Wine_Quality_Prediction_Using_Support_Vector_Machine.ipynb

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        "<a href=\"https://colab.research.google.com/github/akashkchavan9900/Data-Science-Project/blob/main/Wine_Quality_Prediction_Using_Support_Vector_Machine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "Title of Project : Wine Quality Prediction Using Support Vector Machine"
      ],
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        "id": "DQjlYwaqhcqY"
      },
      "id": "DQjlYwaqhcqY"
    },
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      "cell_type": "markdown",
      "source": [
        "Import Library"
      ],
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      },
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      "id": "8287cf14",
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        "id": "8287cf14"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Data"
      ],
      "metadata": {
        "id": "MS4AjxB6h0hl"
      },
      "id": "MS4AjxB6h0hl"
    },
    {
      "cell_type": "code",
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      "id": "935131c1",
      "metadata": {
        "id": "935131c1"
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      "outputs": [],
      "source": [
        "df=pd.read_csv(\"https://github.com/akashkchavan9900/Data-Science-Project/raw/main/WhiteWineQuality.csv?plain=1\",sep=';')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Describe Data"
      ],
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        "id": "nQjMCuibh_-0"
      },
      "id": "nQjMCuibh_-0"
    },
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            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.0              0.27         0.36            20.7      0.045   \n",
              "1            6.3              0.30         0.34             1.6      0.049   \n",
              "2            8.1              0.28         0.40             6.9      0.050   \n",
              "3            7.2              0.23         0.32             8.5      0.058   \n",
              "4            7.2              0.23         0.32             8.5      0.058   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
              "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
              "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
              "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      8.8        6  \n",
              "1      9.5        6  \n",
              "2     10.1        6  \n",
              "3      9.9        6  \n",
              "4      9.9        6  "
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              "      <th>fixed acidity</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4898 entries, 0 to 4897\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         4898 non-null   float64\n",
            " 1   volatile acidity      4898 non-null   float64\n",
            " 2   citric acid           4898 non-null   float64\n",
            " 3   residual sugar        4898 non-null   float64\n",
            " 4   chlorides             4898 non-null   float64\n",
            " 5   free sulfur dioxide   4898 non-null   float64\n",
            " 6   total sulfur dioxide  4898 non-null   float64\n",
            " 7   density               4898 non-null   float64\n",
            " 8   pH                    4898 non-null   float64\n",
            " 9   sulphates             4898 non-null   float64\n",
            " 10  alcohol               4898 non-null   float64\n",
            " 11  quality               4898 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 459.3 KB\n"
          ]
        }
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        "df.info()"
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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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.854788</td>\n",
              "      <td>0.278241</td>\n",
              "      <td>0.334192</td>\n",
              "      <td>6.391415</td>\n",
              "      <td>0.045772</td>\n",
              "      <td>35.308085</td>\n",
              "      <td>138.360657</td>\n",
              "      <td>0.994027</td>\n",
              "      <td>3.188267</td>\n",
              "      <td>0.489847</td>\n",
              "      <td>10.514267</td>\n",
              "      <td>5.877909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.843868</td>\n",
              "      <td>0.100795</td>\n",
              "      <td>0.121020</td>\n",
              "      <td>5.072058</td>\n",
              "      <td>0.021848</td>\n",
              "      <td>17.007137</td>\n",
              "      <td>42.498065</td>\n",
              "      <td>0.002991</td>\n",
              "      <td>0.151001</td>\n",
              "      <td>0.114126</td>\n",
              "      <td>1.230621</td>\n",
              "      <td>0.885639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.987110</td>\n",
              "      <td>2.720000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>0.036000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.991723</td>\n",
              "      <td>3.090000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.800000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.043000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.993740</td>\n",
              "      <td>3.180000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.300000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>0.996100</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.200000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>65.800000</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>1.038980</td>\n",
              "      <td>3.820000</td>\n",
              "      <td>1.080000</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\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",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "691aa604",
      "metadata": {
        "id": "691aa604",
        "outputId": "0b367ab9-0780-45f3-ac2d-c6457eeb2c43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 9
        }
      ],
      "source": [
        "df.columns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "a5684489",
      "metadata": {
        "id": "a5684489",
        "outputId": "36555e25-284c-4618-e08b-4dba0af79f77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing"
      ],
      "metadata": {
        "id": "Vz3zYmlciZ5D"
      },
      "id": "Vz3zYmlciZ5D"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "ab171583",
      "metadata": {
        "id": "ab171583",
        "outputId": "773702f7-2f61-400a-e0b6-7d93808b4977",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 11
        }
      ],
      "source": [
        "df['quality']. value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "b8fb9ca3",
      "metadata": {
        "id": "b8fb9ca3",
        "outputId": "6973c88e-9f06-4095-9d2e-46df851623e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "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-9658b5cd-a9d0-4ff9-99ef-c8ac6fbe9c1c\" class=\"colab-df-container\">\n",
              "    <div>\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",
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              "      <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",
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              "    async function quickchart(key) {\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df.groupby('quality').mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define Target Variable (y) and Feature Variables (X)"
      ],
      "metadata": {
        "id": "3Ty-SGKcijNL"
      },
      "id": "3Ty-SGKcijNL"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "832a7216",
      "metadata": {
        "id": "832a7216"
      },
      "outputs": [],
      "source": [
        "y=df['quality']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "f4094aa0",
      "metadata": {
        "id": "f4094aa0",
        "outputId": "8e27b9a5-8ee4-4018-bb67-e61ffd9c5bad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4898,)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "76d942c1",
      "metadata": {
        "id": "76d942c1",
        "outputId": "29106566-975a-4e17-d50a-5c776e866732",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 15
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "4121bdc1",
      "metadata": {
        "id": "4121bdc1"
      },
      "outputs": [],
      "source": [
        "x=df.drop(['quality'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "a7f02a73",
      "metadata": {
        "id": "a7f02a73",
        "outputId": "0b2b54b8-2d70-42cc-f803-41b7ea02dc4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "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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              "  <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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.045</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1.00100</td>\n",
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              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.99400</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.99510</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.99560</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.99560</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4893</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.039</td>\n",
              "      <td>24.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.99114</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.50</td>\n",
              "      <td>11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4894</th>\n",
              "      <td>6.6</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>57.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4895</th>\n",
              "      <td>6.5</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.041</td>\n",
              "      <td>30.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0.99254</td>\n",
              "      <td>2.99</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4896</th>\n",
              "      <td>5.5</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.022</td>\n",
              "      <td>20.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.98869</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.38</td>\n",
              "      <td>12.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4897</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.020</td>\n",
              "      <td>22.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.98941</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.32</td>\n",
              "      <td>11.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4898 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e9fe658-ae2f-4e9a-89f4-c81035e9fef1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        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",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\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",
              "      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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "  <div id=\"id_91655521-73c6-4f9b-a388-850c4c20e72f\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\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",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "29bb909e",
      "metadata": {
        "id": "29bb909e"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "ba7fa608",
      "metadata": {
        "id": "ba7fa608"
      },
      "outputs": [],
      "source": [
        "ss=StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "83fb166f",
      "metadata": {
        "id": "83fb166f"
      },
      "outputs": [],
      "source": [
        "X=ss.fit_transform(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "8e4e4597",
      "metadata": {
        "id": "8e4e4597",
        "outputId": "87bfd3e2-62c6-4b5c-9d6c-26a050a5cfeb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Test Split"
      ],
      "metadata": {
        "id": "uyIJEQDpitLa"
      },
      "id": "uyIJEQDpitLa"
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "4d80ed4c",
      "metadata": {
        "id": "4d80ed4c"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "6606a2de",
      "metadata": {
        "id": "6606a2de"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test =train_test_split(X,y,test_size =.3,stratify=y,random_state =0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "cd9a0b87",
      "metadata": {
        "id": "cd9a0b87",
        "outputId": "d2947af8-ce33-4f0c-ed82-8cfe45ceef9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3428, 11), (1470, 11), (3428,), (1470,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "ebe8557c",
      "metadata": {
        "id": "ebe8557c"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "3625b246",
      "metadata": {
        "id": "3625b246"
      },
      "outputs": [],
      "source": [
        "svc=SVC()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modeling"
      ],
      "metadata": {
        "id": "rUfd0XTCi307"
      },
      "id": "rUfd0XTCi307"
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "857108b9",
      "metadata": {
        "id": "857108b9",
        "outputId": "dc2f5c30-d674-4da5-acf0-5ddb60d8885b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "svc.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "0d84b30f",
      "metadata": {
        "id": "0d84b30f"
      },
      "outputs": [],
      "source": [
        "y_pred=svc.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "0e14ddf4",
      "metadata": {
        "id": "0e14ddf4",
        "outputId": "c209b01b-e7cb-490c-8c8e-57cefe436523",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "7f53fd82",
      "metadata": {
        "id": "7f53fd82",
        "outputId": "b49e4ea8-72ec-4033-8f8c-2cc8e7fe5477",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 6, 6, ..., 7, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "NGMTcQCejAip"
      },
      "id": "NGMTcQCejAip"
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "d7d36158",
      "metadata": {
        "id": "d7d36158"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "cdc3a196",
      "metadata": {
        "id": "cdc3a196",
        "outputId": "323d722d-78c3-43d0-e1e3-ccd0c3463daf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  0   0   2   4   0   0   0]\n",
            " [  0   3  28  18   0   0   0]\n",
            " [  0   2 242 193   0   0   0]\n",
            " [  0   2 123 513  22   0   0]\n",
            " [  0   0   8 190  66   0   0]\n",
            " [  0   0   0  38  15   0   0]\n",
            " [  0   0   0   0   1   0   0]]\n"
          ]
        }
      ],
      "source": [
        "print(confusion_matrix(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "3783907a",
      "metadata": {
        "id": "3783907a",
        "outputId": "934c9035-79b1-445c-d48a-42cf8df86d32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           3       0.00      0.00      0.00         6\n",
            "           4       0.43      0.06      0.11        49\n",
            "           5       0.60      0.55      0.58       437\n",
            "           6       0.54      0.78      0.63       660\n",
            "           7       0.63      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       0.31      0.23      0.24      1470\n",
            "weighted avg       0.55      0.56      0.52      1470\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: 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/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: 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/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: 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"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "55068871",
      "metadata": {
        "id": "55068871"
      },
      "outputs": [],
      "source": [
        "y=df['quality'].apply(lambda y_value: 1 if y_value>=6 else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "91400a39",
      "metadata": {
        "id": "91400a39",
        "outputId": "8320159a-7671-4848-fd82-3b8a974d5e26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3258\n",
              "0    1640\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "y.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "a4f5e5fa",
      "metadata": {
        "id": "a4f5e5fa"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "936e1ffd",
      "metadata": {
        "id": "936e1ffd"
      },
      "outputs": [],
      "source": [
        "X_train, x_test, y_train, y_test =train_test_split(X,y,test_size =.3,stratify =y,random_state =0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "35ab6e56",
      "metadata": {
        "id": "35ab6e56",
        "outputId": "d7427ac3-ec67-48c9-d9d4-2beefaf8601e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3428, 11), (1470, 11), (3428,), (1470,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "X_train.shape,x_test.shape,y_train.shape,y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "2c2778b3",
      "metadata": {
        "id": "2c2778b3"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "905a3836",
      "metadata": {
        "id": "905a3836"
      },
      "outputs": [],
      "source": [
        "svc=SVC()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "00230743",
      "metadata": {
        "id": "00230743",
        "outputId": "e05138ef-5213-4320-e697-09cefdd13183",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "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>SVC()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "svc.fit(X_train, y_train )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "sM7kaEicjPbZ"
      },
      "id": "sM7kaEicjPbZ"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "8fa2d0e4",
      "metadata": {
        "id": "8fa2d0e4"
      },
      "outputs": [],
      "source": [
        "y_pred=svc.predict(x_test )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "7f7b839d",
      "metadata": {
        "id": "7f7b839d",
        "outputId": "1d0a30e6-ae53-40a9-cd86-a2921292b26b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1470,)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "cc4f38a1",
      "metadata": {
        "id": "cc4f38a1",
        "outputId": "c1545506-48ca-4061-d647-9dba2b4cecb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "d21a3073",
      "metadata": {
        "id": "d21a3073"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "ad1812eb",
      "metadata": {
        "id": "ad1812eb",
        "outputId": "824c829e-9274-4b64-dee5-b104d6b92856",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[280 212]\n",
            " [117 861]]\n"
          ]
        }
      ],
      "source": [
        "print(confusion_matrix(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "f6c590f1",
      "metadata": {
        "id": "f6c590f1",
        "outputId": "41909639-87cf-440b-dba0-391661e40d3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.57      0.63       492\n",
            "           1       0.80      0.88      0.84       978\n",
            "\n",
            "    accuracy                           0.78      1470\n",
            "   macro avg       0.75      0.72      0.73      1470\n",
            "weighted avg       0.77      0.78      0.77      1470\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "3b60ee0d",
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