Wine_Quality.ipynb

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      "name": "Wine_Quality.ipynb",
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    {
      "cell_type": "markdown",
      "source": [
        "# Wine_Quality"
      ],
      "metadata": {
        "id": "uKMLyysWX-_c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import Library**"
      ],
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        "id": "AGIk7n3RL9OA"
      }
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H6eEQ9agLhre"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "7kbUZpkNL42s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import CSV as Dataframe**"
      ],
      "metadata": {
        "id": "AamEWwb6MBjw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/Lorddhaval/Dataset/main/WhiteWineQuality.csv', sep=';')"
      ],
      "metadata": {
        "id": "blXRFmstL76c"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.0              0.27         0.36            20.7      0.045   \n",
              "1            6.3              0.30         0.34             1.6      0.049   \n",
              "2            8.1              0.28         0.40             6.9      0.050   \n",
              "3            7.2              0.23         0.32             8.5      0.058   \n",
              "4            7.2              0.23         0.32             8.5      0.058   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
              "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
              "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
              "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      8.8        6  \n",
              "1      9.5        6  \n",
              "2     10.1        6  \n",
              "3      9.9        6  \n",
              "4      9.9        6  "
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              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
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              "      <th>chlorides</th>\n",
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              "      <th>quality</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.045</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
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              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
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              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4898 entries, 0 to 4897\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         4898 non-null   float64\n",
            " 1   volatile acidity      4898 non-null   float64\n",
            " 2   citric acid           4898 non-null   float64\n",
            " 3   residual sugar        4898 non-null   float64\n",
            " 4   chlorides             4898 non-null   float64\n",
            " 5   free sulfur dioxide   4898 non-null   float64\n",
            " 6   total sulfur dioxide  4898 non-null   float64\n",
            " 7   density               4898 non-null   float64\n",
            " 8   pH                    4898 non-null   float64\n",
            " 9   sulphates             4898 non-null   float64\n",
            " 10  alcohol               4898 non-null   float64\n",
            " 11  quality               4898 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 459.3 KB\n"
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              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    4898.000000       4898.000000  4898.000000     4898.000000   \n",
              "mean        6.854788          0.278241     0.334192        6.391415   \n",
              "std         0.843868          0.100795     0.121020        5.072058   \n",
              "min         3.800000          0.080000     0.000000        0.600000   \n",
              "25%         6.300000          0.210000     0.270000        1.700000   \n",
              "50%         6.800000          0.260000     0.320000        5.200000   \n",
              "75%         7.300000          0.320000     0.390000        9.900000   \n",
              "max        14.200000          1.100000     1.660000       65.800000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  4898.000000          4898.000000           4898.000000  4898.000000   \n",
              "mean      0.045772            35.308085            138.360657     0.994027   \n",
              "std       0.021848            17.007137             42.498065     0.002991   \n",
              "min       0.009000             2.000000              9.000000     0.987110   \n",
              "25%       0.036000            23.000000            108.000000     0.991723   \n",
              "50%       0.043000            34.000000            134.000000     0.993740   \n",
              "75%       0.050000            46.000000            167.000000     0.996100   \n",
              "max       0.346000           289.000000            440.000000     1.038980   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  4898.000000  4898.000000  4898.000000  4898.000000  \n",
              "mean      3.188267     0.489847    10.514267     5.877909  \n",
              "std       0.151001     0.114126     1.230621     0.885639  \n",
              "min       2.720000     0.220000     8.000000     3.000000  \n",
              "25%       3.090000     0.410000     9.500000     5.000000  \n",
              "50%       3.180000     0.470000    10.400000     6.000000  \n",
              "75%       3.280000     0.550000    11.400000     6.000000  \n",
              "max       3.820000     1.080000    14.200000     9.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
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              "      <td>4898.000000</td>\n",
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              "      <td>4898.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.854788</td>\n",
              "      <td>0.278241</td>\n",
              "      <td>0.334192</td>\n",
              "      <td>6.391415</td>\n",
              "      <td>0.045772</td>\n",
              "      <td>35.308085</td>\n",
              "      <td>138.360657</td>\n",
              "      <td>0.994027</td>\n",
              "      <td>3.188267</td>\n",
              "      <td>0.489847</td>\n",
              "      <td>10.514267</td>\n",
              "      <td>5.877909</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.843868</td>\n",
              "      <td>0.100795</td>\n",
              "      <td>0.121020</td>\n",
              "      <td>5.072058</td>\n",
              "      <td>0.021848</td>\n",
              "      <td>17.007137</td>\n",
              "      <td>42.498065</td>\n",
              "      <td>0.002991</td>\n",
              "      <td>0.151001</td>\n",
              "      <td>0.114126</td>\n",
              "      <td>1.230621</td>\n",
              "      <td>0.885639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.987110</td>\n",
              "      <td>2.720000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>0.036000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.991723</td>\n",
              "      <td>3.090000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.800000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.043000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.993740</td>\n",
              "      <td>3.180000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.300000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>0.996100</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.200000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>65.800000</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>1.038980</td>\n",
              "      <td>3.820000</td>\n",
              "      <td>1.080000</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22acceb1-3286-4775-8fc9-b1a56f4d85c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-22acceb1-3286-4775-8fc9-b1a56f4d85c8 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-22acceb1-3286-4775-8fc9-b1a56f4d85c8');\n",
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              "            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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLAB53rSMxmU",
        "outputId": "c0a8aa1e-2c2d-487c-d9c9-45efbf4515bf"
      },
      "execution_count": null,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WBGYJB3M1nf",
        "outputId": "9699def4-0434-48b1-a48c-34b5892483c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['quality'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQo4MEZdM49L",
        "outputId": "0ac29810-942d-4fff-fe38-2ebb9c1f466a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6    2198\n",
              "5    1457\n",
              "7     880\n",
              "8     175\n",
              "4     163\n",
              "3      20\n",
              "9       5\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('quality').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "lmD_6sscNERb",
        "outputId": "09caabce-de59-410f-be5b-8b62c42f7063"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "quality                                                                 \n",
              "3             7.600000          0.333250     0.336000        6.392500   \n",
              "4             7.129448          0.381227     0.304233        4.628221   \n",
              "5             6.933974          0.302011     0.337653        7.334969   \n",
              "6             6.837671          0.260564     0.338025        6.441606   \n",
              "7             6.734716          0.262767     0.325625        5.186477   \n",
              "8             6.657143          0.277400     0.326514        5.671429   \n",
              "9             7.420000          0.298000     0.386000        4.120000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
              "quality                                                                   \n",
              "3         0.054300            53.325000            170.600000  0.994884   \n",
              "4         0.050098            23.358896            125.279141  0.994277   \n",
              "5         0.051546            36.432052            150.904598  0.995263   \n",
              "6         0.045217            35.650591            137.047316  0.993961   \n",
              "7         0.038191            34.125568            125.114773  0.992452   \n",
              "8         0.038314            36.720000            126.165714  0.992236   \n",
              "9         0.027400            33.400000            116.000000  0.991460   \n",
              "\n",
              "               pH  sulphates    alcohol  \n",
              "quality                                  \n",
              "3        3.187500   0.474500  10.345000  \n",
              "4        3.182883   0.476135  10.152454  \n",
              "5        3.168833   0.482203   9.808840  \n",
              "6        3.188599   0.491106  10.575372  \n",
              "7        3.213898   0.503102  11.367936  \n",
              "8        3.218686   0.486229  11.636000  \n",
              "9        3.308000   0.466000  12.180000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3e9ce3a-529d-489c-9616-6b4a48b6d2df\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.600000</td>\n",
              "      <td>0.333250</td>\n",
              "      <td>0.336000</td>\n",
              "      <td>6.392500</td>\n",
              "      <td>0.054300</td>\n",
              "      <td>53.325000</td>\n",
              "      <td>170.600000</td>\n",
              "      <td>0.994884</td>\n",
              "      <td>3.187500</td>\n",
              "      <td>0.474500</td>\n",
              "      <td>10.345000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.129448</td>\n",
              "      <td>0.381227</td>\n",
              "      <td>0.304233</td>\n",
              "      <td>4.628221</td>\n",
              "      <td>0.050098</td>\n",
              "      <td>23.358896</td>\n",
              "      <td>125.279141</td>\n",
              "      <td>0.994277</td>\n",
              "      <td>3.182883</td>\n",
              "      <td>0.476135</td>\n",
              "      <td>10.152454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.933974</td>\n",
              "      <td>0.302011</td>\n",
              "      <td>0.337653</td>\n",
              "      <td>7.334969</td>\n",
              "      <td>0.051546</td>\n",
              "      <td>36.432052</td>\n",
              "      <td>150.904598</td>\n",
              "      <td>0.995263</td>\n",
              "      <td>3.168833</td>\n",
              "      <td>0.482203</td>\n",
              "      <td>9.808840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.837671</td>\n",
              "      <td>0.260564</td>\n",
              "      <td>0.338025</td>\n",
              "      <td>6.441606</td>\n",
              "      <td>0.045217</td>\n",
              "      <td>35.650591</td>\n",
              "      <td>137.047316</td>\n",
              "      <td>0.993961</td>\n",
              "      <td>3.188599</td>\n",
              "      <td>0.491106</td>\n",
              "      <td>10.575372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6.734716</td>\n",
              "      <td>0.262767</td>\n",
              "      <td>0.325625</td>\n",
              "      <td>5.186477</td>\n",
              "      <td>0.038191</td>\n",
              "      <td>34.125568</td>\n",
              "      <td>125.114773</td>\n",
              "      <td>0.992452</td>\n",
              "      <td>3.213898</td>\n",
              "      <td>0.503102</td>\n",
              "      <td>11.367936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>6.657143</td>\n",
              "      <td>0.277400</td>\n",
              "      <td>0.326514</td>\n",
              "      <td>5.671429</td>\n",
              "      <td>0.038314</td>\n",
              "      <td>36.720000</td>\n",
              "      <td>126.165714</td>\n",
              "      <td>0.992236</td>\n",
              "      <td>3.218686</td>\n",
              "      <td>0.486229</td>\n",
              "      <td>11.636000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.420000</td>\n",
              "      <td>0.298000</td>\n",
              "      <td>0.386000</td>\n",
              "      <td>4.120000</td>\n",
              "      <td>0.027400</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>0.991460</td>\n",
              "      <td>3.308000</td>\n",
              "      <td>0.466000</td>\n",
              "      <td>12.180000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3e9ce3a-529d-489c-9616-6b4a48b6d2df')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "      <script>\n",
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              "          document.querySelector('#df-a3e9ce3a-529d-489c-9616-6b4a48b6d2df 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-a3e9ce3a-529d-489c-9616-6b4a48b6d2df');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define y & x**"
      ],
      "metadata": {
        "id": "-ocj3svANSID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['quality']"
      ],
      "metadata": {
        "id": "huXeiuOHNXJ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exX7GHlyNhJo",
        "outputId": "b0959f13-ffc3-4eba-fc6a-9fab17999a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898,)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Frkn3oUWNjXd",
        "outputId": "cafc9a9d-2875-4c74-af38-7ea0aa90e4fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       6\n",
              "1       6\n",
              "2       6\n",
              "3       6\n",
              "4       6\n",
              "       ..\n",
              "4893    6\n",
              "4894    5\n",
              "4895    6\n",
              "4896    7\n",
              "4897    6\n",
              "Name: quality, Length: 4898, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
        "        'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
        "        'pH', 'sulphates', 'alcohol']]"
      ],
      "metadata": {
        "id": "uf1CqpaUNpFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop(['quality'], axis=1)"
      ],
      "metadata": {
        "id": "1lp6vboMRF9_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZNeZeuWRQ3P",
        "outputId": "b3d7fb1a-7270-47f7-a751-350f5ff12dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
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          "height": 485
        },
        "id": "EuwXm8W2RTfS",
        "outputId": "408f1a84-82ef-4504-8118-2c11ae73a81c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.0              0.27         0.36            20.7      0.045   \n",
              "1               6.3              0.30         0.34             1.6      0.049   \n",
              "2               8.1              0.28         0.40             6.9      0.050   \n",
              "3               7.2              0.23         0.32             8.5      0.058   \n",
              "4               7.2              0.23         0.32             8.5      0.058   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "4893            6.2              0.21         0.29             1.6      0.039   \n",
              "4894            6.6              0.32         0.36             8.0      0.047   \n",
              "4895            6.5              0.24         0.19             1.2      0.041   \n",
              "4896            5.5              0.29         0.30             1.1      0.022   \n",
              "4897            6.0              0.21         0.38             0.8      0.020   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    45.0                 170.0  1.00100  3.00       0.45   \n",
              "1                    14.0                 132.0  0.99400  3.30       0.49   \n",
              "2                    30.0                  97.0  0.99510  3.26       0.44   \n",
              "3                    47.0                 186.0  0.99560  3.19       0.40   \n",
              "4                    47.0                 186.0  0.99560  3.19       0.40   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
              "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
              "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
              "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
              "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
              "\n",
              "      alcohol  \n",
              "0         8.8  \n",
              "1         9.5  \n",
              "2        10.1  \n",
              "3         9.9  \n",
              "4         9.9  \n",
              "...       ...  \n",
              "4893     11.2  \n",
              "4894      9.6  \n",
              "4895      9.4  \n",
              "4896     12.8  \n",
              "4897     11.8  \n",
              "\n",
              "[4898 rows x 11 columns]"
            ],
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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",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
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              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
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              "      <th>4</th>\n",
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              "      <td>6.5</td>\n",
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              "      <th>4896</th>\n",
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              "          if (!dataTable) return;\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get X Variables Standardized**"
      ],
      "metadata": {
        "id": "B9vEDxUWRclR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "829wfw00RlVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()"
      ],
      "metadata": {
        "id": "38ykcRb5RsP1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = ss.fit_transform(x)"
      ],
      "metadata": {
        "id": "H1nfVdu7RwUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6G6nya35R5Ag",
        "outputId": "19927944-a748-45a9-98d6-f61c979ecaf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.72096961e-01, -8.17699008e-02,  2.13280202e-01, ...,\n",
              "        -1.24692128e+00, -3.49184257e-01, -1.39315246e+00],\n",
              "       [-6.57501128e-01,  2.15895632e-01,  4.80011213e-02, ...,\n",
              "         7.40028640e-01,  1.34184656e-03, -8.24275678e-01],\n",
              "       [ 1.47575110e+00,  1.74519434e-02,  5.43838363e-01, ...,\n",
              "         4.75101984e-01, -4.36815783e-01, -3.36667007e-01],\n",
              "       ...,\n",
              "       [-4.20473102e-01, -3.79435433e-01, -1.19159198e+00, ...,\n",
              "        -1.31315295e+00, -2.61552731e-01, -9.05543789e-01],\n",
              "       [-1.60561323e+00,  1.16673788e-01, -2.82557040e-01, ...,\n",
              "         1.00495530e+00, -9.62604939e-01,  1.85757201e+00],\n",
              "       [-1.01304317e+00, -6.77100966e-01,  3.78559282e-01, ...,\n",
              "         4.75101984e-01, -1.48839409e+00,  1.04489089e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Train Test Split**"
      ],
      "metadata": {
        "id": "4rRYV2KAR_mx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "A15s03PPSEvG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test, = train_test_split(x,y, test_size = 0.3, stratify = y, random_state=222529)"
      ],
      "metadata": {
        "id": "OSZCUJ0VSO8A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-9KPQ3ESksg",
        "outputId": "e95499bd-702c-41a1-fbbc-4615f404069b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3428, 11), (1470, 11), (3428,), (1470,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Train**"
      ],
      "metadata": {
        "id": "J-gzECy7S4wt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "Y4Rn5hPFS8D7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC()"
      ],
      "metadata": {
        "id": "mrfnBRARTBwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLD5hLjbTFQs",
        "outputId": "798fcf24-25bd-49df-a992-d76da7d5f5af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Prediction**"
      ],
      "metadata": {
        "id": "Tkz264WuTOfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svc.predict(x_test)"
      ],
      "metadata": {
        "id": "qRuM0cY2TWHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ne0IadaTd3C",
        "outputId": "e0620a5e-acff-4f3f-87b8-09efdf7883b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUDfSpOkTg2j",
        "outputId": "63b9a404-afbb-40cd-ece0-6f7aabe6e24e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 6, 5, ..., 6, 5, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Evaluation**"
      ],
      "metadata": {
        "id": "86xfKGvVTsRh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "gANxjTK2TwTd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckoJCkRUT7N6",
        "outputId": "1b2ef176-98b5-4adf-de78-6680e45c2ee5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   1   5   0   0   0]\n",
            " [  0   1  33  15   0   0   0]\n",
            " [  0   0 243 193   1   0   0]\n",
            " [  0   0 126 508  26   0   0]\n",
            " [  0   0  12 185  67   0   0]\n",
            " [  0   0   0  37  16   0   0]\n",
            " [  0   0   0   0   1   0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qeiLb1kxUByC",
        "outputId": "0843ffc4-61e8-45d0-b241-283515deb004"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         6\n",
            "           4       1.00      0.02      0.04        49\n",
            "           5       0.59      0.56      0.57       437\n",
            "           6       0.54      0.77      0.63       660\n",
            "           7       0.60      0.25      0.36       264\n",
            "           8       0.00      0.00      0.00        53\n",
            "           9       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.56      1470\n",
            "   macro avg       0.39      0.23      0.23      1470\n",
            "weighted avg       0.56      0.56      0.52      1470\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Re-run with Two Class Created for Wine Quality**"
      ],
      "metadata": {
        "id": "kmLFonkfUWGu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['quality'].apply(lambda y_value: 1 if y_value>=6 else 0)"
      ],
      "metadata": {
        "id": "7_1YWyYGUfAL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boiZ-nLuU19U",
        "outputId": "b2df8d22-2a63-4bca-927b-d6eba3b0ad73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3258\n",
              "0    1640\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Train Test Split**"
      ],
      "metadata": {
        "id": "unmo3WvcU_71"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "cbgRW0sLVQIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.3, stratify = y, random_state=222529)"
      ],
      "metadata": {
        "id": "KWv8JfYFVflv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mm2ozABMV5Z6",
        "outputId": "6629576f-a072-40b8-836d-c60ae8934403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3428, 11), (1470, 11), (3428,), (1470,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Train**"
      ],
      "metadata": {
        "id": "bYHgIQMrWF9H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "weWkvWZ1WKeL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC()"
      ],
      "metadata": {
        "id": "i0aZYL1kWXku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qqw-wqocWXvW",
        "outputId": "7bb822d6-f4fb-4997-e944-1f406270791c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Prediction**"
      ],
      "metadata": {
        "id": "7HJiIuOZWgDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svc.predict(x_test)"
      ],
      "metadata": {
        "id": "euv9yKcKWnAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd4R213sWngg",
        "outputId": "f8fe2665-dd35-4ee2-e22d-f40863045f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470,)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4UwC73ZWnr4",
        "outputId": "7c55b6ce-ff15-4b74-dfe1-c21a54b1aa7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Model Evaluation**"
      ],
      "metadata": {
        "id": "nAEypYdLWz_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "O2grVvwCW4xI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyoVFRkTXMuB",
        "outputId": "88e20c0e-c38b-4e25-bc09-e141833ce496"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[293 199]\n",
            " [136 842]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHIHblf2XM3H",
        "outputId": "ee3a006e-54fd-4720-adf8-1c7af8f6054a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.60      0.64       492\n",
            "           1       0.81      0.86      0.83       978\n",
            "\n",
            "    accuracy                           0.77      1470\n",
            "   macro avg       0.75      0.73      0.74      1470\n",
            "weighted avg       0.77      0.77      0.77      1470\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Future Predictions**"
      ],
      "metadata": {
        "id": "qmEhq1G-XbeG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df.sample(1)"
      ],
      "metadata": {
        "id": "eU4HeCclXe6k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "0_BcCBk2XtHO",
        "outputId": "606e9bbc-6308-48bf-ba6d-1928eb8131ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "4781            5.6               0.3          0.1             6.4      0.043   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "4781                 34.0                 142.0  0.99382  3.14       0.48   \n",
              "\n",
              "      alcohol  quality  \n",
              "4781      9.8        5  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd820ea7-34a5-4010-9ddc-93d75d99e03c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\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",
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              "      <th>alcohol</th>\n",
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              "      <th>4781</th>\n",
              "      <td>5.6</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>6.4</td>\n",
              "      <td>0.043</td>\n",
              "      <td>34.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>0.99382</td>\n",
              "      <td>3.14</td>\n",
              "      <td>0.48</td>\n",
              "      <td>9.8</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-dd820ea7-34a5-4010-9ddc-93d75d99e03c');\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSw0whn-X04e",
        "outputId": "256c74b0-4e57-43f9-e041-caaba0a6d5da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_new = df_new.drop(['quality'], axis = 1)"
      ],
      "metadata": {
        "id": "1B37b-04X6gr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_new = ss.fit_transform(x_new)"
      ],
      "metadata": {
        "id": "Hc7p1Cn7X7F5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_new = svc.predict(x_new)"
      ],
      "metadata": {
        "id": "e1eUB93KX7d7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVVdEbdxX7yK",
        "outputId": "093b674c-491b-4e30-ad09-cb717fe55175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    }
  ]
}