wineQualityKNNClassifier.ipynb

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        "\n",
        "### December 13, 2021\n",
        "### Fayruz Kibria\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
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      "source": [
        "#pip install plotnine\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import plotnine\n",
        "from plotnine import *\n",
        "from plotnine.data import *\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
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      "source": [
        "# Import dataset\n",
        "\n",
        "---\n",
        "\n"
      ]
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              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
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              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
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              "      <th>quality</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
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              "      <td>0.34</td>\n",
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              "      <td>0.049</td>\n",
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              "      <td>132.0</td>\n",
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              "      <td>0.40</td>\n",
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              "      <td>97.0</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
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              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
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              "      <td>186.0</td>\n",
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              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
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              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
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              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.0              0.27         0.36  ...       0.45      8.8        6\n",
              "1            6.3              0.30         0.34  ...       0.49      9.5        6\n",
              "2            8.1              0.28         0.40  ...       0.44     10.1        6\n",
              "3            7.2              0.23         0.32  ...       0.40      9.9        6\n",
              "4            7.2              0.23         0.32  ...       0.40      9.9        6\n",
              "\n",
              "[5 rows x 12 columns]"
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      "source": [
        "# Import dataset\n",
        "mydata0 = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\", sep=';')\n",
        "mydata=mydata0.copy()\n",
        "\n",
        "# View the first few rows of the data\n",
        "mydata.head()"
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              "      <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",
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              "      <th>count</th>\n",
              "      <td>4898.000000</td>\n",
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              "      <td>4898.000000</td>\n",
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              "      <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",
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              "    <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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-df862e53-de37-4e36-8a53-96095a7e5e6f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-df862e53-de37-4e36-8a53-96095a7e5e6f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      alcohol      quality\n",
              "count    4898.000000       4898.000000  ...  4898.000000  4898.000000\n",
              "mean        6.854788          0.278241  ...    10.514267     5.877909\n",
              "std         0.843868          0.100795  ...     1.230621     0.885639\n",
              "min         3.800000          0.080000  ...     8.000000     3.000000\n",
              "25%         6.300000          0.210000  ...     9.500000     5.000000\n",
              "50%         6.800000          0.260000  ...    10.400000     6.000000\n",
              "75%         7.300000          0.320000  ...    11.400000     6.000000\n",
              "max        14.200000          1.100000  ...    14.200000     9.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "mydata.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "gU4Oc86noWC6",
      "metadata": {
        "id": "gU4Oc86noWC6"
      },
      "source": [
        "# Q1. Check the datatypes of the attributes. (3 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ede1845d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ede1845d",
        "outputId": "547cb3e1-7fa7-4cbd-9d39-65437ac66760"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ],
      "source": [
        "mydata.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0Zi758c2ok2c",
      "metadata": {
        "id": "0Zi758c2ok2c"
      },
      "source": [
        "# Q2. Are there any missing values in the dataset? (4 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7612aee1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7612aee1",
        "outputId": "bc42712e-f4ed-4d4b-a9d4-7e2bd4296883"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 0 missing values in the data set.\n"
          ]
        }
      ],
      "source": [
        "mVal = mydata.isnull().sum().sum()\n",
        "\n",
        "print(\"There are\",mVal,\"missing values in the data set.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CRg6tGihov7B",
      "metadata": {
        "id": "CRg6tGihov7B"
      },
      "source": [
        "# Q3. What is the correlation between the attributes other than Quality? (10 points)\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "658a21dc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "658a21dc",
        "outputId": "26af1f60-b84a-4fe0-d732-9650ea2e3f7a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "mydata_nolabel = mydata.iloc[:, 0:11]\n",
        "corrMatrix = mydata_nolabel.corr()\n",
        "plt.figure(figsize = (11,11))\n",
        "sn.heatmap(corrMatrix, annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6G2oo74-o5jN",
      "metadata": {
        "id": "6G2oo74-o5jN"
      },
      "source": [
        "# Q4. Graph the frequency distribution of wine quality by using Quality. (10 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "57141f02",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "57141f02",
        "outputId": "2968dd10-e0c7-4686-b67e-f82d29522322"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<ggplot: (8764816313513)>\n"
          ]
        }
      ],
      "source": [
        "quality_hist = ggplot(mydata, aes(x='quality')) + geom_bar() \\\n",
        "    + ggtitle('Wine Quality Histogram')  \\\n",
        "    + scale_x_continuous(breaks= [3, 4, 5, 6, 7, 8, 9], labels= [\"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]) \\\n",
        "    + scale_y_continuous(expand = [0,0.5], limits = [0, 2300]) \\\n",
        "    + xlab(\"Quality\") \\\n",
        "    + ylab(\"Count\") \\\n",
        "    +theme_bw()\n",
        "\n",
        "print(quality_hist)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jpGRqtDYpFGU",
      "metadata": {
        "id": "jpGRqtDYpFGU"
      },
      "source": [
        "# Q5. Reduce the levels of rating for quality to three levels as high, medium and low. (10 points)\n",
        "Assign the levels of 3 and 4 to level 0; 5 and 6 to level 1; and 7 ,8 and 9 to level 2 . \n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12a6e52c",
      "metadata": {
        "id": "12a6e52c"
      },
      "outputs": [],
      "source": [
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "\n",
        "# re mapping the quality ratings to 3 categories only\n",
        "\n",
        "mydata.quality.loc[mydata.quality==3]=0\n",
        "mydata.quality.loc[mydata.quality==4]=0\n",
        "mydata.quality.loc[mydata.quality==5]=1\n",
        "mydata.quality.loc[mydata.quality==6]=1\n",
        "mydata.quality.loc[mydata.quality==7]=2\n",
        "mydata.quality.loc[mydata.quality==8]=2\n",
        "mydata.quality.loc[mydata.quality==9]=2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba5e5462",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "ba5e5462",
        "outputId": "dd52ce69-2fc0-4fbf-dfb1-8131ca5d10c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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_categorical   4898 non-null   category\n",
            "dtypes: category(1), float64(11)\n",
            "memory usage: 426.0 KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-87c9ef42-dcef-4945-a4e2-1a2096b75beb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality_categorical</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>1.0010</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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>0.9940</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "      <td>1</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>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>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>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>1</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>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87c9ef42-dcef-4945-a4e2-1a2096b75beb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-87c9ef42-dcef-4945-a4e2-1a2096b75beb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-87c9ef42-dcef-4945-a4e2-1a2096b75beb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  ...  alcohol  quality_categorical\n",
              "0            7.0              0.27  ...      8.8                    1\n",
              "1            6.3              0.30  ...      9.5                    1\n",
              "2            8.1              0.28  ...     10.1                    1\n",
              "3            7.2              0.23  ...      9.9                    1\n",
              "4            7.2              0.23  ...      9.9                    1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# making quality a categorical variable\n",
        "\n",
        "mydata['quality_categorical'] = mydata.quality.astype(\"category\")\n",
        "mydata.drop(['quality'], axis = 1, inplace=True)\n",
        "\n",
        "mydata.info()\n",
        "mydata.head() # checking the numeric columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6LWOcbzht6LO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LWOcbzht6LO",
        "outputId": "ee10ad27-5e7b-4477-e091-1096d2c02841"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3655\n",
              "2    1060\n",
              "0     183\n",
              "Name: quality_categorical, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "mydata['quality_categorical'].value_counts() # checking the categorical quality column"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GpVcii2iqWa5",
      "metadata": {
        "id": "GpVcii2iqWa5"
      },
      "source": [
        "# Q6. Normalize the numeric attributes. (12 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc31cb83",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "bc31cb83",
        "outputId": "7896bee2-ce00-42cc-eb1b-c642cc365f46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5fedc28f-8279-4123-b945-9f271dd3bcbe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.293730</td>\n",
              "      <td>0.194354</td>\n",
              "      <td>0.201320</td>\n",
              "      <td>0.088825</td>\n",
              "      <td>0.109117</td>\n",
              "      <td>0.116056</td>\n",
              "      <td>0.300141</td>\n",
              "      <td>0.133360</td>\n",
              "      <td>0.425697</td>\n",
              "      <td>0.313775</td>\n",
              "      <td>0.405527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.081141</td>\n",
              "      <td>0.098818</td>\n",
              "      <td>0.072903</td>\n",
              "      <td>0.077792</td>\n",
              "      <td>0.064831</td>\n",
              "      <td>0.059258</td>\n",
              "      <td>0.098603</td>\n",
              "      <td>0.057662</td>\n",
              "      <td>0.137273</td>\n",
              "      <td>0.132704</td>\n",
              "      <td>0.198487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.240385</td>\n",
              "      <td>0.127451</td>\n",
              "      <td>0.162651</td>\n",
              "      <td>0.016871</td>\n",
              "      <td>0.080119</td>\n",
              "      <td>0.073171</td>\n",
              "      <td>0.229698</td>\n",
              "      <td>0.088924</td>\n",
              "      <td>0.336364</td>\n",
              "      <td>0.220930</td>\n",
              "      <td>0.241935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.288462</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.192771</td>\n",
              "      <td>0.070552</td>\n",
              "      <td>0.100890</td>\n",
              "      <td>0.111498</td>\n",
              "      <td>0.290023</td>\n",
              "      <td>0.127820</td>\n",
              "      <td>0.418182</td>\n",
              "      <td>0.290698</td>\n",
              "      <td>0.387097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.336538</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>0.234940</td>\n",
              "      <td>0.142638</td>\n",
              "      <td>0.121662</td>\n",
              "      <td>0.153310</td>\n",
              "      <td>0.366589</td>\n",
              "      <td>0.173318</td>\n",
              "      <td>0.509091</td>\n",
              "      <td>0.383721</td>\n",
              "      <td>0.548387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fedc28f-8279-4123-b945-9f271dd3bcbe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5fedc28f-8279-4123-b945-9f271dd3bcbe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5fedc28f-8279-4123-b945-9f271dd3bcbe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...    sulphates      alcohol\n",
              "count    4898.000000       4898.000000  ...  4898.000000  4898.000000\n",
              "mean        0.293730          0.194354  ...     0.313775     0.405527\n",
              "std         0.081141          0.098818  ...     0.132704     0.198487\n",
              "min         0.000000          0.000000  ...     0.000000     0.000000\n",
              "25%         0.240385          0.127451  ...     0.220930     0.241935\n",
              "50%         0.288462          0.176471  ...     0.290698     0.387097\n",
              "75%         0.336538          0.235294  ...     0.383721     0.548387\n",
              "max         1.000000          1.000000  ...     1.000000     1.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "scaled_df = pd.DataFrame(min_max_scaler.fit_transform(mydata.iloc[:, 0:11])) # scaling the numeric columns and saving in a ned scaled dataframe\n",
        "scaled_df['quality_categorical'] = mydata.quality_categorical # adding the categorical quality column to the scaled dataframe\n",
        "scaled_df.columns = mydata.columns # copying the columns names over to the scaled dataframe\n",
        "\n",
        "#scaled_df.head()\n",
        "scaled_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3-aN_IoHqawr",
      "metadata": {
        "id": "3-aN_IoHqawr"
      },
      "source": [
        "# Q7. Divide the dataset to training and test sets. (12 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98881c42",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98881c42",
        "outputId": "4ea2c784-19cb-4d36-dd7f-775b8d5b8471"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train set has 3428 data rows\n",
            "The test set has 1470 data rows\n"
          ]
        }
      ],
      "source": [
        "label_col=scaled_df['quality_categorical'] # defining the label column\n",
        "data_col=scaled_df.iloc[:, 0:11] # defining the numerical columns\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(data_col, label_col, test_size=0.3,random_state=109) # 70-30 train test split, and a random seed to start\n",
        "\n",
        "print(\"The train set has\",len(x_train),\"data rows\")\n",
        "print(\"The test set has\",len(x_test),\"data rows\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wiYpxsyTqhcm",
      "metadata": {
        "id": "wiYpxsyTqhcm"
      },
      "source": [
        "# Q8. Use the KNN algorithm to predict the quality of wine using its attributes. (12 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e144d5b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e144d5b",
        "outputId": "96af9d07-9427-4e06-8315-1848550efeaf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 2, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "classifier = KNeighborsClassifier(n_neighbors=5) # using KNN classifier with 5 nearest neighbours\n",
        "classifier.fit(x_train, y_train) # fitting data to train\n",
        "\n",
        "y_pred = classifier.predict(x_test) # getting prediction from test data \n",
        "y_pred # predicted test labels"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UkSCQnzGqoIY",
      "metadata": {
        "id": "UkSCQnzGqoIY"
      },
      "source": [
        "# Q9. Display the confusion matrix to evaluate the model performance. (12 points)\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c33b703",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "1c33b703",
        "outputId": "b394b61f-1d4c-496f-f382-8f30fd61867e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                Confusion Matrix\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "confmat = confusion_matrix(y_test, y_pred) # confusion matrix from actual and predicted test labels\n",
        "print (\"                Confusion Matrix\\n\")\n",
        "ConfusionMatrixDisplay(confmat, display_labels = ['Low','Medium','High']).plot(); # displaying confusion matrix\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "NUtGQf06qvur",
      "metadata": {
        "id": "NUtGQf06qvur"
      },
      "source": [
        "# Q10. Evaluate the model performance by computing Accuracy, Sensitivity and Specificity. (15 points)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c38f074",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c38f074",
        "outputId": "56628ad7-e9ae-4758-88b2-e56deb4f662e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy = 0.78\n",
            "\n",
            "sensitivity Low = 0.11\n",
            "sensitivity Medium = 0.9\n",
            "sensitivity High = 0.48\n",
            "\n",
            "specificity Low = 1.0\n",
            "specificity Medium = 0.43\n",
            "specificity High = 0.91\n"
          ]
        }
      ],
      "source": [
        "n00, n01, n02, n10, n11, n12, n20, n21, n22 = confmat.ravel() # saving all the confusion matrix data into labelled variables for ease of use\n",
        "\n",
        "accuracy = round(sum([n00, n11, n22]) / sum([n00, n01, n02, n10, n11, n12, n20, n21, n22]), 2) # calculating accuracy\n",
        "\n",
        "sensitivity0 = round(n00 / sum([n00, n01, n02]), 2) # calculating sensitivities\n",
        "sensitivity1 = round(n11 / sum([n10, n11, n12]), 2)\n",
        "sensitivity2 = round(n22 / sum([n20, n21, n22]), 2)\n",
        "\n",
        "specificity0 = round(sum([n11,n12,n21,n22]) / sum([n10, n11, n12, n20, n21, n22]), 2) # calculating specificities\n",
        "specificity1 = round(sum([n00,n02,n20,n22]) / sum([n00, n01, n02, n20, n21, n22]), 2)\n",
        "specificity2 = round(sum([n00,n01,n10,n11]) / sum([n00, n01, n02, n10, n11, n12]), 2)\n",
        "\n",
        "# print all values\n",
        "\n",
        "print(\"accuracy =\",accuracy)\n",
        "\n",
        "print(\"\\nsensitivity Low =\",sensitivity0)\n",
        "print(\"sensitivity Medium =\",sensitivity1)\n",
        "print(\"sensitivity High =\",sensitivity2)\n",
        "\n",
        "print(\"\\nspecificity Low =\",specificity0)\n",
        "print(\"specificity Medium =\",specificity1)\n",
        "print(\"specificity High =\",specificity2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "29c8b610",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29c8b610",
        "outputId": "949ed2e0-5fc9-4e74-d2e0-72e1b1e9a88e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.55      0.11      0.19        53\n",
            "           1       0.82      0.90      0.86      1098\n",
            "           2       0.58      0.48      0.53       319\n",
            "\n",
            "    accuracy                           0.78      1470\n",
            "   macro avg       0.65      0.50      0.52      1470\n",
            "weighted avg       0.76      0.78      0.76      1470\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred)) # check calculated sensitivities and accuracy with the report output"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "wineQualityKNNClassifier.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}