ml_cdcs_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PrLMGsDgjS-z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2d78a94-65f6-421b-8d66-d7a7b2b453bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-98e010612c42>:6: DeprecationWarning: `np.math` is a deprecated alias for the standard library `math` module (Deprecated Numpy 1.25). Replace usages of `np.math` with `math`\n",
            "  from numpy import math\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from numpy import math\n",
        "from sklearn import model_selection\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Step 1: Understanding Our Data"
      ],
      "metadata": {
        "id": "BlYCIcyv2E4y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we need to load in our dataset using the read_csv function in pandas"
      ],
      "metadata": {
        "id": "If8JwqCdBWr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  wine_quality_white = pd.read_csv(\"winequality-white.csv\")"
      ],
      "metadata": {
        "id": "Pe50dbb2jd19"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we will want to inspect our data and understand which fields are included. Looking at this data, what should our target variable be? Any ideas?"
      ],
      "metadata": {
        "id": "-8A0uZHCBcsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wine_quality_white.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "4frlIagsBtKu",
        "outputId": "5550b4c6-7675-45b7-a8da-a787bc0baffb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.0              0.27         0.36            20.7      0.045   \n",
              "1            6.3              0.30         0.34             1.6      0.049   \n",
              "2            8.1              0.28         0.40             6.9      0.050   \n",
              "3            7.2              0.23         0.32             8.5      0.058   \n",
              "4            7.2              0.23         0.32             8.5      0.058   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
              "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
              "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
              "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      8.8        6  \n",
              "1      9.5        6  \n",
              "2     10.1        6  \n",
              "3      9.9        6  \n",
              "4      9.9        6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88c1339e-3492-4f61-8bf2-4da2b6b46752\" 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>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>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.045</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1.0010</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</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-88c1339e-3492-4f61-8bf2-4da2b6b46752')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-88c1339e-3492-4f61-8bf2-4da2b6b46752 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-88c1339e-3492-4f61-8bf2-4da2b6b46752');\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-244089fd-b170-4323-8974-71793c394c9e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-244089fd-b170-4323-8974-71793c394c9e')\"\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-244089fd-b170-4323-8974-71793c394c9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wine_quality_white",
              "summary": "{\n  \"name\": \"wine_quality_white\",\n  \"rows\": 4898,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.843868227687513,\n        \"min\": 3.8,\n        \"max\": 14.2,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          10.3,\n          5.8,\n          6.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10079454842486534,\n        \"min\": 0.08,\n        \"max\": 1.1,\n        \"num_unique_values\": 125,\n        \"samples\": [\n          0.14,\n          0.595,\n          0.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12101980420298249,\n        \"min\": 0.0,\n        \"max\": 1.66,\n        \"num_unique_values\": 87,\n        \"samples\": [\n          0.64,\n          0.36,\n          0.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.072057784014881,\n        \"min\": 0.6,\n        \"max\": 65.8,\n        \"num_unique_values\": 310,\n        \"samples\": [\n          15.5,\n          19.25,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.021847968093728798,\n        \"min\": 0.009,\n        \"max\": 0.346,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          0.167,\n          0.133,\n          0.015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.00713732523259,\n        \"min\": 2.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 132,\n        \"samples\": [\n          24.0,\n          122.5,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.49806455414291,\n        \"min\": 9.0,\n        \"max\": 440.0,\n        \"num_unique_values\": 251,\n        \"samples\": [\n          260.0,\n          63.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0029909069169369337,\n        \"min\": 0.98711,\n        \"max\": 1.03898,\n        \"num_unique_values\": 890,\n        \"samples\": [\n          0.99362,\n          0.99388,\n          0.9929\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1510005996150668,\n        \"min\": 2.72,\n        \"max\": 3.82,\n        \"num_unique_values\": 103,\n        \"samples\": [\n          3.34,\n          3.41,\n          3.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1141258339488323,\n        \"min\": 0.22,\n        \"max\": 1.08,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          0.41,\n          0.45,\n          0.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2306205677522684,\n        \"min\": 8.0,\n        \"max\": 14.2,\n        \"num_unique_values\": 103,\n        \"samples\": [\n          12.6,\n          11.36666667,\n          10.03333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we'll want to see if there are any N/As in the dataset"
      ],
      "metadata": {
        "id": "gn5G5vARBqiS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wine_quality_white.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcTqdp9qwcBA",
        "outputId": "a80cf146-c929-4ec7-c512-a3fcd512cd51",
        "collapsed": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           False\n",
              "volatile acidity        False\n",
              "citric acid             False\n",
              "residual sugar          False\n",
              "chlorides               False\n",
              "free sulfur dioxide     False\n",
              "total sulfur dioxide    False\n",
              "density                 False\n",
              "pH                      False\n",
              "sulphates               False\n",
              "alcohol                 False\n",
              "quality                 False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine_quality_white.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPa9uHYNWpj4",
        "outputId": "924208b6-6486-4c2b-bd54-aa25fa2e819c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4898 entries, 0 to 4897\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         4898 non-null   float64\n",
            " 1   volatile acidity      4898 non-null   float64\n",
            " 2   citric acid           4898 non-null   float64\n",
            " 3   residual sugar        4898 non-null   float64\n",
            " 4   chlorides             4898 non-null   float64\n",
            " 5   free sulfur dioxide   4898 non-null   float64\n",
            " 6   total sulfur dioxide  4898 non-null   float64\n",
            " 7   density               4898 non-null   float64\n",
            " 8   pH                    4898 non-null   float64\n",
            " 9   sulphates             4898 non-null   float64\n",
            " 10  alcohol               4898 non-null   float64\n",
            " 11  quality               4898 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 459.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, we will inspect our dataset to see what kinds of values are provided for each column."
      ],
      "metadata": {
        "id": "RmtO7SF7Bz9s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wine_quality_white.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "6RcmMY-3xZUG",
        "outputId": "3742f8c4-d4ff-4f0e-d453-544d284877c5",
        "collapsed": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
              "mean        6.854788          0.278241     0.334192        6.391415   \n",
              "std         0.843868          0.100795     0.121020        5.072058   \n",
              "min         3.800000          0.080000     0.000000        0.600000   \n",
              "25%         6.300000          0.210000     0.270000        1.700000   \n",
              "50%         6.800000          0.260000     0.320000        5.200000   \n",
              "75%         7.300000          0.320000     0.390000        9.900000   \n",
              "max        14.200000          1.100000     1.660000       65.800000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
              "mean      0.045772            35.308085            138.360657     0.994027   \n",
              "std       0.021848            17.007137             42.498065     0.002991   \n",
              "min       0.009000             2.000000              9.000000     0.987110   \n",
              "25%       0.036000            23.000000            108.000000     0.991723   \n",
              "50%       0.043000            34.000000            134.000000     0.993740   \n",
              "75%       0.050000            46.000000            167.000000     0.996100   \n",
              "max       0.346000           289.000000            440.000000     1.038980   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
              "mean      3.188267     0.489847    10.514267     5.877909  \n",
              "std       0.151001     0.114126     1.230621     0.885639  \n",
              "min       2.720000     0.220000     8.000000     3.000000  \n",
              "25%       3.090000     0.410000     9.500000     5.000000  \n",
              "50%       3.180000     0.470000    10.400000     6.000000  \n",
              "75%       3.280000     0.550000    11.400000     6.000000  \n",
              "max       3.820000     1.080000    14.200000     9.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-296a6246-8e74-4853-90f4-48cbc1c016cf\" 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>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>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.854788</td>\n",
              "      <td>0.278241</td>\n",
              "      <td>0.334192</td>\n",
              "      <td>6.391415</td>\n",
              "      <td>0.045772</td>\n",
              "      <td>35.308085</td>\n",
              "      <td>138.360657</td>\n",
              "      <td>0.994027</td>\n",
              "      <td>3.188267</td>\n",
              "      <td>0.489847</td>\n",
              "      <td>10.514267</td>\n",
              "      <td>5.877909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.843868</td>\n",
              "      <td>0.100795</td>\n",
              "      <td>0.121020</td>\n",
              "      <td>5.072058</td>\n",
              "      <td>0.021848</td>\n",
              "      <td>17.007137</td>\n",
              "      <td>42.498065</td>\n",
              "      <td>0.002991</td>\n",
              "      <td>0.151001</td>\n",
              "      <td>0.114126</td>\n",
              "      <td>1.230621</td>\n",
              "      <td>0.885639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.987110</td>\n",
              "      <td>2.720000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>0.036000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.991723</td>\n",
              "      <td>3.090000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.800000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.043000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.993740</td>\n",
              "      <td>3.180000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.300000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>0.996100</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.200000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>65.800000</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>1.038980</td>\n",
              "      <td>3.820000</td>\n",
              "      <td>1.080000</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>9.000000</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-296a6246-8e74-4853-90f4-48cbc1c016cf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-296a6246-8e74-4853-90f4-48cbc1c016cf 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-296a6246-8e74-4853-90f4-48cbc1c016cf');\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-f8a316b4-f61c-4a8e-9330-d68ddefd4112\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f8a316b4-f61c-4a8e-9330-d68ddefd4112')\"\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-f8a316b4-f61c-4a8e-9330-d68ddefd4112 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"wine_quality_white\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1729.3802796015577,\n        \"min\": 0.843868227687513,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.854787668436097,\n          6.8,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1731.585893012408,\n        \"min\": 0.08,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.27824111882400976,\n          0.26,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1731.5482512784367,\n        \"min\": 0.0,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.33419150673744386,\n          0.32,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1727.057466455006,\n        \"min\": 0.6,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.391414863209474,\n          5.2,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1731.676649579488,\n        \"min\": 0.009,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.04577235606369946,\n          0.043,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1711.6847487709078,\n        \"min\": 2.0,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.30808493262556,\n          34.0,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1684.2445242150275,\n        \"min\": 9.0,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          138.36065741118824,\n          134.0,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1731.4012606981378,\n        \"min\": 0.0029909069169369337,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9940273764801959,\n          0.99374,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1730.7235387365736,\n        \"min\": 0.1510005996150668,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.1882666394446715,\n          3.18,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1731.5361396446815,\n        \"min\": 0.1141258339488323,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.48984687627603113,\n          0.47,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1728.4131904079504,\n        \"min\": 1.2306205677522684,\n        \"max\": 4898.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.51426704777011,\n          10.4,\n          4898.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1729.8998109229558,\n        \"min\": 0.8856385749678312,\n        \"max\": 4898.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4898.0,\n          5.87790935075541,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It might be visually easier for us to see the values using a histogram"
      ],
      "metadata": {
        "id": "atblLW3kCC33"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['figure.figsize'] = 16, 12\n",
        "wine_quality_white.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "sskp4X-wxjNn",
        "outputId": "5dde983e-21e8-4f9b-bb1d-bc0b6a3714b9",
        "collapsed": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Do any of the values look strange?\n",
        "Which feature do you think is most likely to be useful in our wine analysis?"
      ],
      "metadata": {
        "id": "Thd-L3YICUYL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we want to see what the relationships between different variables are. This is an important step for us to understand which variables might be very correlated with each other. If the variables seem extremely correlated, we may choose to drop one of the variables from the model to ensure we still have good predictive power."
      ],
      "metadata": {
        "id": "6TG3RoKbGEto"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlations = wine_quality_white.corr()\n",
        "correlations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "qD4Gl211yAKC",
        "outputId": "4f386633-a207-4592-97c4-158d3cde785a",
        "collapsed": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      fixed acidity  volatile acidity  citric acid  \\\n",
              "fixed acidity              1.000000         -0.022697     0.289181   \n",
              "volatile acidity          -0.022697          1.000000    -0.149472   \n",
              "citric acid                0.289181         -0.149472     1.000000   \n",
              "residual sugar             0.089021          0.064286     0.094212   \n",
              "chlorides                  0.023086          0.070512     0.114364   \n",
              "free sulfur dioxide       -0.049396         -0.097012     0.094077   \n",
              "total sulfur dioxide       0.091070          0.089261     0.121131   \n",
              "density                    0.265331          0.027114     0.149503   \n",
              "pH                        -0.425858         -0.031915    -0.163748   \n",
              "sulphates                 -0.017143         -0.035728     0.062331   \n",
              "alcohol                   -0.120881          0.067718    -0.075729   \n",
              "quality                   -0.113663         -0.194723    -0.009209   \n",
              "\n",
              "                      residual sugar  chlorides  free sulfur dioxide  \\\n",
              "fixed acidity               0.089021   0.023086            -0.049396   \n",
              "volatile acidity            0.064286   0.070512            -0.097012   \n",
              "citric acid                 0.094212   0.114364             0.094077   \n",
              "residual sugar              1.000000   0.088685             0.299098   \n",
              "chlorides                   0.088685   1.000000             0.101392   \n",
              "free sulfur dioxide         0.299098   0.101392             1.000000   \n",
              "total sulfur dioxide        0.401439   0.198910             0.615501   \n",
              "density                     0.838966   0.257211             0.294210   \n",
              "pH                         -0.194133  -0.090439            -0.000618   \n",
              "sulphates                  -0.026664   0.016763             0.059217   \n",
              "alcohol                    -0.450631  -0.360189            -0.250104   \n",
              "quality                    -0.097577  -0.209934             0.008158   \n",
              "\n",
              "                      total sulfur dioxide   density        pH  sulphates  \\\n",
              "fixed acidity                     0.091070  0.265331 -0.425858  -0.017143   \n",
              "volatile acidity                  0.089261  0.027114 -0.031915  -0.035728   \n",
              "citric acid                       0.121131  0.149503 -0.163748   0.062331   \n",
              "residual sugar                    0.401439  0.838966 -0.194133  -0.026664   \n",
              "chlorides                         0.198910  0.257211 -0.090439   0.016763   \n",
              "free sulfur dioxide               0.615501  0.294210 -0.000618   0.059217   \n",
              "total sulfur dioxide              1.000000  0.529881  0.002321   0.134562   \n",
              "density                           0.529881  1.000000 -0.093591   0.074493   \n",
              "pH                                0.002321 -0.093591  1.000000   0.155951   \n",
              "sulphates                         0.134562  0.074493  0.155951   1.000000   \n",
              "alcohol                          -0.448892 -0.780138  0.121432  -0.017433   \n",
              "quality                          -0.174737 -0.307123  0.099427   0.053678   \n",
              "\n",
              "                       alcohol   quality  \n",
              "fixed acidity        -0.120881 -0.113663  \n",
              "volatile acidity      0.067718 -0.194723  \n",
              "citric acid          -0.075729 -0.009209  \n",
              "residual sugar       -0.450631 -0.097577  \n",
              "chlorides            -0.360189 -0.209934  \n",
              "free sulfur dioxide  -0.250104  0.008158  \n",
              "total sulfur dioxide -0.448892 -0.174737  \n",
              "density              -0.780138 -0.307123  \n",
              "pH                    0.121432  0.099427  \n",
              "sulphates            -0.017433  0.053678  \n",
              "alcohol               1.000000  0.435575  \n",
              "quality               0.435575  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bec0a4f8-a98d-415c-8037-b99dfccc064d\" 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>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>-0.022697</td>\n",
              "      <td>0.289181</td>\n",
              "      <td>0.089021</td>\n",
              "      <td>0.023086</td>\n",
              "      <td>-0.049396</td>\n",
              "      <td>0.091070</td>\n",
              "      <td>0.265331</td>\n",
              "      <td>-0.425858</td>\n",
              "      <td>-0.017143</td>\n",
              "      <td>-0.120881</td>\n",
              "      <td>-0.113663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>-0.022697</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.149472</td>\n",
              "      <td>0.064286</td>\n",
              "      <td>0.070512</td>\n",
              "      <td>-0.097012</td>\n",
              "      <td>0.089261</td>\n",
              "      <td>0.027114</td>\n",
              "      <td>-0.031915</td>\n",
              "      <td>-0.035728</td>\n",
              "      <td>0.067718</td>\n",
              "      <td>-0.194723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>0.289181</td>\n",
              "      <td>-0.149472</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.094212</td>\n",
              "      <td>0.114364</td>\n",
              "      <td>0.094077</td>\n",
              "      <td>0.121131</td>\n",
              "      <td>0.149503</td>\n",
              "      <td>-0.163748</td>\n",
              "      <td>0.062331</td>\n",
              "      <td>-0.075729</td>\n",
              "      <td>-0.009209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>0.089021</td>\n",
              "      <td>0.064286</td>\n",
              "      <td>0.094212</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.088685</td>\n",
              "      <td>0.299098</td>\n",
              "      <td>0.401439</td>\n",
              "      <td>0.838966</td>\n",
              "      <td>-0.194133</td>\n",
              "      <td>-0.026664</td>\n",
              "      <td>-0.450631</td>\n",
              "      <td>-0.097577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>0.023086</td>\n",
              "      <td>0.070512</td>\n",
              "      <td>0.114364</td>\n",
              "      <td>0.088685</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101392</td>\n",
              "      <td>0.198910</td>\n",
              "      <td>0.257211</td>\n",
              "      <td>-0.090439</td>\n",
              "      <td>0.016763</td>\n",
              "      <td>-0.360189</td>\n",
              "      <td>-0.209934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>-0.049396</td>\n",
              "      <td>-0.097012</td>\n",
              "      <td>0.094077</td>\n",
              "      <td>0.299098</td>\n",
              "      <td>0.101392</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.615501</td>\n",
              "      <td>0.294210</td>\n",
              "      <td>-0.000618</td>\n",
              "      <td>0.059217</td>\n",
              "      <td>-0.250104</td>\n",
              "      <td>0.008158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>0.091070</td>\n",
              "      <td>0.089261</td>\n",
              "      <td>0.121131</td>\n",
              "      <td>0.401439</td>\n",
              "      <td>0.198910</td>\n",
              "      <td>0.615501</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.529881</td>\n",
              "      <td>0.002321</td>\n",
              "      <td>0.134562</td>\n",
              "      <td>-0.448892</td>\n",
              "      <td>-0.174737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>0.265331</td>\n",
              "      <td>0.027114</td>\n",
              "      <td>0.149503</td>\n",
              "      <td>0.838966</td>\n",
              "      <td>0.257211</td>\n",
              "      <td>0.294210</td>\n",
              "      <td>0.529881</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.093591</td>\n",
              "      <td>0.074493</td>\n",
              "      <td>-0.780138</td>\n",
              "      <td>-0.307123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>-0.425858</td>\n",
              "      <td>-0.031915</td>\n",
              "      <td>-0.163748</td>\n",
              "      <td>-0.194133</td>\n",
              "      <td>-0.090439</td>\n",
              "      <td>-0.000618</td>\n",
              "      <td>0.002321</td>\n",
              "      <td>-0.093591</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.155951</td>\n",
              "      <td>0.121432</td>\n",
              "      <td>0.099427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>-0.017143</td>\n",
              "      <td>-0.035728</td>\n",
              "      <td>0.062331</td>\n",
              "      <td>-0.026664</td>\n",
              "      <td>0.016763</td>\n",
              "      <td>0.059217</td>\n",
              "      <td>0.134562</td>\n",
              "      <td>0.074493</td>\n",
              "      <td>0.155951</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.017433</td>\n",
              "      <td>0.053678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>-0.120881</td>\n",
              "      <td>0.067718</td>\n",
              "      <td>-0.075729</td>\n",
              "      <td>-0.450631</td>\n",
              "      <td>-0.360189</td>\n",
              "      <td>-0.250104</td>\n",
              "      <td>-0.448892</td>\n",
              "      <td>-0.780138</td>\n",
              "      <td>0.121432</td>\n",
              "      <td>-0.017433</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.435575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>-0.113663</td>\n",
              "      <td>-0.194723</td>\n",
              "      <td>-0.009209</td>\n",
              "      <td>-0.097577</td>\n",
              "      <td>-0.209934</td>\n",
              "      <td>0.008158</td>\n",
              "      <td>-0.174737</td>\n",
              "      <td>-0.307123</td>\n",
              "      <td>0.099427</td>\n",
              "      <td>0.053678</td>\n",
              "      <td>0.435575</td>\n",
              "      <td>1.000000</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-bec0a4f8-a98d-415c-8037-b99dfccc064d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bec0a4f8-a98d-415c-8037-b99dfccc064d 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-bec0a4f8-a98d-415c-8037-b99dfccc064d');\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-4960f157-3f6d-46b0-a01e-1642a004dbff\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4960f157-3f6d-46b0-a01e-1642a004dbff')\"\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-4960f157-3f6d-46b0-a01e-1642a004dbff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "correlations",
              "summary": "{\n  \"name\": \"correlations\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3435420867862913,\n        \"min\": -0.4258582909913803,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.12088112319359764,\n          -0.017142985021138365,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30795724673980207,\n        \"min\": -0.19472296892113433,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.06771794278071731,\n          -0.035728146905814676,\n          -0.022697290146645732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3039281585677746,\n        \"min\": -0.16374821140062115,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.07572873005712515,\n          0.0623309402852321,\n          0.28918069769367544\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41169445734982235,\n        \"min\": -0.4506312220327153,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4506312220327153,\n          -0.02666436585385816,\n          0.08902070136217159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33065224753947603,\n        \"min\": -0.3601887121078744,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.3601887121078744,\n          0.016762883708175663,\n          0.02308564365634794\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3428929026335162,\n        \"min\": -0.250103941479281,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.250103941479281,\n          0.05921724582526308,\n          -0.04939585908117326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38079985956770573,\n        \"min\": -0.4488921020582551,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4488921020582551,\n          0.13456236692980836,\n          0.09106975615864002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47901571785248204,\n        \"min\": -0.7801376214258195,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.7801376214258195,\n          0.07449314851168053,\n          0.265331013839189\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3438832752865674,\n        \"min\": -0.4258582909913803,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.12143209874946236,\n          0.1559514972796509,\n          -0.4258582909913803\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2834008512545046,\n        \"min\": -0.035728146905814676,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.017432771861015186,\n          1.0,\n          -0.017142985021138365\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46424488883218046,\n        \"min\": -0.7801376214258195,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.0,\n          -0.017432771861015186,\n          -0.12088112319359764\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3582703091667256,\n        \"min\": -0.3071233127347341,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.43557471546165705,\n          0.05367787713279206,\n          -0.11366283071301829\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's a bit hard to see in table format, so we are going to go ahead and look at a correlation matrix"
      ],
      "metadata": {
        "id": "4kJcMWfMGZYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\tplt.rcParams['figure.figsize'] = 10, 10\n",
        "\tnames = correlations.columns\n",
        "\tfig = plt.figure()\n",
        "\tax = fig.add_subplot(111)\n",
        "\tcax = ax.matshow(correlations, vmin=-1, vmax=1)\n",
        "\tfig.colorbar(cax)\n",
        "\tticks = np.arange(0,len(names),1)\n",
        "\tax.set_xticks(ticks)\n",
        "\tax.set_yticks(ticks)\n",
        "\tax.set_xticklabels(names, rotation=30, rotation_mode='anchor', ha='left')\n",
        "\tax.set_yticklabels(names)\n",
        "\tplt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "LXtmbTZiyOBu",
        "outputId": "710558f7-fd93-457d-fe66-eaf02d589466",
        "collapsed": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now,we might want to understand the relative outliers still present in our data."
      ],
      "metadata": {
        "id": "w3eYenNvHPEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\tplt.rcParams['figure.figsize'] = 16, 12\n",
        "\twine_quality_white.plot(kind='box', subplots=True,\n",
        "\tlayout=(5, math.ceil(len(wine_quality_white.columns)/5)),\n",
        "\tsharex=False, sharey=False)\n",
        "\tplt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "SN3HR8KLzadM",
        "outputId": "fe77555a-c681-4e4b-c1d1-1159fbb70209",
        "collapsed": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 15 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we understand our data, we want to take some additional steps to pre-process our data before putting it into the model."
      ],
      "metadata": {
        "id": "98QhKFzhHnIm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df = wine_quality_white.copy(deep=True)\n",
        "#processed_df = processed_df.fillna(processed_df.median())"
      ],
      "metadata": {
        "id": "uvurx3yS0SKB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ry2t1c_8odM9",
        "outputId": "d64a76bd-e2ac-435a-ec4f-6dc8d848735b",
        "collapsed": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           False\n",
              "volatile acidity        False\n",
              "citric acid             False\n",
              "residual sugar          False\n",
              "chlorides               False\n",
              "free sulfur dioxide     False\n",
              "total sulfur dioxide    False\n",
              "density                 False\n",
              "pH                      False\n",
              "sulphates               False\n",
              "alcohol                 False\n",
              "quality                 False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we look at our dataset again now, we can see what the distribution of the dataset is after we did the preprocessing step."
      ],
      "metadata": {
        "id": "nDyL7OLnH5hH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We might also want to know what variable types each of the variables in the dataset, and convert them if need be."
      ],
      "metadata": {
        "id": "9bJlPe8lIHjT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0pVxzCA_eKv",
        "outputId": "6c55b80c-ef8b-4093-a3d1-4a47bcadf129",
        "collapsed": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the above, we see that most of these are floats, except for quality which is an integer. Because we will be doing a binary classifier model, we need to make a binary target variable."
      ],
      "metadata": {
        "id": "-7S9cPMxIN62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df['quality_binary'] = processed_df['quality'].apply(lambda x: 1 if x < 5 else 0)"
      ],
      "metadata": {
        "id": "6tC24OHhCTVv"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KI7ars-gC2uv",
        "outputId": "deb5c9de-1d53-45ab-bbe9-5c058fc67fbf",
        "collapsed": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "quality_binary            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_df['quality_binary'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "ezmHYDapTgEs",
        "outputId": "01cf7f6e-5809-4370-b05e-15c93e2c6bec"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality_binary\n",
              "0    4715\n",
              "1     183\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Step 2: Putting together our model!"
      ],
      "metadata": {
        "id": "5csokWPLIfap"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we need to decide which features to include in our model. Depending on which model we use, we may need to complete additional pre-processing steps. In today's class, we will be fitting a simple logistic regression model, which accepts continuous variables, so we do not need to do much extra work to pre-process the data."
      ],
      "metadata": {
        "id": "9r0sdfD8IwZI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's come back to the same question again: What is our x variable? What is our y variable?"
      ],
      "metadata": {
        "id": "Cxd11rc4JIU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']\n",
        "selected_y = 'quality_binary'"
      ],
      "metadata": {
        "id": "VyYyM9aLUkvF"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = processed_df[selected_features]\n",
        "y = processed_df[selected_y]"
      ],
      "metadata": {
        "id": "rkYMdGwmCXPy"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRAP4sBcapJa",
        "outputId": "52d22a95-3d64-4b6a-f279-a0f14dd9ef10"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N83WBXkOawg_",
        "outputId": "49305953-2e2c-494f-9f73-90cb92ed04e4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898,)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the purposes of evaluating our model at a later date, we need to split our dataset into training data and testing data. We will use the sklearn function modules for this. In some cases, you may also want to split into 3 datasets (train, validation, and test). For simplicity, I won't do that now but we will cover this more when we discuss model evaluation next time."
      ],
      "metadata": {
        "id": "NwGbCfuNJR-G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.2, random_state=1, shuffle=False)"
      ],
      "metadata": {
        "id": "bbj0rvK3EKci"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, I check the size of the test data (the number of rows). This is relevant later on when we think about evaluation."
      ],
      "metadata": {
        "id": "9xoWwNxeJrYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "v-D1Ae_RWXpo",
        "outputId": "f2797319-9660-4728-8652-64a494f9a645"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(980,)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhbCbsneUSAs",
        "outputId": "69f0db94-8c95-4ff0-c866-41fbcb9600c0"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3918,)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Time to train a logistic regression model!\n",
        "\n"
      ],
      "metadata": {
        "id": "6x9PmKn9L_Jw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf_log = LogisticRegression(C=0.1, max_iter=500)\n",
        "clf_log = clf_log.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "yL_NgGgjPCyk"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(clf_log)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "cgDed-pEUmWn",
        "outputId": "e3402d0b-af22-4fbc-9b16-0009af77c07f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression(C=0.1, max_iter=500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that our model is trained, we will want to apply our model to the test set (which the model has never seen before). Using the testing data, we will try to create a vector of predicted y values, for each of the x_test values."
      ],
      "metadata": {
        "id": "kBbPBLAwKHek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_log.predict(X_test)"
      ],
      "metadata": {
        "id": "SRTqAIGmU5rg"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "OpBnO9PjVJ2W",
        "outputId": "cf99e5e0-aa47-4775-89f6-3e9a44a33843"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(980,)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCPFviRkdI82",
        "outputId": "7a09a5c3-b8bc-4f4b-9639-47e027455bf2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "TbCElMl3KbPp",
        "outputId": "34a2c55b-7936-4565-df9c-f69f72430dad"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3918    0\n",
              "3919    0\n",
              "3920    0\n",
              "3921    0\n",
              "3922    0\n",
              "       ..\n",
              "4893    0\n",
              "4894    0\n",
              "4895    0\n",
              "4896    0\n",
              "4897    0\n",
              "Name: quality_binary, Length: 980, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can use a built-in sklearn function to get our accuracy score for the model."
      ],
      "metadata": {
        "id": "Whhb_bAtK0Jb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics.accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "JDl73yAqKhIq",
        "outputId": "8e0b1ef5-6716-4e5f-ac10-5b35a6ee2de6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9734693877551021"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    }
  ]
}