Assignment1_ML_Question_4_Tanmay_Kumar_Shrivastava.ipynb

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      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Question 4"
      ],
      "metadata": {
        "id": "fbeJ6zduwaqh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the Wine Quality Data Set, convert all the values in the quality attribute to\n",
        "0 (bad) if the value is less than or equal to 6 and to 1 (good) otherwise. Normalize\n",
        "all the other attributes between 0 and 1 by min-max scaling. Mention why we use\n",
        "min-max scaling."
      ],
      "metadata": {
        "id": "AeYGNcMMwXPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('WineQT.csv')\n",
        "desc = df.describe()\n",
        "desc_styled = desc.style.set_table_styles(\n",
        "    [{\n",
        "        'selector': 'thead th',\n",
        "        'props': [('background-color', '#f4f4f4'), ('color', '#333'), ('border', '1px solid black')]\n",
        "    },\n",
        "    {\n",
        "        'selector': 'tbody td',\n",
        "        'props': [('border', '1px solid black'), ('text-align', 'right'), ('padding', '8px')]\n",
        "    }]\n",
        ").set_caption(\"Descriptive Statistics of Wine Quality Data (Normalized)\")\n",
        "\n",
        "# Display the styled DataFrame\n",
        "desc_styled\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "CAwbH_FlxSem",
        "outputId": "8b44851f-6287-4565-b053-1c53685292ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7bf2b365f550>"
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              "<style type=\"text/css\">\n",
              "#T_f7d96 thead th {\n",
              "  background-color: #f4f4f4;\n",
              "  color: #333;\n",
              "  border: 1px solid black;\n",
              "}\n",
              "#T_f7d96 tbody td {\n",
              "  border: 1px solid black;\n",
              "  text-align: right;\n",
              "  padding: 8px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_f7d96\" class=\"dataframe\">\n",
              "  <caption>Descriptive Statistics of Wine Quality Data (Normalized)</caption>\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_f7d96_level0_col0\" class=\"col_heading level0 col0\" >fixed acidity</th>\n",
              "      <th id=\"T_f7d96_level0_col1\" class=\"col_heading level0 col1\" >volatile acidity</th>\n",
              "      <th id=\"T_f7d96_level0_col2\" class=\"col_heading level0 col2\" >citric acid</th>\n",
              "      <th id=\"T_f7d96_level0_col3\" class=\"col_heading level0 col3\" >residual sugar</th>\n",
              "      <th id=\"T_f7d96_level0_col4\" class=\"col_heading level0 col4\" >chlorides</th>\n",
              "      <th id=\"T_f7d96_level0_col5\" class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n",
              "      <th id=\"T_f7d96_level0_col6\" class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n",
              "      <th id=\"T_f7d96_level0_col7\" class=\"col_heading level0 col7\" >density</th>\n",
              "      <th id=\"T_f7d96_level0_col8\" class=\"col_heading level0 col8\" >pH</th>\n",
              "      <th id=\"T_f7d96_level0_col9\" class=\"col_heading level0 col9\" >sulphates</th>\n",
              "      <th id=\"T_f7d96_level0_col10\" class=\"col_heading level0 col10\" >alcohol</th>\n",
              "      <th id=\"T_f7d96_level0_col11\" class=\"col_heading level0 col11\" >quality</th>\n",
              "      <th id=\"T_f7d96_level0_col12\" class=\"col_heading level0 col12\" >Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
              "      <td id=\"T_f7d96_row0_col0\" class=\"data row0 col0\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col1\" class=\"data row0 col1\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col2\" class=\"data row0 col2\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col3\" class=\"data row0 col3\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col4\" class=\"data row0 col4\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col5\" class=\"data row0 col5\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col6\" class=\"data row0 col6\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col7\" class=\"data row0 col7\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col8\" class=\"data row0 col8\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col9\" class=\"data row0 col9\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col10\" class=\"data row0 col10\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col11\" class=\"data row0 col11\" >1143.000000</td>\n",
              "      <td id=\"T_f7d96_row0_col12\" class=\"data row0 col12\" >1143.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
              "      <td id=\"T_f7d96_row1_col0\" class=\"data row1 col0\" >8.311111</td>\n",
              "      <td id=\"T_f7d96_row1_col1\" class=\"data row1 col1\" >0.531339</td>\n",
              "      <td id=\"T_f7d96_row1_col2\" class=\"data row1 col2\" >0.268364</td>\n",
              "      <td id=\"T_f7d96_row1_col3\" class=\"data row1 col3\" >2.532152</td>\n",
              "      <td id=\"T_f7d96_row1_col4\" class=\"data row1 col4\" >0.086933</td>\n",
              "      <td id=\"T_f7d96_row1_col5\" class=\"data row1 col5\" >15.615486</td>\n",
              "      <td id=\"T_f7d96_row1_col6\" class=\"data row1 col6\" >45.914698</td>\n",
              "      <td id=\"T_f7d96_row1_col7\" class=\"data row1 col7\" >0.996730</td>\n",
              "      <td id=\"T_f7d96_row1_col8\" class=\"data row1 col8\" >3.311015</td>\n",
              "      <td id=\"T_f7d96_row1_col9\" class=\"data row1 col9\" >0.657708</td>\n",
              "      <td id=\"T_f7d96_row1_col10\" class=\"data row1 col10\" >10.442111</td>\n",
              "      <td id=\"T_f7d96_row1_col11\" class=\"data row1 col11\" >5.657043</td>\n",
              "      <td id=\"T_f7d96_row1_col12\" class=\"data row1 col12\" >804.969379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
              "      <td id=\"T_f7d96_row2_col0\" class=\"data row2 col0\" >1.747595</td>\n",
              "      <td id=\"T_f7d96_row2_col1\" class=\"data row2 col1\" >0.179633</td>\n",
              "      <td id=\"T_f7d96_row2_col2\" class=\"data row2 col2\" >0.196686</td>\n",
              "      <td id=\"T_f7d96_row2_col3\" class=\"data row2 col3\" >1.355917</td>\n",
              "      <td id=\"T_f7d96_row2_col4\" class=\"data row2 col4\" >0.047267</td>\n",
              "      <td id=\"T_f7d96_row2_col5\" class=\"data row2 col5\" >10.250486</td>\n",
              "      <td id=\"T_f7d96_row2_col6\" class=\"data row2 col6\" >32.782130</td>\n",
              "      <td id=\"T_f7d96_row2_col7\" class=\"data row2 col7\" >0.001925</td>\n",
              "      <td id=\"T_f7d96_row2_col8\" class=\"data row2 col8\" >0.156664</td>\n",
              "      <td id=\"T_f7d96_row2_col9\" class=\"data row2 col9\" >0.170399</td>\n",
              "      <td id=\"T_f7d96_row2_col10\" class=\"data row2 col10\" >1.082196</td>\n",
              "      <td id=\"T_f7d96_row2_col11\" class=\"data row2 col11\" >0.805824</td>\n",
              "      <td id=\"T_f7d96_row2_col12\" class=\"data row2 col12\" >463.997116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
              "      <td id=\"T_f7d96_row3_col0\" class=\"data row3 col0\" >4.600000</td>\n",
              "      <td id=\"T_f7d96_row3_col1\" class=\"data row3 col1\" >0.120000</td>\n",
              "      <td id=\"T_f7d96_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
              "      <td id=\"T_f7d96_row3_col3\" class=\"data row3 col3\" >0.900000</td>\n",
              "      <td id=\"T_f7d96_row3_col4\" class=\"data row3 col4\" >0.012000</td>\n",
              "      <td id=\"T_f7d96_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
              "      <td id=\"T_f7d96_row3_col6\" class=\"data row3 col6\" >6.000000</td>\n",
              "      <td id=\"T_f7d96_row3_col7\" class=\"data row3 col7\" >0.990070</td>\n",
              "      <td id=\"T_f7d96_row3_col8\" class=\"data row3 col8\" >2.740000</td>\n",
              "      <td id=\"T_f7d96_row3_col9\" class=\"data row3 col9\" >0.330000</td>\n",
              "      <td id=\"T_f7d96_row3_col10\" class=\"data row3 col10\" >8.400000</td>\n",
              "      <td id=\"T_f7d96_row3_col11\" class=\"data row3 col11\" >3.000000</td>\n",
              "      <td id=\"T_f7d96_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
              "      <td id=\"T_f7d96_row4_col0\" class=\"data row4 col0\" >7.100000</td>\n",
              "      <td id=\"T_f7d96_row4_col1\" class=\"data row4 col1\" >0.392500</td>\n",
              "      <td id=\"T_f7d96_row4_col2\" class=\"data row4 col2\" >0.090000</td>\n",
              "      <td id=\"T_f7d96_row4_col3\" class=\"data row4 col3\" >1.900000</td>\n",
              "      <td id=\"T_f7d96_row4_col4\" class=\"data row4 col4\" >0.070000</td>\n",
              "      <td id=\"T_f7d96_row4_col5\" class=\"data row4 col5\" >7.000000</td>\n",
              "      <td id=\"T_f7d96_row4_col6\" class=\"data row4 col6\" >21.000000</td>\n",
              "      <td id=\"T_f7d96_row4_col7\" class=\"data row4 col7\" >0.995570</td>\n",
              "      <td id=\"T_f7d96_row4_col8\" class=\"data row4 col8\" >3.205000</td>\n",
              "      <td id=\"T_f7d96_row4_col9\" class=\"data row4 col9\" >0.550000</td>\n",
              "      <td id=\"T_f7d96_row4_col10\" class=\"data row4 col10\" >9.500000</td>\n",
              "      <td id=\"T_f7d96_row4_col11\" class=\"data row4 col11\" >5.000000</td>\n",
              "      <td id=\"T_f7d96_row4_col12\" class=\"data row4 col12\" >411.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
              "      <td id=\"T_f7d96_row5_col0\" class=\"data row5 col0\" >7.900000</td>\n",
              "      <td id=\"T_f7d96_row5_col1\" class=\"data row5 col1\" >0.520000</td>\n",
              "      <td id=\"T_f7d96_row5_col2\" class=\"data row5 col2\" >0.250000</td>\n",
              "      <td id=\"T_f7d96_row5_col3\" class=\"data row5 col3\" >2.200000</td>\n",
              "      <td id=\"T_f7d96_row5_col4\" class=\"data row5 col4\" >0.079000</td>\n",
              "      <td id=\"T_f7d96_row5_col5\" class=\"data row5 col5\" >13.000000</td>\n",
              "      <td id=\"T_f7d96_row5_col6\" class=\"data row5 col6\" >37.000000</td>\n",
              "      <td id=\"T_f7d96_row5_col7\" class=\"data row5 col7\" >0.996680</td>\n",
              "      <td id=\"T_f7d96_row5_col8\" class=\"data row5 col8\" >3.310000</td>\n",
              "      <td id=\"T_f7d96_row5_col9\" class=\"data row5 col9\" >0.620000</td>\n",
              "      <td id=\"T_f7d96_row5_col10\" class=\"data row5 col10\" >10.200000</td>\n",
              "      <td id=\"T_f7d96_row5_col11\" class=\"data row5 col11\" >6.000000</td>\n",
              "      <td id=\"T_f7d96_row5_col12\" class=\"data row5 col12\" >794.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
              "      <td id=\"T_f7d96_row6_col0\" class=\"data row6 col0\" >9.100000</td>\n",
              "      <td id=\"T_f7d96_row6_col1\" class=\"data row6 col1\" >0.640000</td>\n",
              "      <td id=\"T_f7d96_row6_col2\" class=\"data row6 col2\" >0.420000</td>\n",
              "      <td id=\"T_f7d96_row6_col3\" class=\"data row6 col3\" >2.600000</td>\n",
              "      <td id=\"T_f7d96_row6_col4\" class=\"data row6 col4\" >0.090000</td>\n",
              "      <td id=\"T_f7d96_row6_col5\" class=\"data row6 col5\" >21.000000</td>\n",
              "      <td id=\"T_f7d96_row6_col6\" class=\"data row6 col6\" >61.000000</td>\n",
              "      <td id=\"T_f7d96_row6_col7\" class=\"data row6 col7\" >0.997845</td>\n",
              "      <td id=\"T_f7d96_row6_col8\" class=\"data row6 col8\" >3.400000</td>\n",
              "      <td id=\"T_f7d96_row6_col9\" class=\"data row6 col9\" >0.730000</td>\n",
              "      <td id=\"T_f7d96_row6_col10\" class=\"data row6 col10\" >11.100000</td>\n",
              "      <td id=\"T_f7d96_row6_col11\" class=\"data row6 col11\" >6.000000</td>\n",
              "      <td id=\"T_f7d96_row6_col12\" class=\"data row6 col12\" >1209.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f7d96_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
              "      <td id=\"T_f7d96_row7_col0\" class=\"data row7 col0\" >15.900000</td>\n",
              "      <td id=\"T_f7d96_row7_col1\" class=\"data row7 col1\" >1.580000</td>\n",
              "      <td id=\"T_f7d96_row7_col2\" class=\"data row7 col2\" >1.000000</td>\n",
              "      <td id=\"T_f7d96_row7_col3\" class=\"data row7 col3\" >15.500000</td>\n",
              "      <td id=\"T_f7d96_row7_col4\" class=\"data row7 col4\" >0.611000</td>\n",
              "      <td id=\"T_f7d96_row7_col5\" class=\"data row7 col5\" >68.000000</td>\n",
              "      <td id=\"T_f7d96_row7_col6\" class=\"data row7 col6\" >289.000000</td>\n",
              "      <td id=\"T_f7d96_row7_col7\" class=\"data row7 col7\" >1.003690</td>\n",
              "      <td id=\"T_f7d96_row7_col8\" class=\"data row7 col8\" >4.010000</td>\n",
              "      <td id=\"T_f7d96_row7_col9\" class=\"data row7 col9\" >2.000000</td>\n",
              "      <td id=\"T_f7d96_row7_col10\" class=\"data row7 col10\" >14.900000</td>\n",
              "      <td id=\"T_f7d96_row7_col11\" class=\"data row7 col11\" >8.000000</td>\n",
              "      <td id=\"T_f7d96_row7_col12\" class=\"data row7 col12\" >1597.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqQAA1G5wRp4",
        "outputId": "e9d6671a-3c3f-48d1-b2f3-61fcc3cfff0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial DataFrame:\n",
            "   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  Id  \n",
            "0      9.4        5   0  \n",
            "1      9.8        5   1  \n",
            "2      9.8        5   2  \n",
            "3      9.8        6   3  \n",
            "4      9.4        5   4  \n",
            "\n",
            "Converted 'quality' attribute distribution:\n",
            "quality\n",
            "0    984\n",
            "1    159\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Normalized DataFrame:\n",
            "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
            "0       0.247788          0.397260         0.00        0.068493   0.106845   \n",
            "1       0.283186          0.520548         0.00        0.116438   0.143573   \n",
            "2       0.283186          0.438356         0.04        0.095890   0.133556   \n",
            "3       0.584071          0.109589         0.56        0.068493   0.105175   \n",
            "4       0.247788          0.397260         0.00        0.068493   0.106845   \n",
            "\n",
            "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
            "0             0.149254              0.098940  0.567548  0.606299   0.137725   \n",
            "1             0.358209              0.215548  0.494126  0.362205   0.209581   \n",
            "2             0.208955              0.169611  0.508811  0.409449   0.191617   \n",
            "3             0.238806              0.190813  0.582232  0.330709   0.149701   \n",
            "4             0.149254              0.098940  0.567548  0.606299   0.137725   \n",
            "\n",
            "    alcohol  quality        Id  \n",
            "0  0.153846        0  0.000000  \n",
            "1  0.215385        0  0.000626  \n",
            "2  0.215385        0  0.001252  \n",
            "3  0.215385        0  0.001879  \n",
            "4  0.153846        0  0.002505  \n",
            "\n",
            "The DataFrame has been normalized and saved to 'WineQT_normalized.csv'.\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "df = pd.read_csv('/content/WineQT.csv')\n",
        "\n",
        "# Display the first few rows of the dataframe to understand its structure\n",
        "print(\"Initial DataFrame:\")\n",
        "print(df.head())\n",
        "\n",
        "# Step 3: Convert 'quality' attribute to binary classification\n",
        "# If quality is <= 6, it will be 0 (bad), else it will be 1 (good)\n",
        "df['quality'] = df['quality'].apply(lambda x: 0 if x <= 6 else 1)\n",
        "\n",
        "# Display the distribution of the converted 'quality' attribute\n",
        "print(\"\\nConverted 'quality' attribute distribution:\")\n",
        "print(df['quality'].value_counts())\n",
        "\n",
        "# Step 4: Normalize all other attributes between 0 and 1 using Min-Max Scaling\n",
        "# Min-Max scaling formula: X_scaled = (X - X_min) / (X_max - X_min)\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Select the feature columns (excluding 'quality')\n",
        "feature_columns = df.columns[df.columns != 'quality']\n",
        "\n",
        "# Apply min-max scaling to the selected features\n",
        "df[feature_columns] = scaler.fit_transform(df[feature_columns])\n",
        "\n",
        "# Display the first few rows of the normalized dataframe\n",
        "print(\"\\nNormalized DataFrame:\")\n",
        "print(df.head())\n",
        "\n",
        "# Step 5: Save the modified dataframe to a new CSV file\n",
        "df.to_csv('WineQT_normalized.csv', index=False)\n",
        "\n",
        "print(\"\\nThe DataFrame has been normalized and saved to 'WineQT_normalized.csv'.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('WineQT_normalized.csv')\n",
        "\n",
        "# Step 3: Generate descriptive statistics\n",
        "desc = df.describe()\n",
        "\n",
        "# Step 4: Style the output to resemble an Excel sheet\n",
        "desc_styled = desc.style.set_table_styles(\n",
        "    [{\n",
        "        'selector': 'thead th',\n",
        "        'props': [('background-color', '#f4f4f4'), ('color', '#333'), ('border', '1px solid black')]\n",
        "    },\n",
        "    {\n",
        "        'selector': 'tbody td',\n",
        "        'props': [('border', '1px solid black'), ('text-align', 'right'), ('padding', '8px')]\n",
        "    }]\n",
        ").set_caption(\"Descriptive Statistics of Wine Quality Data (Normalized)\")\n",
        "\n",
        "# Display the styled DataFrame\n",
        "desc_styled\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "BqB1RfdlxPMe",
        "outputId": "682b7b4b-7b73-4da8-aa5b-f006c6faa6b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7bf2b36bd030>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_16a35 thead th {\n",
              "  background-color: #f4f4f4;\n",
              "  color: #333;\n",
              "  border: 1px solid black;\n",
              "}\n",
              "#T_16a35 tbody td {\n",
              "  border: 1px solid black;\n",
              "  text-align: right;\n",
              "  padding: 8px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_16a35\" class=\"dataframe\">\n",
              "  <caption>Descriptive Statistics of Wine Quality Data (Normalized)</caption>\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_16a35_level0_col0\" class=\"col_heading level0 col0\" >fixed acidity</th>\n",
              "      <th id=\"T_16a35_level0_col1\" class=\"col_heading level0 col1\" >volatile acidity</th>\n",
              "      <th id=\"T_16a35_level0_col2\" class=\"col_heading level0 col2\" >citric acid</th>\n",
              "      <th id=\"T_16a35_level0_col3\" class=\"col_heading level0 col3\" >residual sugar</th>\n",
              "      <th id=\"T_16a35_level0_col4\" class=\"col_heading level0 col4\" >chlorides</th>\n",
              "      <th id=\"T_16a35_level0_col5\" class=\"col_heading level0 col5\" >free sulfur dioxide</th>\n",
              "      <th id=\"T_16a35_level0_col6\" class=\"col_heading level0 col6\" >total sulfur dioxide</th>\n",
              "      <th id=\"T_16a35_level0_col7\" class=\"col_heading level0 col7\" >density</th>\n",
              "      <th id=\"T_16a35_level0_col8\" class=\"col_heading level0 col8\" >pH</th>\n",
              "      <th id=\"T_16a35_level0_col9\" class=\"col_heading level0 col9\" >sulphates</th>\n",
              "      <th id=\"T_16a35_level0_col10\" class=\"col_heading level0 col10\" >alcohol</th>\n",
              "      <th id=\"T_16a35_level0_col11\" class=\"col_heading level0 col11\" >quality</th>\n",
              "      <th id=\"T_16a35_level0_col12\" class=\"col_heading level0 col12\" >Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
              "      <td id=\"T_16a35_row0_col0\" class=\"data row0 col0\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col1\" class=\"data row0 col1\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col2\" class=\"data row0 col2\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col3\" class=\"data row0 col3\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col4\" class=\"data row0 col4\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col5\" class=\"data row0 col5\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col6\" class=\"data row0 col6\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col7\" class=\"data row0 col7\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col8\" class=\"data row0 col8\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col9\" class=\"data row0 col9\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col10\" class=\"data row0 col10\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col11\" class=\"data row0 col11\" >1143.000000</td>\n",
              "      <td id=\"T_16a35_row0_col12\" class=\"data row0 col12\" >1143.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
              "      <td id=\"T_16a35_row1_col0\" class=\"data row1 col0\" >0.328417</td>\n",
              "      <td id=\"T_16a35_row1_col1\" class=\"data row1 col1\" >0.281739</td>\n",
              "      <td id=\"T_16a35_row1_col2\" class=\"data row1 col2\" >0.268364</td>\n",
              "      <td id=\"T_16a35_row1_col3\" class=\"data row1 col3\" >0.111791</td>\n",
              "      <td id=\"T_16a35_row1_col4\" class=\"data row1 col4\" >0.125096</td>\n",
              "      <td id=\"T_16a35_row1_col5\" class=\"data row1 col5\" >0.218142</td>\n",
              "      <td id=\"T_16a35_row1_col6\" class=\"data row1 col6\" >0.141041</td>\n",
              "      <td id=\"T_16a35_row1_col7\" class=\"data row1 col7\" >0.489017</td>\n",
              "      <td id=\"T_16a35_row1_col8\" class=\"data row1 col8\" >0.449618</td>\n",
              "      <td id=\"T_16a35_row1_col9\" class=\"data row1 col9\" >0.196232</td>\n",
              "      <td id=\"T_16a35_row1_col10\" class=\"data row1 col10\" >0.314171</td>\n",
              "      <td id=\"T_16a35_row1_col11\" class=\"data row1 col11\" >0.139108</td>\n",
              "      <td id=\"T_16a35_row1_col12\" class=\"data row1 col12\" >0.504051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
              "      <td id=\"T_16a35_row2_col0\" class=\"data row2 col0\" >0.154654</td>\n",
              "      <td id=\"T_16a35_row2_col1\" class=\"data row2 col1\" >0.123036</td>\n",
              "      <td id=\"T_16a35_row2_col2\" class=\"data row2 col2\" >0.196686</td>\n",
              "      <td id=\"T_16a35_row2_col3\" class=\"data row2 col3\" >0.092871</td>\n",
              "      <td id=\"T_16a35_row2_col4\" class=\"data row2 col4\" >0.078910</td>\n",
              "      <td id=\"T_16a35_row2_col5\" class=\"data row2 col5\" >0.152992</td>\n",
              "      <td id=\"T_16a35_row2_col6\" class=\"data row2 col6\" >0.115838</td>\n",
              "      <td id=\"T_16a35_row2_col7\" class=\"data row2 col7\" >0.141341</td>\n",
              "      <td id=\"T_16a35_row2_col8\" class=\"data row2 col8\" >0.123358</td>\n",
              "      <td id=\"T_16a35_row2_col9\" class=\"data row2 col9\" >0.102035</td>\n",
              "      <td id=\"T_16a35_row2_col10\" class=\"data row2 col10\" >0.166492</td>\n",
              "      <td id=\"T_16a35_row2_col11\" class=\"data row2 col11\" >0.346210</td>\n",
              "      <td id=\"T_16a35_row2_col12\" class=\"data row2 col12\" >0.290543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
              "      <td id=\"T_16a35_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col1\" class=\"data row3 col1\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col6\" class=\"data row3 col6\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col8\" class=\"data row3 col8\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col10\" class=\"data row3 col10\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
              "      <td id=\"T_16a35_row4_col0\" class=\"data row4 col0\" >0.221239</td>\n",
              "      <td id=\"T_16a35_row4_col1\" class=\"data row4 col1\" >0.186644</td>\n",
              "      <td id=\"T_16a35_row4_col2\" class=\"data row4 col2\" >0.090000</td>\n",
              "      <td id=\"T_16a35_row4_col3\" class=\"data row4 col3\" >0.068493</td>\n",
              "      <td id=\"T_16a35_row4_col4\" class=\"data row4 col4\" >0.096828</td>\n",
              "      <td id=\"T_16a35_row4_col5\" class=\"data row4 col5\" >0.089552</td>\n",
              "      <td id=\"T_16a35_row4_col6\" class=\"data row4 col6\" >0.053004</td>\n",
              "      <td id=\"T_16a35_row4_col7\" class=\"data row4 col7\" >0.403818</td>\n",
              "      <td id=\"T_16a35_row4_col8\" class=\"data row4 col8\" >0.366142</td>\n",
              "      <td id=\"T_16a35_row4_col9\" class=\"data row4 col9\" >0.131737</td>\n",
              "      <td id=\"T_16a35_row4_col10\" class=\"data row4 col10\" >0.169231</td>\n",
              "      <td id=\"T_16a35_row4_col11\" class=\"data row4 col11\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row4_col12\" class=\"data row4 col12\" >0.257358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
              "      <td id=\"T_16a35_row5_col0\" class=\"data row5 col0\" >0.292035</td>\n",
              "      <td id=\"T_16a35_row5_col1\" class=\"data row5 col1\" >0.273973</td>\n",
              "      <td id=\"T_16a35_row5_col2\" class=\"data row5 col2\" >0.250000</td>\n",
              "      <td id=\"T_16a35_row5_col3\" class=\"data row5 col3\" >0.089041</td>\n",
              "      <td id=\"T_16a35_row5_col4\" class=\"data row5 col4\" >0.111853</td>\n",
              "      <td id=\"T_16a35_row5_col5\" class=\"data row5 col5\" >0.179104</td>\n",
              "      <td id=\"T_16a35_row5_col6\" class=\"data row5 col6\" >0.109541</td>\n",
              "      <td id=\"T_16a35_row5_col7\" class=\"data row5 col7\" >0.485316</td>\n",
              "      <td id=\"T_16a35_row5_col8\" class=\"data row5 col8\" >0.448819</td>\n",
              "      <td id=\"T_16a35_row5_col9\" class=\"data row5 col9\" >0.173653</td>\n",
              "      <td id=\"T_16a35_row5_col10\" class=\"data row5 col10\" >0.276923</td>\n",
              "      <td id=\"T_16a35_row5_col11\" class=\"data row5 col11\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row5_col12\" class=\"data row5 col12\" >0.497182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
              "      <td id=\"T_16a35_row6_col0\" class=\"data row6 col0\" >0.398230</td>\n",
              "      <td id=\"T_16a35_row6_col1\" class=\"data row6 col1\" >0.356164</td>\n",
              "      <td id=\"T_16a35_row6_col2\" class=\"data row6 col2\" >0.420000</td>\n",
              "      <td id=\"T_16a35_row6_col3\" class=\"data row6 col3\" >0.116438</td>\n",
              "      <td id=\"T_16a35_row6_col4\" class=\"data row6 col4\" >0.130217</td>\n",
              "      <td id=\"T_16a35_row6_col5\" class=\"data row6 col5\" >0.298507</td>\n",
              "      <td id=\"T_16a35_row6_col6\" class=\"data row6 col6\" >0.194346</td>\n",
              "      <td id=\"T_16a35_row6_col7\" class=\"data row6 col7\" >0.570852</td>\n",
              "      <td id=\"T_16a35_row6_col8\" class=\"data row6 col8\" >0.519685</td>\n",
              "      <td id=\"T_16a35_row6_col9\" class=\"data row6 col9\" >0.239521</td>\n",
              "      <td id=\"T_16a35_row6_col10\" class=\"data row6 col10\" >0.415385</td>\n",
              "      <td id=\"T_16a35_row6_col11\" class=\"data row6 col11\" >0.000000</td>\n",
              "      <td id=\"T_16a35_row6_col12\" class=\"data row6 col12\" >0.757358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16a35_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
              "      <td id=\"T_16a35_row7_col0\" class=\"data row7 col0\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col1\" class=\"data row7 col1\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col2\" class=\"data row7 col2\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col3\" class=\"data row7 col3\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col4\" class=\"data row7 col4\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col5\" class=\"data row7 col5\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col6\" class=\"data row7 col6\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col8\" class=\"data row7 col8\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col9\" class=\"data row7 col9\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col10\" class=\"data row7 col10\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col11\" class=\"data row7 col11\" >1.000000</td>\n",
              "      <td id=\"T_16a35_row7_col12\" class=\"data row7 col12\" >1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "df = pd.read_csv('WineQT_normalized.csv')\n",
        "desc = df.describe()\n",
        "\n",
        "# Step 4: Display descriptive statistics with styling\n",
        "# Apply pandas styling to enhance readability\n",
        "styled_desc = desc.style.background_gradient(cmap='coolwarm', axis=None) \\\n",
        "    .format(precision=2) \\\n",
        "    .set_caption(\"Descriptive Statistics of Wine Quality Data (Normalized)\")\n",
        "\n",
        "styled_desc\n",
        "\n",
        "# Optional: Display a heatmap for a visual overview of the data distribution\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(desc.transpose(), annot=True, cmap='coolwarm', cbar=True)\n",
        "plt.title('Heatmap of Descriptive Statistics')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "kHyg5XR3ww6c",
        "outputId": "e7013cac-ebc9-4e04-e4db-9f34eec9be36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}