso450SqqZmZWZQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws1+FCQdIYSc9KWizpnHLXY2bWnXSoUJBUAfwcGAvsCxwrad/yVmVm1n10qFAARgKLI+L5iPgncDMwvsw1mZl1G4qIcteQkzQRGBMR/zvdPwE4ICK+XjDNZGByurs38GzJC91y/YE15S6iC3F/th/3ZfvqLP25e0QMaG5Ez1JX8l5FxFXAVeWuY0tIqomI6nLX0VW4P9uP+7J9dYX+7Gi7j1YCQwvuD0ltZmZWAh0tFGYDwyTtIWlbYBJwZ5lrMjPrNjrU7qOIeEfS14F7gArguohYWOay2kOn2t3VCbg/24/7sn11+v7sUAeazcysvDra7iMzMysjh4KZmeUcCu1A0ihJBxfcvz5956LYj/sVSbsW+3HKodR92lX7UtKEwqsCSHpQUtFPmZQ0RdL7iv045VDqPi11XzoU2sco4ODNTVQEXwG63AdZMorS9ulX6GJ9KaknMIHskjGlNgXocqFQpj4tbV9GRLe+Ae8H7gLmAQuALwNHAk8CTwHXAb3StEuB/mm4GngQqARWkX2fohY4FLgeuAz4C/A8MDHN83Pg82l4OtnZVQCnABem4eOBWWlZ/0N2FlZFWuaCVNO/AhOBN8i+0V0LbFfuvixHn6b5vkN2OvN8YGpB++3AHGAhMDm1dba+rASeAa5Oz+NPwHZAFfB4es7TgZ3S9A8CPwNqgO8DrwBL0vP6UBp/UXqP/RU4NM13F/DRNPwkcG4avgA4raV+buG1/gbwz9S/D5S7D8vYpxXAxQV99tXU3ge4D5ib+mh8R+rLsr9A5b4BxwBXF9zfEVgO7JXu3wBMScNLafIBlobPB75dsIzrgd+QbYntS3Y9J8i+d3FxGp4FPJ6GfwF8BtgH+D2wTWq/AjgR+Dgws2D5fQverNXl7sMy9+lostMAlcb9ATgsjeuX/m6X/sl27oR9WQm8A1Sl+7eSrTjMBz6V2i4AflbwPK5o0m+FAfogcEkaHgfcm4bPAc5Mr9Vs4J7U/gDZ5WSa7efmXuumr2tHu5WwTycDP0jDvchCZQ+yrwLskNr7A4tTv3aIvvTuoyyBj5J0kaRDyd4wSyLir2n8NLI3/5a6PSI2RMTTwC6p7RHg0LQ/8mngJUmDgIPI1oCPJPvQmi2pNt3/INma8QclXS5pDPCPrXmiJVTKPh2dbk+SrXkNB4alcd+QNI9s7W9oau9sfQlZ39Wm4Tlka6d9I+Kh1Na0P2/ZzPJ+V7CsyjT8SFrGIWRrq33Sfuw9IuJZWu7nd73WEfHa1j3FkitFn44GTkz/y0+QrZQMIwuAH0maD9wLDCZ7P3eIvuxQX14rh4j4q6T9yRL+h8D9rUz+DhuPw/TezKLfLhhWeqyVkvoCY4CHgX7Al4A3IuJ1SQKmRcR3my5M0giyrYmvpXlO2dxzK5dS9mn6++OI+J/CCSWNAj4NHBQRb0l6EOgdEa92pr5MCp93A9B3M9O/2cblNbDxM2A22Zba88BMsjXY08g+5KCFfgYofK0l3RcRF2zm8TuCUvSpgLMi4p7CCSV9BRgAfDwi6iUtJXtv/rUj9GW331JIZ5y8FRG/JNv/dxBQKWnPNMkJQOPaw1KyNXnINvUavQ5s38aHfJzswNHDZGtn305/IdvPOFHSwFRbP0m7S+oP9IiI3wI/APbfisctmRL36T3AKZL6pMcenPpvR+DVFAjDgQPT+E7Vly14DXg1bYXBu/uzqTY9r8guVb8c+CLwGBvfmw+nSZrt52Ze687Yn1CEPiXrs9MlbQMgaS9J7yd7b65OgXA4sHsa3yH6sttvKQAfAS6WtAGoB04ne9F+k840mA38d5p2KnCtpP8g24/Y6PfAbZLGA2dt5vEeAUZHxGJJy8i2Fh4BiIinJf0A+JOkHqmeM4F1wC9SG0DjlsT1wH9LWke2RrxuazqgCErWpxHxJ0n7AI9lG1q8QbZ/eAbwNUnPkB1AfjzNMpjO1ZctOYms3veRrd2f3MJ0NwNXS/oG2QH11jwCHBkR6yQ9QnZBysb3Zkv9vCebvtaQHX+YIenvEXH41j7JEmvvPr2GbFfS3LQXoI7szKVfAb+X9BTZcYZFafrm/m+gxH3py1yYmVmu2+8+MjOzjRwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZkUk6QJJny53HWZt5VNSzdoonWuuiNhQ7lrMisVbCmatkFQp6VlJN5BdVO/fJc2WNF/S1ILp/j1N96ikmyR9O7XnvwMh6UhJT0p6StJ1knql9qWSpkqam8YNL8dzNQOHgllbDCO7Yu2/kn0jeiTZZZY/LukwSZ8gu0THCGAs2TWE3kVSb7JvTX85Ij5CdjWB0wsmWRMR+wNXkl1ewqwsHApmm7csIh6n5SuFHgLcERHrI+J1skt0NLU3rV8ptrmrbJqVnK99ZLZ5jVfIbOmKrFPa4TGau8qmWcl5S8Gs7Vq6Iuufgc9J6p3GfbaZeZ+l5SvFmnUYXiMxa6OWrhQaEbMl3Un2y10vkf1YymtN5l0v6WSav1KsWYfhU1LN2oGkPhHxRrrs8sNkvwk9t9x1mW0pbymYtY+rlP3Mam+yX89zIFin5C0FMzPL+UCzmZnlHApmZpZzKJiZWc6hYGZmOYeCmZnl/j/JnyQU+ANGtwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Count plot of attributes\n",
        "for col in subset_df.columns:\n",
        "  ax = sns.countplot(data=subset_df, x=col)\n",
        "  plt.title(f\"{col} by Count\")\n",
        "  for p in ax.patches:\n",
        "    ax.annotate(p.get_height(), (p.get_x()+0.35, p.get_height()+0.05))\n",
        "  plt.show()\n",
        "  print()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJn9UlJXnpAh"
      },
      "source": [
        "#### **Observation**: \n",
        "*   *Age 18 & 19 have more patients and the rest of the ages have equal distribution*\n",
        "*   *Interestingly Non-Smoker patients count is more than the Smokers count*\n",
        "*   *Southeast region has more patients and the rest of the regions have equal distribution*\n",
        "*   *Good to see less number of patients with 'Severity Level' high*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zOW2FVt_Sed_",
        "outputId": "9136367c-8bb8-4474-f3d7-b3fe546f836f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_theme(style='darkgrid')\n",
        "sns.set(rc = {'figure.figsize':(8,4)})\n",
        "\n",
        "for col in num_features:\n",
        "  p = sns.boxplot(data=work_df,x=col)\n",
        "  p.set(title=f'{col}')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRNMNQwXcU4a"
      },
      "source": [
        "#### **Observation**: *The above charts indicate, we have outliers for 'viral load' and 'hospitalization charges'*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXqX4ArCx_Me"
      },
      "outputs": [],
      "source": [
        "def outlierAnalysis(x):\n",
        "  Q3, Q1 = np.percentile(work_df[x], [75 ,25])\n",
        "  IQR = Q3 - Q1\n",
        "  maxExcludingOutlier = Q3 + 1.5 * IQR\n",
        "  minExcludingOutlier = Q1 - 1.5 * IQR\n",
        "  mean = round(work_df[x].mean(),2)\n",
        "  median = round(work_df[x].median(),2)\n",
        "  mode = round(work_df[x].mode(), 2)\n",
        "  print(\"For the given sample\" +x +\"Analysis is as Follows: \")\n",
        "  print(\"Q1: \", Q1)\n",
        "  print(\"Q3: \", Q3)\n",
        "  print(\"Mean: \", round(work_df[x].mean(),2))\n",
        "  print(\"Median: \", work_df[x].median())\n",
        "  print(\"Mode: \" , round(work_df[x].mode(), 2))\n",
        "  print(\"IQR: \" , IQR)\n",
        "  print(\"Maximum \" + x +\" Excluding Outlier: \" , maxExcludingOutlier) \n",
        "  print(\"Minimum \" + x + \" Purchase Excluding Outlier: \" , minExcludingOutlier)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iK76wIyoyF5J",
        "outputId": "14686226-80e0-4a0e-91e6-6e74a177d68f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "For the given sampleviral loadAnalysis is as Follows: \n",
            "Q1:  8.7625\n",
            "Q3:  11.5675\n",
            "Mean:  10.22\n",
            "Median:  10.13\n",
            "Mode:  0     9.63\n",
            "1    10.77\n",
            "dtype: float64\n",
            "IQR:  2.8050000000000015\n",
            "Maximum viral load Excluding Outlier:  15.775000000000002\n",
            "Minimum viral load Purchase Excluding Outlier:  4.554999999999997\n"
          ]
        }
      ],
      "source": [
        "# outlier analysis of 'viral load'\n",
        "outlierAnalysis('viral load')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FopUN1Sdymc8",
        "outputId": "e518b612-479a-4294-eae8-7bd1a9dcc8a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "For the given samplehospitalization chargesAnalysis is as Follows: \n",
            "Q1:  11851.0\n",
            "Q3:  41599.5\n",
            "Mean:  33176.06\n",
            "Median:  23455.0\n",
            "Mode:  0      4055\n",
            "1      4080\n",
            "2      4099\n",
            "3      4319\n",
            "4      4593\n",
            "5      5509\n",
            "6      5544\n",
            "7      6773\n",
            "8     11799\n",
            "9     16501\n",
            "10    20836\n",
            "11    23618\n",
            "12    26486\n",
            "13    26504\n",
            "14    33677\n",
            "15    35003\n",
            "16    35986\n",
            "17    62288\n",
            "dtype: int64\n",
            "IQR:  29748.5\n",
            "Maximum hospitalization charges Excluding Outlier:  86222.25\n",
            "Minimum hospitalization charges Purchase Excluding Outlier:  -32771.75\n"
          ]
        }
      ],
      "source": [
        "# outlier analysis of 'hospitalization charges'\n",
        "outlierAnalysis('hospitalization charges')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8Kq097mizoq"
      },
      "source": [
        "#### Outlier Treatment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o6JW88W1iow5"
      },
      "outputs": [],
      "source": [
        "# Remove the columns from the Numerical Features list where we don't have any outliers\n",
        "num_features.remove('age')\n",
        "num_features.remove('severity level')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YzfNvo8erKWe"
      },
      "outputs": [],
      "source": [
        "# Treat outliers for the numerical features where outliiers present\n",
        "for i, j in enumerate(num_features):\n",
        "  q1 = work_df[j].quantile(0.25)\n",
        "  q3 = work_df[j].quantile(0.75)\n",
        "  iqr = q3 - q1\n",
        "  work_df = work_df[(work_df[j] >=  q1 - 1.5*iqr) & (work_df[j] <= q3 + 1.5*iqr)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "hs26pm37cOTp",
        "outputId": "cc7bc246-8748-4759-a361-b12f084bf974"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEWCAYAAABYGk2QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWP0lEQVR4nO3df5hfVWHn8feHJJBA+BVgI4TIiNOCUVwEFN1aKz66hVBLu1urPiA/dHUtbaTPrmt1SSsollW2VsS6Sn8FkIqoFUHptio8glIRIoQfCmXEIATlR4JA+OEGOPvHPZN8M2SSmTCZk+/k/Xqe++R+z7333HPu3PnMnfOd70lKKUiSJt92rRsgSdsqA1iSGjGAJakRA1iSGjGAJakRA1iSGjGA+1CS5UleP8nnXJ1k/8089sQk35mIujZyjufXeqdNZL2bOOdpST43WefT1GMAa0xKKbNLKXcCJFmS5IyJqGtzjfwhVEr5aa336edSrzSZDGBpK5Bkeus2aPIZwP3r4CQ3JXk4yReSzBzekOSdSYaSrEpyaZJ9anmS/GWS+5M8kuTmJC+p25Yk+UySbyR5NMm3k+zXU2dJMpjkXcCxwPvqr/yX1e3vT/LjeuwPk/zuaA3vqWufWsfw8niSUvd5YZIrkqxM8mCSC5PsVrddADwfuKwe974kA7Xe6XWffWrfV9Vr8c6e85+W5OIk59f23prksI2098X1uqxKcl+S/9mzefvR6tnYNanDMt+tX4+VwGlJ9khyWf3aXJfkjBFDNwf2tOP2JL/fs21hPcejSVYkee9o/dFWpJTi0mcLsBz4PrAPMAf4EfDuuu11wIPAIcAOwDnAVXXbbwJLgd2AAC8C9q7blgCPAq+px50NfKfnnAUY7Nn3jBFtelNtz3bAm4HHeuo+cbS6RtRxIfD5uj4IvKG2ZS/gKuATI67B63teD9R6p9fXVwGfBmYCBwMPAK+r204DngQWAtOAM4HvjXKtdwZ+Bvz3WtfOwOFjqWcM1+QpYBEwHZgFXFSXHYEFwN3D1w3Yqb4+qe7/svp1XlC3/wz49bq+O3BI6/vUZQzfy60b4LIZX7QufI7ref0x4DN1/W+Bj/Vsmw2sqQH1OuDfgFcC242ocwlw0Yjjngbm19cbDeANtPFG4Ji6vskABv6E7ofDrFHq+x3ghhHXYIMBDMyvbd+5Z/uZwJK6fhrwzZ5tC4AnRjnvW3vPO2LbmOsZ5Zr8tGfbtPp1OqCn7IyeAH4zcPWI+j4LfLCu/xT4r8Aure9Pl7EvDkH0r5/3rD9OF5jQPXHdNbyhlLIaWAnMK6VcAXwK+Cvg/iTnJtmlp567Rxy3qta3SUmOT3Jjkl8k+QXwEmDPMR57FHAK8DullCdq2dwkF9Vfpx8BPjfW+mqbV5VSHu0puwuY1/N65PWbOco47Hzgxxs516j1jOGa3N2zvhfdD4+7R9m+H3D4cF21vmOB59Xt/5nuSfyuOnz0qo20WVsJA3jquZfumxWAJDsBewArAEopnyylHEr3tParwP/oOXZ+z3Gz6YY37t3AOdabQq+OFf818EfAHqWU3YBb6IY5NirJAcB5wO+XUnoD58/reQ4qpewCHDeivo1N43cvMCfJzj1lz6deg3G6Gxj3n8yN8Zr09uEBuiGJfXvK5ves3w18u5SyW88yu5TyBwCllOtKKccA/w64BLh4vG3W5DOAp57PAyclOTjJDnRBdm0pZXmSlyc5PMkMuvHIJ4Fneo5dmOTVSbYHPkw3nnn3s84A97F+KO1EFyYPACQ5ie5pb6Pq0/dXgVNLKd8ZsXlnYDXwcJJ5rP+DYkNtWKu2+RrgzCQzk7wUeAfdU/R4fQ3YO8kfJ9khyc5JDh/DceO6JqX787l/pHszbsckBwLHj2jHryZ5W5IZdXl5khcl2T7JsUl2LaWsAR5h/a+rtlIG8BRTSvkm8KfAl+nemHkh8Ja6eRe6p7KH6H4lXwmc1XP4PwAfpBt6OJTuqXND/hZYUH8VvqSU8kPgL4B/pQvGg4DvjqG5hwAHAH/Z+9cQddvpdfvDwNfpwqnXmcDi2oYNveP/Vrpx4XuBr9CNlX5zDG1aTx3GeAPwRrrhhjuAI8Zw3OZckz8Cdq3nuYDuh+kve9rxH+m+lvfWfT5K9yYlwNuA5XW45t10wxPayqUUJ2RX92dowD2llMWt26JOko8CzyulnNC6LdoyfAKWthL173xfms4r6IZNvtK6Xdpy/PSNtPXYmW7YYR+6YYu/oBsj1xTlEIQkNeIQhCQ1Mq4hiD333LMMDAxsoaZI0tS0dOnSB0spe40sH1cADwwMcP31109cqyRpG5Dkrg2VOwQhSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY2M6/+Em4rOOecchoaGWjdj3FasWAHAvHnzGrdkYg0ODrJo0aLWzZAmxTYfwENDQ9x4y494esc5rZsyLtMefxiAn/9y6nwJpz2+qnUTpEk1db57n4Ond5zDEwcubN2McZl12+UAfdfujRnuk7StcAxYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhqZlAA+55xzOOeccybjVJIE9EfuTJ+MkwwNDU3GaSRprX7IHYcgJKkRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGAJU1Ja9asYWhoiKGhIU4++WSOO+44jjjiCI499liOOuoorrzySo488kiOOuooli5dynve8x5WrlzJ0NAQCxcu5MQTT+Tkk09m6dKlHH300QwNDU14G6dPeI2StBW47777eOyxxzjjjDNYvnz52vIVK1YA8JGPfISnnnoKgA9+8IM89thjnH/++SxbtozHH3987THD28444wyWLFkyoW30CVjSlLNy5UpWrVoFsF749hoOX4DVq1dTSuHyyy9/1v6rV69eW89EPwVPyhPwihUreOKJJzjllFMm43TjMjQ0xHb/r7RuhoDtnnyEoaFHt8r7RP3lnnvuoZTxf1+vWbNmo9sn+il4k0/ASd6V5Pok1z/wwAMTdmJJ2lIeeuihLVLvaE/Tm2uTT8CllHOBcwEOO+ywzXpUnDdvHgBnn3325hy+RZ1yyiksvfO+1s0Q8MzMXRjcf+5WeZ+ov3z84x/n0ksvnfB6BwYGJrQ+x4AlTTknnHACScZ93IwZMza6ffHixZvbpA0ygCVNOXvssQdz5swBRn9qnT593QDA7NmzScLChQuftf/s2bPX1jM4ODih7TSAJU1Jc+fOZaeddmLx4sUsWLCAfffdlyTMmzePWbNmceqppzJz5kxmzZrF6aefzkEHHcTxxx/P4sWL2XHHHRkYGGDBggWcfvrpa+uZaP4dsKQpacaMGQwODjI4OMinP/3pDe5zxBFHrF0/9NBDge7p+fLLL19vv69//etbpI0+AUtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDUyfTJOMjg4OBmnkaS1+iF3JiWAFy1aNBmnkaS1+iF3HIKQpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqZHrrBmwNpj2+ilm3Xd66GeMy7fGVAH3X7o2Z9vgqYG7rZkiTZpsP4MHBwdZN2CwrVjwFwLx5Uymw5vbt10PaHNt8AC9atKh1EyRtoxwDlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJaiSllLHvnDwAPAY8uMVaNHn2pP/7MRX6AFOjH1OhDzA1+rE19mG/UspeIwvHFcAASa4vpRw2Yc1qZCr0Yyr0AaZGP6ZCH2Bq9KOf+uAQhCQ1YgBLUiObE8DnTngr2pgK/ZgKfYCp0Y+p0AeYGv3omz6MewxYkjQxHIKQpEYMYElqZFwBnOTIJLcnGUry/i3VqHG05++S3J/klp6yOUm+keSO+u/utTxJPlnbflOSQ3qOOaHuf0eSE3rKD01ycz3mk0myBfowP8mVSX6Y5NYkp/RpP2Ym+X6SZbUfp9fyFyS5tp77C0m2r+U71NdDdftAT10fqOW3J/nNnvJJuf+STEtyQ5Kv9XEfltev+Y1Jrq9lfXVP1fPsluRLSW5L8qMkr+rHfoyqlDKmBZgG/BjYH9geWAYsGOvxW2IBXgMcAtzSU/Yx4P11/f3AR+v6QuCfgACvBK6t5XOAO+u/u9f13eu279d9U489agv0YW/gkLq+M/BvwII+7EeA2XV9BnBtPefFwFtq+WeAP6jrJwOfqetvAb5Q1xfUe2sH4AX1nps2mfcf8N+AfwC+Vl/3Yx+WA3uOKOure6qe5zzgv9T17YHd+rEfo/ZvHBfiVcA/97z+APCByWzsKO0aYP0Avh3Yu67vDdxe1z8LvHXkfsBbgc/2lH+2lu0N3NZTvt5+W7A/XwXe0M/9AHYEfgAcTveJpOkj7yHgn4FX1fXpdb+MvK+G95us+w/YF/gW8Drga7VNfdWHWvdynh3AfXVPAbsCP6H+sUC/9mNjy3iGIOYBd/e8vqeWbW3mllJ+Vtd/Dsyt66O1f2Pl92ygfIupv8K+jO7pse/6UX91vxG4H/gG3dPeL0opT23g3GvbW7c/DOyxiX5Mxv33CeB9wDP19R70Xx8ACvAvSZYmeVct67d76gXAA8Df1yGhv0myE/3Xj1FN6TfhSvdjrS/+zi7JbODLwB+XUh7p3dYv/SilPF1KOZjuKfIVwIGNmzQuSX4LuL+UsrR1WybAq0sphwBHAX+Y5DW9G/vknppON8T4f0opL6Obh2a9cfM+6ceoxhPAK4D5Pa/3rWVbm/uS7A1Q/72/lo/W/o2V77uB8gmXZAZd+F5YSvnHWtx3/RhWSvkFcCXdr9y7JZm+gXOvbW/dviuwkvH3byL9GvDbSZYDF9ENQ5zdZ30AoJSyov57P/AVuh+I/XZP3QPcU0q5tr7+El0g91s/RjeO8ZjpdIPXL2DdGwgvnszxklHaNcD6Y8Bnsf4A/cfq+tGsP0D//Vo+h26cafe6/ASYU7eNHKBfuAXaH+B84BMjyvutH3sBu9X1WcDVwG8BX2T9N7BOrut/yPpvYF1c11/M+m9g3Un35tWk3n/Aa1n3Jlxf9QHYCdi5Z/0a4Mh+u6fqea4GDqjrp9U+9F0/Ru3fOC/GQrp36X8MnDqZDR2lPZ8Hfgasoftp+Q66MbhvAXcA3+y50AH+qrb9ZuCwnnreDgzV5aSe8sOAW+oxn2LEmwET1IdX0/0KdRNwY10W9mE/XgrcUPtxC/BntXz/epMP0QXZDrV8Zn09VLfv31PXqbWtt9PzrvRk3n+sH8B91Yfa3mV1uXX4PP12T9XzHAxcX++rS+gCtO/6MdriR5ElqZEp/SacJG3NDGBJasQAlqRGDGBJasQAlqRGDOApLslAemaL2wL175PkS3X94CQLx3DMa3tmGvvtzZkVbOS5NreecZxvSZLf21L1a9tkAOs5KaXcW0oZDqaD6f7OdTzHX1pK+V+bcer1zvUc6tni6jSJfq/pWbwptg3Tkvx1unl6/yXJLFj7FPm9OnfqV3rmVX1PuvmJb0pyUS07LckFSf61zqn6zlo+kOSWdHPkfgh4c52D9s1JXlH3vyHJNUkOGNmwJCcm+VRdv7FneSLJb2yojlHO1VvPQJIravu/leT5tXxJnfP1miR3jvZEm+T4euyyJBf0bHrNyGOTzK7n+EGdV/aYnjbcnuR8uj/0n5/kT2vZd5J8Psl7674vTPJ/68Q5Vyc5sJa/qV7bZUmuek53gLZOk/mpD5fJX+g+qv0UcHB9fTFwXF2/CfiNuv4h6sehgXtZ92mv4Y8Xn0b3yapZwJ50s0vtQ89HwYETgU/1nHsX1k3j+Hrgy3X9taz7lNl6x9SyN9J9BHXGRuoYea61r4HLgBPq+tuBS+r6ErpPrm1HN2fv0Aau14vpPqm2Z309Z2PH0n28eJe6vifdJ61Sr8szwCvrtpfTfcpxJt28z3cA763bvgX8Sl0/HLiirt8MzOv9OrhMrWV4ghFNbT8ppdxY15cCA0l2pfum/nYtP48uYKAL5guTXEL38c9hXy2lPAE8keRKuglebmR0uwLnJfkVuo9bz9hUQ+u+ZwFHlFLWJHneeOugmwToP9X1C+gm8B52SSnlGeCHSeY+68huAp4vllIeBCilrNrEsQH+PN1sY8/QTWc4vO2uUsr36vqv0V2/J4Enk1xW+zsb+A/AF7PuP2PYof77XWBJkouB4UmaNIU4BLFt+GXP+tOwyR+8R9N9pv4Q4LqemcBGfm59U59j/zBwZSnlJXRPtTM3tnMNo4uBd5Z1872Oq44x6L0W4/3vZzZ07LF0ExEdWrqpOO9jXRsfG0Od29HNN3xwz/IigFLKu4HFdDN5LU2yxzjbq62cAbyNKqU8DDyU5Ndr0duAb9c3i+aXUq4E/oTuKXZ23eeYdP/32x50wwjXjaj2Ubpfr4ftyrrp/U4cQ7P+Dvj7UsrVY6hj5Ll6XUM3Oxl0AXn1KPttyBXAm4bDLsmcTey/K90cwmuSHAHsN8p+3wXeWK/fbLqZ4ijd3M8/SfKmer4k+fd1/YWllGtLKX9GNzH5/FHqVp8ygLdtJwBnJbmJ7q8KPkQ3beLnktxMN7vZJ0s3vy90QxNXAt8DPlxKuXdEfVcCC4bfGKP71f/MJDewiafuJPsBvwe8veeNuMM2UsfIc/VaBJxU+/U24JSxXpBSyq3AR+h+GC0DPr6JQy4EDqvX63jgtlHqvQ64lO4a/hPd+O7DdfOxwDvq+W4FjqnlZ9U39m6h+6GybKz9UH9wNjSNSZLTgNWllP/dui39KsnsUsrqJDsCVwHvKqX8oHW71I5vwkmT59wkC+jGiM8zfOUTsCQ14hiwJDViAEtSIwawJDViAEtSIwawJDXy/wHRWcBGYOS1cAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plots post outlier treatment\n",
        "for col in num_features:\n",
        "  p = sns.boxplot(data=work_df,x=col)\n",
        "  p.set(title=f'{col}')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LOrUzn1-zWi"
      },
      "source": [
        "#### Bivariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "a7715jT3CafS",
        "outputId": "cbbde671-6cc5-4ee5-b8e9-0fe5822f39e3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Bi-variate analysis of Categorical Features\n",
        "variable = \"age\"\n",
        "for i in cat_features:\n",
        " plt.figure(figsize = (7,5))\n",
        " sns.boxplot(x=i, hue=i, y=variable, data=work_df, dodge=False)\n",
        " plt.title(variable + \" Distribution w.r.to \" + i)\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eJBJ6yDqNvZ"
      },
      "source": [
        "#### **Observation**: \n",
        "*   *Most of the patients are seem to be b/w the ages 28 to 52 and equally distributed b/w both the gender*\n",
        "*   *Similarly irrespective of smoking habit, the patients seem to be highly distributed b/w 28 and 50 ages*\n",
        "*   *Similar distribution observed across the regions*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IW7RCeZSECtV",
        "outputId": "03fe5fe2-a029-48f9-b8cb-2dcfc79f0557"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "variable = \"viral load\"\n",
        "for i in cat_features:\n",
        " plt.figure(figsize = (7,5))\n",
        " sns.boxplot(x=i, hue=i, y=variable, data=work_df, dodge=False)\n",
        " plt.title(variable + \" Distribution w.r.to \" + i)\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rve58_uesFw9"
      },
      "source": [
        "#### **Observation**: \n",
        "*   *Female patients seems to be little more vulnerable to viral load than Male patients*\n",
        "*   *Incidently non-smokers tend to have high viral load than smokers*\n",
        "*   *Amongst the regions, Southeast is impacted with high viral load than other regions*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "50d7DK6fEw5H",
        "outputId": "2df2026a-13ad-4f03-c93f-1c0b093dfac6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Impact of Severity Level on Hospitalization Charges\n",
        "plt.figure(figsize = (7,5))\n",
        "sns.boxplot(x='severity level', y='hospitalization charges', data=work_df, dodge=False)\n",
        "plt.title(variable + \"Severity level Vs hospitalization charges\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ke45OtuqwfsL"
      },
      "source": [
        "#### **Observation**: \n",
        "*   *Severity Levels 0, 3 and 4 seem to have higher hospitalization charges*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1rb4O5drE3xv",
        "outputId": "f3acd719-1160-4d29-a02c-8188310cdd28"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "variable = \"hospitalization charges\"\n",
        "for i in cat_features:\n",
        " plt.figure(figsize = (7,5))\n",
        " sns.boxplot(x=i, hue=i, y=variable, data=work_df, dodge=False)\n",
        " plt.title(variable + \" Distribution w.r.to \" + i)\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1buai2k4wzcG"
      },
      "source": [
        "#### **Observation**: In terms of hospitalization charges, \n",
        "*   *Male patients tend to incur high charges*\n",
        "*   *Evidently Smokers are paying more than the non-smokers*\n",
        "*   *Southeast region has higher hospitalization charges amongst regions*\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "H5Wsi6AbzaYj",
        "outputId": "ce612073-439b-4aa3-d991-be95396cada7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(16, 6), squeeze=False)\n",
        "plt.suptitle(\"Hospitalization Charges Analysis\", fontsize = 15)\n",
        "g1 = sns.histplot(work_df['hospitalization charges'], kde = True, edgecolor='green', ax = axs[0, 0], bins = 20) \n",
        "g1.axvline(work_df['hospitalization charges'].mean(), ls = '--', color = \"red\", lw = 2.5, label = \"mean\") \n",
        "g1.axvline(work_df['hospitalization charges'].median(), ls = '--' ,color = 'blue', lw = 2.5, label = 'Median') \n",
        "g1.axvline(work_df['hospitalization charges'].mode()[0], ls = '--', color = 'green', lw = 2.5, label = 'Mode') \n",
        "g1.legend()\n",
        "stats.probplot(work_df['hospitalization charges'], dist=\"norm\", plot=axs[0, 1])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "GcQRDTPs0tX6",
        "outputId": "215f6086-a3e8-4675-ffc6-70132e37ce14"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(16, 6), squeeze=False)\n",
        "plt.suptitle(\"Viral Load Analysis\", fontsize = 15)\n",
        "g1 = sns.histplot(work_df['viral load'], kde = True, edgecolor='green', ax = axs[0, 0], bins = 20) \n",
        "g1.axvline(work_df['viral load'].mean(), ls = '--', color = \"red\", lw = 2.5, label = \"mean\") \n",
        "g1.axvline(work_df['viral load'].median(), ls = '--' ,color = 'blue', lw = 2.5, label = 'Median') \n",
        "g1.axvline(work_df['viral load'].mode()[0], ls = '--', color = 'green', lw = 2.5, label = 'Mode') \n",
        "g1.legend()\n",
        "stats.probplot(work_df['viral load'], dist=\"norm\", plot=axs[0, 1])\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4hxH169GiwZY",
        "outputId": "3e678cd4-de7e-420d-8bbf-438465b9dd8b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+zs/Qo0iSJEkqJCsTkiRJUiHZgC1JkiRpjdiALUmSJKnUWZmQJEmSCihlaJqTlQlJkiRJa8TKhCRJklRIGfo6P0OPIkmSJKmQrExIkiRJhZSh1ZxMJiRJkqRCsgFbkiRJUqmzMiFJkiQVUoamOVmZkCRJkrRGrExIkiRJhZSdwoTJhCRJklRIyWlOkiRJkkqdlQlJkiSpkKxMSJIkSSp1ViYkSZKkQsrQS+tWmExExBkruzCldHX9hyNJkiRlXIbmBq2sMtGq+s9NgR2AB6r3DwReymdQkiRJkhq+FSYTKaULASLiGWDblNLc6v0BwMMFiU6SJEnKmgxNc8qlyFIOLKq1v6j6mCRJkqQSlksD9hDgpYi4r3r/EGBw/kKSJEmSMixDS8OuMplIKV0SEY8Bu1Yf+mlK6bX8hiVJkiSpoctpadiU0isR8THQDCAi1k8pTcprZJIkSVIWlVJlIiIOAq4CugAVwPrAO0Cv/IYmSZIkZU8qsQbsgcBOwHsppR7A3sALeY1KkiRJUoOXSzKxOKU0AyiLiLKU0r+B7fMclyRJkpRNZXnYiiSXnolZEdESeBa4LSIqgC/yG5YkSZKkhi6XPOZgYAFwOvAY8AFVb8GWJEmStLoi6n8rklyWhv0iIsqBHYAZwKPV054kSZIkra4Mrea0yspERBwFvAQcCRwFvBgRR+Q7MEmSJEkNWy49E78HdkgpVQBEREfgSeDufAYmSZIkZVIpVSaAsq8SiWozcrxOkiRJUoblUpl4LCIeB+6o3j8aeCR/IUmSJEkZlp3CxKorDCmls4BBQO/qbVBK6bf5DqzUvPrfd/jVkZfxy8Mv5e7BI792fuxrH/A/P76aQ3c5i+dHvvG18/PnfcnPfnARN15xbyHClWrccMUv+ejVGxj9xOXFDkUlIqXEZZcM4YDvn8Hhh5zDuHETlztu3NiJHHbw7zjg+2dw2SVDSCkBMOKxFzn0wN/Sp9ePGPvWhK9d99nk6ey43YnccvPDeX0OZVNKiYsvvpF+/fpz4IG/ZuzY8csd99Zb4znwwFPo168/F198Y83fT4Bbb32Qfff9fxxwwElcfvk/a46/885Ejj76NxxwwEkceOApLFy4KO/Po/xIZVHvW7HkNF0ppXRPSumM6u2+fAdVaiorl3LjFfdywZ9/wXV3ns2zI15j0oQpdcZ0KG/LaX84hr77bLPce9x242P02mbDQoQr1XHrsFEc/OPLih2GSshzz7zBRx9N4aHHruL8C0/k4gtvWe64iy/6Jxdc9HMeeuwqPvpoCs89OwaAjTbuxtXXnMZ222+63OuuuPw2dt2tT77CV8Y988wrfPjhZEaMuJGBA09mwIC/LXfcgAF/ZeDAUxgx4kY+/HAyzzzzCgAvvDCGkSNf5IEHruXhh//KiSceCsCSJZWcddbVXHjhyTz88F8ZMuRSGjduVLDnUjZExL4R8W5EjI+I361gzFERMS4ixkbE7au65wqTiYiYGxFzlrPNjYg53+RBVNf74ybRuVt7OndtT5Mmjdmt3za89MzYOmPKu7Tj2xt3oWw5mef4tz9m1sy5bL3jJoUKWarx/EvvMHPWvGKHoRLy76de4cCDdyUi6NNnI+bO/YJp0z6vM2batM+ZN28BffpsRERw4MG78u+RowHYsGdXevTostx7P/XkaLp27UjPjbrm/TmUTSNHvsAhh+xJRLD11psxZ84XVFTMrDOmomIm8+bNZ+utNyMiOOSQPRk58gUA7rjjEfr3P4J11mkCQPv2bQB4/vnX2HTTb7PZZj0AaNt2PRo1MplYaxXhPRMR0Qi4HtgP2AI4NiK2WGbMxsA5wHdTSr2oes/cSq0wmUgptUoprbecrVVKab1VRqyczaiYTYfyNjX77Tu1Zsa02Tldu3TpUv55zYP89FTfIyipNFRUfE7nzu1r9svL21ExtW4yUTH1c8rL29UdU1F3zLLmf/ElN//jIX510mH1G7BKytSpM+jcuUPNfufO7Zk6dcYqxnSoGfPhh5MZPXosRx55Jj/84e8YM+Y9ACZO/JQIOPHE8zn00NO46aZ7CvA0ypjvAONTShNSSouAO6l6OXVtvwCuTyl9DrDMIkzLlUsDthqwR+/5D9vtslmdZESStPr+ev29/OjH+9Ji3WbFDkUlrLKyktmz5zF06JW8+eb7nH76/zJy5N+prKzklVfGcffdV9O8eVNOOOE8ttxyI3be2Sl5a6Xi9Dh0BT6utf8JsOMyYzYBiIjngUbAgJTSYyu7aV6SiYjoD/QHuPBPJ3PUCfvm42Myo32n1kyfOqtmf0bFbNp3bJ3Tte+8+SHjXp/Io/f8hwXzF7JkcSXNWqzDT07+Qb7ClaSCu/P2J7hn2L8B6LXVhkyZ8n/f9E6dOpNO5W3rjO9U3papU2fWHdOp7phlvTlmPE+OeIk/XXUnc+fOJyJo2rQJxx6/Tz0+ibLottseZujQxwHYaquNmTJles25KVNmUF7evs748vL2y4yZXjOmvLwD/frtTETQu/cmlJWV8fnnc+jcuQM77LAl7dpV/X7Qt+/2jB37gcnE2ioPuUTt37+rDUopDVrN2zQGNga+B3QDnomIrVJKs1Z2Qb2rDnwQwDuzHkqrGF7yNt68O599PJ2pk2fQrmNrnn3iNc4c+MOcrj3zov8bN/Khlxj/9icmEpIy55jj+nHMcf0AeGbUa9xx2xPst//OjBnzAa1ataBjx7qJQseObWnZsjlvvDGe3r178uDw5zhuFUnB4H+dX/PzX6+7hxYtmplIKCfHH38Axx9/AABPP/0y//rXQxxwQF/eeONdWrVqQadO7eqM79SpHS1btuD119+hT59Nuf/+p/jRj6qmK++99068+OIYdtqpNxMnfsrixUto23Y9dt11W/7+93tYsOBLmjRpwssvv8UJJyw7Q0WlrPbv3yvwKdC91n636mO1fQK8mFJaDEyMiPeoSi5eXtFNnebUADRq3Ij+vzmMAacOYunSxF4Hfof1N+zMbTc+xkabd2PHvlvy/rhJ/PHsW5g3dwEvPzuOO256nOvuPLvYoUsMvvbX7Lbz5nRo24rxL17HwKvvZvBdTxc7LGXYbn235tln3uCAfc+kWbN1GHjJ/30Rd+Sh5zLsvksB+P0fTuC8cwexcOEidt2tD7v2rfoGd+STL/PHS4bw+cy5nPyrK9lssw244SZXPFf92H337Rk1ajT9+vWnefOmXHrpaTXnDj74VIYPvwaACy74Feec82e+/HIRfftuR9++2wFw+OF7c+651/CDH5xMkyaNueyy04kIWrduyQknHMIRR5xBRNC37/Z873s7FOUZ9c2VFef1zy8DG0dED6qSiGOA45YZcz9wLPDPiOhA1bSnr6+hXUvUXte4zomIucDyTgaQcm3CtjKhhmqb3rcVOwRphWZNPKPYIUjL1bRRbtNwpeLYZK14Hdy3rxtV778ff3jK7qt89ojYH/gzVf0QN6eULomIi4DRKaUHIiKAq4B9gUrgkpTSnSu75worEymlVqvzAJIkSZJWLYeVXPMipfQI8Mgyx86v9XMCzqjecpLzNKeI6ATULHGRUpqU67WSJEmSsmeVyUREHERVuaMLUAFsALwN9MpvaJIkSVL2FKsykQ+5tH8MBHYC3ksp9QD2Al7Ia1SSJElSRkVEvW/FkksysTilNAMoi4iylNK/ge3zHJckSZKkBi6XnolZEdESeAa4LSIqgC/yG5YkSZKUTaU2zelgYD7wP8BjwAfAgfkMSpIkSVLDt9LKREQ0Ah5KKe0BLAUGFyQqSZIkKaOyVJlYaTKRUqqMiKUR0TqlNLtQQUmSJElZFcV5A3Ze5NIzMQ94MyKeoFavRErp1LxFJUmSJKnByyWZuLd6kyRJkvQNlcw0J4CUkn0SkiRJkr5mhclERAxNKR0VEW8CadnzKaXeeY1MkiRJyqCyEqlMnFb95w8KEYgkSZJUCkpimlNK6bPqHw8H7kwpTS5MSJIkSZLWBrk0YLcCnoiImcBdwLCU0tT8hiVJkiRlU5YqE6tc5TaldGFKqRdwMvAtYFREPJn3yCRJkiQ1aLlUJr5SAUwBZgCd8hOOJEmSlG2RodLEKisTEXFSRDwNjATaA79wJSdJkiRJuVQmugOnp5Rez3cwkiRJUtbFKr/OX3vk8tK6cwoRiCRJklQKMjTLadXTnCRJkiRpeVanAVuSJEnSN2RlQpIkSVLJszIhSZIkFVCWKhMmE5IkSVIBlWUomXCakyRJkqQ1YmVCkiRJKqAsTXOyMiFJkiRpjViZkCRJkgooS5UJkwlJkiSpgCJDHdhOc5IkSZK0RqxMSJIkSQWUpWlOViYkSZIkrRErE5IkSVIBWZmQJEmSVPKsTEiSJEkFlKXKhMmEJEmSVEAZWhnWaU6SJEmS1oyVCUmSJKmAsjTNycqEJEmSpDViZUKSJEkqoMjQ1/kmE5IkSVIBOc1JkiRJUsmzMiFJkiQVUGSoNGFlQpIkSdIasTIhSZIkFVCGChMmE5IkSVIhZSmZcJqTJEmSpDViZUKSJEkqoCxVJvKeTHRZt1m+P0JaI7MmnlHsEKQVatPj6mKHIC3X2LHHFTsEaYU2bLVJsUMoOVYmJEmSpAIqy1Blwp4JSZIkSWvEyoQkSZJUQFmqTJhMSJIkSQVUFqnYIdQbpzlJkiRJWiNWJiRJkqQCytI0JysTkiRJktaIlQlJkiSpgLL0bb7JhCRJklRANmBLkiRJKnlWJiRJkqQCsgFbkiRJUsmzMiFJkiQVUJa+zTeZkCRJkgrIaU6SJEmSSp6VCUmSJKmAwqVhJUmSJJU6KxOSJElSAdkzIUmSJKnkWZmQJEmSCihL3+abTEiSJEkFVGYDtiRJkqRSt8LKRERsu7ILU0qv1n84kiRJUrZlqQF7ZdOcrlrJuQTsWc+xSJIkScqTiNgX+AvQCPh7SumyFYw7HLgb2CGlNHpl91xhMpFS2uMbxCpJkiRpOYrRZxARjYDrgX7AJ8DLEfFASmncMuNaAacBL+Zy31U+S0S0iIjzImJQ9f7GEfGD1X0ASZIkSVXTnOp7y8F3gPEppQkppUXAncDByxk3EPhf4MucniWHMf8EFgG7VO9/Clycy80lSZIkNQhdgY9r7X9SfaxGdc9095TSw7neNJelYXumlI6OiGMBUkrzIyJDbSOSJElS4eRjadiI6A/0r3VoUEpp0GpcXwZcDZywOp+bSzKxKCKaU9V0TUT0BBauzodIkiRJyp/qxGFlycOnQPda+92qj32lFbAl8HR13aAz8EBEHLSyJuxckokBwGNA94i4Dfguq5mxSJIkSapSpKVhXwY2jogeVCURxwDHfXUypTQb6PDVfkQ8DfxmjVdzqnXjERHxCrATEMBpKaXpa/IEkiRJUqkrxmpOKaUlEXEK8DhVS8PenFIaGxEXAaNTSg+syX1XmUxExIPA7cADKaUv1uRDJEmSJBVXSukR4JFljp2/grHfy+WeuSRGVwK7AeMi4u6IOCIimuVyc0mSJEl1lUWq961YcpnmNAoYVf2iiz2BXwA3A+vlOTZJkiRJDVguDdhUr+Z0IHA0sC0wOJ9BSZIkSVlVpAbsvMilZ2IoVW/Mewy4DhiVUlqa78AkSZIkNWy5VCb+ARybUqrMdzCSJElS1mWpMpFLA/azwDkRMQggIjaOiB/kNyxJkiQpm8rysBVLLp/9T2ARsEv1/qfAxXmLSJIkSdJaIZdpTj1TSkdHxLEAKaX5Uf2ObUmSJEmrp5hLuda3XCoTi6pXc0oAEdETWJjXqCRJkiQ1eLlUJi6gaiWn7hFxG/Bd4IR8BiVJkiRlVZYasHN5ad0TEfEqsBMQwGkppel5j0ySJEnKoGI2TNe3FSYTEbHtMoc+q/5z/YhYP6X0av7CkiRJktTQrawycdVKziVgz3qORZIkScq8kpjmlFLao5CBSJIkSVq75NKALUmSJKmeRIaWhjWZkCRJkgooS9OcstRMLkmSJKmAVlmZiIh7gX8Aj6aUluY/pNLxn+fGctVld7O0cikHH/5dTvj5PnXOL1q0mAvOGcI74ybRus26XHrliXTp2p6xb37IJQNurxqU4Bcn7c8ee28NwEH7/IEW6zajrCxo3KgRQ4b+ttCPpYxIKfG/l97Ks+ipa8IAACAASURBVM+8TrPmTRl4aX+22KLH18aNGzuR8869kYVfLmK3vlvz23N/REQw4rEX+dv19zJhwmRuv+tCem25YZ3rPps8nUMO/C2/OvkwTvjZAYV6LJWYG674JfvttQ3TZsxh+35nFzsclZjR/3mHG64cztKlS9n3kB056oS6a9e8+eoH3HjVA0wc/xm/u+R4dtu7DwAfvPsp1112L/O/+JKysjKO+dle7L7P1sV4BOVJlr7Nz+VZ/gocB7wfEZdFxKZ5jqkkVFYu5fKLh/KXv53M0Af+wIhHRjPhg8/qjBl+739Zb70W3PfohRz3oz259ur7Aei5UReG3PVbbr/nXK658WT+eNEdLFlSWXPdDTefxu33nGsioW/kuWfe4KOPpvDQY1dx/oUncvGFtyx33MUX/ZMLLvo5Dz12FR99NIXnnh0DwEYbd+Pqa05ju+2X/0/GFZffxq679clX+BIAtw4bxcE/vqzYYagEVVYu5fr/vY+B1/ycG4edxdOPv8ZHE6bUGdOpc1vOHHA0e3x/mzrHmzZbh99ceAw3Dj2Li6/9OTdeNZx5cxcUMnwpZ6tMJlJKT6aUjge2BT4EnoyI/0TETyOiSb4DzKqxb35I9/U70q17B5o0aUy//bZj1FNj6ox55qkxHHDwjgDsuc82vPziu6SUaNZ8HRo3bgTAwoWLCTI08U4Nxr+feoUDD96ViKBPn42YO/cLpk37vM6YadM+Z968BfTpsxERwYEH78q/R44GYMOeXenRo8ty7/3Uk6Pp2rUjPTfqmvfnUGl7/qV3mDlrXrHDUAl6b+wkunRvz7e6tadJk8bsvs/WvDBqbJ0x5V3a0WPjLsQyE+i7bdCRrut3BKB9x9a0adeS2Z/79zhLyiLV+1a0Z8llUES0B04Afg68BvyFquTiibxFlnHTKmZR3rltzX55eRumVcyqM6ai1pjGjRvRsmVzZs/6AoC3xkzkqIMHcuyhl/C784+pSS4iglP6X8ePjrqMe4c9V6CnURZVVHxO587ta/bLy9tRMbVuMlEx9XPKy9vVHVNRd8yy5n/xJTf/4yF+ddJh9RuwJDUg0ytm07G8Tc1+h05tmFExe7Xv8+5bk1iyuJJvdWu/6sFSEeTSM3EfsClwK3BgSumruTh3RcTofAanFduydw+GDv8DEz+YwoDfD2GX3XrRtGkTbhpyBp3K2zBzxlxO+cW1fLtHOdtuv3Gxw5Vq/PX6e/nRj/elxbrNih2KJDVoM6fP4Yrz7+DMC4+hrCxLs+yVpdWcclka9qaU0iO1D0RE05TSwpTS9su7ICL6A/0B/vzX0/npz22uXFbHTm2YOuX/vsGdOnUWHTu1qTOmU/WY8s5tWbKkknnzFtC6zbp1xvTo2ZkWLZrywfuT2WLLDehU/S1Iu/at+N5efRj75kcmE8rZnbc/wT3D/g1Ar602ZMqUGTXnpk6dSafytnXGdypvy9SpM+uO6VR3zLLeHDOeJ0e8xJ+uupO5c+cTETRt2oRjj99npddJ0tqkQ6fWTJv6fzMOplfMon2n1jlf/8W8Lzn/tH/wk5P2ZfOtNshHiCqiLCUTuaS5Fy/n2H9XdkFKaVBKafuU0vYmEsu3xZYbMGlSBZ9+Mp3Fi5fwxKOv0HePreqM2W2PrXh4+IsAPDXiNXbYcRMigk8/mV7TcP3Z5Bl8OHEqXbq2Z8H8hXzxxZcALJi/kBf+8zY9N/5WYR9Ma7VjjuvHsPsuZdh9l7LnXtvx4PDnSCnxxhvjadWqBR071k0UOnZsS8uWzXnjjfGklHhw+HPssed2K/2Mwf86n8ee/DOPPflnjv/R9/l5/4NMJCRlziZbdGfyx9OZ8ukMFi9ewqgRr7NT3145Xbt48RIGnnULex2wXc0KT1JDtcLKRER0BroCzSNiG6jp8l0PaFGA2DKtceNGnH3uUZz6y+uprFzKQYfuTM+NunDDdQ+xea/12X2P3hx82C5ccM5gDt3vAtZrvS6XXPEzAN549QNu+ccIGjduRFlZGb8972jatG3JJx9P5+zTBgGwpLKSffffgV12ze0fLmlZu/XdmmefeYMD9j2TZs3WYeAl/WvOHXnouQy771IAfv+HEzjv3EEsXLiIXXfrw659q/7DN/LJl/njJUP4fOZcTv7VlWy22QbccJMrjKmwBl/7a3bbeXM6tG3F+BevY+DVdzP4rqeLHZZKQKPGjfjVWYdy3q9vorIysc9BO7BBz84MueExNtm8Ozvt3ot3x05i4FmDmTdnPi8+O45/DRrBjUPP4tkn3uCtVycwd/Z8nnyoakb5GRccTc9NXbQiKxoVO4B6FCktv/s7In5CVdP19kDt3oi5wC0ppXtz+YA5i5/MzvvClSlNy3IvN0uF1qbH1cUOQVqusWOPK3YI0gpt2OrAtWIC0cWv1f/vx+dts3dRnn2FlYmU0mBgcEQcnlK6p4AxSZIkSZlVzKVc69vKpjn9MKX0L+DbEXHGsudTSn5tJkmSJK2mLDVgr2w1p6+WDWpZiEAkSZIkrV1WNs3pxohoBMxJKf2pgDFJkiRJmZWlysRKl4ZNKVUCxxYoFkmSJElrkVxeWvd8RFwH3AV88dXBlNKreYtKkiRJyqhGGapM5JJMbF3950W1jiVgz/oPR5IkScq2LE1zWmUykVLaoxCBSJIkSVq7rLRnAiAiyiPiHxHxaPX+FhFxYv5DkyRJkrKnLFK9b0V7lhzG3AI8DnSp3n8POD1fAUmSJElaO+SSTHRIKQ0FlgKklJYAlXmNSpIkScqosqj/rWjPksOYLyKiPVVN10TETsDsvEYlSZIkqcHLZTWnM4EHgJ4R8TzQETgir1FJkiRJGdWo2AHUo1xWc3olInYHNgUCeDeltDjvkUmSJEkZlKWlYXNZzWkMcDbwZUrpLRMJSZIkSZBbz8SBwBJgaES8HBG/iYj18xyXJEmSlEkltTRsSumjlNLlKaXtgOOA3sDEvEcmSZIkqUHLpQGbiNgAOLp6q6Rq2pMkSZKk1dQoQz0Tq0wmIuJFoAkwDDgypTQh71FJkiRJGZWlBuxcKhM/Tim9m/dIJEmSJK1VcmnAnhUR/4iIRwEiYouIODHPcUmSJEmZVGpvwL4FeBzoUr3/HnB6vgKSJEmStHbIJZnokFIaCiwFSCktoaoJW5IkSdJqylJlIpeeiS8ioj2QACJiJ2B2XqOSJEmSMqpREd8LUd9ySSbOAB4AekbE80BH4Ii8RiVJkiSpwVtlMpFSejUidgc2BQJ4N6W0OO+RSZIkSRmUS5/B2mKVzxIRRwLNU0pjgUOAuyJi27xHJkmSJKlByyUx+kNKaW5E7ArsBfwD+Ft+w5IkSZKyKUsN2LkkE1+t3HQAcFNK6WFgnfyFJEmSJGVXqSUTn0bEjcDRwCMR0TTH6yRJkiRlWC6rOR0F7AtcmVKaFRHfAs7Kb1iSJElSNpXU0rAppfnAvbX2PwM+y2dQkiRJkhq+XCoTkiRJkupJMXsc6pu9D5IkSZLWiJUJSZIkqYCyVJkwmZAkSZIKKEvJhNOcJEmSJK0RKxOSJElSATWyMiFJkiSp1FmZkCRJkgqorJReWidJkiSp/mRpalCWnkWSJElSAVmZkCRJkgrIpWElSZIklTwrE5IkSVIBZWlpWJMJSZIkqYCytJqT05wkSZIkrRGTCUmSJKmAyqL+t1xExL4R8W5EjI+I3y3n/BkRMS4ixkTEyIjYYJXPsjoPHhFtI6L36lwjSZIkqbgiohFwPbAfsAVwbERsscyw14DtU0q9gbuBy1d131UmExHxdESsFxHtgFeBmyLi6tV9AEmSJElFq0x8BxifUpqQUloE3AkcXHtASunfKaX51bsvAN1WddNcGrBbp5TmRMTPgSEppQsiYkxOIQN7398s16FSQb10ZOtihyCt0NixxxU7BGm5evW6vdghSCu0YNKBxQ6haCKiP9C/1qFBKaVBtfa7Ah/X2v8E2HEltzwReHRVn5tLMtE4Ir4FHAX8PofxkiRJklYgH03L1YnDoFUOzEFE/BDYHth9VWNzSSYuAh4Hnk8pvRwRGwLvf7MQJUmSpNIUxXnPxKdA91r73aqP1RERe1NVQNg9pbRwVTddZTKRUhoGDKu1PwE4PIeAJUmSJDUMLwMbR0QPqpKIY4A6c2ojYhvgRmDflFJFLjfNpQF7k+qlod6q3u8dEeetbvSSJEmSIPKwrUpKaQlwClUzjt4GhqaUxkbERRFxUPWwK4CWwLCIeD0iHljVfXOZ5nQTcBZVWQoppTERcTtwcQ7XSpIkSWoAUkqPAI8sc+z8Wj/vvbr3zCWZaJFSeinqTu5asrofJEmSJKloPRN5kUsyMT0iegIJICKOAD7La1SSJElSRuVjNadiySWZOJmqZaY2i4hPgYnAD/MalSRJkqQGL5fVnCYAe0fEukBZSmlu/sOSJEmSsikiFTuEerPKZCIizlhmH2A28EpK6fU8xSVJkiSpgctlmtP21duD1fs/AMYA/y8ihqWULs9XcJIkSVLWZKj/OqdkohuwbUppHkBEXAA8DPQFXgFMJiRJkqQcZWk1p1yayTsBtV+lvRgoTyktWOa4JEmSpBKSS2XiNuDFiBhevX8gcHt1Q/a4vEUmSZIkZVCGChMrTyaiqtv6FuBR4LvVh/9fSml09c/H5y80SZIkSQ3ZSpOJlFKKiEdSSlsBo1c2VpIkSdKqlWWoNJFLz8SrEbFD3iORJEmStFbJpWdiR+D4iPgI+IKqaV4ppdQ7r5FJkiRJGZShwkROycT38x6FJEmSVCKytDTsKpOJlNJHABHRCWiW94gkSZIkrRVW2TMREQdFxPvARGAU8CFVqztJkiRJWk2Rh61YcmnAHgjsBLyXUuoB7AW8kNeoJEmSJDV4uSQTi1NKM4CyiChLKf0b2D7PcUmSJEmZlKXKRC4N2LMioiXwDHBbRFRQtaqTJEmSpNVUau+ZOBhYAPwP8BjwAXBgPoOSJEmS1PDlsppT7SrE4DzGIkmSJGVehgoTOa3mdFhEvB8RsyNiTkTMjYg5hQhOkiRJUsOVS8/E5cCBKaW38x2MJEmSlHURqdgh1JtckompJhKSJElS/cjSNKcVJhMRcVj1j6Mj4i7gfmDhV+dTSvfmOTZJkiRJDdjKKhO1V2yaD+xTaz8BJhOSJEnSaooMlSZWmEyklH5ayEAkSZIkrV1yWc1pcES0qbXfNiJuzm9YkiRJUjaV5WErllw+u3dKadZXOymlz4Ft8heSJEmSpLVBLqs5lUVE2+okgohol+N1kiRJkpZREj0TtVwF/DcihlXvHwlckr+QJEmSpOzKUC6x6mQipTQkIkYDe1YfOiylNC6/YUmSJElq6HKarlSdPJhASJIkSd9QlqY5FbP5W5IkSdJazEZqSZIkqYAyVJgwmZAkSZIKqSxD2UQuL607LCLej4jZETEnIuZGxJxCBCdJkiSp4cqlMnE5cGBK6e18ByNJkiRlXYYKEzk1YE81kZAkSZK0rFwqE6Mj4i7gfmDhVwdTSvfmLSpJkiQpoyJSsUOoN7kkE+sB84F9ah1LgMmEJEmStJqyNM0plzdg/7QQgUiSJElau+SymlO3iLgvIiqqt3siolshgpMkSZKyJqL+t2LJpQH7n8ADQJfq7cHqY5IkSZJKWC49Ex1TSrWTh1si4vR8BVSqdipvw5nbbEhZBMMnTGXIu5/UOX/ABp04tU8Ppi2o6oEfNv4zhk+cCsBfduvFlu1a8cb0OZzx/LiCx65sSilxySWDGDXqFZo1a8pll51Gr14bfW3cW2+N55xz/syXXy5i99234/e/709Uf0Vy660PctttD9OoURm7774DZ59dNWvynXcmcsEF1zNv3nzKysq4++6radp0nYI+n7Jh9H/e4YYrh7N06VL2PWRHjjphzzrn33z1A2686gEmjv+M311yPLvt3QeAD979lOsuu5f5X3xJWVkZx/xsL3bfZ+tiPIJK1A1X/JL99tqGaTPmsH2/s4sdjgqspHomgBkR8UPgjur9Y4EZ+Qup9JQBZ2/bk1OeeYuK+YsYvPfWPDt5BhPnLqgz7omPp3HlaxO+dv2/3v2Epo0acdiGnQsUsUrBM8+8wocfTmbEiBt54413GTDgbwwbdtXXxg0Y8FcGDjyFPn025Re/GMAzz7zC7rtvzwsvjGHkyBd54IFrWWedJsyYMQuAJUsqOeusq7niijPYbLMefP75HBo3blTox1MGVFYu5fr/vY9Lr+9Ph/LWnPbjv7Bj3y3YoNa/hZ06t+XMAUdzz62j6lzbtNk6/ObCY+i6fkdmTJvNr3/4Z7bbeVNatmpe6MdQibp12ChuGPw4f//TScUORfpGcpnm9DPgKGAK8BlwBGBTdj3q1a4Vn8z7kslfLGRJSoz4eBp9u7bP+fqXK2Yzf0llHiNUKRo58gUOOWRPIoKtt96MOXO+oKJiZp0xFRUzmTdvPltvvRkRwSGH7MnIkS8AcMcdj9C//xGss04TANq3bwPA88+/xqabfpvNNusBQNu269GokcmEVt97YyfRpXt7vtWtPU2aNGb3fbbmhVFj64wp79KOHht3Icrqfg/YbYOOdF2/IwDtO7amTbuWzP58XsFil55/6R1mzvLvXKkqy8NWLLms5vQRcFABYilZHZuvw9T5Na/woGL+Qnq1b/W1cXt27cA2HVozad4C/vT6BCoWLCpkmCoxU6fOoHPnDjX7nTu3Z+rUGXTq1G4lYzowdWpV4fLDDyczevRY/vSnW2natAlnn/0zevfehIkTPyUCTjzxfGbOnM3++/flF784vHAPpsyYXjGbjuVtavY7dGrDu299tNr3efetSSxZXMm3uuX+JY4kfRPFbJiubytMJiLi7JTS5RFxLVXvlagjpXTqSq7tD/QH2KD/WXTa21zkm3rus5mM+Hgai5cmDt2wMwO+swknjXqr2GFJK1RZWcns2fMYOvRK3nzzfU4//X8ZOfLvVFZW8sor47j77qtp3rwpJ5xwHltuuRE779yn2CGrBM2cPocrzr+DMy88hrKyYn63J0lrp5VVJt6u/nP06t40pTQIGATwnWHPZecVf3kybcEiyls0rdnv1KIp05apOsxetKTm5+ETpvDr3t8uVHgqIbfd9jBDhz4OwFZbbcyUKdNrzk2ZMoPy8rrf3JaXt19mzPSaMeXlHejXb2cigt69N6GsrIzPP59D584d2GGHLWnXrjUAfftuz9ixH5hMaLV16NSaaVNn1exPr5hF+06tc77+i3lfcv5p/+AnJ+3L5lttkI8QJWkFslOaWOHXMCmlB6t/nJ9SGlx7o+qN2Kon4z6fS/eWzenSoimNI9ine0eenVx3bnr7Zk1qfu7bpT0T5/h/gerf8ccfwPDh1zB8+DXsvfdO3H//U6SUeP31d2jVqkWdKU4AnTq1o2XLFrz++juklLj//qfYa6+dANh775148cUxAEyc+CmLFy+hbdv12HXXbXnvvQ9ZsOBLliyp5OWX32KjjboX/Fm19ttki+5M/ng6Uz6dweLFSxg14nV26tsrp2sXL17CwLNuYa8DtqtZ4UmStPpyWc3pHGBYDse0hioTXPHaB1zTd0vKAh6cOJUJc+bTv9f6vD1zHs9+NpOjN+pC3y7tqEwwe9FiLnr5/ZrrB31vKzZYrwXNG5fx4AE7cMno93mh1rd10prYffftGTVqNP369ad586ZceulpNecOPvhUhg+/BoALLvhVzdKwfftuR9++2wFw+OF7c+651/CDH5xMkyaNueyy04kIWrduyQknHMIRR5xBRNC37/Z873s7FOUZtXZr1LgRvzrrUM779U1UVib2OWgHNujZmSE3PMYmm3dnp9178e7YSQw8azDz5sznxWfH8a9BI7hx6Fk8+8QbvPXqBObOns+TD1UV4M+44Gh6btq1yE+lUjH42l+z286b06FtK8a/eB0Dr76bwXc9XeywVCCRocpEpLT8WUgRsR+wP1UrOd1V69R6wBYppe/k8gFOc1JD9dKRnYodgrRCE+a+W+wQpOXq1ev2YocgrdCCSXesFb+lz1r0SL3/ftxmnf2L8uwrq0xMpqpf4iDglVrH5wL/k8+gJEmSJDV8K0wmUkpvAG9ExO0ppcUFjEmSJEnKsLWigJKTXHomvh0RfwS2AJp9dTCltGHeopIkSZLU4OWyqPY/gb8BS4A9gCHAv/IZlCRJkpRVkYf/FUsuyUTzlNJIqpq1P0opDQAOyG9YkiRJUlZFHrbiyGWa08KIKAPej4hTgE+BlvkNS5IkSVJDl0sycRrQAjgVGEjVVKef5DMoSZIkKauqvqfPhlySiWbAgpTSPOCnABGxbV6jkiRJktTg5ZIWPQ48FRG13/D19zzFI0mSJGVcdnomckkm3gWuAEZFxC7Vx7KzOK4kSZKkNZLLNKeUUnooIt4F7oqIm4F6fwW4JEmSVAqKuZRrfcslmQiAlNL7EdEXuBnondeoJEmSpIwqqWQipbRNrZ/nAUdFxPp5jUqSJElSg7fCZCIizk4pXR4R16xgyKl5ikmSJEnKsNJYGvbt6j9fKUQgkiRJktYuK0wmUkoPVv85uHDhSJIkSdkWUQI9ExHxICtZtSmldFBeIpIkSZIyrQSSCeDKgkWh/9/evQfbWdVnHP8+uXBLSCqQqqSRwyAtjVyiSbkIcmlpR25GhRgduQQoGWaKMG3R2lFpRKsihToZWi2OSbgESIImRIoIkxuRGHIxObkhEBEEoq0CcilJNPjrH2tt8nJy9t7vOZx9zj77PJ/MO3vttde73rV31nnfd73vWus1MzMzM+t3anVzWtabBTEzMzMzGwhaaWrYukPJJR0u6W5JWyQ9WVl6o3BmZmZmZtYzJH1Q0mOStkr6bCef7y1pTv78EUlt9fIsMy/VTOCbwC7gNOBW4PauFd3MzMzMzJJBDVhqkzQY+A/gDGAs8AlJYzskuxR4MSLeDfw7cF2Zb1LPvhGxCFBEPB0R04CzSqxnZmZmZmYdqAH/SjgW2BoRT0bE74C7gIkd0kwEKjO53g38lepMPVX3CdjATkmDgCckXQE8BwwvU2IzMzMzM2sKo4FnCu+fBY6rliYidkl6CTgQ+E21TMvcmbgK2I/0xOvxwPnAhaWLbWZmZmZmb5DUiGWqpDWFZWpvfJcydybaImI18CpwMYCkScAjjSyYmZmZmZmVExE3AzfXSPIcMKbw/k9yXGdpnpU0BBgJPF9ru2XuTPxzyTgzMzMzM6tLDVjqWg0cLulQSXsBHwcWdkizELgoh88DFkdE1YdYQ+0nYJ8BnAmMljS98NEI0sxOZmZmZmbWD+QxEFcAPwQGAzMiYrOka4E1EbEQ+A5wm6StwAukBkdNtbo5bQPWAB8C1hbiXwH+vntfw8zMzMxsYFOpzkE9LyLuA+7rEHdNIbwDmNSVPGs9AbsdaJc0OyJ8J8LMzMzMrEe0zhOwa3VzmhsRHwPWSdqjr1REHN3QkpmZmZmZWVOr1c3pqvx6dm8UxMzMzMxsIKjzHLh+pWqHrYj4ZX59GtgJHAMcDezMcWZmZmZmNoDVHf0h6W+BVcBHSVNErZR0SaMLZmZmZmbWmvpkatiGKPPQuk8D742I5wEkHQisAGY0smBmZmZmZq2or2ZzaoQy3+R50nSwFa9Q50l4ZmZmZmbW+srcmdgKPCLpHiCAicAGSf8AEBE3NrB8ZmZmZmYtpnUGYJdpTPwsLxX35Nf9e744ZmZmZmbWX9RtTETEFythSYOA4RHxckNLZWZmZmbWotRCdybKzOZ0h6QRkoYBm4Atkj7d+KKZmZmZmbUeST2+9JUyA7DH5jsRHwZ+ABwKXNDQUpmZmZmZWdMrM2ZiqKShpMbETRHxe0nR4HKZmZmZmbWogTU17H8BTwHDgIckHQJ4zISZmZmZ2QBXZgD2dGB6IeppSac1rkhmZmZmZq1roA3AHinpRklr8nID6S6FmZmZmZkNYGW6Oc0gPfX6Y3l5GZjZyEKZmZmZmbUuNWDpG2UGYB8WEecW3n9R0vpGFcjMzMzMrJX15VSuPa3MnYntkk6qvJF0IrC9cUUyMzMzM7P+oMydicuBWyWNJN1DeQGY0shCmZmZmZm1rtaZGrbMbE7twDGSRuT3nhbWzMzMzMzqNyYk7Q2cC7QBQyp9vCLi2oaWzMzMzMysBbXS1LCKqP0wa0n3Ay8Ba4HXK/ERcUNji2adkTQ1Im7u63KYdeS6ac3M9dOaleum9XdlGhObIuLIXiqP1SFpTURM6OtymHXkumnNzPXTmpXrpvV3ZUZ/rJB0VMNLYmZmZmZm/UrVMROSNgKR01ws6UlgJ2lGp4iIo3uniGZmZmZm1oxqDcA+u9dKYV3hfpXWrFw3rZm5flqzct20fq3umAkzMzMzM7POtM4TM8zMzMzMrFe5MWFmSDpY0t3dWO/VrsR3I/9pkq7uibxsYJB0uaQLc3iKpIO7uP5SST06s04j8hwIJLVJ2tTA/N/Y70kaJ+nMEuucKuneHP6QpM92Y7tv2lZ38+nC9mZJOq9R+ZvVfWidmbW+iNgG7HGwkTQkInb1QZHMuizX128VoqYAm4BtfVMia2Yd9nvjgAnAfV1YfyGwsBubftO23kI+Daf0pGJFxB/6uizWvHxnoslIWiBpraTNkqbmuEslPS5plaRvS7opx4+S9F1Jq/NyYt+W3pqdpK9J+rvC+2mSri5eAcxXcxdKWgwskjRc0iJJP5G0UdLELmxPkq6XtCmvOznHV81T0udyff8R8Gc99+2tL0gaJum/JbXnelCpA+MlLcv7ux9KeqekIyStKqzblmcW7DR9jl8q6RuS1gBXFer0eaQTttmS1ks6S9KCQt5/LWl+nbL/jaQf53o6L9fbD0qaV0hTvFK9R/oe/CkHqsH5uLdZ0gOS9oU3ru6vlLRB0nxJb8vxV0rakuPvynHTJN2W/2+ekHRZjm/LdXIv4Fpgcq4rkyUdm9Ovk7RC0h77oryvSsWQvQAABvtJREFUrByP1xeW7ZJO6SyPKtsq5tMmaXEu/yJJ78rxsyRNz/k8qSp3GiRdmNdtl3Rb4aOTO65bbT+cy/CYpFtJjfExkr6Q434k6U7lO8aSDpN0f/67XC7piBw/Kf+27ZIeeks1wJpfRHhpogU4IL/uS/ojHg08BRwADAWWAzflNHcAJ+Xwu4BH+7r8Xpp7Ad4LLCu83wKMAdqATTluCvBsoS4OAUbk8EHAVnZP3vBqle28ml/PBR4EBgNvB34BvLNansB4YCOwHzAix1/d17+bl7dU584Fvl14PzLvy1YAo3LcZGBGDq8HDs3hfwI+Xyf9UuA/C/lPq9SZ/NmEHBbw00IedwDndFLepaRGyEHAQ8CwQlmuyXX3F4X4bwLnV0vfsRxeulR32oBdwLj8fi5wfg5vAE7J4WuBb+TwNmDvHP6jQp1oJx1XDwKeAQ5mz/3eTYVtjwCG5PDpwHdz+FTg3s7WyXHnkI7TQ2vk0XFbb7wHvg9clMOXAAtyeBYwj3QReCywtZPf6z3A48BB+f0Btdal+n64DfgDcHz+7C9If5f7APsDT7D7b2wRcHgOHwcszuGNwOji/4OX1l3czan5XCnpIzk8BriAdPL3AkC+Ivan+fPTgbGSKuuOkDQ8Inqkv7q1nohYJ+mPlfqRjwJejIhnJLV1SPpgpc6RDi5fkXQy6QAzmtQw+FWJTZ4E3BkRrwP/I2kZ6cD0gyp5fgCYHxGvAUhqylv/1iUbgRskXUc6CVsu6UjgSODBvP8aDPwyp59Laix8Lb9OJt2hqpYeYE69QkRE5Cu150uaCZwAXFhjleNJJ14P523uBfw4InZJuh84R6m//VnAZ4BTOktfr1xW188jYn0OrwXaJI0knaAuy/G3kE6WITUyZue7UAsK+dwTEduB7ZKWAMeSTpCrGQncIulw0jO3htYraE57PXBaRPxe0ju6mgepXn40h28Dvl74bEGk7kZbJL29k3X/EpgXEb8BKOzDq61bbd8O8HRErMzhE0m/3w5gh6Tv5+87HHg/MK9wHrJ3fn0YmCVpLvC9Et/b+jE3JpqIpFNJDYQTIuI1SUtJV9L+vMoqg0hXDnb0TgmtRcwj9RN+B9VPwv6vEP4kqeExPh8gnyJdoXorGpGnNaGIeFzS+4AzgS9LWgTMBzZHxAmdrDKHdHLyvbR6PCHpqBrp4c31tZaZpCu/O0gnXbXGA4nUqP5EJ5/dBVwBvACsiYhXlM6mqqW37ttZCL9OurtQy1nAyaQ7BJ/LdQfSyXxRvXnxvwQsiYiP5IstS2slzifWc4HLIqLS0O1SHiUUfwtVTVV+3Vr74TJ/U4OA30bEuI4fRMTlko4j/X+slTQ+Ip7vYpmtn/CYieYyknSl+LXc7/B4YBhwiqS3SRpC6jJQ8QDwqcobSXv8QZt1Yg7wcVKDYl6dtJDq5f/mg81pwCFd2NZyUt/gwZJGkQ7yq2rk+RDwYUn7StqfdEJg/Vi+C/ZaRNxOumr7PuAxYJSkE3KaoZLeAxARPyOdNH6B3Y3dqunreIXULYOc9zZSN5jPkxoWtawETpT07rzNYZIqd4WX5e9xGalhUS+99aCIeAl4UdIHctQFwDJJg4AxEbGE1M1sJFAZtzJR0j6SDiR1VVrdIds31ZW87nM5PKVEsWYAMyNieYk8Om6raAVp/wzpZH95lXSdWQxMyt8RSQfUSV923/4w6U7cPrnRdDZARLwM/FzSpLw9STomhw+LiEci4hrg16SeFtai3JhoLvcDQyQ9SrrFv5K0I/oK6QTsYdL4iZdy+iuBCXmw1Rbg8l4vsfU7EbGZdCB7rnAFrZbZpHq2kdQt5Kdd2Nx8UreDdtKB7jMR8atqeUbET0gnkO2krlAdD/jW/xwFrJK0HvgX4MsR8TtSY/Y6Se2k7ibvL6wzhzQOYS5AifTVzAK+pTTQtXJFezbwTEQ8WmvFiPg16QTwTkkbSF2WjsifvQ7cC5yRX2umt4a4CLg+/9bjSOMmBgO35/3KOmB6RPw2p98ALCEdV7+UG5ZFS0jdhtcrTRLwdeCrktZRpxeHpENI9fMS7R6EPaFGHh23VfQp4OL8vS4Arir7g+R9+7+SGlbtwI11Vim1b4+I1aTZpjaQ9ssb2X0e8kng0ry9zUBlMo3rlQZ1byI1kNrLfg/rf/wE7H6gMg4i35mYTxp4WHMWEjMz25PSrDnrIuI7fV0W6x2SppEmhfi3vi5Lf1U4D9mPdAd5ar74Y+YxE/3ENEmnk/oyPsCbB5WZmVkJktaS+oL/Y1+XxayfuVnSWNJ5yC1uSFiR70yYmZmZmVm3eMyEmZmZmZl1ixsTZmZmZmbWLW5MmJmZmZlZt7gxYWZmZmZm3eLGhJmZmZmZdYsbE2ZmZmZm1i3/D6bfqyMhN2EBAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Check for Correlation\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(work_df.corr(), annot=True, cmap='YlGnBu')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgkhkCssvMec"
      },
      "source": [
        "#### **Observation**: *Notably, there is a high correlatioin b/w Age & Hospitalization Charges*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "m423hr47jgbN",
        "outputId": "e0542769-0814-4aba-cc45-0c4c83396306"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4fccabf9-f305-470b-b80a-714ff91f7d0e\">\n",
              "    <div 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>viral load</th>\n",
              "      <th>severity level</th>\n",
              "      <th>hospitalization charges</th>\n",
              "      <th>age_bins</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0</td>\n",
              "      <td>42212</td>\n",
              "      <td>18-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>11.26</td>\n",
              "      <td>1</td>\n",
              "      <td>4314</td>\n",
              "      <td>0-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>11.00</td>\n",
              "      <td>3</td>\n",
              "      <td>11124</td>\n",
              "      <td>18-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>7.57</td>\n",
              "      <td>0</td>\n",
              "      <td>54961</td>\n",
              "      <td>18-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>9.63</td>\n",
              "      <td>0</td>\n",
              "      <td>9667</td>\n",
              "      <td>18-35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fccabf9-f305-470b-b80a-714ff91f7d0e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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",
              "    [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-4fccabf9-f305-470b-b80a-714ff91f7d0e 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-4fccabf9-f305-470b-b80a-714ff91f7d0e');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age     sex smoker     region  viral load  severity level  \\\n",
              "0   19  female    yes  southwest        9.30               0   \n",
              "1   18    male     no  southeast       11.26               1   \n",
              "2   28    male     no  southeast       11.00               3   \n",
              "3   33    male     no  northwest        7.57               0   \n",
              "4   32    male     no  northwest        9.63               0   \n",
              "\n",
              "   hospitalization charges age_bins  \n",
              "0                    42212    18-35  \n",
              "1                     4314     0-18  \n",
              "2                    11124    18-35  \n",
              "3                    54961    18-35  \n",
              "4                     9667    18-35  "
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "work_df['age_bins'] = pd.cut(x=work_df['age'], bins = [0, 18, 35, 50, 70, 100], labels = ['0-18', '18-35', '35-50', '50-70', '70-100'])\n",
        "work_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "cYtvtvZowUjq",
        "outputId": "d95eddf1-b508-45a4-fdd1-992808d46e68"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd3937f4640>"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lineplot(x='age', y='hospitalization charges', data=work_df, hue='sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "8if_sqf3wi6w",
        "outputId": "aaa73859-e987-4260-d8e8-9133b2399b14"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd393307b80>"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x='age_bins', y='hospitalization charges', data=work_df, hue='sex')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XK1Uimvmwrhs"
      },
      "source": [
        "#### **Observation**: *It is evident from the above graphs that hospitalization charges increase with the age and that too in women segment*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "2zcNUCPXxVsF",
        "outputId": "a3fc81d2-71a5-4a59-acc4-41468ed5f9ce"
      },
      "outputs": [
        {
          "data": {
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              "                                                     [key], {});\n",
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              "          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",
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              "  "
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            "text/plain": [
              "smoker                      no           yes\n",
              "region    sex                               \n",
              "northeast female  22013.256000  46492.823529\n",
              "          male    19633.533333  46155.153846\n",
              "northwest female  20019.523077  48740.166667\n",
              "          male    18420.714286  48740.666667\n",
              "southeast female  18421.461538  49618.000000\n",
              "          male    17718.819672  48649.357143\n",
              "southwest female  18842.235294  45495.000000\n",
              "          male    18778.569106  48505.833333"
            ]
          },
          "execution_count": 69,
          "metadata": {},
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      ],
      "source": [
        "work_df.groupby(['region', 'sex', 'smoker']).mean()['hospitalization charges'].unstack()"
      ]
    },
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              "smoker               no    yes\n",
              "region    sex                 \n",
              "northeast female  58222  60267\n",
              "          male    58104  56155\n",
              "northwest female  60149  59719\n",
              "          male    56234  59918\n",
              "southeast female  55481  60452\n",
              "          male    58908  55545\n",
              "southwest female  50444  56196\n",
              "          male    57707  58921"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "# Hospitalization charges max by region by sex and by smoking habit\n",
        "hosp_charge_max_by_region = work_df.groupby(['region', 'sex', 'smoker']).max()['hospitalization charges'].sort_values().unstack()\n",
        "hosp_charge_max_by_region"
      ]
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "smoker              no    yes\n",
              "region    sex                \n",
              "northeast female  5491  35709\n",
              "          male    4237  32074\n",
              "northwest female  5293  36779\n",
              "          male    4053  39545\n",
              "southeast female  4019  41444\n",
              "          male    2805  42608\n",
              "southwest female  4319  34611\n",
              "          male    3104  39552"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Hospitalization charges min by region by sex and by smoking habit\n",
        "hosp_charge_min_by_region = work_df.groupby(['region', 'sex', 'smoker']).min()['hospitalization charges'].sort_values().unstack()\n",
        "hosp_charge_min_by_region"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8G8UOOSJ33XO"
      },
      "source": [
        "### Hypothesis Testing\n",
        "\n",
        "#### Prove (or disprove) that the hospitalization of people who do smoking is greater than those who don't? (T-test Right tailed)"
      ]
    },
    {
      "cell_type": "code",
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          "height": 143
        },
        "id": "kQpKkUE9z-Wj",
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        {
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              "      <td>42830.0</td>\n",
              "      <td>47769.0</td>\n",
              "      <td>53103.50</td>\n",
              "      <td>60452.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56df7fc3-c791-4f40-aee8-0bf703fc9588')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
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              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56df7fc3-c791-4f40-aee8-0bf703fc9588 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-56df7fc3-c791-4f40-aee8-0bf703fc9588');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         count          mean           std      min      25%      50%  \\\n",
              "smoker                                                                  \n",
              "no      1012.0  19229.271739  11641.575233   2805.0   9692.0  17880.0   \n",
              "yes       99.0  47808.929293   6808.260660  32074.0  42830.0  47769.0   \n",
              "\n",
              "             75%      max  \n",
              "smoker                     \n",
              "no      27420.75  60149.0  \n",
              "yes     53103.50  60452.0  "
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Hypothesis 1 - Hospitalization of people who are smokers is greater than the non-smokers\n",
        "work_df.groupby('smoker')['hospitalization charges'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdElufZw4KIu"
      },
      "source": [
        "##### \n",
        "\n",
        "Null Hypothesis :  Mean Hospitalization charges of Smokers (m1) <= the Mean Hospitalization charges of nonsmokers (m2)\n",
        "\n",
        "Alternate Hypothesis: m1 > m2 (The average hospitalization charges of smokers is greater than that of nonsmokers)\n",
        "\n",
        "Decide the significance level. If P values is less than alpha, then 'fail to accept' the null hypothesis.\n",
        "\n",
        "Let's do a T-stat test. The > sign in alternate hypothesis indicate test is right tailed, which means all z values that would reject null hypothesis are in just one tail to the right of sampling distribution curve."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "050bm3Dv4ceH",
        "outputId": "56810988-c6d7-40cf-8292-8e19926460d5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "79681.65"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Mean value of Smokers and their hospitalization charges from a random sample\n",
        "smoker_sample = df[df['smoker']=='yes']['hospitalization charges'].sample(200)\n",
        "m1 = smoker_sample.mean()\n",
        "m1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkmcXwxb5UmM",
        "outputId": "d0161037-a763-4254-e8ea-9fc555a90784"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "18982.11"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Mean value of Non-smokers and their hospitalization charges from a random sample\n",
        "non_smoker_sample = df[df['smoker']=='no']['hospitalization charges'].sample(200)\n",
        "m2 = non_smoker_sample.mean()\n",
        "m2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbD0tK7f6JVW",
        "outputId": "b7a4755f-5e34-44f9-8332-c904bf6fea7f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Statistic = 27.088426845510426, P-Value = 2.149878888748719e-81, One Tail P-Value = 1.0749394443743594e-81\n",
            "We fail to accept the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "# If mean1 is > mean2 then alternate value is 'greater' else it is 'less' else if mean1 equal to mean2 then the value is 'two-sided'\n",
        "alpha = 0.05\n",
        "alt_val = [\"greater\" if m1>m2 else 'two-sided' if m1==m2 else \"less\"]\n",
        "t_stat, p_val = stats.ttest_ind(smoker_sample, non_smoker_sample, equal_var=False, alternative=alt_val[0])\n",
        "print(\"Test Statistic = {}, P-Value = {}, One Tail P-Value = {}\".format(t_stat, p_val, p_val_onetail))\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "D6nBYSB79Yb-",
        "outputId": "04a9c4dd-7cfe-4fad-f61b-2fb94c3854e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd38c1afd90>"
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(smoker_sample, color='green')\n",
        "sns.distplot(non_smoker_sample, color='red')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PsYe7ZMADTc"
      },
      "source": [
        "#### Prove (or disprove) with statistical evidence that the viral load of females is different from that of males (T-test Two tailed)\n",
        "\n",
        "##### \n",
        "\n",
        "Null Hypothesis : the viral load of females is different from that of males\n",
        "\n",
        "Alternate Hypothesis: the viral load of females is not different from that of males\n",
        "\n",
        "Decide the significance level. If P values is less than alpha, then 'fail to accept' the null hypothesis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_J9wvzdv6zgV",
        "outputId": "086c7517-8b02-44d9-de9f-73211811cffe"
      },
      "outputs": [
        {
          "data": {
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              "      <th>count</th>\n",
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              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
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              "      <th>sex</th>\n",
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              "      <th>female</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.952285</td>\n",
              "      <td>1.944578</td>\n",
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              "      <td>8.58</td>\n",
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              "      <th>male</th>\n",
              "      <td>542.0</td>\n",
              "      <td>10.003653</td>\n",
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              "      <td>5.32</td>\n",
              "      <td>8.58</td>\n",
              "      <td>9.935</td>\n",
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              "                                                     [key], {});\n",
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            ],
            "text/plain": [
              "        count       mean       std   min   25%    50%      75%    max\n",
              "sex                                                                  \n",
              "female  569.0   9.952285  1.944578  5.60  8.58  9.860  11.1800  15.11\n",
              "male    542.0  10.003653  1.931349  5.32  8.58  9.935  11.2975  15.14"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "work_df.groupby('sex')['viral load'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2sHE6Cx8KII",
        "outputId": "6937b3fd-3155-4852-e017-4a4bf18311e0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10.339899999999998"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "male_sample = work_df[work_df['sex']=='male']['viral load'].sample(500)\n",
        "male_mean = male_sample.mean()\n",
        "male_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKCJQo0E8egB",
        "outputId": "ee2640b2-38bd-42eb-d2a2-be31afc1ad0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10.04166"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "female_sample = work_df[work_df['sex']=='female']['viral load'].sample(500)\n",
        "fem_mean = female_sample.mean()\n",
        "fem_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOwkOK918oz5",
        "outputId": "f3661967-eb3b-4478-fbd1-de1810062747"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Statistic = 2.297856074924988, P-Value = 0.021776091551530378\n",
            "We fail to accept the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "# As the means of both the samples of male and female, let's do 'two-sided'.\n",
        "alpha = 0.05\n",
        "t_stat, p_val = stats.ttest_ind(male_sample, female_sample)\n",
        "print(\"Test Statistic = {}, P-Value = {}\".format(t_stat, p_val))\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "-NP_06hW9EGN",
        "outputId": "7c5e0566-0e71-466c-de31-5804858853b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd38c275520>"
            ]
          },
          "execution_count": 159,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(male_sample, color='green')\n",
        "sns.distplot(female_sample, color='red')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4ZiJiQiCCBt"
      },
      "source": [
        "#### **Observation**: *We fail to reject the Null hypothesis, which means gender doesn't have much effect on Viral load*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6THWs5J98LX"
      },
      "source": [
        "### Chi-Squared Test\n",
        "\n",
        "#### Is the proportion of smoking significantly different across different regions? (Chi-square)\n",
        "\n",
        "Both Smoker and Region are categorical variables\n",
        "\n",
        "#####\n",
        "\n",
        "Null Hypothesis : Smokers proportions is same across all regions\n",
        "\n",
        "Alternate Hypothesis: Smokers proportions is different across different regions\n",
        "\n",
        "If P values is less than alpha, then 'fail to accept' the null hypothesis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Py2FbH009N4H",
        "outputId": "62be87c6-88b4-4a58-8c71-4b8c5f7c3157"
      },
      "outputs": [
        {
          "data": {
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              "          const element = document.querySelector('#df-d0dcbbd7-8489-473c-936d-c6fc0b90bd11');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "smoker      no  yes\n",
              "region             \n",
              "northeast  257   67\n",
              "northwest  267   58\n",
              "southeast  273   91\n",
              "southwest  267   58"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "contingency = pd.crosstab(work_df.region, work_df.smoker)\n",
        "contingency"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzVONjeA-N5x",
        "outputId": "cc72f432-fca7-4a43-e842-1d649d3471f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi Square Statistic: 7.343477761407071, P-Value: 0.06171954839170541, Degree of Freedom: 3, Expected Frequency: [[257.65022422  66.34977578]\n",
            " [258.44544096  66.55455904]\n",
            " [289.45889387  74.54110613]\n",
            " [258.44544096  66.55455904]]\n",
            "We fail to reject the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "chi2, p_val, dof, exp_freq = chi2_contingency(contingency, correction=False)\n",
        "print(\"Chi Square Statistic: {}, P-Value: {}, Degree of Freedom: {}, Expected Frequency: {}\".format(chi2, p_val, dof, exp_freq))\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZO8266Yx_UDS"
      },
      "source": [
        "### ANOVA Test\n",
        "\n",
        "#### Is the mean viral load of women with 0 Severity level , 1 Severity level, and 2 Severity level the same? Explain your answer with statistical evidence (One way Anova)\n",
        "\n",
        "#####\n",
        "\n",
        "Null Hypothesis : The mean viral load of women with no severity level μ1, one severity level μ2 and two severity level μ3 are the same.  \n",
        "\n",
        "Alternate Hypothesis: μ1 or μ2 or μ3 may not be same.\n",
        "\n",
        "If P values is less than alpha, then 'fail to accept' the null hypothesis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "-juEi8tg-p1v",
        "outputId": "1f28c686-90fa-44c7-853c-9a3e0d49b14a"
      },
      "outputs": [
        {
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              "      <th>2</th>\n",
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              "  "
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            "text/plain": [
              "                count       mean       std   min     25%     50%      75%  \\\n",
              "severity level                                                              \n",
              "0               249.0  10.016426  1.962150  5.76  8.6300   9.870  11.3000   \n",
              "1               138.0   9.822609  1.854912  5.60  8.5850   9.655  11.0100   \n",
              "2                98.0   9.879184  2.012418  5.73  8.2550  10.010  11.1650   \n",
              "3                68.0  10.042353  1.968850  6.33  8.6075  10.035  11.3175   \n",
              "4                 9.0  10.681111  1.906321  8.53  9.6000   9.770  11.2300   \n",
              "\n",
              "                  max  \n",
              "severity level         \n",
              "0               14.92  \n",
              "1               15.11  \n",
              "2               14.45  \n",
              "3               14.90  \n",
              "4               13.82  "
            ]
          },
          "execution_count": 165,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "work_df[work_df['sex']=='female'].groupby('severity level')['viral load'].describe().head()"
      ]
    },
    {
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      "execution_count": null,
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        "id": "QuT248a-_hEC",
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              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>viral load</th>\n",
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              "      <th>0</th>\n",
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            "text/plain": [
              "    age     sex smoker     region  viral load  severity level  \\\n",
              "0    19  female    yes  southwest        9.30               0   \n",
              "5    31  female     no  southeast        8.58               0   \n",
              "6    46  female     no  southeast       11.15               1   \n",
              "9    60  female     no  northwest        8.61               0   \n",
              "11   62  female    yes  southeast        8.76               0   \n",
              "\n",
              "    hospitalization charges  \n",
              "0                     42212  \n",
              "5                      9392  \n",
              "6                     20601  \n",
              "9                     72308  \n",
              "11                    69522  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# female_severity_df = work_df[work_df['sex']=='female'].loc[work_df[work_df['sex']=='female']['severity level']<=2]\n",
        "female_severity_df = work_df[work_df['sex']=='female'].loc[work_df[work_df['sex']=='female']['severity level']<=2]\n",
        "female_severity_df.head()"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
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        "outputId": "b52f9278-b694-4d4a-a497-b4d75b899aa9"
      },
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P-Value is:  0.011093495413661003\n",
            "We fail to accept the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "# Shapiro-Wilk’s test - Let's test null hypothesis: viral load follows normal distribution against the alternative hypothesis: viral load doesn't follow normal distribution\n",
        "w, p_val = shapiro(female_severity_df['viral load'])\n",
        "print(\"P-Value is: \", p_val)\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9t67Wg8fFeuR"
      },
      "source": [
        "#### **Observation**: *Null hypothesis fails, which means it doesn't follow normal distribution. This can be validated by EDA as follows*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "zNu4TMYVCOJW",
        "outputId": "9a067ab9-6260-42e7-d623-5a5992738331"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd38bc7c4f0>"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(female_severity_df['viral load'], bins=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LIrYCSMbCUoI",
        "outputId": "9b001882-daba-46f7-8a78-a1e4dfb23902"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd38ba7bc10>"
            ]
          },
          "execution_count": 181,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(np.log(female_severity_df['viral load']), bins=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJi_UklIClqo",
        "outputId": "73d83204-d693-44dd-fa6e-c45df14fe672"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P-Value is:  0.006363806780427694\n",
            "We fail to accept the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "# For the same above Ho and H1, let's do a log normal test and check the p-value. \n",
        "w, p_val = shapiro(np.log(female_severity_df['viral load']))\n",
        "print('P-Value is: ', p_val)\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u917WncJGIhs"
      },
      "source": [
        "#### **Observation**: *Even the log normal test fails the Ho, which means the distribution is not normal*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "pwwBZuVcwszr",
        "outputId": "4caa2218-b6a9-4d93-c90a-cc84dae5f3d8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "transformed_data, best_lambda = stats.boxcox(work_df['viral load']) \n",
        "sns.distplot(transformed_data, hist=False, kde=True, label = \"TransformedData\") \n",
        "sns.distplot(work_df['viral load'], hist=False, kde=True, label = \"Non Transformed Data\") \n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a_TIZ_0428z",
        "outputId": "948735c9-e2e7-4e46-c3b7-29432965b75b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P-Value is:  0.34881141781806946\n",
            "We fail to reject the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "tstat, p_val = stats.shapiro(transformed_data)\n",
        "print('P-Value is: ', p_val)\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHkTg-VI56a-"
      },
      "source": [
        "#### **Observation**: *Boxcox transformation technique for viral load indicates p-value > alpha. So distribution is Normal on transformed data*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_x1z2WYGpNI"
      },
      "source": [
        "#### Levene’s test\n",
        "\n",
        "Null hypothesis : All the viral load variances are equal\n",
        "\n",
        "Alternative hypothesis : At least one variance is different from the rest."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wCwublDDQVk",
        "outputId": "bf6b309b-f9bf-4b79-c83f-16136e316f16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P-Value is:  0.1716618340538876\n",
            "We fail to reject the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "# Levene's test\n",
        "statistics, p_val = levene(female_severity_df[female_severity_df['severity level']==0]['viral load'].sample(100), \n",
        "                           female_severity_df[female_severity_df['severity level']==1]['viral load'].sample(100),\n",
        "                           female_severity_df[female_severity_df['severity level']==2]['viral load'].sample(100, replace=True))\n",
        "print('P-Value is: ', p_val)\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZH9rUMz8ZtF-",
        "outputId": "7f78295a-e859-460f-93ac-6a8481e46bdf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P-Value is:  0.31898469765817655\n",
            "We fail to reject the null hypothesis\n"
          ]
        }
      ],
      "source": [
        "test_stat, p_val = f_oneway(female_severity_df[female_severity_df['severity level']==0]['viral load'].sample(100), \n",
        "                            female_severity_df[female_severity_df['severity level']==1]['viral load'].sample(100),\n",
        "                            female_severity_df[female_severity_df['severity level']==2]['viral load'].sample(100, replace=True))\n",
        "print('P-Value is: ', p_val)\n",
        "if p_val > alpha:\n",
        "  print(\"We fail to reject the null hypothesis\")\n",
        "else:\n",
        "  print(\"We fail to accept the null hypothesis\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GCc6XtIHNc7"
      },
      "source": [
        "#### **Observation**: *P-Value is high, thus Null Hypothesis is true, indicating Means are equal for all severity levels.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMW4i95yoeU_"
      },
      "source": [
        "# Observations & Insights\n",
        "\n",
        "\n",
        "\n",
        "*   There is no missing or duplicate value in the given dataset\n",
        "*   Age 18 & 19 have more patients and the rest of the ages have equal distribution\n",
        "*   Interestingly Non-Smoker patients count is more than the Smokers count\n",
        "*   Southeast region has more patients and the rest of the regions have equal distribution\n",
        "*   Good to see less number of patients with 'Severity Level' high\n",
        "*   There are outliers in 'viral load' and 'hospitalization charges' columns\n",
        "*   There is a high correlatioin b/w Age & Hospitalization Charges. As the age increases, hospitalization charges also increases\n",
        "*   Also, hospitalization seem to increase with the age, and more specifically for women   \n",
        "*   Female patients seems to be little more vulnerable to viral load than Male patients\n",
        "*   Incidently non-smokers tend to have high viral load than smokers\n",
        "*   Amongst regions, Southeast is impacted high viral load than other regions\n",
        "*   Male patients tend to incur high charges\n",
        "*   Evidently Smokers are paying more than the non-smokers\n",
        "*   Southeast region has higher hospitalization charges amongst regions\n",
        "*   Hospitalization charges increase with age and that too in women segment\n",
        "\n",
        "**Observations from Hypothesis Testing:**\n",
        "\n",
        "*   Hospitalization Charges for smokers is higher than for non-smokers\n",
        "*   Viral Load in Females is more compared to Males\n",
        "*   Proportion of smoking is almost similar across the regions\n",
        "*   Viral Load of women with Severity levels - 0,1,2 are more or less same\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HB1HbMLXqbOi"
      },
      "source": [
        "# Recommendations\n",
        "\n",
        "Based on the above observations and insights, some of the recommendations to consider are: \n",
        "*   It is alarming to see high number of patients at lower age group 18-20. It's high time to role out stable nutrition programs for children & teens.\n",
        "*   Notably Southeast region is impacted with high viral load and increased hospitalization charges. Special attention needs to be given in this region\n",
        "*   As age has direct proportion to the increased hospitalization charges, Apollo can come up with targeted healthcare policy & programs for higher age groups\n",
        "*   Female gender group requires specific, focused attention \n",
        "*   Special medi-claim policy can be launced for Smokers "
      ]
    }
  ],
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