Boosting.ipynb

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    "colab": {
      "name": "Boosting.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "yIdHRmqdmhGM",
        "D48m5EqjSyhl",
        "cBhplKR0Xr16",
        "X0l_eqFjYbz5",
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      "include_colab_link": true
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/george-zakharov/ml_notebooks/blob/master/Boosting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOBAFZY9l5gY",
        "colab_type": "text"
      },
      "source": [
        "### Homework 6\n",
        "\n",
        "Датасет: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009\n",
        "\n",
        "#### Light: \n",
        "\n",
        "решить задачу классификации на предложенном датасете с использованием Boosting моделей.\n",
        "\n",
        "\n",
        "\n",
        "#### Pro: \n",
        "\n",
        "- нормализовать, стандартизовать датасет и применить техники работы с не сбалансированным датасетом;\n",
        "\n",
        "- решить задачу классификации на предложенном датасете с использованием Boosting моделей.."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkVOCIrhmds7",
        "colab_type": "text"
      },
      "source": [
        "# Load Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z0EFucOl5gb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Normalization tests\n",
        "from scipy.stats import kurtosis\n",
        "from scipy.stats import skew\n",
        "from scipy.stats import shapiro\n",
        "from scipy.stats import normaltest\n",
        "\n",
        "# Data normalization / standartization\n",
        "from sklearn.preprocessing import PowerTransformer, MinMaxScaler, StandardScaler\n",
        "\n",
        "# libs for train tuning\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
        "\n",
        "# Data resampling\n",
        "from sklearn.utils import resample\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# Boosting\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "\n",
        "# Model parameters fitting\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score,auc, f1_score, confusion_matrix,precision_score, recall_score, roc_auc_score, roc_curve"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIdHRmqdmhGM",
        "colab_type": "text"
      },
      "source": [
        "# Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwl7poZeml6S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "04eb2a80-6c03-4d55-c38c-5a5b9a3ecd9b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7GqIQ7vl5gf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Loading dataset\n",
        "wine = pd.read_csv('/content/drive/My Drive/Colab Notebooks/ML/6/data/winequality-red.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFiUeUgDX24M",
        "colab_type": "text"
      },
      "source": [
        "# User defined functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kuDzFurl8bD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Functions for normalization test\n",
        "def draw_hist_for_feature(data):\n",
        "  plt.style.use('ggplot')\n",
        "  data.hist(bins = 60)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def chech_skew(feature):\n",
        "  method_name = '\\nSKEW TEST: '\n",
        "  skew_ = np.abs(skew(feature))\n",
        "  if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "    print(method_name + 'Данные рекомендуется логарифмировать\\n')\n",
        "  elif skew_ >= 1:\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def check_shapiro(feature):\n",
        "  method_name = '\\nSHAPIRO TEST: '\n",
        "  shapiro_ = np.abs(shapiro(feature))\n",
        "  if (shapiro_[1] < 0.50):\n",
        "    print(method_name + 'Данные рекомендуется нормализовать\\n')\n",
        "  else:\n",
        "    print(method_name + 'Данные достаточно стандартизировать\\n')\n",
        "\n",
        "\n",
        "def print_stats(data, need_hist = True):\n",
        "  if (need_hist == True):\n",
        "    draw_hist_for_feature(data)\n",
        "\n",
        "  print(\"mean : \", np.mean(data))\n",
        "  print(\"var  : \", np.var(data))\n",
        "  print(\"skew : \", skew(data))\n",
        "  print(\"kurt : \", kurtosis(data))\n",
        "  print(\"shapiro : \", shapiro(data))\n",
        "  print(\"normaltest : \", normaltest(data))\n",
        "\n",
        "\n",
        "def print_stats_all(df, need_hist = True):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    print_stats(df[feature_name], need_hist)\n",
        "    chech_skew(df[feature_name])\n",
        "    check_shapiro(df[feature_name])\n",
        "    n += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZO1s0Csls3A-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data preprocessing according to skew metrics\n",
        "def data_preprocessing(df):\n",
        "  n = 1\n",
        "  for feature_name in df.columns:\n",
        "    print(f'\\n\\n{n}. {feature_name}')\n",
        "    feature = df[feature_name]\n",
        "\n",
        "    print_stats(feature)\n",
        "    \n",
        "    skew_ = np.abs(skew(feature))\n",
        "\n",
        "    if (skew_ >= 0.75) and (skew_ < 1.0):\n",
        "      print('\\nДанные рекомендуется логарифмировать\\n')\n",
        "      feature = np.log(feature)\n",
        "    elif skew_ >= 1:\n",
        "      if feature.min() > 0:\n",
        "        print('\\nДанные рекомендуется нормализовать методом boxcox\\n')\n",
        "        box_cox_transform = PowerTransformer(method = 'box-cox', standardize = False)\n",
        "        feature = box_cox_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "      else:\n",
        "        print('\\nДанные рекомендуется нормализовать методом yeo-johnson\\n')\n",
        "        yeo_johnson_transform = PowerTransformer(method = 'yeo-johnson', standardize = False)\n",
        "        feature = yeo_johnson_transform.fit_transform(feature.values.reshape(df.shape[0], -1))\n",
        "    else:\n",
        "      print('\\nДанные достаточно стандартизировать\\n')\n",
        "\n",
        "    print_stats(pd.DataFrame(feature, columns = [feature_name]))\n",
        "    chech_skew(feature)\n",
        "\n",
        "    df[feature_name] = feature\n",
        "\n",
        "    n += 1\n",
        "\n",
        "  return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhetk2Fps5eZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data standartization\n",
        "def scale_std_minmax(df):\n",
        "  print(df.shape)\n",
        "\n",
        "  scaler = StandardScaler()\n",
        "  print(scaler.fit(df.copy()))\n",
        "  StandardScaler()\n",
        "  result = scaler.transform(df.copy())\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "\n",
        "\n",
        "  scaler = MinMaxScaler()\n",
        "  print(scaler.fit(result))\n",
        "  MinMaxScaler(copy = True, feature_range = (0, 1))\n",
        "  print(scaler.data_max_)\n",
        "  result = scaler.transform(result)\n",
        "  result = pd.DataFrame(result, columns = df.columns)\n",
        "  \n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uzqwf62e7zAo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fit models on Grid SearchCV\n",
        "def grid_search_fitting(models, parameters, X_train, y_train, X_test, y_test): \n",
        "  clf = GridSearchCV(estimator = models, scoring = 'accuracy', param_grid = parameters, cv = 5, iid = False)\n",
        "\n",
        "  clf.fit(X_train, y_train)\n",
        "    \n",
        "  score_test = clf.best_estimator_.score(X_test, y_test) \n",
        "  y_pred = clf.predict(X_test)\n",
        "        \n",
        "  print(clf.best_estimator_)\n",
        "  print(clf.best_score_)\n",
        "  print(\"\\n\")\n",
        "\n",
        "  print(\"Confusion matrix:\")\n",
        "  print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "  print('accuracy_on_test: ', score_test)\n",
        "  print(\"\\n\")\n",
        "  \n",
        "  return clf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D48m5EqjSyhl",
        "colab_type": "text"
      },
      "source": [
        "# Dataset exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBhplKR0Xr16",
        "colab_type": "text"
      },
      "source": [
        "## View data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrPac-2Rl5gi",
        "colab_type": "code",
        "outputId": "cc1fde8b-ba44-49a6-908c-aae8526b4f1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "wine.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JW4sHKhNl5gm",
        "colab_type": "code",
        "outputId": "c780c60e-5be1-4912-a0e5-9c2e83b90079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# Check class balance\n",
        "wine['quality'].value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    681\n",
              "6    638\n",
              "7    199\n",
              "4     53\n",
              "8     18\n",
              "3     10\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EayOCWnbFANm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "f9830481-ba14-4104-caf1-40788fdd4a0c"
      },
      "source": [
        "wine.describe(include = 'all')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</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>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...      alcohol      quality\n",
              "count    1599.000000       1599.000000  ...  1599.000000  1599.000000\n",
              "mean        8.319637          0.527821  ...    10.422983     5.636023\n",
              "std         1.741096          0.179060  ...     1.065668     0.807569\n",
              "min         4.600000          0.120000  ...     8.400000     3.000000\n",
              "25%         7.100000          0.390000  ...     9.500000     5.000000\n",
              "50%         7.900000          0.520000  ...    10.200000     6.000000\n",
              "75%         9.200000          0.640000  ...    11.100000     6.000000\n",
              "max        15.900000          1.580000  ...    14.900000     8.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TmXh-eHiFE6l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "f0734b32-6d3c-47af-8101-7adc0a399b77"
      },
      "source": [
        "wine.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1599 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            "fixed acidity           1599 non-null float64\n",
            "volatile acidity        1599 non-null float64\n",
            "citric acid             1599 non-null float64\n",
            "residual sugar          1599 non-null float64\n",
            "chlorides               1599 non-null float64\n",
            "free sulfur dioxide     1599 non-null float64\n",
            "total sulfur dioxide    1599 non-null float64\n",
            "density                 1599 non-null float64\n",
            "pH                      1599 non-null float64\n",
            "sulphates               1599 non-null float64\n",
            "alcohol                 1599 non-null float64\n",
            "quality                 1599 non-null int64\n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 150.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDDbzpNLS3GU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "d4c256d4-da8e-4808-b72b-32fc535f7bf9"
      },
      "source": [
        "# Check missing values\n",
        "wine.isnull().sum()"
      ],
      "execution_count": 15,
      "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",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0l_eqFjYbz5",
        "colab_type": "text"
      },
      "source": [
        "## Divide features and labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJEs1baIYocd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = wine.drop(['quality'], axis = 1)\n",
        "y = wine.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApTf0ebYY1G4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "e7134688-6ec4-4553-ac3d-024329d47fc5"
      },
      "source": [
        "X.head(3)"
      ],
      "execution_count": 20,
      "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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...    pH  sulphates  alcohol\n",
              "0            7.4              0.70         0.00  ...  3.51       0.56      9.4\n",
              "1            7.8              0.88         0.00  ...  3.20       0.68      9.8\n",
              "2            7.8              0.76         0.04  ...  3.26       0.65      9.8\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiyCnQUxY3WF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "77783c19-3511-496b-9ace-d9eb7be4dc46"
      },
      "source": [
        "y.head(3)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5\n",
              "1    5\n",
              "2    5\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g9MKXr4Xv8m",
        "colab_type": "text"
      },
      "source": [
        "## Check normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fm3-UubqmdHM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3178d982-72fa-4ed9-c612-00d9b3b805be"
      },
      "source": [
        "print_stats_all(X)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1. fixed acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QCSb2ZaWn8vLyB96X1mqZyclJNTc3a+vWrdq8ebMkyev1amRkRJI0MjKioqKidA4BALDA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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  8.319637273295838\n",
            "var  :  3.0295205688671296\n",
            "skew :  0.9818292953262073\n",
            "kurt :  1.1248563352037486\n",
            "shapiro :  (0.942030668258667, 1.5255951637239901e-24)\n",
            "normaltest :  NormaltestResult(statistic=224.53087840457746, pvalue=1.7528277735470436e-49)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется логарифмировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "2. volatile acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yD4VC2rt3r5599lkVFxdry5YtKisr0+zZs5M9FIBxiPWviImYFTP7viKVj0PS19yPHz+u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\nUe4AYCDKHQAM9L993jHqC3jLkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.5278205128205131\n",
            "var  :  0.03204232613332036\n",
            "skew :  0.6709623963499574\n",
            "kurt :  1.2179633893005306\n",
            "shapiro :  (0.9743340611457825, 2.686806772838566e-16)\n",
            "normaltest :  NormaltestResult(statistic=143.4193435598286, pvalue=7.192589039756692e-32)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "3. citric acid\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.2709756097560964\n",
            "var  :  0.037923751124940605\n",
            "skew :  0.3180385895475358\n",
            "kurt :  -0.7902825153036646\n",
            "shapiro :  (0.9552900195121765, 1.0208196357632134e-21)\n",
            "normaltest :  NormaltestResult(statistic=152.039214793795, pvalue=9.662822259281018e-34)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "4. residual sugar\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  2.5388055034396517\n",
            "var  :  1.9866539202699018\n",
            "skew :  4.536394788805638\n",
            "kurt :  28.524437824252235\n",
            "shapiro :  (0.5660765171051025, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1520.3239698236891, pvalue=0.0)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "5. chlorides\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.08746654158849257\n",
            "var  :  0.0022137573233114395\n",
            "skew :  5.675016527504259\n",
            "kurt :  41.581708043675775\n",
            "shapiro :  (0.4842468500137329, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1783.1059225626427, pvalue=0.0)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "6. free sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  15.874921826141339\n",
            "var  :  109.3464567637451\n",
            "skew :  1.249393847434253\n",
            "kurt :  2.01348999733378\n",
            "shapiro :  (0.9018407464027405, 7.6973984391279615e-31)\n",
            "normaltest :  NormaltestResult(statistic=342.2591484251238, pvalue=4.779365332171477e-75)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "7. total sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  46.46779237023139\n",
            "var  :  1081.4256355891553\n",
            "skew :  1.5141091878506638\n",
            "kurt :  3.7941717664905665\n",
            "shapiro :  (0.8732253909111023, 3.5741436086741964e-34)\n",
            "normaltest :  NormaltestResult(statistic=487.42725648953467, pvalue=1.433890834343538e-106)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "8. density\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.9967466791744831\n",
            "var  :  3.559801792630711e-06\n",
            "skew :  0.07122077153539946\n",
            "kurt :  0.9274107990478333\n",
            "shapiro :  (0.9908668398857117, 1.9400959061499634e-08)\n",
            "normaltest :  NormaltestResult(statistic=30.70774994095191, pvalue=2.1473202738102222e-07)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "9. pH\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  3.311113195747343\n",
            "var  :  0.023820274241131745\n",
            "skew :  0.19350175891005525\n",
            "kurt :  0.8006714441211544\n",
            "shapiro :  (0.993486762046814, 1.7136950418716879e-06)\n",
            "normaltest :  NormaltestResult(statistic=33.684697471483915, pvalue=4.8468645347727716e-08)\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "10. sulphates\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.6581488430268921\n",
            "var  :  0.028714647013983425\n",
            "skew :  2.426393455449087\n",
            "kurt :  11.6798841115311\n",
            "shapiro :  (0.8330425024032593, 5.821617678881608e-38)\n",
            "normaltest :  NormaltestResult(statistic=906.8944479227036, pvalue=1.1759065222978855e-197)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n",
            "\n",
            "\n",
            "11. alcohol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  10.422983114446502\n",
            "var  :  1.1349371714888994\n",
            "skew :  0.8600210646566755\n",
            "kurt :  0.1956543762123122\n",
            "shapiro :  (0.9288389682769775, 6.643664824998876e-27)\n",
            "normaltest :  NormaltestResult(statistic=154.17806951912516, pvalue=3.3163288473185496e-34)\n",
            "\n",
            "SKEW TEST: Данные рекомендуется логарифмировать\n",
            "\n",
            "\n",
            "SHAPIRO TEST: Данные рекомендуется нормализовать\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C9sbmsaCu9KT",
        "colab_type": "text"
      },
      "source": [
        "# Dataset transformation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "On7TUl_Ix5Yp",
        "colab_type": "text"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZWek1f4vCwP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc9b603b-f810-4893-ae2b-e9d127f03acf"
      },
      "source": [
        "X_normalized = data_preprocessing(X)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "1. fixed acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QCSb2ZaWn8vLyB96X1mqZyclJNTc3a+vWrdq8ebMkyev1amRkRJI0MjKioqKidA4BALDA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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  8.319637273295838\n",
            "var  :  3.0295205688671296\n",
            "skew :  0.9818292953262073\n",
            "kurt :  1.1248563352037486\n",
            "shapiro :  (0.942030668258667, 1.5255951637239901e-24)\n",
            "normaltest :  NormaltestResult(statistic=224.53087840457746, pvalue=1.7528277735470436e-49)\n",
            "\n",
            "Данные рекомендуется логарифмировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  fixed acidity    2.098226\n",
            "dtype: float64\n",
            "var  :  fixed acidity    0.03974\n",
            "dtype: float64\n",
            "skew :  [0.39354537]\n",
            "kurt :  [0.09695618]\n",
            "shapiro :  (0.9848697185516357, 6.456312047486978e-12)\n",
            "normaltest :  NormaltestResult(statistic=array([39.54165981]), pvalue=array([2.59201595e-09]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "2. volatile acidity\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yD4VC2rt3r5599lkVFxdry5YtKisr0+zZs5M9FIBxiPWviImYFTP7viKVj0PS19yPHz+u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\nUe4AYCDKHQAM9L993jHqC3jLkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.5278205128205131\n",
            "var  :  0.03204232613332036\n",
            "skew :  0.6709623963499574\n",
            "kurt :  1.2179633893005306\n",
            "shapiro :  (0.9743340611457825, 2.686806772838566e-16)\n",
            "normaltest :  NormaltestResult(statistic=143.4193435598286, pvalue=7.192589039756692e-32)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EWO5ERBJiuRMRSYjlTkQkIZY7EZGEWO5ERBJiuRMRSYjlTkQkIZY7EZGEWO5ERBLi9dzJ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\niOVORCQhljsRkYRY7kREEvpf8w4yzRVi0I0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  volatile acidity    0.527821\n",
            "dtype: float64\n",
            "var  :  volatile acidity    0.032042\n",
            "dtype: float64\n",
            "skew :  [0.6709624]\n",
            "kurt :  [1.21796339]\n",
            "shapiro :  (0.9743340611457825, 2.686806772838566e-16)\n",
            "normaltest :  NormaltestResult(statistic=array([143.41934356]), pvalue=array([7.19258904e-32]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "3. citric acid\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.2709756097560964\n",
            "var  :  0.037923751124940605\n",
            "skew :  0.3180385895475358\n",
            "kurt :  -0.7902825153036646\n",
            "shapiro :  (0.9552900195121765, 1.0208196357632134e-21)\n",
            "normaltest :  NormaltestResult(statistic=152.039214793795, pvalue=9.662822259281018e-34)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fX6SkpLi8ASIiep7NcF+6dKnV7evXr39umyRJmDt3rvNVERGRU/gOVSIiATHciYgExHAn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\nTkQkIIY7EZGAGO5ERAJiuBMRCej/ABvvioyem4oRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  citric acid    0.270976\n",
            "dtype: float64\n",
            "var  :  citric acid    0.037924\n",
            "dtype: float64\n",
            "skew :  [0.31803859]\n",
            "kurt :  [-0.79028252]\n",
            "shapiro :  (0.9552900195121765, 1.0208196357632134e-21)\n",
            "normaltest :  NormaltestResult(statistic=array([152.03921479]), pvalue=array([9.66282226e-34]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "4. residual sugar\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  2.5388055034396517\n",
            "var  :  1.9866539202699018\n",
            "skew :  4.536394788805638\n",
            "kurt :  28.524437824252235\n",
            "shapiro :  (0.5660765171051025, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1520.3239698236891, pvalue=0.0)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bm2EymTBr1izcfffdzi1CJru0t7fLzz77rHzu3Dm5ra1Nfv755+UzZ85YjPn8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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  residual sugar    0.513556\n",
            "dtype: float64\n",
            "var  :  residual sugar    0.01211\n",
            "dtype: float64\n",
            "skew :  [-0.09584002]\n",
            "kurt :  [1.60404381]\n",
            "shapiro :  (0.9759386777877808, 1.011885569943366e-15)\n",
            "normaltest :  NormaltestResult(statistic=array([62.41154248]), pvalue=array([2.80224271e-14]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "5. chlorides\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.08746654158849257\n",
            "var  :  0.0022137573233114395\n",
            "skew :  5.675016527504259\n",
            "kurt :  41.581708043675775\n",
            "shapiro :  (0.4842468500137329, 0.0)\n",
            "normaltest :  NormaltestResult(statistic=1783.1059225626427, pvalue=0.0)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  chlorides   -4.763474\n",
            "dtype: float64\n",
            "var  :  chlorides    0.926346\n",
            "dtype: float64\n",
            "skew :  [-0.49917442]\n",
            "kurt :  [14.5513695]\n",
            "shapiro :  (0.8716785907745361, 2.4586373518860107e-34)\n",
            "normaltest :  NormaltestResult(statistic=array([412.77325778]), pvalue=array([2.33037565e-90]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "6. free sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  15.874921826141339\n",
            "var  :  109.3464567637451\n",
            "skew :  1.249393847434253\n",
            "kurt :  2.01348999733378\n",
            "shapiro :  (0.9018407464027405, 7.6973984391279615e-31)\n",
            "normaltest :  NormaltestResult(statistic=342.2591484251238, pvalue=4.779365332171477e-75)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DnYhIQgx3IiIJMdyJiCTEcCcikhDDnYhIQgx3IiIJMdyJiCTEcCcikhDDnYhIQgx3IiIJ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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  free sulfur dioxide    3.129049\n",
            "dtype: float64\n",
            "var  :  free sulfur dioxide    0.971227\n",
            "dtype: float64\n",
            "skew :  [-0.01975919]\n",
            "kurt :  [-0.59777277]\n",
            "shapiro :  (0.9874722361564636, 1.5522860774552782e-10)\n",
            "normaltest :  NormaltestResult(statistic=array([49.9466963]), pvalue=array([1.42630598e-11]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "7. total sulfur dioxide\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  46.46779237023139\n",
            "var  :  1081.4256355891553\n",
            "skew :  1.5141091878506638\n",
            "kurt :  3.7941717664905665\n",
            "shapiro :  (0.8732253909111023, 3.5741436086741964e-34)\n",
            "normaltest :  NormaltestResult(statistic=487.42725648953467, pvalue=1.433890834343538e-106)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  total sulfur dioxide    3.965719\n",
            "dtype: float64\n",
            "var  :  total sulfur dioxide    0.715565\n",
            "dtype: float64\n",
            "skew :  [-0.00802061]\n",
            "kurt :  [-0.67038533]\n",
            "shapiro :  (0.990358293056488, 8.859659672566522e-09)\n",
            "normaltest :  NormaltestResult(statistic=array([71.36209297]), pvalue=array([3.19093827e-16]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "8. density\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.9967466791744831\n",
            "var  :  3.559801792630711e-06\n",
            "skew :  0.07122077153539946\n",
            "kurt :  0.9274107990478333\n",
            "shapiro :  (0.9908668398857117, 1.9400959061499634e-08)\n",
            "normaltest :  NormaltestResult(statistic=30.70774994095191, pvalue=2.1473202738102222e-07)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hzJs3j5EjRwLnj93m5ubS0NDAqFGjWLhwIf369fPrzD/5yU8YNGgQAwYMAGDq\n1Kncd999fp35QrNnz/bqKZu+yNvY2EheXh41NTUMGDDgkq/FnzJ/+OGHvP766wQFBREWFkZGRobX\nDj/k5uZy4MAB6uvriYyMZObMmbS2tgJw2223YRgGmzZtYt++fYSEhJCZmUlCQgIAxcXFFBYWAud/\nS/nmB9ihQ4dYt24dLS0tTJ48mYcfftirp2x6O/PJkyfJyMhg6NChrlOkv//973Prrbf6beYLVVdX\n8/zzz3fplE2/KX0REfE9vzi8IyIiPUOlLyJiIip9ERETUemLiJiISl9ExERU+iIiJqLSFxExkf8P\nR27z3YvADIkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  density    0.996747\n",
            "dtype: float64\n",
            "var  :  density    0.000004\n",
            "dtype: float64\n",
            "skew :  [0.07122077]\n",
            "kurt :  [0.9274108]\n",
            "shapiro :  (0.9908668398857117, 1.9400959061499634e-08)\n",
            "normaltest :  NormaltestResult(statistic=array([30.70774994]), pvalue=array([2.14732027e-07]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "9. pH\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  3.311113195747343\n",
            "var  :  0.023820274241131745\n",
            "skew :  0.19350175891005525\n",
            "kurt :  0.8006714441211544\n",
            "shapiro :  (0.993486762046814, 1.7136950418716879e-06)\n",
            "normaltest :  NormaltestResult(statistic=33.684697471483915, pvalue=4.8468645347727716e-08)\n",
            "\n",
            "Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  pH    3.311113\n",
            "dtype: float64\n",
            "var  :  pH    0.02382\n",
            "dtype: float64\n",
            "skew :  [0.19350176]\n",
            "kurt :  [0.80067144]\n",
            "shapiro :  (0.993486762046814, 1.7136950418716879e-06)\n",
            "normaltest :  NormaltestResult(statistic=array([33.68469747]), pvalue=array([4.84686453e-08]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "10. sulphates\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  0.6581488430268921\n",
            "var  :  0.028714647013983425\n",
            "skew :  2.426393455449087\n",
            "kurt :  11.6798841115311\n",
            "shapiro :  (0.8330425024032593, 5.821617678881608e-38)\n",
            "normaltest :  NormaltestResult(statistic=906.8944479227036, pvalue=1.1759065222978855e-197)\n",
            "\n",
            "Данные рекомендуется нормализовать методом boxcox\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  sulphates   -0.609663\n",
            "dtype: float64\n",
            "var  :  sulphates    0.115999\n",
            "dtype: float64\n",
            "skew :  [-0.02475606]\n",
            "kurt :  [0.39890141]\n",
            "shapiro :  (0.9964831471443176, 0.0010049439733847976)\n",
            "normaltest :  NormaltestResult(statistic=array([8.24567945]), pvalue=array([0.01619845]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n",
            "\n",
            "\n",
            "11. alcohol\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mj3NftWqV3n77bW3cuFFXrlxRZWVlrIcAAIwjJgegz58/X/Pnz5ckFRYWas+ePbG4WwBA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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  10.422983114446502\n",
            "var  :  1.1349371714888994\n",
            "skew :  0.8600210646566755\n",
            "kurt :  0.1956543762123122\n",
            "shapiro :  (0.9288389682769775, 6.643664824998876e-27)\n",
            "normaltest :  NormaltestResult(statistic=154.17806951912516, pvalue=3.3163288473185496e-34)\n",
            "\n",
            "Данные рекомендуется логарифмировать\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rzpw5qqmpUU1Nje68885w1QsACEDQyzInTpzQRx99pNLSUklSYmKixowZI6/Xq+LiYklS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\n8ZPuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "mean :  alcohol    2.339021\n",
            "dtype: float64\n",
            "var  :  alcohol    0.009775\n",
            "dtype: float64\n",
            "skew :  [0.66123544]\n",
            "kurt :  [-0.28947897]\n",
            "shapiro :  (0.9464339017868042, 1.154532723556871e-23)\n",
            "normaltest :  NormaltestResult(statistic=array([105.75210886]), pvalue=array([1.08697946e-23]))\n",
            "\n",
            "SKEW TEST: Данные достаточно стандартизировать\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6CLUN-Bxpd9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "44281368-8dcd-467a-8e4b-68a330010841"
      },
      "source": [
        "# Check normalized data\n",
        "X_normalized.head()"
      ],
      "execution_count": 62,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.001480</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.450049</td>\n",
              "      <td>-4.920401</td>\n",
              "      <td>2.867882</td>\n",
              "      <td>3.861490</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>-0.801405</td>\n",
              "      <td>2.240710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.054124</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.572939</td>\n",
              "      <td>-4.140122</td>\n",
              "      <td>4.102521</td>\n",
              "      <td>4.686835</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>-0.476630</td>\n",
              "      <td>2.282382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.054124</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.530233</td>\n",
              "      <td>-4.325636</td>\n",
              "      <td>3.317116</td>\n",
              "      <td>4.421290</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>-0.546246</td>\n",
              "      <td>2.282382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.415914</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.450049</td>\n",
              "      <td>-4.963593</td>\n",
              "      <td>3.504207</td>\n",
              "      <td>4.550629</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>-0.737642</td>\n",
              "      <td>2.282382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.001480</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.450049</td>\n",
              "      <td>-4.920401</td>\n",
              "      <td>2.867882</td>\n",
              "      <td>3.861490</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>-0.801405</td>\n",
              "      <td>2.240710</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...    pH  sulphates   alcohol\n",
              "0       2.001480              0.70         0.00  ...  3.51  -0.801405  2.240710\n",
              "1       2.054124              0.88         0.00  ...  3.20  -0.476630  2.282382\n",
              "2       2.054124              0.76         0.04  ...  3.26  -0.546246  2.282382\n",
              "3       2.415914              0.28         0.56  ...  3.16  -0.737642  2.282382\n",
              "4       2.001480              0.70         0.00  ...  3.51  -0.801405  2.240710\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OW9NoSHx-IP",
        "colab_type": "text"
      },
      "source": [
        "## Standartization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NQZKUQUyAlz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9a394c9a-1d95-425d-9d39-b1d6076ab887"
      },
      "source": [
        "X_scaled = scale_std_minmax(X_normalized)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1599, 11)\n",
            "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
            "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
            "[3.35136224 5.87797605 3.74357393 2.72895662 4.37508071 2.83835204\n",
            " 3.07267011 3.68005512 4.52828197 3.23051437 3.66487349]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPg13Vw4ycDe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8fc3f8dd-0e7c-48f7-e13d-091052f24399"
      },
      "source": [
        "X_scaled.head()"
      ],
      "execution_count": 67,
      "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",
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              "\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.520548</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.739835</td>\n",
              "      <td>0.739956</td>\n",
              "      <td>0.692260</td>\n",
              "      <td>0.599486</td>\n",
              "      <td>0.494126</td>\n",
              "      <td>0.362205</td>\n",
              "      <td>0.628770</td>\n",
              "      <td>0.268963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.693719</td>\n",
              "      <td>0.726509</td>\n",
              "      <td>0.559731</td>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.508811</td>\n",
              "      <td>0.409449</td>\n",
              "      <td>0.602052</td>\n",
              "      <td>0.268963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.717475</td>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.680266</td>\n",
              "      <td>0.591301</td>\n",
              "      <td>0.570440</td>\n",
              "      <td>0.582232</td>\n",
              "      <td>0.330709</td>\n",
              "      <td>0.528593</td>\n",
              "      <td>0.268963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...        pH  sulphates   alcohol\n",
              "0       0.383325          0.397260         0.00  ...  0.606299   0.504120  0.196252\n",
              "1       0.425771          0.520548         0.00  ...  0.362205   0.628770  0.268963\n",
              "2       0.425771          0.438356         0.04  ...  0.409449   0.602052  0.268963\n",
              "3       0.717475          0.109589         0.56  ...  0.330709   0.528593  0.268963\n",
              "4       0.383325          0.397260         0.00  ...  0.606299   0.504120  0.196252\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFUqucZcyflA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "462b6c49-784e-437d-e549-8bca0786cea8"
      },
      "source": [
        "X_scaled.describe()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.461329</td>\n",
              "      <td>0.279329</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>0.675711</td>\n",
              "      <td>0.694772</td>\n",
              "      <td>0.527996</td>\n",
              "      <td>0.445704</td>\n",
              "      <td>0.490211</td>\n",
              "      <td>0.449695</td>\n",
              "      <td>0.577712</td>\n",
              "      <td>0.367786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.160782</td>\n",
              "      <td>0.122644</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>0.118870</td>\n",
              "      <td>0.069787</td>\n",
              "      <td>0.166347</td>\n",
              "      <td>0.180452</td>\n",
              "      <td>0.138571</td>\n",
              "      <td>0.121564</td>\n",
              "      <td>0.130759</td>\n",
              "      <td>0.172560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.349957</td>\n",
              "      <td>0.184932</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.663648</td>\n",
              "      <td>0.379391</td>\n",
              "      <td>0.313672</td>\n",
              "      <td>0.406021</td>\n",
              "      <td>0.370079</td>\n",
              "      <td>0.491195</td>\n",
              "      <td>0.214716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.436042</td>\n",
              "      <td>0.273973</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.675281</td>\n",
              "      <td>0.692440</td>\n",
              "      <td>0.542572</td>\n",
              "      <td>0.451923</td>\n",
              "      <td>0.490455</td>\n",
              "      <td>0.448819</td>\n",
              "      <td>0.572663</td>\n",
              "      <td>0.338765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.558871</td>\n",
              "      <td>0.356164</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.739835</td>\n",
              "      <td>0.721739</td>\n",
              "      <td>0.645917</td>\n",
              "      <td>0.579054</td>\n",
              "      <td>0.570117</td>\n",
              "      <td>0.519685</td>\n",
              "      <td>0.668280</td>\n",
              "      <td>0.486301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed acidity  volatile acidity  ...    sulphates      alcohol\n",
              "count    1599.000000       1599.000000  ...  1599.000000  1599.000000\n",
              "mean        0.461329          0.279329  ...     0.577712     0.367786\n",
              "std         0.160782          0.122644  ...     0.130759     0.172560\n",
              "min         0.000000          0.000000  ...     0.000000     0.000000\n",
              "25%         0.349957          0.184932  ...     0.491195     0.214716\n",
              "50%         0.436042          0.273973  ...     0.572663     0.338765\n",
              "75%         0.558871          0.356164  ...     0.668280     0.486301\n",
              "max         1.000000          1.000000  ...     1.000000     1.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izJ9PRosyoqy",
        "colab_type": "text"
      },
      "source": [
        "## Prepare train/test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQGc_tV5yvI8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine_preprocessed = pd.concat([X_scaled, y], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlONRLBHy7Lm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dc7724c8-ee73-489b-b16a-fe8019ea6d68"
      },
      "source": [
        "wine_preprocessed.head()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.520548</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.739835</td>\n",
              "      <td>0.739956</td>\n",
              "      <td>0.692260</td>\n",
              "      <td>0.599486</td>\n",
              "      <td>0.494126</td>\n",
              "      <td>0.362205</td>\n",
              "      <td>0.628770</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.425771</td>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.693719</td>\n",
              "      <td>0.726509</td>\n",
              "      <td>0.559731</td>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.508811</td>\n",
              "      <td>0.409449</td>\n",
              "      <td>0.602052</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.717475</td>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.680266</td>\n",
              "      <td>0.591301</td>\n",
              "      <td>0.570440</td>\n",
              "      <td>0.582232</td>\n",
              "      <td>0.330709</td>\n",
              "      <td>0.528593</td>\n",
              "      <td>0.268963</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.383325</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.607134</td>\n",
              "      <td>0.683397</td>\n",
              "      <td>0.483927</td>\n",
              "      <td>0.423477</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.504120</td>\n",
              "      <td>0.196252</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...  sulphates   alcohol  quality\n",
              "0       0.383325          0.397260         0.00  ...   0.504120  0.196252        5\n",
              "1       0.425771          0.520548         0.00  ...   0.628770  0.268963        5\n",
              "2       0.425771          0.438356         0.04  ...   0.602052  0.268963        5\n",
              "3       0.717475          0.109589         0.56  ...   0.528593  0.268963        6\n",
              "4       0.383325          0.397260         0.00  ...   0.504120  0.196252        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gqGT3IZzXyE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = wine_preprocessed.drop('quality', axis = 1)\n",
        "y = wine_preprocessed.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhSjYxrDzde_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Disbalanced dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9RQkHIv1MNr",
        "colab_type": "text"
      },
      "source": [
        "## Data balancing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9NW7kSV1rPo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f664269a-5a63-4bea-e4fd-779bcdf9bdb4"
      },
      "source": [
        "wine_preprocessed.quality.value_counts()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5    681\n",
              "6    638\n",
              "7    199\n",
              "4     53\n",
              "8     18\n",
              "3     10\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfOZRql11Psr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get data classes for further balancing\n",
        "wine_3 = wine_preprocessed[wine_preprocessed.quality == 3]\n",
        "wine_4 = wine_preprocessed[wine_preprocessed.quality == 4]\n",
        "wine_5 = wine_preprocessed[wine_preprocessed.quality == 5]\n",
        "wine_6 = wine_preprocessed[wine_preprocessed.quality == 6]\n",
        "wine_7 = wine_preprocessed[wine_preprocessed.quality == 7]\n",
        "wine_8 = wine_preprocessed[wine_preprocessed.quality == 8]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZ6ZJYc61iYj",
        "colab_type": "text"
      },
      "source": [
        "### Upsampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuMukkQX1kPS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transform classes\n",
        "wine_3_upsampled = resample(wine_3, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "wine_4_upsampled = resample(wine_4, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "wine_6_upsampled = resample(wine_6, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "wine_7_upsampled = resample(wine_7, replace = True, n_samples = len(wine_5), random_state = 42)\n",
        "wine_8_upsampled = resample(wine_8, replace = True, n_samples = len(wine_5), random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84dAfqel3YF2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine_upsampled = pd.concat([wine_5, wine_3_upsampled, wine_4_upsampled, wine_6_upsampled, wine_7_upsampled, wine_8_upsampled])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTw4UGfu3bUi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4bf6127d-cb52-4e0f-f7bd-8847391d58ee"
      },
      "source": [
        "wine_upsampled.quality.value_counts()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7    681\n",
              "5    681\n",
              "3    681\n",
              "8    681\n",
              "6    681\n",
              "4    681\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIgOUdU11vRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_ups = wine_upsampled.drop('quality', axis = 1)\n",
        "y_ups = wine_upsampled.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_z0Xe0S3f1f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_ups, X_test_ups, y_train_ups, y_test_ups = train_test_split(X_ups, y_ups, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oa2Myc1S1zgw",
        "colab_type": "text"
      },
      "source": [
        "### Downsampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-ASGMwH11Ne",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine_4_downsampled = resample(wine_4, replace = False, n_samples = len(wine_3), random_state = 42)\n",
        "wine_5_downsampled = resample(wine_5, replace = False, n_samples = len(wine_3), random_state = 42)\n",
        "wine_6_downsampled = resample(wine_6, replace = False, n_samples = len(wine_3), random_state = 42)\n",
        "wine_7_downsampled = resample(wine_7, replace = False, n_samples = len(wine_3), random_state = 42)\n",
        "wine_8_downsampled = resample(wine_8, replace = False, n_samples = len(wine_3), random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcnZ3gI14YzL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wine_downsampled = pd.concat([wine_3, wine_4_downsampled, wine_5_downsampled, wine_6_downsampled, wine_7_downsampled, wine_8_downsampled])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNtJWxwn4Zem",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "ed38aeac-d8b5-4ff1-979c-a1b1715a244e"
      },
      "source": [
        "wine_downsampled.quality.value_counts()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8    10\n",
              "7    10\n",
              "6    10\n",
              "5    10\n",
              "4    10\n",
              "3    10\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GY4k_Bw139G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_downs = wine_downsampled.drop('quality', axis=1)\n",
        "y_downs = wine_downsampled.quality"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHWJgwBe4m1W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_downs, X_test_downs, y_train_downs, y_test_downs = train_test_split(X_downs, y_downs, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrjewYDN15-K",
        "colab_type": "text"
      },
      "source": [
        "### SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FATkWvEU47f_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sm = SMOTE('minority')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJBjEWHF6V3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_smote, y_train_smote = sm.fit_sample(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vU9Ed9Qt449Z",
        "colab_type": "text"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3vmPMLAF_mb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Gradient Boosting parameters\n",
        "gb_params = [{'loss': ['deviance'], 'n_estimators': [5, 20, 50], 'max_depth': [2, 5, 10], 'criterion': ['mse']}]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiWKFwjrGDOB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# XGBoost parameters\n",
        "xgb_params = [{'max_depth':[5, 10, 30],'min_child_weight':[3, 5, 9], \"n_estimators\": [150]}]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmYTF6eu7eTz",
        "colab_type": "text"
      },
      "source": [
        "## Disbalances dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adbrii7g7lCW",
        "colab_type": "text"
      },
      "source": [
        "### GradientBoosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4uE1qeh7tav",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "d162c5a8-0ed8-460e-b423-9f6df7ecbc4d"
      },
      "source": [
        "grid_search_fitting(GradientBoostingClassifier(), gb_params, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GradientBoostingClassifier(criterion='mse', init=None, learning_rate=0.1,\n",
            "                           loss='deviance', max_depth=10, max_features=None,\n",
            "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "                           min_impurity_split=None, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=50, n_iter_no_change=None,\n",
            "                           presort='auto', random_state=None, subsample=1.0,\n",
            "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "0.673387537689538\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[ 0  0  1  0  0  0]\n",
            " [ 1  1  4  4  0  0]\n",
            " [ 1  0 94 33  2  0]\n",
            " [ 1  1 38 83  7  2]\n",
            " [ 0  0  1 16 23  2]\n",
            " [ 0  0  0  2  3  0]]\n",
            "accuracy_on_test:  0.628125\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
              "                                                  init=None, learning_rate=0.1,\n",
              "                                                  loss='deviance', max_depth=3,\n",
              "                                                  max_features=None,\n",
              "                                                  max_leaf_nodes=None,\n",
              "                                                  min_impurity_decrease=0.0,\n",
              "                                                  min_impurity_split=None,\n",
              "                                                  min_samples_leaf=1,\n",
              "                                                  min_samples_split=2,\n",
              "                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                  n_estimators=100,\n",
              "                                                  n_iter_no_change=None,\n",
              "                                                  presort='auto',\n",
              "                                                  random_state=None,\n",
              "                                                  subsample=1.0, tol=0.0001,\n",
              "                                                  validation_fraction=0.1,\n",
              "                                                  verbose=0, warm_start=False),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'criterion': ['mse'], 'loss': ['deviance'],\n",
              "                          'max_depth': [2, 5, 10],\n",
              "                          'n_estimators': [5, 20, 50]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLJsCO5ECBgc",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aru_dK-uCYf1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "8c2e1695-65c9-4704-9664-4be9d6b7c84b"
      },
      "source": [
        "grid_search_fitting(XGBClassifier(), xgb_params, X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
            "              learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
            "              min_child_weight=3, missing=None, n_estimators=150, n_jobs=1,\n",
            "              nthread=None, objective='multi:softprob', random_state=0,\n",
            "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
            "              silent=None, subsample=1, verbosity=1)\n",
            "0.6616363842065913\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[ 0  0  1  0  0  0]\n",
            " [ 0  0  7  3  0  0]\n",
            " [ 0  0 93 35  2  0]\n",
            " [ 0  1 27 94  9  1]\n",
            " [ 0  0  0 19 23  0]\n",
            " [ 0  0  0  3  2  0]]\n",
            "accuracy_on_test:  0.65625\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                     colsample_bylevel=1, colsample_bynode=1,\n",
              "                                     colsample_bytree=1, gamma=0,\n",
              "                                     learning_rate=0.1, max_delta_step=0,\n",
              "                                     max_depth=3, min_child_weight=1,\n",
              "                                     missing=None, n_estimators=100, n_jobs=1,\n",
              "                                     nthread=None, objective='binary:logistic',\n",
              "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
              "                                     scale_pos_weight=1, seed=None, silent=None,\n",
              "                                     subsample=1, verbosity=1),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'max_depth': [5, 10, 30],\n",
              "                          'min_child_weight': [3, 5, 9],\n",
              "                          'n_estimators': [150]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVPOLT08Eg19",
        "colab_type": "text"
      },
      "source": [
        "## Upsampled dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UOgczsmmElfh",
        "colab_type": "text"
      },
      "source": [
        "### GradientBoosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72hhur8sErT8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "511e0659-24bd-4dd1-9c42-5edf3f13213e"
      },
      "source": [
        "grid_search_fitting(GradientBoostingClassifier(), gb_params, X_train_ups, y_train_ups, X_test, y_test)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GradientBoostingClassifier(criterion='mse', init=None, learning_rate=0.1,\n",
            "                           loss='deviance', max_depth=10, max_features=None,\n",
            "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "                           min_impurity_split=None, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=50, n_iter_no_change=None,\n",
            "                           presort='auto', random_state=None, subsample=1.0,\n",
            "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "0.9161530783509413\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[  1   0   0   0   0   0]\n",
            " [  0  10   0   0   0   0]\n",
            " [  0   0 124   5   0   1]\n",
            " [  0   0  18 106   6   2]\n",
            " [  0   0   1   1  40   0]\n",
            " [  0   0   0   0   0   5]]\n",
            "accuracy_on_test:  0.89375\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
              "                                                  init=None, learning_rate=0.1,\n",
              "                                                  loss='deviance', max_depth=3,\n",
              "                                                  max_features=None,\n",
              "                                                  max_leaf_nodes=None,\n",
              "                                                  min_impurity_decrease=0.0,\n",
              "                                                  min_impurity_split=None,\n",
              "                                                  min_samples_leaf=1,\n",
              "                                                  min_samples_split=2,\n",
              "                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                  n_estimators=100,\n",
              "                                                  n_iter_no_change=None,\n",
              "                                                  presort='auto',\n",
              "                                                  random_state=None,\n",
              "                                                  subsample=1.0, tol=0.0001,\n",
              "                                                  validation_fraction=0.1,\n",
              "                                                  verbose=0, warm_start=False),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'criterion': ['mse'], 'loss': ['deviance'],\n",
              "                          'max_depth': [2, 5, 10],\n",
              "                          'n_estimators': [5, 20, 50]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEDL9cEuEoCq",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xFv1apOE1i4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "56f2a43f-b0ae-4102-84a1-ed4bf2dc7027"
      },
      "source": [
        "grid_search_fitting(XGBClassifier(), xgb_params, X_train_ups, y_train_ups, X_test, y_test)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
            "              learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
            "              min_child_weight=3, missing=None, n_estimators=150, n_jobs=1,\n",
            "              nthread=None, objective='multi:softprob', random_state=0,\n",
            "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
            "              silent=None, subsample=1, verbosity=1)\n",
            "0.9192083809834584\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[  1   0   0   0   0   0]\n",
            " [  0  10   0   0   0   0]\n",
            " [  0   0 126   3   1   0]\n",
            " [  0   0  16 108   6   2]\n",
            " [  0   0   1   1  40   0]\n",
            " [  0   0   0   0   0   5]]\n",
            "accuracy_on_test:  0.90625\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                     colsample_bylevel=1, colsample_bynode=1,\n",
              "                                     colsample_bytree=1, gamma=0,\n",
              "                                     learning_rate=0.1, max_delta_step=0,\n",
              "                                     max_depth=3, min_child_weight=1,\n",
              "                                     missing=None, n_estimators=100, n_jobs=1,\n",
              "                                     nthread=None, objective='binary:logistic',\n",
              "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
              "                                     scale_pos_weight=1, seed=None, silent=None,\n",
              "                                     subsample=1, verbosity=1),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'max_depth': [5, 10, 30],\n",
              "                          'min_child_weight': [3, 5, 9],\n",
              "                          'n_estimators': [150]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dYrHBWzlFkX4",
        "colab_type": "text"
      },
      "source": [
        "## Dowsampled data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oF3V_yx8Fme8",
        "colab_type": "text"
      },
      "source": [
        "### GradientBoosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lxRvSaZkFw--",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "c52f7f41-1faa-4149-fac4-d291e2df65d0"
      },
      "source": [
        "grid_search_fitting(GradientBoostingClassifier(), gb_params, X_train_downs, y_train_downs, X_test, y_test)"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GradientBoostingClassifier(criterion='mse', init=None, learning_rate=0.1,\n",
            "                           loss='deviance', max_depth=2, max_features=None,\n",
            "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "                           min_impurity_split=None, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=5, n_iter_no_change=None,\n",
            "                           presort='auto', random_state=None, subsample=1.0,\n",
            "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "0.3361904761904762\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[ 1  0  0  0  0  0]\n",
            " [ 7  0  2  1  0  0]\n",
            " [ 7 28 55 27  5  8]\n",
            " [ 5 21 47 26 11 22]\n",
            " [ 1  2  6  8 11 14]\n",
            " [ 0  0  0  0  1  4]]\n",
            "accuracy_on_test:  0.303125\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
              "                                                  init=None, learning_rate=0.1,\n",
              "                                                  loss='deviance', max_depth=3,\n",
              "                                                  max_features=None,\n",
              "                                                  max_leaf_nodes=None,\n",
              "                                                  min_impurity_decrease=0.0,\n",
              "                                                  min_impurity_split=None,\n",
              "                                                  min_samples_leaf=1,\n",
              "                                                  min_samples_split=2,\n",
              "                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                  n_estimators=100,\n",
              "                                                  n_iter_no_change=None,\n",
              "                                                  presort='auto',\n",
              "                                                  random_state=None,\n",
              "                                                  subsample=1.0, tol=0.0001,\n",
              "                                                  validation_fraction=0.1,\n",
              "                                                  verbose=0, warm_start=False),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'criterion': ['mse'], 'loss': ['deviance'],\n",
              "                          'max_depth': [2, 5, 10],\n",
              "                          'n_estimators': [5, 20, 50]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kn5kNQEYFqAR",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaZAe-HgF3Py",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "8e05828f-0c7e-4b68-b037-bbee4b09506d"
      },
      "source": [
        "grid_search_fitting(XGBClassifier(), xgb_params, X_train_downs, y_train_downs, X_test, y_test)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
            "              learning_rate=0.1, max_delta_step=0, max_depth=5,\n",
            "              min_child_weight=3, missing=None, n_estimators=150, n_jobs=1,\n",
            "              nthread=None, objective='multi:softprob', random_state=0,\n",
            "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
            "              silent=None, subsample=1, verbosity=1)\n",
            "0.2909523809523809\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[ 1  0  0  0  0  0]\n",
            " [ 7  1  1  1  0  0]\n",
            " [18 34 52 16  9  1]\n",
            " [ 7 30 44 20 15 16]\n",
            " [ 0  2  5  4 17 14]\n",
            " [ 0  0  0  0  0  5]]\n",
            "accuracy_on_test:  0.3\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                     colsample_bylevel=1, colsample_bynode=1,\n",
              "                                     colsample_bytree=1, gamma=0,\n",
              "                                     learning_rate=0.1, max_delta_step=0,\n",
              "                                     max_depth=3, min_child_weight=1,\n",
              "                                     missing=None, n_estimators=100, n_jobs=1,\n",
              "                                     nthread=None, objective='binary:logistic',\n",
              "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
              "                                     scale_pos_weight=1, seed=None, silent=None,\n",
              "                                     subsample=1, verbosity=1),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'max_depth': [5, 10, 30],\n",
              "                          'min_child_weight': [3, 5, 9],\n",
              "                          'n_estimators': [150]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhIPhz9SGauQ",
        "colab_type": "text"
      },
      "source": [
        "## SMOTE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLg0EkrXGdoI",
        "colab_type": "text"
      },
      "source": [
        "### GradientBoosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "va2ttStDGigG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "1f606981-fb7c-4fff-bf30-fb1c836c241d"
      },
      "source": [
        "grid_search_fitting(GradientBoostingClassifier(), gb_params, X_train_smote, y_train_smote, X_test, y_test)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GradientBoostingClassifier(criterion='mse', init=None, learning_rate=0.1,\n",
            "                           loss='deviance', max_depth=5, max_features=None,\n",
            "                           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
            "                           min_impurity_split=None, min_samples_leaf=1,\n",
            "                           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
            "                           n_estimators=50, n_iter_no_change=None,\n",
            "                           presort='auto', random_state=None, subsample=1.0,\n",
            "                           tol=0.0001, validation_fraction=0.1, verbose=0,\n",
            "                           warm_start=False)\n",
            "0.7617617780328911\n",
            "\n",
            "\n",
            "Confusion matrix:\n",
            "[[ 0  0  1  0  0  0]\n",
            " [ 0  3  5  2  0  0]\n",
            " [ 1  2 96 30  1  0]\n",
            " [ 1  1 29 88  9  4]\n",
            " [ 0  0  1 19 21  1]\n",
            " [ 0  0  0  2  3  0]]\n",
            "accuracy_on_test:  0.65\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
              "             estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
              "                                                  init=None, learning_rate=0.1,\n",
              "                                                  loss='deviance', max_depth=3,\n",
              "                                                  max_features=None,\n",
              "                                                  max_leaf_nodes=None,\n",
              "                                                  min_impurity_decrease=0.0,\n",
              "                                                  min_impurity_split=None,\n",
              "                                                  min_samples_leaf=1,\n",
              "                                                  min_samples_split=2,\n",
              "                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                  n_estimators=100,\n",
              "                                                  n_iter_no_change=None,\n",
              "                                                  presort='auto',\n",
              "                                                  random_state=None,\n",
              "                                                  subsample=1.0, tol=0.0001,\n",
              "                                                  validation_fraction=0.1,\n",
              "                                                  verbose=0, warm_start=False),\n",
              "             iid=False, n_jobs=None,\n",
              "             param_grid=[{'criterion': ['mse'], 'loss': ['deviance'],\n",
              "                          'max_depth': [2, 5, 10],\n",
              "                          'n_estimators': [5, 20, 50]}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz7oml7gGfrC",
        "colab_type": "text"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPNC-xPRLlMz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "bd3d5452-4038-42bd-c16a-8cdb73b60ccf"
      },
      "source": [
        "clf = GridSearchCV(estimator = XGBClassifier(), scoring = 'accuracy', param_grid = xgb_params, cv = 5, iid = False)\n",
        "\n",
        "clf.fit(X_train, y_train)\n",
        "  \n",
        "score_test = clf.best_estimator_.score(X_test, y_test) \n",
        "      \n",
        "print(clf.best_estimator_)\n",
        "print(clf.best_score_)\n",
        "print(\"\\n\")\n",
        "\n",
        "print('accuracy_on_test: ', score_test)\n",
        "print(\"\\n\")"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
            "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
            "              learning_rate=0.1, max_delta_step=0, max_depth=10,\n",
            "              min_child_weight=3, missing=None, n_estimators=150, n_jobs=1,\n",
            "              nthread=None, objective='multi:softprob', random_state=0,\n",
            "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
            "              silent=None, subsample=1, verbosity=1)\n",
            "0.6616363842065913\n",
            "\n",
            "\n",
            "accuracy_on_test:  0.65625\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}