{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset wine (C:/Users/s487176/.cache/huggingface/datasets/mstz___wine/wine/1.0.0/0913b614badc418a000d75d098776831f39ebf5ee208ecd3cfad4d5db1418d76)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8f1b9db0c8b41e1904e16e22ae351e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"mstz/wine\", \"wine\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality', 'color'],\n",
       "    num_rows: 6497\n",
       "})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset = pd.DataFrame(dataset[\"train\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed_acidity  volatile_acidity  citric_acid  residual_sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free_sulfur_dioxide  total_sulfur_dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      9.4        5      0  \n",
       "1      9.8        5      0  \n",
       "2      9.8        5      0  \n",
       "3      9.8        6      0  \n",
       "4      9.4        5      0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.head()# podgląd danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.215307</td>\n",
       "      <td>0.339666</td>\n",
       "      <td>0.318633</td>\n",
       "      <td>5.443235</td>\n",
       "      <td>0.056034</td>\n",
       "      <td>30.525319</td>\n",
       "      <td>115.744574</td>\n",
       "      <td>0.994697</td>\n",
       "      <td>3.218501</td>\n",
       "      <td>0.531268</td>\n",
       "      <td>10.491801</td>\n",
       "      <td>5.818378</td>\n",
       "      <td>0.753886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.296434</td>\n",
       "      <td>0.164636</td>\n",
       "      <td>0.145318</td>\n",
       "      <td>4.757804</td>\n",
       "      <td>0.035034</td>\n",
       "      <td>17.749400</td>\n",
       "      <td>56.521855</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.160787</td>\n",
       "      <td>0.148806</td>\n",
       "      <td>1.192712</td>\n",
       "      <td>0.873255</td>\n",
       "      <td>0.430779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.992340</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>0.994890</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.996990</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed_acidity  volatile_acidity  citric_acid  residual_sugar  \\\n",
       "count    6497.000000       6497.000000  6497.000000     6497.000000   \n",
       "mean        7.215307          0.339666     0.318633        5.443235   \n",
       "std         1.296434          0.164636     0.145318        4.757804   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.400000          0.230000     0.250000        1.800000   \n",
       "50%         7.000000          0.290000     0.310000        3.000000   \n",
       "75%         7.700000          0.400000     0.390000        8.100000   \n",
       "max        15.900000          1.580000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free_sulfur_dioxide  total_sulfur_dioxide      density  \\\n",
       "count  6497.000000          6497.000000           6497.000000  6497.000000   \n",
       "mean      0.056034            30.525319            115.744574     0.994697   \n",
       "std       0.035034            17.749400             56.521855     0.002999   \n",
       "min       0.009000             1.000000              6.000000     0.987110   \n",
       "25%       0.038000            17.000000             77.000000     0.992340   \n",
       "50%       0.047000            29.000000            118.000000     0.994890   \n",
       "75%       0.065000            41.000000            156.000000     0.996990   \n",
       "max       0.611000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality        color  \n",
       "count  6497.000000  6497.000000  6497.000000  6497.000000  6497.000000  \n",
       "mean      3.218501     0.531268    10.491801     5.818378     0.753886  \n",
       "std       0.160787     0.148806     1.192712     0.873255     0.430779  \n",
       "min       2.720000     0.220000     8.000000     3.000000     0.000000  \n",
       "25%       3.110000     0.430000     9.500000     5.000000     1.000000  \n",
       "50%       3.210000     0.510000    10.300000     6.000000     1.000000  \n",
       "75%       3.320000     0.600000    11.300000     6.000000     1.000000  \n",
       "max       4.010000     2.000000    14.900000     9.000000     1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wine_dataset[\"color\"].value_counts().plot(kind=\"bar\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2964337577998153"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset[\"fixed_acidity\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64), array([], dtype=int64))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.where(pd.isnull(wine_dataset))## sprawdzanie czy istnieją puste wartości"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in wine_dataset.columns:\n",
    "    wine_dataset[column] = wine_dataset[column]  / wine_dataset[column].abs().max() # normalizacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "      <td>6497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.453793</td>\n",
       "      <td>0.214978</td>\n",
       "      <td>0.191948</td>\n",
       "      <td>0.082724</td>\n",
       "      <td>0.091708</td>\n",
       "      <td>0.105624</td>\n",
       "      <td>0.263056</td>\n",
       "      <td>0.957378</td>\n",
       "      <td>0.802619</td>\n",
       "      <td>0.265634</td>\n",
       "      <td>0.704148</td>\n",
       "      <td>0.646486</td>\n",
       "      <td>0.753886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.081537</td>\n",
       "      <td>0.104200</td>\n",
       "      <td>0.087541</td>\n",
       "      <td>0.072307</td>\n",
       "      <td>0.057338</td>\n",
       "      <td>0.061417</td>\n",
       "      <td>0.128459</td>\n",
       "      <td>0.002886</td>\n",
       "      <td>0.040097</td>\n",
       "      <td>0.074403</td>\n",
       "      <td>0.080048</td>\n",
       "      <td>0.097028</td>\n",
       "      <td>0.430779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.238994</td>\n",
       "      <td>0.050633</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009119</td>\n",
       "      <td>0.014730</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.013636</td>\n",
       "      <td>0.950076</td>\n",
       "      <td>0.678304</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.536913</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.402516</td>\n",
       "      <td>0.145570</td>\n",
       "      <td>0.150602</td>\n",
       "      <td>0.027356</td>\n",
       "      <td>0.062193</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.955110</td>\n",
       "      <td>0.775561</td>\n",
       "      <td>0.215000</td>\n",
       "      <td>0.637584</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.440252</td>\n",
       "      <td>0.183544</td>\n",
       "      <td>0.186747</td>\n",
       "      <td>0.045593</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.100346</td>\n",
       "      <td>0.268182</td>\n",
       "      <td>0.957564</td>\n",
       "      <td>0.800499</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.484277</td>\n",
       "      <td>0.253165</td>\n",
       "      <td>0.234940</td>\n",
       "      <td>0.123100</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.141869</td>\n",
       "      <td>0.354545</td>\n",
       "      <td>0.959585</td>\n",
       "      <td>0.827930</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.758389</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed_acidity  volatile_acidity  citric_acid  residual_sugar  \\\n",
       "count    6497.000000       6497.000000  6497.000000     6497.000000   \n",
       "mean        0.453793          0.214978     0.191948        0.082724   \n",
       "std         0.081537          0.104200     0.087541        0.072307   \n",
       "min         0.238994          0.050633     0.000000        0.009119   \n",
       "25%         0.402516          0.145570     0.150602        0.027356   \n",
       "50%         0.440252          0.183544     0.186747        0.045593   \n",
       "75%         0.484277          0.253165     0.234940        0.123100   \n",
       "max         1.000000          1.000000     1.000000        1.000000   \n",
       "\n",
       "         chlorides  free_sulfur_dioxide  total_sulfur_dioxide      density  \\\n",
       "count  6497.000000          6497.000000           6497.000000  6497.000000   \n",
       "mean      0.091708             0.105624              0.263056     0.957378   \n",
       "std       0.057338             0.061417              0.128459     0.002886   \n",
       "min       0.014730             0.003460              0.013636     0.950076   \n",
       "25%       0.062193             0.058824              0.175000     0.955110   \n",
       "50%       0.076923             0.100346              0.268182     0.957564   \n",
       "75%       0.106383             0.141869              0.354545     0.959585   \n",
       "max       1.000000             1.000000              1.000000     1.000000   \n",
       "\n",
       "                pH    sulphates      alcohol      quality        color  \n",
       "count  6497.000000  6497.000000  6497.000000  6497.000000  6497.000000  \n",
       "mean      0.802619     0.265634     0.704148     0.646486     0.753886  \n",
       "std       0.040097     0.074403     0.080048     0.097028     0.430779  \n",
       "min       0.678304     0.110000     0.536913     0.333333     0.000000  \n",
       "25%       0.775561     0.215000     0.637584     0.555556     1.000000  \n",
       "50%       0.800499     0.255000     0.691275     0.666667     1.000000  \n",
       "75%       0.827930     0.300000     0.758389     0.666667     1.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe(include='all') # sprawdzanie wartości po znormalizowaniu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "652     1.000000\n",
       "442     0.981132\n",
       "557     0.981132\n",
       "554     0.974843\n",
       "555     0.974843\n",
       "243     0.943396\n",
       "244     0.943396\n",
       "544     0.899371\n",
       "3125    0.893082\n",
       "374     0.880503\n",
       "Name: fixed_acidity, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset[\"fixed_acidity\"].nlargest(10) #sprawdza czy najwyższe wartości mają sens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    4408\n",
       "0.0    1439\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset[\"color\"])\n",
    "wine_train[\"color\"].value_counts() \n",
    "# podzielenie na train i test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    490\n",
       "0.0    160\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test[\"color\"]) # podzielenie na test i validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    245\n",
       "0.0     80\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    245\n",
       "0.0     80\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_val[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wine_dataset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(data=wine_dataset, hue=\"color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed_acidity</th>\n",
       "      <th>volatile_acidity</th>\n",
       "      <th>citric_acid</th>\n",
       "      <th>residual_sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free_sulfur_dioxide</th>\n",
       "      <th>total_sulfur_dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.127077</td>\n",
       "      <td>0.342969</td>\n",
       "      <td>0.299846</td>\n",
       "      <td>5.197538</td>\n",
       "      <td>0.054222</td>\n",
       "      <td>29.773846</td>\n",
       "      <td>113.283077</td>\n",
       "      <td>0.994568</td>\n",
       "      <td>3.222246</td>\n",
       "      <td>0.527754</td>\n",
       "      <td>10.488564</td>\n",
       "      <td>5.815385</td>\n",
       "      <td>0.753846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.181391</td>\n",
       "      <td>0.170050</td>\n",
       "      <td>0.129556</td>\n",
       "      <td>4.608978</td>\n",
       "      <td>0.031405</td>\n",
       "      <td>15.822670</td>\n",
       "      <td>55.072566</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>0.159630</td>\n",
       "      <td>0.144550</td>\n",
       "      <td>1.172682</td>\n",
       "      <td>0.855128</td>\n",
       "      <td>0.431433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.988190</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.037000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.992400</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>0.048000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>0.994800</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.062000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.415000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>1.002890</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>1.170000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed_acidity  volatile_acidity  citric_acid  residual_sugar  \\\n",
       "count     325.000000        325.000000   325.000000      325.000000   \n",
       "mean        7.127077          0.342969     0.299846        5.197538   \n",
       "std         1.181391          0.170050     0.129556        4.608978   \n",
       "min         5.000000          0.100000     0.000000        0.800000   \n",
       "25%         6.400000          0.230000     0.240000        1.800000   \n",
       "50%         6.900000          0.280000     0.300000        2.800000   \n",
       "75%         7.500000          0.400000     0.370000        7.500000   \n",
       "max        13.000000          0.900000     0.740000       22.000000   \n",
       "\n",
       "        chlorides  free_sulfur_dioxide  total_sulfur_dioxide     density  \\\n",
       "count  325.000000           325.000000            325.000000  325.000000   \n",
       "mean     0.054222            29.773846            113.283077    0.994568   \n",
       "std      0.031405            15.822670             55.072566    0.002895   \n",
       "min      0.019000             3.000000              9.000000    0.988190   \n",
       "25%      0.037000            17.000000             74.000000    0.992400   \n",
       "50%      0.048000            29.000000            115.000000    0.994800   \n",
       "75%      0.062000            41.000000            151.000000    0.996750   \n",
       "max      0.415000            67.000000            253.000000    1.002890   \n",
       "\n",
       "               pH   sulphates     alcohol     quality       color  \n",
       "count  325.000000  325.000000  325.000000  325.000000  325.000000  \n",
       "mean     3.222246    0.527754   10.488564    5.815385    0.753846  \n",
       "std      0.159630    0.144550    1.172682    0.855128    0.431433  \n",
       "min      2.860000    0.260000    8.500000    3.000000    0.000000  \n",
       "25%      3.110000    0.420000    9.500000    5.000000    1.000000  \n",
       "50%      3.210000    0.500000   10.300000    6.000000    1.000000  \n",
       "75%      3.320000    0.600000   11.300000    6.000000    1.000000  \n",
       "max      3.680000    1.170000   14.000000    9.000000    1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "      <td>5847.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.216179</td>\n",
       "      <td>0.339796</td>\n",
       "      <td>0.319111</td>\n",
       "      <td>5.417402</td>\n",
       "      <td>0.056310</td>\n",
       "      <td>30.535403</td>\n",
       "      <td>115.673508</td>\n",
       "      <td>0.994682</td>\n",
       "      <td>3.218303</td>\n",
       "      <td>0.531596</td>\n",
       "      <td>10.494455</td>\n",
       "      <td>5.820592</td>\n",
       "      <td>0.753891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.299695</td>\n",
       "      <td>0.164817</td>\n",
       "      <td>0.146141</td>\n",
       "      <td>4.736399</td>\n",
       "      <td>0.035816</td>\n",
       "      <td>17.845522</td>\n",
       "      <td>56.432512</td>\n",
       "      <td>0.002995</td>\n",
       "      <td>0.159919</td>\n",
       "      <td>0.149728</td>\n",
       "      <td>1.189801</td>\n",
       "      <td>0.872353</td>\n",
       "      <td>0.430780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.009000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.987110</td>\n",
       "      <td>2.720000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.038000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>0.992300</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>0.994840</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>155.500000</td>\n",
       "      <td>0.996985</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.660000</td>\n",
       "      <td>65.800000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>1.038980</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed_acidity  volatile_acidity  citric_acid  residual_sugar  \\\n",
       "count    5847.000000       5847.000000  5847.000000     5847.000000   \n",
       "mean        7.216179          0.339796     0.319111        5.417402   \n",
       "std         1.299695          0.164817     0.146141        4.736399   \n",
       "min         3.800000          0.080000     0.000000        0.600000   \n",
       "25%         6.400000          0.230000     0.250000        1.800000   \n",
       "50%         7.000000          0.290000     0.310000        3.000000   \n",
       "75%         7.700000          0.400000     0.390000        8.100000   \n",
       "max        15.900000          1.580000     1.660000       65.800000   \n",
       "\n",
       "         chlorides  free_sulfur_dioxide  total_sulfur_dioxide      density  \\\n",
       "count  5847.000000          5847.000000           5847.000000  5847.000000   \n",
       "mean      0.056310            30.535403            115.673508     0.994682   \n",
       "std       0.035816            17.845522             56.432512     0.002995   \n",
       "min       0.009000             1.000000              6.000000     0.987110   \n",
       "25%       0.038000            17.000000             77.500000     0.992300   \n",
       "50%       0.047000            29.000000            118.000000     0.994840   \n",
       "75%       0.065000            41.000000            155.500000     0.996985   \n",
       "max       0.611000           289.000000            440.000000     1.038980   \n",
       "\n",
       "                pH    sulphates      alcohol      quality        color  \n",
       "count  5847.000000  5847.000000  5847.000000  5847.000000  5847.000000  \n",
       "mean      3.218303     0.531596    10.494455     5.820592     0.753891  \n",
       "std       0.159919     0.149728     1.189801     0.872353     0.430780  \n",
       "min       2.720000     0.220000     8.000000     3.000000     0.000000  \n",
       "25%       3.110000     0.430000     9.500000     5.000000     1.000000  \n",
       "50%       3.210000     0.510000    10.300000     6.000000     1.000000  \n",
       "75%       3.320000     0.600000    11.300000     6.000000     1.000000  \n",
       "max       4.010000     2.000000    14.900000     9.000000     1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "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",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>325.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.287846</td>\n",
       "      <td>0.334031</td>\n",
       "      <td>0.328831</td>\n",
       "      <td>6.153692</td>\n",
       "      <td>0.052874</td>\n",
       "      <td>31.095385</td>\n",
       "      <td>119.484615</td>\n",
       "      <td>0.995091</td>\n",
       "      <td>3.218308</td>\n",
       "      <td>0.528892</td>\n",
       "      <td>10.447282</td>\n",
       "      <td>5.781538</td>\n",
       "      <td>0.753846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.345471</td>\n",
       "      <td>0.156023</td>\n",
       "      <td>0.144192</td>\n",
       "      <td>5.220944</td>\n",
       "      <td>0.021471</td>\n",
       "      <td>17.861741</td>\n",
       "      <td>59.481580</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.177176</td>\n",
       "      <td>0.136171</td>\n",
       "      <td>1.265593</td>\n",
       "      <td>0.908617</td>\n",
       "      <td>0.431433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.987460</td>\n",
       "      <td>2.870000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>0.992700</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>4.550000</td>\n",
       "      <td>0.048000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>0.995320</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>0.997450</td>\n",
       "      <td>3.320000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>31.600000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>1.010300</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed_acidity  volatile_acidity  citric_acid  residual_sugar  \\\n",
       "count     325.000000        325.000000   325.000000      325.000000   \n",
       "mean        7.287846          0.334031     0.328831        6.153692   \n",
       "std         1.345471          0.156023     0.144192        5.220944   \n",
       "min         4.700000          0.090000     0.000000        0.800000   \n",
       "25%         6.400000          0.230000     0.260000        2.000000   \n",
       "50%         7.100000          0.290000     0.310000        4.550000   \n",
       "75%         7.800000          0.400000     0.400000        8.800000   \n",
       "max        15.000000          1.180000     0.740000       31.600000   \n",
       "\n",
       "        chlorides  free_sulfur_dioxide  total_sulfur_dioxide     density  \\\n",
       "count  325.000000           325.000000            325.000000  325.000000   \n",
       "mean     0.052874            31.095385            119.484615    0.995091   \n",
       "std      0.021471            17.861741             59.481580    0.003150   \n",
       "min      0.012000             3.000000              8.000000    0.987460   \n",
       "25%      0.039000            16.000000             79.000000    0.992700   \n",
       "50%      0.048000            29.000000            125.000000    0.995320   \n",
       "75%      0.060000            45.000000            163.000000    0.997450   \n",
       "max      0.170000            77.000000            251.000000    1.010300   \n",
       "\n",
       "               pH   sulphates     alcohol     quality       color  \n",
       "count  325.000000  325.000000  325.000000  325.000000  325.000000  \n",
       "mean     3.218308    0.528892   10.447282    5.781538    0.753846  \n",
       "std      0.177176    0.136171    1.265593    0.908617    0.431433  \n",
       "min      2.870000    0.280000    8.400000    3.000000    0.000000  \n",
       "25%      3.100000    0.430000    9.400000    5.000000    1.000000  \n",
       "50%      3.210000    0.500000   10.200000    6.000000    1.000000  \n",
       "75%      3.320000    0.610000   11.300000    6.000000    1.000000  \n",
       "max      4.010000    1.140000   14.000000    8.000000    1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_val.describe()"
   ]
  }
 ],
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