Medical_Cost.ipynb

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        "The following data pre-processing steps may be performed:\n",
        "\n",
        "Check for duplicates\n",
        "Check for missing values\n",
        "Check for outliers/irregular entries\n",
        "Treat records and attributes that are flagged in the pre-processing steps.\n",
        "\n",
        "The following analysis may be of interest:\n",
        "\n",
        "Distribution of insurance subscribers’ age and bmi : summary statistics and graphs\n",
        "Distribution of medical cost billed by the insurance company: summary statistics and graph\n",
        "Testing hypothesis: For example, on average, medical cost is different for subscribers in South-East and South-West regions.\n",
        "ANOVA: Is there a difference in the average medical cost charged to insurance subscribers in the four regions?"
      ],
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      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/cmich-stats-club/spring2024-dac/main/datasets/medical-cost.csv\")"
      ],
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        "read the data"
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      "source": [
        "df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "display the data info"
      ],
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      "source": [
        "duplicateRows = df[df.duplicated()]\n",
        "duplicateRows"
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            "text/plain": [
              "     age   sex    bmi  children smoker     region    charges\n",
              "581   19  male  30.59         0     no  northwest  1639.5631"
            ],
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              "summary": "{\n  \"name\": \"duplicateRows\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 19,\n        \"max\": 19,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 30.59,\n        \"max\": 30.59,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          30.59\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"northwest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1639.5631,\n        \"max\": 1639.5631,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1639.5631\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "check duplicate rows"
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        "remove duplicate row"
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      "source": [
        "# check outliers\n",
        "import seaborn as sns\n",
        "\n",
        "# age\n",
        "min_age = df['age'].min()\n",
        "max_age = df['age'].max()\n",
        "print('min age: ',min_age, '\\nmax age: ' ,max_age)\n",
        "\n",
        "# sex\n",
        "sex_check = df['sex'].value_counts()\n",
        "print(sex_check)\n",
        "\n",
        "# bmi\n",
        "min_bmi = df['bmi'].min()\n",
        "max_bmi = df['bmi'].max()\n",
        "print('min bmi: ',min_age, '\\nmax bmi: ' ,max_age)\n",
        "\n",
        "# children\n",
        "sns.histplot(df['children'])\n",
        "\n"
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          "text": [
            "min age:  18 \n",
            "max age:  64\n",
            "male      675\n",
            "female    662\n",
            "Name: sex, dtype: int64\n",
            "min bmi:  18 \n",
            "max bmi:  64\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<Axes: xlabel='children', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check the outliers in about age, sex, bmi, children, the result seem ok"
      ],
      "metadata": {
        "id": "OLG3e2KReOs8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "1-itYXOzeLrW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# smoker\n",
        "smoker_check = df['smoker'].value_counts()\n",
        "print(smoker_check)\n",
        "\n",
        "# region\n",
        "region_check = df['region'].value_counts()\n",
        "print(region_check)\n",
        "\n",
        "# charges\n",
        "sns.boxplot(x='charges', data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "bBDnX9PqXd3W",
        "outputId": "8c86d129-6cfe-46b8-da08-158e139f1faf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "no     1063\n",
            "yes     274\n",
            "Name: smoker, dtype: int64\n",
            "southeast    364\n",
            "southwest    325\n",
            "northwest    324\n",
            "northeast    324\n",
            "Name: region, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='charges'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r1DnPZ3Fe7kE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "check the outliers in smoker, region and charges.\n",
        "Many outliers are observe, most of the outliers are betweeen 35,000 and 50,000 and the rest are below 60,000. It makes sense in real world as there are medical bills as high as 60,000."
      ],
      "metadata": {
        "id": "iQk-exbCeco1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create a plot to find the correlation between 'age' and  'bmi'\n",
        "\n",
        "sns.scatterplot(data=df, x=\"age\", y=\"bmi\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "q5mdUbVwfMB-",
        "outputId": "0734e698-e4ef-4ecb-c51d-1a942f98e15e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='bmi'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the average value of 'charges' group by 'region'\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "average_by_region = df.groupby('region')['charges'].mean().reset_index()\n",
        "sns.barplot(data=average_by_region,x='region',y='charges')\n",
        "plt.title('Average bill cost by region')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "tSyy174lf0kX",
        "outputId": "bc44b15f-d234-40dd-c310-3a76d33c87a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_df = pd.DataFrame({\n",
        "    'ne_charge': df[df['region']=='northeast']['charges'],\n",
        "    'nw_charge': df[df['region']=='northwest']['charges'],\n",
        "    'se_charge': df[df['region']=='southeast']['charges'],\n",
        "    'sw_charge': df[df['region']=='southwest']['charges']\n",
        "})\n",
        "result_df.info()\n",
        "# Checking for significant differences among the columns\n",
        "cv = result_df.describe().T['std']/ result_df.describe().T['mean']\n",
        "print(\"coefficient of variance:\")\n",
        "print(cv)"
      ],
      "metadata": {
        "id": "iAcbnU-vh-1t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c0ad6f4-54d4-4a93-d921-17f85fb871af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1338 entries, 0 to 1337\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   ne_charge  324 non-null    float64\n",
            " 1   nw_charge  325 non-null    float64\n",
            " 2   se_charge  364 non-null    float64\n",
            " 3   sw_charge  325 non-null    float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 52.3 KB\n",
            "coefficient of variance:\n",
            "ne_charge    0.839585\n",
            "nw_charge    0.891662\n",
            "se_charge    0.948131\n",
            "sw_charge    0.936036\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6ux2SJB2o-XE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}