Day 10.ipynb

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        "## 1. INTRODUCTION\n",
        "\n",
        "In this study, I will give you an example of EDA (Exploratory Data analysis) and I will make an analysis of the various Regression algorithms in Machine Learning. In this analysis, I will use the \"Medical Cost Personal Datasets\" dataset. In this dataset, 1338 people have anonymous information. In addition, annual insurance premiums given to these people are also included in the dataset. We will create and test regression models that estimate their annual premiums based on their other information. Then we will discuss the results and see which algorithm is successful. We will also perform visualization using various data in dataset.\n",
        "\n",
        "\n",
        "1. INTRODUCTION\n",
        "2. USED LIBRARIES\n",
        "3. DATA EXPLORATION\n",
        "    * 3.1. Detailed Information of the Dataset\n",
        "    * 3.2. Correlation of Columns(Attributes)\n",
        "    * 3.3. Various Visualizations from Dataset\n",
        "4. DATA PREPARATION AND CLEANING\n",
        "    * 4.1. Drop Irrelevant Columns\n",
        "    * 4.2. Correction of Column(Attribute) Names\n",
        "    * 4.3. Split Data and Target\n",
        "    * 4.4. Handling Missing Values\n",
        "    * 4.5. Type Conversions and Encoding\n",
        "    * 4.6. Preparation of Test and Train Data\n",
        "    * 4.7. Normalization of Data\n",
        "* 5. BUILDING MODELS\n",
        "    * 5.1. Multiple Linear Regression\n",
        "    * 5.2. Polynomial Regression\n",
        "* 6. EVALUATING MODELS\n",
        "    * 6.1. Evaluating Multiple Linear Regression Model\n",
        "    * 6.2. Evaluating Polynomial Regression Model\n",
        "* 7. EXPLORATION OF RESULTS\n",
        "* 8. CONCLUSION"
      ]
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      "source": [
        "## 2. Importing LIBRARIES\n",
        "\n"
      ]
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      "source": [
        "import numpy as np  # Importing NumPy library\n",
        "import pandas as pd  # Importing Pandas library\n",
        "import matplotlib.pyplot as plt  # Importing Matplotlib library's \"pyplot\" module\n",
        "import seaborn as sns  # Imorting Seaborn library"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
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        "id": "FLxnXmUDnwRq",
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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      "source": [
        "<a id=\"3\"></a> <br>\n",
        "## 3. DATA EXPLORATION\n",
        "\n",
        "In this section, various explanations will be made about dataset."
      ]
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      "source": [
        "<a id=\"31\"></a> <br>\n",
        "### 3.1. Detailed Information of the Dataset\n",
        "\n",
        "Here we will import the dataset first. Then, we will explain the columns(features) of the dataset one by one."
      ]
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        "outputId": "8fe7a8ab-3ed1-43d8-d028-f7f1c9ef8ef7"
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/My Drive/Corizo/Class 10 27052024/insurance.csv')  # Read CSV file and load into \"data\" variable\n",
        "data.info()  # Show detailed information for dataset columns(attributes)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
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      "source": [
        "data.head()"
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        {
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          "data": {
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236694001,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "As you can see from the output here, there are 1338 rows so record. There are also 7 columns(attributes). Fortunately, our dataset doesn't have any missing values. In other words, all the columns of all rows are filled with data. The rows are also indexed from 0 to 1337. Now, let's explain what the columns mean:\n",
        "\n",
        "* **age :** Indicates the age of the person. It contains data of type \"_int64_\".\n",
        "* **sex :** It refers to the gender of the person. It contains \"_object_\" type data.\n",
        "* **bmi :** It refers to the Body Mass Index of the person and contains the data of type \"_float64_\". BMI is a measure of the weight of a person, divided by the square of its length. Determines the person's obesity value. The formula for USA and METRIC units is as follows:\n",
        "![](https://i2.wp.com/www.marathonnewbie.com/wp-content/uploads/2016/07/body-mass-index-formula.jpg)\n",
        "* **children :** It refers to the number of children that a person has. It contains data of type \"_int64_\".\n",
        "* **smoker :** Indicates whether the person smokes or not. It contains \"_object_\" type data.\n",
        "* **region :** Specifies which region the person is from. It contains \"_object_\" type data.\n",
        "* **charges :** The person's total insurance premium is specified. Although not specified, it is assumed to be in dollars ($). It contains \"__float64__\" type data.\n",
        "\n",
        "We gave the necessary information about dataset. Now, looking at the first 5 and last 5 entries of dataset, what are the values that are being held:"
      ]
    },
    {
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        "data.head()  # Print first 5 entry of the dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ],
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"southeast\",\n          \"northeast\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12110.011236694001,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 1337,\n        \"samples\": [\n          8688.85885,\n          5708.867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 27
        }
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        "data.tail()  # Prints last 5 entries of the dataset"
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              "      age     sex    bmi  children smoker     region     charges\n",
              "1333   50    male  30.97         3     no  northwest  10600.5483\n",
              "1334   18  female  31.92         0     no  northeast   2205.9808\n",
              "1335   18  female  36.85         0     no  southeast   1629.8335\n",
              "1336   21  female  25.80         0     no  southwest   2007.9450\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-91696cdb-4a93-4767-85f4-011e2ee4b6c5 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-91696cdb-4a93-4767-85f4-011e2ee4b6c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-404a320f-1465-489a-ba4b-7eec098b4eee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-404a320f-1465-489a-ba4b-7eec098b4eee')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-404a320f-1465-489a-ba4b-7eec098b4eee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 18,\n        \"max\": 61,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          18,\n          61,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.055991863897165,\n        \"min\": 25.8,\n        \"max\": 36.85,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          31.92,\n          29.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"northeast\",\n          \"southwest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11806.083521514944,\n        \"min\": 1629.8335,\n        \"max\": 29141.3603,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2205.9808,\n          29141.3603\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "82d8e27fbec20d7aba92544b45797301ffb2f23d",
        "id": "bNFkRMGUmTTP"
      },
      "cell_type": "markdown",
      "source": [
        "There is no problem with numeric data. However, we may need to apply numerical transformation in the future for categorical data of type \"object\". Now let's see the various statistics about the numeric data:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f5d6150dcbf545249d9bda8a674c0b83b2ceae65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5kMPau35mTTP",
        "outputId": "20bffb20-35ea-4e09-ecfc-5a21f8546d37"
      },
      "cell_type": "code",
      "source": [
        "data.describe()  # Print table which contain statistical data of the dataset"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b73431d-c2d6-4b74-8b60-9fcc9683f282\" 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>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
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              "        document.querySelector('#df-8b73431d-c2d6-4b74-8b60-9fcc9683f282 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-8b73431d-c2d6-4b74-8b60-9fcc9683f282');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3b98a72-1dba-4d02-8f58-06333c34d2e8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 460.6106090399993,\n        \"min\": 14.049960379216154,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.20702541106129,\n          39.0,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 463.29524977918294,\n        \"min\": 6.098186911679014,\n        \"max\": 1338.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.66339686098655,\n          30.4,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 472.5368318870757,\n        \"min\": 0.0,\n        \"max\": 1338.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1338.0,\n          1.0949177877429,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20381.922846226596,\n        \"min\": 1121.8739,\n        \"max\": 63770.42801,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13270.422265141257,\n          9382.033,\n          1338.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "e4f961893a29bb820ae2d0f021eea7937cfbd4d2",
        "id": "HLWIf62XmTTP"
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      "cell_type": "markdown",
      "source": [
        "<a id=\"32\"></a> <br>\n",
        "### 3.2. Correlation of Columns(Attributes)\n",
        "\n",
        "In this section, we'll find the correlation matrix between the columns and we'll visualize it into a Heatmap. In this way, we will be able to see the relationship between the attributes more clearly and visualize them in the future."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4227a731f2e063135e1de65a116a33360006bb4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "UJaE0gSxmTTP",
        "outputId": "b36ee86b-00a2-45d4-c48b-0ba74d3e674c"
      },
      "cell_type": "code",
      "source": [
        "corr = data[['age','bmi','children','charges']].corr()  # Prints correlation matrix for the numerical columns\n",
        "corr"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age       bmi  children   charges\n",
              "age       1.000000  0.109272  0.042469  0.299008\n",
              "bmi       0.109272  1.000000  0.012759  0.198341\n",
              "children  0.042469  0.012759  1.000000  0.067998\n",
              "charges   0.299008  0.198341  0.067998  1.000000"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corr",
              "summary": "{\n  \"name\": \"corr\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43855126689828755,\n        \"min\": 0.04246899855884958,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.1092718815485351,\n          0.299008193330648,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45298891937204494,\n        \"min\": 0.012758900820673994,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.19834096883362912,\n          0.1092718815485351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4799933789762917,\n        \"min\": 0.012758900820673994,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.012758900820673994,\n          0.06799822684790495,\n          0.04246899855884958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"charges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.416649607535863,\n        \"min\": 0.06799822684790495,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.19834096883362912,\n          1.0,\n          0.299008193330648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 7
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      "cell_type": "markdown",
      "source": [
        "Correlation is a number that indicates how the two attributes are related to each other. As this number approaches 1.0, the relationship is strengthened in the right direction. As it approaches -1.0, it is strengthened in the opposite direction. If this value is close to zero, the bond between the two data is weak. For example in the above matrix, we see a little (but no more) bound with person's age and charge values. Other bounds are so weak. Now we visualize this correlation matrix with Heatmap:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "3064edd99f90aeb67e1115c6bc8087c7eeb4cf2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "h9FzVi71mTTQ",
        "outputId": "44ea9a18-c7d7-453a-a300-52791a16e535"
      },
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(figsize=(8, 8))  # This method creates a figure and a set of subplots\n",
        "sns.heatmap(data=corr, annot=True, linewidths=.5, ax=axes)  # Figure out heatmap\n",
        "# Parameters:\n",
        "# data : 2D data for the heatmap.\n",
        "# annot : If True, write the data value in each cell.\n",
        "# linewidths : Width of the lines that will divide each cell.\n",
        "# ax : Axes in which to draw the plot, otherwise use the currently-active Axes.\n",
        "plt.show()  # Shows only plot and remove other informations"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "170623b96b3d1b0ec8ce219b9dcd3ca2500c8cc1",
        "id": "_cqZF8KPmTTQ"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"33\"></a> <br>\n",
        "### 3.3. Various Visualizations from Dataset\n",
        "\n",
        "In this section, we will make various visualizations related to dataset and after understanding the connection between the data in dataset we will move to the next section. Let's look at the distribution of numerical attributes:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6229a6f97739c33091af89779b86757afe03ec2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "lCriWZxamTTQ",
        "outputId": "a9eb2bb8-1f85-4f76-9810-a9efc000ea31"
      },
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))\n",
        "data.plot(kind=\"hist\", y=\"age\", bins=70, color=\"b\", ax=axes[0][0])\n",
        "data.plot(kind=\"hist\", y=\"bmi\", bins=200, color=\"r\", ax=axes[0][1])\n",
        "data.plot(kind=\"hist\", y=\"children\", bins=5, color=\"g\", ax=axes[1][0])\n",
        "data.plot(kind=\"hist\", y=\"charges\", bins=200, color=\"orange\", ax=axes[1][1])\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "a23516f5adad2c025209c051f9d02af62309a218",
        "id": "pzKy2IL8mTTQ"
      },
      "cell_type": "markdown",
      "source": [
        "We can make inferences from these images. For example, the number of children who have no children in the dataset is more than the others. In addition, the total charge amount usually looks less than 20000. Now look at the male and female numbers in dataset:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5e4a9819503653d501924ac71c64a1e358f89343",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "47eUUTOBmTTQ",
        "outputId": "63f4ad6c-5c75-4986-c31c-414803905639"
      },
      "cell_type": "code",
      "source": [
        "sns.catplot(x=\"sex\", kind=\"count\", palette=\"Set1\", data=data)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-3c7ca644ef46>:1: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.catplot(x=\"sex\", kind=\"count\", palette=\"Set1\", data=data)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x78eb20c62290>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "331308eff00ab2a847decd52920cf9ba032eda5a",
        "id": "07nhSj6RmTTQ"
      },
      "cell_type": "markdown",
      "source": [
        "Since the value we will examine as an output here is \"_charges_\", we need to examine the relationship of the other columns with it. To do this, let's draw Scatter Plots between the numeric columns and the \"_charges_\" column:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4faadb467d75e86fe0b52458491d5e88141f2101",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "vfDGipqomTTQ",
        "outputId": "fef2cae5-1d22-4563-fa0f-94cceaf2367b"
      },
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))\n",
        "data.plot(kind='scatter', x='age', y='charges', alpha=0.5, color='green', ax=axes[0], title=\"Age vs. Charges\")\n",
        "data.plot(kind='scatter', x='bmi', y='charges', alpha=0.5, color='red', ax=axes[1], title=\"Sex vs. Charges\")\n",
        "data.plot(kind='scatter', x='children', y='charges', alpha=0.5, color='blue', ax=axes[2], title=\"Children vs. Charges\")\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "ea0dcc844ddf5e98c7fac10492dc19769795f231",
        "id": "EhGYS3BdmTTR"
      },
      "cell_type": "markdown",
      "source": [
        "Finally, look at the distribution of smokers and non-smokers in the BMI vs. Charges Scatter Plot:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "24fd908016a9de761e95a1195dfab9872ecd6b2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "mNC1EzTlmTTR",
        "outputId": "c0cd9c56-f1d7-4e4a-d03b-5eb0ceed99ba"
      },
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=\"bmi\", y=\"charges\", data=data, palette='Set2', hue='smoker')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='bmi', ylabel='charges'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "682701fedd3c0c742710b6573684271dba6d910a",
        "id": "oQjdM9YGmTTR"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"4\"></a> <br>\n",
        "## 4. DATA PREPARATION AND CLEANING\n",
        "\n",
        "In this section, we will be able to use various preprocesses in order to use the data correctly. First, let us explain what data will be used as a target. Here we will use \"age\", \"sex\", \"bmi\", \"children\" and \"smoker\" columns as data ie X. We will use \"charges\" column as the target ie Y. We will drop the \"region\" column from this dataset. Because we haven't done much analysis about it, so we can slow down our process. Now, let's do the related operations one by one."
      ]
    },
    {
      "metadata": {
        "_uuid": "5d07f27fd69d0ca53f35e1aa3868325d39e25686",
        "id": "L-5SGbSqmTTR"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"41\"></a> <br>\n",
        "### 4.1. Drop Irrelevant Columns\n",
        "\n",
        "In this section, we will delete the columns that will not work for us from the dataset. Here is the only one we will not use the column is \"_region_\", so we just need to delete it:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4b572331a805fb1aa6d5630258880cef1d565a04",
        "id": "l7RSKMcymTTR"
      },
      "cell_type": "code",
      "source": [
        "data.drop([\"region\"], axis=1, inplace=True)  # Drop \"region\" column from dataset"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "f6176b8be81cb31a37cb31022a6d77ff3c01592b",
        "id": "8VomNaljmTTR"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"42\"></a> <br>\n",
        "### 4.2. Correction of Column(Attribute) Names\n",
        "\n",
        "In this section we will use more readable column names instead existing column names if there are spaces and unwanted characters. There is no problem with column names in the dataset we use. But in order to show how the process is done, I'll change them all to the name I want and all to be capitalized:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0aec70fb71194d03754c6e876a744dad149f4f34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4gu8TJImTTS",
        "outputId": "f9c0d2f0-012c-4f7f-8c06-862dc811c830"
      },
      "cell_type": "code",
      "source": [
        "data.rename(columns={\"age\" : \"AGE\", \"sex\" : \"GENDER\", \"bmi\" : \"BMI\", \"children\" : \"CHILDREN\", \"smoker\": \"SMOKER\", \"charges\" : \"CHARGES\"}, inplace=True)\n",
        "data.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['AGE', 'GENDER', 'BMI', 'CHILDREN', 'SMOKER', 'CHARGES'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "883ae46c54dccf4914284b2b5c9136eecc3e02c3",
        "id": "HCker33jmTTS"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"43\"></a> <br>\n",
        "### 4.3. Split Data and Target\n",
        "\n",
        "We can't give the whole dataset to the model as it is. First we need to set the data and target part of it. Here, the data part is called X, while the target part is called Y. Now split the data and target partitions and assign each of them to variables named X and Y:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "341525c8aa03ec1e5617844f584edc4eb7628e9c",
        "id": "_vyGAdNlmTTS"
      },
      "cell_type": "code",
      "source": [
        "X = data.drop([\"CHARGES\"], axis=1)  # Put all data (except \"__CHARGES__\" column) to the X variable\n",
        "y = data.CHARGES.values  # Put only \"__CHARGES__\" column to the Y variable"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "edd0c97743b68cd004a9c7bda6280adb6e099c84",
        "id": "LuT9LkqOmTTS"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"44\"></a> <br>\n",
        "### 4.4. Handling Missing Values\n",
        "\n",
        "There is no missing value in this dataset. Therefore, we do not have any action here."
      ]
    },
    {
      "metadata": {
        "_uuid": "2a0b2e25f120512b6e66d19a41761909f0400054",
        "id": "zFb3DeR_mTTS"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"45\"></a> <br>\n",
        "### 4.5. Type Conversions and Encoding\n",
        "\n",
        "Here we will convert categorical data into numeric data. In our dataset, categorical columns are gender and smoking. We will convert them numerically:\n",
        "\n",
        "* **GENDER :** If \"male\" is 0, \"female\" is 1.\n",
        "* **SMOKER :** If \"no\" is 0, \"yes\" is 1."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a56392ab8349b7644aaaa2c1f2edb1f09edeb5a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ighANioJmTTS",
        "outputId": "6f6fcf99-3f68-4639-b3c9-c323bd8671b6"
      },
      "cell_type": "code",
      "source": [
        "X.GENDER = [1 if each == \"female\" else 0 for each in X.GENDER]\n",
        "X.SMOKER = [1 if each == \"yes\" else 0 for each in X.SMOKER]\n",
        "X.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AGE  GENDER     BMI  CHILDREN  SMOKER\n",
              "0   19       1  27.900         0       1\n",
              "1   18       0  33.770         1       0\n",
              "2   28       0  33.000         3       0\n",
              "3   33       0  22.705         0       0\n",
              "4   32       0  28.880         0       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56e265a5-e621-4a7b-b697-1a280ebf66e2\" 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>GENDER</th>\n",
              "      <th>BMI</th>\n",
              "      <th>CHILDREN</th>\n",
              "      <th>SMOKER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56e265a5-e621-4a7b-b697-1a280ebf66e2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-56e265a5-e621-4a7b-b697-1a280ebf66e2 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-56e265a5-e621-4a7b-b697-1a280ebf66e2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-69572446-78e9-4421-bdbc-d8bc73d77279\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69572446-78e9-4421-bdbc-d8bc73d77279')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69572446-78e9-4421-bdbc-d8bc73d77279 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 1338,\n  \"fields\": [\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          21,\n          45,\n          36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.098186911679014,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 548,\n        \"samples\": [\n          23.18,\n          26.885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHILDREN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "98e70ea069a6525855eaca9e6e3cd5bdff03388e",
        "id": "MsgsajzDmTTT"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"47\"></a> <br>\n",
        "### 4.6. Preparation of Test and Train Data\n",
        "\n",
        "The final process here is the smooth and random separation of test and train data. For this, we will benefit from the method named \"_train_test_split_\" from the Scikit-Learn library. I would like to use 20% of our data for testing and 80% for training purposes. The process is very simple:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "9f32638e7f88b35e63099580c1da0da263c93795",
        "id": "w9PgQtJsmTTT"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Import \"train_test_split\" method\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "# Parameters:\n",
        "# test_size : It decides how many test data in percentage.\n",
        "# random_state : This parameter can take any value. This value decides randomness seed."
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cGFFSGkHqBe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QAevlL8VqZ1l",
        "outputId": "2c642b49-cc15-411a-f184-6bbdae06abe5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      AGE  GENDER    BMI  CHILDREN  SMOKER\n",
              "560    46       1  19.95         2       0\n",
              "1285   47       1  24.32         0       0\n",
              "1142   52       1  24.86         0       0\n",
              "969    39       1  34.32         5       0\n",
              "486    54       1  21.47         3       0"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c9cbb4b-0d76-46ba-8923-bccd9b6d4033')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3c9cbb4b-0d76-46ba-8923-bccd9b6d4033 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-3c9cbb4b-0d76-46ba-8923-bccd9b6d4033');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-58fd9915-2e9d-4d20-9a3b-6cd6946b987f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58fd9915-2e9d-4d20-9a3b-6cd6946b987f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-58fd9915-2e9d-4d20-9a3b-6cd6946b987f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 1070,\n  \"fields\": [\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 64,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          35,\n          33,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.043385672727495,\n        \"min\": 15.96,\n        \"max\": 53.13,\n        \"num_unique_values\": 494,\n        \"samples\": [\n          27.3,\n          31.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHILDREN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "3b37475af2a9091c6fdd8207a0003006c233e16a",
        "id": "4ENwfbfDmTTT"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"46\"></a> <br>\n",
        "### 4.7 Normalization of Data\n",
        "\n",
        "The values of the data may be so far from each other. This can sometimes lead to undesirable situations in regression algorithms. Therefore, we need to normalize the data."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1033622967a2bb2d67b040d6a50a4459afed65b0",
        "id": "ay0gR0_imTTT"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Initialize the MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Fit the scaler to the 'bmi' column in x_train and transform it\n",
        "x_train['BMI'] = scaler.fit_transform(x_train[['BMI']])\n",
        "\n",
        "# Transform the 'bmi' column in x_test using the same scaler\n",
        "x_test['BMI'] = scaler.transform(x_test[['BMI']])\n",
        "\n",
        "# Fit the scaler to the 'bmi' column in x_train and transform it\n",
        "x_train['AGE'] = scaler.fit_transform(x_train[['AGE']])\n",
        "\n",
        "# Transform the 'bmi' column in x_test using the same scaler\n",
        "x_test['AGE'] = scaler.transform(x_test[['AGE']])\n",
        "\n",
        "# Fit the scaler to the 'bmi' column in x_train and transform it\n",
        "x_train['CHILDREN'] = scaler.fit_transform(x_train[['CHILDREN']])\n",
        "\n",
        "# Transform the 'bmi' column in x_test using the same scaler\n",
        "x_test['CHILDREN'] = scaler.transform(x_test[['CHILDREN']])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NZy3NEltqn3j",
        "outputId": "76e05c9a-cc1a-43da-8fcd-6d31747fa97e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           AGE  GENDER       BMI  CHILDREN  SMOKER\n",
              "560   0.608696       1  0.107345       0.4       0\n",
              "1285  0.630435       1  0.224913       0.0       0\n",
              "1142  0.739130       1  0.239440       0.0       0\n",
              "969   0.456522       1  0.493947       1.0       0\n",
              "486   0.782609       1  0.148238       0.6       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0ec6216-c374-46b6-9bfe-d1fa5663032e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>GENDER</th>\n",
              "      <th>BMI</th>\n",
              "      <th>CHILDREN</th>\n",
              "      <th>SMOKER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>560</th>\n",
              "      <td>0.608696</td>\n",
              "      <td>1</td>\n",
              "      <td>0.107345</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
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              "      <th>1285</th>\n",
              "      <td>0.630435</td>\n",
              "      <td>1</td>\n",
              "      <td>0.224913</td>\n",
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              "      <th>1142</th>\n",
              "      <td>0.739130</td>\n",
              "      <td>1</td>\n",
              "      <td>0.239440</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>969</th>\n",
              "      <td>0.456522</td>\n",
              "      <td>1</td>\n",
              "      <td>0.493947</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>0.782609</td>\n",
              "      <td>1</td>\n",
              "      <td>0.148238</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0ec6216-c374-46b6-9bfe-d1fa5663032e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c0ec6216-c374-46b6-9bfe-d1fa5663032e 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-c0ec6216-c374-46b6-9bfe-d1fa5663032e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-93aaa611-deda-40e2-9071-b160fbcf8876\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93aaa611-deda-40e2-9071-b160fbcf8876')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-93aaa611-deda-40e2-9071-b160fbcf8876 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 1070,\n  \"fields\": [\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3059556552029841,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          0.3695652173913043,\n          0.32608695652173914,\n          0.043478260869565244\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16258772323722076,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 494,\n        \"samples\": [\n          0.30508474576271183,\n          0.41404358353510895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHILDREN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24319668611085113,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.4,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "508730e8f2b6876d4545d5ffe70b15f98588b913",
        "id": "bQEG_VhTmTTT"
      },
      "cell_type": "markdown",
      "source": [
        "As you can see, the values were normalized between 0 and 1. Now let's take our Train and Test data and finish this part."
      ]
    },
    {
      "metadata": {
        "_uuid": "d1dfa0987295e13b69fd59c16b6daadd11715929",
        "id": "_H-gmoHWmTTY"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"5\"></a> <br>\n",
        "## 5. BUILDING MODELS\n",
        "\n",
        "In this section, we will build regression models and fit them wit data. The regression algorithms used in this section are:\n",
        "\n",
        "1. Multiple Linear Regression\n",
        "2. Polynomial Regression"
      ]
    },
    {
      "metadata": {
        "_uuid": "a8853f194dca24a0134691544e52b216a0c56be4",
        "id": "kyaVhlGBmTTY"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"51\"></a> <br>\n",
        "### 5.1. Multiple Linear Regression\n",
        "\n",
        "Create the Multiple Linear Regression model and fit the data:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "338eb5eeb7ba4d28d8a13b851e753ae9eb43b41b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "3lRGJQJ4mTTY",
        "outputId": "21d736a0-adfe-48b9-a66f-357dc2a773a4"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression  # Import Linear Regression model\n",
        "\n",
        "multiple_linear_reg = LinearRegression(fit_intercept=False)  # Create a instance for Linear Regression model\n",
        "multiple_linear_reg.fit(x_train, y_train)  # Fit data to the model"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(fit_intercept=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "0e74e260c1e2195c96745fe236c903fc6d24284e",
        "id": "mb0ZMZVYmTTY"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"52\"></a> <br>\n",
        "### 5.2. Polynomial Regression\n",
        "\n",
        "In the Polynomial Regression model, we must first convert the data to the specified degree as a polynomial feature. For this, we will take advantage of the PolynomialFeatures class of the Scikit-Learn library. Now we will use the X data for training and testing as a polynomial feature and perform the fit process:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "de5b702ce8092acdf7c7c2fc5876dfdfd82e75b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BWX4BsGsmTTZ",
        "outputId": "8084a82a-35bb-45db-9c21-3a62d142c03f"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "polynomial_features = PolynomialFeatures(degree=3)  # Create a PolynomialFeatures instance in degree 3\n",
        "x_train_poly = polynomial_features.fit_transform(x_train)  # Fit and transform the training data to polynomial\n",
        "x_test_poly = polynomial_features.transform(x_test)  # Fit and transform the testing data to polynomial\n",
        "\n",
        "polynomial_reg = LinearRegression(fit_intercept=False)  # Create a instance for Linear Regression model\n",
        "polynomial_reg.fit(x_train_poly, y_train)  # Fit data to the model"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(fit_intercept=False)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression(fit_intercept=False)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "44b764960855c477d1553c7e0c3f5b644dfa895a",
        "id": "b2Kh2jR8mTTa"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"6\"></a> <br>\n",
        "## 6. EVALUATING MODELS\n",
        "\n",
        "In this section we will do some measurements to evaluate the performance on the models we fit. In addition, 10-Fold Cross Validation method will perform the validation process. R Squared Score method will be used for calculating the accuracy of the models. Mean Squared Error (MSE) method will be used for error measurement. As the MSE result can be very large, its square root will be taken and converted into RMSE. The error and accuracy calculation shall be performed on both the test and training dataset. Let's first perform evaluation for each model by importing the necessary functions:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7751c8cb820ee535fcd61754559c34184703a5fc",
        "id": "FCanU9RbmTTa"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict  # For K-Fold Cross Validation\n",
        "from sklearn.metrics import r2_score  # For find accuracy with R2 Score\n",
        "from sklearn.metrics import mean_squared_error  # For MSE\n",
        "from math import sqrt  # For squareroot operation"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "01fd4702a36e8903241537691d074c2d1b75900f",
        "id": "cL1nhdKbmTTa"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"61\"></a> <br>\n",
        "### 6.1. Evaluating Multiple Linear Regression Model"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f925dfad3fd6f412558d5b294b23589b343d90a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbTB3vL5mTTa",
        "outputId": "4788cf4c-7ea8-4aa0-b0d6-0659190ee591"
      },
      "cell_type": "code",
      "source": [
        "# Prediction with training dataset:\n",
        "y_pred_MLR_train = multiple_linear_reg.predict(x_train)\n",
        "\n",
        "# Prediction with testing dataset:\n",
        "y_pred_MLR_test = multiple_linear_reg.predict(x_test)\n",
        "\n",
        "# Find training accuracy for this model:\n",
        "accuracy_MLR_train = r2_score(y_train, y_pred_MLR_train)\n",
        "print(\"Training Accuracy for Multiple Linear Regression Model: \", accuracy_MLR_train)\n",
        "\n",
        "# Find testing accuracy for this model:\n",
        "accuracy_MLR_test = r2_score(y_test, y_pred_MLR_test)\n",
        "print(\"Testing Accuracy for Multiple Linear Regression Model: \", accuracy_MLR_test)\n",
        "\n",
        "# Find RMSE for training data:\n",
        "RMSE_MLR_train = sqrt(mean_squared_error(y_train, y_pred_MLR_train))\n",
        "print(\"RMSE for Training Data: \", RMSE_MLR_train)\n",
        "\n",
        "# Find RMSE for testing data:\n",
        "RMSE_MLR_test = sqrt(mean_squared_error(y_test, y_pred_MLR_test))\n",
        "print(\"RMSE for Testing Data: \", RMSE_MLR_test)\n",
        "\n",
        "# Prediction with 10-Fold Cross Validation:\n",
        "y_pred_cv_MLR = cross_val_predict(multiple_linear_reg, X, y, cv=10)\n",
        "\n",
        "# Find accuracy after 10-Fold Cross Validation\n",
        "accuracy_cv_MLR = r2_score(y, y_pred_cv_MLR)\n",
        "print(\"Accuracy for 10-Fold Cross Predicted Multiple Linaer Regression Model: \", accuracy_cv_MLR)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy for Multiple Linear Regression Model:  0.7377125164037337\n",
            "Testing Accuracy for Multiple Linear Regression Model:  0.7763297304887649\n",
            "RMSE for Training Data:  6152.796315105857\n",
            "RMSE for Testing Data:  5892.75195592368\n",
            "Accuracy for 10-Fold Cross Predicted Multiple Linaer Regression Model:  0.717113419200113\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "11c5d39c81465f176368cd3e94cecff927dc2e83",
        "id": "ZFXaE1ocmTTb"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"62\"></a> <br>\n",
        "### 6.2. Evaluating Polynomial Regression Model"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cab7ae537d218e481c428d51f541f652379bdf9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hd_p1OPmTTb",
        "outputId": "913c54a2-ef19-40df-c324-7eb3ac13422b"
      },
      "cell_type": "code",
      "source": [
        "# Prediction with training dataset:\n",
        "y_pred_PR_train = polynomial_reg.predict(x_train_poly)\n",
        "\n",
        "# Prediction with testing dataset:\n",
        "y_pred_PR_test = polynomial_reg.predict(x_test_poly)\n",
        "\n",
        "# Find training accuracy for this model:\n",
        "accuracy_PR_train = r2_score(y_train, y_pred_PR_train)\n",
        "print(\"Training Accuracy for Polynomial Regression Model: \", accuracy_PR_train)\n",
        "\n",
        "# Find testing accuracy for this model:\n",
        "accuracy_PR_test = r2_score(y_test, y_pred_PR_test)\n",
        "print(\"Testing Accuracy for Polynomial Regression Model: \", accuracy_PR_test)\n",
        "\n",
        "# Find RMSE for training data:\n",
        "RMSE_PR_train = sqrt(mean_squared_error(y_train, y_pred_PR_train))\n",
        "print(\"RMSE for Training Data: \", RMSE_PR_train)\n",
        "\n",
        "# Find RMSE for testing data:\n",
        "RMSE_PR_test = sqrt(mean_squared_error(y_test, y_pred_PR_test))\n",
        "print(\"RMSE for Testing Data: \", RMSE_PR_test)\n",
        "\n",
        "# Prediction with 10-Fold Cross Validation:\n",
        "y_pred_cv_PR = cross_val_predict(polynomial_reg, polynomial_features.fit_transform(X), y, cv=10)\n",
        "\n",
        "# Find accuracy after 10-Fold Cross Validation\n",
        "accuracy_cv_PR = r2_score(y, y_pred_cv_PR)\n",
        "print(\"Accuracy for 10-Fold Cross Predicted Polynomial Regression Model: \", accuracy_cv_PR)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy for Polynomial Regression Model:  0.8440044461603674\n",
            "Testing Accuracy for Polynomial Regression Model:  0.8640001614931052\n",
            "RMSE for Training Data:  4745.03986191281\n",
            "RMSE for Testing Data:  4594.975779183332\n",
            "Accuracy for 10-Fold Cross Predicted Polynomial Regression Model:  0.8391072917692678\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "dfa4608ed3fbc5b0c5ceca4074339386c9f7ed7a",
        "id": "c4x90ft9mTTc"
      },
      "cell_type": "markdown",
      "source": [
        "<a id=\"7\"></a> <br>\n",
        "## 7. EXPLORATION OF RESULTS\n",
        "\n",
        "In general, let's put all the results of the models into the table:"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0c7984a08d8b4b55bdb8353127a2dd29a0f6112a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "SVM0AJ_zmTTd",
        "outputId": "6ff530b6-4690-41f4-b4de-136a2dd8d28d"
      },
      "cell_type": "code",
      "source": [
        "training_accuracies = [accuracy_MLR_train, accuracy_PR_train]\n",
        "testing_accuracies = [accuracy_MLR_test, accuracy_PR_test]\n",
        "training_RMSE = [RMSE_MLR_train, RMSE_PR_train]\n",
        "testing_RMSE = [RMSE_MLR_test, RMSE_PR_test]\n",
        "cv_accuracies = [accuracy_cv_MLR, accuracy_cv_PR]\n",
        "\n",
        "table_data = {\"Training Accuracy\": training_accuracies, \"Testing Accuracy\": testing_accuracies,\n",
        "              \"Training RMSE\": training_RMSE, \"Testing RMSE\": testing_RMSE, \"10-Fold Score\": cv_accuracies}\n",
        "model_names = [\"Multiple Linear Regression\", \"Polynomial Regression\",]\n",
        "\n",
        "table_dataframe = pd.DataFrame(data=table_data, index=model_names)\n",
        "table_dataframe"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Training Accuracy  Testing Accuracy  \\\n",
              "Multiple Linear Regression           0.737713           0.77633   \n",
              "Polynomial Regression                0.844004           0.86400   \n",
              "\n",
              "                            Training RMSE  Testing RMSE  10-Fold Score  \n",
              "Multiple Linear Regression    6152.796315   5892.751956       0.717113  \n",
              "Polynomial Regression         4745.039862   4594.975779       0.839107  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Training Accuracy</th>\n",
              "      <th>Testing Accuracy</th>\n",
              "      <th>Training RMSE</th>\n",
              "      <th>Testing RMSE</th>\n",
              "      <th>10-Fold Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Multiple Linear Regression</th>\n",
              "      <td>0.737713</td>\n",
              "      <td>0.77633</td>\n",
              "      <td>6152.796315</td>\n",
              "      <td>5892.751956</td>\n",
              "      <td>0.717113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polynomial Regression</th>\n",
              "      <td>0.844004</td>\n",
              "      <td>0.86400</td>\n",
              "      <td>4745.039862</td>\n",
              "      <td>4594.975779</td>\n",
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          "metadata": {},
          "execution_count": 26
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        "Now let's compare the training and testing accuracy of each model:"
      ]
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        "<a id=\"8\"></a> <br>\n",
        "## 8. CONCLUSION\n",
        "\n",
        "This kernel is designed to completely illustrate regression and EDA stages in machine learning. You can use the code and information in the example as desired. But please use his license to use the dataset. In this kernel, we tried to get information about:\n",
        "\n",
        "* Data Science processes\n",
        "* How a Dataset Exploratory Data Analysis (EDA) is made\n",
        "* Find and visualize the correlation between features in data\n",
        "* How to make various visualizations about dataset\n",
        "* Use of libraries such as Pandas, Matplotlib, Seaborn and Scikit-Learn in Python\n",
        "* How to do Data Cleaning simply\n",
        "* How to split Dataset into Test and Train\n",
        "* How to install and learn Machine Learning models\n",
        "* How polynomial is made in Polynomial Regression\n",
        "* How to do the Evaluating on the installed models\n",
        "* How the R-Squared Score is located\n",
        "* How RMSE values are located\n",
        "* How K-Fold Cross Validation is done and how the score is calculated\n",
        "* How the models evaluated are visually compared and interpreted\n"
      ]
    },
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NAVXU6z0oZp9"
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