GitHub_Series_1,_Wine_Quality_Prediction.ipynb

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    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "0HI1PKyX7gOz"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Load the wine quality dataset\n",
        "Dataset = pd.read_csv('/content/1. winequality.csv')\n",
        "print(Dataset.head())  # Display the first five rows of the dataset\n"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "FKrvQGzcQb5b",
        "outputId": "53ba96e6-3fb1-4f4f-fbce-1b9001f2e975"
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      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "0            7.4              0.70         0.00             1.9      0.076   \n",
            "1            7.8              0.88         0.00             2.6      0.098   \n",
            "2            7.8              0.76         0.04             2.3      0.092   \n",
            "3           11.2              0.28         0.56             1.9      0.075   \n",
            "4            7.4              0.70         0.00             1.9      0.076   \n",
            "\n",
            "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
            "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
            "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
            "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
            "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
            "\n",
            "   alcohol  quality  good color  \n",
            "0      9.4        5     0   red  \n",
            "1      9.8        5     0   red  \n",
            "2      9.8        5     0   red  \n",
            "3      9.8        6     0   red  \n",
            "4      9.4        5     0   red  \n"
          ]
        }
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      "source": [
        "# Define the target variable\n",
        "target_variable = 'color'\n",
        "\n",
        "# Separate the features and the target variable\n",
        "X = Dataset.drop(columns=[target_variable])  # Features\n",
        "y = Dataset[target_variable]  # Target variable\n"
      ],
      "metadata": {
        "id": "_3WKRsAfW4dJ"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
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          "text": [
            "0         red\n",
            "1         red\n",
            "2         red\n",
            "3         red\n",
            "4         red\n",
            "        ...  \n",
            "6492    white\n",
            "6493    white\n",
            "6494    white\n",
            "6495    white\n",
            "6496    white\n",
            "Name: color, Length: 6497, dtype: object\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "print(X)"
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      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "0               7.4              0.70         0.00             1.9      0.076   \n",
            "1               7.8              0.88         0.00             2.6      0.098   \n",
            "2               7.8              0.76         0.04             2.3      0.092   \n",
            "3              11.2              0.28         0.56             1.9      0.075   \n",
            "4               7.4              0.70         0.00             1.9      0.076   \n",
            "...             ...               ...          ...             ...        ...   \n",
            "6492            6.2              0.21         0.29             1.6      0.039   \n",
            "6493            6.6              0.32         0.36             8.0      0.047   \n",
            "6494            6.5              0.24         0.19             1.2      0.041   \n",
            "6495            5.5              0.29         0.30             1.1      0.022   \n",
            "6496            6.0              0.21         0.38             0.8      0.020   \n",
            "\n",
            "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
            "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
            "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
            "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
            "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
            "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
            "...                   ...                   ...      ...   ...        ...   \n",
            "6492                 24.0                  92.0  0.99114  3.27       0.50   \n",
            "6493                 57.0                 168.0  0.99490  3.15       0.46   \n",
            "6494                 30.0                 111.0  0.99254  2.99       0.46   \n",
            "6495                 20.0                 110.0  0.98869  3.34       0.38   \n",
            "6496                 22.0                  98.0  0.98941  3.26       0.32   \n",
            "\n",
            "      alcohol  quality  good  \n",
            "0         9.4        5     0  \n",
            "1         9.8        5     0  \n",
            "2         9.8        5     0  \n",
            "3         9.8        6     0  \n",
            "4         9.4        5     0  \n",
            "...       ...      ...   ...  \n",
            "6492     11.2        6     0  \n",
            "6493      9.6        5     0  \n",
            "6494      9.4        6     0  \n",
            "6495     12.8        7     1  \n",
            "6496     11.8        6     0  \n",
            "\n",
            "[6497 rows x 13 columns]\n"
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        "\n",
        "# Step 1: Handling Missing Values (NaN Imputation)\n",
        "df=X\n",
        "# Impute missing values with the mean\n",
        "df.fillna(df.mean(), inplace=True)"
      ],
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        "\n",
        "# Explore the data\n",
        "df.info()  # Display information about columns\n",
        "df.describe().T  # Display descriptive statistical measures\n"
      ],
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          "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   fixed acidity         6497 non-null   float64\n",
            " 1   volatile acidity      6497 non-null   float64\n",
            " 2   citric acid           6497 non-null   float64\n",
            " 3   residual sugar        6497 non-null   float64\n",
            " 4   chlorides             6497 non-null   float64\n",
            " 5   free sulfur dioxide   6497 non-null   float64\n",
            " 6   total sulfur dioxide  6497 non-null   float64\n",
            " 7   density               6497 non-null   float64\n",
            " 8   pH                    6497 non-null   float64\n",
            " 9   sulphates             6497 non-null   float64\n",
            " 10  alcohol               6497 non-null   float64\n",
            " 11  quality               6497 non-null   int64  \n",
            " 12  good                  6497 non-null   int64  \n",
            "dtypes: float64(11), int64(2)\n",
            "memory usage: 660.0 KB\n"
          ]
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              "                       count        mean        std      min       25%  \\\n",
              "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
              "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
              "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
              "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
              "chlorides             6497.0    0.056034   0.035034  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                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
              "sulphates             6497.0    0.531268   0.148806  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",
              "good                  6497.0    0.196552   0.397421  0.00000   0.00000   \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  \n",
              "good                    0.00000    0.00000    1.00000  "
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fixed acidity</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>7.215307</td>\n",
              "      <td>1.296434</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",
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              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>0.339666</td>\n",
              "      <td>0.164636</td>\n",
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              "      <td>0.40000</td>\n",
              "      <td>1.58000</td>\n",
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              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>0.318633</td>\n",
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              "      <td>1.66000</td>\n",
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              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>5.443235</td>\n",
              "      <td>4.757804</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>6497.0</td>\n",
              "      <td>0.056034</td>\n",
              "      <td>0.035034</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",
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              "    <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>6497.0</td>\n",
              "      <td>3.218501</td>\n",
              "      <td>0.160787</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",
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              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>0.531268</td>\n",
              "      <td>0.148806</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",
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              "    <tr>\n",
              "      <th>good</th>\n",
              "      <td>6497.0</td>\n",
              "      <td>0.196552</td>\n",
              "      <td>0.397421</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\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",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-88a1b2d1-fb47-45d1-b748-716aad68e917\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88a1b2d1-fb47-45d1-b748-716aad68e917')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-88a1b2d1-fb47-45d1-b748-716aad68e917 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "correlation_matrix = df.corr()\n",
        "\n",
        "# Sort the features by their correlation with the target variable (quality in this case)\n",
        "sorted_correlations = correlation_matrix['quality'].abs().sort_values(ascending=False)\n",
        "\n",
        "# Plot the feature importance\n",
        "plt.figure(figsize=(10, 6))\n",
        "sorted_correlations.drop('quality').plot(kind='bar')\n",
        "plt.xlabel('Feature')\n",
        "plt.ylabel('Absolute Correlation with Quality')\n",
        "plt.title('Feature Importance for Wine Quality (based on Correlation)')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "SeaZhAgbU6UI",
        "outputId": "d62fff90-d684-4c22-f8a0-4a22c884d0bd"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Visualize data distribution\n",
        "df.hist(bins=20, figsize=(10, 10))\n",
        "plt.show()  # Draw histograms for continuous data columns\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "3TYUYM8oQldF",
        "outputId": "83f587fd-38c1-49a4-dd73-523dc9888fc8"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Visualize data for each quality of wine\n",
        "plt.bar(df['quality'], df['alcohol'])\n",
        "plt.xlabel('quality')\n",
        "plt.ylabel('alcohol')\n",
        "plt.show()  # Draw a count plot for each quality of wine\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "DQd1k4S_Qnq2",
        "outputId": "d0b85f48-ad05-4901-8dc0-a2b41ce4bbea"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# List of columns to exclude from normalization\n",
        "columns_to_exclude = ['color']\n",
        "\n",
        "# Initialize the MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Normalize the numeric features in your DataFrame\n",
        "numeric_columns = [col for col in df.columns if col not in columns_to_exclude]\n",
        "df_normalized = df.copy()  # Create a copy of the DataFrame to preserve the original data\n",
        "df_normalized[numeric_columns] = scaler.fit_transform(df[numeric_columns])\n",
        "\n",
        "\n",
        "print(df_normalized.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkTknLJGQtUs",
        "outputId": "e4e3388e-695b-4025-905b-7284ddde1572"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "0       0.297521          0.413333     0.000000        0.019939   0.111296   \n",
            "1       0.330579          0.533333     0.000000        0.030675   0.147841   \n",
            "2       0.330579          0.453333     0.024096        0.026074   0.137874   \n",
            "3       0.611570          0.133333     0.337349        0.019939   0.109635   \n",
            "4       0.297521          0.413333     0.000000        0.019939   0.111296   \n",
            "\n",
            "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
            "0             0.034722              0.064516  0.206092  0.612403   0.191011   \n",
            "1             0.083333              0.140553  0.186813  0.372093   0.258427   \n",
            "2             0.048611              0.110599  0.190669  0.418605   0.241573   \n",
            "3             0.055556              0.124424  0.209948  0.341085   0.202247   \n",
            "4             0.034722              0.064516  0.206092  0.612403   0.191011   \n",
            "\n",
            "    alcohol   quality  good  \n",
            "0  0.202899  0.333333   0.0  \n",
            "1  0.260870  0.333333   0.0  \n",
            "2  0.260870  0.333333   0.0  \n",
            "3  0.260870  0.500000   0.0  \n",
            "4  0.202899  0.333333   0.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Initialize the label encoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply label encoding to the target variable\n",
        "y_encoded = label_encoder.fit_transform(y)"
      ],
      "metadata": {
        "id": "5_QPl_ERYTnJ"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into a training set and a testing set (e.g., 80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_normalized, y_encoded, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "bI0xIFITQwBy"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the classifiers\n",
        "rf_classifier = RandomForestClassifier(random_state=42)\n",
        "dt_classifier = DecisionTreeClassifier(random_state=42)\n",
        "knn_classifier = KNeighborsClassifier()\n",
        "\n",
        "# Train the Random Forest Classifier\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Train the Decision Tree Classifier\n",
        "dt_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Train the K-Nearest Neighbors Classifier\n",
        "knn_classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "expXNy3oQzn6",
        "outputId": "5f7b2d3d-56b7-4f34-84f6-008d3feebeb8"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "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>KNeighborsClassifier()</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "# Predict with the trained classifiers on the test data\n",
        "rf_predictions = rf_classifier.predict(X_test)\n",
        "dt_predictions = dt_classifier.predict(X_test)\n",
        "knn_predictions = knn_classifier.predict(X_test)\n",
        "\n",
        "# Calculate accuracy and F1 score for each classifier\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
        "rf_f1_score = f1_score(y_test, rf_predictions, average='weighted')\n",
        "\n",
        "dt_accuracy = accuracy_score(y_test, dt_predictions)\n",
        "dt_f1_score = f1_score(y_test, dt_predictions, average='weighted')\n",
        "\n",
        "knn_accuracy = accuracy_score(y_test, knn_predictions)\n",
        "knn_f1_score = f1_score(y_test, knn_predictions, average='weighted')\n",
        "\n",
        "# Print the accuracy and F1 score for each classifier\n",
        "print(f\"Random Forest Classifier Accuracy: {rf_accuracy:.2f}\")\n",
        "print(f\"Random Forest Classifier F1 Score: {rf_f1_score:.2f}\")\n",
        "\n",
        "print(f\"Decision Tree Classifier Accuracy: {dt_accuracy:.2f}\")\n",
        "print(f\"Decision Tree Classifier F1 Score: {dt_f1_score:.2f}\")\n",
        "\n",
        "print(f\"K-Nearest Neighbors Classifier Accuracy: {knn_accuracy:.2f}\")\n",
        "print(f\"K-Nearest Neighbors Classifier F1 Score: {knn_f1_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_UcfCAwZW23",
        "outputId": "655933f3-e79c-4891-9459-9a6c88897728"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Classifier Accuracy: 0.99\n",
            "Random Forest Classifier F1 Score: 0.99\n",
            "Decision Tree Classifier Accuracy: 0.98\n",
            "Decision Tree Classifier F1 Score: 0.98\n",
            "K-Nearest Neighbors Classifier Accuracy: 0.99\n",
            "K-Nearest Neighbors Classifier F1 Score: 0.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute ROC and AUC for Random Forest Classifier\n",
        "rf_probabilities = rf_classifier.predict_proba(X_test)[:, 1]\n",
        "rf_fpr, rf_tpr, _ = roc_curve(y_test, rf_probabilities)\n",
        "rf_roc_auc = auc(rf_fpr, rf_tpr)\n",
        "\n",
        "# Compute ROC and AUC for Decision Tree Classifier\n",
        "dt_probabilities = dt_classifier.predict_proba(X_test)[:, 1]\n",
        "dt_fpr, dt_tpr, _ = roc_curve(y_test, dt_probabilities)\n",
        "dt_roc_auc = auc(dt_fpr, dt_tpr)\n",
        "\n",
        "# Compute ROC and AUC for K-Nearest Neighbors Classifier\n",
        "knn_probabilities = knn_classifier.predict_proba(X_test)[:, 1]\n",
        "knn_fpr, knn_tpr, _ = roc_curve(y_test, knn_probabilities)\n",
        "knn_roc_auc = auc(knn_fpr, knn_tpr)\n",
        "\n",
        "# Compute precision-recall curve for Random Forest Classifier\n",
        "rf_precision, rf_recall, _ = precision_recall_curve(y_test, rf_probabilities)\n",
        "\n",
        "# Compute precision-recall curve for Decision Tree Classifier\n",
        "dt_precision, dt_recall, _ = precision_recall_curve(y_test, dt_probabilities)\n",
        "\n",
        "# Compute precision-recall curve for K-Nearest Neighbors Classifier\n",
        "knn_precision, knn_recall, _ = precision_recall_curve(y_test, knn_probabilities)"
      ],
      "metadata": {
        "id": "aeRU1eMbZp54"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Plot ROC curves\n",
        "plt.figure(figsize=(12, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(rf_fpr, rf_tpr, color='blue', lw=2, label='Random Forest (AUC = %0.2f)' % rf_roc_auc)\n",
        "plt.plot(dt_fpr, dt_tpr, color='green', lw=2, label='Decision Tree (AUC = %0.2f)' % dt_roc_auc)\n",
        "plt.plot(knn_fpr, knn_tpr, color='red', lw=2, label='KNN (AUC = %0.2f)' % knn_roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='gray', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc='lower right')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "7Gf9mR2MZtSq",
        "outputId": "62c309cd-6e6b-4524-bc32-873319dced57"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7b446e410e50>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot Precision-Recall curves\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(rf_recall, rf_precision, color='blue', lw=2, label='Random Forest')\n",
        "plt.plot(dt_recall, dt_precision, color='green', lw=2, label='Decision Tree')\n",
        "plt.plot(knn_recall, knn_precision, color='red', lw=2, label='KNN')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend(loc='best')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "T2mMD7EgZZ6-",
        "outputId": "03882154-8de4-47c2-cc47-390e4b0c79d8"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}