wine_quality.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "aj1_lam8dUiO"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "data = {\n",
        "    'A': [random.randint(0, 100) for _ in range(100)],\n",
        "    'B': [random.randint(0, 100) for _ in range(100)],\n",
        "    'C': [random.randint(0, 100) for _ in range(100)]\n",
        "    }\n",
        "\n",
        "df = pd.DataFrame(data)\n"
      ],
      "metadata": {
        "id": "biTU4bTWddBd"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    A   B   C\n",
              "0  29  38  96\n",
              "1  56  44  69\n",
              "2  87  81  62\n",
              "3   8  61   5\n",
              "4   9  94  25"
            ],
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              "      <th>A</th>\n",
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              "      <th>4</th>\n",
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              "\n",
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              "        document.querySelector('#df-da079132-6740-4bf4-a36c-e8f70322280e 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-da079132-6740-4bf4-a36c-e8f70322280e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      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-0b79cf27-1043-4d75-be9f-4fe2d0df01c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          31,\n          29,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          38,\n          47,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          59,\n          95,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# countplot\n",
        "sns.countplot(x='A', data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8T3vO3MGduPd",
        "outputId": "05d9ff33-9a96-4ec2-c3d8-e46c6ab79451",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"A\"].value_counts()"
      ],
      "metadata": {
        "id": "z1Ff7NSfd-yx",
        "outputId": "e2153ecd-7b80-4846-f7e4-020d9696b6ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A\n",
              "86     3\n",
              "5      3\n",
              "52     3\n",
              "80     3\n",
              "56     3\n",
              "      ..\n",
              "48     1\n",
              "78     1\n",
              "82     1\n",
              "100    1\n",
              "19     1\n",
              "Name: count, Length: 70, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_value = df[\"A\"].shape[0]"
      ],
      "metadata": {
        "id": "9avcIwRyeY_u"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=new_value, data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Qz3iRX4eeeiX",
        "outputId": "4e262dab-77bc-4043-e6d6-7e410a590380",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'int' object is not iterable",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-110-408298004f74>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, fill, hue_norm, stat, width, dodge, gap, log_scale, native_scale, formatter, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2624\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2625\u001b[0m         \u001b[0morient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"x\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2626\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2627\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2628\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot pass values for both `x` and `y`.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "** This is what has to happen **"
      ],
      "metadata": {
        "id": "9pOlaZzze-PM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''what is this '''"
      ],
      "metadata": {
        "id": "RtIPfQY2emPy",
        "outputId": "bb35a238-aaab-482e-9a7b-33735bd546b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'what is this '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "SV6rngCye8_t"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = range(0, 10)"
      ],
      "metadata": {
        "id": "lYPY8P-Eh8ry"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "OyfNpbc0iAKV",
        "outputId": "9c595aea-c3ad-432a-d641-b713bf2ac6fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "range(0, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = list(range(0, 10))"
      ],
      "metadata": {
        "id": "TnOC0E4EiAo9"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "id": "8Vdt0RG1iFQz",
        "outputId": "a5210c35-e66f-4e1b-9de3-4855bb7f448f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = list(range(-10, 0))"
      ],
      "metadata": {
        "id": "MvGbARVKiGU5"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x\n",
        "y"
      ],
      "metadata": {
        "id": "o03K9zAgiJov",
        "outputId": "b5298d79-0973-477b-f94c-2be94227419d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1]"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x)"
      ],
      "metadata": {
        "id": "ENt3bz3DiTFB",
        "outputId": "f6191336-15cc-42cb-d99f-996e351062dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78daceb45420>]"
            ]
          },
          "metadata": {},
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, y)"
      ],
      "metadata": {
        "id": "t5vtijNkiYPx",
        "outputId": "a8f3a457-fde4-44dd-9950-0e15738e95f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dacf25e590>]"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, y, marker='o')"
      ],
      "metadata": {
        "id": "ulj93IiGiaCN",
        "outputId": "5a30ffcb-8ca2-41e9-da6b-503a1aa2e5ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78daced193c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, y, marker='o', linestyle='--')"
      ],
      "metadata": {
        "id": "SPR2LOUHifk5",
        "outputId": "3925c5e5-2a03-4e89-ece7-842bc96ae481",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dace963af0>]"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, y, marker='o', linestyle='--', color='red')"
      ],
      "metadata": {
        "id": "2EvbePV-iigv",
        "outputId": "bf05c74f-ab57-48a2-b198-c1b2f66ad5cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dace70aa10>]"
            ]
          },
          "metadata": {},
          "execution_count": 124
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine = pd.read_csv('winequality-red.csv')"
      ],
      "metadata": {
        "id": "Mthrcm52in_F"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wine.shape"
      ],
      "metadata": {
        "id": "9c8h45RdksVA",
        "outputId": "e7b3f8df-fb0d-4160-b5ce-595a6e834db3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.head()"
      ],
      "metadata": {
        "id": "7K8hPViLkt62",
        "outputId": "80ad6835-9b64-43a5-9f34-a8c92f80a30e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
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              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>0.076</td>\n",
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              "      <td>11.2</td>\n",
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              "      <td>1.9</td>\n",
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              "      <td>7.4</td>\n",
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              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
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              "        const element = document.querySelector('#df-51c060dd-c568-49b3-b7a9-bfa9719dd79a');\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-ee223a22-ecae-426e-8fe6-565a745b3df3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wine",
              "summary": "{\n  \"name\": \"wine\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\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\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.isnull().sum()"
      ],
      "metadata": {
        "id": "nO3rNpC6kvSp",
        "outputId": "4e082aba-c55f-4bfe-f5ad-1ff058242bba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.info()"
      ],
      "metadata": {
        "id": "4nQTCnTiky0I",
        "outputId": "8f14fe1d-8a2d-46e1-ef40-d282fa21b4d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1599 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1599 non-null   float64\n",
            " 1   volatile acidity      1599 non-null   float64\n",
            " 2   citric acid           1599 non-null   float64\n",
            " 3   residual sugar        1599 non-null   float64\n",
            " 4   chlorides             1599 non-null   float64\n",
            " 5   free sulfur dioxide   1599 non-null   float64\n",
            " 6   total sulfur dioxide  1599 non-null   float64\n",
            " 7   density               1599 non-null   float64\n",
            " 8   pH                    1599 non-null   float64\n",
            " 9   sulphates             1599 non-null   float64\n",
            " 10  alcohol               1599 non-null   float64\n",
            " 11  quality               1599 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 150.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.describe()"
      ],
      "metadata": {
        "id": "qO-SllYNk1f2",
        "outputId": "a4f8ea64-e523-4504-f89a-5332b62f0d04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
              "mean        8.319637          0.527821     0.270976        2.538806   \n",
              "std         1.741096          0.179060     0.194801        1.409928   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.390000     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.260000        2.200000   \n",
              "75%         9.200000          0.640000     0.420000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
              "mean      0.087467            15.874922             46.467792     0.996747   \n",
              "std       0.047065            10.460157             32.895324     0.001887   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996750   \n",
              "75%       0.090000            21.000000             62.000000     0.997835   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
              "mean      3.311113     0.658149    10.422983     5.636023  \n",
              "std       0.154386     0.169507     1.065668     0.807569  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d30af102-8aa3-42f4-a1d3-6a633aff4d99\" 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>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"wine\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 562.5806745048905,\n        \"min\": 1.7410963181276953,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.31963727329581,\n          7.9,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 565.1321980080039,\n        \"min\": 0.12,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5278205128205128,\n          0.52,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 565.2190298438521,\n        \"min\": 0.0,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2709756097560976,\n          0.26,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 563.9859918397555,\n        \"min\": 0.9,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.53880550343965,\n          2.2,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 565.2815720420849,\n        \"min\": 0.012,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.08746654158849279,\n          0.079,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 558.6255652064851,\n        \"min\": 1.0,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.874921826141339,\n          14.0,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 547.7946740169228,\n        \"min\": 6.0,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          46.46779237023139,\n          38.0,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 565.0298129526795,\n        \"min\": 0.0018873339538425554,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9967466791744841,\n          0.99675,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564.3160746075234,\n        \"min\": 0.15438646490354277,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.3111131957473416,\n          3.31,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 565.0766914059569,\n        \"min\": 0.16950697959010996,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6581488430268917,\n          0.62,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 562.0325260314485,\n        \"min\": 1.0656675818473946,\n        \"max\": 1599.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.422983114446529,\n          10.2,\n          1599.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 563.5963817822505,\n        \"min\": 0.807569439734705,\n        \"max\": 1599.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1599.0,\n          5.6360225140712945,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.head()"
      ],
      "metadata": {
        "id": "hyh3vApsk4_o",
        "outputId": "ffdb92b4-f522-4866-abe8-c840261d733e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d20b051-5e37-4855-b1e4-40e63fe3718b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d20b051-5e37-4855-b1e4-40e63fe3718b')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d20b051-5e37-4855-b1e4-40e63fe3718b 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-2d20b051-5e37-4855-b1e4-40e63fe3718b');\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-57e40d7d-318c-4e85-b96e-f0bc536d42dc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57e40d7d-318c-4e85-b96e-f0bc536d42dc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57e40d7d-318c-4e85-b96e-f0bc536d42dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wine",
              "summary": "{\n  \"name\": \"wine\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\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\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine['quality'].value_counts()"
      ],
      "metadata": {
        "id": "9LEax20Yk9t5",
        "outputId": "3cc9df80-ff79-4f8d-fc3c-49eb869a34a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality\n",
              "5    681\n",
              "6    638\n",
              "7    199\n",
              "4     53\n",
              "8     18\n",
              "3     10\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine['quality'].value_counts().plot(kind=\"bar\")"
      ],
      "metadata": {
        "id": "EjdMNtJ5lDBx",
        "outputId": "64491240-a202-4246-8a78-fe795440a69b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='quality'>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=wine, x='quality')"
      ],
      "metadata": {
        "id": "GQHuzwehlLO9",
        "outputId": "5a91b17d-95fa-4804-d5d3-87d70379eda2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='quality', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mean value of fixed acidity w.r.t to quality of wine\n",
        "\n",
        "wine.groupby('quality').mean()['fixed acidity']"
      ],
      "metadata": {
        "id": "XiD95uEulUJs",
        "outputId": "3e4f755c-e5b1-42fe-c12e-9fd7eb598ef0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality\n",
              "3    8.360000\n",
              "4    7.779245\n",
              "5    8.167254\n",
              "6    8.347179\n",
              "7    8.872362\n",
              "8    8.566667\n",
              "Name: fixed acidity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.head()"
      ],
      "metadata": {
        "id": "qKjNLWQblnhx",
        "outputId": "c471921a-602a-4e76-df50-4e03b61a62d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
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              "    <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",
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              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
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              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
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              "      <td>9.8</td>\n",
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              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
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              "      <td>0.70</td>\n",
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              "      <td>0.9978</td>\n",
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              "      <td>9.4</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wine",
              "summary": "{\n  \"name\": \"wine\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\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\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_group = wine.groupby('quality')"
      ],
      "metadata": {
        "id": "IXT1HlP0l2Nj"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use new_group and provide some useful output\n",
        "new_group.mean()['fixed acidity']"
      ],
      "metadata": {
        "id": "cBWhuAewmFal",
        "outputId": "290e2bb6-c15c-441b-fc31-1faa3e346448",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality\n",
              "3    8.360000\n",
              "4    7.779245\n",
              "5    8.167254\n",
              "6    8.347179\n",
              "7    8.872362\n",
              "8    8.566667\n",
              "Name: fixed acidity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_group.describe()"
      ],
      "metadata": {
        "id": "j8kL9u0JmJSc",
        "outputId": "2af81571-193d-4c6d-f432-5a9313744880",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        fixed acidity                                                     \\\n",
              "                count      mean       std  min   25%   50%     75%   max   \n",
              "quality                                                                    \n",
              "3                10.0  8.360000  1.770875  6.7  7.15  7.50   9.875  11.6   \n",
              "4                53.0  7.779245  1.626624  4.6  6.80  7.50   8.400  12.5   \n",
              "5               681.0  8.167254  1.563988  5.0  7.10  7.80   8.900  15.9   \n",
              "6               638.0  8.347179  1.797849  4.7  7.00  7.90   9.400  14.3   \n",
              "7               199.0  8.872362  1.992483  4.9  7.40  8.80  10.100  15.6   \n",
              "8                18.0  8.566667  2.119656  5.0  7.25  8.25  10.225  12.6   \n",
              "\n",
              "        volatile acidity            ... sulphates       alcohol             \\\n",
              "                   count      mean  ...       75%   max   count       mean   \n",
              "quality                             ...                                      \n",
              "3                   10.0  0.884500  ...     0.615  0.86    10.0   9.955000   \n",
              "4                   53.0  0.693962  ...     0.600  2.00    53.0  10.265094   \n",
              "5                  681.0  0.577041  ...     0.660  1.98   681.0   9.899706   \n",
              "6                  638.0  0.497484  ...     0.750  1.95   638.0  10.629519   \n",
              "7                  199.0  0.403920  ...     0.830  1.36   199.0  11.465913   \n",
              "8                   18.0  0.423333  ...     0.820  1.10    18.0  12.094444   \n",
              "\n",
              "                                                      \n",
              "              std  min     25%     50%     75%   max  \n",
              "quality                                               \n",
              "3        0.818009  8.4   9.725   9.925  10.575  11.0  \n",
              "4        0.934776  9.0   9.600  10.000  11.000  13.1  \n",
              "5        0.736521  8.5   9.400   9.700  10.200  14.9  \n",
              "6        1.049639  8.4   9.800  10.500  11.300  14.0  \n",
              "7        0.961933  9.2  10.800  11.500  12.100  14.0  \n",
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              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">fixed acidity</th>\n",
              "      <th colspan=\"2\" halign=\"left\">volatile acidity</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"2\" halign=\"left\">sulphates</th>\n",
              "      <th colspan=\"8\" halign=\"left\">alcohol</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
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              "      <th>mean</th>\n",
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              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8.360000</td>\n",
              "      <td>1.770875</td>\n",
              "      <td>6.7</td>\n",
              "      <td>7.15</td>\n",
              "      <td>7.50</td>\n",
              "      <td>9.875</td>\n",
              "      <td>11.6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.884500</td>\n",
              "      <td>...</td>\n",
              "      <td>0.615</td>\n",
              "      <td>0.86</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.955000</td>\n",
              "      <td>0.818009</td>\n",
              "      <td>8.4</td>\n",
              "      <td>9.725</td>\n",
              "      <td>9.925</td>\n",
              "      <td>10.575</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53.0</td>\n",
              "      <td>7.779245</td>\n",
              "      <td>1.626624</td>\n",
              "      <td>4.6</td>\n",
              "      <td>6.80</td>\n",
              "      <td>7.50</td>\n",
              "      <td>8.400</td>\n",
              "      <td>12.5</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.693962</td>\n",
              "      <td>...</td>\n",
              "      <td>0.600</td>\n",
              "      <td>2.00</td>\n",
              "      <td>53.0</td>\n",
              "      <td>10.265094</td>\n",
              "      <td>0.934776</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.600</td>\n",
              "      <td>10.000</td>\n",
              "      <td>11.000</td>\n",
              "      <td>13.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>681.0</td>\n",
              "      <td>8.167254</td>\n",
              "      <td>1.563988</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.10</td>\n",
              "      <td>7.80</td>\n",
              "      <td>8.900</td>\n",
              "      <td>15.9</td>\n",
              "      <td>681.0</td>\n",
              "      <td>0.577041</td>\n",
              "      <td>...</td>\n",
              "      <td>0.660</td>\n",
              "      <td>1.98</td>\n",
              "      <td>681.0</td>\n",
              "      <td>9.899706</td>\n",
              "      <td>0.736521</td>\n",
              "      <td>8.5</td>\n",
              "      <td>9.400</td>\n",
              "      <td>9.700</td>\n",
              "      <td>10.200</td>\n",
              "      <td>14.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>638.0</td>\n",
              "      <td>8.347179</td>\n",
              "      <td>1.797849</td>\n",
              "      <td>4.7</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.90</td>\n",
              "      <td>9.400</td>\n",
              "      <td>14.3</td>\n",
              "      <td>638.0</td>\n",
              "      <td>0.497484</td>\n",
              "      <td>...</td>\n",
              "      <td>0.750</td>\n",
              "      <td>1.95</td>\n",
              "      <td>638.0</td>\n",
              "      <td>10.629519</td>\n",
              "      <td>1.049639</td>\n",
              "      <td>8.4</td>\n",
              "      <td>9.800</td>\n",
              "      <td>10.500</td>\n",
              "      <td>11.300</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>199.0</td>\n",
              "      <td>8.872362</td>\n",
              "      <td>1.992483</td>\n",
              "      <td>4.9</td>\n",
              "      <td>7.40</td>\n",
              "      <td>8.80</td>\n",
              "      <td>10.100</td>\n",
              "      <td>15.6</td>\n",
              "      <td>199.0</td>\n",
              "      <td>0.403920</td>\n",
              "      <td>...</td>\n",
              "      <td>0.830</td>\n",
              "      <td>1.36</td>\n",
              "      <td>199.0</td>\n",
              "      <td>11.465913</td>\n",
              "      <td>0.961933</td>\n",
              "      <td>9.2</td>\n",
              "      <td>10.800</td>\n",
              "      <td>11.500</td>\n",
              "      <td>12.100</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8.566667</td>\n",
              "      <td>2.119656</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.25</td>\n",
              "      <td>8.25</td>\n",
              "      <td>10.225</td>\n",
              "      <td>12.6</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.423333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.820</td>\n",
              "      <td>1.10</td>\n",
              "      <td>18.0</td>\n",
              "      <td>12.094444</td>\n",
              "      <td>1.224011</td>\n",
              "      <td>9.8</td>\n",
              "      <td>11.325</td>\n",
              "      <td>12.150</td>\n",
              "      <td>12.875</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6 rows × 88 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(new_group)"
      ],
      "metadata": {
        "id": "i56LPWrYmaj8",
        "outputId": "79127de3-1bd8-4372-b61a-469e8fb4267b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x78dace845210>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(new_group))"
      ],
      "metadata": {
        "id": "6KSSjxKtmlAC",
        "outputId": "5b4e36dd-0eac-4b61-eee1-7a18b404ece3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for quality, group in new_group:\n",
        "    print(quality, group)"
      ],
      "metadata": {
        "id": "QwnLv5t4m3yl",
        "outputId": "efb00ec6-1390-484d-cf1b-d5952aa5e1cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "459            11.6             0.580         0.66            2.20      0.074   \n",
            "517            10.4             0.610         0.49            2.10      0.200   \n",
            "690             7.4             1.185         0.00            4.25      0.097   \n",
            "832            10.4             0.440         0.42            1.50      0.145   \n",
            "899             8.3             1.020         0.02            3.40      0.084   \n",
            "1299            7.6             1.580         0.00            2.10      0.137   \n",
            "1374            6.8             0.815         0.00            1.20      0.267   \n",
            "1469            7.3             0.980         0.05            2.10      0.061   \n",
            "1478            7.1             0.875         0.05            5.70      0.082   \n",
            "1505            6.7             0.760         0.02            1.80      0.078   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "459                  10.0                  47.0  1.00080  3.25       0.57   \n",
            "517                   5.0                  16.0  0.99940  3.16       0.63   \n",
            "690                   5.0                  14.0  0.99660  3.63       0.54   \n",
            "832                  34.0                  48.0  0.99832  3.38       0.86   \n",
            "899                   6.0                  11.0  0.99892  3.48       0.49   \n",
            "1299                  5.0                   9.0  0.99476  3.50       0.40   \n",
            "1374                 16.0                  29.0  0.99471  3.32       0.51   \n",
            "1469                 20.0                  49.0  0.99705  3.31       0.55   \n",
            "1478                  3.0                  14.0  0.99808  3.40       0.52   \n",
            "1505                  6.0                  12.0  0.99600  3.55       0.63   \n",
            "\n",
            "      alcohol  quality  \n",
            "459      9.00        3  \n",
            "517      8.40        3  \n",
            "690     10.70        3  \n",
            "832      9.90        3  \n",
            "899     11.00        3  \n",
            "1299    10.90        3  \n",
            "1374     9.80        3  \n",
            "1469     9.70        3  \n",
            "1478    10.20        3  \n",
            "1505     9.95        3  \n",
            "4       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "18              7.4             0.590         0.08             4.4      0.086   \n",
            "38              5.7             1.130         0.09             1.5      0.172   \n",
            "41              8.8             0.610         0.30             2.8      0.088   \n",
            "45              4.6             0.520         0.15             2.1      0.054   \n",
            "73              8.3             0.675         0.26             2.1      0.084   \n",
            "79              8.3             0.625         0.20             1.5      0.080   \n",
            "94              5.0             1.020         0.04             1.4      0.045   \n",
            "151             9.2             0.520         1.00             3.4      0.610   \n",
            "161             7.6             0.680         0.02             1.3      0.072   \n",
            "167             7.3             0.550         0.03             1.6      0.072   \n",
            "170             7.9             0.885         0.03             1.8      0.058   \n",
            "199             6.9             1.090         0.06             2.1      0.061   \n",
            "224             8.4             0.635         0.36             2.0      0.089   \n",
            "261             7.0             0.975         0.04             2.0      0.087   \n",
            "266             8.1             0.870         0.00             3.3      0.096   \n",
            "409            12.5             0.460         0.49             4.5      0.070   \n",
            "573            10.5             0.590         0.49             2.1      0.070   \n",
            "576             9.9             0.500         0.24             2.3      0.103   \n",
            "600             8.2             0.915         0.27             2.1      0.088   \n",
            "633            10.1             0.935         0.22             3.4      0.105   \n",
            "647             8.3             0.845         0.01             2.2      0.070   \n",
            "659             7.1             0.840         0.02             4.4      0.096   \n",
            "703             7.5             0.380         0.48             2.6      0.073   \n",
            "704             9.1             0.765         0.04             1.6      0.078   \n",
            "724             7.5             1.115         0.10             3.1      0.086   \n",
            "813             6.9             0.390         0.24             2.1      0.102   \n",
            "830             7.5             0.685         0.07             2.5      0.058   \n",
            "833            11.6             0.470         0.44             1.6      0.147   \n",
            "872             7.3             0.350         0.24             2.0      0.067   \n",
            "876             7.1             0.470         0.00             2.2      0.067   \n",
            "927             8.4             0.670         0.19             2.2      0.093   \n",
            "937            12.0             0.630         0.50             1.4      0.071   \n",
            "1124            6.5             0.580         0.00             2.2      0.096   \n",
            "1176            6.5             0.880         0.03             5.6      0.079   \n",
            "1189            8.8             0.955         0.05             1.8      0.075   \n",
            "1233           10.2             0.230         0.37             2.2      0.057   \n",
            "1235            6.0             0.330         0.32            12.9      0.054   \n",
            "1238            8.1             0.730         0.00             2.5      0.081   \n",
            "1239            6.5             0.670         0.00             4.3      0.057   \n",
            "1261            6.3             1.020         0.00             2.0      0.083   \n",
            "1263            8.2             0.780         0.00             2.2      0.089   \n",
            "1276            8.5             0.400         0.40             6.3      0.050   \n",
            "1293            7.5             0.755         0.00             1.9      0.084   \n",
            "1307            6.8             0.680         0.09             3.9      0.068   \n",
            "1363            8.0             0.830         0.27             2.0      0.080   \n",
            "1369            6.6             0.610         0.00             1.6      0.069   \n",
            "1423            6.4             0.530         0.09             3.9      0.123   \n",
            "1461            6.2             0.785         0.00             2.1      0.060   \n",
            "1467            6.7             1.040         0.08             2.3      0.067   \n",
            "1480            5.6             0.620         0.03             1.5      0.080   \n",
            "1482            7.2             0.580         0.54             2.1      0.114   \n",
            "1484            6.8             0.910         0.06             2.0      0.060   \n",
            "1521            6.9             0.480         0.20             1.9      0.082   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "18                    6.0                  29.0  0.99740  3.38       0.50   \n",
            "38                    7.0                  19.0  0.99400  3.50       0.48   \n",
            "41                   17.0                  46.0  0.99760  3.26       0.51   \n",
            "45                    8.0                  65.0  0.99340  3.90       0.56   \n",
            "73                   11.0                  43.0  0.99760  3.31       0.53   \n",
            "79                   27.0                 119.0  0.99720  3.16       1.12   \n",
            "94                   41.0                  85.0  0.99380  3.75       0.48   \n",
            "151                  32.0                  69.0  0.99960  2.74       2.00   \n",
            "161                   9.0                  20.0  0.99650  3.17       1.08   \n",
            "167                  17.0                  42.0  0.99560  3.37       0.48   \n",
            "170                   4.0                   8.0  0.99720  3.36       0.33   \n",
            "199                  12.0                  31.0  0.99480  3.51       0.43   \n",
            "224                  15.0                  55.0  0.99745  3.31       0.57   \n",
            "261                  12.0                  67.0  0.99565  3.35       0.60   \n",
            "266                  26.0                  61.0  1.00025  3.60       0.72   \n",
            "409                  26.0                  49.0  0.99810  3.05       0.57   \n",
            "573                  14.0                  47.0  0.99910  3.30       0.56   \n",
            "576                   6.0                  14.0  0.99780  3.34       0.52   \n",
            "600                   7.0                  23.0  0.99620  3.26       0.47   \n",
            "633                  11.0                  86.0  1.00100  3.43       0.64   \n",
            "647                   5.0                  14.0  0.99670  3.32       0.58   \n",
            "659                   5.0                  13.0  0.99700  3.41       0.57   \n",
            "703                  22.0                  84.0  0.99720  3.32       0.70   \n",
            "704                   4.0                  14.0  0.99800  3.29       0.54   \n",
            "724                   5.0                  12.0  0.99580  3.54       0.60   \n",
            "813                   4.0                   7.0  0.99462  3.44       0.58   \n",
            "830                   5.0                   9.0  0.99632  3.38       0.55   \n",
            "833                  36.0                  51.0  0.99836  3.38       0.86   \n",
            "872                  28.0                  48.0  0.99576  3.43       0.54   \n",
            "876                   7.0                  14.0  0.99517  3.40       0.58   \n",
            "927                  11.0                  75.0  0.99736  3.20       0.59   \n",
            "937                   6.0                  26.0  0.99791  3.07       0.60   \n",
            "1124                  3.0                  13.0  0.99557  3.62       0.62   \n",
            "1176                 23.0                  47.0  0.99572  3.58       0.50   \n",
            "1189                  5.0                  19.0  0.99616  3.30       0.44   \n",
            "1233                 14.0                  36.0  0.99614  3.23       0.49   \n",
            "1235                  6.0                 113.0  0.99572  3.30       0.56   \n",
            "1238                 12.0                  24.0  0.99798  3.38       0.46   \n",
            "1239                 11.0                  20.0  0.99488  3.45       0.56   \n",
            "1261                 17.0                  24.0  0.99437  3.59       0.55   \n",
            "1263                 13.0                  26.0  0.99780  3.37       0.46   \n",
            "1276                  3.0                  10.0  0.99566  3.28       0.56   \n",
            "1293                  6.0                  12.0  0.99672  3.34       0.49   \n",
            "1307                 15.0                  29.0  0.99524  3.41       0.52   \n",
            "1363                 11.0                  63.0  0.99652  3.29       0.48   \n",
            "1369                  4.0                   8.0  0.99396  3.33       0.37   \n",
            "1423                 14.0                  31.0  0.99680  3.50       0.67   \n",
            "1461                  6.0                  13.0  0.99664  3.59       0.61   \n",
            "1467                 19.0                  32.0  0.99648  3.52       0.57   \n",
            "1480                  6.0                  13.0  0.99498  3.66       0.62   \n",
            "1482                  3.0                   9.0  0.99719  3.33       0.57   \n",
            "1484                  4.0                  11.0  0.99592  3.53       0.64   \n",
            "1521                  9.0                  23.0  0.99585  3.39       0.43   \n",
            "\n",
            "      alcohol  quality  \n",
            "18       9.00        4  \n",
            "38       9.80        4  \n",
            "41       9.30        4  \n",
            "45      13.10        4  \n",
            "73       9.20        4  \n",
            "79       9.10        4  \n",
            "94      10.50        4  \n",
            "151      9.40        4  \n",
            "161      9.20        4  \n",
            "167      9.00        4  \n",
            "170      9.10        4  \n",
            "199     11.40        4  \n",
            "224     10.40        4  \n",
            "261      9.40        4  \n",
            "266      9.80        4  \n",
            "409      9.60        4  \n",
            "573      9.60        4  \n",
            "576     10.00        4  \n",
            "600     10.00        4  \n",
            "633     11.30        4  \n",
            "647     11.00        4  \n",
            "659     11.00        4  \n",
            "703      9.60        4  \n",
            "704      9.70        4  \n",
            "724     11.20        4  \n",
            "813     11.40        4  \n",
            "830     10.90        4  \n",
            "833      9.90        4  \n",
            "872     10.00        4  \n",
            "876     10.90        4  \n",
            "927      9.20        4  \n",
            "937     10.40        4  \n",
            "1124    11.50        4  \n",
            "1176    11.20        4  \n",
            "1189     9.60        4  \n",
            "1233     9.30        4  \n",
            "1235    11.50        4  \n",
            "1238     9.60        4  \n",
            "1239    11.80        4  \n",
            "1261    11.20        4  \n",
            "1263     9.60        4  \n",
            "1276    12.00        4  \n",
            "1293     9.70        4  \n",
            "1307    11.10        4  \n",
            "1363     9.80        4  \n",
            "1369    10.40        4  \n",
            "1423    11.00        4  \n",
            "1461    10.00        4  \n",
            "1467    11.00        4  \n",
            "1480    10.10        4  \n",
            "1482    10.30        4  \n",
            "1484    10.90        4  \n",
            "1521     9.05        4  \n",
            "5       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "0               7.4             0.700         0.00             1.9      0.076   \n",
            "1               7.8             0.880         0.00             2.6      0.098   \n",
            "2               7.8             0.760         0.04             2.3      0.092   \n",
            "4               7.4             0.700         0.00             1.9      0.076   \n",
            "5               7.4             0.660         0.00             1.8      0.075   \n",
            "...             ...               ...          ...             ...        ...   \n",
            "1582            6.1             0.715         0.10             2.6      0.053   \n",
            "1583            6.2             0.460         0.29             2.1      0.074   \n",
            "1589            6.6             0.725         0.20             7.8      0.073   \n",
            "1594            6.2             0.600         0.08             2.0      0.090   \n",
            "1597            5.9             0.645         0.12             2.0      0.075   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
            "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
            "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
            "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
            "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
            "...                   ...                   ...      ...   ...        ...   \n",
            "1582                 13.0                  27.0  0.99362  3.57       0.50   \n",
            "1583                 32.0                  98.0  0.99578  3.33       0.62   \n",
            "1589                 29.0                  79.0  0.99770  3.29       0.54   \n",
            "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
            "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
            "\n",
            "      alcohol  quality  \n",
            "0         9.4        5  \n",
            "1         9.8        5  \n",
            "2         9.8        5  \n",
            "4         9.4        5  \n",
            "5         9.4        5  \n",
            "...       ...      ...  \n",
            "1582     11.9        5  \n",
            "1583      9.8        5  \n",
            "1589      9.2        5  \n",
            "1594     10.5        5  \n",
            "1597     10.2        5  \n",
            "\n",
            "[681 rows x 12 columns]\n",
            "6       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "3              11.2             0.280         0.56             1.9      0.075   \n",
            "19              7.9             0.320         0.51             1.8      0.341   \n",
            "20              8.9             0.220         0.48             1.8      0.077   \n",
            "24              6.9             0.400         0.14             2.4      0.085   \n",
            "29              7.8             0.645         0.00             2.0      0.082   \n",
            "...             ...               ...          ...             ...        ...   \n",
            "1592            6.3             0.510         0.13             2.3      0.076   \n",
            "1593            6.8             0.620         0.08             1.9      0.068   \n",
            "1595            5.9             0.550         0.10             2.2      0.062   \n",
            "1596            6.3             0.510         0.13             2.3      0.076   \n",
            "1598            6.0             0.310         0.47             3.6      0.067   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
            "19                   17.0                  56.0  0.99690  3.04       1.08   \n",
            "20                   29.0                  60.0  0.99680  3.39       0.53   \n",
            "24                   21.0                  40.0  0.99680  3.43       0.63   \n",
            "29                    8.0                  16.0  0.99640  3.38       0.59   \n",
            "...                   ...                   ...      ...   ...        ...   \n",
            "1592                 29.0                  40.0  0.99574  3.42       0.75   \n",
            "1593                 28.0                  38.0  0.99651  3.42       0.82   \n",
            "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
            "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
            "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
            "\n",
            "      alcohol  quality  \n",
            "3         9.8        6  \n",
            "19        9.2        6  \n",
            "20        9.4        6  \n",
            "24        9.7        6  \n",
            "29        9.8        6  \n",
            "...       ...      ...  \n",
            "1592     11.0        6  \n",
            "1593      9.5        6  \n",
            "1595     11.2        6  \n",
            "1596     11.0        6  \n",
            "1598     11.0        6  \n",
            "\n",
            "[638 rows x 12 columns]\n",
            "7       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "7               7.3              0.65         0.00             1.2      0.065   \n",
            "8               7.8              0.58         0.02             2.0      0.073   \n",
            "16              8.5              0.28         0.56             1.8      0.092   \n",
            "37              8.1              0.38         0.28             2.1      0.066   \n",
            "62              7.5              0.52         0.16             1.9      0.085   \n",
            "...             ...               ...          ...             ...        ...   \n",
            "1534            6.6              0.56         0.14             2.4      0.064   \n",
            "1541            7.4              0.25         0.29             2.2      0.054   \n",
            "1544            8.4              0.37         0.43             2.3      0.063   \n",
            "1555            7.0              0.56         0.17             1.7      0.065   \n",
            "1584            6.7              0.32         0.44             2.4      0.061   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "7                    15.0                  21.0  0.99460  3.39       0.47   \n",
            "8                     9.0                  18.0  0.99680  3.36       0.57   \n",
            "16                   35.0                 103.0  0.99690  3.30       0.75   \n",
            "37                   13.0                  30.0  0.99680  3.23       0.73   \n",
            "62                   12.0                  35.0  0.99680  3.38       0.62   \n",
            "...                   ...                   ...      ...   ...        ...   \n",
            "1534                 13.0                  29.0  0.99397  3.42       0.62   \n",
            "1541                 19.0                  49.0  0.99666  3.40       0.76   \n",
            "1544                 12.0                  19.0  0.99550  3.17       0.81   \n",
            "1555                 15.0                  24.0  0.99514  3.44       0.68   \n",
            "1584                 24.0                  34.0  0.99484  3.29       0.80   \n",
            "\n",
            "      alcohol  quality  \n",
            "7       10.00        7  \n",
            "8        9.50        7  \n",
            "16      10.50        7  \n",
            "37       9.70        7  \n",
            "62       9.50        7  \n",
            "...       ...      ...  \n",
            "1534    11.70        7  \n",
            "1541    10.90        7  \n",
            "1544    11.20        7  \n",
            "1555    10.55        7  \n",
            "1584    11.60        7  \n",
            "\n",
            "[199 rows x 12 columns]\n",
            "8       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "267             7.9              0.35         0.46             3.6      0.078   \n",
            "278            10.3              0.32         0.45             6.4      0.073   \n",
            "390             5.6              0.85         0.05             1.4      0.045   \n",
            "440            12.6              0.31         0.72             2.2      0.072   \n",
            "455            11.3              0.62         0.67             5.2      0.086   \n",
            "481             9.4              0.30         0.56             2.8      0.080   \n",
            "495            10.7              0.35         0.53             2.6      0.070   \n",
            "498            10.7              0.35         0.53             2.6      0.070   \n",
            "588             5.0              0.42         0.24             2.0      0.060   \n",
            "828             7.8              0.57         0.09             2.3      0.065   \n",
            "1061            9.1              0.40         0.50             1.8      0.071   \n",
            "1090           10.0              0.26         0.54             1.9      0.083   \n",
            "1120            7.9              0.54         0.34             2.5      0.076   \n",
            "1202            8.6              0.42         0.39             1.8      0.068   \n",
            "1269            5.5              0.49         0.03             1.8      0.044   \n",
            "1403            7.2              0.33         0.33             1.7      0.061   \n",
            "1449            7.2              0.38         0.31             2.0      0.056   \n",
            "1549            7.4              0.36         0.30             1.8      0.074   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "267                  15.0                  37.0  0.99730  3.35       0.86   \n",
            "278                   5.0                  13.0  0.99760  3.23       0.82   \n",
            "390                  12.0                  88.0  0.99240  3.56       0.82   \n",
            "440                   6.0                  29.0  0.99870  2.88       0.82   \n",
            "455                   6.0                  19.0  0.99880  3.22       0.69   \n",
            "481                   6.0                  17.0  0.99640  3.15       0.92   \n",
            "495                   5.0                  16.0  0.99720  3.15       0.65   \n",
            "498                   5.0                  16.0  0.99720  3.15       0.65   \n",
            "588                  19.0                  50.0  0.99170  3.72       0.74   \n",
            "828                  34.0                  45.0  0.99417  3.46       0.74   \n",
            "1061                  7.0                  16.0  0.99462  3.21       0.69   \n",
            "1090                 42.0                  74.0  0.99451  2.98       0.63   \n",
            "1120                  8.0                  17.0  0.99235  3.20       0.72   \n",
            "1202                  6.0                  12.0  0.99516  3.35       0.69   \n",
            "1269                 28.0                  87.0  0.99080  3.50       0.82   \n",
            "1403                  3.0                  13.0  0.99600  3.23       1.10   \n",
            "1449                 15.0                  29.0  0.99472  3.23       0.76   \n",
            "1549                 17.0                  24.0  0.99419  3.24       0.70   \n",
            "\n",
            "      alcohol  quality  \n",
            "267      12.8        8  \n",
            "278      12.6        8  \n",
            "390      12.9        8  \n",
            "440       9.8        8  \n",
            "455      13.4        8  \n",
            "481      11.7        8  \n",
            "495      11.0        8  \n",
            "498      11.0        8  \n",
            "588      14.0        8  \n",
            "828      12.7        8  \n",
            "1061     12.5        8  \n",
            "1090     11.8        8  \n",
            "1120     13.1        8  \n",
            "1202     11.7        8  \n",
            "1269     14.0        8  \n",
            "1403     10.0        8  \n",
            "1449     11.3        8  \n",
            "1549     11.4        8  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wind"
      ],
      "metadata": {
        "id": "zDqBRaQlm93F",
        "outputId": "5ab7ba0e-2b28-4e3e-f60f-432cb9ee5b7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'wind' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-148-c537dfed0900>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwind\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'wind' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine"
      ],
      "metadata": {
        "id": "N-bXEOhbnKLd",
        "outputId": "af8a9962-5bf5-491a-d88a-8e0d57c97607",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "4               7.4             0.700         0.00             1.9      0.076   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "0         9.4        5  \n",
              "1         9.8        5  \n",
              "2         9.8        5  \n",
              "3         9.8        6  \n",
              "4         9.4        5  \n",
              "...       ...      ...  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  \n",
              "\n",
              "[1599 rows x 12 columns]"
            ],
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              "      <th>fixed acidity</th>\n",
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              "      <td>9.4</td>\n",
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              "<p>1599 rows × 12 columns</p>\n",
              "</div>\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf2bff31-d003-43b2-8451-5f1f23bb1282 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_326b2dc9-9887-4197-b4d0-1eca1402df71\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('wine')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_326b2dc9-9887-4197-b4d0-1eca1402df71 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('wine');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wine",
              "summary": "{\n  \"name\": \"wine\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\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\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.columns"
      ],
      "metadata": {
        "id": "3m37GyMtnKrK",
        "outputId": "26353053-6e3a-4bbe-ada8-73f65518ad06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
              "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
              "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.groupby('quality')['fixed acidity'].mean()"
      ],
      "metadata": {
        "id": "5MGIGR9_nNja",
        "outputId": "a402e5d4-ebc3-4c16-f7f3-41ab1d2e118c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality\n",
              "3    8.360000\n",
              "4    7.779245\n",
              "5    8.167254\n",
              "6    8.347179\n",
              "7    8.872362\n",
              "8    8.566667\n",
              "Name: fixed acidity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=wine, x='quality', y='fixed acidity')\n",
        "plt.ylim(6,10)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_Vatwj3QnSwb",
        "outputId": "61312d8a-5ff5-475d-b195-7908a4ae9707",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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bq5CQEGfksfHGG28oISFBo0eP1vnz5xUUFKRnn31WU6ZMsW6Tm5urrKws63ppaalefPFF5eTkqHbt2mrXrp22bdumnj17Oj0vAADwDHYXoI0bN2rmzJnq3r27RowYoccff9xp18/Uq1dPycnJSk5OrnSb1NRUm/VJkyZp0qRJTskDAADuDHZfA3To0CFlZGQoPDxcL7zwggICAvTcc88pIyPDGfkAAAAc7qYugu7QoYMWLFigb7/9Vu+++66++eYbdevWTe3atdP8+fNVUFDg6JwAAAAOc0u/ArNYLLp27ZpKS0tlsVjUoEEDLVy4UMHBwfrggw8clREAAMChbqoAHThwQGPGjFFgYKDGjRunDh066J///Kd27typkydPaubMmb/6sFIAAABXsbsAtW3bVv/xH/+hs2fP6t1331V2drZeffVVhYWFWbcZNGiQvvvuO4cGBQAAcBS7fwU2cOBAxcbGqkmTJpVu85vf/MbumxUCAADcLnYXoH9d6/Pvrly5otdee83mHj13sk4Tl7vsvU0/lcr3F+s9EtbIUr2mS7IceG2IS94XAIBbYXcBmj59ukaNGqXatWvbjF++fFnTp083TAECcOfif3B+xv/g4E5m9zVAFotFJpPpuvHDhw+rYcOGDgkFAADgTFU+AtSgQQOZTCaZTCbdfffdNiWorKxMRUVFGjVqlFNCAgAAOFKVC1BycrIsFotiY2M1ffp0+fr+/0HamjVrqlmzZoqMjHRKSADuz2KxqLi42Lpep06dCo8WA4A7qHIBGjp0qCQpNDRUUVFRqlGjhtNCAfA8xcXF6tevn3V948aNqlu3rgsTAUDlqlSACgsL5ePjI+nnx2BcuXJFV65cqXDbf20HAADgrqpUgBo0aKDc3Fz5+/urfv36FR7W/tfF0WVlZQ4PCQAA4EhVKkCfffaZ9Rde//u//+vUQAAAAM5WpQLUvXv3Cv8MAADgiapUgL788ssq77Bdu3Y3HQYAAOB2qFIBioiIkMlkqvQmiL/ENUDwJPx0GwCMqUoF6OzZs9Y/Hzx4UBMmTNDEiROt9/1JT09XUlKS5syZ45yUgJPw020AMKYqFaCQkBDrnwcMGKAFCxbo0UcftY61a9dOwcHBSkhIUP/+/R0eEgAAwJHsfhbYkSNHFBoaet14aGiovvrqK4eEAgAAcCa7C1CbNm2UmJio0tJS61hpaakSExPVpk0bh4YDAABwhio/CuNfFi9erL59++quu+6y/uLryy+/lMlk0scff+zwgABgJBavGipoN8hmHYDj2V2AunbtqjNnzuj999/XsWPHJElPPPGE/vCHP6hOnToODwig6jpNXO6y9zb9VCrfX6z3SFgjS/WaLsly4LUhLnlfhzCZXPa9AUZidwGSfv6p8B//+EdHZwEAALgtqlSANm3apN69e6tGjRratGnTDbd97LHHHBIMAADAWapUgPr376+8vDz5+/vf8GfuPAwVAAB4gioVoPLy8gr/DAAA4Ins/hk8AACAp7O7AI0dO1YLFiy4bnzhwoWKi4tzRCYAAACnsrsArVu3Tt26dbtuPCoqSh9++KFDQgEAADiT3QXohx9+kK+v73XjPj4++v777x0SCgAAwJnsLkBhYWHasmXLdeOffPKJmjdv7pBQAAAAzmT3jRDHjx+vMWPG6LvvvtNDDz0kSdq+fbuSkpKUnJzs6HwAAAAOZ3cBio2NVUlJiWbOnKkZM2ZIkpo1a6ZFixZpyBAPvv08AAAwjJv6Gfxzzz2nb775Rvn5+SosLNSZM2ecUn7KysqUkJCg0NBQeXt7q0WLFpoxY4YsFssN5+3YsUMdO3aU2WxWWFiYUlNTHZ4NAAB4rpt6Fti/+Pn5OSpHhWbPnq1FixZp2bJlCg8P1/79+zV8+HD5+vpq7NixFc45e/as+vTpo1GjRun999/X9u3bNXLkSAUGBiomJsapeQEj4ynmADzJTRWgDz/8UGvXrlVWVpZKS0ttXsvMzHRIMEnas2eP+vXrpz59+kj6+VTb6tWrtW/fvkrnLF68WKGhoUpKSpIktWnTRrt27dK8efMoQIAz8RRzAB7E7lNgCxYs0PDhw9W4cWMdPHhQXbt2VaNGjXTmzBn17t3boeGioqK0fft2nThxQpJ0+PBh7dq164bvk56erujoaJuxmJgYpaenVzqnpKREhYWFNgsAALhz2V2A3nrrLS1ZskRvvPGGatasqUmTJiktLU1jx45VQUGBQ8NNnjxZTz75pFq3bq0aNWqoQ4cOiouL0+DBgyudk5eXp8aNG9uMNW7cWIWFhbpy5UqFcxITE+Xr62tdgoODHfo5AACAe7G7AGVlZSkqKkqS5O3trUuXLkmSnn76aa1evdqh4dauXav3339fq1atUmZmppYtW6bXX39dy5Ytc+j7xMfHq6CgwLpkZ2c7dP8AAMC92H0NUEBAgH788UeFhISoadOm+uKLL9S+fXudPXv2V3+dZa+JEydajwJJUtu2bXXu3DklJiZq6NChlebLz8+3GcvPz5ePj4+8vb0rnGM2m2U2mx2aHQAAuC+7jwA99NBD2rRpkyRp+PDhGjdunHr16qUnnnhCv//97x0a7vLly6pWzTail5eXysvLK50TGRmp7du324ylpaUpMjLSodkAAIDnsvsI0JIlS6wF5Pnnn1ejRo20Z88ePfbYY3r22WcdGq5v376aOXOmmjZtqvDwcB08eFBz585VbGysdZv4+Hjl5ORo+fLlkqRRo0Zp4cKFmjRpkmJjY/XZZ59p7dq12rx5s0OzAQAAz2V3AapWrZrNUZknn3zSeorK0d544w0lJCRo9OjROn/+vIKCgvTss89qypQp1m1yc3OVlZVlXQ8NDdXmzZs1btw4zZ8/X3fddZeWLl3KT+ABAIDVLd0I0dnq1aun5OTkGz5jrKK7PPfo0UMHDx50XjAAAODRbupRGAAAAJ6MAgQAAAyHAgQAAAyHAgQAAAynShdBd+jQQSaTqUo7dOTDUAEAAJyhSgWof//+1j9fvXpVb731lu655x7rzQW/+OIL/eMf/9Do0aOdEhIAAMCRqlSApk6dav3zyJEjNXbsWM2YMeO6bXiGFgAA8AR2XwP0l7/8RUOGDLlu/KmnntK6descEgoAAMCZ7C5A3t7e2r1793Xju3fvVq1atRwSCgAAwJnsvhN0XFycnnvuOWVmZqpr166SpL179+q9995TQkKCwwMCAAA4mt0FaPLkyWrevLnmz5+vlStXSpLatGmjlJQUDRw40OEBAQAAHO2mngU2cOBAyg4AAPBYN3UjxIsXL2rp0qX605/+pB9//FHSz/f/ycnJcWg4AAAAZ7D7CNCXX36p6Oho+fr66uuvv9bIkSPVsGFDffTRR8rKytLy5cudkRN3sE4TXfd3xvRTqXx/sd4jYY0s1Wu6JMuB167/dSUAwDnsPgI0fvx4DRs2TCdPnrT51dejjz6qzz//3KHhAAAAnMHuApSRkaFnn332uvEmTZooLy/PIaEAAACcye4CZDabVVhYeN34iRMn5Ofn55BQAAAAzmR3AXrsscf0yiuv6Nq1a5Ikk8mkrKwsvfTSS3r88ccdHhAAAMDR7C5ASUlJKioqkr+/v65cuaLu3bsrLCxM9erV08yZM52REQAAwKHs/hWYr6+v0tLStHv3bh0+fFhFRUXq2LGjoqOjnZEPAADA4ewuQMeOHVPr1q3VrVs3devWzea1rVu3KiYmxmHhAAAAnMHuU2AdO3bUm2++aTNWUlKiMWPGqF+/fg4LBgAA4Cx2F6DU1FRNmTJFjz76qPLz83Xo0CF16NBB27Zt09///ndnZAQAAHAouwvQwIEDdfjwYV27dk3h4eGKjIxU9+7dlZmZqS5dujgjIwAAgEPd1LPAJKm0tFRlZWUqKytTYGCgzV2hAQAA3JndBWjNmjVq27atfH19deLECW3evFlLlizRAw88oDNnzjgjIwAAgEPZXYBGjBihWbNmadOmTfLz81OvXr105MgRNWnSRBEREU6ICAAA4Fh2/ww+MzNTrVq1shlr0KCB1q5dqxUrVjgsGAAAgLPYfQTo38vPLz399NO3FAYAAOB2qNIRoPHjx2vGjBmqU6eOxo8ff8Nt586d65BgAAAAzlKlAnTw4EHrw08zMzNlMpkq3K6ycQAA4LksXjVU0G6Qzbqnq1IBmj9/vnx8fCRJO3bscGYeAADgbkwmWarXdHUKh6rSNUAdOnTQ999/L0lq3ry5fvjhB6eGAgAAcKYqFaD69evr7NmzkqSvv/5a5eXlTg0FAADgTFU6Bfb444+re/fuCgwMlMlkUufOneXl5VXhttwMEQAAuLsqFaAlS5boP//zP3Xq1CmNHTtWzzzzjOrVq+fsbJKkZs2a6dy5c9eNjx49+rqn0ks/P6x1+PDhNmNms1lXr151WkYAAOBZqnwjxN/+9reSpAMHDuiFF164bQUoIyNDZWVl1vWjR4+qV69eGjBgQKVzfHx8dPz4ces6v04DAAC/ZPedoFNSUpyRo1J+fn4266+++qpatGih7t27VzrHZDIpICDA2dEAAICHuumnwbtCaWmpVq5cqdjY2Bse1SkqKlJISIiCg4PVr18//eMf/7jhfktKSlRYWGizAACAO5dHFaANGzbo4sWLGjZsWKXbtGrVSu+99542btyolStXqry8XFFRUfrmm28qnZOYmChfX1/rEhwc7IT0AADAXXhUAXr33XfVu3dvBQUFVbpNZGSkhgwZooiICHXv3l0fffSR/Pz89Pbbb1c6Jz4+XgUFBdYlOzvbGfEBAICbsPsaIFc5d+6ctm3bpo8++siueTVq1FCHDh106tSpSrcxm80ym823GhEAAHgIjzkClJKSIn9/f/Xp08eueWVlZTpy5IgCAwOdlAwAAHgajyhA5eXlSklJ0dChQ1W9uu1BqyFDhig+Pt66/sorr+jTTz/VmTNnlJmZqaeeekrnzp3TyJEjb3dsAADgpjziFNi2bduUlZWl2NjY617LyspStWr/3+MuXLigZ555Rnl5eWrQoIE6deqkPXv26J577rmdkQEAgBvziAL0yCOPyGKxVPjavz+dft68eZo3b95tSAUAADyVR5wCAwAAcCQKEAAAMBwKEAAAMByPuAYItixeNVTQbpDNOgAAqDoKkCcymWSpXtPVKQAA8FicAgMAAIZDAQIAAIZDAQIAAIZDAQIAAIZDAQIAAIbDr8BgaNxSAACMiQIEY+OWAgBgSJwCAwAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhkMBAgAAhuP2BahZs2YymUzXLc8//3ylc/7yl7+odevWqlWrltq2bau//e1vtzExAABwd25fgDIyMpSbm2td0tLSJEkDBgyocPs9e/Zo0KBBGjFihA4ePKj+/furf//+Onr06O2MDQAA3JjbFyA/Pz8FBARYl7/+9a9q0aKFunfvXuH28+fP129/+1tNnDhRbdq00YwZM9SxY0ctXLjwNicHAADuyu0L0C+VlpZq5cqVio2NlclkqnCb9PR0RUdH24zFxMQoPT290v2WlJSosLDQZgEAAHcujypAGzZs0MWLFzVs2LBKt8nLy1Pjxo1txho3bqy8vLxK5yQmJsrX19e6BAcHOyoyAABwQx5VgN5991317t1bQUFBDt1vfHy8CgoKrEt2drZD9w8AANxLdVcHqKpz585p27Zt+uijj264XUBAgPLz823G8vPzFRAQUOkcs9kss9nskJwAAMD9ecwRoJSUFPn7+6tPnz433C4yMlLbt2+3GUtLS1NkZKQz4wEAAA/iEQWovLxcKSkpGjp0qKpXtz1oNWTIEMXHx1vXX3jhBW3ZskVJSUk6duyYpk2bpv3792vMmDG3OzYAAHBTHlGAtm3bpqysLMXGxl73WlZWlnJzc63rUVFRWrVqlZYsWaL27dvrww8/1IYNG3TvvffezsgAAMCNecQ1QI888ogsFkuFr+3YseO6sQEDBlR6o0QAAACPOAIEAADgSBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOG5fgHJycvTUU0+pUaNG8vb2Vtu2bbV///5Kt9+xY4dMJtN1S15e3m1MDQAA3Fl1Vwe4kQsXLqhbt27q2bOnPvnkE/n5+enkyZNq0KDBr849fvy4fHx8rOv+/v7OjAoAADyIWxeg2bNnKzg4WCkpKdax0NDQKs319/dX/fr1nZQMAAB4Mrc+BbZp0yZ17txZAwYMkL+/vzp06KB33nmnSnMjIiIUGBioXr16affu3U5OCgAAPIlbF6AzZ85o0aJFatmypbZu3arnnntOY8eO1bJlyyqdExgYqMWLF2vdunVat26dgoOD1aNHD2VmZlY6p6SkRIWFhTYLAAC4c7n1KbDy8nJ17txZs2bNkiR16NBBR48e1eLFizV06NAK57Rq1UqtWrWyrkdFRen06dOaN2+eVqxYUeGcxMRETZ8+3fEfAAAAuCW3PgIUGBioe+65x2asTZs2ysrKsms/Xbt21alTpyp9PT4+XgUFBdYlOzv7pvICAADP4NZHgLp166bjx4/bjJ04cUIhISF27efQoUMKDAys9HWz2Syz2XxTGQEAgOdx6wI0btw4RUVFadasWRo4cKD27dunJUuWaMmSJdZt4uPjlZOTo+XLl0uSkpOTFRoaqvDwcF29elVLly7VZ599pk8//dRVHwMAALgZty5AXbp00fr16xUfH69XXnlFoaGhSk5O1uDBg63b5Obm2pwSKy0t1YsvvqicnBzVrl1b7dq107Zt29SzZ09XfAQAAOCG3LoASdLvfvc7/e53v6v09dTUVJv1SZMmadKkSU5OBQAAPJlbXwQNAADgDBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOBQgAABgOG5fgHJycvTUU0+pUaNG8vb2Vtu2bbV///4bztmxY4c6duwos9mssLAwpaam3p6wAADAI7h1Abpw4YK6deumGjVq6JNPPtFXX32lpKQkNWjQoNI5Z8+eVZ8+fdSzZ08dOnRIcXFxGjlypLZu3XobkwMAAHdW3dUBbmT27NkKDg5WSkqKdSw0NPSGcxYvXqzQ0FAlJSVJktq0aaNdu3Zp3rx5iomJcWpeAADgGdy6AG3atEkxMTEaMGCAdu7cqSZNmmj06NF65plnKp2Tnp6u6Ohom7GYmBjFxcVVOqekpEQlJSXW9YKCAklSYWFhpXPKSq5U8VPc2W70HVUV3+XP+C4d51a/S77Hn/F30nH4Lh3nRt/lv16zWCy/viOLGzObzRaz2WyJj4+3ZGZmWt5++21LrVq1LKmpqZXOadmypWXWrFk2Y5s3b7ZIsly+fLnCOVOnTrVIYmFhYWFhYbkDluzs7F/tGG59BKi8vFydO3fWrFmzJEkdOnTQ0aNHtXjxYg0dOtRh7xMfH6/x48fbvO+PP/6oRo0ayWQyOex9HKmwsFDBwcHKzs6Wj4+Pq+N4NL5Lx+G7dAy+R8fhu3QcT/guLRaLLl26pKCgoF/d1q0LUGBgoO655x6bsTZt2mjdunWVzgkICFB+fr7NWH5+vnx8fOTt7V3hHLPZLLPZbDNWv379mwt9m/n4+LjtX0RPw3fpOHyXjsH36Dh8l47j7t+lr69vlbZz61+BdevWTcePH7cZO3HihEJCQiqdExkZqe3bt9uMpaWlKTIy0ikZAQCA53HrAjRu3Dh98cUXmjVrlk6dOqVVq1ZpyZIlev75563bxMfHa8iQIdb1UaNG6cyZM5o0aZKOHTumt956S2vXrtW4ceNc8REAAIAbcusC1KVLF61fv16rV6/WvffeqxkzZig5OVmDBw+2bpObm6usrCzremhoqDZv3qy0tDS1b99eSUlJWrp06R33E3iz2aypU6ded+oO9uO7dBy+S8fge3QcvkvHudO+S5PFUpXfigEAANw53PoIEAAAgDNQgAAAgOFQgAAAgOFQgAAAgOFQgDzMokWL1K5dO+uNqCIjI/XJJ5+4OpbHe/XVV2UymW74zDhUbNq0aTKZTDZL69atXR3LY+Xk5Oipp55So0aN5O3trbZt22r//v2ujuVxmjVrdt3fS5PJZHMbFfy6srIyJSQkKDQ0VN7e3mrRooVmzJhRtWdtuTm3vhM0rnfXXXfp1VdfVcuWLWWxWLRs2TL169dPBw8eVHh4uKvjeaSMjAy9/fbbateunaujeKzw8HBt27bNul69Ov9puRkXLlxQt27d1LNnT33yySfy8/PTyZMn1aBBA1dH8zgZGRkqKyuzrh89elS9evXSgAEDXJjK88yePVuLFi3SsmXLFB4erv3792v48OHy9fXV2LFjXR3vlvBfKQ/Tt29fm/WZM2dq0aJF+uKLLyhAN6GoqEiDBw/WO++8oz//+c+ujuOxqlevroCAAFfH8HizZ89WcHCwUlJSrGOhoaEuTOS5/Pz8bNZfffVVtWjRQt27d3dRIs+0Z88e9evXT3369JH085G11atXa9++fS5Odus4BebBysrKtGbNGhUXF/Ooj5v0/PPPq0+fPoqOjnZ1FI928uRJBQUFqXnz5ho8eLDNzUlRdZs2bVLnzp01YMAA+fv7q0OHDnrnnXdcHcvjlZaWauXKlYqNjXXbB1y7q6ioKG3fvl0nTpyQJB0+fFi7du1S7969XZzs1nEEyAMdOXJEkZGRunr1qurWrav169df99BY/Lo1a9YoMzNTGRkZro7i0e677z6lpqaqVatWys3N1fTp0/XAAw/o6NGjqlevnqvjeZQzZ85o0aJFGj9+vP70pz8pIyNDY8eOVc2aNTV06FBXx/NYGzZs0MWLFzVs2DBXR/E4kydPVmFhoVq3bi0vLy+VlZVp5syZNk9k8FTcCdoDlZaWKisrSwUFBfrwww+1dOlS7dy5kxJkh+zsbHXu3FlpaWnWa3969OihiIgIJScnuzach7t48aJCQkI0d+5cjRgxwtVxPErNmjXVuXNn7dmzxzo2duxYZWRkKD093YXJPFtMTIxq1qypjz/+2NVRPM6aNWs0ceJEvfbaawoPD9ehQ4cUFxenuXPnenwp5wiQB6pZs6bCwsIkSZ06dVJGRobmz5+vt99+28XJPMeBAwd0/vx5dezY0TpWVlamzz//XAsXLlRJSYm8vLxcmNBz1a9fX3fffbdOnTrl6igeJzAw8Lr/kWnTpo3WrVvnokSe79y5c9q2bZs++ugjV0fxSBMnTtTkyZP15JNPSpLatm2rc+fOKTExkQIE1ysvL1dJSYmrY3iUhx9+WEeOHLEZGz58uFq3bq2XXnqJ8nMLioqKdPr0aT399NOujuJxunXrpuPHj9uMnThxQiEhIS5K5PlSUlLk7+9vvYgX9rl8+bKqVbO9XNjLy0vl5eUuSuQ4FCAPEx8fr969e6tp06a6dOmSVq1apR07dmjr1q2ujuZR6tWrp3vvvddmrE6dOmrUqNF147ixCRMmqG/fvgoJCdG3336rqVOnysvLS4MGDXJ1NI8zbtw4RUVFadasWRo4cKD27dunJUuWaMmSJa6O5pHKy8uVkpKioUOHcmuGm9S3b1/NnDlTTZs2VXh4uA4ePKi5c+cqNjbW1dFuGX8jPMz58+c1ZMgQ5ebmytfXV+3atdPWrVvVq1cvV0eDQX3zzTcaNGiQfvjhB/n5+en+++/XF198cd3PkPHrunTpovXr1ys+Pl6vvPKKQkNDlZycfEdccOoK27ZtU1ZW1h3xj7WrvPHGG0pISNDo0aN1/vx5BQUF6dlnn9WUKVNcHe2WcRE0AAAwHO4DBAAADIcCBAAADIcCBAAADIcCBAAADIcCBAAADIcCBAAADIcCBAAADIcCBMDQpk2bpoiICOv6sGHD1L9/f5flAXB7cCdoAPiF+fPn65f3h+3Ro4ciIiKUnJzsulAAHI4CBAC/4Ovr6+oIAG4DToEBcFvFxcUaMmSI6tatq8DAQCUlJalHjx6Ki4uTJJlMJm3YsMFmTv369ZWammpdf+mll3T33Xerdu3aat68uRISEnTt2rVK3/OXp8CGDRumnTt3av78+TKZTDKZTDp79qzCwsL0+uuv28w7dOiQTCaTTp065YiPDsDJKEAA3NbEiRO1c+dObdy4UZ9++ql27NihzMxMu/ZRr149paam6quvvtL8+fP1zjvvaN68eVWaO3/+fEVGRuqZZ55Rbm6ucnNz1bRpU8XGxiolJcVm25SUFD344IMKCwuzKx8A16AAAXBLRUVFevfdd/X666/r4YcfVtu2bbVs2TL99NNPdu3nf/7nfxQVFaVmzZqpb9++mjBhgtauXVulub6+vqpZs6Zq166tgIAABQQEyMvLS8OGDdPx48e1b98+SdK1a9e0atUqnjoOeBCuAQLglk6fPq3S0lLdd9991rGGDRuqVatWdu3ngw8+0IIFC3T69GkVFRXpp59+ko+Pzy1lCwoKUp8+ffTee++pa9eu+vjjj1VSUqIBAwbc0n4B3D4cAQLgsUwmk80vtiTZXN+Tnp6uwYMH69FHH9Vf//pXHTx4UC+//LJKS0tv+b1HjhypNWvW6MqVK0pJSdETTzyh2rVr3/J+AdweHAEC4JZatGihGjVqaO/evWratKkk6cKFCzpx4oS6d+8uSfLz81Nubq51zsmTJ3X58mXr+p49exQSEqKXX37ZOnbu3Dm7ctSsWVNlZWXXjT/66KOqU6eOFi1apC1btujzzz+3a78AXIsCBMAt1a1bVyNGjNDEiRPVqFEj+fv76+WXX1a1av9/4Pqhhx7SwoULFRkZqbKyMr300kuqUaOG9fWWLVsqKytLa9asUZcuXbR582atX7/erhzNmjXT3r179fXXX6tu3bpq2LChqlWrZr0WKD4+Xi1btlRkZKTDPjsA5+MUGAC39dprr+mBBx5Q3759FR0drfvvv1+dOnWyvp6UlKTg4GA98MAD+sMf/qAJEybYnIZ67LHHNG7cOI0ZM0YRERHas2ePEhIS7MowYcIEeXl56Z577pGfn5+ysrKsr40YMUKlpaUaPnz4rX9YALeVyfLvJ9ABwI25052Z//73v+vhhx9Wdna2Gjdu7Oo4AOzAKTAAsFNJSYm+++47TZs2TQMGDKD8AB6IU2AAYKfVq1crJCREFy9e1Jw5c1wdB8BN4BQYAAAwHI4AAQAAw6EAAQAAw6EAAQAAw6EAAQAAw6EAAQAAw6EAAQAAw6EAAQAAw6EAAQAAw6EAAQAAw/k/piKbO8EL1VsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wine.groupby('quality')['volatile acidity'].mean()"
      ],
      "metadata": {
        "id": "9YWhGSi5n0Kl",
        "outputId": "bf14d80d-e16f-4bb8-a29a-d059357d0511",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "quality\n",
              "3    0.884500\n",
              "4    0.693962\n",
              "5    0.577041\n",
              "6    0.497484\n",
              "7    0.403920\n",
              "8    0.423333\n",
              "Name: volatile acidity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot these data now\n",
        "sns.barplot(data=wine, x='quality', y='volatile acidity')\n",
        "plt.ylim(0,2)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ybD52uhun8r6",
        "outputId": "07b259ff-775e-415f-c574-4eaaf64fdf4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = wine.corr()"
      ],
      "metadata": {
        "id": "b7J-LDEtpKcU"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation"
      ],
      "metadata": {
        "id": "rhmBHQk4pVot",
        "outputId": "5ed6a2e3-8d08-446f-f82f-8d88515be1c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      fixed acidity  volatile acidity  citric acid  \\\n",
              "fixed acidity              1.000000         -0.256131     0.671703   \n",
              "volatile acidity          -0.256131          1.000000    -0.552496   \n",
              "citric acid                0.671703         -0.552496     1.000000   \n",
              "residual sugar             0.114777          0.001918     0.143577   \n",
              "chlorides                  0.093705          0.061298     0.203823   \n",
              "free sulfur dioxide       -0.153794         -0.010504    -0.060978   \n",
              "total sulfur dioxide      -0.113181          0.076470     0.035533   \n",
              "density                    0.668047          0.022026     0.364947   \n",
              "pH                        -0.682978          0.234937    -0.541904   \n",
              "sulphates                  0.183006         -0.260987     0.312770   \n",
              "alcohol                   -0.061668         -0.202288     0.109903   \n",
              "quality                    0.124052         -0.390558     0.226373   \n",
              "\n",
              "                      residual sugar  chlorides  free sulfur dioxide  \\\n",
              "fixed acidity               0.114777   0.093705            -0.153794   \n",
              "volatile acidity            0.001918   0.061298            -0.010504   \n",
              "citric acid                 0.143577   0.203823            -0.060978   \n",
              "residual sugar              1.000000   0.055610             0.187049   \n",
              "chlorides                   0.055610   1.000000             0.005562   \n",
              "free sulfur dioxide         0.187049   0.005562             1.000000   \n",
              "total sulfur dioxide        0.203028   0.047400             0.667666   \n",
              "density                     0.355283   0.200632            -0.021946   \n",
              "pH                         -0.085652  -0.265026             0.070377   \n",
              "sulphates                   0.005527   0.371260             0.051658   \n",
              "alcohol                     0.042075  -0.221141            -0.069408   \n",
              "quality                     0.013732  -0.128907            -0.050656   \n",
              "\n",
              "                      total sulfur dioxide   density        pH  sulphates  \\\n",
              "fixed acidity                    -0.113181  0.668047 -0.682978   0.183006   \n",
              "volatile acidity                  0.076470  0.022026  0.234937  -0.260987   \n",
              "citric acid                       0.035533  0.364947 -0.541904   0.312770   \n",
              "residual sugar                    0.203028  0.355283 -0.085652   0.005527   \n",
              "chlorides                         0.047400  0.200632 -0.265026   0.371260   \n",
              "free sulfur dioxide               0.667666 -0.021946  0.070377   0.051658   \n",
              "total sulfur dioxide              1.000000  0.071269 -0.066495   0.042947   \n",
              "density                           0.071269  1.000000 -0.341699   0.148506   \n",
              "pH                               -0.066495 -0.341699  1.000000  -0.196648   \n",
              "sulphates                         0.042947  0.148506 -0.196648   1.000000   \n",
              "alcohol                          -0.205654 -0.496180  0.205633   0.093595   \n",
              "quality                          -0.185100 -0.174919 -0.057731   0.251397   \n",
              "\n",
              "                       alcohol   quality  \n",
              "fixed acidity        -0.061668  0.124052  \n",
              "volatile acidity     -0.202288 -0.390558  \n",
              "citric acid           0.109903  0.226373  \n",
              "residual sugar        0.042075  0.013732  \n",
              "chlorides            -0.221141 -0.128907  \n",
              "free sulfur dioxide  -0.069408 -0.050656  \n",
              "total sulfur dioxide -0.205654 -0.185100  \n",
              "density              -0.496180 -0.174919  \n",
              "pH                    0.205633 -0.057731  \n",
              "sulphates             0.093595  0.251397  \n",
              "alcohol               1.000000  0.476166  \n",
              "quality               0.476166  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6390a7f4-fb51-4492-8407-87f95eea09ac\" 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>-0.256131</td>\n",
              "      <td>0.671703</td>\n",
              "      <td>0.114777</td>\n",
              "      <td>0.093705</td>\n",
              "      <td>-0.153794</td>\n",
              "      <td>-0.113181</td>\n",
              "      <td>0.668047</td>\n",
              "      <td>-0.682978</td>\n",
              "      <td>0.183006</td>\n",
              "      <td>-0.061668</td>\n",
              "      <td>0.124052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>-0.256131</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.552496</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>0.061298</td>\n",
              "      <td>-0.010504</td>\n",
              "      <td>0.076470</td>\n",
              "      <td>0.022026</td>\n",
              "      <td>0.234937</td>\n",
              "      <td>-0.260987</td>\n",
              "      <td>-0.202288</td>\n",
              "      <td>-0.390558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>0.671703</td>\n",
              "      <td>-0.552496</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.143577</td>\n",
              "      <td>0.203823</td>\n",
              "      <td>-0.060978</td>\n",
              "      <td>0.035533</td>\n",
              "      <td>0.364947</td>\n",
              "      <td>-0.541904</td>\n",
              "      <td>0.312770</td>\n",
              "      <td>0.109903</td>\n",
              "      <td>0.226373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>0.114777</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>0.143577</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055610</td>\n",
              "      <td>0.187049</td>\n",
              "      <td>0.203028</td>\n",
              "      <td>0.355283</td>\n",
              "      <td>-0.085652</td>\n",
              "      <td>0.005527</td>\n",
              "      <td>0.042075</td>\n",
              "      <td>0.013732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>0.093705</td>\n",
              "      <td>0.061298</td>\n",
              "      <td>0.203823</td>\n",
              "      <td>0.055610</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005562</td>\n",
              "      <td>0.047400</td>\n",
              "      <td>0.200632</td>\n",
              "      <td>-0.265026</td>\n",
              "      <td>0.371260</td>\n",
              "      <td>-0.221141</td>\n",
              "      <td>-0.128907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>-0.153794</td>\n",
              "      <td>-0.010504</td>\n",
              "      <td>-0.060978</td>\n",
              "      <td>0.187049</td>\n",
              "      <td>0.005562</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.667666</td>\n",
              "      <td>-0.021946</td>\n",
              "      <td>0.070377</td>\n",
              "      <td>0.051658</td>\n",
              "      <td>-0.069408</td>\n",
              "      <td>-0.050656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>-0.113181</td>\n",
              "      <td>0.076470</td>\n",
              "      <td>0.035533</td>\n",
              "      <td>0.203028</td>\n",
              "      <td>0.047400</td>\n",
              "      <td>0.667666</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.071269</td>\n",
              "      <td>-0.066495</td>\n",
              "      <td>0.042947</td>\n",
              "      <td>-0.205654</td>\n",
              "      <td>-0.185100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>0.668047</td>\n",
              "      <td>0.022026</td>\n",
              "      <td>0.364947</td>\n",
              "      <td>0.355283</td>\n",
              "      <td>0.200632</td>\n",
              "      <td>-0.021946</td>\n",
              "      <td>0.071269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.341699</td>\n",
              "      <td>0.148506</td>\n",
              "      <td>-0.496180</td>\n",
              "      <td>-0.174919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>-0.682978</td>\n",
              "      <td>0.234937</td>\n",
              "      <td>-0.541904</td>\n",
              "      <td>-0.085652</td>\n",
              "      <td>-0.265026</td>\n",
              "      <td>0.070377</td>\n",
              "      <td>-0.066495</td>\n",
              "      <td>-0.341699</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.196648</td>\n",
              "      <td>0.205633</td>\n",
              "      <td>-0.057731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>0.183006</td>\n",
              "      <td>-0.260987</td>\n",
              "      <td>0.312770</td>\n",
              "      <td>0.005527</td>\n",
              "      <td>0.371260</td>\n",
              "      <td>0.051658</td>\n",
              "      <td>0.042947</td>\n",
              "      <td>0.148506</td>\n",
              "      <td>-0.196648</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.093595</td>\n",
              "      <td>0.251397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>-0.061668</td>\n",
              "      <td>-0.202288</td>\n",
              "      <td>0.109903</td>\n",
              "      <td>0.042075</td>\n",
              "      <td>-0.221141</td>\n",
              "      <td>-0.069408</td>\n",
              "      <td>-0.205654</td>\n",
              "      <td>-0.496180</td>\n",
              "      <td>0.205633</td>\n",
              "      <td>0.093595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.476166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>0.124052</td>\n",
              "      <td>-0.390558</td>\n",
              "      <td>0.226373</td>\n",
              "      <td>0.013732</td>\n",
              "      <td>-0.128907</td>\n",
              "      <td>-0.050656</td>\n",
              "      <td>-0.185100</td>\n",
              "      <td>-0.174919</td>\n",
              "      <td>-0.057731</td>\n",
              "      <td>0.251397</td>\n",
              "      <td>0.476166</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "variable_name": "correlation",
              "summary": "{\n  \"name\": \"correlation\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4602975372935466,\n        \"min\": -0.6829781945685302,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.061668270628151035,\n          0.18300566393215362,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.392134221880031,\n        \"min\": -0.5524956845595799,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.20228802715325636,\n          -0.26098668528328917,\n          -0.2561308947703812\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4376099014517484,\n        \"min\": -0.5524956845595799,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.109903246641567,\n          0.3127700438544169,\n          0.6717034347641064\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28649753054022475,\n        \"min\": -0.08565242221887187,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.04207543720973079,\n          0.005527121339138446,\n          0.11477672449492153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33087564388511387,\n        \"min\": -0.2650261311732267,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.22114054478828285,\n          0.37126048128542677,\n          0.09370518632130556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34462174083616215,\n        \"min\": -0.15379419286482468,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.06940835356500007,\n          0.05165757184282859,\n          -0.15379419286482468\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35555858592086564,\n        \"min\": -0.20565394374367102,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.20565394374367102,\n          0.04294683623953878,\n          -0.11318144304548165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4154746728306213,\n        \"min\": -0.4961797702417342,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4961797702417342,\n          0.1485064117207901,\n          0.6680472921189573\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43267079976952083,\n        \"min\": -0.6829781945685302,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.2056325085054961,\n          -0.19664760230436984,\n          -0.6829781945685302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3219469604293676,\n        \"min\": -0.26098668528328917,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.0935947504104671,\n          1.0,\n          0.18300566393215362\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3862448960297187,\n        \"min\": -0.4961797702417342,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.0,\n          0.0935947504104671,\n          -0.061668270628151035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3686741370679561,\n        \"min\": -0.390557780264006,\n        \"max\": 1.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.47616632400113656,\n          0.25139707906925995,\n          0.12405164911322257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = wine.drop(columns=['quality'], axis=1)"
      ],
      "metadata": {
        "id": "Eb7b1HMCpWaE"
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = wine['quality']"
      ],
      "metadata": {
        "id": "R5wqx-Wbpz6M"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = wine['quality'].apply(lambda x:1 if x>= 7 else 0)"
      ],
      "metadata": {
        "id": "L6zpCAxxp3dt"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "XNrxhqcoqLP5"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=2)\n"
      ],
      "metadata": {
        "id": "KqPlurmoqOvg"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "JCb2GoZ0rP8D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_model = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "yPtgQ0QgqQoa"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "lIY4AkcpqWCZ",
        "outputId": "7d0e5d7d-e93f-4fac-a118-f721d5f880f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "AiASNLP1qbBE"
      },
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy score for Random Forest Classifier\n",
        "\n",
        "y_test_random_predicted = random_model.predict(X_test)\n",
        "acc_test_random = accuracy_score(y_test_random_predicted, y_test)\n",
        "acc_test_random"
      ],
      "metadata": {
        "id": "sIFBgA8bqgvc",
        "outputId": "e02058b6-096e-4340-bb99-1f2431bd8d86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.928125"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_random_predicted = random_model.predict(X_train)\n",
        "acc_train_random = accuracy_score(y_train_random_predicted, y_train)\n",
        "acc_train_random\n"
      ],
      "metadata": {
        "id": "_Eu1dOtOqueS",
        "outputId": "c858025d-9389-4c69-a99c-c7f78bc01001",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the DecisionTreeClassifier model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Now you can predict using the trained model\n",
        "y_test_logistic_predicted = model.predict(X_test)\n",
        "acc_test_logistic = accuracy_score(y_test_logistic_predicted, y_test)\n",
        "print(acc_test_logistic)"
      ],
      "metadata": {
        "id": "NknYXOj8qw-u",
        "outputId": "67e5fb76-7284-4a70-d353-9170f8c24fc1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.878125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_logistic_predicted = model.predict(X_train)\n",
        "acc_train_logistic = accuracy_score(y_train, y_train_logistic_predicted)\n",
        "acc_train_logistic"
      ],
      "metadata": {
        "id": "5jA1t2dJqzvH",
        "outputId": "8d4e852b-3c15-4c11-8dd0-e78993b0fa6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0VHNVt6zrCes"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Welcome To Colab",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}