{
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  "metadata": {
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      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BehpvrbkbFp0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('winequality.csv')\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwYstdKsjmxf",
        "outputId": "57336c77-2378-4ce9-eccd-cb1c6434bb21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    type  fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
            "0  white            7.0              0.27         0.36            20.7   \n",
            "1  white            6.3              0.30         0.34             1.6   \n",
            "2  white            8.1              0.28         0.40             6.9   \n",
            "3  white            7.2              0.23         0.32             8.5   \n",
            "4  white            7.2              0.23         0.32             8.5   \n",
            "\n",
            "   chlorides  free sulfur dioxide  total sulfur dioxide  density    pH  \\\n",
            "0      0.045                 45.0                 170.0   1.0010  3.00   \n",
            "1      0.049                 14.0                 132.0   0.9940  3.30   \n",
            "2      0.050                 30.0                  97.0   0.9951  3.26   \n",
            "3      0.058                 47.0                 186.0   0.9956  3.19   \n",
            "4      0.058                 47.0                 186.0   0.9956  3.19   \n",
            "\n",
            "   sulphates  alcohol  quality  \n",
            "0       0.45      8.8        6  \n",
            "1       0.49      9.5        6  \n",
            "2       0.44     10.1        6  \n",
            "3       0.40      9.9        6  \n",
            "4       0.40      9.9        6  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnOVSC2Pi731",
        "outputId": "a4e15df0-3e90-4df7-e786-1705e54a8d73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6497 entries, 0 to 6496\n",
            "Data columns (total 13 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   type                  6497 non-null   object \n",
            " 1   fixed acidity         6487 non-null   float64\n",
            " 2   volatile acidity      6489 non-null   float64\n",
            " 3   citric acid           6494 non-null   float64\n",
            " 4   residual sugar        6495 non-null   float64\n",
            " 5   chlorides             6495 non-null   float64\n",
            " 6   free sulfur dioxide   6497 non-null   float64\n",
            " 7   total sulfur dioxide  6497 non-null   float64\n",
            " 8   density               6497 non-null   float64\n",
            " 9   pH                    6488 non-null   float64\n",
            " 10  sulphates             6493 non-null   float64\n",
            " 11  alcohol               6497 non-null   float64\n",
            " 12  quality               6497 non-null   int64  \n",
            "dtypes: float64(11), int64(1), object(1)\n",
            "memory usage: 660.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "_JUgNHKwjAgO",
        "outputId": "f2b20b59-c088-412a-de70-aafae5f50e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       count        mean        std      min       25%  \\\n",
              "fixed acidity         6487.0    7.216579   1.296750  3.80000   6.40000   \n",
              "volatile acidity      6489.0    0.339691   0.164649  0.08000   0.23000   \n",
              "citric acid           6494.0    0.318722   0.145265  0.00000   0.25000   \n",
              "residual sugar        6495.0    5.444326   4.758125  0.60000   1.80000   \n",
              "chlorides             6495.0    0.056042   0.035036  0.00900   0.03800   \n",
              "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
              "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
              "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
              "pH                    6488.0    3.218395   0.160748  2.72000   3.11000   \n",
              "sulphates             6493.0    0.531215   0.148814  0.22000   0.43000   \n",
              "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
              "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
              "\n",
              "                            50%        75%        max  \n",
              "fixed acidity           7.00000    7.70000   15.90000  \n",
              "volatile acidity        0.29000    0.40000    1.58000  \n",
              "citric acid             0.31000    0.39000    1.66000  \n",
              "residual sugar          3.00000    8.10000   65.80000  \n",
              "chlorides               0.04700    0.06500    0.61100  \n",
              "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
              "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
              "density                 0.99489    0.99699    1.03898  \n",
              "pH                      3.21000    3.32000    4.01000  \n",
              "sulphates               0.51000    0.60000    2.00000  \n",
              "alcohol                10.30000   11.30000   14.90000  \n",
              "quality                 6.00000    6.00000    9.00000  "
            ],
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              "\n",
              "  <div id=\"df-bd04fd11-dc13-4024-ada7-69b69d137c63\" 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",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fixed acidity</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>7.216579</td>\n",
              "      <td>1.296750</td>\n",
              "      <td>3.80000</td>\n",
              "      <td>6.40000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>7.70000</td>\n",
              "      <td>15.90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>6489.0</td>\n",
              "      <td>0.339691</td>\n",
              "      <td>0.164649</td>\n",
              "      <td>0.08000</td>\n",
              "      <td>0.23000</td>\n",
              "      <td>0.29000</td>\n",
              "      <td>0.40000</td>\n",
              "      <td>1.58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>6494.0</td>\n",
              "      <td>0.318722</td>\n",
              "      <td>0.145265</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.25000</td>\n",
              "      <td>0.31000</td>\n",
              "      <td>0.39000</td>\n",
              "      <td>1.66000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>6495.0</td>\n",
              "      <td>5.444326</td>\n",
              "      <td>4.758125</td>\n",
              "      <td>0.60000</td>\n",
              "      <td>1.80000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>8.10000</td>\n",
              "      <td>65.80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>6495.0</td>\n",
              "      <td>0.056042</td>\n",
              "      <td>0.035036</td>\n",
              "      <td>0.00900</td>\n",
              "      <td>0.03800</td>\n",
              "      <td>0.04700</td>\n",
              "      <td>0.06500</td>\n",
              "      <td>0.61100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>30.525319</td>\n",
              "      <td>17.749400</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>17.00000</td>\n",
              "      <td>29.00000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>289.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>115.744574</td>\n",
              "      <td>56.521855</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>77.00000</td>\n",
              "      <td>118.00000</td>\n",
              "      <td>156.00000</td>\n",
              "      <td>440.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>0.994697</td>\n",
              "      <td>0.002999</td>\n",
              "      <td>0.98711</td>\n",
              "      <td>0.99234</td>\n",
              "      <td>0.99489</td>\n",
              "      <td>0.99699</td>\n",
              "      <td>1.03898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>6488.0</td>\n",
              "      <td>3.218395</td>\n",
              "      <td>0.160748</td>\n",
              "      <td>2.72000</td>\n",
              "      <td>3.11000</td>\n",
              "      <td>3.21000</td>\n",
              "      <td>3.32000</td>\n",
              "      <td>4.01000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>6493.0</td>\n",
              "      <td>0.531215</td>\n",
              "      <td>0.148814</td>\n",
              "      <td>0.22000</td>\n",
              "      <td>0.43000</td>\n",
              "      <td>0.51000</td>\n",
              "      <td>0.60000</td>\n",
              "      <td>2.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>10.491801</td>\n",
              "      <td>1.192712</td>\n",
              "      <td>8.00000</td>\n",
              "      <td>9.50000</td>\n",
              "      <td>10.30000</td>\n",
              "      <td>11.30000</td>\n",
              "      <td>14.90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>5.818378</td>\n",
              "      <td>0.873255</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>9.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2cc5c723-ea43-40cd-a47d-cfffcf27c134 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.785938897200183,\n        \"min\": 6487.0,\n        \"max\": 6497.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6487.0,\n          6489.0,\n          6488.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.81500424331123,\n        \"min\": 0.05604157043879908,\n        \"max\": 115.7445744189626,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          10.491800831149455,\n          0.531215154782073,\n          7.2165793124710955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.408410661669553,\n        \"min\": 0.002998673003719039,\n        \"max\": 56.52185452263028,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.192711748868981,\n          0.14881412131628377,\n          1.296749856526477\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6125007442265145,\n        \"min\": 0.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          8.0,\n          0.22,\n          3.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.644118401513243,\n        \"min\": 0.038,\n        \"max\": 77.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          9.5,\n          0.43,\n          6.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.462082345111284,\n        \"min\": 0.047,\n        \"max\": 118.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          10.3,\n          0.51,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.39695226556568,\n        \"min\": 0.065,\n        \"max\": 156.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11.3,\n          0.6,\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142.2100644966134,\n        \"min\": 0.611,\n        \"max\": 440.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          14.9,\n          2.0,\n          15.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "vykbIcZYjBCI",
        "outputId": "9f50af6a-aab4-4f9e-906e-83a78252b6cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type                     0\n",
              "fixed acidity           10\n",
              "volatile acidity         8\n",
              "citric acid              3\n",
              "residual sugar           2\n",
              "chlorides                2\n",
              "free sulfur dioxide      0\n",
              "total sulfur dioxide     0\n",
              "density                  0\n",
              "pH                       9\n",
              "sulphates                4\n",
              "alcohol                  0\n",
              "quality                  0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fixed acidity</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  if df[col].isnull().sum() > 0:\n",
        "    df[col] = df[col].fillna(df[col].mean())\n",
        "\n",
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ep3em1sjDbZ",
        "outputId": "44c8a69a-7924-4551-9d4b-d02cc0de19cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=20, figsize=(10, 10))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "Z6Y2km0_jJRT",
        "outputId": "31bc3a5b-e099-4dda-d21c-01e65408dc1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(df['quality'], df['alcohol'])\n",
        "plt.xlabel('quality')\n",
        "plt.ylabel('alcohol')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "v_GhRQqBjK1s",
        "outputId": "994f55aa-d831-4e9d-9b54-15eb68f3b3dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "# Convert 'object' columns to numerical if they represent numbers\n",
        "for col in df.columns:\n",
        "    if df[col].dtype == 'object':\n",
        "        try:\n",
        "            df[col] = pd.to_numeric(df[col], errors='coerce')  # Convert to numeric, replace non-convertibles with NaN\n",
        "        except:\n",
        "            pass  # Skip columns that cannot be converted\n",
        "\n",
        "plt.figure(figsize=(12, 12))\n",
        "sb.heatmap(df.corr() > 0.7, annot=True, cbar=False)\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5J2vdnRlkTTl",
        "outputId": "592f03d1-a695-40e2-f2c0-e8e305a8f216"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('total sulfur dioxide', axis=1)\n"
      ],
      "metadata": {
        "id": "kk1doyc1jQCG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['best quality'] = [1 if x > 5 else 0 for x in df.quality]\n"
      ],
      "metadata": {
        "id": "xS0xN4VEjRmT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'white': 1, 'red': 0}, inplace=True)\n"
      ],
      "metadata": {
        "id": "oYqO31cOjS56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = features.fillna(features.mean())\n",
        "features = df.drop(['quality', 'best quality'], axis=1)\n",
        "target = df['best quality']\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(\n",
        "\tfeatures, target, test_size=0.2, random_state=40)\n",
        "\n",
        "# Impute missing values after splitting\n",
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(strategy='mean')  # Or another strategy like 'median'\n",
        "xtrain = imputer.fit_transform(xtrain)\n",
        "xtest = imputer.transform(xtest)\n",
        "\n",
        "xtrain.shape, xtest.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2UoCKoT7ly_x",
        "outputId": "5c025e8d-d72c-4746-d9e7-146d2a4cb36d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5197, 10), (1300, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "norm = MinMaxScaler()\n",
        "xtrain = norm.fit_transform(xtrain)\n",
        "xtest = norm.transform(xtest)\n"
      ],
      "metadata": {
        "id": "m-YsVj7SjWX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [LogisticRegression(), XGBClassifier(), SVC(kernel='rbf')]\n",
        "\n",
        "for i in range(3):\n",
        "\tmodels[i].fit(xtrain, ytrain)\n",
        "\n",
        "\tprint(f'{models[i]} : ')\n",
        "\tprint('Training Accuracy : ', metrics.roc_auc_score(ytrain, models[i].predict(xtrain)))\n",
        "\tprint('Validation Accuracy : ', metrics.roc_auc_score(\n",
        "\t\tytest, models[i].predict(xtest)))\n",
        "\tprint()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HooNpzT0jZA7",
        "outputId": "0aadbffc-e80c-4e22-c953-4168e03000f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression() : \n",
            "Training Accuracy :  0.6975101024661644\n",
            "Validation Accuracy :  0.6855058693719925\n",
            "\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
            "              num_parallel_tree=None, random_state=None, ...) : \n",
            "Training Accuracy :  0.9762240429934201\n",
            "Validation Accuracy :  0.8045662590288206\n",
            "\n",
            "SVC() : \n",
            "Training Accuracy :  0.7203202525576721\n",
            "Validation Accuracy :  0.7073819229472522\n",
            "\n"
          ]
        }
      ]
    },
    {
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'models[1]' is your trained classifier\n",
        "cm = confusion_matrix(ytest, models[1].predict(xtest))\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=models[1].classes_) # Assuming your model has a 'classes_' attribute\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JDWvTLpCmFvs",
        "outputId": "6266a171-b000-4cdc-ba54-202997362532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.classification_report(ytest,\n",
        "\t\t\t\t\t\t\t\t\tmodels[1].predict(xtest)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKGW30BJjcgt",
        "outputId": "f8550d74-1319-4117-d18b-393ea5a25684"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.74      0.75       474\n",
            "           1       0.86      0.86      0.86       826\n",
            "\n",
            "    accuracy                           0.82      1300\n",
            "   macro avg       0.81      0.80      0.81      1300\n",
            "weighted avg       0.82      0.82      0.82      1300\n",
            "\n"
          ]
        }
      ]
    }
  ]
}