Machine_Learning/Red Wine Quality Detection/Red_Wine_Quality (1).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Red Wine Qualtiy\n",
        "\n",
        "Configuring Kaggle API"
      ],
      "metadata": {
        "id": "DRJNGe2jtXu_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "whNPMka0ZheY"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"janvichoudhary\",\"key\":\"\"}\n",
        "\n",
        "import json\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d uciml/red-wine-quality-cortez-et-al-2009"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXSPDp2LtY1m",
        "outputId": "39693d0e-c339-4b26-9da9-9389ae944a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading red-wine-quality-cortez-et-al-2009.zip to /content\n",
            "\r  0% 0.00/25.6k [00:00<?, ?B/s]\n",
            "\r100% 25.6k/25.6k [00:00<00:00, 46.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip 'red-wine-quality-cortez-et-al-2009.zip' -d redwine"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVKgc5I-tmVI",
        "outputId": "8cbb9cc3-c862-46f9-fde8-d1a5c198d886"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  red-wine-quality-cortez-et-al-2009.zip\n",
            "  inflating: redwine/winequality-red.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing libraries."
      ],
      "metadata": {
        "id": "82rwRMDKtv3c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix,classification_report\n",
        "import imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "print(os.listdir(\"/content\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDXOA6C-tktw",
        "outputId": "908bb87b-de34-48c2-cea0-53eb75d87c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'red-wine-quality-cortez-et-al-2009.zip', 'redwine', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/redwine/winequality-red.csv\")\n",
        "df.head()"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   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  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
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              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
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              "      <td>7.4</td>\n",
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              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
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              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
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              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>0.9978</td>\n",
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              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
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      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "    # cat_cols, cat_but_car\n",
        "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
        "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and\n",
        "                   dataframe[col].dtypes != \"O\"]\n",
        "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
        "                   dataframe[col].dtypes == \"O\"]\n",
        "    cat_cols = cat_cols + num_but_cat\n",
        "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "    # num_cols\n",
        "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
        "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
        "\n",
        "    print(f\"Observations: {dataframe.shape[0]}\")\n",
        "    print(f\"Variables: {dataframe.shape[1]}\")\n",
        "    print(f'cat_cols: {len(cat_cols)}')\n",
        "    print(f'num_cols: {len(num_cols)}')\n",
        "    print(f'cat_but_car: {len(cat_but_car)}')\n",
        "    print(f'num_but_cat: {len(num_but_cat)}')\n",
        "\n",
        "    return cat_cols, num_cols, cat_but_car\n",
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
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        "id": "0ES83FWWuAYE",
        "outputId": "563116fa-9a0f-4af1-cf91-4c4b29e0bb98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 1599\n",
            "Variables: 12\n",
            "cat_cols: 1\n",
            "num_cols: 11\n",
            "cat_but_car: 0\n",
            "num_but_cat: 1\n"
          ]
        }
      ]
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        "df.info"
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        "outputId": "e2d29605-d428-4b65-aaa9-e4fc8cdda751"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "4               7.4             0.700         0.00             1.9      0.076   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "0         9.4        5  \n",
              "1         9.8        5  \n",
              "2         9.8        5  \n",
              "3         9.8        6  \n",
              "4         9.4        5  \n",
              "...       ...      ...  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  \n",
              "\n",
              "[1599 rows x 12 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Quality Points:\",df['quality'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dC9Rdy4uEzZ",
        "outputId": "b94cbe5f-a10d-437c-e0d5-71716d2a6020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quality Points: [5 6 7 4 8 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['quality'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wi_LslaCuGzy",
        "outputId": "f35ae9c1-c145-4650-bcb5-0da4f2db8be2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5    681\n",
            "6    638\n",
            "7    199\n",
            "4     53\n",
            "8     18\n",
            "3     10\n",
            "Name: quality, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(1, figsize=(10,10))\n",
        "df['quality'].value_counts().plot.pie(autopct=\"%1.2f%%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 824
        },
        "id": "rO0ACw70uIkA",
        "outputId": "3574b970-542f-4f43-b818-4050419b9d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='quality'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quality = df[\"quality\"].value_counts().sort_index(ascending=True)\n",
        "explode_list = [0.1, 0.1, 0, 0, 0, 0.1]\n",
        "ax = quality.plot(kind='pie',\n",
        "             figsize = (10,10),\n",
        "             autopct='%1.1f%%',\n",
        "             startangle=45,\n",
        "             labels=None,\n",
        "             pctdistance=1.1,\n",
        "             explode=explode_list,\n",
        "             textprops={'fontsize': 14})\n",
        "ax.patch.set_facecolor('white')\n",
        "plt.title('Wine Quality', size = 20)\n",
        "ax.set(ylabel=None)\n",
        "plt.axis('equal')\n",
        "plt.legend(labels=quality.index, loc=\"upper left\", fontsize=16);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "ImjjkeI7uK9I",
        "outputId": "f153d8ac-c1c7-46c1-bf2b-01f42609ba05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "ax = sns.distplot(df['alcohol'])\n",
        "ax.set_title('Distribution of Alcohol Concentration', size=20)\n",
        "ax.set(ylabel='Density', xlabel='Alcohol');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "B8OFe_XBuNTX",
        "outputId": "6e5edb54-b821-4ac2-e4eb-e2c262044019"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "veZFRwn0uPLf",
        "outputId": "1adcf7f9-79fd-4b15-ad78-bd1f733e335a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       count       mean        std      min      25%  \\\n",
              "fixed acidity         1599.0   8.319637   1.741096  4.60000   7.1000   \n",
              "volatile acidity      1599.0   0.527821   0.179060  0.12000   0.3900   \n",
              "citric acid           1599.0   0.270976   0.194801  0.00000   0.0900   \n",
              "residual sugar        1599.0   2.538806   1.409928  0.90000   1.9000   \n",
              "chlorides             1599.0   0.087467   0.047065  0.01200   0.0700   \n",
              "free sulfur dioxide   1599.0  15.874922  10.460157  1.00000   7.0000   \n",
              "total sulfur dioxide  1599.0  46.467792  32.895324  6.00000  22.0000   \n",
              "density               1599.0   0.996747   0.001887  0.99007   0.9956   \n",
              "pH                    1599.0   3.311113   0.154386  2.74000   3.2100   \n",
              "sulphates             1599.0   0.658149   0.169507  0.33000   0.5500   \n",
              "alcohol               1599.0  10.422983   1.065668  8.40000   9.5000   \n",
              "quality               1599.0   5.636023   0.807569  3.00000   5.0000   \n",
              "\n",
              "                           50%        75%        max  \n",
              "fixed acidity          7.90000   9.200000   15.90000  \n",
              "volatile acidity       0.52000   0.640000    1.58000  \n",
              "citric acid            0.26000   0.420000    1.00000  \n",
              "residual sugar         2.20000   2.600000   15.50000  \n",
              "chlorides              0.07900   0.090000    0.61100  \n",
              "free sulfur dioxide   14.00000  21.000000   72.00000  \n",
              "total sulfur dioxide  38.00000  62.000000  289.00000  \n",
              "density                0.99675   0.997835    1.00369  \n",
              "pH                     3.31000   3.400000    4.01000  \n",
              "sulphates              0.62000   0.730000    2.00000  \n",
              "alcohol               10.20000  11.100000   14.90000  \n",
              "quality                6.00000   6.000000    8.00000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e3b9f68-9051-4a4d-a689-0bbbbb061967\" 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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fixed acidity</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>8.319637</td>\n",
              "      <td>1.741096</td>\n",
              "      <td>4.60000</td>\n",
              "      <td>7.1000</td>\n",
              "      <td>7.90000</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>15.90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.12000</td>\n",
              "      <td>0.3900</td>\n",
              "      <td>0.52000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>1.58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric acid</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0900</td>\n",
              "      <td>0.26000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual sugar</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.90000</td>\n",
              "      <td>1.9000</td>\n",
              "      <td>2.20000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>15.50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>0.01200</td>\n",
              "      <td>0.0700</td>\n",
              "      <td>0.07900</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.61100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>14.00000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>72.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>22.0000</td>\n",
              "      <td>38.00000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>289.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.99007</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>0.99675</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>1.00369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>2.74000</td>\n",
              "      <td>3.2100</td>\n",
              "      <td>3.31000</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>4.01000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>0.33000</td>\n",
              "      <td>0.5500</td>\n",
              "      <td>0.62000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>2.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>8.40000</td>\n",
              "      <td>9.5000</td>\n",
              "      <td>10.20000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>14.90000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>1599.0</td>\n",
              "      <td>5.636023</td>\n",
              "      <td>0.807569</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.00000</td>\n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining all the features and exploring them."
      ],
      "metadata": {
        "id": "3HXcdgznuRrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = ['residual sugar', 'total sulfur dioxide', 'sulphates',\n",
        "                   'alcohol', 'volatile acidity', 'quality']\n",
        "df_features = df[features]"
      ],
      "metadata": {
        "id": "7SqESHbHuRzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "case_1 = (df_features['quality']==3)|(df_features['quality']==4)\n",
        "case_2 = (df_features['quality']==5)|(df_features['quality']==6)\n",
        "case_3 = (df_features['quality']==7)|(df_features['quality']==8)\n",
        "level_34 = round(df_features[case_1].describe(),2)\n",
        "level_56 = round(df_features[case_2].describe(),2)\n",
        "level_78 = round(df_features[case_3].describe(),2)"
      ],
      "metadata": {
        "id": "pp5nyPXQuTWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "level_all = pd.concat([level_34,level_56, level_78],\n",
        "                    axis=1,\n",
        "                    keys=['3,4','5,6','7,8',])\n",
        "level_all.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "OspQeAiiuUx-",
        "outputId": "c10ed4b8-c9cd-430b-a386-cf29341ec74e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           count   mean    std   min    25%    50%    75%  \\\n",
              "3,4 residual sugar          63.0   2.68   1.72  1.20   1.90   2.10   2.95   \n",
              "    total sulfur dioxide    63.0  34.44  26.40  7.00  13.50  26.00  48.00   \n",
              "    sulphates               63.0   0.59   0.22  0.33   0.50   0.56   0.60   \n",
              "    alcohol                 63.0  10.22   0.92  8.40   9.60  10.00  11.00   \n",
              "    volatile acidity        63.0   0.72   0.25  0.23   0.56   0.68   0.88   \n",
              "    quality                 63.0   3.84   0.37  3.00   4.00   4.00   4.00   \n",
              "5,6 residual sugar        1319.0   2.50   1.40  0.90   1.90   2.20   2.60   \n",
              "    total sulfur dioxide  1319.0  48.95  32.71  6.00  24.00  40.00  65.00   \n",
              "    sulphates             1319.0   0.65   0.17  0.37   0.54   0.61   0.70   \n",
              "    alcohol               1319.0  10.25   0.97  8.40   9.50  10.00  10.90   \n",
              "    volatile acidity      1319.0   0.54   0.17  0.16   0.41   0.54   0.64   \n",
              "    quality               1319.0   5.48   0.50  5.00   5.00   5.00   6.00   \n",
              "7,8 residual sugar         217.0   2.71   1.36  1.20   2.00   2.30   2.70   \n",
              "    total sulfur dioxide   217.0  34.89  32.57  7.00  17.00  27.00  43.00   \n",
              "    sulphates              217.0   0.74   0.13  0.39   0.65   0.74   0.82   \n",
              "    alcohol                217.0  11.52   1.00  9.20  10.80  11.60  12.20   \n",
              "    volatile acidity       217.0   0.41   0.14  0.12   0.30   0.37   0.49   \n",
              "    quality                217.0   7.08   0.28  7.00   7.00   7.00   7.00   \n",
              "\n",
              "                             max  \n",
              "3,4 residual sugar         12.90  \n",
              "    total sulfur dioxide  119.00  \n",
              "    sulphates               2.00  \n",
              "    alcohol                13.10  \n",
              "    volatile acidity        1.58  \n",
              "    quality                 4.00  \n",
              "5,6 residual sugar         15.50  \n",
              "    total sulfur dioxide  165.00  \n",
              "    sulphates               1.98  \n",
              "    alcohol                14.90  \n",
              "    volatile acidity        1.33  \n",
              "    quality                 6.00  \n",
              "7,8 residual sugar          8.90  \n",
              "    total sulfur dioxide  289.00  \n",
              "    sulphates               1.36  \n",
              "    alcohol                14.00  \n",
              "    volatile acidity        0.92  \n",
              "    quality                 8.00  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
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              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">3,4</th>\n",
              "      <th>residual sugar</th>\n",
              "      <td>63.0</td>\n",
              "      <td>2.68</td>\n",
              "      <td>1.72</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.10</td>\n",
              "      <td>2.95</td>\n",
              "      <td>12.90</td>\n",
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              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>63.0</td>\n",
              "      <td>34.44</td>\n",
              "      <td>26.40</td>\n",
              "      <td>7.00</td>\n",
              "      <td>13.50</td>\n",
              "      <td>26.00</td>\n",
              "      <td>48.00</td>\n",
              "      <td>119.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>63.0</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.60</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>63.0</td>\n",
              "      <td>10.22</td>\n",
              "      <td>0.92</td>\n",
              "      <td>8.40</td>\n",
              "      <td>9.60</td>\n",
              "      <td>10.00</td>\n",
              "      <td>11.00</td>\n",
              "      <td>13.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>63.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>63.0</td>\n",
              "      <td>3.84</td>\n",
              "      <td>0.37</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">5,6</th>\n",
              "      <th>residual sugar</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.90</td>\n",
              "      <td>2.20</td>\n",
              "      <td>2.60</td>\n",
              "      <td>15.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>48.95</td>\n",
              "      <td>32.71</td>\n",
              "      <td>6.00</td>\n",
              "      <td>24.00</td>\n",
              "      <td>40.00</td>\n",
              "      <td>65.00</td>\n",
              "      <td>165.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>10.25</td>\n",
              "      <td>0.97</td>\n",
              "      <td>8.40</td>\n",
              "      <td>9.50</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.90</td>\n",
              "      <td>14.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>1319.0</td>\n",
              "      <td>5.48</td>\n",
              "      <td>0.50</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"6\" valign=\"top\">7,8</th>\n",
              "      <th>residual sugar</th>\n",
              "      <td>217.0</td>\n",
              "      <td>2.71</td>\n",
              "      <td>1.36</td>\n",
              "      <td>1.20</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.30</td>\n",
              "      <td>2.70</td>\n",
              "      <td>8.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <td>217.0</td>\n",
              "      <td>34.89</td>\n",
              "      <td>32.57</td>\n",
              "      <td>7.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>27.00</td>\n",
              "      <td>43.00</td>\n",
              "      <td>289.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>217.0</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>217.0</td>\n",
              "      <td>11.52</td>\n",
              "      <td>1.00</td>\n",
              "      <td>9.20</td>\n",
              "      <td>10.80</td>\n",
              "      <td>11.60</td>\n",
              "      <td>12.20</td>\n",
              "      <td>14.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile acidity</th>\n",
              "      <td>217.0</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>217.0</td>\n",
              "      <td>7.08</td>\n",
              "      <td>0.28</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ecc0e81-ebbd-4b68-83d9-1822311ce1f5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-1ecc0e81-ebbd-4b68-83d9-1822311ce1f5 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-1ecc0e81-ebbd-4b68-83d9-1822311ce1f5');\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",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-046b5825-25c0-4055-b3e7-96092a7ec049')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-046b5825-25c0-4055-b3e7-96092a7ec049 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Suuccess rates"
      ],
      "metadata": {
        "id": "pVS-XqmWxFTl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_models():\n",
        "    models=[]\n",
        "    models.append((\"LR\",LogisticRegression()))\n",
        "    models.append((\"NB\",GaussianNB()))\n",
        "    models.append((\"KNN\",KNeighborsClassifier()))\n",
        "    models.append((\"DT\",DecisionTreeClassifier()))\n",
        "    models.append((\"SVM rbf\",SVC()))\n",
        "    models.append((\"SVM linear\",SVC(kernel='linear')))\n",
        "    models.append(('LDA', LinearDiscriminantAnalysis()))\n",
        "\n",
        "    return models\n",
        "\n",
        "def cross_validation_scores_for_various_ml_models(X_cv, y_cv):\n",
        "    print(\"success rates\".upper())\n",
        "    models=get_models()\n",
        "\n",
        "\n",
        "    results=[]\n",
        "    names= []\n",
        "\n",
        "    for name, model in models:\n",
        "        kfold=StratifiedKFold(n_splits=5,shuffle=True,random_state=22)\n",
        "        cv_result=cross_val_score(model,X_cv, y_cv, cv=kfold,scoring=\"accuracy\")\n",
        "        names.append(name)\n",
        "        results.append(cv_result)\n",
        "        print(\"{} success rate:{:0.2f}\".format(name, cv_result.mean()))\n",
        "\n",
        "df_temp = df.copy(deep=True)\n",
        "X = df.drop('quality', axis=1)\n",
        "y = df['quality']\n",
        "\n",
        "X=StandardScaler().fit_transform(X)\n",
        "cross_validation_scores_for_various_ml_models(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLFeepyAuX1n",
        "outputId": "7af32f27-2600-43a9-a62f-53698fa88497"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SUCCESS RATES\n",
            "LR success rate:0.60\n",
            "NB success rate:0.55\n",
            "KNN success rate:0.58\n",
            "DT success rate:0.62\n",
            "SVM rbf success rate:0.62\n",
            "SVM linear success rate:0.58\n",
            "LDA success rate:0.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_temp = df.copy(deep=True)\n",
        "X = df.drop('quality', axis=1)\n",
        "y = df['quality']\n",
        "\n",
        "X=StandardScaler().fit_transform(X)\n",
        "cross_validation_scores_for_various_ml_models(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3GYEjfpuaXv",
        "outputId": "8b146a9e-eb39-48a9-9f2b-82ec2c2badad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SUCCESS RATES\n",
            "LR success rate:0.60\n",
            "NB success rate:0.55\n",
            "KNN success rate:0.58\n",
            "DT success rate:0.62\n",
            "SVM rbf success rate:0.62\n",
            "SVM linear success rate:0.58\n",
            "LDA success rate:0.59\n"
          ]
        }
      ]
    }
  ]
}