Regression_Notes/SKlearn_PolynomialRegression.ipynb

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      "name": "SKlearn_PolynomialRegression.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFM5sl2ef0Cg"
      },
      "source": [
        "## Polynomial Linear Regression \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHYXNoDvORIF"
      },
      "source": [
        "### Polynomial Regression\n",
        "#### Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x).\n",
        "\n",
        "#### Pros \n",
        " - very simple to work with \n",
        " - easy to interpret results\n",
        "\n",
        "#### Cons\n",
        " - numerical values only, categorical will not work \n",
        " - assumes a non-linear relationship between dependent and independent variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhRHMqRaQfiG"
      },
      "source": [
        "### Model Set Up\n",
        "\n",
        "#### Steps\n",
        " - load the data\n",
        " - inspect, clean, organize data\n",
        " - check for, handle outliers \n",
        " - encode data if necessary \n",
        " - set features and target \n",
        " - train, test split the data \n",
        " - scale the data if necessary \n",
        " - build the model, fit on the data, run the model\n",
        " - run metrics, analyze, view results, adjust parameters, repeat until satisfied... \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGu2oFZGu9qQ"
      },
      "source": [
        "### Regression Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9Z_XK3rO7H6"
      },
      "source": [
        "#### Polynomial-Linear Regression\n",
        "![photo](https://thaddeus-segura.com/wp-content/uploads/2020/09/poly-cover.png)\n",
        " - a curved-line fit to data\n",
        " - a generalized case of linear regression \n",
        " - assume the polynomial dependence between the output and inputs and, consequently, the polynomial estimated regression function\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qYrx7bZVW21"
      },
      "source": [
        "#### Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L06FgehMCRNd"
      },
      "source": [
        "##### Import + Inspect"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "o6as8gqJCnlB",
        "outputId": "8a54e847-7ab0-4ccf-f5c5-25ca11a04fbe"
      },
      "source": [
        "### imports ###\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/CVanchieri/CS_Notes/main/Regression_Notes/winequality-red.csv') # read in the file \n",
        "print('data frame shape:', df.shape)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data frame shape: (1599, 12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 921
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        "id": "7ZhDaU4ICX-X",
        "outputId": "74bca91e-bfaf-4005-9184-6f5a0a9d1a0f"
      },
      "source": [
        "### inspecting the data ###\n",
        "print('--- INSPECTING THE DATA --- ')\n",
        "print('--- columns --- ')\n",
        "print(df.columns)\n",
        "print('--- types --- ')\n",
        "print(df.dtypes)\n",
        "print('--- NA counts --- ')\n",
        "print(df.isna().sum())\n",
        "print('--- numericals descriptions --- ')\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- INSPECTING THE DATA --- \n",
            "--- columns --- \n",
            "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
            "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
            "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
            "      dtype='object')\n",
            "--- types --- \n",
            "fixed acidity           float64\n",
            "volatile acidity        float64\n",
            "citric acid             float64\n",
            "residual sugar          float64\n",
            "chlorides               float64\n",
            "free sulfur dioxide     float64\n",
            "total sulfur dioxide    float64\n",
            "density                 float64\n",
            "pH                      float64\n",
            "sulphates               float64\n",
            "alcohol                 float64\n",
            "quality                   int64\n",
            "dtype: object\n",
            "--- NA counts --- \n",
            "fixed acidity           0\n",
            "volatile acidity        0\n",
            "citric acid             0\n",
            "residual sugar          0\n",
            "chlorides               0\n",
            "free sulfur dioxide     0\n",
            "total sulfur dioxide    0\n",
            "density                 0\n",
            "pH                      0\n",
            "sulphates               0\n",
            "alcohol                 0\n",
            "quality                 0\n",
            "dtype: int64\n",
            "--- numericals descriptions --- \n"
          ],
          "name": "stdout"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      alcohol      quality\n",
              "count    1599.000000       1599.000000  ...  1599.000000  1599.000000\n",
              "mean        8.319637          0.527821  ...    10.422983     5.636023\n",
              "std         1.741096          0.179060  ...     1.065668     0.807569\n",
              "min         4.600000          0.120000  ...     8.400000     3.000000\n",
              "25%         7.100000          0.390000  ...     9.500000     5.000000\n",
              "50%         7.900000          0.520000  ...    10.200000     6.000000\n",
              "75%         9.200000          0.640000  ...    11.100000     6.000000\n",
              "max        15.900000          1.580000  ...    14.900000     8.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "2dtxj26xCYG-",
        "outputId": "0b07b741-8b39-4e01-dc0f-b8a90d994cf0"
      },
      "source": [
        "### view basic feature correlations ###\n",
        "print('--- feature correlations ---')\n",
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- feature correlations ---\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fixed acidity</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.256131</td>\n",
              "      <td>0.671703</td>\n",
              "      <td>0.114777</td>\n",
              "      <td>0.093705</td>\n",
              "      <td>-0.153794</td>\n",
              "      <td>-0.113181</td>\n",
              "      <td>0.668047</td>\n",
              "      <td>-0.682978</td>\n",
              "      <td>0.183006</td>\n",
              "      <td>-0.061668</td>\n",
              "      <td>0.124052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>-0.256131</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.552496</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>0.061298</td>\n",
              "      <td>-0.010504</td>\n",
              "      <td>0.076470</td>\n",
              "      <td>0.022026</td>\n",
              "      <td>0.234937</td>\n",
              "      <td>-0.260987</td>\n",
              "      <td>-0.202288</td>\n",
              "      <td>-0.390558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>0.671703</td>\n",
              "      <td>-0.552496</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.143577</td>\n",
              "      <td>0.203823</td>\n",
              "      <td>-0.060978</td>\n",
              "      <td>0.035533</td>\n",
              "      <td>0.364947</td>\n",
              "      <td>-0.541904</td>\n",
              "      <td>0.312770</td>\n",
              "      <td>0.109903</td>\n",
              "      <td>0.226373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>0.114777</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>0.143577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055610</td>\n",
              "      <td>0.187049</td>\n",
              "      <td>0.203028</td>\n",
              "      <td>0.355283</td>\n",
              "      <td>-0.085652</td>\n",
              "      <td>0.005527</td>\n",
              "      <td>0.042075</td>\n",
              "      <td>0.013732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>0.093705</td>\n",
              "      <td>0.061298</td>\n",
              "      <td>0.203823</td>\n",
              "      <td>0.055610</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005562</td>\n",
              "      <td>0.047400</td>\n",
              "      <td>0.200632</td>\n",
              "      <td>-0.265026</td>\n",
              "      <td>0.371260</td>\n",
              "      <td>-0.221141</td>\n",
              "      <td>-0.128907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>-0.153794</td>\n",
              "      <td>-0.010504</td>\n",
              "      <td>-0.060978</td>\n",
              "      <td>0.187049</td>\n",
              "      <td>0.005562</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.667666</td>\n",
              "      <td>-0.021946</td>\n",
              "      <td>0.070377</td>\n",
              "      <td>0.051658</td>\n",
              "      <td>-0.069408</td>\n",
              "      <td>-0.050656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>-0.113181</td>\n",
              "      <td>0.076470</td>\n",
              "      <td>0.035533</td>\n",
              "      <td>0.203028</td>\n",
              "      <td>0.047400</td>\n",
              "      <td>0.667666</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.071269</td>\n",
              "      <td>-0.066495</td>\n",
              "      <td>0.042947</td>\n",
              "      <td>-0.205654</td>\n",
              "      <td>-0.185100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>0.668047</td>\n",
              "      <td>0.022026</td>\n",
              "      <td>0.364947</td>\n",
              "      <td>0.355283</td>\n",
              "      <td>0.200632</td>\n",
              "      <td>-0.021946</td>\n",
              "      <td>0.071269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.341699</td>\n",
              "      <td>0.148506</td>\n",
              "      <td>-0.496180</td>\n",
              "      <td>-0.174919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>-0.682978</td>\n",
              "      <td>0.234937</td>\n",
              "      <td>-0.541904</td>\n",
              "      <td>-0.085652</td>\n",
              "      <td>-0.265026</td>\n",
              "      <td>0.070377</td>\n",
              "      <td>-0.066495</td>\n",
              "      <td>-0.341699</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.196648</td>\n",
              "      <td>0.205633</td>\n",
              "      <td>-0.057731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>0.183006</td>\n",
              "      <td>-0.260987</td>\n",
              "      <td>0.312770</td>\n",
              "      <td>0.005527</td>\n",
              "      <td>0.371260</td>\n",
              "      <td>0.051658</td>\n",
              "      <td>0.042947</td>\n",
              "      <td>0.148506</td>\n",
              "      <td>-0.196648</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.093595</td>\n",
              "      <td>0.251397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>-0.061668</td>\n",
              "      <td>-0.202288</td>\n",
              "      <td>0.109903</td>\n",
              "      <td>0.042075</td>\n",
              "      <td>-0.221141</td>\n",
              "      <td>-0.069408</td>\n",
              "      <td>-0.205654</td>\n",
              "      <td>-0.496180</td>\n",
              "      <td>0.205633</td>\n",
              "      <td>0.093595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.476166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>0.124052</td>\n",
              "      <td>-0.390558</td>\n",
              "      <td>0.226373</td>\n",
              "      <td>0.013732</td>\n",
              "      <td>-0.128907</td>\n",
              "      <td>-0.050656</td>\n",
              "      <td>-0.185100</td>\n",
              "      <td>-0.174919</td>\n",
              "      <td>-0.057731</td>\n",
              "      <td>0.251397</td>\n",
              "      <td>0.476166</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      fixed acidity  volatile acidity  ...   alcohol   quality\n",
              "fixed acidity              1.000000         -0.256131  ... -0.061668  0.124052\n",
              "volatile acidity          -0.256131          1.000000  ... -0.202288 -0.390558\n",
              "citric acid                0.671703         -0.552496  ...  0.109903  0.226373\n",
              "residual sugar             0.114777          0.001918  ...  0.042075  0.013732\n",
              "chlorides                  0.093705          0.061298  ... -0.221141 -0.128907\n",
              "free sulfur dioxide       -0.153794         -0.010504  ... -0.069408 -0.050656\n",
              "total sulfur dioxide      -0.113181          0.076470  ... -0.205654 -0.185100\n",
              "density                    0.668047          0.022026  ... -0.496180 -0.174919\n",
              "pH                        -0.682978          0.234937  ...  0.205633 -0.057731\n",
              "sulphates                  0.183006         -0.260987  ...  0.093595  0.251397\n",
              "alcohol                   -0.061668         -0.202288  ...  1.000000  0.476166\n",
              "quality                    0.124052         -0.390558  ...  0.476166  1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "Kly3hmnRCYOI",
        "outputId": "72b8b9aa-c80a-4ed7-af7f-b799b1f9cf82"
      },
      "source": [
        "### view basic feature correlations in a heatmap ###\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "print('--- feature correlations heatmap ---')\n",
        "sns.heatmap(df.corr() , cmap = 'Wistia' , annot = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- feature correlations heatmap ---\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdebf576c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qnphtcX4CYWY",
        "outputId": "208f303b-cf7c-4a49-d104-c92237447b86"
      },
      "source": [
        "### view scatter plots for each feature vs. target ###\n",
        "import matplotlib.pyplot as plt \n",
        "target_ = 'quality' # set the target\n",
        "features_ = df.iloc[:, 0:11] # set the features\n",
        "print('--- bar plots ---')\n",
        "for feature in features_:\n",
        "    figure = plt.figure\n",
        "    f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "    ax = plt.gca()\n",
        "    ax.bar(df[target_], df[feature])\n",
        "    ax.set_xlabel(target_)\n",
        "    ax.set_ylabel(feature)\n",
        "    ax.set_title(f'''{target_} vs {feature}''')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- bar plots ---\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1LQTOqKpP9FWnJElSS/qcrXnsLNtfBrxshvargUfefQ9JkqTJ18psTUmSJGE4kyRJaorhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqyEjhLMlfJ9kzyaIkn0yyMcmL+i5OkiRpvhl15OyIqroReCZwDfAg4LV9FSVJkjRfjRrOtrwg/Sjgw1V1Q0/1SJIkzWsLZ+8CwEeTfA24FXh5kiXAj/orS5IkaX4aaeSsqk4AfgWYqqrbgVuAo/ssTJIkaT4adULAvYHfA97ZNf08MNVXUZIkSfPVqM+cvRe4jcHoGcAG4C96qUiSJGkeGzWcPbCq/hq4HaCqbgHSW1WSJEnz1Kjh7LYk9wIKIMkDgR/3VpUkSdI8NepszTcAnwD2S/IB4AnA8X0VJUmSNF+NFM6q6rwklwCPY3A781VV9d1eK5MkSZqHRgpnST5ZVYcDH5uhTZIk7YKWnfCx2TvNQ9e86aixnn+b4SzJ7sC9gcVJ9uGuSQB7Akt7rk2SJGnemW3k7HeAP2TwvWaXcFc4uxF4e491SZIkzUvbDGdV9RbgLUl+v6retpNqkiRJmrdGnRDwtiQPBw4Gdh9qf39fhUmSJM1Ho04IOAk4jEE4Ww0cCXwGMJxJkiTNoVG/hPb5wOHAt6vqeOCRwF69VSVJkjRPjRrObq2qO4HNSfYErgf2668sSZKk+WnUNwSsTbI38G4GszZvBj7fW1WSJEnz1KgTAn6vWzw1ySeAPavqy/2VJUmSND+NOnJGkqXAAVv2SfKkqrqwr8IkSZLmo1Fna74ZeAFwBXBH11yA4UySJGkOjTpy9hzgwVX14z6LkSRJmu9Gna15NbCoz0IkSZI0+4vP38bg9uUtwGVJPgn8z+hZVf1Bv+VJkiTNL7Pd1lzb/bwEWNVzLZIkSfPebC8+f9+W5SS7AQ9hMJJ2VVXdNtvBk6wEnglcX1UPn2F7gLcAz2AwOveSqrq023Yc8Lqu618M1yJJkjSpRnrmLMkzgG8CbwXeDqxLcuQIu54OLN/G9iOBg7rPCuCd3fnuB5wEPBY4FDgpyT6j1CpJkrQrG3W25t8BT6mqdQBJHgh8DPj4tnaqqguTLNtGl6OB91dVARcn2TvJAxi8ZP38qtrUne98BiHvzBHrlSRJ2iWNOlvzpi3BrHM1cNMcnH8pcO3Q+vqubWvtd5NkRZK1SdZu3LhxDkqSJEkan+15t+Zq4EMMnjn7DWBNkucCVNVHeqpvVlV1GnAawNTUVI2rDkmSpLkw6sjZ7sB3gCczuOW4EbgX8CwGD/zvqA3AfkPr+3ZtW2uXJEmaaKO++Pz4ns6/CnhlkrMYPPx/Q1Vdl+Rc4K+GJgEcAZzYUw2SJEnNGPVLaGc025fQJjmTwUjb4iTrGczAXNTteyqwmsHXaKxj8FUax3fbNiV5I7CmO9TJWyYHSJIkTbJRv4R2h1TVsbNsL+AVW9m2Elj505xfkiRpVzPyl9BKkiSpfyM9c5bkU8xwe7OqnjrnFUmSJM1jo36Vxh8PLe8OPA/YPPflSJIkzW+jzta8ZFrTZ5P8Rw/1SJIkzWuj3ta839DqPYApYK9eKpIkSZrHRr2teQmDZ84C3A5cA7y0p5okSZLmrVHfEPC/gUOq6kDgn4AfMvheMkmSJM2hUcPZ66rqxiRPBJ4KvAd4Z39lSZIkzU+jhrM7up9HAe+uqo8Bu/VTkiRJ0vw1ajjbkORdwAuA1UnuuR37SpIkaUSjBqzfBM4Fnl5VPwDuB7y2t6okSZLmqVG/5+wW4CND69cB1/VVlCRJ0nzlrUlJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJashI33Omuyw74WPjLqFJ17zpqHGXIEnSRHDkTJIkqSGGM0mSpIYYziRJkhpiOJMkSWqI4UySJKkhhjNJkqSGGM4kSZIaYjiTJElqiOFMkiSpIYYzSZKkhhjOJEmSGmI4kyRJaojhTJIkqSGGM0mSpIYYziRJkhpiOJMkSWpIr+EsyfIkVyVZl+SEGbb/fZLLus/Xk/xgaNsdQ9tW9VmnJElSKxb2deAkC4BTgKcB64E1SVZV1RVb+lTVq4f6/z7wqKFD3FpVh/RVnyRJUov6HDk7FFhXVVdX1W3AWcDR2+h/LHBmj/VIkiQ1r89wthS4dmh9fdd2N0kOAA4ELhhq3j3J2iQXJ3nO1k6SZEXXb+3GjRvnom5JkqSxaWVCwDHA2VV1x1DbAVU1BfwW8A9JHjjTjlV1WlVNVdXUkiVLdkatkiRJvekznG0A9hta37drm8kxTLulWVUbup9XA5/mJ59HkyRJmkh9hrM1wEFJDkyyG4MAdrdZl0keAuwDfH6obZ8k9+yWFwNPAK6Yvq8kSdKk6W22ZlVtTvJK4FxgAbCyqi5PcjKwtqq2BLVjgLOqqoZ2fyjwriR3MgiQbxqe5SlJkjSpegtnAFW1Glg9re3109bfMMN+nwMe0WdtkiRJLWplQoAkSZIwnEmSJDXFcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkN6DWdJlie5Ksm6JCfMsP0lSTYmuaz7vGxo23FJvtF9juuzTkmSpFYs7OvASRYApwBPA9YDa5KsqqorpnX9YFW9ctq+9wNOAqaAAi7p9v1+X/VKkiS1oM+Rs0OBdVV1dVXdBpwFHD3ivk8Hzq+qTV0gOx9Y3lOdkiRJzegznC0Frh1aX9+1Tfe8JF9OcnaS/bZzX5KsSLI2ydqNGzfORd2SJEljM+4JAf8GLKuqX2IwOva+7T1AVZ1WVVNVNbVkyZI5L1CSJGln6jOcbQD2G1rft2v7H1X1var6cbf6HuAxo+4rSZI0ifoMZ2uAg5IcmGQ34Bhg1XCHJA8YWn02cGW3fC5wRJJ9kuwDHNG1SZIkTbTeZmtW1eYkr2QQqhYAK6vq8iQnA2urahXwB0meDWwGNgEv6fbdlOSNDAIewMlVtamvWiVJklrRWzgDqKrVwOppba8fWj4ROHEr+64EVvZZnyRJUmvGPSFAkiRJQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUkF7DWZLlSa5Ksi7JCTNs/6MkVyT5cpJPJjlgaNsdSS7rPqv6rFOSJKkVC/s6cJIFwCnA04D1wJokq6rqiqFuXwSmquqWJC8H/hp4Qbft1qo6pK/6JEmSWtTnyNmhwLqqurqqbgPOAo4e7lBVn6qqW7rVi4F9e6xHkiSpeX2Gs6XAtUPr67u2rXkp8PGh9d2TrE1ycZLn9FGgJElSa3q7rbk9krwImAKePNR8QFVtSPILwAVJvlJV35xh3xXACoD9999/p9QrSZLUlz5HzjYA+w2t79u1/YQkvwb8GfDsqvrxlvaq2tD9vBr4NPComU5SVadV1VRVTS1ZsmTuqpckSRqDPsPZGuCgJAcm2Q04BviJWZdJHgW8i0Ewu36ofZ8k9+yWFwNPAIYnEkiSJE2k3m5rVtXmJK8EzgUWACur6vIkJwNrq2oV8DfAfYEPJwH4r6p6NvBQ4F1J7mQQIN80bZanJEnSROr1mbOqWg2sntb2+qHlX9vKfp8DHtFnbZIkSS3yDQGSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQwxnkiRJDTGcSZIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJktQQw5kkSVJDDGeSJEkN6TWcJVme5Kok65KcMMP2eyb5YLf9C0mWDW07sWu/KsnT+6xTkiSpFb2FsyQLgFOAI4GDgWOTHDyt20uB71fVg4C/B97c7XswcAzwMGA58I7ueJIkSROtz5GzQ4F1VXV1Vd0GnAUcPa3P0cD7uuWzgcOTpGs/q6p+XFXfAtZ1x5MkSZpoC3s89lLg2qH19cBjt9anqjYnuQH4ma794mn7Lp3pJElWACu61ZuTXPXTl77LWAx8d9xFAOTN465gznlt++X17Y/Xtl9e3/7Mx2t7wEyNfYaznaKqTgNOG3cd45BkbVVNjbuOSeS17ZfXtz9e2355ffvjtb1Ln7c1NwD7Da3v27XN2CfJQmAv4Hsj7itJkjRx+gxna4CDkhyYZDcGD/ivmtZnFXBct/x84IKqqq79mG4254HAQcB/9FirJElSE3q7rdk9Q/ZK4FxgAbCyqi5PcjKwtqpWAf8I/FOSdcAmBgGOrt+HgCuAzcArquqOvmrdhc3L27k7ide2X17f/nht++X17Y/XtpPBQJUkSZJa4BsCJEmSGmI4kyRJaojhbBeUZPck/5HkS0kuT/Ln465p0iRZkOSLST467lomSZJrknwlyWVJ1o67nkmTZO8kZyf5WpIrkzx+3DVNgiQP7v6d3fK5MckfjruuSZLk1d3/z76a5Mwku4+7pnHymbNdUPcWhftU1c1JFgGfAV5VVRfPsqtGlOSPgClgz6p65rjrmRRJrgGmqqqJL5qcNEneB1xUVe/pZsnfu6p+MO66Jkn3KsENwGOr6j/HXc8kSLKUwf/HDq6qW7sJgaur6vTxVjY+jpztgmrg5m51UfcxZc+RJPsCRwHvGXct0qiS7AU8icEseKrqNoNZLw4Hvmkwm3MLgXt133l6b+C/x1zPWBnOdlHdbbfLgOuB86vqC+OuaYL8A/AnwJ3jLmQCFXBekku6V69p7hwIbATe292Sf0+S+4y7qAl0DHDmuIuYJFW1Afhb4L+A64Abquq88VY1XoazXVRV3VFVhzB4e8KhSR4+7pomQZJnAtdX1SXjrmVCPbGqHg0cCbwiyZPGXdAEWQg8GnhnVT0K+CFwwnhLmizdreJnAx8edy2TJMk+wNEM/sD4eeA+SV403qrGy3C2i+tuW3wKWD7uWibEE4Bnd89GnQU8Nck/j7ekydH9hUxVXQ+cAxw63oomynpg/dAo+tkMwprmzpHApVX1nXEXMmF+DfhWVW2sqtuBjwC/MuaaxspwtgtKsiTJ3t3yvYCnAV8bb1WToapOrKp9q2oZg9sXF1TVvP4Lbq4kuU+SPbYsA0cAXx1vVZOjqr4NXJvkwV3T4QzesqK5cyze0uzDfwGPS3LvbsLb4cCVY65prHp7fZN69QDgfd2soXsAH6oqv/JBrbs/cM7gv70sBM6oqk+Mt6SJ8/vAB7rbb1cDx4+5nonR/UHxNOB3xl3LpKmqLyQ5G7iUwSsbv8g8f5WTX6UhSZLUEG9rSpIkNcRwJkmS1BDDmSRJUkMMZ5IkSQ0xnEmSJDXEcCZJI0iyLMlXu+WpJG/tlg9LMq+/MFPS3PJ7ziRpO1XVWmBtt3oYcDPwubEVJGmiOHImaeIl+bMkX0/ymSRnJvnjJJ9OMtVtX9y9smvLCNlFSS7tPncbFetGyz6aZBnwu8Crk1yW5FeTfCvJoq7fnsPrkjQKR84kTbQkj2HwKq5DGPw37xn7bnMAAAFQSURBVFJgWy+2vx54WlX9KMlBDF7XMzVTx6q6JsmpwM1V9bfd+T4NHAX8a3fej3TvC5SkkThyJmnS/SpwTlXdUlU3Aqtm6b8IeHeSrwAfBg7ezvO9h7tem3Q88N7t3F/SPOfImaT5ajN3/YG6+1D7q4HvAI/stv9oew5aVZ/tbo0eBiyoKl/uLmm7OHImadJdCDwnyb2S7AE8q2u/BnhMt/z8of57AddV1Z3Ai4EFsxz/JmCPaW3vB87AUTNJO8BwJmmiVdWlwAeBLwEfB9Z0m/4WeHmSLwKLh3Z5B3Bcki8BDwF+OMsp/g349S0TArq2DwD7MHheTZK2S6pq3DVI0k6T5A0MPcDf0zmeDxxdVS/u6xySJpfPnEnSHEryNuBI4BnjrkXSrsmRM0mSpIb4zJkkSVJDDGeSJEkNMZxJkiQ1xHAmSZLUEMOZJElSQ/4/GdKHqznBlegAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZPjD3YsCNKH"
      },
      "source": [
        "##### Encode + Clean + Organize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0rHAloKB4iu"
      },
      "source": [
        "### no encoding necessary all numericals ###"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NvY40FLwB4rT",
        "outputId": "f65b6cd0-22c1-4ab5-de52-8d77588914fc"
      },
      "source": [
        "### check for outliers in the data ### \n",
        "import matplotlib.pyplot as plt\n",
        "# view each feature in a boxplot \n",
        "print('--- box plots --- ')\n",
        "for column in df:\n",
        "    plt.figure() # plot figure \n",
        "    f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "    df.boxplot([column]) # set data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- box plots --- \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
            "  import sys\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W1maXJqniHMmSVGnnnXceN910E+eeey6333475557LjfddBPnnXdes0uTVHGHHcmKiNXAbwM/zsz2A/RfCiwFAngOuDwzv1vve7zeNgLsysyOxpUuSYe3bds2Fi5cyOrVq/nyl79Ma2srCxcu5OGHH252aZIqbjy3C28Ergf+6iD9m4HzMnN7RFwMrATeOqa/KzOfPqIqJelVGhoa4jvf+Q5TpkyhVqvR2dnJzp07mTZtWrNLk1Rxh71dmJnfAp45RP99mbm9vvlt4LQG1SZJR8wV3yU1S6MnvvcAd43ZTuDuiEjgK5m58mAHRsQiYBHAjBkzqNVqDS5N0kT0nve8h0svvZSPfexjzJo1i89//vNcc8019PT0eJ2RVFTDQlZEdDEassa+iXVeZm6LiF8E7omIf6yPjL1MPYCtBOjo6MjOzs5GlSZpAuvs7GT27NksW7Zs79OFn/3sZ13xXVJxDQlZEfHrwF8AF2fmv+xpz8xt9X9/HBG3AecABwxZklRKd3c33d3de+dkSdLRcMRLOETELwN/C3wgM384pv2EiDhxz3fgAuChI/09SXql+vv7aW9vZ/78+bS3t9Pf39/skiRNAONZwqEf6ASmR8RW4CpgCkBmrgD+DPi3wJciAv51qYYZwG31tsnAzZn59QJ/gyQdVH9/P319faxatYqRkREmTZpET08PgLcMJRUVmdnsGl6mo6MjBwcHm12GpApob2/nuuuuo6ura+/twoGBARYvXsxDDzm4LunIRcQDB1oL1BXfJVXa0NAQ8+bN26dt3rx5DA0NNakiSROF7y6UVGltbW184hOfYM2aNXufLly4cKHrZEkqzpAlqdK6urpYvnw5y5cvZ/bs2WzatImlS5fS29vb7NIkVZwhS1KlDQwMsHTpUlavXr13JGvp0qWsWbOm2aVJqjgnvkuqtEmTJvHSSy8d8N2FIyMjzS5PUgUcbOK7I1mSKq2trY25c+fywAMPkJlEBG95y1uckyWpOJ8ulFRpLS0tDA4OsmDBAm677TYWLFjA4OAgLS1e/iSV5VVGUqU99NBDzJ8/n0cffZT3vve9PProo8yfP981siQV5+1CSZWWmQwNDfHkk08CsHHjRrZv386xOB9VUrU4kiWp8p588knmzp3Lrbfeyty5c/cGLkkqyZEsSRPC0NAQ73//+3nta1/b7FIkTRCOZEmqvClTpuy9Rbh9+3amTJnS7JIkTQCGLEmVd8oppzBnzhxaWlqYM2cOp5xySrNLkjQBGLIkVd6eOVg33XTTPtuSVJJzsiRVWmtrK7t372bjxo10d3cDo7cPXSdLUmleZSRV2vDwMDt37uTyyy9n3bp1XH755ezcuZPh4eFmlyap4nx3oaRKa2lp4dRTT2Xbtm17X6uzZ3v37t3NLk9SBRzs3YWOZEmqtMxk69at9Pb2sm7dOnp7e9m6dauLkUoqzpEsSZUWEZx++uk89dRTDA8P09rayute9zq2bNli0JLUEI5kSZqwtmzZwmWXXca6deu47LLL2LJlS7NLkjQBOJIlqdKckyWpNEeyJE1IzsmS1CyOZEmqtGnTpnHyySfz1FNP7W173etex/bt23nppZeaWJmkqnAkS9KENDw8zFNPPbXPOll7JsFLUkmOZEmqtJaWFmbPns0jjzyy9+nCN77xjWzatMk5WZIa4mAjWb5WR1KlZSZDQ0Ncc801zJ49m02bNvGxj33MOVmSijNkSaq0iOCkk07iox/96N62k08+mZ/85CdNrErSROCcLEmVlpls376duXPncuuttzJ37ly2b9/uSJak4hzJklR5J554Ivfddx/33Xff3u3nnnuuyVVJqjpHsiRV3nPPPbfP04UGLElHg08XSqq0iGDKlCns3Llzb9ue7WPx+ifp+OM6WZImrJ07dzJz5kz++q//mpkzZ+4TuCSpFOdkSaq8lpYWHn/8cT7wgQ/s3XaNLEmlOZIlqfJ27969z9OFBixJR4MjWZIqb/Lkyfs8XTh58mR27drV5KokVZ0jWZIqb9euXcyZM4f+/n7mzJljwJJ0VDiSJanyXvOa17Bx40a6u7v3br/44otNrkpS1TmSJanyXnzxRVpaRi93LS0tBixJR4UhS1Kl7QlXeya77/l3T7skleJVRlKlRcQrapekRjFkSaq0kZERgH1uF45tl6RSDFmSKu/MM8+kra2NlpYW2traOPPMM5tdkqQJwJAlqfIefvhhzj33XG6//XbOPfdcHn744WaXJGkCGFfIiojVEfHjiHjoIP0REX8eEY9ExPci4s1j+j4YEQ/XPx9sVOGSNF7Tp09nxYoVLFiwgBUrVjB9+vRmlyRpAhjvSNaNwEWH6L8YOLP+WQR8GSAifgG4CngrcA5wVUSc/GqLlaRX4+mnn2bBggXcdtttLFiwgKeffrrZJUmaAMa1GGlmfisiZh5il3cDf5WZCXw7Il4bEa8HOoF7MvMZgIi4h9Gw1n8kRUvSeLW2tnL66aezbt061q5dS0Twpje9iS1btjS7NEkV16gV308FnhizvbXedrD2l4mIRYyOgjFjxgxqtVqDSpM0ke3YsYOf/vSnfPazn2XWrFls3ryZz3zmM+zYscPrjKSijpnX6mTmSmAlQEdHR3Z2dja3IEmVMHv2bBYuXMiqVasYGhqira2Nnp4e1qxZg9cZSSU1KmRtA94wZvu0ets2Rm8Zjm2vNeg3JVVYIxcL3bhx4z7f92w36jdGZ0pI0r4atYTDWuD36k8Zvg14NjN/BHwDuCAiTq5PeL+g3iZJh5SZDfvcfPPNzJkzB6KFOXPmcPPNNzf0/JJ0IOMayYqIfkZHpKZHxFZGnxicApCZK4A7gXcAjwA/A36/3vdMRHwKuL9+qk/umQQvSUdLd3c33d3dzLzyDh66+p3NLkfSBDHepwu7D9OfwB8cpG81sPqVlyZJknT8csV3SZKkAgxZkiRJBRiyJEmSCjBkSZIkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgGGLEmSpAIMWZIkSQUYsiRJkgowZEmSJBVgyJIkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFGLIkSZIKMGRJkiQVYMiSJEkqwJAlSZJUgCFLkiSpAEOWJElSAYYsSZKkAgxZkiRJBRiyJEmSCjBkSZIkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgGGLEmSpAIMWZIkSQUYsiRJkgowZEmSJBVgyJIkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFjCtkRcRFEfGDiHgkIq48QP/nI+LB+ueHEfGTMX0jY/rWNrJ4SZKkY9Xkw+0QEZOALwLnA1uB+yNibWZu2rNPZv7xmP0XA2ePOcWLmXlW40qWJEk69o1nJOsc4JHMfCwzdwBfBd59iP27gf5GFCdJknS8Gk/IOhV4Ysz21nrby0TE6cAsYP2Y5mkRMRgR346Iha+6UkmSpOPIYW8XvkKXAH+TmSNj2k7PzG0R8SvA+oj4fmY+uv+BEbEIWAQwY8YMarVag0uTJLy2SDpqxhOytgFvGLN9Wr3tQC4B/mBsQ2Zuq//7WETUGJ2v9bKQlZkrgZUAHR0d2dnZOY7SJOkV+PodeG2RdLSM53bh/cCZETErIqYyGqRe9pRgRPwqcDLw92PaTo6I1vr36cDbgU37HytJklQ1hx3JysxdEXEF8A1gErA6MzdGxCeBwczcE7guAb6amTnm8DbgKxGxm9FAd/XYpxIlSZKqKvbNRMeGjo6OHBwcbHYZksbpNz5xN8++uLPZZVTCSa+ZwnevuqDZZUh6BSLigczs2L+90RPfJU1Az764k8evfmezyzisWq12zM/JmnnlHc0uQVKD+FodSZKkAgxZkiRJBRiyJEmSCjBkSZIkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgGGLEmSpAIMWZIkSQUYsiRJkgowZEmSJBVgyJIkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFGLIkSZIKMGRJkiQVMLnZBUg6/p3YdiW/9pdXNruM8fnLZhdwaCe2Abyz2WVIagBDlqQj9tzQ1Tx+9bEfDGq1Gp2dnc0u45BmXnlHs0uQ1CDeLpQkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFGLIkSZIKMGRJkiQVYMiSJEkqwJAlSZJUgCFLkiSpAEOWJElSAYYsSZKkAiY3uwBJ1TDzyjuaXcL4fP3YrvOk10xpdgmSGsSQJemIPX71O5tdwrjMvPKO46ZWScc/bxdKkiQVYMiSJEkqwJAlSZJUgCFLkiSpAEOWJElSAeMKWRFxUUT8ICIeiYgrD9D/oYj454h4sP75L2P6PhgRD9c/H2xk8ZIkSceqwy7hEBGTgC8C5wNbgfsjYm1mbtpv11sy84r9jv0F4CqgA0jggfqx2xtSvSRJ0jFqPCNZ5wCPZOZjmbkD+Crw7nGe/0Lgnsx8ph6s7gEuenWlSpIkHT/GsxjpqcATY7a3Am89wH7vjYhzgR8Cf5yZTxzk2FMP9CMRsQhYBDBjxgxqtdo4SpOkV8Zri6SjpVErvq8D+jNzOCL+K/CXwG++khNk5kpgJUBHR0d2dnY2qDRJqvv6HXhtkXS0jOd24TbgDWO2T6u37ZWZ/5KZw/XNvwDeMt5jJUmSqmg8Iet+4MyImBURU4FLgLVjd4iI14/ZfBcwVP/+DeCCiDg5Ik4GLqi3SZIkVdphbxdm5q6IuILRcDQJWJ2ZGyPik8BgZq4F/jAi3gXsAp4BPlQ/9pmI+BSjQQ3gk5n5TIG/Q5Ik6ZgyrjlZmXkncOd+bX825vufAn96kGNXA6uPoEZJkqTjjiu+S5IkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgGGLEmSpAIMWZIkSQUYsiRJkgowZEmSJBVgyJIkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFGLIkSZIKMGRJkiQVYMiSJEkqwJAlSZJUgCFLkiSpAEOWJElSAYYsSZKkAgxZkiRJBRiyJEmSCjBkSZIkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgGGLEmSpAImN7sASTqQiChz3uWNP2dmNv6kko57jmRJOiZlZsM/AwMDRc4rSQdiyJIkSSrAkCVJklSAIUuSJKkAQ5YkSVIBhixJkqQCDFmSJEkFGLIkSZIKMGRJkiQVYMiSJEkqwJAlSZJUgCFLkiSpgHGFrIi4KCJ+EBGPRMSVB+hfEhGbIuJ7EXFvRJw+pm8kIh6sf9Y2snhJkqRj1eTD7RARk4AvAucDW4H7I2JtZm4as9t3gI7M/FlEXA58Bvidet+LmXlWg+uWJEk6po1nJOsc4JHMfCwzdwBfBd49dofMHMjMn9U3vw2c1tgyJUmSji+HHckCTgWeGLO9FXjrIfbvAe4asz0tIgaBXcDVmbnmQAdFxCJgEcCMGTOo1WrjKE2Sxu/555/32iLpqBlPyBq3iPjPQAdw3pjm0zNzW0T8CrA+Ir6fmY/uf2xmrgRW1s/zz11dXVsaWZskAdOBp5tdhKTKOf1AjeMJWduAN4zZPq3eto+I+C2gDzgvM4f3tGfmtvq/j0VEDTgbeFnIGiszTxlHXZL0ikTEYGZ2NLsOSRPDeOZk3Q+cGRGzImIqcAmwz1OCEXE28BXgXZn54zHtJ0dEa/37dODtwNgJ85IkSZV02JGszNwVEVcA3wAmAaszc2NEfBIYzMy1wDXAzwO3RgTAP2Xmu4A24CsRsZvRQHf1fk8lSpIkVVJkZrNrkKSjIiIW1ed/SlJxhixJkqQCfK2OJElSAYYsSZKkAgxZko5LEfGhiLj+SPc5wDF/FBE/d2TVSZIhS5L290eAIUvSETNkSTpmRMQJEXFHRHw3Ih6KiN+JiMfr6+wRER31RY33P+7GiFgREYMR8cOI+O0x3b8UEV+PiIcj4jNjjvlyff+NEfGJetsfAr8EDETEQL3tgoj4+4j4h4i4NSJ+vt5+dURsiojvRcS15f6rSDpeNfS1OpJ0hC4CnszMdwJExEnA8nEeO5PRF9qfwWhIemO9/SxG3zQxDPwgIq7LzCeAvsx8JiImAd7iyfAAAAHMSURBVPdGxK9n5p9HxBKgKzOfroe7/wb8Vma+EBFLgSUR8UXgPcCvZmZGxGsb8tdLqhRHsiQdS74PnB8RyyPiP2Tms6/g2P+Zmbsz82HgMeBX6+33ZuazmfkSo2+c2POOsfdHxD8A3wHmALMPcM631dv/LiIeBD5YP/5Z4CVgVUT8R+Bnr+zPlDQROJIl6ZiRmT+MiDcD7wD+e0TcC+ziX/+HcNqhDj/I9vCYthFgckTMAv4E+HeZuT0ibjzIuQO4JzO7X9YRcQ4wH3gfcAXwm4f62yRNPI5kSTpmRMQvAT/LzP/B6Ou63gw8Drylvst7D3H4f4qIlog4A/gV4AeH2PffAC8Az0bEDODiMX3PASfWv38bePueW4/1OWNvqs/LOikz7wT+GPiNV/BnSpogHMmSdCz5NeCa+vtOdwKXA69h9Lbcp4DaIY79J+D/MRqgejPzpfq7VF8mM78bEd8B/hF4Avi7Md0rga9HxJOZ2RURHwL697zsntE5Ws8Bt0fENEZHu5a8mj9WUrX5Wh1Jx7367b7/lZl/0+xaJGkPbxdKkiQV4EiWJElSAY5kSZIkFWDIkiRJKsCQJUmSVIAhS5IkqQBDliRJUgH/H1+zaHGKDNs1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L8sBnzy1JTjzKS+5O8vLu/lpVfU+SG5OM37bd3XdU1R8n2enu63ffb5HkiiR/tfu+7+3u/9unfx3gAmAnCThbXpLkpu7+and/Jclf77H+oiR/WlWfTPLuJM89w/d7S5LX7j5+bZK3nuHrgQucnSRg3b6Zh/7C9sRTjr8+yReTPH/35187k5N299/vXrLbSnKwu2/bh1mBC4idJOBs+UiSn6mqJ1XV4SQ/tXv8jiQv3H38qlPWPyXJF7r7/iSvSXJwj/Pfk+Twacf+PMk7YxcJ+DaIJOCs6O5bkvxlkk8k+UCSf9r90fVJrqqqjye59JSX/FGSX6yqTyS5LMm9e7zF3yT52ZM3bu8ee0eSp+WB+5kAzoj/LQmwFlX1eznlRusVvcerkryyu1+zqvcAzl/uSQLOS1X1piSvSPIT654FeHyykwQAMHBPEgDAQCQBAAxEEgDAQCQBAAxEEgDA4P8BwPysEpwhdhQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPKO3bryJUgv"
      },
      "source": [
        "### function to find outliers in the data ###\n",
        "def outlier_zscore(data):\n",
        "    global outliers,zscore\n",
        "    outliers = [] \n",
        "    zscore = [] \n",
        "    threshold = 3.5 # set threshold \n",
        "    mean = np.mean(data)\n",
        "    std = np.std(data)\n",
        "    for i in data:\n",
        "        z_score = (i - mean)/std # calculate the z_score\n",
        "        zscore.append(z_score) # append the score to the zscore\n",
        "        if np.abs(z_score) > threshold: \n",
        "            outliers.append(i) # append z_score the outliers\n",
        "    print(outliers)\n",
        "    return len(outliers), outliers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sMUZkpJTJUxl",
        "outputId": "bce9407b-5e64-4ba4-a9b6-5c0b1e245c35"
      },
      "source": [
        "### run each feature 'wanted' through the function ### \n",
        "print('--- possible outliers --- ')\n",
        "total_sulfur_dioxide_number, total_sulfur_dioxide_outliers = outlier_zscore(df['total sulfur dioxide'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- possible outliers --- \n",
            "[165.0, 278.0, 289.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hzfRM2SJU-C",
        "outputId": "f660c3fa-24f3-435a-e947-b38f22fe0d2a"
      },
      "source": [
        "### removal of outliers per feature ###\n",
        "for num, i in enumerate(df['total sulfur dioxide']): \n",
        "    if i in total_sulfur_dioxide_outliers:\n",
        "        df['total sulfur dioxide'][num] = 150 # choose replacement score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQo1pSuXvbZ8"
      },
      "source": [
        "#### Polynomial Regression Model\n",
        " - GridSearch CV \n",
        " - RandomizedSearch CV "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdTP6Jo8Oa0P"
      },
      "source": [
        "### copy the data frame ###\n",
        "df1 = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULyU31lqMERr",
        "outputId": "ebd863eb-1443-420d-9741-73ea51d3042a"
      },
      "source": [
        "### split the data into features & target sets ###\n",
        "X = df1.iloc[:, 0:11].values # features \n",
        "y = df1.iloc[:, 11].values # target \n",
        "print('--- data shapes --- ')\n",
        "print('X shape:', X.shape)\n",
        "print('y shape:', y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- data shapes --- \n",
            "X shape: (1599, 11)\n",
            "y shape: (1599,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXFoHrp5MGsg"
      },
      "source": [
        "### set the train test split parameters ###\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # split 80/20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqAT0-4VOhRb"
      },
      "source": [
        "### feature scaling ### \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler() # initiate the scalar   \n",
        "X_train = sc.fit_transform(X_train) # fit transform the train data \n",
        "X_test = sc.transform(X_test) # transform the test data "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wN1cf1SFGTa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c597d9b-3ac6-47c8-b227-1b97f8fac95f"
      },
      "source": [
        "### polynomial regression model ###\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn import metrics\n",
        "poly = PolynomialFeatures(degree = 1) # degree = 1\n",
        "X_poly = poly.fit_transform(X_train)\n",
        "poly.fit(X_poly, y_train) \n",
        "X_test_poly = poly.fit_transform(X_test) \n",
        "### linear regression model ###\n",
        "model = LinearRegression() \n",
        "print('--- model runtime --- ')\n",
        "%time model.fit(X_poly, y_train)\n",
        "y_pred = model.predict(X_test_poly) \n",
        "### create data frame of predictions and results ### \n",
        "y_pred_df = pd.DataFrame(y_pred, columns=[\"Predicted_Values\" ])\n",
        "y_test_df = pd.DataFrame(np.array(y_test), columns=[\"Real_Values\"]) \n",
        "df_poly_final = pd.concat([y_test_df , y_pred_df] , axis=1)\n",
        "print('--- real values vs predicted values ---')\n",
        "print(df_poly_final.head())\n",
        "### get the model metrics ###\n",
        "print('--- model metrics ---') \n",
        "print('mean absolute error:', metrics.mean_absolute_error(y_test, y_pred)) # mae \n",
        "print('mean squared error:', metrics.mean_squared_error(y_test, y_pred)) # mse \n",
        "print('root mean squared error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) # rmse\n",
        "print('intercept:', model.intercept_) # get the intercept\n",
        "print('coefficients:', model.coef_) # get the coefficients\n",
        "score = metrics.r2_score(y_test , y_pred) # get the r2 score \n",
        "print(\"r2 score : {}\".format(score)) # show the r2 score\n",
        "print('model score:', model.score(X_poly, y_train)) # show the model score \n",
        "print(\"model accuracy: {}%\".format(score * 100)) # show the model accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- model runtime --- \n",
            "CPU times: user 509 µs, sys: 1.01 ms, total: 1.52 ms\n",
            "Wall time: 1.18 ms\n",
            "--- real values vs predicted values ---\n",
            "   Real_Values  Predicted_Values\n",
            "0            6          5.780812\n",
            "1            5          5.044411\n",
            "2            7          6.595246\n",
            "3            6          5.348717\n",
            "4            5          5.939156\n",
            "--- model metrics ---\n",
            "mean absolute error: 0.4683455520201097\n",
            "mean squared error: 0.3829431762291927\n",
            "root mean squared error: 0.6188240268680529\n",
            "intercept: 5.646598905394846\n",
            "coefficients: [ 0.          0.06407222 -0.2072839  -0.03226829  0.03693782 -0.09071668\n",
            "  0.03777729 -0.10631762 -0.05242127 -0.04208797  0.16254793  0.28374258]\n",
            "r2 score : 0.3310579794290458\n",
            "model score: 0.3670422930835868\n",
            "model accuracy: 33.10579794290458%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "o-kJhNYNNqhb",
        "outputId": "3ca855ab-67ca-432b-8739-d2d5350c6ae6"
      },
      "source": [
        "### visualize the model prediction accuracy ###\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "### configure the plot ###\n",
        "print('--- distplot accuracy --- ')\n",
        "f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "ax1 = sns.distplot(y_test, hist=False, color=\"b\", label=\"Actual Values\")\n",
        "sns.distplot(y_pred, hist=False, color=\"r\", label=\"Predicted Values\" , axlabel='Charges', ax=ax1)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- distplot accuracy --- \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeb402ce48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVLKiiySvXq0"
      },
      "source": [
        "##### GridSearch CV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_wILaIvO31b"
      },
      "source": [
        "### copy the data frame ###\n",
        "df2 = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQaRrGFQQM89",
        "outputId": "9440a167-7754-45ea-b53e-64c95e2a0574"
      },
      "source": [
        "### split the data into features & target sets ###\n",
        "X = df2.iloc[:, 0:11].values # set the features \n",
        "y = df2.iloc[:, 11].values # set the target \n",
        "print('--- data shapes --- ')\n",
        "print('X shape:', X.shape) \n",
        "print('y shape:', y.shape) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- data shapes --- \n",
            "X shape: (1599, 11)\n",
            "y shape: (1599,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixeirZnFQYYG"
      },
      "source": [
        "### set the train test split parameters ###\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 80/20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtZV2xmoQYuB"
      },
      "source": [
        "### feature scaling ### \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler() # initiate the scalar   \n",
        "X_train = sc.fit_transform(X_train) # fit transform the data with scalar\n",
        "X_test = sc.transform(X_test) # fit transform the data with scalar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXU2b_ZcvaWE",
        "outputId": "70126af8-0c33-4e5a-eb01-5a06bf320a6b"
      },
      "source": [
        "### polynomial regression + grid search CV model ###\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "model1 = LinearRegression()\n",
        "param_grid = {  # create the param grid \n",
        "            'copy_X': [True, False],\n",
        "            'fit_intercept': [True, False],\n",
        "            'n_jobs' : [2, 6, 10, 15],\n",
        "            'normalize' : [True, False],\n",
        "              }\n",
        "CV_rfc = GridSearchCV(estimator=model1, param_grid=param_grid, cv=3) \n",
        "print('--- model runtime --- ')\n",
        "%time CV_rfc.fit(X_train, y_train)\n",
        "print('--- best params --- ')\n",
        "CV_rfc.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- model runtime --- \n",
            "CPU times: user 264 ms, sys: 193 ms, total: 457 ms\n",
            "Wall time: 252 ms\n",
            "--- best params --- \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'copy_X': True, 'fit_intercept': True, 'n_jobs': 2, 'normalize': True}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7LFz_9tO6mC",
        "outputId": "141bc714-52f3-4dff-ab4b-a0e53f0b8e3a"
      },
      "source": [
        "### polynomial regression model + grid best params ###\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn import metrics\n",
        "poly = PolynomialFeatures(degree = 1) # degree = 4 \n",
        "X_poly = poly.fit_transform(X_train)\n",
        "poly.fit(X_poly, y_train)\n",
        "X_test_poly = poly.fit_transform(X_test)\n",
        "### linear regression model ###\n",
        "model1 = LinearRegression(\n",
        "                          copy_X= True,\n",
        "                          fit_intercept= True,\n",
        "                          n_jobs= 2,\n",
        "                          normalize= True\n",
        "                          ) \n",
        "print('--- model runtime --- ')\n",
        "%time model1.fit(X_poly, y_train)\n",
        "y_pred = model1.predict(X_test_poly)\n",
        "### create data frame of predictions and results ### \n",
        "y_pred_df = pd.DataFrame(y_pred, columns=[\"Predicted_Values\" ])\n",
        "y_test_df = pd.DataFrame(np.array(y_test), columns=[\"Real_Values\"])\n",
        "df_poly_final = pd.concat([y_test_df , y_pred_df] , axis=1)\n",
        "print('--- real values vs predicted values ---')\n",
        "print(df_poly_final.head())\n",
        "### get the model metrics ###\n",
        "print('--- model metrics ---') \n",
        "print('mean absolute error:', metrics.mean_absolute_error(y_test, y_pred)) # mae \n",
        "print('mean squared error:', metrics.mean_squared_error(y_test, y_pred)) # mse \n",
        "print('root mean squared error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) # rmse\n",
        "print('intercept:', model1.intercept_) # get the intercept\n",
        "print('coefficients:', model1.coef_) # get the coefficients\n",
        "score = metrics.r2_score(y_test , y_pred) # get the r2 score \n",
        "print(\"r2 score : {}\".format(score)) # show the r2 score\n",
        "print('model score:', model1.score(X_poly, y_train)) # show the model score \n",
        "print(\"model accuracy: {}%\".format(score * 100)) # show the model accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- model runtime --- \n",
            "CPU times: user 1.64 ms, sys: 7 µs, total: 1.65 ms\n",
            "Wall time: 1.3 ms\n",
            "--- real values vs predicted values ---\n",
            "   Real_Values  Predicted_Values\n",
            "0            6          5.780812\n",
            "1            5          5.044411\n",
            "2            7          6.595246\n",
            "3            6          5.348717\n",
            "4            5          5.939156\n",
            "--- model metrics ---\n",
            "mean absolute error: 0.46834555202010975\n",
            "mean squared error: 0.38294317622919277\n",
            "root mean squared error: 0.618824026868053\n",
            "intercept: 5.646598905394846\n",
            "coefficients: [ 0.          0.06407222 -0.2072839  -0.03226829  0.03693782 -0.09071668\n",
            "  0.03777729 -0.10631762 -0.05242127 -0.04208797  0.16254793  0.28374258]\n",
            "r2 score : 0.3310579794290457\n",
            "model score: 0.3670422930835868\n",
            "model accuracy: 33.105797942904566%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "4skEaKA-QE-X",
        "outputId": "6bab2a49-1e53-4767-9dc7-1300e962b009"
      },
      "source": [
        "### visualize the model prediction accuracy ###\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "### configure the plot ###\n",
        "print('--- distplot accuracy --- ')\n",
        "f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "ax1 = sns.distplot(y_test, hist=False, color=\"b\", label=\"Actual Values\")\n",
        "sns.distplot(y_pred, hist=False, color=\"r\", label=\"Predicted Values\" , axlabel='Charges', ax=ax1)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- distplot accuracy --- \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeb34e5c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhe8XhSPPxEU"
      },
      "source": [
        "##### RandomizedSearch CV "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIKSJXGYPFE5"
      },
      "source": [
        "### copy the data frame ###\n",
        "df3 = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmFNTMxiQPyR",
        "outputId": "277cd5d7-f326-4711-cd11-b5b57a748cba"
      },
      "source": [
        "### split the data into features & target sets ###\n",
        "X = df3.iloc[:, 0:11].values \n",
        "y = df3.iloc[:, 11].values \n",
        "print('--- data shapes --- ')\n",
        "print('X shape:', X.shape) \n",
        "print('y shape:', y.shape) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- data shapes --- \n",
            "X shape: (1599, 11)\n",
            "y shape: (1599,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTvS2qkjQSyt"
      },
      "source": [
        "### set the train test split parameters ###\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 80/20 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vucvFQhVQUpv"
      },
      "source": [
        "### feature scaling ### \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0UTP8juP0L4",
        "outputId": "7c851858-959f-4943-a270-fe0b5cfa1765"
      },
      "source": [
        "### polynomial regression + randomized search CV model ###\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "model2 = LinearRegression()\n",
        "param_grid = {  # create the param grid \n",
        "            'copy_X': [True, False],\n",
        "            'fit_intercept': [True, False],\n",
        "            'n_jobs' : [2, 20], # \n",
        "            'normalize' : [True, False],\n",
        "              }\n",
        "CV_rfc = RandomizedSearchCV(model2, param_grid, cv=3) \n",
        "print('--- model runtime --- ')\n",
        "%time CV_rfc.fit(X_train, y_train)\n",
        "print('--- best params --- ')\n",
        "CV_rfc.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- model runtime --- \n",
            "CPU times: user 75.9 ms, sys: 60.9 ms, total: 137 ms\n",
            "Wall time: 76.8 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'copy_X': True, 'fit_intercept': True, 'n_jobs': 20, 'normalize': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1rsKkPYP0m1",
        "outputId": "ae9fcbbe-fbc4-46ec-8dc6-abf9a126161b"
      },
      "source": [
        "### polynomial regression model + random best params ###\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn import metrics\n",
        "poly = PolynomialFeatures(degree = 2) # degree = 2 \n",
        "X_poly = poly.fit_transform(X_train)\n",
        "poly.fit(X_poly, y_train)\n",
        "X_test_poly = poly.fit_transform(X_test)\n",
        "### linear regression model ###\n",
        "model2 = LinearRegression(\n",
        "                          copy_X= True,\n",
        "                          fit_intercept= True,\n",
        "                          n_jobs= 20,\n",
        "                          normalize= False\n",
        "                          ) \n",
        "print('--- model runtime --- ')\n",
        "%time model2.fit(X_poly, y_train) \n",
        "y_pred = model2.predict(X_test_poly)\n",
        "### create data frame of predictions and results ### \n",
        "y_pred_df = pd.DataFrame(y_pred, columns=[\"Predicted_Values\" ])\n",
        "y_test_df = pd.DataFrame(np.array(y_test), columns=[\"Real_Values\"])\n",
        "df_poly_final = pd.concat([y_test_df , y_pred_df] , axis=1)\n",
        "print('--- real values vs predicted values ---')\n",
        "print(df_poly_final.head())\n",
        "print('--- model metrics ---') \n",
        "print('mean absolute error:', metrics.mean_absolute_error(y_test, y_pred)) # mae \n",
        "print('mean squared error:', metrics.mean_squared_error(y_test, y_pred)) # mse \n",
        "print('root mean squared error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred))) # rmse\n",
        "print('intercept:', model2.intercept_) # get the intercept\n",
        "print('coefficients:', model2.coef_) # get the coefficients\n",
        "score = metrics.r2_score(y_test , y_pred) # get the r2 score \n",
        "print(\"r2 score : {}\".format(score)) # show the r2 score\n",
        "print('model score:', model2.score(X_poly, y_train)) # show the model score \n",
        "print(\"model accuracy: {}%\".format(score * 100)) # show the model accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- model runtime --- \n",
            "CPU times: user 7.11 ms, sys: 7.01 ms, total: 14.1 ms\n",
            "Wall time: 13.9 ms\n",
            "--- real values vs predicted values ---\n",
            "   Real_Values  Predicted_Values\n",
            "0            6          5.483383\n",
            "1            5          5.022187\n",
            "2            7          6.909452\n",
            "3            6          5.083932\n",
            "4            5          5.834304\n",
            "--- model metrics ---\n",
            "mean absolute error: 0.4827426944601928\n",
            "mean squared error: 0.4018809603800432\n",
            "root mean squared error: 0.6339408177267364\n",
            "intercept: 5.735155127927229\n",
            "coefficients: [-1.48547329e-16  1.07268253e-01 -1.90047219e-01 -7.46384103e-02\n",
            "  4.20015980e-02 -6.47881866e-02  8.95662811e-02 -1.43251961e-01\n",
            " -7.79772447e-02 -3.98638849e-02  2.68691515e-01  2.40079638e-01\n",
            " -1.60707484e-01 -4.84552592e-02 -9.41046512e-02 -1.28213529e-01\n",
            " -1.28821722e-01 -1.23730748e-01  9.82753305e-03  2.52937555e-01\n",
            " -1.75642314e-01  5.07224203e-02  1.10761394e-01 -9.68459932e-03\n",
            "  5.90933665e-02 -5.51227895e-02  3.28935859e-02 -4.57216955e-02\n",
            "  1.37823485e-01  1.86122840e-02 -2.43679795e-02 -5.85642047e-02\n",
            "  7.33438534e-02  2.89841022e-02 -2.57955392e-02  6.16858883e-02\n",
            "  7.27931386e-02  8.80636432e-03  6.72811827e-02 -1.26161557e-01\n",
            " -7.48445914e-02  1.95014213e-01 -2.98138203e-02  4.91690654e-02\n",
            " -1.77353961e-02  3.97254513e-02  1.12703407e-01 -8.52184451e-02\n",
            " -5.29266461e-02  7.85271133e-02 -2.97645708e-02 -7.62017702e-02\n",
            " -3.40681075e-02  8.49902033e-02 -4.18535005e-02  2.88467846e-02\n",
            " -5.16492371e-03 -2.29524679e-02 -1.42108593e-02  1.38466862e-01\n",
            " -3.87604480e-02 -9.63044833e-02  1.35994757e-01 -2.17213732e-02\n",
            " -1.20130876e-01  2.95474820e-02  5.26413214e-02 -1.37259612e-01\n",
            " -5.99510507e-02  2.25364041e-01 -5.01363513e-02 -1.51812408e-01\n",
            " -9.47359524e-02  1.29890794e-04  1.81749491e-01 -3.68654913e-02\n",
            "  3.56525551e-02 -7.15945280e-02]\n",
            "r2 score : 0.2979766232869938\n",
            "model score: 0.4522371482825506\n",
            "model accuracy: 29.79766232869938%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "J4X5W_ecPFHZ",
        "outputId": "c06d909f-ddf5-4d30-a8b0-9c1d31bd1311"
      },
      "source": [
        "### visualize the model prediction accuracy ###\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "### configure the plot ###\n",
        "print('--- distplot accuracy --- ')\n",
        "f, ax = plt.subplots(1, 1, figsize = (10, 7))\n",
        "ax1 = sns.distplot(y_test, hist=False, color=\"b\", label=\"Actual Values\")\n",
        "sns.distplot(y_pred, hist=False, color=\"r\", label=\"Predicted Values\" , axlabel='Charges', ax=ax1)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--- distplot accuracy --- \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeb3e796a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}