Multi-Output_Wine-Quality.ipynb

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  {
   "cell_type": "markdown",
   "id": "e6054e02-a61a-4552-b806-8fa64b179fde",
   "metadata": {},
   "source": [
    "# *Multi-Output: Wine Quality Estimator & Type Predictor*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9725cc46-f992-4ad1-96a8-1f97849e29ff",
   "metadata": {},
   "source": [
    "### *Code by @Om Mule*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc301bf7-ac8b-4d50-ad28-3542397c9e44",
   "metadata": {},
   "source": [
    "## *Dataset Information:*\n",
    "The two datasets are related to red and white variants of the Portuguese \"Vinho Verde\" wine. For more details, consult: [Web Link] or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).\n",
    "\n",
    "These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3309aacc-d848-4d6f-9b94-3007257a3d67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b21e9d7a-fa36-4dc0-8d22-e81312858730",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import zipfile\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81155738-95e7-42c7-952e-b0c17b02c0c1",
   "metadata": {},
   "source": [
    "## *Preprocess Datasets*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c546b4c2-342e-4457-8a10-52753524b0ee",
   "metadata": {},
   "source": [
    "### *White Wine Dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef0a0700-7150-4deb-8bd8-8e779adff981",
   "metadata": {},
   "outputs": [
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       "  <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>is_red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.045</td>\n",
       "      <td>30.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.9949</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.47</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "6            6.2              0.32         0.16             7.0      0.045   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "6                 30.0                 136.0   0.9949  3.18       0.47   \n",
       "\n",
       "   alcohol  quality  is_red  \n",
       "0      8.8        6       0  \n",
       "1      9.5        6       0  \n",
       "2     10.1        6       0  \n",
       "3      9.9        6       0  \n",
       "6      9.6        6       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# White wine dataset \n",
    "white_df = pd.read_csv(\"winequality-white.csv\", sep=';')\n",
    "white_df[\"is_red\"] = 0\n",
    "white_df.drop_duplicates(keep='first', inplace=True)\n",
    "white_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "599a29c9-95e5-4717-8c00-cfa38ab09479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51493"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "white_df.size"
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  {
   "cell_type": "markdown",
   "id": "92c97586-35ea-456a-bd10-4beed5065ccb",
   "metadata": {},
   "source": [
    "### *Red Wine Dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5c24bbe-0671-4886-8db1-efa111ed4ce0",
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       "      <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",
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       "      <th>quality</th>\n",
       "      <th>is_red</th>\n",
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       "      <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>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "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",
       "5            7.4              0.66         0.00             1.8      0.075   \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",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  is_red  \n",
       "0      9.4        5       1  \n",
       "1      9.8        5       1  \n",
       "2      9.8        5       1  \n",
       "3      9.8        6       1  \n",
       "5      9.4        5       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Red Wine dataset\n",
    "red_df = pd.read_csv(\"winequality-red.csv\", sep=\";\")\n",
    "red_df[\"is_red\"] = 1\n",
    "red_df.drop_duplicates(keep='first', inplace=True)\n",
    "red_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46c83427-8725-417e-ab7a-00a05beaf0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17667"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b28871c-a18a-4789-8b92-12101608af03",
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
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       "      <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",
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       "      <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",
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       "    <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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "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              0.66         0.00             1.8      0.075   \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                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  is_red  \n",
       "0      9.4        5       1  \n",
       "1      9.8        5       1  \n",
       "2      9.8        5       1  \n",
       "3      9.8        6       1  \n",
       "4      9.4        5       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate Datasets\n",
    "\n",
    "df = pd.concat([red_df, white_df], ignore_index=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1bc11a1e-9810-428e-b5ff-29a75eed7403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69160"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d49daca7-a37a-42e3-b72b-d7ea5a3f56f5",
   "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>is_red</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5315</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5316</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5317</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5318</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5319</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</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",
       "5315            6.2              0.21         0.29             1.6      0.039   \n",
       "5316            6.6              0.32         0.36             8.0      0.047   \n",
       "5317            6.5              0.24         0.19             1.2      0.041   \n",
       "5318            5.5              0.29         0.30             1.1      0.022   \n",
       "5319            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "5315                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "5316                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "5317                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "5318                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "5319                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality  is_red  \n",
       "5315     11.2        6       0  \n",
       "5316      9.6        5       0  \n",
       "5317      9.4        6       0  \n",
       "5318     12.8        7       0  \n",
       "5319     11.8        6       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5672512d-388f-4c33-9621-8410ffe46a3a",
   "metadata": {},
   "source": [
    "### *Wine Quality Plot*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c5aa9a5-6bb7-49fc-ac03-f74f9333fcab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style=\"darkgrid\")\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(df[\"quality\"], color=\"orange\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0981e617-3d50-462a-9f38-57215b98c026",
   "metadata": {},
   "source": [
    "#### *From the above plot, the wines with 3,4,8 & 9 have low number of examples but other have a good number of samples; thus removing the 3,4,8 & 9 samples.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb6cb70f-f47b-44a2-82e3-1f9f7cda156f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "6    2323\n",
       "5    1752\n",
       "7     856\n",
       "4     206\n",
       "8     148\n",
       "3      30\n",
       "9       5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts(df['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3983c67-a0ff-4d0b-8783-c385f18d86af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality\n",
       "6    2323\n",
       "5    1752\n",
       "7     856\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[(df['quality']>4) & (df['quality']<8)]\n",
    "df = df.reset_index(drop=True)\n",
    "df.value_counts(df['quality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6adfab96-a32b-4389-bbc8-6771b455b807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4931"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fixed acidity'].size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93db9bc7-db36-4257-92c5-3b80d69a5c47",
   "metadata": {},
   "source": [
    "### *Plot after processing*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25e327d8-e3be-465f-853b-5a662e83b059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style=\"darkgrid\")\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(df[\"quality\"], color=\"orange\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23131a7b-3234-4d48-82ee-9938640653e1",
   "metadata": {},
   "source": [
    "## *Train Test Split of the data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76c16346-3d9f-4150-9216-f2064656b01b",
   "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>is_red</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</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>1</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              0.66         0.00             1.8      0.075   \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                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  is_red  \n",
       "0      9.4        5       1  \n",
       "1      9.8        5       1  \n",
       "2      9.8        5       1  \n",
       "3      9.8        6       1  \n",
       "4      9.4        5       1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f06da8f-9739-4999-a9b4-a0063c340d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64103"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9409bdc6-9603-4c0b-acaa-196a306efc21",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
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       "      <th>residual sugar</th>\n",
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       "      <td>0.65</td>\n",
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      "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              0.66         0.00             1.8      0.075   \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                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  \n",
       "0      9.4  \n",
       "1      9.8  \n",
       "2      9.8  \n",
       "3      9.8  \n",
       "4      9.4  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
    "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
    "       'pH', 'sulphates', 'alcohol']]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67c4716a-4a23-4f37-8528-0c8231d46994",
   "metadata": {},
   "outputs": [
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       "      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              0.66         0.00             1.8      0.075   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "4926            6.2              0.21         0.29             1.6      0.039   \n",
       "4927            6.6              0.32         0.36             8.0      0.047   \n",
       "4928            6.5              0.24         0.19             1.2      0.041   \n",
       "4929            5.5              0.29         0.30             1.1      0.022   \n",
       "4930            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    13.0                  40.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "4926                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "4927                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "4928                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "4929                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "4930                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  \n",
       "0         9.4  \n",
       "1         9.8  \n",
       "2         9.8  \n",
       "3         9.8  \n",
       "4         9.4  \n",
       "...       ...  \n",
       "4926     11.2  \n",
       "4927      9.6  \n",
       "4928      9.4  \n",
       "4929     12.8  \n",
       "4930     11.8  \n",
       "\n",
       "[4931 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f6aa1df-f88c-420d-9d62-7572eb54830f",
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   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>quality</th>\n",
       "      <th>is_red</th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "   quality  is_red\n",
       "0        5       1\n",
       "1        5       1\n",
       "2        5       1\n",
       "3        6       1\n",
       "4        5       1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[['quality', 'is_red']]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4b1a94c-6537-42e3-9130-448d9b042b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9862"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc7ef3f4-6ebd-4301-84f4-c1b7b7520967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43384\n",
      "10857\n",
      "8679\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, random_state=1)\n",
    "print(train_x.size)\n",
    "print(test_x.size)\n",
    "train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size=0.2, random_state=1)\n",
    "print(val_x.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7fe469e1-30b5-4e58-9d19-9624aa8665aa",
   "metadata": {},
   "outputs": [
    {
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       "      <th>residual sugar</th>\n",
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       "      <td>0.65</td>\n",
       "      <td>0.18</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.088</td>\n",
       "      <td>27.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.99915</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.77</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.29</td>\n",
       "      <td>12.20</td>\n",
       "      <td>0.044</td>\n",
       "      <td>59.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.99782</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3825</th>\n",
       "      <td>8.8</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.024</td>\n",
       "      <td>52.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.99250</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.49</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>6.4</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.030</td>\n",
       "      <td>10.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.99050</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.28</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.058</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.99573</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.37</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.042</td>\n",
       "      <td>36.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0.99580</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>9.2</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.078</td>\n",
       "      <td>28.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.99788</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.075</td>\n",
       "      <td>15.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.99467</td>\n",
       "      <td>3.58</td>\n",
       "      <td>0.67</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>6.8</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.070</td>\n",
       "      <td>16.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.99572</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.60</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2887</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.22</td>\n",
       "      <td>12.15</td>\n",
       "      <td>0.048</td>\n",
       "      <td>42.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.99570</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.46</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3155 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "225             7.5              0.65         0.18            7.00      0.088   \n",
       "3557            6.3              0.27         0.29           12.20      0.044   \n",
       "3825            8.8              0.27         0.25            5.00      0.024   \n",
       "1740            6.4              0.45         0.07            1.10      0.030   \n",
       "1221            7.2              0.53         0.13            2.00      0.058   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1916            7.0              0.21         0.37            7.20      0.042   \n",
       "606             9.2              0.53         0.24            2.60      0.078   \n",
       "1255            6.0              0.58         0.20            2.40      0.075   \n",
       "959             6.8              0.66         0.07            1.60      0.070   \n",
       "2887            6.0              0.28         0.22           12.15      0.048   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "225                  27.0                  94.0  0.99915  3.38       0.77   \n",
       "3557                 59.0                 196.0  0.99782  3.14       0.40   \n",
       "3825                 52.0                  99.0  0.99250  2.87       0.49   \n",
       "1740                 10.0                 131.0  0.99050  2.97       0.28   \n",
       "1221                 18.0                  22.0  0.99573  3.21       0.68   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1916                 36.0                 167.0  0.99580  3.26       0.56   \n",
       "606                  28.0                 139.0  0.99788  3.21       0.57   \n",
       "1255                 15.0                  50.0  0.99467  3.58       0.67   \n",
       "959                  16.0                  61.0  0.99572  3.29       0.60   \n",
       "2887                 42.0                 163.0  0.99570  3.20       0.46   \n",
       "\n",
       "      alcohol  \n",
       "225       9.4  \n",
       "3557      8.8  \n",
       "3825     11.4  \n",
       "1740     10.8  \n",
       "1221      9.9  \n",
       "...       ...  \n",
       "1916      9.8  \n",
       "606       9.5  \n",
       "1255     12.5  \n",
       "959       9.3  \n",
       "2887     10.1  \n",
       "\n",
       "[3155 rows x 11 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3639e62d-fc1f-43f7-ac22-e30f585b53f1",
   "metadata": {},
   "source": [
    "## *train_x, train_y, test_x, test_y, val_x, val_y: Are all the sets created*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df7110b2-cbae-434b-822f-e7f88566a546",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f83ab77e-3301-41c1-b7a4-225a3bf2b0bf",
   "metadata": {},
   "source": [
    "## *Normalization*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "166d2ccb-b71c-405b-9b83-69b683e34a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>7.221616</td>\n",
       "      <td>1.325297</td>\n",
       "      <td>3.80000</td>\n",
       "      <td>6.40000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>7.7000</td>\n",
       "      <td>15.60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.338929</td>\n",
       "      <td>0.162476</td>\n",
       "      <td>0.08000</td>\n",
       "      <td>0.23000</td>\n",
       "      <td>0.29000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>1.24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.321569</td>\n",
       "      <td>0.147970</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.31000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>1.66000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>5.155911</td>\n",
       "      <td>4.639632</td>\n",
       "      <td>0.60000</td>\n",
       "      <td>1.80000</td>\n",
       "      <td>2.80000</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>65.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.056976</td>\n",
       "      <td>0.036802</td>\n",
       "      <td>0.01200</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>0.04700</td>\n",
       "      <td>0.0660</td>\n",
       "      <td>0.61100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>30.388590</td>\n",
       "      <td>17.236784</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>131.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>115.062282</td>\n",
       "      <td>56.706617</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>75.00000</td>\n",
       "      <td>117.00000</td>\n",
       "      <td>156.0000</td>\n",
       "      <td>344.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.994633</td>\n",
       "      <td>0.003005</td>\n",
       "      <td>0.98711</td>\n",
       "      <td>0.99232</td>\n",
       "      <td>0.99481</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>1.03898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>3.223201</td>\n",
       "      <td>0.161272</td>\n",
       "      <td>2.72000</td>\n",
       "      <td>3.11000</td>\n",
       "      <td>3.21000</td>\n",
       "      <td>3.3300</td>\n",
       "      <td>4.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>0.534051</td>\n",
       "      <td>0.149149</td>\n",
       "      <td>0.22000</td>\n",
       "      <td>0.43000</td>\n",
       "      <td>0.51000</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>1.95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>3155.0</td>\n",
       "      <td>10.504466</td>\n",
       "      <td>1.154654</td>\n",
       "      <td>8.50000</td>\n",
       "      <td>9.50000</td>\n",
       "      <td>10.30000</td>\n",
       "      <td>11.3000</td>\n",
       "      <td>14.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count        mean        std      min       25%  \\\n",
       "fixed acidity         3155.0    7.221616   1.325297  3.80000   6.40000   \n",
       "volatile acidity      3155.0    0.338929   0.162476  0.08000   0.23000   \n",
       "citric acid           3155.0    0.321569   0.147970  0.00000   0.25000   \n",
       "residual sugar        3155.0    5.155911   4.639632  0.60000   1.80000   \n",
       "chlorides             3155.0    0.056976   0.036802  0.01200   0.03800   \n",
       "free sulfur dioxide   3155.0   30.388590  17.236784  1.00000  17.00000   \n",
       "total sulfur dioxide  3155.0  115.062282  56.706617  6.00000  75.00000   \n",
       "density               3155.0    0.994633   0.003005  0.98711   0.99232   \n",
       "pH                    3155.0    3.223201   0.161272  2.72000   3.11000   \n",
       "sulphates             3155.0    0.534051   0.149149  0.22000   0.43000   \n",
       "alcohol               3155.0   10.504466   1.154654  8.50000   9.50000   \n",
       "\n",
       "                            50%       75%        max  \n",
       "fixed acidity           7.00000    7.7000   15.60000  \n",
       "volatile acidity        0.29000    0.4000    1.24000  \n",
       "citric acid             0.31000    0.4000    1.66000  \n",
       "residual sugar          2.80000    7.6500   65.80000  \n",
       "chlorides               0.04700    0.0660    0.61100  \n",
       "free sulfur dioxide    28.00000   41.0000  131.00000  \n",
       "total sulfur dioxide  117.00000  156.0000  344.00000  \n",
       "density                 0.99481    0.9968    1.03898  \n",
       "pH                      3.21000    3.3300    4.01000  \n",
       "sulphates               0.51000    0.6000    1.95000  \n",
       "alcohol                10.30000   11.3000   14.00000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_x.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "78e2047a-6c33-40c6-a825-b892b105fda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5b38a4a5-31e8-4a34-a268-3da51f6dc306",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b135e176-44fe-4edd-8eed-ee1c5d63f149",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>pH</th>\n",
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       "      <th>225</th>\n",
       "      <td>0.210054</td>\n",
       "      <td>1.914565</td>\n",
       "      <td>-0.956741</td>\n",
       "      <td>0.397464</td>\n",
       "      <td>0.842996</td>\n",
       "      <td>-0.196591</td>\n",
       "      <td>-0.371425</td>\n",
       "      <td>1.503235</td>\n",
       "      <td>0.972264</td>\n",
       "      <td>1.581966</td>\n",
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       "      <th>3557</th>\n",
       "      <td>-0.695404</td>\n",
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       "      <td>-0.213347</td>\n",
       "      <td>1.518243</td>\n",
       "      <td>-0.352585</td>\n",
       "      <td>1.659904</td>\n",
       "      <td>1.427306</td>\n",
       "      <td>1.060647</td>\n",
       "      <td>-0.515907</td>\n",
       "      <td>-0.898768</td>\n",
       "      <td>-1.476170</td>\n",
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       "      <th>3825</th>\n",
       "      <td>1.190966</td>\n",
       "      <td>-0.424239</td>\n",
       "      <td>-0.483672</td>\n",
       "      <td>-0.033604</td>\n",
       "      <td>-0.896032</td>\n",
       "      <td>1.253796</td>\n",
       "      <td>-0.283252</td>\n",
       "      <td>-0.709707</td>\n",
       "      <td>-2.190100</td>\n",
       "      <td>-0.295346</td>\n",
       "      <td>0.775586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740</th>\n",
       "      <td>-0.619949</td>\n",
       "      <td>0.683616</td>\n",
       "      <td>-1.700136</td>\n",
       "      <td>-0.874188</td>\n",
       "      <td>-0.732998</td>\n",
       "      <td>-1.182853</td>\n",
       "      <td>0.281056</td>\n",
       "      <td>-1.375253</td>\n",
       "      <td>-1.570029</td>\n",
       "      <td>-1.703331</td>\n",
       "      <td>0.255950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>-0.016311</td>\n",
       "      <td>1.175995</td>\n",
       "      <td>-1.294648</td>\n",
       "      <td>-0.680207</td>\n",
       "      <td>0.027827</td>\n",
       "      <td>-0.718730</td>\n",
       "      <td>-1.641119</td>\n",
       "      <td>0.365151</td>\n",
       "      <td>-0.081857</td>\n",
       "      <td>0.978544</td>\n",
       "      <td>-0.523504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "225        0.210054          1.914565    -0.956741        0.397464   0.842996   \n",
       "3557      -0.695404         -0.424239    -0.213347        1.518243  -0.352585   \n",
       "3825       1.190966         -0.424239    -0.483672       -0.033604  -0.896032   \n",
       "1740      -0.619949          0.683616    -1.700136       -0.874188  -0.732998   \n",
       "1221      -0.016311          1.175995    -1.294648       -0.680207   0.027827   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
       "225             -0.196591             -0.371425  1.503235  0.972264   \n",
       "3557             1.659904              1.427306  1.060647 -0.515907   \n",
       "3825             1.253796             -0.283252 -0.709707 -2.190100   \n",
       "1740            -1.182853              0.281056 -1.375253 -1.570029   \n",
       "1221            -0.718730             -1.641119  0.365151 -0.081857   \n",
       "\n",
       "      sulphates   alcohol  \n",
       "225    1.581966 -0.956534  \n",
       "3557  -0.898768 -1.476170  \n",
       "3825  -0.295346  0.775586  \n",
       "1740  -1.703331  0.255950  \n",
       "1221   0.978544 -0.523504  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize\n",
    "\n",
    "norm_train_x = (train_x - train_x.mean())/train_x.std()\n",
    "norm_train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2a04cb4b-fa40-411b-a665-91de81d779bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4149</th>\n",
       "      <td>0.134599</td>\n",
       "      <td>-0.731976</td>\n",
       "      <td>-0.348510</td>\n",
       "      <td>-0.766421</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.847688</td>\n",
       "      <td>-0.300887</td>\n",
       "      <td>-0.546648</td>\n",
       "      <td>0.414200</td>\n",
       "      <td>-0.630581</td>\n",
       "      <td>-0.523504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4340</th>\n",
       "      <td>-0.846313</td>\n",
       "      <td>-0.547333</td>\n",
       "      <td>-0.280928</td>\n",
       "      <td>1.044067</td>\n",
       "      <td>-0.053690</td>\n",
       "      <td>1.485858</td>\n",
       "      <td>0.281056</td>\n",
       "      <td>-0.210547</td>\n",
       "      <td>-0.019850</td>\n",
       "      <td>-1.234002</td>\n",
       "      <td>0.342556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>-0.619949</td>\n",
       "      <td>-0.362691</td>\n",
       "      <td>0.597629</td>\n",
       "      <td>0.354358</td>\n",
       "      <td>-0.325413</td>\n",
       "      <td>1.775935</td>\n",
       "      <td>1.779999</td>\n",
       "      <td>0.188781</td>\n",
       "      <td>-0.825943</td>\n",
       "      <td>-0.496487</td>\n",
       "      <td>-0.956534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>-0.846313</td>\n",
       "      <td>-0.362691</td>\n",
       "      <td>0.192141</td>\n",
       "      <td>1.647564</td>\n",
       "      <td>-0.243896</td>\n",
       "      <td>1.891966</td>\n",
       "      <td>2.009249</td>\n",
       "      <td>0.954159</td>\n",
       "      <td>-0.887950</td>\n",
       "      <td>-0.898768</td>\n",
       "      <td>-1.389564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3452</th>\n",
       "      <td>-0.242675</td>\n",
       "      <td>0.375878</td>\n",
       "      <td>-0.145766</td>\n",
       "      <td>1.173388</td>\n",
       "      <td>-0.651481</td>\n",
       "      <td>-0.370637</td>\n",
       "      <td>-0.494868</td>\n",
       "      <td>-0.659791</td>\n",
       "      <td>-0.453900</td>\n",
       "      <td>-0.563534</td>\n",
       "      <td>1.988071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "4149       0.134599         -0.731976    -0.348510       -0.766421   0.000655   \n",
       "4340      -0.846313         -0.547333    -0.280928        1.044067  -0.053690   \n",
       "2045      -0.619949         -0.362691     0.597629        0.354358  -0.325413   \n",
       "1966      -0.846313         -0.362691     0.192141        1.647564  -0.243896   \n",
       "3452      -0.242675          0.375878    -0.145766        1.173388  -0.651481   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
       "4149             0.847688             -0.300887 -0.546648  0.414200   \n",
       "4340             1.485858              0.281056 -0.210547 -0.019850   \n",
       "2045             1.775935              1.779999  0.188781 -0.825943   \n",
       "1966             1.891966              2.009249  0.954159 -0.887950   \n",
       "3452            -0.370637             -0.494868 -0.659791 -0.453900   \n",
       "\n",
       "      sulphates   alcohol  \n",
       "4149  -0.630581 -0.523504  \n",
       "4340  -1.234002  0.342556  \n",
       "2045  -0.496487 -0.956534  \n",
       "1966  -0.898768 -1.389564  \n",
       "3452  -0.563534  1.988071  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_val_x = (val_x - train_x.mean())/train_x.std()\n",
    "norm_val_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5b47886d-2a4b-4ab0-8bb5-23c0e46a69a0",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>-0.242675</td>\n",
       "      <td>-0.670428</td>\n",
       "      <td>-0.551254</td>\n",
       "      <td>1.949312</td>\n",
       "      <td>-0.108035</td>\n",
       "      <td>-0.660714</td>\n",
       "      <td>-0.371425</td>\n",
       "      <td>1.187100</td>\n",
       "      <td>-0.329886</td>\n",
       "      <td>-0.228299</td>\n",
       "      <td>-0.783322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>-0.469039</td>\n",
       "      <td>-0.793523</td>\n",
       "      <td>-0.078184</td>\n",
       "      <td>1.345815</td>\n",
       "      <td>-0.488447</td>\n",
       "      <td>0.905703</td>\n",
       "      <td>0.880633</td>\n",
       "      <td>1.103907</td>\n",
       "      <td>1.158285</td>\n",
       "      <td>-0.630581</td>\n",
       "      <td>-0.610110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1.266420</td>\n",
       "      <td>0.560521</td>\n",
       "      <td>0.867954</td>\n",
       "      <td>-0.701761</td>\n",
       "      <td>-0.135207</td>\n",
       "      <td>-1.414915</td>\n",
       "      <td>-1.746926</td>\n",
       "      <td>0.055672</td>\n",
       "      <td>0.786243</td>\n",
       "      <td>1.112638</td>\n",
       "      <td>1.728253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>-0.619949</td>\n",
       "      <td>2.099207</td>\n",
       "      <td>-0.416091</td>\n",
       "      <td>-0.378459</td>\n",
       "      <td>0.326722</td>\n",
       "      <td>-0.312622</td>\n",
       "      <td>0.545575</td>\n",
       "      <td>-0.386917</td>\n",
       "      <td>-0.267879</td>\n",
       "      <td>-0.898768</td>\n",
       "      <td>-1.043140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>-0.469039</td>\n",
       "      <td>-0.670428</td>\n",
       "      <td>-0.416091</td>\n",
       "      <td>-0.831081</td>\n",
       "      <td>-0.325413</td>\n",
       "      <td>-0.834761</td>\n",
       "      <td>0.228152</td>\n",
       "      <td>-0.410211</td>\n",
       "      <td>0.848250</td>\n",
       "      <td>0.442169</td>\n",
       "      <td>-0.436898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "2372      -0.242675         -0.670428    -0.551254        1.949312  -0.108035   \n",
       "1900      -0.469039         -0.793523    -0.078184        1.345815  -0.488447   \n",
       "275        1.266420          0.560521     0.867954       -0.701761  -0.135207   \n",
       "3276      -0.619949          2.099207    -0.416091       -0.378459   0.326722   \n",
       "2360      -0.469039         -0.670428    -0.416091       -0.831081  -0.325413   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
       "2372            -0.660714             -0.371425  1.187100 -0.329886   \n",
       "1900             0.905703              0.880633  1.103907  1.158285   \n",
       "275             -1.414915             -1.746926  0.055672  0.786243   \n",
       "3276            -0.312622              0.545575 -0.386917 -0.267879   \n",
       "2360            -0.834761              0.228152 -0.410211  0.848250   \n",
       "\n",
       "      sulphates   alcohol  \n",
       "2372  -0.228299 -0.783322  \n",
       "1900  -0.630581 -0.610110  \n",
       "275    1.112638  1.728253  \n",
       "3276  -0.898768 -1.043140  \n",
       "2360   0.442169 -0.436898  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_test_x = (test_x - train_x.mean())/train_x.std()\n",
    "norm_test_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41428c7d-6c64-4b12-bb47-d1f8f673b204",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4b82a74a-5012-4f3b-804f-5032051f392f",
   "metadata": {},
   "source": [
    "## *Model Development*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "997ca51b-a4f3-4fc8-b23e-a1df268e8902",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize base model\n",
    "def base_model(inputs):\n",
    "    x = Dense(128, activation = 'relu', name = \"First_Dense\")(inputs)\n",
    "    x = Dense(128, activation = 'relu', name='Second_Dense')(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4313aae6-9385-4446-bf30-432e198464a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def final_model(inputs):\n",
    "    x = base_model(inputs)\n",
    "    # Output layers\n",
    "    \n",
    "    wine_quality = Dense(1, name='Wine_Quality')(x)\n",
    "    wine_type = Dense(1, activation='sigmoid', name=\"Wine_Type\")(x)\n",
    "    model = Model(inputs = inputs, outputs =[wine_quality, wine_type])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9f71ba-37ff-49bb-8531-932c9a39d774",
   "metadata": {},
   "source": [
    "## *Compile the model*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8eb3927e-a6ae-4036-a088-32410b0f641b",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(11,))\n",
    "model = final_model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "66bbb7fb-7d3e-4017-afdf-ebef1e00f983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, show_shapes= True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9397fbb8-425f-4251-ae5f-db0216443b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = keras.optimizers.RMSprop(learning_rate= 0.0001), \n",
    "              loss = {'Wine_Type':'binary_crossentropy',\n",
    "                      'Wine_Quality': 'mean_squared_error'},\n",
    "              metrics = {'Wine_Type': 'accuracy',\n",
    "                         'Wine_Quality': tf.keras.metrics.RootMeanSquaredError()},\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "64790618-7db7-42b8-ace3-72c0f8ada857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 11)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "First_Dense (Dense)             (None, 128)          1536        input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Second_Dense (Dense)            (None, 128)          16512       First_Dense[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Wine_Quality (Dense)            (None, 1)            129         Second_Dense[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "Wine_Type (Dense)               (None, 1)            129         Second_Dense[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 18,306\n",
      "Trainable params: 18,306\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8b59dc-f246-4285-a602-28cb48d45c3b",
   "metadata": {},
   "source": [
    "## *Train the model*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a9d7ea7c-9997-4926-8b56-766ca3aea649",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_red = train_y['is_red']\n",
    "is_red = np.array(is_red)\n",
    "quality = train_y['quality']\n",
    "quality = np.array(quality)\n",
    "train_y = (quality, is_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cca61510-6dc6-4004-99de-83c8d98b6ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5, 6, 5, ..., 6, 5, 5], dtype=int64),\n",
       " array([1, 0, 0, ..., 1, 1, 0], dtype=int64))"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "00126b0e-13c3-4437-9344-a420979eee86",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_red = val_y['is_red']\n",
    "is_red = np.array(is_red)\n",
    "quality = val_y['quality']\n",
    "quality = np.array(quality)\n",
    "val_y = (quality, is_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "0bb52ee0-bae5-4698-b636-c5d78b37d931",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_red = test_y['is_red']\n",
    "is_red = np.array(is_red)\n",
    "quality = test_y['quality']\n",
    "quality = np.array(quality)\n",
    "test_y = (quality, is_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a97cbd87-4674-4250-9b17-1595d5433511",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/180\n",
      "99/99 [==============================] - 2s 16ms/step - loss: 23.2641 - Wine_Quality_loss: 22.5111 - Wine_Type_loss: 0.7531 - Wine_Quality_root_mean_squared_error: 4.7446 - Wine_Type_accuracy: 0.2802 - val_loss: 14.9148 - val_Wine_Quality_loss: 14.2052 - val_Wine_Type_loss: 0.7096 - val_Wine_Quality_root_mean_squared_error: 3.7690 - val_Wine_Type_accuracy: 0.4816\n",
      "Epoch 2/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 8.9329 - Wine_Quality_loss: 8.2718 - Wine_Type_loss: 0.6611 - Wine_Quality_root_mean_squared_error: 2.8761 - Wine_Type_accuracy: 0.6818 - val_loss: 4.5573 - val_Wine_Quality_loss: 3.9463 - val_Wine_Type_loss: 0.6109 - val_Wine_Quality_root_mean_squared_error: 1.9865 - val_Wine_Type_accuracy: 0.8441\n",
      "Epoch 3/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 3.2378 - Wine_Quality_loss: 2.6950 - Wine_Type_loss: 0.5427 - Wine_Quality_root_mean_squared_error: 1.6417 - Wine_Type_accuracy: 0.8304 - val_loss: 2.6698 - val_Wine_Quality_loss: 2.1857 - val_Wine_Type_loss: 0.4841 - val_Wine_Quality_root_mean_squared_error: 1.4784 - val_Wine_Type_accuracy: 0.8213\n",
      "Epoch 4/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 2.4378 - Wine_Quality_loss: 2.0194 - Wine_Type_loss: 0.4184 - Wine_Quality_root_mean_squared_error: 1.4211 - Wine_Type_accuracy: 0.8586 - val_loss: 2.2696 - val_Wine_Quality_loss: 1.8942 - val_Wine_Type_loss: 0.3754 - val_Wine_Quality_root_mean_squared_error: 1.3763 - val_Wine_Type_accuracy: 0.8821\n",
      "Epoch 5/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 2.1098 - Wine_Quality_loss: 1.7857 - Wine_Type_loss: 0.3240 - Wine_Quality_root_mean_squared_error: 1.3363 - Wine_Type_accuracy: 0.9192 - val_loss: 1.9948 - val_Wine_Quality_loss: 1.7047 - val_Wine_Type_loss: 0.2901 - val_Wine_Quality_root_mean_squared_error: 1.3056 - val_Wine_Type_accuracy: 0.9341\n",
      "Epoch 6/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 1.8642 - Wine_Quality_loss: 1.6169 - Wine_Type_loss: 0.2473 - Wine_Quality_root_mean_squared_error: 1.2716 - Wine_Type_accuracy: 0.9601 - val_loss: 1.7862 - val_Wine_Quality_loss: 1.5633 - val_Wine_Type_loss: 0.2229 - val_Wine_Quality_root_mean_squared_error: 1.2503 - val_Wine_Type_accuracy: 0.9683\n",
      "Epoch 7/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 1.6736 - Wine_Quality_loss: 1.4820 - Wine_Type_loss: 0.1916 - Wine_Quality_root_mean_squared_error: 1.2174 - Wine_Type_accuracy: 0.9721 - val_loss: 1.6113 - val_Wine_Quality_loss: 1.4377 - val_Wine_Type_loss: 0.1735 - val_Wine_Quality_root_mean_squared_error: 1.1991 - val_Wine_Type_accuracy: 0.9772\n",
      "Epoch 8/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 1.5230 - Wine_Quality_loss: 1.3725 - Wine_Type_loss: 0.1505 - Wine_Quality_root_mean_squared_error: 1.1716 - Wine_Type_accuracy: 0.9807 - val_loss: 1.4835 - val_Wine_Quality_loss: 1.3452 - val_Wine_Type_loss: 0.1384 - val_Wine_Quality_root_mean_squared_error: 1.1598 - val_Wine_Type_accuracy: 0.9848\n",
      "Epoch 9/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 1.4035 - Wine_Quality_loss: 1.2819 - Wine_Type_loss: 0.1216 - Wine_Quality_root_mean_squared_error: 1.1322 - Wine_Type_accuracy: 0.9861 - val_loss: 1.3590 - val_Wine_Quality_loss: 1.2453 - val_Wine_Type_loss: 0.1137 - val_Wine_Quality_root_mean_squared_error: 1.1159 - val_Wine_Type_accuracy: 0.9886\n",
      "Epoch 10/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 1.3020 - Wine_Quality_loss: 1.2006 - Wine_Type_loss: 0.1013 - Wine_Quality_root_mean_squared_error: 1.0957 - Wine_Type_accuracy: 0.9876 - val_loss: 1.2573 - val_Wine_Quality_loss: 1.1623 - val_Wine_Type_loss: 0.0950 - val_Wine_Quality_root_mean_squared_error: 1.0781 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 11/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 1.2147 - Wine_Quality_loss: 1.1282 - Wine_Type_loss: 0.0865 - Wine_Quality_root_mean_squared_error: 1.0621 - Wine_Type_accuracy: 0.9883 - val_loss: 1.1749 - val_Wine_Quality_loss: 1.0928 - val_Wine_Type_loss: 0.0821 - val_Wine_Quality_root_mean_squared_error: 1.0454 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 12/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 1.1325 - Wine_Quality_loss: 1.0569 - Wine_Type_loss: 0.0756 - Wine_Quality_root_mean_squared_error: 1.0280 - Wine_Type_accuracy: 0.9886 - val_loss: 1.0887 - val_Wine_Quality_loss: 1.0165 - val_Wine_Type_loss: 0.0722 - val_Wine_Quality_root_mean_squared_error: 1.0082 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 13/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 1.0598 - Wine_Quality_loss: 0.9926 - Wine_Type_loss: 0.0672 - Wine_Quality_root_mean_squared_error: 0.9963 - Wine_Type_accuracy: 0.9889 - val_loss: 1.0155 - val_Wine_Quality_loss: 0.9506 - val_Wine_Type_loss: 0.0648 - val_Wine_Quality_root_mean_squared_error: 0.9750 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 14/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.9854 - Wine_Quality_loss: 0.9244 - Wine_Type_loss: 0.0610 - Wine_Quality_root_mean_squared_error: 0.9615 - Wine_Type_accuracy: 0.9895 - val_loss: 0.9370 - val_Wine_Quality_loss: 0.8779 - val_Wine_Type_loss: 0.0590 - val_Wine_Quality_root_mean_squared_error: 0.9370 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 15/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.9241 - Wine_Quality_loss: 0.8679 - Wine_Type_loss: 0.0562 - Wine_Quality_root_mean_squared_error: 0.9316 - Wine_Type_accuracy: 0.9905 - val_loss: 0.8747 - val_Wine_Quality_loss: 0.8198 - val_Wine_Type_loss: 0.0549 - val_Wine_Quality_root_mean_squared_error: 0.9054 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 16/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.8731 - Wine_Quality_loss: 0.8206 - Wine_Type_loss: 0.0525 - Wine_Quality_root_mean_squared_error: 0.9059 - Wine_Type_accuracy: 0.9902 - val_loss: 0.8267 - val_Wine_Quality_loss: 0.7751 - val_Wine_Type_loss: 0.0516 - val_Wine_Quality_root_mean_squared_error: 0.8804 - val_Wine_Type_accuracy: 0.9899\n",
      "Epoch 17/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.8217 - Wine_Quality_loss: 0.7722 - Wine_Type_loss: 0.0495 - Wine_Quality_root_mean_squared_error: 0.8788 - Wine_Type_accuracy: 0.9905 - val_loss: 0.7829 - val_Wine_Quality_loss: 0.7342 - val_Wine_Type_loss: 0.0487 - val_Wine_Quality_root_mean_squared_error: 0.8568 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 18/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.7787 - Wine_Quality_loss: 0.7318 - Wine_Type_loss: 0.0469 - Wine_Quality_root_mean_squared_error: 0.8554 - Wine_Type_accuracy: 0.9905 - val_loss: 0.7288 - val_Wine_Quality_loss: 0.6824 - val_Wine_Type_loss: 0.0464 - val_Wine_Quality_root_mean_squared_error: 0.8261 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 19/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.7352 - Wine_Quality_loss: 0.6904 - Wine_Type_loss: 0.0448 - Wine_Quality_root_mean_squared_error: 0.8309 - Wine_Type_accuracy: 0.9905 - val_loss: 0.6951 - val_Wine_Quality_loss: 0.6506 - val_Wine_Type_loss: 0.0445 - val_Wine_Quality_root_mean_squared_error: 0.8066 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 20/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.6949 - Wine_Quality_loss: 0.6518 - Wine_Type_loss: 0.0431 - Wine_Quality_root_mean_squared_error: 0.8073 - Wine_Type_accuracy: 0.9905 - val_loss: 0.6557 - val_Wine_Quality_loss: 0.6127 - val_Wine_Type_loss: 0.0431 - val_Wine_Quality_root_mean_squared_error: 0.7827 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 21/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.6590 - Wine_Quality_loss: 0.6173 - Wine_Type_loss: 0.0416 - Wine_Quality_root_mean_squared_error: 0.7857 - Wine_Type_accuracy: 0.9908 - val_loss: 0.6193 - val_Wine_Quality_loss: 0.5777 - val_Wine_Type_loss: 0.0416 - val_Wine_Quality_root_mean_squared_error: 0.7601 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 22/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.6247 - Wine_Quality_loss: 0.5843 - Wine_Type_loss: 0.0404 - Wine_Quality_root_mean_squared_error: 0.7644 - Wine_Type_accuracy: 0.9908 - val_loss: 0.5896 - val_Wine_Quality_loss: 0.5490 - val_Wine_Type_loss: 0.0405 - val_Wine_Quality_root_mean_squared_error: 0.7410 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 23/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.6003 - Wine_Quality_loss: 0.5610 - Wine_Type_loss: 0.0393 - Wine_Quality_root_mean_squared_error: 0.7490 - Wine_Type_accuracy: 0.9911 - val_loss: 0.5614 - val_Wine_Quality_loss: 0.5216 - val_Wine_Type_loss: 0.0398 - val_Wine_Quality_root_mean_squared_error: 0.7222 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 24/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.5732 - Wine_Quality_loss: 0.5347 - Wine_Type_loss: 0.0385 - Wine_Quality_root_mean_squared_error: 0.7312 - Wine_Type_accuracy: 0.9911 - val_loss: 0.5404 - val_Wine_Quality_loss: 0.5015 - val_Wine_Type_loss: 0.0389 - val_Wine_Quality_root_mean_squared_error: 0.7081 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 25/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.5495 - Wine_Quality_loss: 0.5118 - Wine_Type_loss: 0.0377 - Wine_Quality_root_mean_squared_error: 0.7154 - Wine_Type_accuracy: 0.9914 - val_loss: 0.5202 - val_Wine_Quality_loss: 0.4819 - val_Wine_Type_loss: 0.0383 - val_Wine_Quality_root_mean_squared_error: 0.6942 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 26/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.5263 - Wine_Quality_loss: 0.4893 - Wine_Type_loss: 0.0369 - Wine_Quality_root_mean_squared_error: 0.6995 - Wine_Type_accuracy: 0.9911 - val_loss: 0.4990 - val_Wine_Quality_loss: 0.4614 - val_Wine_Type_loss: 0.0376 - val_Wine_Quality_root_mean_squared_error: 0.6793 - val_Wine_Type_accuracy: 0.9924\n",
      "Epoch 27/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.5063 - Wine_Quality_loss: 0.4701 - Wine_Type_loss: 0.0362 - Wine_Quality_root_mean_squared_error: 0.6856 - Wine_Type_accuracy: 0.9911 - val_loss: 0.4834 - val_Wine_Quality_loss: 0.4464 - val_Wine_Type_loss: 0.0370 - val_Wine_Quality_root_mean_squared_error: 0.6681 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 28/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.4885 - Wine_Quality_loss: 0.4528 - Wine_Type_loss: 0.0357 - Wine_Quality_root_mean_squared_error: 0.6729 - Wine_Type_accuracy: 0.9921 - val_loss: 0.4776 - val_Wine_Quality_loss: 0.4410 - val_Wine_Type_loss: 0.0365 - val_Wine_Quality_root_mean_squared_error: 0.6641 - val_Wine_Type_accuracy: 0.9911\n",
      "Epoch 29/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.4743 - Wine_Quality_loss: 0.4392 - Wine_Type_loss: 0.0351 - Wine_Quality_root_mean_squared_error: 0.6627 - Wine_Type_accuracy: 0.9921 - val_loss: 0.4593 - val_Wine_Quality_loss: 0.4233 - val_Wine_Type_loss: 0.0360 - val_Wine_Quality_root_mean_squared_error: 0.6506 - val_Wine_Type_accuracy: 0.9924\n",
      "Epoch 30/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.4601 - Wine_Quality_loss: 0.4255 - Wine_Type_loss: 0.0346 - Wine_Quality_root_mean_squared_error: 0.6523 - Wine_Type_accuracy: 0.9927 - val_loss: 0.4411 - val_Wine_Quality_loss: 0.4055 - val_Wine_Type_loss: 0.0356 - val_Wine_Quality_root_mean_squared_error: 0.6368 - val_Wine_Type_accuracy: 0.9924\n",
      "Epoch 31/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.4467 - Wine_Quality_loss: 0.4126 - Wine_Type_loss: 0.0342 - Wine_Quality_root_mean_squared_error: 0.6423 - Wine_Type_accuracy: 0.9927 - val_loss: 0.4300 - val_Wine_Quality_loss: 0.3950 - val_Wine_Type_loss: 0.0351 - val_Wine_Quality_root_mean_squared_error: 0.6285 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 32/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.4359 - Wine_Quality_loss: 0.4022 - Wine_Type_loss: 0.0337 - Wine_Quality_root_mean_squared_error: 0.6342 - Wine_Type_accuracy: 0.9933 - val_loss: 0.4214 - val_Wine_Quality_loss: 0.3866 - val_Wine_Type_loss: 0.0348 - val_Wine_Quality_root_mean_squared_error: 0.6218 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 33/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.4253 - Wine_Quality_loss: 0.3921 - Wine_Type_loss: 0.0332 - Wine_Quality_root_mean_squared_error: 0.6262 - Wine_Type_accuracy: 0.9930 - val_loss: 0.4138 - val_Wine_Quality_loss: 0.3792 - val_Wine_Type_loss: 0.0346 - val_Wine_Quality_root_mean_squared_error: 0.6158 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 34/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.4166 - Wine_Quality_loss: 0.3837 - Wine_Type_loss: 0.0329 - Wine_Quality_root_mean_squared_error: 0.6194 - Wine_Type_accuracy: 0.9933 - val_loss: 0.4079 - val_Wine_Quality_loss: 0.3737 - val_Wine_Type_loss: 0.0342 - val_Wine_Quality_root_mean_squared_error: 0.6113 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 35/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.4078 - Wine_Quality_loss: 0.3753 - Wine_Type_loss: 0.0325 - Wine_Quality_root_mean_squared_error: 0.6126 - Wine_Type_accuracy: 0.9930 - val_loss: 0.3964 - val_Wine_Quality_loss: 0.3624 - val_Wine_Type_loss: 0.0340 - val_Wine_Quality_root_mean_squared_error: 0.6020 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 36/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.4015 - Wine_Quality_loss: 0.3693 - Wine_Type_loss: 0.0321 - Wine_Quality_root_mean_squared_error: 0.6077 - Wine_Type_accuracy: 0.9933 - val_loss: 0.3928 - val_Wine_Quality_loss: 0.3591 - val_Wine_Type_loss: 0.0337 - val_Wine_Quality_root_mean_squared_error: 0.5992 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 37/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3943 - Wine_Quality_loss: 0.3624 - Wine_Type_loss: 0.0318 - Wine_Quality_root_mean_squared_error: 0.6020 - Wine_Type_accuracy: 0.9930 - val_loss: 0.3894 - val_Wine_Quality_loss: 0.3560 - val_Wine_Type_loss: 0.0334 - val_Wine_Quality_root_mean_squared_error: 0.5967 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 38/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3877 - Wine_Quality_loss: 0.3562 - Wine_Type_loss: 0.0315 - Wine_Quality_root_mean_squared_error: 0.5968 - Wine_Type_accuracy: 0.9940 - val_loss: 0.3823 - val_Wine_Quality_loss: 0.3491 - val_Wine_Type_loss: 0.0332 - val_Wine_Quality_root_mean_squared_error: 0.5908 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 39/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3822 - Wine_Quality_loss: 0.3510 - Wine_Type_loss: 0.0312 - Wine_Quality_root_mean_squared_error: 0.5925 - Wine_Type_accuracy: 0.9937 - val_loss: 0.3789 - val_Wine_Quality_loss: 0.3458 - val_Wine_Type_loss: 0.0331 - val_Wine_Quality_root_mean_squared_error: 0.5880 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 40/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3794 - Wine_Quality_loss: 0.3485 - Wine_Type_loss: 0.0310 - Wine_Quality_root_mean_squared_error: 0.5903 - Wine_Type_accuracy: 0.9937 - val_loss: 0.3778 - val_Wine_Quality_loss: 0.3449 - val_Wine_Type_loss: 0.0329 - val_Wine_Quality_root_mean_squared_error: 0.5873 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 41/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3744 - Wine_Quality_loss: 0.3437 - Wine_Type_loss: 0.0307 - Wine_Quality_root_mean_squared_error: 0.5862 - Wine_Type_accuracy: 0.9933 - val_loss: 0.3792 - val_Wine_Quality_loss: 0.3465 - val_Wine_Type_loss: 0.0328 - val_Wine_Quality_root_mean_squared_error: 0.5886 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 42/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3703 - Wine_Quality_loss: 0.3399 - Wine_Type_loss: 0.0304 - Wine_Quality_root_mean_squared_error: 0.5830 - Wine_Type_accuracy: 0.9937 - val_loss: 0.3678 - val_Wine_Quality_loss: 0.3352 - val_Wine_Type_loss: 0.0326 - val_Wine_Quality_root_mean_squared_error: 0.5789 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 43/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3675 - Wine_Quality_loss: 0.3373 - Wine_Type_loss: 0.0302 - Wine_Quality_root_mean_squared_error: 0.5808 - Wine_Type_accuracy: 0.9937 - val_loss: 0.3650 - val_Wine_Quality_loss: 0.3326 - val_Wine_Type_loss: 0.0323 - val_Wine_Quality_root_mean_squared_error: 0.5767 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 44/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3644 - Wine_Quality_loss: 0.3344 - Wine_Type_loss: 0.0300 - Wine_Quality_root_mean_squared_error: 0.5783 - Wine_Type_accuracy: 0.9937 - val_loss: 0.3664 - val_Wine_Quality_loss: 0.3343 - val_Wine_Type_loss: 0.0321 - val_Wine_Quality_root_mean_squared_error: 0.5782 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 45/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3600 - Wine_Quality_loss: 0.3303 - Wine_Type_loss: 0.0297 - Wine_Quality_root_mean_squared_error: 0.5747 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3660 - val_Wine_Quality_loss: 0.3339 - val_Wine_Type_loss: 0.0321 - val_Wine_Quality_root_mean_squared_error: 0.5778 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 46/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3575 - Wine_Quality_loss: 0.3281 - Wine_Type_loss: 0.0295 - Wine_Quality_root_mean_squared_error: 0.5728 - Wine_Type_accuracy: 0.9943 - val_loss: 0.3701 - val_Wine_Quality_loss: 0.3381 - val_Wine_Type_loss: 0.0320 - val_Wine_Quality_root_mean_squared_error: 0.5815 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 47/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3550 - Wine_Quality_loss: 0.3257 - Wine_Type_loss: 0.0293 - Wine_Quality_root_mean_squared_error: 0.5707 - Wine_Type_accuracy: 0.9940 - val_loss: 0.3640 - val_Wine_Quality_loss: 0.3323 - val_Wine_Type_loss: 0.0316 - val_Wine_Quality_root_mean_squared_error: 0.5765 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 48/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3527 - Wine_Quality_loss: 0.3237 - Wine_Type_loss: 0.0290 - Wine_Quality_root_mean_squared_error: 0.5690 - Wine_Type_accuracy: 0.9940 - val_loss: 0.3621 - val_Wine_Quality_loss: 0.3306 - val_Wine_Type_loss: 0.0314 - val_Wine_Quality_root_mean_squared_error: 0.5750 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 49/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3493 - Wine_Quality_loss: 0.3204 - Wine_Type_loss: 0.0288 - Wine_Quality_root_mean_squared_error: 0.5661 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3577 - val_Wine_Quality_loss: 0.3264 - val_Wine_Type_loss: 0.0313 - val_Wine_Quality_root_mean_squared_error: 0.5713 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 50/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3471 - Wine_Quality_loss: 0.3184 - Wine_Type_loss: 0.0287 - Wine_Quality_root_mean_squared_error: 0.5643 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3753 - val_Wine_Quality_loss: 0.3442 - val_Wine_Type_loss: 0.0312 - val_Wine_Quality_root_mean_squared_error: 0.5867 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 51/180\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 0.3462 - Wine_Quality_loss: 0.3178 - Wine_Type_loss: 0.0284 - Wine_Quality_root_mean_squared_error: 0.5637 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3582 - val_Wine_Quality_loss: 0.3272 - val_Wine_Type_loss: 0.0310 - val_Wine_Quality_root_mean_squared_error: 0.5720 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 52/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3440 - Wine_Quality_loss: 0.3158 - Wine_Type_loss: 0.0282 - Wine_Quality_root_mean_squared_error: 0.5619 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3530 - val_Wine_Quality_loss: 0.3222 - val_Wine_Type_loss: 0.0308 - val_Wine_Quality_root_mean_squared_error: 0.5676 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 53/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3424 - Wine_Quality_loss: 0.3143 - Wine_Type_loss: 0.0280 - Wine_Quality_root_mean_squared_error: 0.5607 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3561 - val_Wine_Quality_loss: 0.3253 - val_Wine_Type_loss: 0.0308 - val_Wine_Quality_root_mean_squared_error: 0.5704 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 54/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3394 - Wine_Quality_loss: 0.3115 - Wine_Type_loss: 0.0279 - Wine_Quality_root_mean_squared_error: 0.5581 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3521 - val_Wine_Quality_loss: 0.3214 - val_Wine_Type_loss: 0.0307 - val_Wine_Quality_root_mean_squared_error: 0.5669 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 55/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3385 - Wine_Quality_loss: 0.3109 - Wine_Type_loss: 0.0277 - Wine_Quality_root_mean_squared_error: 0.5576 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3549 - val_Wine_Quality_loss: 0.3243 - val_Wine_Type_loss: 0.0306 - val_Wine_Quality_root_mean_squared_error: 0.5694 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 56/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3380 - Wine_Quality_loss: 0.3105 - Wine_Type_loss: 0.0275 - Wine_Quality_root_mean_squared_error: 0.5572 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3474 - val_Wine_Quality_loss: 0.3168 - val_Wine_Type_loss: 0.0306 - val_Wine_Quality_root_mean_squared_error: 0.5628 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 57/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3364 - Wine_Quality_loss: 0.3091 - Wine_Type_loss: 0.0273 - Wine_Quality_root_mean_squared_error: 0.5559 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3508 - val_Wine_Quality_loss: 0.3204 - val_Wine_Type_loss: 0.0304 - val_Wine_Quality_root_mean_squared_error: 0.5661 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 58/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3336 - Wine_Quality_loss: 0.3064 - Wine_Type_loss: 0.0272 - Wine_Quality_root_mean_squared_error: 0.5536 - Wine_Type_accuracy: 0.9946 - val_loss: 0.3503 - val_Wine_Quality_loss: 0.3200 - val_Wine_Type_loss: 0.0303 - val_Wine_Quality_root_mean_squared_error: 0.5657 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 59/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3331 - Wine_Quality_loss: 0.3061 - Wine_Type_loss: 0.0269 - Wine_Quality_root_mean_squared_error: 0.5533 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3483 - val_Wine_Quality_loss: 0.3180 - val_Wine_Type_loss: 0.0303 - val_Wine_Quality_root_mean_squared_error: 0.5639 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 60/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3315 - Wine_Quality_loss: 0.3047 - Wine_Type_loss: 0.0268 - Wine_Quality_root_mean_squared_error: 0.5520 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3456 - val_Wine_Quality_loss: 0.3154 - val_Wine_Type_loss: 0.0302 - val_Wine_Quality_root_mean_squared_error: 0.5616 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 61/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3317 - Wine_Quality_loss: 0.3050 - Wine_Type_loss: 0.0267 - Wine_Quality_root_mean_squared_error: 0.5523 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3445 - val_Wine_Quality_loss: 0.3145 - val_Wine_Type_loss: 0.0300 - val_Wine_Quality_root_mean_squared_error: 0.5608 - val_Wine_Type_accuracy: 0.9937\n",
      "Epoch 62/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3294 - Wine_Quality_loss: 0.3029 - Wine_Type_loss: 0.0264 - Wine_Quality_root_mean_squared_error: 0.5504 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3586 - val_Wine_Quality_loss: 0.3285 - val_Wine_Type_loss: 0.0301 - val_Wine_Quality_root_mean_squared_error: 0.5732 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 63/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3283 - Wine_Quality_loss: 0.3019 - Wine_Type_loss: 0.0263 - Wine_Quality_root_mean_squared_error: 0.5495 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3452 - val_Wine_Quality_loss: 0.3153 - val_Wine_Type_loss: 0.0299 - val_Wine_Quality_root_mean_squared_error: 0.5615 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 64/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3264 - Wine_Quality_loss: 0.3003 - Wine_Type_loss: 0.0261 - Wine_Quality_root_mean_squared_error: 0.5480 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3444 - val_Wine_Quality_loss: 0.3146 - val_Wine_Type_loss: 0.0298 - val_Wine_Quality_root_mean_squared_error: 0.5609 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 65/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3263 - Wine_Quality_loss: 0.3003 - Wine_Type_loss: 0.0260 - Wine_Quality_root_mean_squared_error: 0.5480 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3460 - val_Wine_Quality_loss: 0.3163 - val_Wine_Type_loss: 0.0297 - val_Wine_Quality_root_mean_squared_error: 0.5624 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 66/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3249 - Wine_Quality_loss: 0.2991 - Wine_Type_loss: 0.0259 - Wine_Quality_root_mean_squared_error: 0.5469 - Wine_Type_accuracy: 0.9949 - val_loss: 0.3448 - val_Wine_Quality_loss: 0.3151 - val_Wine_Type_loss: 0.0297 - val_Wine_Quality_root_mean_squared_error: 0.5614 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 67/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3238 - Wine_Quality_loss: 0.2982 - Wine_Type_loss: 0.0256 - Wine_Quality_root_mean_squared_error: 0.5461 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3430 - val_Wine_Quality_loss: 0.3135 - val_Wine_Type_loss: 0.0295 - val_Wine_Quality_root_mean_squared_error: 0.5599 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 68/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3229 - Wine_Quality_loss: 0.2975 - Wine_Type_loss: 0.0255 - Wine_Quality_root_mean_squared_error: 0.5454 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3527 - val_Wine_Quality_loss: 0.3233 - val_Wine_Type_loss: 0.0294 - val_Wine_Quality_root_mean_squared_error: 0.5686 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 69/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3221 - Wine_Quality_loss: 0.2968 - Wine_Type_loss: 0.0254 - Wine_Quality_root_mean_squared_error: 0.5448 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3461 - val_Wine_Quality_loss: 0.3169 - val_Wine_Type_loss: 0.0292 - val_Wine_Quality_root_mean_squared_error: 0.5629 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 70/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3215 - Wine_Quality_loss: 0.2963 - Wine_Type_loss: 0.0252 - Wine_Quality_root_mean_squared_error: 0.5444 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3492 - val_Wine_Quality_loss: 0.3199 - val_Wine_Type_loss: 0.0293 - val_Wine_Quality_root_mean_squared_error: 0.5656 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 71/180\n",
      "99/99 [==============================] - ETA: 0s - loss: 0.3201 - Wine_Quality_loss: 0.2949 - Wine_Type_loss: 0.0252 - Wine_Quality_root_mean_squared_error: 0.5430 - Wine_Type_accuracy: 0.99 - 0s 3ms/step - loss: 0.3202 - Wine_Quality_loss: 0.2951 - Wine_Type_loss: 0.0251 - Wine_Quality_root_mean_squared_error: 0.5432 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3456 - val_Wine_Quality_loss: 0.3163 - val_Wine_Type_loss: 0.0293 - val_Wine_Quality_root_mean_squared_error: 0.5624 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 72/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3197 - Wine_Quality_loss: 0.2948 - Wine_Type_loss: 0.0249 - Wine_Quality_root_mean_squared_error: 0.5430 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3416 - val_Wine_Quality_loss: 0.3125 - val_Wine_Type_loss: 0.0291 - val_Wine_Quality_root_mean_squared_error: 0.5590 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 73/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3170 - Wine_Quality_loss: 0.2922 - Wine_Type_loss: 0.0247 - Wine_Quality_root_mean_squared_error: 0.5406 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3424 - val_Wine_Quality_loss: 0.3134 - val_Wine_Type_loss: 0.0290 - val_Wine_Quality_root_mean_squared_error: 0.5598 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 74/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3172 - Wine_Quality_loss: 0.2925 - Wine_Type_loss: 0.0246 - Wine_Quality_root_mean_squared_error: 0.5409 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3423 - val_Wine_Quality_loss: 0.3134 - val_Wine_Type_loss: 0.0290 - val_Wine_Quality_root_mean_squared_error: 0.5598 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 75/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3171 - Wine_Quality_loss: 0.2927 - Wine_Type_loss: 0.0244 - Wine_Quality_root_mean_squared_error: 0.5410 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3421 - val_Wine_Quality_loss: 0.3133 - val_Wine_Type_loss: 0.0288 - val_Wine_Quality_root_mean_squared_error: 0.5597 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 76/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3152 - Wine_Quality_loss: 0.2909 - Wine_Type_loss: 0.0243 - Wine_Quality_root_mean_squared_error: 0.5393 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3546 - val_Wine_Quality_loss: 0.3259 - val_Wine_Type_loss: 0.0287 - val_Wine_Quality_root_mean_squared_error: 0.5709 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 77/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3149 - Wine_Quality_loss: 0.2907 - Wine_Type_loss: 0.0242 - Wine_Quality_root_mean_squared_error: 0.5392 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3400 - val_Wine_Quality_loss: 0.3113 - val_Wine_Type_loss: 0.0287 - val_Wine_Quality_root_mean_squared_error: 0.5580 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 78/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3137 - Wine_Quality_loss: 0.2897 - Wine_Type_loss: 0.0240 - Wine_Quality_root_mean_squared_error: 0.5382 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3380 - val_Wine_Quality_loss: 0.3093 - val_Wine_Type_loss: 0.0287 - val_Wine_Quality_root_mean_squared_error: 0.5562 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 79/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3133 - Wine_Quality_loss: 0.2894 - Wine_Type_loss: 0.0239 - Wine_Quality_root_mean_squared_error: 0.5380 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3436 - val_Wine_Quality_loss: 0.3150 - val_Wine_Type_loss: 0.0286 - val_Wine_Quality_root_mean_squared_error: 0.5612 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 80/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3121 - Wine_Quality_loss: 0.2883 - Wine_Type_loss: 0.0238 - Wine_Quality_root_mean_squared_error: 0.5369 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3401 - val_Wine_Quality_loss: 0.3115 - val_Wine_Type_loss: 0.0286 - val_Wine_Quality_root_mean_squared_error: 0.5582 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 81/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3113 - Wine_Quality_loss: 0.2877 - Wine_Type_loss: 0.0237 - Wine_Quality_root_mean_squared_error: 0.5363 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3460 - val_Wine_Quality_loss: 0.3175 - val_Wine_Type_loss: 0.0286 - val_Wine_Quality_root_mean_squared_error: 0.5635 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 82/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3108 - Wine_Quality_loss: 0.2873 - Wine_Type_loss: 0.0235 - Wine_Quality_root_mean_squared_error: 0.5360 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3451 - val_Wine_Quality_loss: 0.3165 - val_Wine_Type_loss: 0.0286 - val_Wine_Quality_root_mean_squared_error: 0.5626 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 83/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3109 - Wine_Quality_loss: 0.2875 - Wine_Type_loss: 0.0234 - Wine_Quality_root_mean_squared_error: 0.5361 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3416 - val_Wine_Quality_loss: 0.3131 - val_Wine_Type_loss: 0.0285 - val_Wine_Quality_root_mean_squared_error: 0.5595 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 84/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3092 - Wine_Quality_loss: 0.2860 - Wine_Type_loss: 0.0233 - Wine_Quality_root_mean_squared_error: 0.5348 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3447 - val_Wine_Quality_loss: 0.3162 - val_Wine_Type_loss: 0.0284 - val_Wine_Quality_root_mean_squared_error: 0.5623 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 85/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3092 - Wine_Quality_loss: 0.2860 - Wine_Type_loss: 0.0232 - Wine_Quality_root_mean_squared_error: 0.5348 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3397 - val_Wine_Quality_loss: 0.3114 - val_Wine_Type_loss: 0.0284 - val_Wine_Quality_root_mean_squared_error: 0.5580 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 86/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3093 - Wine_Quality_loss: 0.2862 - Wine_Type_loss: 0.0231 - Wine_Quality_root_mean_squared_error: 0.5350 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3420 - val_Wine_Quality_loss: 0.3138 - val_Wine_Type_loss: 0.0282 - val_Wine_Quality_root_mean_squared_error: 0.5602 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 87/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3072 - Wine_Quality_loss: 0.2843 - Wine_Type_loss: 0.0229 - Wine_Quality_root_mean_squared_error: 0.5332 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3381 - val_Wine_Quality_loss: 0.3100 - val_Wine_Type_loss: 0.0281 - val_Wine_Quality_root_mean_squared_error: 0.5568 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 88/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3078 - Wine_Quality_loss: 0.2849 - Wine_Type_loss: 0.0228 - Wine_Quality_root_mean_squared_error: 0.5338 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3410 - val_Wine_Quality_loss: 0.3129 - val_Wine_Type_loss: 0.0281 - val_Wine_Quality_root_mean_squared_error: 0.5594 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 89/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3065 - Wine_Quality_loss: 0.2838 - Wine_Type_loss: 0.0227 - Wine_Quality_root_mean_squared_error: 0.5327 - Wine_Type_accuracy: 0.9952 - val_loss: 0.3363 - val_Wine_Quality_loss: 0.3082 - val_Wine_Type_loss: 0.0281 - val_Wine_Quality_root_mean_squared_error: 0.5552 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 90/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3049 - Wine_Quality_loss: 0.2824 - Wine_Type_loss: 0.0226 - Wine_Quality_root_mean_squared_error: 0.5314 - Wine_Type_accuracy: 0.9956 - val_loss: 0.3389 - val_Wine_Quality_loss: 0.3108 - val_Wine_Type_loss: 0.0280 - val_Wine_Quality_root_mean_squared_error: 0.5575 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 91/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3047 - Wine_Quality_loss: 0.2822 - Wine_Type_loss: 0.0225 - Wine_Quality_root_mean_squared_error: 0.5312 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3365 - val_Wine_Quality_loss: 0.3086 - val_Wine_Type_loss: 0.0279 - val_Wine_Quality_root_mean_squared_error: 0.5555 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 92/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3046 - Wine_Quality_loss: 0.2821 - Wine_Type_loss: 0.0224 - Wine_Quality_root_mean_squared_error: 0.5312 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3417 - val_Wine_Quality_loss: 0.3139 - val_Wine_Type_loss: 0.0279 - val_Wine_Quality_root_mean_squared_error: 0.5602 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 93/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3030 - Wine_Quality_loss: 0.2807 - Wine_Type_loss: 0.0223 - Wine_Quality_root_mean_squared_error: 0.5298 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3387 - val_Wine_Quality_loss: 0.3110 - val_Wine_Type_loss: 0.0277 - val_Wine_Quality_root_mean_squared_error: 0.5576 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 94/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3031 - Wine_Quality_loss: 0.2810 - Wine_Type_loss: 0.0221 - Wine_Quality_root_mean_squared_error: 0.5301 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3441 - val_Wine_Quality_loss: 0.3162 - val_Wine_Type_loss: 0.0278 - val_Wine_Quality_root_mean_squared_error: 0.5623 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 95/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.3029 - Wine_Quality_loss: 0.2809 - Wine_Type_loss: 0.0220 - Wine_Quality_root_mean_squared_error: 0.5300 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3379 - val_Wine_Quality_loss: 0.3102 - val_Wine_Type_loss: 0.0277 - val_Wine_Quality_root_mean_squared_error: 0.5570 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 96/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3014 - Wine_Quality_loss: 0.2794 - Wine_Type_loss: 0.0220 - Wine_Quality_root_mean_squared_error: 0.5286 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3416 - val_Wine_Quality_loss: 0.3139 - val_Wine_Type_loss: 0.0276 - val_Wine_Quality_root_mean_squared_error: 0.5603 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 97/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3016 - Wine_Quality_loss: 0.2798 - Wine_Type_loss: 0.0218 - Wine_Quality_root_mean_squared_error: 0.5289 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3377 - val_Wine_Quality_loss: 0.3102 - val_Wine_Type_loss: 0.0276 - val_Wine_Quality_root_mean_squared_error: 0.5569 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 98/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3011 - Wine_Quality_loss: 0.2794 - Wine_Type_loss: 0.0217 - Wine_Quality_root_mean_squared_error: 0.5286 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3351 - val_Wine_Quality_loss: 0.3077 - val_Wine_Type_loss: 0.0275 - val_Wine_Quality_root_mean_squared_error: 0.5547 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 99/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2990 - Wine_Quality_loss: 0.2775 - Wine_Type_loss: 0.0215 - Wine_Quality_root_mean_squared_error: 0.5268 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3419 - val_Wine_Quality_loss: 0.3144 - val_Wine_Type_loss: 0.0275 - val_Wine_Quality_root_mean_squared_error: 0.5607 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 100/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.3003 - Wine_Quality_loss: 0.2789 - Wine_Type_loss: 0.0215 - Wine_Quality_root_mean_squared_error: 0.5281 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3363 - val_Wine_Quality_loss: 0.3089 - val_Wine_Type_loss: 0.0274 - val_Wine_Quality_root_mean_squared_error: 0.5557 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 101/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2990 - Wine_Quality_loss: 0.2776 - Wine_Type_loss: 0.0214 - Wine_Quality_root_mean_squared_error: 0.5269 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3384 - val_Wine_Quality_loss: 0.3111 - val_Wine_Type_loss: 0.0273 - val_Wine_Quality_root_mean_squared_error: 0.5577 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 102/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2976 - Wine_Quality_loss: 0.2764 - Wine_Type_loss: 0.0212 - Wine_Quality_root_mean_squared_error: 0.5257 - Wine_Type_accuracy: 0.9959 - val_loss: 0.3369 - val_Wine_Quality_loss: 0.3098 - val_Wine_Type_loss: 0.0271 - val_Wine_Quality_root_mean_squared_error: 0.5566 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 103/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2987 - Wine_Quality_loss: 0.2776 - Wine_Type_loss: 0.0212 - Wine_Quality_root_mean_squared_error: 0.5269 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3359 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0272 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 104/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2970 - Wine_Quality_loss: 0.2759 - Wine_Type_loss: 0.0211 - Wine_Quality_root_mean_squared_error: 0.5253 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3402 - val_Wine_Quality_loss: 0.3130 - val_Wine_Type_loss: 0.0271 - val_Wine_Quality_root_mean_squared_error: 0.5595 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 105/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2968 - Wine_Quality_loss: 0.2758 - Wine_Type_loss: 0.0210 - Wine_Quality_root_mean_squared_error: 0.5252 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3348 - val_Wine_Quality_loss: 0.3077 - val_Wine_Type_loss: 0.0270 - val_Wine_Quality_root_mean_squared_error: 0.5547 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 106/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2957 - Wine_Quality_loss: 0.2748 - Wine_Type_loss: 0.0209 - Wine_Quality_root_mean_squared_error: 0.5243 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3359 - val_Wine_Quality_loss: 0.3089 - val_Wine_Type_loss: 0.0271 - val_Wine_Quality_root_mean_squared_error: 0.5558 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 107/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2949 - Wine_Quality_loss: 0.2741 - Wine_Type_loss: 0.0208 - Wine_Quality_root_mean_squared_error: 0.5236 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3390 - val_Wine_Quality_loss: 0.3119 - val_Wine_Type_loss: 0.0270 - val_Wine_Quality_root_mean_squared_error: 0.5585 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 108/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2954 - Wine_Quality_loss: 0.2747 - Wine_Type_loss: 0.0207 - Wine_Quality_root_mean_squared_error: 0.5242 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3442 - val_Wine_Quality_loss: 0.3174 - val_Wine_Type_loss: 0.0269 - val_Wine_Quality_root_mean_squared_error: 0.5633 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 109/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2948 - Wine_Quality_loss: 0.2743 - Wine_Type_loss: 0.0206 - Wine_Quality_root_mean_squared_error: 0.5237 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3369 - val_Wine_Quality_loss: 0.3101 - val_Wine_Type_loss: 0.0268 - val_Wine_Quality_root_mean_squared_error: 0.5569 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 110/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2935 - Wine_Quality_loss: 0.2730 - Wine_Type_loss: 0.0205 - Wine_Quality_root_mean_squared_error: 0.5225 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3419 - val_Wine_Quality_loss: 0.3151 - val_Wine_Type_loss: 0.0268 - val_Wine_Quality_root_mean_squared_error: 0.5614 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 111/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2932 - Wine_Quality_loss: 0.2729 - Wine_Type_loss: 0.0203 - Wine_Quality_root_mean_squared_error: 0.5224 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3343 - val_Wine_Quality_loss: 0.3075 - val_Wine_Type_loss: 0.0268 - val_Wine_Quality_root_mean_squared_error: 0.5546 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 112/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2929 - Wine_Quality_loss: 0.2726 - Wine_Type_loss: 0.0203 - Wine_Quality_root_mean_squared_error: 0.5221 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3348 - val_Wine_Quality_loss: 0.3080 - val_Wine_Type_loss: 0.0268 - val_Wine_Quality_root_mean_squared_error: 0.5550 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 113/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2922 - Wine_Quality_loss: 0.2720 - Wine_Type_loss: 0.0202 - Wine_Quality_root_mean_squared_error: 0.5215 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3368 - val_Wine_Quality_loss: 0.3101 - val_Wine_Type_loss: 0.0267 - val_Wine_Quality_root_mean_squared_error: 0.5569 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 114/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2918 - Wine_Quality_loss: 0.2717 - Wine_Type_loss: 0.0201 - Wine_Quality_root_mean_squared_error: 0.5212 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3360 - val_Wine_Quality_loss: 0.3093 - val_Wine_Type_loss: 0.0266 - val_Wine_Quality_root_mean_squared_error: 0.5562 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 115/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2903 - Wine_Quality_loss: 0.2703 - Wine_Type_loss: 0.0200 - Wine_Quality_root_mean_squared_error: 0.5200 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3363 - val_Wine_Quality_loss: 0.3096 - val_Wine_Type_loss: 0.0267 - val_Wine_Quality_root_mean_squared_error: 0.5564 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 116/180\n",
      "99/99 [==============================] - ETA: 0s - loss: 0.2923 - Wine_Quality_loss: 0.2708 - Wine_Type_loss: 0.0216 - Wine_Quality_root_mean_squared_error: 0.5204 - Wine_Type_accuracy: 0.99 - 0s 3ms/step - loss: 0.2908 - Wine_Quality_loss: 0.2709 - Wine_Type_loss: 0.0199 - Wine_Quality_root_mean_squared_error: 0.5205 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3412 - val_Wine_Quality_loss: 0.3146 - val_Wine_Type_loss: 0.0266 - val_Wine_Quality_root_mean_squared_error: 0.5609 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 117/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2901 - Wine_Quality_loss: 0.2703 - Wine_Type_loss: 0.0198 - Wine_Quality_root_mean_squared_error: 0.5199 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3379 - val_Wine_Quality_loss: 0.3113 - val_Wine_Type_loss: 0.0266 - val_Wine_Quality_root_mean_squared_error: 0.5580 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 118/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2898 - Wine_Quality_loss: 0.2701 - Wine_Type_loss: 0.0197 - Wine_Quality_root_mean_squared_error: 0.5197 - Wine_Type_accuracy: 0.9962 - val_loss: 0.3333 - val_Wine_Quality_loss: 0.3067 - val_Wine_Type_loss: 0.0265 - val_Wine_Quality_root_mean_squared_error: 0.5538 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 119/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2893 - Wine_Quality_loss: 0.2697 - Wine_Type_loss: 0.0196 - Wine_Quality_root_mean_squared_error: 0.5194 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3397 - val_Wine_Quality_loss: 0.3130 - val_Wine_Type_loss: 0.0266 - val_Wine_Quality_root_mean_squared_error: 0.5595 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 120/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2901 - Wine_Quality_loss: 0.2705 - Wine_Type_loss: 0.0196 - Wine_Quality_root_mean_squared_error: 0.5201 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3354 - val_Wine_Quality_loss: 0.3089 - val_Wine_Type_loss: 0.0265 - val_Wine_Quality_root_mean_squared_error: 0.5558 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 121/180\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 0.2880 - Wine_Quality_loss: 0.2686 - Wine_Type_loss: 0.0194 - Wine_Quality_root_mean_squared_error: 0.5183 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3371 - val_Wine_Quality_loss: 0.3105 - val_Wine_Type_loss: 0.0266 - val_Wine_Quality_root_mean_squared_error: 0.5572 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 122/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2873 - Wine_Quality_loss: 0.2679 - Wine_Type_loss: 0.0194 - Wine_Quality_root_mean_squared_error: 0.5176 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3342 - val_Wine_Quality_loss: 0.3078 - val_Wine_Type_loss: 0.0265 - val_Wine_Quality_root_mean_squared_error: 0.5548 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 123/180\n",
      "99/99 [==============================] - 0s 5ms/step - loss: 0.2878 - Wine_Quality_loss: 0.2685 - Wine_Type_loss: 0.0193 - Wine_Quality_root_mean_squared_error: 0.5182 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3366 - val_Wine_Quality_loss: 0.3102 - val_Wine_Type_loss: 0.0263 - val_Wine_Quality_root_mean_squared_error: 0.5570 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 124/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2858 - Wine_Quality_loss: 0.2666 - Wine_Type_loss: 0.0192 - Wine_Quality_root_mean_squared_error: 0.5164 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3319 - val_Wine_Quality_loss: 0.3055 - val_Wine_Type_loss: 0.0264 - val_Wine_Quality_root_mean_squared_error: 0.5527 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 125/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2871 - Wine_Quality_loss: 0.2680 - Wine_Type_loss: 0.0191 - Wine_Quality_root_mean_squared_error: 0.5177 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3373 - val_Wine_Quality_loss: 0.3110 - val_Wine_Type_loss: 0.0263 - val_Wine_Quality_root_mean_squared_error: 0.5577 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 126/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2851 - Wine_Quality_loss: 0.2660 - Wine_Type_loss: 0.0191 - Wine_Quality_root_mean_squared_error: 0.5158 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3320 - val_Wine_Quality_loss: 0.3057 - val_Wine_Type_loss: 0.0263 - val_Wine_Quality_root_mean_squared_error: 0.5529 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 127/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2848 - Wine_Quality_loss: 0.2658 - Wine_Type_loss: 0.0190 - Wine_Quality_root_mean_squared_error: 0.5156 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3321 - val_Wine_Quality_loss: 0.3058 - val_Wine_Type_loss: 0.0263 - val_Wine_Quality_root_mean_squared_error: 0.5530 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 128/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2852 - Wine_Quality_loss: 0.2663 - Wine_Type_loss: 0.0189 - Wine_Quality_root_mean_squared_error: 0.5160 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3355 - val_Wine_Quality_loss: 0.3093 - val_Wine_Type_loss: 0.0262 - val_Wine_Quality_root_mean_squared_error: 0.5561 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 129/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2840 - Wine_Quality_loss: 0.2651 - Wine_Type_loss: 0.0188 - Wine_Quality_root_mean_squared_error: 0.5149 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3435 - val_Wine_Quality_loss: 0.3172 - val_Wine_Type_loss: 0.0262 - val_Wine_Quality_root_mean_squared_error: 0.5632 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 130/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2838 - Wine_Quality_loss: 0.2650 - Wine_Type_loss: 0.0188 - Wine_Quality_root_mean_squared_error: 0.5148 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3347 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0261 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 131/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2819 - Wine_Quality_loss: 0.2633 - Wine_Type_loss: 0.0186 - Wine_Quality_root_mean_squared_error: 0.5131 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3348 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0261 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 132/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2833 - Wine_Quality_loss: 0.2648 - Wine_Type_loss: 0.0186 - Wine_Quality_root_mean_squared_error: 0.5146 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3381 - val_Wine_Quality_loss: 0.3120 - val_Wine_Type_loss: 0.0261 - val_Wine_Quality_root_mean_squared_error: 0.5585 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 133/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2827 - Wine_Quality_loss: 0.2642 - Wine_Type_loss: 0.0184 - Wine_Quality_root_mean_squared_error: 0.5140 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3366 - val_Wine_Quality_loss: 0.3105 - val_Wine_Type_loss: 0.0261 - val_Wine_Quality_root_mean_squared_error: 0.5572 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 134/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2816 - Wine_Quality_loss: 0.2632 - Wine_Type_loss: 0.0184 - Wine_Quality_root_mean_squared_error: 0.5131 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3423 - val_Wine_Quality_loss: 0.3162 - val_Wine_Type_loss: 0.0260 - val_Wine_Quality_root_mean_squared_error: 0.5624 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 135/180\n",
      "99/99 [==============================] - ETA: 0s - loss: 0.2819 - Wine_Quality_loss: 0.2636 - Wine_Type_loss: 0.0183 - Wine_Quality_root_mean_squared_error: 0.5134 - Wine_Type_accuracy: 0.99 - 0s 4ms/step - loss: 0.2819 - Wine_Quality_loss: 0.2636 - Wine_Type_loss: 0.0183 - Wine_Quality_root_mean_squared_error: 0.5134 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3329 - val_Wine_Quality_loss: 0.3069 - val_Wine_Type_loss: 0.0260 - val_Wine_Quality_root_mean_squared_error: 0.5540 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 136/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2821 - Wine_Quality_loss: 0.2639 - Wine_Type_loss: 0.0183 - Wine_Quality_root_mean_squared_error: 0.5137 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3317 - val_Wine_Quality_loss: 0.3058 - val_Wine_Type_loss: 0.0259 - val_Wine_Quality_root_mean_squared_error: 0.5530 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 137/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2792 - Wine_Quality_loss: 0.2610 - Wine_Type_loss: 0.0182 - Wine_Quality_root_mean_squared_error: 0.5109 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3366 - val_Wine_Quality_loss: 0.3107 - val_Wine_Type_loss: 0.0259 - val_Wine_Quality_root_mean_squared_error: 0.5574 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 138/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2800 - Wine_Quality_loss: 0.2619 - Wine_Type_loss: 0.0181 - Wine_Quality_root_mean_squared_error: 0.5117 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3511 - val_Wine_Quality_loss: 0.3252 - val_Wine_Type_loss: 0.0258 - val_Wine_Quality_root_mean_squared_error: 0.5703 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 139/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2796 - Wine_Quality_loss: 0.2616 - Wine_Type_loss: 0.0180 - Wine_Quality_root_mean_squared_error: 0.5115 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3373 - val_Wine_Quality_loss: 0.3115 - val_Wine_Type_loss: 0.0259 - val_Wine_Quality_root_mean_squared_error: 0.5581 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 140/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2791 - Wine_Quality_loss: 0.2612 - Wine_Type_loss: 0.0179 - Wine_Quality_root_mean_squared_error: 0.5111 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3337 - val_Wine_Quality_loss: 0.3078 - val_Wine_Type_loss: 0.0258 - val_Wine_Quality_root_mean_squared_error: 0.5548 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 141/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2786 - Wine_Quality_loss: 0.2608 - Wine_Type_loss: 0.0178 - Wine_Quality_root_mean_squared_error: 0.5107 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3339 - val_Wine_Quality_loss: 0.3080 - val_Wine_Type_loss: 0.0259 - val_Wine_Quality_root_mean_squared_error: 0.5549 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 142/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2790 - Wine_Quality_loss: 0.2613 - Wine_Type_loss: 0.0178 - Wine_Quality_root_mean_squared_error: 0.5111 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3367 - val_Wine_Quality_loss: 0.3109 - val_Wine_Type_loss: 0.0259 - val_Wine_Quality_root_mean_squared_error: 0.5576 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 143/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2765 - Wine_Quality_loss: 0.2588 - Wine_Type_loss: 0.0177 - Wine_Quality_root_mean_squared_error: 0.5087 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3507 - val_Wine_Quality_loss: 0.3249 - val_Wine_Type_loss: 0.0258 - val_Wine_Quality_root_mean_squared_error: 0.5700 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 144/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2779 - Wine_Quality_loss: 0.2603 - Wine_Type_loss: 0.0176 - Wine_Quality_root_mean_squared_error: 0.5102 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3389 - val_Wine_Quality_loss: 0.3131 - val_Wine_Type_loss: 0.0258 - val_Wine_Quality_root_mean_squared_error: 0.5596 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 145/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2780 - Wine_Quality_loss: 0.2604 - Wine_Type_loss: 0.0176 - Wine_Quality_root_mean_squared_error: 0.5103 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3329 - val_Wine_Quality_loss: 0.3071 - val_Wine_Type_loss: 0.0258 - val_Wine_Quality_root_mean_squared_error: 0.5542 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 146/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2773 - Wine_Quality_loss: 0.2598 - Wine_Type_loss: 0.0175 - Wine_Quality_root_mean_squared_error: 0.5097 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3295 - val_Wine_Quality_loss: 0.3038 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5512 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 147/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2764 - Wine_Quality_loss: 0.2590 - Wine_Type_loss: 0.0174 - Wine_Quality_root_mean_squared_error: 0.5089 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3343 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 148/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2761 - Wine_Quality_loss: 0.2588 - Wine_Type_loss: 0.0173 - Wine_Quality_root_mean_squared_error: 0.5087 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3347 - val_Wine_Quality_loss: 0.3090 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5559 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 149/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2761 - Wine_Quality_loss: 0.2588 - Wine_Type_loss: 0.0173 - Wine_Quality_root_mean_squared_error: 0.5087 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3336 - val_Wine_Quality_loss: 0.3080 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5549 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 150/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2746 - Wine_Quality_loss: 0.2574 - Wine_Type_loss: 0.0172 - Wine_Quality_root_mean_squared_error: 0.5073 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3314 - val_Wine_Quality_loss: 0.3057 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5529 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 151/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2749 - Wine_Quality_loss: 0.2578 - Wine_Type_loss: 0.0171 - Wine_Quality_root_mean_squared_error: 0.5078 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3373 - val_Wine_Quality_loss: 0.3117 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5583 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 152/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2742 - Wine_Quality_loss: 0.2571 - Wine_Type_loss: 0.0170 - Wine_Quality_root_mean_squared_error: 0.5071 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3395 - val_Wine_Quality_loss: 0.3138 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5602 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 153/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2739 - Wine_Quality_loss: 0.2569 - Wine_Type_loss: 0.0170 - Wine_Quality_root_mean_squared_error: 0.5068 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3410 - val_Wine_Quality_loss: 0.3154 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5616 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 154/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2741 - Wine_Quality_loss: 0.2572 - Wine_Type_loss: 0.0169 - Wine_Quality_root_mean_squared_error: 0.5071 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3336 - val_Wine_Quality_loss: 0.3080 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5550 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 155/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2724 - Wine_Quality_loss: 0.2556 - Wine_Type_loss: 0.0169 - Wine_Quality_root_mean_squared_error: 0.5056 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3382 - val_Wine_Quality_loss: 0.3126 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5591 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 156/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2729 - Wine_Quality_loss: 0.2562 - Wine_Type_loss: 0.0167 - Wine_Quality_root_mean_squared_error: 0.5062 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3346 - val_Wine_Quality_loss: 0.3089 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5558 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 157/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2728 - Wine_Quality_loss: 0.2560 - Wine_Type_loss: 0.0167 - Wine_Quality_root_mean_squared_error: 0.5060 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3356 - val_Wine_Quality_loss: 0.3100 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5568 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 158/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2719 - Wine_Quality_loss: 0.2553 - Wine_Type_loss: 0.0166 - Wine_Quality_root_mean_squared_error: 0.5053 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3422 - val_Wine_Quality_loss: 0.3165 - val_Wine_Type_loss: 0.0257 - val_Wine_Quality_root_mean_squared_error: 0.5626 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 159/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2714 - Wine_Quality_loss: 0.2547 - Wine_Type_loss: 0.0167 - Wine_Quality_root_mean_squared_error: 0.5047 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3342 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 160/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2720 - Wine_Quality_loss: 0.2555 - Wine_Type_loss: 0.0165 - Wine_Quality_root_mean_squared_error: 0.5055 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3420 - val_Wine_Quality_loss: 0.3165 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5626 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 161/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2706 - Wine_Quality_loss: 0.2542 - Wine_Type_loss: 0.0164 - Wine_Quality_root_mean_squared_error: 0.5042 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3344 - val_Wine_Quality_loss: 0.3089 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5558 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 162/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2703 - Wine_Quality_loss: 0.2538 - Wine_Type_loss: 0.0165 - Wine_Quality_root_mean_squared_error: 0.5038 - Wine_Type_accuracy: 0.9965 - val_loss: 0.3325 - val_Wine_Quality_loss: 0.3069 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5540 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 163/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2703 - Wine_Quality_loss: 0.2539 - Wine_Type_loss: 0.0164 - Wine_Quality_root_mean_squared_error: 0.5039 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3344 - val_Wine_Quality_loss: 0.3090 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5558 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 164/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2694 - Wine_Quality_loss: 0.2531 - Wine_Type_loss: 0.0163 - Wine_Quality_root_mean_squared_error: 0.5031 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3366 - val_Wine_Quality_loss: 0.3111 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5578 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 165/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2707 - Wine_Quality_loss: 0.2545 - Wine_Type_loss: 0.0162 - Wine_Quality_root_mean_squared_error: 0.5045 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3385 - val_Wine_Quality_loss: 0.3130 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5595 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 166/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2687 - Wine_Quality_loss: 0.2525 - Wine_Type_loss: 0.0162 - Wine_Quality_root_mean_squared_error: 0.5025 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3385 - val_Wine_Quality_loss: 0.3130 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5595 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 167/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2689 - Wine_Quality_loss: 0.2528 - Wine_Type_loss: 0.0161 - Wine_Quality_root_mean_squared_error: 0.5028 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3320 - val_Wine_Quality_loss: 0.3066 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5537 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 168/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2680 - Wine_Quality_loss: 0.2520 - Wine_Type_loss: 0.0160 - Wine_Quality_root_mean_squared_error: 0.5020 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3349 - val_Wine_Quality_loss: 0.3095 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5563 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 169/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2685 - Wine_Quality_loss: 0.2525 - Wine_Type_loss: 0.0160 - Wine_Quality_root_mean_squared_error: 0.5025 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3353 - val_Wine_Quality_loss: 0.3099 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5567 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 170/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2687 - Wine_Quality_loss: 0.2528 - Wine_Type_loss: 0.0159 - Wine_Quality_root_mean_squared_error: 0.5028 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3320 - val_Wine_Quality_loss: 0.3066 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5537 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 171/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2674 - Wine_Quality_loss: 0.2516 - Wine_Type_loss: 0.0158 - Wine_Quality_root_mean_squared_error: 0.5016 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3318 - val_Wine_Quality_loss: 0.3063 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5535 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 172/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2678 - Wine_Quality_loss: 0.2520 - Wine_Type_loss: 0.0158 - Wine_Quality_root_mean_squared_error: 0.5020 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3337 - val_Wine_Quality_loss: 0.3082 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5551 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 173/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2663 - Wine_Quality_loss: 0.2506 - Wine_Type_loss: 0.0157 - Wine_Quality_root_mean_squared_error: 0.5006 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3454 - val_Wine_Quality_loss: 0.3199 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5656 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 174/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2665 - Wine_Quality_loss: 0.2509 - Wine_Type_loss: 0.0156 - Wine_Quality_root_mean_squared_error: 0.5009 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3401 - val_Wine_Quality_loss: 0.3145 - val_Wine_Type_loss: 0.0256 - val_Wine_Quality_root_mean_squared_error: 0.5608 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 175/180\n",
      "99/99 [==============================] - 0s 2ms/step - loss: 0.2662 - Wine_Quality_loss: 0.2505 - Wine_Type_loss: 0.0156 - Wine_Quality_root_mean_squared_error: 0.5005 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3363 - val_Wine_Quality_loss: 0.3108 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5575 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 176/180\n",
      "99/99 [==============================] - 0s 2ms/step - loss: 0.2664 - Wine_Quality_loss: 0.2508 - Wine_Type_loss: 0.0156 - Wine_Quality_root_mean_squared_error: 0.5008 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3341 - val_Wine_Quality_loss: 0.3087 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5556 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 177/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2660 - Wine_Quality_loss: 0.2506 - Wine_Type_loss: 0.0155 - Wine_Quality_root_mean_squared_error: 0.5006 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3409 - val_Wine_Quality_loss: 0.3154 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5616 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 178/180\n",
      "99/99 [==============================] - 0s 3ms/step - loss: 0.2646 - Wine_Quality_loss: 0.2491 - Wine_Type_loss: 0.0155 - Wine_Quality_root_mean_squared_error: 0.4991 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3371 - val_Wine_Quality_loss: 0.3116 - val_Wine_Type_loss: 0.0255 - val_Wine_Quality_root_mean_squared_error: 0.5582 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 179/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2638 - Wine_Quality_loss: 0.2484 - Wine_Type_loss: 0.0154 - Wine_Quality_root_mean_squared_error: 0.4984 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3357 - val_Wine_Quality_loss: 0.3104 - val_Wine_Type_loss: 0.0253 - val_Wine_Quality_root_mean_squared_error: 0.5571 - val_Wine_Type_accuracy: 0.9949\n",
      "Epoch 180/180\n",
      "99/99 [==============================] - 0s 4ms/step - loss: 0.2648 - Wine_Quality_loss: 0.2495 - Wine_Type_loss: 0.0153 - Wine_Quality_root_mean_squared_error: 0.4995 - Wine_Type_accuracy: 0.9968 - val_loss: 0.3365 - val_Wine_Quality_loss: 0.3111 - val_Wine_Type_loss: 0.0254 - val_Wine_Quality_root_mean_squared_error: 0.5578 - val_Wine_Type_accuracy: 0.9949\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(norm_train_x, train_y, \n",
    "                    epochs=180, \n",
    "                    validation_data=(norm_val_x, val_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951b6898-86db-49b3-9358-dc7c35431451",
   "metadata": {},
   "source": [
    "## *Analyze Model's performance*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "92a5650e-1f8f-4787-8032-1baefe3fd69a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25/25 [==============================] - 0s 880us/step - loss: 0.3365 - Wine_Quality_loss: 0.3111 - Wine_Type_loss: 0.0254 - Wine_Quality_root_mean_squared_error: 0.5578 - Wine_Type_accuracy: 0.9949\n",
      "\n",
      "loss: 0.336535781621933\n",
      "wine_quality_loss: 0.3111044764518738\n",
      "wine_type_loss: 0.02543131448328495\n",
      "wine_quality_rmse: 0.557767391204834\n",
      "wine_type_accuracy: 0.9949302673339844\n"
     ]
    }
   ],
   "source": [
    "loss, wine_quality_loss, wine_type_loss, wine_quality_rmse, wine_type_accuracy = model.evaluate(x=norm_val_x, y=val_y)\n",
    "\n",
    "print()\n",
    "print(f'loss: {loss}')\n",
    "print(f'wine_quality_loss: {wine_quality_loss}')\n",
    "print(f'wine_type_loss: {wine_type_loss}')\n",
    "print(f'wine_quality_rmse: {wine_quality_rmse}')\n",
    "print(f'wine_type_accuracy: {wine_type_accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "096d46e0-2449-44ba-b64a-33b3fa18aa76",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(norm_test_x)\n",
    "quality_preds = predictions[0]\n",
    "type_preds = predictions[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b54c508-9c68-4dc6-88d8-f4b7206f71de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d2cc4c4a-359e-4841-a7bb-7cd3a9832e1d",
   "metadata": {},
   "source": [
    "### *Plot utilities*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3f3b750d-7501-4f73-adbb-f35b75099cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(metric_name, title, ylim=5):\n",
    "    plt.title(title)\n",
    "    plt.ylim(0,ylim)\n",
    "    plt.plot(history.history[metric_name],color='blue',label=metric_name)\n",
    "    plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, title='', labels=[0,1]):\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title('Confusion matrix of the classifier')\n",
    "    fig.colorbar(cax)\n",
    "    ax.set_xticklabels([''] + labels)\n",
    "    ax.set_yticklabels([''] + labels)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    fmt = 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "          plt.text(j, i, format(cm[i, j], fmt),\n",
    "                  horizontalalignment=\"center\",\n",
    "                  color=\"black\" if cm[i, j] > thresh else \"white\")\n",
    "    plt.show()\n",
    "    \n",
    "def plot_diff(y_true, y_pred, title = '' ):\n",
    "    plt.scatter(y_true, y_pred)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('True Values')\n",
    "    plt.ylabel('Predictions')\n",
    "    plt.axis('equal')\n",
    "    plt.axis('square')\n",
    "    plt.plot([-100, 100], [-100, 100])\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea873a82-656f-4514-9c0c-3b68b10d5768",
   "metadata": {},
   "source": [
    "## *Metrics Plots* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cec076ef-a4e3-4e85-9434-c6011f639df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('Wine_Quality_root_mean_squared_error', 'RMSE', ylim=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "32e46167-a7c9-4fc6-9e48-cc5f5f3b34fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics('Wine_Type_loss', 'Wine Type Loss', ylim=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3cdad4-e616-4b6c-b027-9ebeba1d7fea",
   "metadata": {},
   "source": [
    "## *Confusion Matrix*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1bef20a1-ca72-47cd-b415-b40aee64031e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-125-3c2bb5135d98>:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([''] + labels)\n",
      "<ipython-input-125-3c2bb5135d98>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([''] + labels)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(test_y[1], np.round(type_preds), title='Wine Type', labels = [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "dc867e96-cb45-4c9c-91c9-74f231d4492a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "scatter_plot = plot_diff(test_y[0], quality_preds, title='Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b4a397d-9386-452f-a8c8-45a4b21e282e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}