{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "url = \"https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv\"\n",
    "save_path = \"Wine_Quality_Data.csv\"\n",
    "\n",
    "response = requests.get(url)\n",
    "response.raise_for_status()\n",
    "\n",
    "with open(save_path, \"wb\") as f:\n",
    "    f.write(response.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset = pd.read_csv(\"Wine_Quality_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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>red</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>red</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>red</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>red</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>red</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   red  \n",
       "1      9.8        5   red  \n",
       "2      9.8        5   red  \n",
       "3      9.8        6   red  \n",
       "4      9.4        5   red  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.head()# podgląd danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset['color'] = wine_dataset['color'].replace({'red': 1, 'white': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>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>0.246114</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>0.000000</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>0.000000</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>0.000000</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     0.246114  \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     0.000000  \n",
       "50%       3.210000     0.510000    10.300000     6.000000     0.000000  \n",
       "75%       3.320000     0.600000    11.300000     6.000000     0.000000  \n",
       "max       4.010000     2.000000    14.900000     9.000000     1.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 62,
     "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2964337577998153"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset[\"fixed_acidity\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64), array([], dtype=int64))"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "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": 66,
   "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>0.246114</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>0.000000</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>0.000000</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>0.000000</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     0.246114  \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     0.000000  \n",
       "50%       0.800499     0.255000     0.691275     0.666667     0.000000  \n",
       "75%       0.827930     0.300000     0.758389     0.666667     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset.describe(include='all') # sprawdzanie wartości po znormalizowaniu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_dataset[\"fixed_acidity\"].nlargest(10) #sprawdza czy najwyższe wartości mają sens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4408\n",
       "1.0    1439\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    490\n",
       "1.0    160\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    245\n",
       "1.0     80\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    245\n",
       "1.0     80\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_val[\"color\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wine_dataset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(data=wine_dataset, hue=\"color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>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>0.460126</td>\n",
       "      <td>0.209883</td>\n",
       "      <td>0.197294</td>\n",
       "      <td>0.083839</td>\n",
       "      <td>0.096352</td>\n",
       "      <td>0.105307</td>\n",
       "      <td>0.272028</td>\n",
       "      <td>0.957685</td>\n",
       "      <td>0.799770</td>\n",
       "      <td>0.266477</td>\n",
       "      <td>0.691389</td>\n",
       "      <td>0.636239</td>\n",
       "      <td>0.246154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.087321</td>\n",
       "      <td>0.100971</td>\n",
       "      <td>0.086532</td>\n",
       "      <td>0.072172</td>\n",
       "      <td>0.066017</td>\n",
       "      <td>0.061895</td>\n",
       "      <td>0.131981</td>\n",
       "      <td>0.002780</td>\n",
       "      <td>0.038640</td>\n",
       "      <td>0.082243</td>\n",
       "      <td>0.073293</td>\n",
       "      <td>0.088732</td>\n",
       "      <td>0.431433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.308176</td>\n",
       "      <td>0.066456</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.026187</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.020455</td>\n",
       "      <td>0.952030</td>\n",
       "      <td>0.698254</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.577181</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.408805</td>\n",
       "      <td>0.139241</td>\n",
       "      <td>0.156627</td>\n",
       "      <td>0.027356</td>\n",
       "      <td>0.062193</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.188636</td>\n",
       "      <td>0.955322</td>\n",
       "      <td>0.773067</td>\n",
       "      <td>0.215000</td>\n",
       "      <td>0.630872</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.440252</td>\n",
       "      <td>0.189873</td>\n",
       "      <td>0.186747</td>\n",
       "      <td>0.048632</td>\n",
       "      <td>0.078560</td>\n",
       "      <td>0.100346</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.957978</td>\n",
       "      <td>0.795511</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.671141</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.484277</td>\n",
       "      <td>0.240506</td>\n",
       "      <td>0.246988</td>\n",
       "      <td>0.121581</td>\n",
       "      <td>0.116203</td>\n",
       "      <td>0.145329</td>\n",
       "      <td>0.356818</td>\n",
       "      <td>0.959787</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>0.738255</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.943396</td>\n",
       "      <td>0.715190</td>\n",
       "      <td>0.469880</td>\n",
       "      <td>0.303191</td>\n",
       "      <td>0.764321</td>\n",
       "      <td>0.479239</td>\n",
       "      <td>0.781818</td>\n",
       "      <td>0.966034</td>\n",
       "      <td>0.895262</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.906040</td>\n",
       "      <td>0.888889</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        0.460126          0.209883     0.197294        0.083839   \n",
       "std         0.087321          0.100971     0.086532        0.072172   \n",
       "min         0.308176          0.066456     0.000000        0.010638   \n",
       "25%         0.408805          0.139241     0.156627        0.027356   \n",
       "50%         0.440252          0.189873     0.186747        0.048632   \n",
       "75%         0.484277          0.240506     0.246988        0.121581   \n",
       "max         0.943396          0.715190     0.469880        0.303191   \n",
       "\n",
       "        chlorides  free_sulfur_dioxide  total_sulfur_dioxide     density  \\\n",
       "count  325.000000           325.000000            325.000000  325.000000   \n",
       "mean     0.096352             0.105307              0.272028    0.957685   \n",
       "std      0.066017             0.061895              0.131981    0.002780   \n",
       "min      0.026187             0.003460              0.020455    0.952030   \n",
       "25%      0.062193             0.058824              0.188636    0.955322   \n",
       "50%      0.078560             0.100346              0.275000    0.957978   \n",
       "75%      0.116203             0.145329              0.356818    0.959787   \n",
       "max      0.764321             0.479239              0.781818    0.966034   \n",
       "\n",
       "               pH   sulphates     alcohol     quality       color  \n",
       "count  325.000000  325.000000  325.000000  325.000000  325.000000  \n",
       "mean     0.799770    0.266477    0.691389    0.636239    0.246154  \n",
       "std      0.038640    0.082243    0.073293    0.088732    0.431433  \n",
       "min      0.698254    0.115000    0.577181    0.333333    0.000000  \n",
       "25%      0.773067    0.215000    0.630872    0.555556    0.000000  \n",
       "50%      0.795511    0.250000    0.671141    0.666667    0.000000  \n",
       "75%      0.822943    0.305000    0.738255    0.666667    0.000000  \n",
       "max      0.895262    0.975000    0.906040    0.888889    1.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>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>0.453724</td>\n",
       "      <td>0.215128</td>\n",
       "      <td>0.192091</td>\n",
       "      <td>0.082877</td>\n",
       "      <td>0.091656</td>\n",
       "      <td>0.105899</td>\n",
       "      <td>0.262834</td>\n",
       "      <td>0.957374</td>\n",
       "      <td>0.802637</td>\n",
       "      <td>0.265601</td>\n",
       "      <td>0.704572</td>\n",
       "      <td>0.646846</td>\n",
       "      <td>0.246109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.081597</td>\n",
       "      <td>0.104319</td>\n",
       "      <td>0.087166</td>\n",
       "      <td>0.072487</td>\n",
       "      <td>0.057502</td>\n",
       "      <td>0.061908</td>\n",
       "      <td>0.128388</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>0.040030</td>\n",
       "      <td>0.074400</td>\n",
       "      <td>0.080399</td>\n",
       "      <td>0.097212</td>\n",
       "      <td>0.430780</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>0.000000</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>0.957555</td>\n",
       "      <td>0.800499</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.691275</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.484277</td>\n",
       "      <td>0.259494</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>0.000000</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    5847.000000       5847.000000  5847.000000     5847.000000   \n",
       "mean        0.453724          0.215128     0.192091        0.082877   \n",
       "std         0.081597          0.104319     0.087166        0.072487   \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          0.259494     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  5847.000000          5847.000000           5847.000000  5847.000000   \n",
       "mean      0.091656             0.105899              0.262834     0.957374   \n",
       "std       0.057502             0.061908              0.128388     0.002899   \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     0.957555   \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  5847.000000  5847.000000  5847.000000  5847.000000  5847.000000  \n",
       "mean      0.802637     0.265601     0.704572     0.646846     0.246109  \n",
       "std       0.040030     0.074400     0.080399     0.097212     0.430780  \n",
       "min       0.678304     0.110000     0.536913     0.333333     0.000000  \n",
       "25%       0.775561     0.215000     0.637584     0.555556     0.000000  \n",
       "50%       0.800499     0.255000     0.691275     0.666667     0.000000  \n",
       "75%       0.827930     0.300000     0.758389     0.666667     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>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>0.448708</td>\n",
       "      <td>0.217381</td>\n",
       "      <td>0.184022</td>\n",
       "      <td>0.078864</td>\n",
       "      <td>0.088017</td>\n",
       "      <td>0.100985</td>\n",
       "      <td>0.258073</td>\n",
       "      <td>0.957147</td>\n",
       "      <td>0.805141</td>\n",
       "      <td>0.265385</td>\n",
       "      <td>0.709269</td>\n",
       "      <td>0.650256</td>\n",
       "      <td>0.246154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.073960</td>\n",
       "      <td>0.105388</td>\n",
       "      <td>0.094736</td>\n",
       "      <td>0.069232</td>\n",
       "      <td>0.043159</td>\n",
       "      <td>0.051174</td>\n",
       "      <td>0.126120</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>0.042584</td>\n",
       "      <td>0.065946</td>\n",
       "      <td>0.079198</td>\n",
       "      <td>0.101225</td>\n",
       "      <td>0.431433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.075949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.026187</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.950731</td>\n",
       "      <td>0.683292</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.570470</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>0.138554</td>\n",
       "      <td>0.028875</td>\n",
       "      <td>0.062193</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.179545</td>\n",
       "      <td>0.954879</td>\n",
       "      <td>0.775561</td>\n",
       "      <td>0.215000</td>\n",
       "      <td>0.637584</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.186747</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.100346</td>\n",
       "      <td>0.256818</td>\n",
       "      <td>0.957189</td>\n",
       "      <td>0.805486</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.697987</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</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>0.117021</td>\n",
       "      <td>0.101473</td>\n",
       "      <td>0.138408</td>\n",
       "      <td>0.356818</td>\n",
       "      <td>0.959306</td>\n",
       "      <td>0.830424</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>0.758389</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.798742</td>\n",
       "      <td>0.696203</td>\n",
       "      <td>0.602410</td>\n",
       "      <td>0.303191</td>\n",
       "      <td>0.436989</td>\n",
       "      <td>0.280277</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.962935</td>\n",
       "      <td>0.935162</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.953020</td>\n",
       "      <td>0.888889</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        0.448708          0.217381     0.184022        0.078864   \n",
       "std         0.073960          0.105388     0.094736        0.069232   \n",
       "min         0.301887          0.075949     0.000000        0.012158   \n",
       "25%         0.402516          0.145570     0.138554        0.028875   \n",
       "50%         0.433962          0.177215     0.186747        0.042553   \n",
       "75%         0.484277          0.253165     0.234940        0.117021   \n",
       "max         0.798742          0.696203     0.602410        0.303191   \n",
       "\n",
       "        chlorides  free_sulfur_dioxide  total_sulfur_dioxide     density  \\\n",
       "count  325.000000           325.000000            325.000000  325.000000   \n",
       "mean     0.088017             0.100985              0.258073    0.957147   \n",
       "std      0.043159             0.051174              0.126120    0.002746   \n",
       "min      0.026187             0.006920              0.018182    0.950731   \n",
       "25%      0.062193             0.058824              0.179545    0.954879   \n",
       "50%      0.076923             0.100346              0.256818    0.957189   \n",
       "75%      0.101473             0.138408              0.356818    0.959306   \n",
       "max      0.436989             0.280277              0.575000    0.962935   \n",
       "\n",
       "               pH   sulphates     alcohol     quality       color  \n",
       "count  325.000000  325.000000  325.000000  325.000000  325.000000  \n",
       "mean     0.805141    0.265385    0.709269    0.650256    0.246154  \n",
       "std      0.042584    0.065946    0.079198    0.101225    0.431433  \n",
       "min      0.683292    0.150000    0.570470    0.333333    0.000000  \n",
       "25%      0.775561    0.215000    0.637584    0.555556    0.000000  \n",
       "50%      0.805486    0.260000    0.697987    0.666667    0.000000  \n",
       "75%      0.830424    0.305000    0.758389    0.666667    0.000000  \n",
       "max      0.935162    0.490000    0.953020    0.888889    1.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_val.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TabularDataset(Dataset):\n",
    "    def __init__(self, data):\n",
    "        self.data = data.values.astype('float32')\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x = torch.tensor(self.data[index, :-1])\n",
    "        y = torch.tensor(self.data[index, -1])\n",
    "        return x, y\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "train_dataset = TabularDataset(wine_train)\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_dataset = TabularDataset(wine_test)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TabularModel(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
    "        super(TabularModel, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        out = self.softmax(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = wine_train.shape[1] - 1\n",
    "hidden_dim = 32\n",
    "output_dim = 2\n",
    "model = TabularModel(input_dim, hidden_dim, output_dim)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=32, output_dim=2)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, loss: 0.4864\n",
      "Epoch 3, loss: 0.3413\n",
      "Epoch 5, loss: 0.3345\n",
      "Epoch 7, loss: 0.3337\n",
      "Epoch 9, loss: 0.3331\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(train_dataloader, 0):\n",
    "        inputs, labels = data\n",
    "        labels = labels.type(torch.LongTensor)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    # Print the loss every 1000 mini-batches\n",
    "    if (epoch%2)  == 0:\n",
    "        print(f'Epoch {epoch + 1}, loss: {running_loss / len(train_dataloader):.4f}')\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 98 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in test_dataloader:\n",
    "        inputs, labels = data\n",
    "        outputs = model(inputs.float())\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "print('Accuracy on test set: %d %%' % (100 * correct / total))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}