WineQuality_Decision_RandomForest (1).ipynb

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  "metadata": {
    "colab": {
      "name": "WineQuality_Decision_RandomForest.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLgjeTEJBbem"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib as plt\n",
        "import seaborn as sns \n",
        "import plotly.express as px"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCYs5KXjB6-L"
      },
      "source": [
        "df = pd.read_csv(\"winequality-red.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "Nw8zL1xCEJul",
        "outputId": "0235cf90-7251-40ac-d617-d8c2c68e6cc0"
      },
      "source": [
        "print(\"Rows, columns: \" + str(df.shape))\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows, columns: (1599, 12)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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        "outputId": "0139c178-59a0-4283-bbc1-2fd3f6d01805"
      },
      "source": [
        "print(df.isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fixed acidity           0\n",
            "volatile acidity        0\n",
            "citric acid             0\n",
            "residual sugar          0\n",
            "chlorides               0\n",
            "free sulfur dioxide     0\n",
            "total sulfur dioxide    0\n",
            "density                 0\n",
            "pH                      0\n",
            "sulphates               0\n",
            "alcohol                 0\n",
            "quality                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "74de32ad-879a-49d9-d1d6-3964ec39bca0"
      },
      "source": [
        "fig = px.histogram(df, x='quality')\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"quality\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"count\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('d27458c8-0a37-453b-a574-d33de10da0b7');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "k8kiZCthFBh-",
        "outputId": "90f9660c-d636-48a1-941e-6fc6962193f0"
      },
      "source": [
        "corr = df.corr()\n",
        "matplotlib.pyplot.subplots(figsize=(15,10))\n",
        "sns.heatmap(corr, xticklabels=corr.columns,\n",
        "            yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f99fd8ae250>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdgSzrfwIY-n"
      },
      "source": [
        "#Classification target \n",
        "df['goodquality'] = [1 if x>=7 else 0 for x in df['quality']]\n",
        "\n",
        "#Separate feature variables and targer variable\n",
        "X = df.drop(['quality', 'goodquality'], axis = 1)\n",
        "y = df['goodquality']"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4t0HLEtwJoao",
        "outputId": "e93820c8-e88c-408a-87b6-b75a7d4d4496"
      },
      "source": [
        "df['goodquality'].value_counts()\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1382\n",
              "1     217\n",
              "Name: goodquality, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjFm1xDHVKCO"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_features = X\n",
        "X = StandardScaler().fit_transform(X)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvNiyR0TWF9Z"
      },
      "source": [
        "from sklearn.model_selection import  train_test_split\n",
        "X_train, X_test, y_train,  y_test = train_test_split(X, y, test_size=.25, random_state=0)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDufJD2yh5Zb"
      },
      "source": [
        "**Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z16zO9hyhy2V",
        "outputId": "368ebddb-b82b-4ed3-8aad-7c98cedda192"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model1 = DecisionTreeClassifier(random_state= 1)\n",
        "model1.fit(X_train, y_train)\n",
        "y_pred1 = model1.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred1))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.92      0.94       355\n",
            "           1       0.53      0.73      0.62        45\n",
            "\n",
            "    accuracy                           0.90       400\n",
            "   macro avg       0.75      0.83      0.78       400\n",
            "weighted avg       0.92      0.90      0.90       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76SjOQB7h2dw"
      },
      "source": [
        "**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZG3O3H8jU_S",
        "outputId": "b5d20565-2e22-43d3-dc3c-84783491303a"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model2 = RandomForestClassifier(random_state=1)\n",
        "model2.fit(X_train, y_train)\n",
        "y_pred2 = model2.predict(X_test)\n",
        "\n",
        "print(classification_report(y_test, y_pred2))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96       355\n",
            "           1       0.68      0.58      0.63        45\n",
            "\n",
            "    accuracy                           0.92       400\n",
            "   macro avg       0.82      0.77      0.79       400\n",
            "weighted avg       0.92      0.92      0.92       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dH5AQRumkYrG"
      },
      "source": [
        "**Feature Importance**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQsCcu62kd61"
      },
      "source": [
        "***Decision Tree***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "C4KuBbv2kAcq",
        "outputId": "37d70e91-fa80-48d4-f47b-ec53073be767"
      },
      "source": [
        "feat_importances = pd.Series(model1.feature_importances_, index=X_features.columns)\n",
        "feat_importances.nlargest(25).plot(kind='barh', figsize=(10,10));"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGopQPVdlj46"
      },
      "source": [
        "***Random Forest***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "UXYIphf-loxK",
        "outputId": "48349de0-c84c-4293-97ef-74ade5f8062e"
      },
      "source": [
        "feat_importances = pd.Series(model2.feature_importances_, index=X_features.columns)\n",
        "feat_importances.nlargest(25).plot(kind='barh', figsize=(10,10));"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5YgESA8mzI6"
      },
      "source": [
        "***Comparing the Top 4 Features***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "tyFq4N_Um_bo",
        "outputId": "dddf557b-fedd-4090-ae31-57686ff2f3d1"
      },
      "source": [
        "#Filter data for only good quality \n",
        "df_temp = df[df['goodquality']==1]\n",
        "df_temp.describe()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>goodquality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.000000</td>\n",
              "      <td>217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.847005</td>\n",
              "      <td>0.405530</td>\n",
              "      <td>0.376498</td>\n",
              "      <td>2.708756</td>\n",
              "      <td>0.075912</td>\n",
              "      <td>13.981567</td>\n",
              "      <td>34.889401</td>\n",
              "      <td>0.996030</td>\n",
              "      <td>3.288802</td>\n",
              "      <td>0.743456</td>\n",
              "      <td>11.518049</td>\n",
              "      <td>7.082949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.999977</td>\n",
              "      <td>0.144963</td>\n",
              "      <td>0.194438</td>\n",
              "      <td>1.363026</td>\n",
              "      <td>0.028480</td>\n",
              "      <td>10.234615</td>\n",
              "      <td>32.572238</td>\n",
              "      <td>0.002201</td>\n",
              "      <td>0.154478</td>\n",
              "      <td>0.134038</td>\n",
              "      <td>0.998153</td>\n",
              "      <td>0.276443</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.900000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.990640</td>\n",
              "      <td>2.880000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.062000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.994700</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>10.800000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.700000</td>\n",
              "      <td>0.370000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>0.073000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.995720</td>\n",
              "      <td>3.270000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>11.600000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10.100000</td>\n",
              "      <td>0.490000</td>\n",
              "      <td>0.490000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>0.085000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>0.997350</td>\n",
              "      <td>3.380000</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.600000</td>\n",
              "      <td>0.915000</td>\n",
              "      <td>0.760000</td>\n",
              "      <td>8.900000</td>\n",
              "      <td>0.358000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003200</td>\n",
              "      <td>3.780000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...     quality  goodquality\n",
              "count     217.000000        217.000000  ...  217.000000        217.0\n",
              "mean        8.847005          0.405530  ...    7.082949          1.0\n",
              "std         1.999977          0.144963  ...    0.276443          0.0\n",
              "min         4.900000          0.120000  ...    7.000000          1.0\n",
              "25%         7.400000          0.300000  ...    7.000000          1.0\n",
              "50%         8.700000          0.370000  ...    7.000000          1.0\n",
              "75%        10.100000          0.490000  ...    7.000000          1.0\n",
              "max        15.600000          0.915000  ...    8.000000          1.0\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "jJWNbp2qnxdx",
        "outputId": "337b28d0-118e-46ee-9d32-70f8918660d1"
      },
      "source": [
        "#Filtering df for only bad quality \n",
        "df_temp2 = df[df['goodquality']==0]\n",
        "df_temp2.describe()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>goodquality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.000000</td>\n",
              "      <td>1382.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.236831</td>\n",
              "      <td>0.547022</td>\n",
              "      <td>0.254407</td>\n",
              "      <td>2.512120</td>\n",
              "      <td>0.089281</td>\n",
              "      <td>16.172214</td>\n",
              "      <td>48.285818</td>\n",
              "      <td>0.996859</td>\n",
              "      <td>3.314616</td>\n",
              "      <td>0.644754</td>\n",
              "      <td>10.251037</td>\n",
              "      <td>5.408828</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.682726</td>\n",
              "      <td>0.176337</td>\n",
              "      <td>0.189665</td>\n",
              "      <td>1.415778</td>\n",
              "      <td>0.049113</td>\n",
              "      <td>10.467685</td>\n",
              "      <td>32.585604</td>\n",
              "      <td>0.001808</td>\n",
              "      <td>0.154135</td>\n",
              "      <td>0.170629</td>\n",
              "      <td>0.969664</td>\n",
              "      <td>0.601719</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.034000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.082500</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.071000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.995785</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.540000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.800000</td>\n",
              "      <td>0.540000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>39.500000</td>\n",
              "      <td>0.996800</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.100000</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.091000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>0.997900</td>\n",
              "      <td>3.410000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>10.900000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      quality  goodquality\n",
              "count    1382.000000       1382.000000  ...  1382.000000       1382.0\n",
              "mean        8.236831          0.547022  ...     5.408828          0.0\n",
              "std         1.682726          0.176337  ...     0.601719          0.0\n",
              "min         4.600000          0.160000  ...     3.000000          0.0\n",
              "25%         7.100000          0.420000  ...     5.000000          0.0\n",
              "50%         7.800000          0.540000  ...     5.000000          0.0\n",
              "75%         9.100000          0.650000  ...     6.000000          0.0\n",
              "max        15.900000          1.580000  ...     6.000000          0.0\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rbRtFFypjTd"
      },
      "source": [
        "**We can see that good quality wines have higher levels of alcohol on average, have a lower volatile acidity on average, higher levels of sulphates on average, and higher levels of residual sugar on average.**"
      ]
    }
  ]
}