Wine_Quality_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the Libraries"
      ],
      "metadata": {
        "id": "8VvnEUQCvR6A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "XE8HbazHvV05"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the dataset"
      ],
      "metadata": {
        "id": "0G_6VY_nvhPV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('WineQT.csv')"
      ],
      "metadata": {
        "id": "QHewB5pNwRsW"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oMnXI0pwaw3",
        "outputId": "b65d7aee-ad8b-4362-a7ae-60a9ef800eab"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1143, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "1U8PfzLewgMx",
        "outputId": "4cf7a2ec-7fe3-4a26-8700-2aaa0e10ec04"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "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  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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37b07e98-7d41-4cc1-a721-df47b10c3069\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>Id</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",
              "      <td>0</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",
              "      <td>1</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",
              "      <td>2</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",
              "      <td>3</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",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37b07e98-7d41-4cc1-a721-df47b10c3069')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37b07e98-7d41-4cc1-a721-df47b10c3069 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-37b07e98-7d41-4cc1-a721-df47b10c3069');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking for missing values\n"
      ],
      "metadata": {
        "id": "k7bj99abwtqK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_kq_SK1wwOU",
        "outputId": "9c771e82-5de8-4bbb-cc29-54d7cdcfef9f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "Id                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization"
      ],
      "metadata": {
        "id": "qNYg9v3aw4sF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.hist(bins=20, figsize=(10, 10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "k_NUZRJkxAdh",
        "outputId": "17b22e73-4f0a-4ad3-9364-1eb3e69f25f3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(dataset['quality'], dataset['alcohol'])\n",
        "plt.xlabel('quality')\n",
        "plt.ylabel('alcohol')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "jzsliWkDx7qB",
        "outputId": "9bd3d608-4b79-48cc-b3d7-b41c9d3a486f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 12))\n",
        "sns.heatmap(dataset.corr() > 0.7, annot=True, cbar=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "v0qpphHYyInH",
        "outputId": "6d55aa72-34f8-4b82-af63-7589510c0511"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Development"
      ],
      "metadata": {
        "id": "Bu2ZRpQK3EV6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['best quality'] = [1 if x > 5 else 0 for x in dataset.quality]"
      ],
      "metadata": {
        "id": "ozSEIuY_3G-L"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.replace({'white': 1, 'red': 0}, inplace=True)"
      ],
      "metadata": {
        "id": "6R4OCD1j4bBP"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "features = dataset.drop(['quality', 'best quality'], axis=1)\n",
        "target = dataset['best quality']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=40)\n",
        "\n",
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yeXBzFZ4hLM",
        "outputId": "7c4b856f-716a-4549-9ab5-cd69a708f743"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((914, 12), (229, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "norm = MinMaxScaler()\n",
        "xtrain = norm.fit_transform(X_train)\n",
        "xtest = norm.transform(X_test)"
      ],
      "metadata": {
        "id": "SPjTJhz340ZV"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the Models"
      ],
      "metadata": {
        "id": "Eks6oPRv49Pg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def models(X_train,y_train):\n",
        "\n",
        "  #LOGISTIC REGRESSION MODEL\n",
        "  from sklearn.linear_model import LogisticRegression\n",
        "  log = LogisticRegression(random_state = 0)\n",
        "  log.fit(X_train, y_train)\n",
        "\n",
        "  #KERNEL SVM MODEL\n",
        "  from sklearn.svm import SVC\n",
        "  svm = SVC(kernel = 'rbf', random_state = 0)\n",
        "  svm.fit(X_train, y_train)\n",
        "\n",
        "  #RANDOM FOREST CLASSIFIER MODEL\n",
        "  from sklearn.ensemble import RandomForestClassifier\n",
        "  ran= RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "  ran.fit(X_train, y_train)\n",
        "\n",
        "  #MODEL ACCURACY ON TRAINING DATA\n",
        "  print('[0]Logistic Regression Training Accuracy:',log.score(X_train, y_train))\n",
        "  print('[1]Kernel SVM Training Accuracy:',svm.score(X_train, y_train))\n",
        "  print('[2]Random Forest Classifier Training Accuracy:',ran.score(X_train, y_train))\n",
        "\n",
        "  return log,svm,ran"
      ],
      "metadata": {
        "id": "0bLaeRIl5Ol9"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=models(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSbE-Sm_5zR2",
        "outputId": "e5ae0036-b3fe-4ae5-f2ce-72f77ff247a2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]Logistic Regression Training Accuracy: 0.7560175054704595\n",
            "[1]Kernel SVM Training Accuracy: 0.600656455142232\n",
            "[2]Random Forest Classifier Training Accuracy: 0.9879649890590809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making matrix of the models"
      ],
      "metadata": {
        "id": "wwm2W9D_6MzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "for i in range( len(model) ):\n",
        "  print('Model',i)\n",
        "  print(classification_report(y_test,model[i].predict(X_test)))\n",
        "  print(accuracy_score(y_test,model[i].predict(X_test)))\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkMi5jjr6QQ4",
        "outputId": "5b50085a-f6bc-4ce2-a6e0-0b33e093ed28"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.70      0.69        97\n",
            "           1       0.78      0.77      0.77       132\n",
            "\n",
            "    accuracy                           0.74       229\n",
            "   macro avg       0.73      0.73      0.73       229\n",
            "weighted avg       0.74      0.74      0.74       229\n",
            "\n",
            "0.7379912663755459\n",
            "\n",
            "Model 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      0.25      0.34        97\n",
            "           1       0.61      0.86      0.71       132\n",
            "\n",
            "    accuracy                           0.60       229\n",
            "   macro avg       0.58      0.55      0.53       229\n",
            "weighted avg       0.59      0.60      0.55       229\n",
            "\n",
            "0.5982532751091703\n",
            "\n",
            "Model 2\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.79      0.74        97\n",
            "           1       0.83      0.75      0.79       132\n",
            "\n",
            "    accuracy                           0.77       229\n",
            "   macro avg       0.77      0.77      0.77       229\n",
            "weighted avg       0.78      0.77      0.77       229\n",
            "\n",
            "0.7685589519650655\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction of random forest classifier model"
      ],
      "metadata": {
        "id": "BUQwCh1W6Yzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred=model[2].predict(X_test)\n",
        "print(pred)\n",
        "print()\n",
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p6zMQ2F6hu1",
        "outputId": "8c22e649-e180-49c8-9ccb-606d432d7929"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 0 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0\n",
            " 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 0 1 1\n",
            " 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1\n",
            " 1 0 0 0 0 1 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 0\n",
            " 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 1 0 0 1\n",
            " 1 1 0 1 0 1 1 0 1 0 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0\n",
            " 1 1 0 0 0 1 1]\n",
            "\n",
            "471     1\n",
            "192     1\n",
            "1035    1\n",
            "476     0\n",
            "512     0\n",
            "       ..\n",
            "281     0\n",
            "176     1\n",
            "537     0\n",
            "1043    1\n",
            "801     1\n",
            "Name: best quality, Length: 229, dtype: int64\n"
          ]
        }
      ]
    }
  ]
}