wines-multiclass-model.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a95bb244",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f52a7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"./Wines-Dataset/wine.data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b19623d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7cfc107e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Classes', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash',\n",
       "       'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',\n",
       "       'Proanthocyanins', 'Color intensity', 'Hue',\n",
       "       'OD280/OD315 of diluted wines', 'Proline'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a09fd727",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Classes</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
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       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Classes  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0        1    14.23        1.71  2.43               15.6        127   \n",
       "1        1    13.20        1.78  2.14               11.2        100   \n",
       "2        1    13.16        2.36  2.67               18.6        101   \n",
       "3        1    14.37        1.95  2.50               16.8        113   \n",
       "4        1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92e81798",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['Classes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c46c960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178,)"
      ]
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     "execution_count": 7,
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    "y.shape"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "118678fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c4049b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    71\n",
       "1    59\n",
       "3    48\n",
       "Name: Classes, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   "source": [
    "y.value_counts()"
   ]
  },
  {
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   "execution_count": 10,
   "id": "79236c2d",
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   "outputs": [
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       "[178 rows x 3 columns]"
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     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "pd.get_dummies(y)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5830a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_categorical = pd.get_dummies(y)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9662633a",
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       "(178, 3)"
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     "execution_count": 12,
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    "y_categorical.shape"
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82ac80a4",
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     "execution_count": 13,
     "metadata": {},
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    "y_categorical"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "047b4319",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "117bce84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Alcohol', ylabel='Classes'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Alcohol\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "550dc82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Malic acid', ylabel='Classes'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Malic acid\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f120a99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Ash', ylabel='Classes'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#See overlapping values so remove it: Ash ==> Not Important feature\n",
    "sns.scatterplot(x=\"Ash\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "274d826a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Alcalinity of ash', ylabel='Classes'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Alcalinity of ash\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91b48d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Magnesium', ylabel='Classes'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Magenesium is not an Important feature: OVERLAPPING VALUES\n",
    "sns.scatterplot(x=\"Magnesium\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6201a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total phenols', ylabel='Classes'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Total phenols\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ce69af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Flavanoids', ylabel='Classes'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Flavanoids\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c0947706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Nonflavanoid phenols', ylabel='Classes'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Not a good feature as it has distributed values for all classes\n",
    "sns.scatterplot(x=\"Nonflavanoid phenols\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "75d43757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Proanthocyanins', ylabel='Classes'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Proanthocyanins\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "807a558e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Color intensity', ylabel='Classes'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Color intensity\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0f63e709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hue', ylabel='Classes'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Hue\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b128f632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='OD280/OD315 of diluted wines', ylabel='Classes'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"OD280/OD315 of diluted wines\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "60eb9363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Proline', ylabel='Classes'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(x=\"Proline\",y=\"Classes\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2813cde4",
   "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>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>24.5</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Alcohol  Malic acid  Alcalinity of ash  Total phenols  Flavanoids  \\\n",
       "0      14.23        1.71               15.6           2.80        3.06   \n",
       "1      13.20        1.78               11.2           2.65        2.76   \n",
       "2      13.16        2.36               18.6           2.80        3.24   \n",
       "3      14.37        1.95               16.8           3.85        3.49   \n",
       "4      13.24        2.59               21.0           2.80        2.69   \n",
       "..       ...         ...                ...            ...         ...   \n",
       "173    13.71        5.65               20.5           1.68        0.61   \n",
       "174    13.40        3.91               23.0           1.80        0.75   \n",
       "175    13.27        4.28               20.0           1.59        0.69   \n",
       "176    13.17        2.59               20.0           1.65        0.68   \n",
       "177    14.13        4.10               24.5           2.05        0.76   \n",
       "\n",
       "     Proanthocyanins  Color intensity   Hue  OD280/OD315 of diluted wines  \\\n",
       "0               2.29             5.64  1.04                          3.92   \n",
       "1               1.28             4.38  1.05                          3.40   \n",
       "2               2.81             5.68  1.03                          3.17   \n",
       "3               2.18             7.80  0.86                          3.45   \n",
       "4               1.82             4.32  1.04                          2.93   \n",
       "..               ...              ...   ...                           ...   \n",
       "173             1.06             7.70  0.64                          1.74   \n",
       "174             1.41             7.30  0.70                          1.56   \n",
       "175             1.35            10.20  0.59                          1.56   \n",
       "176             1.46             9.30  0.60                          1.62   \n",
       "177             1.35             9.20  0.61                          1.60   \n",
       "\n",
       "     Proline  \n",
       "0       1065  \n",
       "1       1050  \n",
       "2       1185  \n",
       "3       1480  \n",
       "4        735  \n",
       "..       ...  \n",
       "173      740  \n",
       "174      750  \n",
       "175      835  \n",
       "176      840  \n",
       "177      560  \n",
       "\n",
       "[178 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Elimination\n",
    "x=dataset\n",
    "for i in [\"Ash\",\"Magnesium\",\"Nonflavanoid phenols\",\"Classes\"]:\n",
    "    x=x.drop(i,axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b596154a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4bd490b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 10 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Alcohol                       178 non-null    float64\n",
      " 1   Malic acid                    178 non-null    float64\n",
      " 2   Alcalinity of ash             178 non-null    float64\n",
      " 3   Total phenols                 178 non-null    float64\n",
      " 4   Flavanoids                    178 non-null    float64\n",
      " 5   Proanthocyanins               178 non-null    float64\n",
      " 6   Color intensity               178 non-null    float64\n",
      " 7   Hue                           178 non-null    float64\n",
      " 8   OD280/OD315 of diluted wines  178 non-null    float64\n",
      " 9   Proline                       178 non-null    int64  \n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 14.0 KB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8dfbbd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0ea7b5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d6ea38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential', 'layers': []}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0c3c08d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9176fcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units = 16,\n",
    "        input_shape=(10,),\n",
    "        activation=\"relu\",\n",
    "        kernel_initializer=\"he_normal\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "626deb05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                176       \n",
      "=================================================================\n",
      "Total params: 176\n",
      "Trainable params: 176\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d7d39c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units=16,\n",
    "        activation='relu',\n",
    "        kernel_initializer='he_normal'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54330dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                176       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                272       \n",
      "=================================================================\n",
      "Total params: 448\n",
      "Trainable params: 448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fd9e99a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units=16,\n",
    "        activation='relu',\n",
    "        kernel_initializer='he_normal'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "045e8528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sequential',\n",
       " 'layers': [{'class_name': 'InputLayer',\n",
       "   'config': {'batch_input_shape': (None, 10),\n",
       "    'dtype': 'float32',\n",
       "    'sparse': False,\n",
       "    'ragged': False,\n",
       "    'name': 'dense_input'}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense',\n",
       "    'trainable': True,\n",
       "    'batch_input_shape': (None, 10),\n",
       "    'dtype': 'float32',\n",
       "    'units': 16,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'HeNormal', 'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_1',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 16,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'HeNormal', 'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}},\n",
       "  {'class_name': 'Dense',\n",
       "   'config': {'name': 'dense_2',\n",
       "    'trainable': True,\n",
       "    'dtype': 'float32',\n",
       "    'units': 16,\n",
       "    'activation': 'relu',\n",
       "    'use_bias': True,\n",
       "    'kernel_initializer': {'class_name': 'HeNormal', 'config': {'seed': None}},\n",
       "    'bias_initializer': {'class_name': 'Zeros', 'config': {}},\n",
       "    'kernel_regularizer': None,\n",
       "    'bias_regularizer': None,\n",
       "    'activity_regularizer': None,\n",
       "    'kernel_constraint': None,\n",
       "    'bias_constraint': None}}]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b49027f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(\n",
    "    Dense(\n",
    "        units=3,\n",
    "        activation=\"softmax\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5fb079ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                176       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 51        \n",
      "=================================================================\n",
      "Total params: 771\n",
      "Trainable params: 771\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bba2a1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8193a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop(),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "46324ef5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/275\n",
      "6/6 [==============================] - 3s 3ms/step - loss: 144.5636 - accuracy: 0.3768\n",
      "Epoch 2/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 83.9698 - accuracy: 0.3363\n",
      "Epoch 3/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 52.8216 - accuracy: 0.3491\n",
      "Epoch 4/275\n",
      "6/6 [==============================] - 0s 7ms/step - loss: 45.7342 - accuracy: 0.3158\n",
      "Epoch 5/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 37.1226 - accuracy: 0.3185\n",
      "Epoch 6/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 26.8740 - accuracy: 0.3085\n",
      "Epoch 7/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 17.2270 - accuracy: 0.3270\n",
      "Epoch 8/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 9.8508 - accuracy: 0.3583\n",
      "Epoch 9/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.4406 - accuracy: 0.2624\n",
      "Epoch 10/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 3.8696 - accuracy: 0.2752\n",
      "Epoch 11/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.4078 - accuracy: 0.3199\n",
      "Epoch 12/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 2.9204 - accuracy: 0.3969\n",
      "Epoch 13/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.3162 - accuracy: 0.3818\n",
      "Epoch 14/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 3.7335 - accuracy: 0.3114\n",
      "Epoch 15/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.4271 - accuracy: 0.4424\n",
      "Epoch 16/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.2389 - accuracy: 0.4463\n",
      "Epoch 17/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 3.0394 - accuracy: 0.4053\n",
      "Epoch 18/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 3.4298 - accuracy: 0.3103\n",
      "Epoch 19/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.7324 - accuracy: 0.2785\n",
      "Epoch 20/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.7524 - accuracy: 0.4104\n",
      "Epoch 21/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 2.0122 - accuracy: 0.4354\n",
      "Epoch 22/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 1.8401 - accuracy: 0.4848\n",
      "Epoch 23/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.6426 - accuracy: 0.4570\n",
      "Epoch 24/275\n",
      "6/6 [==============================] - 0s 7ms/step - loss: 2.7377 - accuracy: 0.4127\n",
      "Epoch 25/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 4.2901 - accuracy: 0.3538\n",
      "Epoch 26/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9769 - accuracy: 0.4788\n",
      "Epoch 27/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9528 - accuracy: 0.4793\n",
      "Epoch 28/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9156 - accuracy: 0.3679\n",
      "Epoch 29/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.4082 - accuracy: 0.3542\n",
      "Epoch 30/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 1.9995 - accuracy: 0.3659\n",
      "Epoch 31/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 3.4791 - accuracy: 0.4074\n",
      "Epoch 32/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 1.7883 - accuracy: 0.5233\n",
      "Epoch 33/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 2.0815 - accuracy: 0.5174\n",
      "Epoch 34/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 4.0093 - accuracy: 0.3564\n",
      "Epoch 35/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 1.1141 - accuracy: 0.5220\n",
      "Epoch 36/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.0113 - accuracy: 0.5019\n",
      "Epoch 37/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.4601 - accuracy: 0.4833\n",
      "Epoch 38/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.1496 - accuracy: 0.6026\n",
      "Epoch 39/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.2614 - accuracy: 0.5959\n",
      "Epoch 40/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 2.4203 - accuracy: 0.4209\n",
      "Epoch 41/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 2.0584 - accuracy: 0.3838\n",
      "Epoch 42/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.6420 - accuracy: 0.5661\n",
      "Epoch 43/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 1.9502 - accuracy: 0.4397\n",
      "Epoch 44/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 3.5838 - accuracy: 0.4275\n",
      "Epoch 45/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 2.4058 - accuracy: 0.5051\n",
      "Epoch 46/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 1.0836 - accuracy: 0.5936\n",
      "Epoch 47/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 2.1357 - accuracy: 0.4731\n",
      "Epoch 48/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.3605 - accuracy: 0.6047\n",
      "Epoch 49/275\n",
      "6/6 [==============================] - 0s 7ms/step - loss: 0.8350 - accuracy: 0.6502\n",
      "Epoch 50/275\n",
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      "6/6 [==============================] - 0s 3ms/step - loss: 0.5970 - accuracy: 0.7685\n",
      "Epoch 225/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4197 - accuracy: 0.8523\n",
      "Epoch 226/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 1.4726 - accuracy: 0.7139\n",
      "Epoch 227/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.2252 - accuracy: 0.9022\n",
      "Epoch 228/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 1.6515 - accuracy: 0.6199\n",
      "Epoch 229/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3029 - accuracy: 0.8987\n",
      "Epoch 230/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 0.5253 - accuracy: 0.8269\n",
      "Epoch 231/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.3220 - accuracy: 0.8765\n",
      "Epoch 232/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5791 - accuracy: 0.7983\n",
      "Epoch 233/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.4804 - accuracy: 0.8644\n",
      "Epoch 234/275\n",
      "6/6 [==============================] - 0s 6ms/step - loss: 0.6642 - accuracy: 0.7930\n",
      "Epoch 235/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.1418 - accuracy: 0.7134\n",
      "Epoch 236/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.2410 - accuracy: 0.9345\n",
      "Epoch 237/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4270 - accuracy: 0.8333\n",
      "Epoch 238/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5898 - accuracy: 0.8282\n",
      "Epoch 239/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.3149 - accuracy: 0.8894\n",
      "Epoch 240/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.8839 - accuracy: 0.8107\n",
      "Epoch 241/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.7356 - accuracy: 0.8409\n",
      "Epoch 242/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.2735 - accuracy: 0.8674\n",
      "Epoch 243/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6812 - accuracy: 0.8168\n",
      "Epoch 244/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.2667 - accuracy: 0.9033\n",
      "Epoch 245/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.7740 - accuracy: 0.7972\n",
      "Epoch 246/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 0.4401 - accuracy: 0.8859\n",
      "Epoch 247/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5740 - accuracy: 0.8479\n",
      "Epoch 248/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 1.1374 - accuracy: 0.8118\n",
      "Epoch 249/275\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 3ms/step - loss: 0.4612 - accuracy: 0.9217\n",
      "Epoch 250/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.5888 - accuracy: 0.8404\n",
      "Epoch 251/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.3807 - accuracy: 0.8405\n",
      "Epoch 252/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.4468 - accuracy: 0.8997\n",
      "Epoch 253/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.5226 - accuracy: 0.8535\n",
      "Epoch 254/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.4364 - accuracy: 0.8215\n",
      "Epoch 255/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.7820 - accuracy: 0.7524\n",
      "Epoch 256/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.5387 - accuracy: 0.8494\n",
      "Epoch 257/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.5491 - accuracy: 0.7100\n",
      "Epoch 258/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.2508 - accuracy: 0.9055\n",
      "Epoch 259/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.6608 - accuracy: 0.8236\n",
      "Epoch 260/275\n",
      "6/6 [==============================] - 0s 9ms/step - loss: 0.3828 - accuracy: 0.8558\n",
      "Epoch 261/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.2830 - accuracy: 0.8716\n",
      "Epoch 262/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.4034 - accuracy: 0.8739\n",
      "Epoch 263/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.6553 - accuracy: 0.8410\n",
      "Epoch 264/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.2574 - accuracy: 0.9158\n",
      "Epoch 265/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.6298 - accuracy: 0.8106\n",
      "Epoch 266/275\n",
      "6/6 [==============================] - 0s 7ms/step - loss: 1.7179 - accuracy: 0.6914\n",
      "Epoch 267/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4137 - accuracy: 0.8743\n",
      "Epoch 268/275\n",
      "6/6 [==============================] - 0s 3ms/step - loss: 0.3930 - accuracy: 0.8589\n",
      "Epoch 269/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.3017 - accuracy: 0.8915\n",
      "Epoch 270/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.2937 - accuracy: 0.9072\n",
      "Epoch 271/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 1.1442 - accuracy: 0.6838\n",
      "Epoch 272/275\n",
      "6/6 [==============================] - 0s 2ms/step - loss: 0.4331 - accuracy: 0.8675\n",
      "Epoch 273/275\n",
      "6/6 [==============================] - 0s 5ms/step - loss: 0.2671 - accuracy: 0.9166\n",
      "Epoch 274/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 1.0852 - accuracy: 0.7065\n",
      "Epoch 275/275\n",
      "6/6 [==============================] - 0s 4ms/step - loss: 0.5381 - accuracy: 0.8080\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f772a7e6cd0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y_categorical,epochs=275)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8df8d0a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  0.7921348214149475]}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "66fd0429",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss=model.history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6da648f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d437e99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import matplotlib.pyplot as plt\n",
    "#plt.plot(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8eb316b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to pandas datatype: DataFrame\n",
    "loss= pd.DataFrame(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "920ef412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6014f84f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Oc38Q+JiZ7QQ+Fv98SuRnhmhUuIuIJDQsM8A59zLwcvx9M3D1RHzvycrPDNHWFaGrN0o4xT8VJYiInBY8c4UqxMbcAQ3NiMi0561wz4yHuw6qisg058lwb2xVuIvI9ObJcFfPXUSmO0+Fe256EDONuYuIeCrcA34fuelBhbuITHueCneAvIwQTW1dU12GiMiU8ly4F2SFdSGTiEx7ngv3wswQDS3quYvI9Oa5cJ+VHebg0W4i0b7jLywi4lGeC/fCrDB9TqdDisj05rlwn5UVBuCALmQSkWnMc+FeGA93jbuLyHTmvXDPjl2leqBV4S4i05fnwj0vPUTAZzQo3EVkGvNcuPt8RkFmSD13EZnWPBfuAIXZYYW7iExr3gz3zLAOqIrItObJcJ+VHdY93UVkWht3uJtZqZm9ZGbbzGyLmd0Tn55jZi+a2c7468yJK/fEFGaFaeuO0N4dOdU/LSJyWkik5x4B/tQ5dzZwMXC3mS0E7gXWOufmA2vjn0+pWfHTIXXGjIhMV+MOd+dcvXPunfj7NmAbUAwsA1bGF1sJ3JJgjSetMDN+larG3UVkmpqQMXczKwPOA94CCp1z9RDbAQAFY6xzl5lVmVlVU1PTRJQxoDA7Hu66r7uITFMJh7uZZQC/BP7EOdd6ous55x52zlU65yrz8/MTLWOIWQO3INBBVRGZnhIKdzNLIRbsP3POPRWffMDMiuLzi4DGxEo8eemhAJmhgM51F5FpK5GzZQx4FNjmnPveoFmrgRXx9yuAZ8Zf3vgVZOmhHSIyfQUSWPdS4A5gs5m9G5/2F8CDwJNmdiewD7gtoQrHaVZ2WGPuIjJtjTvcnXOvATbG7KvH+70TpTArzJu7m6e6DBGRKeHJK1QhdlC1sa2bvj431aWIiJxy3g337DCRPsfBdp0xIyLTj2fDvSB+IZPuMSMi05Fnw31Wth63JyLTl3fDvf9CJp3rLiLTkGfDPS8jiM/0LFURmZ48G+4Bv4+8DD1uT0SmJ8+GO8TG3Rt0QFVEpiFPh3thVli3/RWRacnT4T4rK6wDqiIyLXk63AuzQrR09tLZE53qUkRETilPh3tpThoANYc7prgSEZFTy9PhXp6XDsAHTe1TXImIyKnl6XAvi4d79UGFu4hML54O96xwCnkZIaoPHp3qUkRETilPhztARV461Qfb6eo9sYOqNYc6cE63CRaR5Ob5cC/PS+ftPYdZfP9v+OoTG2jp7GX93sP86H92827NkSHLPr2hlo/+/Uu8uPXAiO/Z19zByztO+eNgRUTGJZHH7CWF/rtDBvzGms31tHdHeG3XQXoifRTPSOUfbzuHV3Y2ccOSIv7q/20B4MmqGj6+aNbAd+w/0sltP3qdxrZuXvmzqyjNSaOxrYs7HlnH5y6Zy6crS0jx+XDAD9buZHfTUX6w/Dx8vqEPqnr+vQaKZ6SypCQbgD0H21m7vZEVl8wl4B+6nz3Q2kXAZ+RmhE6qvdE+h9831gOyjm3N5nrK8tI5uyhrXOuLyOnDTochiMrKSldVVTUp3/3G7mZu//GbPP75C3ht50Eeea2acIqPP7v2LL797FZCAR/dkT4gdrOxS87IY83metbc81EAdjUe5dvPbqWtK0JHb5RPLS3hooocNtW28PjrewBI8Rs3fmQ2AE9v2A/AA7cu5khHL3kZQZadW8yOhjZueeh3pAcDrLrrYrLCKXz6R2/Q0NrFsnNn891PLuEHa3dxbmk2V51VwNX/9D9E+xzPfe2jzEwPsmZzPX/9q638423ncNn8PABWrdtHb7SPm86ZTWY4hftXv8dzmxv4yR9dwPNbGrjzsnIeebWaOTlpfOr8EoKB2A7kF+trWfn6Hh767NKB00XfrTnCrQ/9jgvm5vDkly6h+mA72+tb+cSSouP+P+6J9NHQ0sWc3LSEtlVXb5SeaB9Z4ZRR5/f1uRE7zMGcc8Se23566uqNEk7xT3UZ4iFmtt45VznqvMkKdzO7Dvg+4Acecc49ONaykxnuAB09EdKCAVo6eln2769x+4VzuPOyci77u5doaO3i7qvOYEfDUe6/aSFdvVGu+/6rRAc9nq94Rio/uuN8Hnp5F89tbhiYft2iWZTnp7OtvpWXdzQB8JWr5rF6Yx37Dn14bv2S4mzaunrp6IkS8BkH23sI+X2Ywa3nFbPyjb0UZYepj98qoSI/nQ+a2jGLDSudWzqDF7YcoL0nQtDv49//cCmdvVG++sQGAObkpLG4OIvnNjcQ8BmOWA/+rFmZbG9oA+DiihwuLM/l/YY2frujkZ5IH2fkp7PqrktwOO54ZB07DsSWXXPPR/nyT9ezp7mD7336HC4sz2Ffcwe1RzqZkZpCbkaQn/xuD3kZIf73lWdw98/foWrvYb510yI+d/FcXtzawIXluaQF/dyzagOFWWG+dMUZ/HZ7I7NnhKksy+HHr3xA8YxUrv9IEX+3ZjuVZTN54q0aGlq7eOHrl1N9sJ3DHT1cVJ6L32cc6ejhkz98nYvKc/nuJ5ewfu8httS1ctWCAkpz0thYc4Q7V1bx7WWLuG7xLFZvrONIRy83nTObnPQgm2qP8Mv1tWSnpnDnZRVkp324A6k+2E5vtI8zCzMB6I5E2dvcQd2RTi4sz6Grt4+MUGBg59ivf2fT0RPhW6u3cOm8PJadWzzqv8GqPYe449F1PHDrYj65tGTIvO0Nrby28yBfuLT8mDuvw+09vH+gjYsqcmnr6uWRV6v59AWlFM9IpbG1i2DAx4y0IBD7a/Op9bV88fKKITuUaJ8j2udGtGVwm8w4qZ3kuupDdPZGueLM/IFpzjl2Nx2lIi/jmG0a7Gh3hLQU/zGX37DvMGcWZpIe8vygwwk55eFuZn7gfeBjQC3wNnC7c27raMtPdrgPNrj39/x7DdQc6uCLl1cMWWZLXQsba1pID/kpmZnGwqIsUoN+qg+2s+rtfZxZkMkv36nlb29ZTEV+Br3RPm77v28QDPj4+R9fxK831/Pgmu386+3n0djWzV88vZmctCB/vWwRCwoz+c5z2zjc0cu3ly1mTm4a//TCDv71t7u45+r5pAb9PLhmOwuLsvjylWfwn2/upeZQB6lBP9//zHn8xdOb2by/BYBzS2fwjY8v4Iv/UUVnb5R7P3EW6UE/96/ewuLibDbVtjCvIIO7PlrBvU9tos9BQWaIYMDHX95wNl//r41kp6bQ0ROhJ9rH39y8mHuf2kR2agqHO3oHdjKjCfp99ET7Bt4vKclm/d7DAzupmWkp5GaE2NUYO1MpMxSgrTsCwLyCjIHpWeEArV0RzKD/n2JpTio1hzoBmF+QwZUL8tlY08K6PYcG2t1/vCTgM754eQVPv7OfhtYuZmeHOb8sh19trAMgJz3IVQsKeGpDLaGAj55ILMSvObuQvIwgG2tbWL2xDp/BZy+aS+3hDl7a0TSwc89ND3Kks5cFhZn86cfP5DdbGnh7z2HauyN09kb5zq1LWL2xbuA4zVUL8rm4Ipc3PmjmcEcvf/R7c5lfkMlXfv4Oe5o7mJ0d5q9uWsSvNtWBgwWzMnnsd9Uc6ejlhiVFNLd3c9M5s1m1roazZmWytb6V7NQU/mBpCf/83+9Te7iTz19axqbaFtbvPczZRVl859bFfP7xtwkFfPzzZ85lSXE2dz5exbo9h/jylWdwUXkO2xvaCPp9/OiV3bR1RVh27mzm5qbz7KY6ctNDfHxRIcUzUvnTJzdiZtxx8VzOLsrk9d3NpIf8fOmKMwDYWtfK+41H6Y30UXu4k7NmZfI3z26lJ9LHs1+7jMKsMNvqW/nbX2/lvf2tfKaylO9+cgldkSg/fHk37d1RbjlvNnubO9jddJTz5sxke30r5Xnp/NkvNnFO6QzuuXo+v95UT1tXLzefO5u2rgjPbqpj0exs/uE3O7ikIpd7rplPTnqQ1BQ/2+pbSQ36aWzt5u09h/jsRXMB+NWmOs4uymR2diqbalvIzQhy5YICdjS0UZaXxu7GdnqiUV7e0UQ4xc/VZxUQDPioO9JF0YwwuxqPcuWCfPxmvL67mfcPtPFuzRGWzpnJrecVU3ekk8Ul2QR8xq7GoxRlpzIjLYVfbazjx69WM78ggwduXYwDMoIBtje08eymOi6bn8d5pTP5+bp9GHDJGbnjHgqdinC/BPiWc+7a+Of7AJxz3x1t+VMZ7pMlEu3DzAbGuwcPERxvuMA5x75DHczJScPMWLvtAOV56VTkZ4xYtqMnwk9+t4dQwMcnl5aQkx7k3ZojHGzr5pqFhQADt1z49I/e4Fs3L+T3zyrklfebMIPL5sWGdMyMqj2H+O6a7czJSePLV57BmYWZfPPpzWzYd4TbL5rDjUuKeHZzPQGfMScnjZKZqbR09sb+URdn8/qu2D/45ReWUpabzuOv7+Gnb+7lDy+aw9t7DtPS2cunK0tZtW4fe5o7eGRFJd//7/d5aUcTf37dAs4syOTrT77LjR8p4oUtB8jNCFKUncqrO5v4xrULmJUV5j/e2MvWulYCfuO+T5zFKzsPsudgO9cvKeKmc4p44NfbeGlHE8UzUvnCZeV8+9mtBHzGn1wznysXFPC1Jzawp7mdFb9Xxtc/dibv7jvC3T9/h6PdEZyDzHCAT51fwv7Dnby47QCFmWFu/EgRS0qySQsGeGLdPvIzQjy3uZ627gjBgI/fX1BAWsjPtvo2ttW3AvCXN5xNT7SPR1+tprm9h8KsEOmhwMDOMej38b+uqOBff7sLgLyMEAGf0dDaxZycWAfi+S0NpAf9tPdEyUkP0tbVS2FWmNbOXlq7IuRnhlg6Zwa/2XKAUMDH5y6ey2O/q8a52E4o4DcODLoLakVeOh8Mu8ajIj+dC+bm8It3aon2Oc4tnUFLZ+/AtSBluWnMK8jgv7fFTh5ID/rp7I1iZkP+moXYjjXS50hN8RNO8XGks3dgB108I5Xz585k9cY6Uvyx5Zz7cJ3R9O/o+/9/BQM+jsY7BD6DPgezs8PUHeNmgIO/f3CH4VhSU/xEnaMnPjw7Fp/F/kre0zz0ivfBv5PiN3qjjvL4WXonUucNS4r4988uPX6ho5iKcP8UcJ1z7o/jn+8ALnLOfWXQMncBdwHMmTPn/L179054HXJ66I5EiUQd6aEAHT0R3tjdzFULCvD5jN5oHyl+H42tXaT4faQEfDS0dDGv4MMd27F2jn19jv1HOimekYrPZzz/Xj3zCjIH1m/vjnCovWfg2EL/OmbQ2NZNdmrKwLDFscb0G9u6qDnUydzcNPLiB7lbOnr56Vt7uWpBAQtnx3pekWgfXZE+0lL8OOCdfYepPtjOVQsKyMsI8l9v11CQFeLy+fn4fUZPtI+Az0efc2ze38LZs7J4esN+rllYQFowQCjgo6M7Sn1rJ2W56YQCPupaushNDxJO8bOp9ghv7znMFWfmk5se5K3qZnY3teP3GZ+pLOWhl3dxcUUulWU5HOnoYVZ2mFAgtt6+Qx3cED+msqWulf95v4lPnV9CYVaYl7Y30hPt4+qzCti8v4XntzSQnZrC2UVZnD0ri1DAR1rIz2Ov7aEiP53MUIAXth6geEYq4aCfP1haTGqKn9Ub69je0EaK38cVZ+aRnxFm8/4WZs8IU5qTxjt7D1ORn8Ez7+7nxo/MZnfTUY52Rbh28SwCPmP93sN0R/pYUpzNf7yxh89dPJeqvYcJ+Izawx30RPq4dF4evVFHit+Ym5vOqrf3UZgZ5vfPKuDVXQdxznHFmfm8+cEhNtYe4bzSGdTE/+oI+IxFxbHe99Mb9hOJ9jG/MJOGli4q8tN5fXczfjPOL5vJotlZBP0+fvbWPhpbuzhvzkx2HGijoyfKWbMyaWztouZwJ5dU5HL12QW8uvMg79YcITXFz9F4x+APlpbwVnUz6/ce5pqzC5lXkEFXb3TUjtyJmIpwvw24dli4X+ic++poy3uh5y4icqodK9wn6zz3WqB00OcSoG6SfktERIaZrHB/G5hvZuVmFgSWA6sn6bdERGSYSTmfyDkXMbOvAL8hdirkY865LZPxWyIiMtKknSzqnHsOeG6yvl9ERMbm+XvLiIhMRwp3EREPUriLiHiQwl1ExINOi7tCmlkTkMglqnnAwQkq53Tj5baBt9vn5baBt9uXLG2b65zLH23GaRHuiTKzqrGu0kp2Xm4beLt9Xm4beLt9XmibhmVERDxI4S4i4kFeCfeHp7qASeTltoG32+fltoG325f0bfPEmLuIiAzllZ67iIgMonAXEfGgpA53M7vOzHaY2S4zu3eq65kIZrbHzDab2btmVhWflmNmL5rZzvjrzKmu80SY2WNm1mhm7w2aNmZbzOy++LbcYWbXTk3VJ26M9n3LzPbHt9+7Znb9oHlJ0z4zKzWzl8xsm5ltMbN74tOTfvsdo22e2HYDnHNJ+R+xWwnvBiqAILARWDjVdU1Au/YAecOm/T1wb/z9vcDfTXWdJ9iWy4GlwHvHawuwML4NQ0B5fNv6p7oN42jft4BvjLJsUrUPKAKWxt9nEnvg/UIvbL9jtM0T267/v2TuuV8I7HLOfeCc6wFWAcumuKbJsgxYGX+/Erhl6ko5cc65V4BDwyaP1ZZlwCrnXLdzrhrYRWwbn7bGaN9Ykqp9zrl659w78fdtwDagGA9sv2O0bSxJ07bBkjnci4GaQZ9rOfYGShYOeMHM1scfIg5Q6Jyrh9g/TKBgyqpL3Fht8dL2/IqZbYoP2/QPWyRt+8ysDDgPeAuPbb9hbQMPbbtkDvfRHlPvhfM6L3XOLQU+AdxtZpdPdUGniFe25w+BM4BzgXrgn+LTk7J9ZpYB/BL4E+dc67EWHWXaad2+UdrmqW2XzOHuyYdwO+fq4q+NwNPE/vw7YGZFAPHXxqmrMGFjtcUT29M5d8A5F3XO9QE/5sM/35OufWaWQiz8fuaceyo+2RPbb7S2eWnbQXKHu+cewm1m6WaW2f8e+DjwHrF2rYgvtgJ4ZmoqnBBjtWU1sNzMQmZWDswH1k1BfQnpD764W4ltP0iy9pmZAY8C25xz3xs0K+m331ht88q2GzDVR3QT+Q+4ntiR7t3AN6e6ngloTwWxo/IbgS39bQJygbXAzvhrzlTXeoLteYLYn7e9xHo/dx6rLcA349tyB/CJqa5/nO37T2AzsIlYKBQlY/uAy4gNPWwC3o3/d70Xtt8x2uaJbdf/n24/ICLiQck8LCMiImNQuIuIeJDCXUTEgxTuIiIepHAXEfEghbuIiAcp3EVEPOj/A1oXGagiA2/gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e9e52bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=model.history.history['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fbb5fbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = pd.DataFrame(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d0cc5b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TXRz7CzZmo18C9Kth21ARVTfAlkFxM5uO7KX+gq0JXWP1Iw6kLfNCFgFEYq3+MF0/Js2kh76YlkvkP0fHThB6W4UeyrFxlmlI+8MPQnkDSFsvEy089CBsHgWnkuneiNBrXiAVPBD/eqE1q77zoakadoz0yOftFPrtu4/hH+/ej50beuT/X2rpP4CmKtSzRnqw56j45TBT8+AGIdb15OTaKWXxxaMDeOjAFIKQwzRYktD3TcAymPzs+ws2jlsmGn5cWPSv//mVKNimtOfIIhsqOvLxUkFbLhpLjrVkucSVotmErhLUUme50BpOZFouWQo9+WuBBjYEGZWirfLQ4wB3c5BbVf9kuVQaPu58Mu5jQzn0qlImHEhNIiLFm0Xo/+cnL+A7e47jH7+/X9o4nMfHB5K55ABw3sbepm2DRRuMiXPc+9wEtg4VsH1dESFXbjpeIDOK5ho+zl7fg8FI2SfSFqPPs+RYsE0DThTjoBvwYMnRQVGN1Q+yBoI2AbDVgk4eetlV+4OEJ9XOthWkQg9IocfERaSadT4vRdZxpShPBEPDkCt56G6yKjN6XUwtSh6fzjk2VcVYVDpfafj4d4XQj80mSVa9Aarl9kBsG2WNllNvHqrqnVV+DZDlsq5H2D4XbBYdF0uOKYOevXkbecvE8dkGfrTvBF5/4SaMRKqdSL/uhdKaAYSlsqlf2CkibdFAwxN92h3TgGmIzpOMMdgmkzfgwaKjC4s0Vj9kQ6e1oNBllkv2tajVh17A2+aFLwScc0kQpJaPKiTZaGe5kP+vdAUEoqCocgOgm9JQyYEfcszW4mvy2/xCcQNRkv/+rz+OomPiteevR7nh4/BMTNQTKU++lgg4JhW6tFw6fHZewJGLCoRUe4cGOFP64H/4qVF8/PqL8LIdw5iICL0nbyFnG/jXxw7DCziufdEmrO+LCL0c+/w9OQtWRNSC0MWAGyJ0NxCWS85OUqpjGrJlwVDJFtOPljB9VxO6xpJDKvQ1kIfeKa9czXteijz0ihvImwn9e3SmJsmmO8slqsDMqBQFYtuCSCsrLTLrF0rDC/DseAU/2HsCv/eanTh3Yy8qboCpqouiI4Kd6Xu6SuiHUlOgWvU/J1DAFQD6CkJBZyn0F48OYENfDoNFG9ddtAUFx5S/LnpzFnKWgYobYFN/Hi8Z7cdIj7hu8uBrXoCCY8qb2bbhIjZGn01fXlgujaiwKN1fx7YMzEaf52C03qVU6ZrQNZYcMnd5DSj0Tv3Qkwq9uyyXcsPHn/3rk101uZpSPG0/FCQyVfUwGmVUtMpDp4EU6rasSlEAmIs6LBKhqz463RSySMkN4srMs0Z6UMpZCEKOY7MNbBsqZl6PaqdUUy2Gs4Kiav8a9Zh9UZHVTM2DbTI4liEJ+YarzsL3/+jVcghHQSHdnrwly/8v2z4ExhhGepMKveGFyCvZN1uHijIdkxQ65+J7LKQInXx0AImc96WCJnSNJQdZLu2KSFYLgqC15QC0UehtyPr+5ybxTz98Do8cnO54/moi6MpxJCpVpxS5VlkunpJi5EbdCqvScuEpQhfEulEqdJXQW/8CcJU8cNsyJPm6foitLQhdJWv12jjnMl1R3ae/EHvZ24cVQo9en6l5cExx7lmliZmqnAtO/Lg3b2Mqyom/ZNuA3N6bs6SHTgqdsG2oiFefux5veNEmbBrIyxvCTM1DPmW5UPGXYxooRZ9Ho02nzpOFJnSNJQcF49aCQu+Utqg2ylIrRbNyqQlEWN1kQKg3Ej8IZTHM6IAgt7hAKEy9L/7sG1F6H30douBItVxIoYubxH3PTeIV/+MuHJmpSfss03JRUjYd00DJidX0Ga0UegtC94L4F4Wq4ouOGXvZwyX5el8+slzqHhzlZgKIIKiKhELPWbK/yyVnDMrXR3pzSlA0SBD1loECdm3uw43/8ZJENstszWtS6OTtl3KmfLyUmS6a0DWWHGupl0untEXq7geQQk9WYKqouj72HpuTpNaNz65+hn7IZTOptOWStm/UZmLpYQxBk+WS9NDvePIoDs/UsX+8ArfNwA5VoTsWk4oUEL5zFojES44pb4aVhi/L64HkzTBvm1Jtqwq9X1HoOcvEzvUinz1nGbDMJM2pap364QDA+Zv65ON1vTnpwde8IGG5pH3yXPR8tu7LxwRS6KXIqweWtvxfE7rGkmMt5aF3as6lKvROHvoXfnIAb/rEj2Sv7G4yYejmmLOMDpZLSqErlkvDDxPDHbwwabmQVdGbt9GTs2Sp+1zdb9vu1vVD+V07pplQyZ0sl76CjZorBk5f+dHvJpp6qTexnGVItXxGBqFPVYVCvziyT/wMEUH2iWUw5CwD24aKMA0myRcQCv2EkraYd0yM9OYyB32TQheWS8pDJ4XuWFqha6wNLKVCD0KOv7njaZmG1g6cc9z43X2JIQ/zRdghKEoKPR81bCLP3Y8qNG999DB+tE+Uj0/XXNSioCbQ3aAIIsyCY0rLZaBoJ/xqIJ7qc+N39+HARDVluSQVup+atkOWi2MZiUwS0Qa59RobfqAodAOlXExu60o5aX2ogUJaR1/eRtUL8M1HDjVNSVKRt03ZHmB0sIgozikDma4fImcZuGSbsE+y/p8jW6Qnb4ExhjveewWe+PDrEvusVyyXRqTQf/BfXo3HPvSzTcejVMWZmodCk4cuFljKmZLcNaFrrGpQMHQpCP35iQr+4Tv78N2nxzvuu/d4GR+9/Wm872uPL/h8fodeLlXXR942ULDNhIdO7/n4nc/gMz98TjxPVWN2pdCjzzJvmdJy2diXl8Us6szKY7MNfPT2p3HbE0eSlkuq1avabRGILZecZWC4RyH0utd2wlDDDyXh2yZLKPSBoo3eyOdWbxJkN/UXbFQbAb7X4XvM27FCHyo58hzrI0IHxM3kxVsHWh5DEnr03rxtJoKegLhBzDV87Dk6i7mGjy2DhYTdo4JuUEHIWyv0nAXHFNuW3XJhjF3DGHuaMbaPMfa+jO39jLF/YYw9yhjbzRj7tcVfqsZqhToUYbEhA2ddpPw9NibGj6mE0g6/+r/vwxfvPZB5vtZZLj6KUek39U4hkHc9p/RLB+JKx27S2eiGUnBMeEGIIzN1bOyPCV1Vf2pQz09bLoo1RFkuZAlQLrdjGRjuoNBtk0m7o5Hw0I2Ehz5QtNFXEM/VmwR9PH0FG24QYrLiyv4qWchZglT7CzZs00BvdA4aBUfnzhrPR8g7SULPAlWLfvbHLwAArrlwY8t9VRumKW0x+jVRciyp5JdVoTPGTAA3Ang9gF0A3sYY25Xa7V0AnuScvwTAVQD+hjHW3V+NxpoHqdQsP/NkIYOALQYZqHg4CrS1CtCpCEOOu58Zxw/2JhVj0MFyqXsh8pYhi03UaybvulxPzpecVIYfd4JU6LYJP+CoNHz05W1J6CpOlOOgnjoD1fXDxA1Q5KEHUkGTQnfMpOUiPPTkd/jbV52Nv3/bxfK4iSyXiDAtQ6h1Ov5wBmET2R+brePs9aWm7YScbYhfDtG6aAyfYxoyz5tuTN/4nVfga7/98qZjEOmqAdE01veJgPBXHzyIi7YOyGrTzDW1CZg6keVSzJlSyS9mo7Y0ulHolwPYxznfzzl3AdwM4LrUPhxALxOZ+z0AJgH40NBAnGGxFEHR+Sj0hw9MA+hOIU3XPIS8uWFUrNCzr6Xhh8jZ4o9XpN41Wx1yAnxaoc8jKFqwDVkQlLOMTEKPPeAwoaxVD50xcZOoe6EkVfLQ87aBoVJMvnMZCv2cDT248pwR2CaDG6QUeqSEB4oOGGOy+Gddxi8kUvlHZ+oYKLTWgjnLxOaBAs5cJ0ifbhKOZWAgInRSxRdvG8RPnTHUdIyY0O2mbQRKs/QCjusu2txyPzo3Ia36aVtP7tQo9G7a524BcFB5Pgbgpal9PgHgVgCHAfQCeCvnvGnVjLF3AHgHAGzbtm0h69VYJbhj91H05m28/KxhWVi0FJaL3yWhVyI/FOhOCVPDqwMTVXDOZZVhp0pRN2rQJIYZBAmFXo36dRNh0k2BClu6UuhhrNC9gCPkIXK2Acto1mZU6Vhz05ZLID+v3pwlS//7mhS6KRtbAUC57iP9DVJOeM4yEzcO2xTpgnnbkJ0JY4XemtBn635COVMPc8c0EHCOvG3gj9/4ElnTQATqWPF5chmZKCoKTkyyrbB9XQnf+8OrUHUDnKt0acyCer4Lt/Qntqlpi6TQl7v0v/nWj6bv9XUAHgGwGcBFAD7BGOtL7QPO+U2c80s555eOjIzMc6kaqwl/d+defPoH+wEsbS+XrOKTLIxN1eY115H6gM81/ETDJ2m5tPijFArdiDx0nrhmGnRQjibS0wR4abl046FTlottRk21xFAINdWaMj+kh+4nLZeGF8r87968LUfTEZFKQrcMvGzHMF61cx12ru8RHnrqszOik4kWssksFwDoydnSCqHjZ1ouilruK9hSzRPpWibDmy/aglectQ4Fx5R2DlkutmK5ZKUWqsgrWS7tsH1dCbs292X++lGhEjpVmxKIxEtOnOWy3KX/YwC2Ks9HIZS4il8D8HUusA/AcwDOW5wlaqxGqDnYS5nlQsfu1Ael3FCnsHe2NtRyd9V26eShu34IxxQeulr6DwAzNepjzhMBxLiwqPO6SAHnHVPmjwvLJf5TpgpNWbruBgmrhDoDAoI8/Wg0HZXP0y+InGXgwi39+PxvvBTrenIo1/2mqVNW5BE7phHloYvtpNz78pZsPTsQ9R4nX14lQrWkvzdv4UWjQukSCVoGw9/84ktw9a4NifNTUDRnGfI8ObODQifLpY1Cnw/UGwh57wRbyXKhG8l//+YT+Ns7nl6Uc6fRzRXdD2AnY+xMAIcAXA/g7al9DgB4DYAfMMY2ADgXwP7FXKjG6oKvZHhQUQt56PvHy9g+XIIR/dHPVD14Ydg2u6EVuvXQ1cEH3VgbaUJ/8ehA4nxtCd0yEHION5XlQvnmgFDpaZWfHJ9WR8400V9M+rzScrFMuYacFZfDA8LSKDd8vDBZASAqLROWixcmLJegleWiesN5C2NTNZkhQiCFnrNFELgRiOsni+rDb7pAEvjbLt+G8zbGirc3b6MR2UIqofflbdz49ktw2+NH8a+PHcax2Uai6EdF0nJx5FraIW93znKZD9SgaBqxQrewrieHD75xF47O1HBRSskvFjoqdM65D+DdAG4H8BSAr3DOdzPGbmCM3RDt9mcAXsEYexzAXQD+K+f8RPYRNU4H+CGPZ4kqCv3YbB2v/dvv4zt7jst9P/wvu/GuLzy0oPPQzaKT5UKByJJjduehl7MVert+4ACkYnYiwvUThB4fs1z3m24K6rre+fkH8eF/2d10fDoe+cCAIDAiVgDYGKnE58YjQk9luTT8AFVXeP0524AXBVdLjgnTYDKtUiX03pyFubrXVClK3r1U6D5PFA5dcc6I9JVHB4v4uZdslk2/tgzEarYvpdAHig7e/tJtsmyffgmkoWa5kEJ3Oij0/oKNgm3K6tqTBRVQ/crLz2japuahA8BvvPJMfOANu/Az521o2ncx0NUtinN+G4DbUq99Snl8GEBzCZXGaQs/8o8555KEQs4xUXYT470AoYbT0+W7BVViqgr93v0TqLoBXh0NJAYgUwWHe3LSuwaAJw7N4NnxMq67aEviuJOVBvryIp/8YIbl4odiUIGR8ldJoQchb8pDn25S6EkLqu4F+Nw9z+O152/A0Zl6ZvCKCFXNdxb9SuK9N0SEXlHazyYsl6h3dyFqdCXz0G3RaKoWBjAYEqq/J29l5qGT2iaF7gZBRw/7km2D+Mn7X4O/ueNpPBrVBiQtl/gxrSEr6Kvum8xyaX/+Us7C9//LVRguzf8XYas1/OT9r0kUNxHo5lLMtVbxiwk9JFpjSeCHQp2q6X1+wOX0GbW3SBCGC07lkt0MFUL/5PeexYlyI0nojXgKj6qEb7p7P77/zHgToU9EBS45W4wni9earPzMG8k/VJpc70fX5IccBhMFNGov89m613TNx+ca+ONbdmOu7qNc93HcaG5noAZFCXnbTCj0oiMKbyiYqxYWMSZ+CTDmi86FpoGGH4gqR8tEzjZQ84KEbQJEXQnrzTchInRVodst1LSKjf35xE0oYbkUYloiQm91zMu2D+IVZw1juMdRslw6k+f63nzHfeYD+tWRBllFi2XvdIImdI0lAWV4qN5tyLkkXs8P8Z6bH8YV54zAC/iCCT3LQ6+6flOQlBpODZUc2aEQEHbKbN1rUtsTZRdDJQccyRFo6VL+dCEJKXQWIFLoIUqOhbmGn/TQ637ilwIA2Qxqpuah7PpypJtKrHT+XFqhK2s3DDGkgQi95gXwIsulx7HQ8AOEnKNgm7BNJvvPOJYhA5VpUuzJi/TGct1LvG4qaYtUWNRJIRNU1a2mKqoZL0T66Y6JhBePDuCLv/UyAOhaoZ9KqM25TgVWzpVrrCn4QQg/SCr0IIxTtryA4649x/HAC1NNzaHmdZ6Q2sXGN4SaFzT55OW6j56cJXusEA5OVpumxQPCEhoqOSjYZsKfV3u6p1P4AEhCI8XqB1z+3KYsFyA7KEqj3o7O1MG5UNJzqSk+2ZaLmUitMxmTpev02dC5SjlLjksrOCZMw8BsRNIFJbUuTYqUEaLaRoCi0C2h9N0gbBnATIPI2jZZ4noShG7EWS6d0G0e+qlE7KGfGstl5Vy5xpqCF408U4NoQTQyTWwXKW5+lN540gpdHWXmZhB6w0NPzpLEI17zpXev5poDwnIZ7nGQt03UlJuFH8Q9T7Jy0Rue2G5bBtzoVwoFxNQ4QbnRHBSlm9IRZagypR4SqEgrn1LoCUI3mBx0DIi2CL4kdKGkq24gFLrBpB1VsE2pJNOBRfKqJ6vJWIeVIPRQpm12AyJ+yzBgGCyzJD9W6J0JfaQ3h5xlLChbaqmwrscRGThd9g86WWjLRWNJQHaLalEEoeKh+0K9+9F/C62ey/LQ627QNFCi3PDFhHfLkL8S1GCnSuhhyDFVFQq93AgSxw5CjmKUKeP5ST8ZgEzb4zyuFO2NbiSHlSHIc3W/ZfuAw9OxJXR8toGzRnqU84ewDJYgOFEpmiT0hEL348HSPXkbDT9EyLmYZG8yObmo4Jh41c512HN0rumGSB4w58KHp/fItEXLgBuIm3T3lkuSrIuOiZoXJAp+OgVFVfTmbXznD6/Chozg5HLhDS/ahEu3DyV+dSwltELXWHRwLpQpZXoQAg7UXOrXLQJxXkT8XsAX1BrAz8hyqWZYLnNRSXkuUpJAMh1RJfSJiosg5Fjfm0fBNpLTfSLvGQDc1GxIUf0ZImcJ64LiCJZpYF3JSdhCWQqdcHQ2JvTxVJ93P+CwTJYIEuYsM+H/m0Y86JgxYW/R59OTM2VPGQqKEoqOiWtftAlA3NiLoJKsOr1HFhZFN8r5KHQ6t5oJUnTMhGVD+3QTaAXEeLhWfvtywDKNto29Fhsr58o11gxIDYqhDjFJh2EcFKXSc/Lagdbl9H/wlUdxyyOH5PM7dh/FNR+7O7ppxNWWPJKNZLlwxe8uN/yoQVJckNNKoRPRbxsqCg89pdCpuIZyu3/nCw/i0z/YL9efIw89EAU9psEwpPQvGSjamXno6jkITZZLwGEZyd4tTUFRZXI9dSWM8/At2W2xYCcLkgq2hYta9BFXszTU+ZqxQjdlc65uFbqdVui21dQBcT4KXUNbLhpLAHVCkZrl4odcpitSP24viF9reM1ZI4AgcAAytfC/fu0xTFU9TFQa0nKhIKIdBSMBcYOgbI25uo9N/Xk4UZoe5zyh0GeV7A0i+q1DReQdM9VqVlXo4qZx2+NHcdvjRyUZOqaBwBLq3PXFNVHXwoJtoi9vi7RF5QZGrQLSSBO6H4bNCt1OeuiWwWRa3khvHifKLmZrHkyDoRDZRVU3QMGxEkRZcEwwxvD537i8qU2uqjLFd+TJcwGIrCyR717qMkXPNJNkXXBMhDxpTcigaJcK/XSHvu1pLDpUha5WKIq0RSL0SKGH8Zi2RpCd6dIIwoSCppzlibKbULN1L0iQr2q7UJZLzjIQcrG2w9M1OZcyS6GPDhZEVkzUkyUMOTgX1oS4zuRw5Y/duRcAZHMuAKh5QqGTUi46YtbmVCpbZKDQ7LGaBsPxuXriNT/MUujJLBfDYNg2JMaz7YjazM41fFgGQ94SWTs1CooqREnX9aqdI4kcfgAYLDmy86J601WzXNzo8+heoSftlA19uaZ8bluSvib0bqAJXWPRQRkVYUqhByGXwcrYcuEycyNLoXIufPjZDEI/PtdIBF1rXpCYxKMGM4XlYid6Utc90T/GMlgToW/sy4vRZBF51b1A9qIhQnP9MKHsKTuFmnPR+yyF0PO2iZ68Jdvzpq9JxbahIsbnGhibquKbDx+KPq+MoGg6y4UxbBsu4s7fvxI/e4EoMZ+r+6I8vmRjqipmmRad5I0gPW0njZ3re+X55LkUhS7z0Oedtij2/8jPvxgfe+tFiX1MactoquoG+lPSWHSo5fHJPPTYQ6dJ95S6CGQ3zfIjVawSLvX9GJ9rJBR6zQ1Qd5NNqABxY6Esl3hqTBzAU6sqAUHo26IBBzRrsuYF8lwFldBr8Q2E2hmIEnom12QahvTQi46J3pwle8VQzdBAsZnQtw4VMVlx8c7PP4j3fPkRTFddJSia9NBNpfiIiPKskR4UozTE2ZoHO5r0Q8HaQioomp6rmcbODT3R9SlBUaVSNOQiw6jrLBfZp0X8O1hymlrrzjcoerpDE7rGooMUd3Meemy5kDUimng1K/TZuodrPnY3Hj04DSBF6PmY0FWvt+YFqHrNXRUrkWrvjYKiYltUBGM1E/rBySq2RoROalwMiaDGWLGHTq1mC7aJaZrFacYFOlXXTyj0oiMUOuWkU8FOWqEXHRN9eQuVhi9vfvuOl+GHXAyPMFQPPWW5KOROAcy5ugfLYIkJRJSHrp6zHWhK0LSSi07ZNfTLZ67hd02+dO52++ug6PygPyWNRQeReFMeOo/T5yjrIpHlohD6j/aewJ6jc/jr20XfaJVwibzG5xoJS6fuJafZqwVEAGQeOp2LFHpfwZaWTt0LcHS2Hit0m6o8PUlkCYUeVZhu6s/L3GxRQi/2qbgBTDMm0rwtPHS62VC5el+K0HtyFnpyFipugE39IiD5zLGyCIoaLKGsc1HfFfpcVHeC1jpX92GbRmJaUDptMd+hB8r2iNDV7CBVoQPis56vQm9XWTqfwiINTegaSwCyWdJ56Im0xah/iBfEPrtquVDJOxGwGHQc57ADomd4wkN3wxShi/2prL83b8VTY3wqgmEJhU5l99RalQjxI9/ag7d88sfiNSUoSgp9k9IKNmcZUtkHIRcKXbVclCITUuY0R5MUdU8+IvSGL+2Yvcfn4AUcphFnuYjH4j1ku6jDLvIKoTvKcGW6DiLkvG00dY5MY9cmMYRs1+Z4GJkh3y/Ow3l7glbRTcBTBk61Qu8K+lPSWHTIPuipPHQ/5Kj7ybRFKioCkgqd2t2qATgKQBJRN3noXiCDrUDsoROhiywXU26jAJ5K6HEBjrBCiLyfOjIrUwhVhU7H3tgXp/U5lpHwo9Usl4JjJnKtJaFHpL05Sg/szVko5SxUlaHSe4+V4Ue9UsiCyApQqmJWWkZRcHZIJXTbVKo0O6cabujL464/uBL//Q275GtExmoMoFuFbkrLpfX+cVBUK/RuoAldY9FBJE654QQ1KErEq25XG3SVM4YsEOkSUY+Xmz30ZNpiMgBbysWWS8MP4EV56+lWs0DsCRMhqn1YiNDV7JtN/UmFXkgFDolIC7aVKNK5ZNsAzt3QKy0eyvcmhQ7Eueh7j8+JtEUlDz2T0I1mDx0QxKn2AC86lrQ9OmW4EM4a6UnsS3696s13neWSSlvMgk5bnB80oWssOlSbRU0dDHmctpjVgyWh0CMSZsqYByJdKsgRCl05l5v20KkTo3gtb5lJDz3qO9IfeehBNKMTiIkyi+hIfTciha6W2gORQk/lalNjMMpDJ1x+5jBuf+8VsnkT3Rh6IoUOAMeiVgDHZhuYrLiwDUMSsdrmNiZ0JXNFWYcd/XKg4GfBMWRgslOGi4p0EROAhDfffXOuzimJpiws0lTVDXSlqMaiI1Hs4yfL5uupiecqAauVkxSALCvtY6VCj445V/dRbgRygIRa/i/2iwg9+jdvG/DD2EOnytLt60oIOfDseFkhdCK91oTuBRyzdQ+9+aTqTrdvNQ0Gxhj++I278JLRARxWuinSLxB6z8YoANqbt2XLVbUI6fhcA2eP2HGFpqLArYygqFoEROQ9VHJQdWso2JYk504ZLunrST9WvXl73kHRNlkuOig6L+jbnsaiQ809Vwk8CHnTMGd1e0N5PFFuJvTZlOUCCPVKZFp1g0SrW1LmUqHbZuyh+6JMPWcZuCQa2PvwgSn5C6KdQqdsEPLQe/NWotw9Z5kJIiVr4ZdedgZeNNqfmDafJvT+go2R3hxGenOJmwS1hC3X/UQeunrzoABlMm1RIfToPWoKJb2W1XKhFSx5HsjhG2o3wa4VehcpiUT2OijaHfSnpLHoSKcSAuKPX/XQCaoqbyiPybMu17MUeggSiXNR0y06V81tzkOXqts2pKKlwiLbNHDmuhIGijYeemFa7kvecxahO1FlJmW59OXtBPlmWS4q1M6FsRce9QLPWfjKO1+O377qrMRNgsrua14ggqKp9wHN7Wjp3ESwpJyHlACt2rq2WxgZXr2aITN/hd6N5aIVejfQhK6x6FADlQ1FHQe8mdBVqD3MqeoyYblE1oPrhzL1r+b6sKPuhtR0Kn08dQ1EbjU3QMghZ2devHUADx+carJc8k7zn4gVpQ26gagUFQo9OXBCtWrSAb0se2bLQAFbhwrYtbkPZ64rNd0kVI/eNJhUrAmFzpoVOgCMDgkbh9oTUzWmSFucX1BUvZ70jYp+eeQWMLGoFeLiI01V3aCrT4kxdg1j7GnG2D7G2Psytv8RY+yR6L8nGGMBY2xo8ZersRqQCIpKxWuKPPQ2k4ncIMS+43P4zc8+IBtktfLQKfWv6gYwDYacbcjmXEQsmUFRWTmZzKK5ZNsg9h4vyz7gRJSOaSDFWzAi1Uu9XHozFHo+I1hJ6E3YE2K//qKNH/yXn8GFW/rltlKG5QIIApQKXfXQzWyi/ZM3XQAA2HN0FkBsuajtc+cTFKUbhsmyf3nYVndqOusXRRpZmTsardExKMoYMwHcCOBqAGMA7meM3co5f5L24Zx/FMBHo/1/DsB7OeeTS7NkjeUGZaOIyTwi0FlwTFRdH0XHShX7xJ6012HUnOuH+MK9B3DnU8fka9mEHmJjvw2gJgc15G0xuKHhhegr2Jhr+AqhC4vGNlncTjc6Lim/bcNFcA6MTVWj9Yr9GBOj0SqK8rcMJrsLztV9oaYVG8UxRZEODdNIK3Q1D71dzraq+lWFbhnZlovM2U6d71U7R/Anb7oA26LOkm+5ZAsGik7CupmP5dLqxtGbt3BkJr5JdTyO0dlyicfUaULvBt0o9MsB7OOc7+ecuwBuBnBdm/3fBuBLi7E4jZWJKz/6XVzx198FAPz42Qlc9Kd3YN/xMi7603/H3c+MJwhdDUiqGS1ZaPhhIp8biDNmHNOI0xb9MKXQDeRtA3UvlF0Ec8rs0LoXIG+LXt+kvMsphU6WA2WUqMo3rV7NJoUeB0VNpSyf3memAnrqQIp2hN6bi5X8OiUt0DKzLRezheUCAL/yiu149bmiJe55G/vw21edBSAmzPlYLq06INIvj67b55qd7ZR0R0aN9ujmU9oC4KDyfCx6rQmMsSKAawB8rcX2dzDGHmCMPTA+Pj7ftWqsAMzWPRyZqcsxaWNTVTT8EHuOzsL1Q+w9Xk405CKLxTGNOLe8hdhy/RCVRjbpDxRtVFxfDpXuiwidKiDzlol6VCkqCd2jtMVAZnHEPUeokVbScqB0STVTI50BYhoMdqS+yw0ffQU7c7gykWTaUmCMxfZEG7shb8d2j2q5WAaDYTAYrHNhUSfElkv3Gcytbhx0k+327FYX6ltXis4P3RB61ifZavjjzwH4USu7hXN+E+f8Us75pSMjI92uUWMF4c4njyWek4VCbWQnUtWb1A/cMJis2FT95vSxKorFotoMNLCZsmJUH9o0GPK2KZtz5W0TOdtMWC75iPjI/05XopLlMFXx4FjJviZp9UoKfbrqgnMRDDQNYc0klL1tyv3ToM+gnZpljEnlnyB0pe1sIj2yhRXSDguyXIxsIqZrqiiZRu2P07mwiJS5TlvsDt18SmMAtirPRwEcbrHv9dB2y5rGt54Q4+CoVJ1Ik/qsTFZcOVIOiAjdZLAMJjNQWk1Ab/gBKq6PdT05PP+RN+C8jb1y20DRRqXhS9Wt3hQsg3VluRByliGDorHlII43U/OaCoPSlotliIlElBdP20s5K1PZZylQSegdrISeDEKXmR+Rl0+Im3PNR6HP33Ihbk2fh9IhO1lrBPrsnS6Colqhd4dufmfdD2AnY+xMAIcgSPvt6Z0YY/0ArgTwS4u6Qo1Fx3wG+aZBrVPJ26YiIiK3iYqbSlsMYRtGQqGnBwGr6wq4mEwPJO2EoZKD509UFYUeH8OMiK3c8FFxfWzLFWVAEhAKXR3KkLNNuV4n5XdPVd2mRlV520z0e6Hzpdvp9uTMRPwg9tCbyag3GrbBWvlPEUihU6dI1w+lon3v1efgkjMGE58D0Jx90g5WynLq6j0Ro6ev6w+uPhcA8OaLMx3Zluduq9B16f+80PFT4pz7AN4N4HYATwH4Cud8N2PsBsbYDcqubwFwB+e8sjRL1VgM/GT/BF78J7djotzovHMGXKXXORBbLkR2kxU3lbYoFLrJ0JHQG5HlQv6ymsExUHQSCl21XGxT9B+veyGqjQAlR1SEyjx0P0g0qSo68TAKO2W5VN2gSaEPFGw5exSILRe65qKi0NX3Fjoo9G5uqkToPTmr6Xi/+aoduGRbBqHPQ6HTL4Csmaat0Eqh9xdt/Ol1F3ZddZqPBnO0suCAOBWyJ9f9Ded0RleREM75bQBuS732qdTzfwbwz4u1MI2lwcHJKupeiCMz9aZxX+3ww70ncOGWPknWpNDdaLAzDXqYKCdb2gqCFAUs1Yhge1tYLm4UZJQBRoXwBqOgKPWGSSv0vC2mzlei1MmcbSTy0NW88KJjyqAuFcEkLBk7SbQfetMF8PwQV//d9+EFXKYtzmRYLsqly2Oms1zoM+hmsg8RWSki9Jma19J+WAihnzFcws3veBkuVZR+J7RS6PNFT87CV294Oc5VrLU0zlwn1nfZdl3W0g10c67TDKSw1eBjJzT8AL/yv+/DH73uXKnI0z3ME5aLSujRBBvDgJzok6XIDCbWVmn42NgnUhdlybrJ0JOzESqzRVVCt5SgaNUNUMqlsly8EOt64v2LjimPk1boQPKXARC3tM3bJrzAlwMm6DJJOV+wuQ/TSiMtIvoshb5rc59si9sOJceSQ6fj42Ure3q906CKNF62Y3he+9Ph52PttMLF2zrfSOa7vtMZmtBPM3ipOZvdoOGHCEKOSsOXBC4VeiooOlf3E0GxqhdgXc5KqLm+QvP/diXHQsMThE42Ayll2zSkUqWmXWmFnosUcxByodAtU3rcdS9IkHTRseJxcUo/Edtk8ALeZLkQCrYp2+Wqvx7Ic//Qz12Q2r+1kr3hyrNww5VnZZ5HhWijG7UhaGPhAEqPlUUg2nagcXe6enPlQRP6aQZS1uUW+d5ZINJ2/Xi6kPTQU0FRIO7fDcSetJqznGW5lHIWGkGIciOQhE5k61iG9FKpx0vRsWTbXMsUCp2qOUsyy0XNQ0966AQ7lZXiBX5rQleCnOr7Chn9XoD2aYvd4ucvGcX50eg3ukG0ChC26rGyFDCZJvSVCE3opxkWYrnEszxD+Tg92HlW6Yp4TLESKKNGVZVZlkvRMWUeOqlxUsGOaUhffYoKf6JBzDUvrhQllHJWcx66nfTQCUmlLRR4rkVQTw1Kqu9rVZSTb5Pl0i1euXMdXrlzXXQecbxW3ruxgLTFhUIr9JUJnQt0moEIWCX0n+yfwGd//Dz2HS8DED71Pc9ONL3HDUIly4WDcy6fqwr9uKLQAZF+mLBcMrJcijkTNddHzYsVOtkkwnIRr5HlkrMMSapUKUqgbBO126JK6CoBZ1V2tlLodAxDaSEAAMUubgCLgU7HO5UK3TKY7q+yAqEJ/TQDZamoTa9+7+aH8aFbd+Ovv70HAPCl+w7gl/7pXjnImd5TbSQHNwchh+s3zwY9liJ0xzI7Wy6OJe2UdBVlzjIkyU9WqBuiKbdTpSihGI1Zo2us+2Eic6XUQqET0bfz0AEkBkyI97Un9MUiWJk108JyMRdQKbpQGFH1r8bKgib00wxehuVCU4NomtCJOZF6SARLZE0ESZaF36J7ojoyDRAqWE21K7WwXMi2iRV6HLCk12jwRS7qgS62sybLZaQnh9m6j7oXwPXDprRFgmpfkEedznKR2xULhc7NWJsbgBPfABYDROh2CyI1W7S1XQpohb4yoQn9NIMryTkOilIzrXSREKXguUGa0AW5+iFPjJvrL9iZjbdydhwUzduxVaKSMM3SBGJClx66FVsu5KHnLEMSqeiHniRs6gMzNlWLztXCcsnIVsnbHRS6Ych0x0LUxTEL7fLQF4L4F8LyB0UN7aGvSGhCP81AWSmqQvdlkVCS0Ik83ZRCJ+UZBEmFXrDNxLxMQs6MPfS8bUqFqZbY79oUF5ekg6K2yWTq3qT00E3FQ082qSo5liT0g1F/86SCz85yoetqpdAlQbNYobdrarXoHnqnoOgp9tA1oa88aEI/zZBlucR9WdKEHvcfF+8Rqp5IzAvDxBzQnG2gvyj8cZV0HMuQNkDeMhPqlkCpeQDQE/UBzykKnbJcTpDloih902CymyIgAqyS0CeJ0BWFrjzODIq2UuhReqJpxlku7XqgLJmHvgKCogZjp8Ta0ZgfNKGfZiBypok9nHOp0D1f/EuEPhMpdCJ66lBIRBWkPPScZaC/QGSc7G4YK3RDqmJV3Z6jlH+XUs257GgCEKU2AoKIiYytVFC0J2dhfa+oNo0Jvdlasc1kYK/oJM+bRiJtsYvBEGcMF+GYBjYrdtLJQLV8skCfcdaAi8WGZWqFvhKh89BPM6QVutp7JJ2CmFboNBRCKvQgTDTiylmmrALN2wao/5ejFPnklTmWKqGXFPslK8sFEN561Q0kEWdluTAW/QroEdsOTkYeuhoUzcXpkCroGC2Dokraoi27FLb+E9q5oRd7/uyaRcsGKdixBZWFViPolgK6sGhlQhP6aYY0oftK7/J0UFR66EFcoAPECrd7hW6iYsSj6NJ2BWNJmyArDx0QRD8+15CvJ/LQI7Ir2qZIqQPDUMlRPHSF0O3k++XrHRT6q89bj9m6D9tkcKI1FFrYM4TFTO1r145Xff1UpBO+7fJt2DywOL88NBYPmtBPM7ip0n+1MyIpbiqhpyyXRio1sSAVeorQ7ZjQVYsj4aEnLJfI+ogshPM39eGpI7NNCt2RCj3Z0yTOcomDokUlKDvSk5OWSy7TcmlB6C1I+uJtg7KZlC0n/Zy6PyGZttgqD/0UVor+1hU7lvwcGvOHJvRVihcmKrBMQ3YC7BZUCEQKXU079IIwUfGZznIhEPEFYVwpCpDl0qzQHSuZ5RLPsUzmaX/xN1+Kxw7NNBE2EdgTh2YBAL/wU6PRceP3k6WiFg2t78vh6WNz8rxy/ZRFM0/LRUU3QdHFRqesGSos0vnhpy90UHSV4r1ffgR/cuvueb+PCLzmBQhCLhW6bTK4fijtFiD20FWfHIhJzA/DlpaLikRQVEk3LKayNgZLDq48J541m1bo1120GQDwntfuFK+bGZaLk1TohKzCombLpX2lqIpugqKLjW3DRVgGw8b+fOZ26xQGRTVWJjShrwJUXR8fuuUJzNVjsh0vN2Ql53ygknPF9aWHXrBNeAGP+43nLJnl0qTQo/mbfpBW6DGh0zxPIKnQC47ZlOXSykKQ3Rajf//2Fy/Cnj+7RippNShKhUVq4y91yHRWlktaoVNaYleEntFHfalx3sY+7P7T1+GM4VLm9lNpuWisTGjLZRXgkQPT+Ow9L+CVO0dw9a4NAEQKYcnpvmMiQSVnNRe9EJXeE6Gfsa4oM0RaWS7NCt2UhE4BVHpdTVu0UhkirSyCXAZxm0YyHZLeLxW6UjT06vPW4579ExgoOokAnryRWMnz0qDoVt0WVdjLoNCB9nbQFeeMYKLiQvP56QtN6KsANHaNGlNxzjFX99GbXwChByEYE9ODKg1fEoRQrQ3ZzfCMoRJ2H55t8smB2HJRSRtIBkVFW1uGIOSJoGjOMuGYBgwWD7poRehphd60nYg+yklnLJn++LIdw7j13a9seh8Ns0gfl4ZmlLpQ3cvhoXfCpduHcKke1XZaQ1suqwA1VxAnNaaqukE0QSg5pIJzjocOTIFz3nQMgheEknTLjUB66GRjjEfJ42cMF8GjkW+tFDopfLJsVcul3PClKneiwiA6T9428elfuRTXX7YNjLXuTeKkgqJN2xUPnTERGO3WAik6VtNxX7ZjGH/31pckBi+3QlZxlIbGckMT+ipAPeqCSH1MqGKznBpS8cShWfz8J3+Mhw5MtzyW64cYLjkAgOlqPP+T8qlPRMMpzhrpAQAcmak1KXQiMerO2CObWsWWi+uHUnnn7GTaIgD8zHkbMFRyYBtGy26ERcfEYNHGlsHsTB7VigGA0cECzhgutrz29LHTQVHTYHjLxaNd5XGT3XOqLRcNjXboynJhjF0D4OMATACf5px/JGOfqwB8DIAN4ATn/MpFW+VpjthyEYRO8zvFSLhQqkXyv2frXsZRBLyA48x1PXh2vIKxqZrMmCDr4ES5gZxl4NLtQqU+cnC6SaGT912N8tV78xbmGn5TlgsRbU5pn5smQMtkMg89Dds08KP3/UwiQ0WFWlgEAP/yu6/sOmWv4JgtrZxuID30U5iHrqHRCR3/b2SMmQBuBHA1gDEA9zPGbuWcP6nsMwDgkwCu4ZwfYIytX6L1npYgr5osFzXbpdLwMVAUipsyWLyMHuUEzw8xOliAYxk4OFnFRVsHAMQBwcmqh968hW1DRQyXHDz0wnRTkI0UekzoNjBTR84yE8MrVIUet89NEXqHrn3tCnek5WJSO97u1fLPX7xF9ntZCM4YLuK152/AZds72zMaGqcK3ciLywHs45zvBwDG2M0ArgPwpLLP2wF8nXN+AAA458cXe6GnM8hymYiCorO12GopZxF60NpDbwRies/WwQIOTFalh04KfbbmoeCIHt8XbxvEwwem8KLR/sQxSGXTBCMKJubsOD3RNpnsA+6YJkgMp3uNU4ByIUgr9Png3T+zc0HnJFAcQENjJaGb35xbABxUno9Fr6k4B8AgY+x7jLEHGWO/nHUgxtg7GGMPMMYeGB8fX9iKVymOzNQwrgxPTmPf8TnU3CBzWyPloc8mFHr8HiLydCEQgXMOLwjhmAa2DRVxYLIqPXQq8pmte5KwL942gP0nKjg+m1w3KeGqF1suQOwrf+7XL8edv3+lJHGRh56toi2TtQyKdkIu5aFraJzu6OYvKeuvJS0BLQA/BeANAF4H4IOMsXOa3sT5TZzzSznnl46MjKQ3r2m898uP4MMtKjtdP8Qb/+GH+MK9L2Rur0mF7sqURYIaGCUiTwcxCUHIwTliQp+oymlFCYUeke5LRgcAAI+NTctjOFasqGuq5YI4R/qKc0ZwxnBJtnnNWQZMmQmTtlyMBZeqn4xC19BYi+jGchkDsFV5PgrgcMY+JzjnFQAVxtjdAF4C4JlFWeUawHTVa6kkp2su6l6y7F4FeegNP0TVDVIKPYPQW3jopOBty8DWoSLmGr4MtMaE7uOsEfF4uEdYORXll4M6fYiGSKcVOkGmLab6oauwTbbgmZtaoWtoJNGNQr8fwE7G2JmMMQfA9QBuTe1zC4BXMcYsxlgRwEsBPLW4S13dEJ0Ms71taojVajt56IDIdGmt0NtbLkT0dqTQAWD/iQqA2Bd3g1CSe1ZfFtuKuyVW0go9RdYyKGrFeejNWS5Gy4ENnaCOoNPQ0OhCoXPOfcbYuwHcDpG2+BnO+W7G2A3R9k9xzp9ijH0bwGMAQojUxieWcuGrDV7AWxLtdIsmWIS6orgnKm6iI2KW5dKS0AOa9sOwNSL051KErj5WCd02GbyAw1EUOlkuA0VqmZska1WhW0Z2los67Hm+SPd00dA43dFVEi3n/DYAt6Ve+1Tq+UcBfHTxlra24AUh/BYKvCOhKwp9otzAXN1HX97CbN3PtFxaKX3a7ij54mTzqCXsROhFR7S69UOOnpyFqaoH24rzxslyecVZw/jzN1+Iy1Nl5yqht0pb/OM37pIDLeaLy7cP4c/ffCEu2TawoPdraKw1aGlzipAe16ZiugvLhao7p6oe5uqebDZVybBcWnnoquVCtgmpbFWh5+UkISaJvycfdyg0U0HRgm3il152RlO2CnnjjmlgpDcH22TSlye8dMcwLtySTIvsFpZpZJ5XQ+N0hf5LOEWgqk7CTNXDJ76zF2HIMZ0axgwAuw/P4P8+ILJFG16IwYjQa16A2bqP4R4Htsnk5CH1/a1uHPS6rQxYpgyaYoZCB2LbpSdny/daMigq3tvK8jANA45lgDGGK88ZwT3vfw3WKT3KNTQ0Fhea0E8RhIceK/C79hzD/7zjGewbL0vbQyXiN/z9D/FHX30MgCDdAeoz7gWYq3vozdko5ayEQve79dAtQ7aOlSq7BaHTBKLeXJzJ0i2hWwaT/jhjTJO5hsYSQxP6EuDQdA0v/cs7ccmf/TsefGESgCBTXyFaIuJKw5ceeiuPve4Fshq07gWYrfnozVsoOUlCd2WWi/j3XV94CF+890C83aegaKzQyZ9PBEWdDIWej+dwptMWW864VAhdQ0Nj6aH/2pYAzxydw7FZMVHomWNlOerNUwYyk1VScwOp0FsVBNX9AL15CwYTOelV10cpZ6EnZ7UsLOKc49+fPIbvPn1c2U7j5gQpMxarbJXE85mWSzy4mTEGy2BdKfSTaYCloaExP+i/tiUADVcGksFQL0OhV91ACYqK7Wo/8yDkqHsh8raYbF/3AtQ9kSteypmouBmWix+i3PDhBqGceq8en0jZNg2psrPSFoFshQ6IgGdDUfxZUEfDaWhoLD10788lAA1XBpL556qlQkRccX05u5O2z6VUd90LkLPEYIiKG4jiH9tEKSdSFw9N1/C9p48rlksoK0APTFbBOQdjTP4CsJXsE6pCTXjoTkzQWQodoGKeMOGTp6EVuobGqYX+a1sCTDcp9CidMEOh1xSFTtuPTNcT729EijxvGfLYBdtEybFQc33c8sghfOAbT2CmRtkyPDHd6EQ5OeyZVLZjGfKcnRQ6ZcE4ikIHgFLOAmsxZf6nz16Hq847vXr2aGgsJ7RCXwJMVV0x9KHuw1PSFRNB0ch/rriBEhSNCH2mJvereyHcIETeMpGzTWnn5B0TOdtAww9Rj45FLQHcIJSzQQGh0kd6cwnLBUCibW3ONmEwIOTZHjrNAk03xOppUxT0m6/a0c3HpaGhsUjQCn0JMF31ZIqeF4RSGYccsv+4muVCzbZIyR+diRU6DbPI26JEnsi/YJvIWQYaXii9bCJ0YbnELW/JR5eEbjbP6rQMFg8+zkhbdCxB5vEcT/FvKac9cg2NlQJN6EuA6aqHgaINxzTgpnq40GMi9KOzdVAM1JMKXSV0sR8NV55SLJecZaLhB5LQKePFC0JpuQBCoQOK5WLFlgvBNJgyVq1ZoTuWqPakSk9KXVxo2b6GhsbiQ/81ngRu330U24dLOHdjb+L1qaqLDX35qKFVssuiF4QiuBmlLR6ZriW2AUmFPqso9LxtyIBrwRGKveGHaEQzR8uk0H2OybKLgm2ir2DhhYmI0GXaYhwUJZgsDmBmeeiOyfDld75MWix0jHaWi4aGxqmFVugngQ984wl85ofPNb1OCt22DPipHi6UyUJZLqTG+/KWJP5jc60VOqnsvB176A0vqdDdKMtlqOTg4q2D+PYTRzA2VZWzRnOmIGxS5AYDDMVyUT30M4aLeMnWAVywuR/re/NyxqdU6HpIsobGioEm9A647fEj+P2vPJK5rdLwE3nghKmqi8GiAzuyXNxMy0Wo6sORQl+nBC0nFbsk9tBN5K1kJkreMhGEXK5BErovLJfhHgcfeMP54AD+4t+ekuugDBVS2eSHZ1kupZyFW971000NtGhfyk/X0NBYfmhC74B7np3Atx4/2vR6GHLUvKBpDmjDD1B1AwxGHroXhFIZA5DVouShz0YKfKQnJxX6RNmVQVUaCC0Uevx1FaIsF3UfeY4gxESlgeGSg61DRVx5zgj2HS9LdZ9LeejqYGegeQhFFswuslw0NDROLTShd4AXCI9ard4ERDk+EHcrJFAWykDRgZXlofshguhmoEJV6BOVBjb2C0KXCt0yElYIBUUBNI2u84IQk2UXQyVxjLxtou4HqHsBLCMeyiyrPiWhZw9yzoIlg6I6y0VDY6VAE3oHuEGIkAN+mCR06mNSTSl0ykIhyyXdB90PQ1lur0IodLGt7oXY2JcHECt48tAJlLYo9kkSumq5iPeKitCGHyaqOtNFQrkoNbGbGZ1BdIPTWS4aGisHmtA7gNR1IzU0gqyWtOUSK3RbeOh+0kN3fS79c0LJMVFwTPgBlwVBGyJCV4Oi6szOfMJySRL6XMNHww/laLicZaLhCYWu3hRiyyVW7N3YLUA8uFpbLhoaKwea0DuA/O9GyiIhZd5suQhCFnnoLFOhV2TbWRbt68A2RK8Vyh/f1E+EHhcSpYOiZLmovV/Ee8RzdXhz3W9W6FmWS/eELq5bZ7loaKwcaELvACLjtEIn26TZchEETJaLH4apwiIuA6IjUeCzv2BLcj0+K1IWmxV67KFbUREQkTPPbqOuDKUQ6Y61lEKnc6qzP9UMl3agz0NbLhoaKwea0DuA7JJ6SonHlktSHTd56D5PzPj0glBaLiO9gtApZx0AjkWEvpEUekPcIHJKlgup6JzVnnzJDskr1oxaHepEU4vIQx8s2nJ2aSfQ56EtFw2NlYOu/hoZY9cA+DgAE8CnOecfSW2/CsAtAKjK5uuc8z9dvGUuH1opdLJaal4g29MCwkPPRUrXtgzUap6s0AREYRGRoUroZHscJUKnoGitWaHTEGfVU88C5YgT8c/W/aSHnlLoH3zjrpaDqtOIFbrOctHQWCnoSOiMMRPAjQCuBjAG4H7G2K2c8ydTu/6Ac/7GJVjjsqJVUJSslpCLbUSUUxVRVAQAtsGa89CD2EMnQu8vOFI5H51pIGcZ6I8CmnN1D4wJ8s2lmmflOyj03nyzQl/fG8/1THvow/OY+Um/OrRC19BYOejGcrkcwD7O+X7OuQvgZgDXLe2yVg6kQm9huaQfT0Vl/wAy0xZVy2Vdhod+bLaO4ZIjy/Pn6j7ylgnGmLxpSMslQ6GrKYe9uTjLBRD56gkPPTGsYmHQHrqGxspBN3/JWwAcVJ6PRa+l8XLG2KOMsW8xxi7IOhBj7B2MsQcYYw+Mj48vYLmnBpWGH8/n9NsHRYFkpstMzY0J3TISE4uAVFA0w3I5NlvHUI8DO/K3/ZDLQCUpbWm5ZEwKKipBzZ6UQp+peW3z0BcCTegaGisH3RB61l972mh9CMAZnPOXAPgHAN/MOhDn/CbO+aWc80tHRlbuJJsLPnQ73vn5BwG0yXJRSLyaUujSconSFhMeupK2SAp9oGBLy+XYbF0GVAl5applkUIXz7OCosVEH5Zk8DQIeYs89IUTurZcNDRWDroh9DEAW5XnowAOqztwzmc55+Xo8W0AbMbYukVb5SkEldF/Z89xAKqH3tpyUTNgpqsuBiJCdzIsF9cPUWn4yNuGJEMqQgJE4LK/ECt2IC7Fz6UtlwyFTnnhjmVIIld7wCTz0Kk518IJ/WRuBhoaGouLbgj9fgA7GWNnMsYcANcDuFXdgTG2kUVpHoyxy6PjTiz2Yk8F9h0vJ57HHnp2UFR9zDnHdNXDYMJD54mgqB9yVNwAPTkLZ63vwab+PM7b2Jcg1d68mNNJlggRubRc2njoxUiV9yrKWVXy7fLQ54N3XrEjcQ4NDY3lR8e/SM65zxh7N4DbIdIWP8M5380YuyHa/ikAvwDgtxljPoAagOt5upvVKsHeY3MAILNBWhcWqYQuLJS5hg8/5IrlYiRmitLxKg0fRcfCloEC7nn/awAAz01U5D6k3GmIc5rISaGrAyoIRVu8V21r20qhOycRFH3/tefj/deeP+/3aWhoLB26kliRjXJb6rVPKY8/AeATi7u05cHeSKFTYDMOiqYtlzgoSpbLdMVLvNc2RTm/G3A5XYiCoulgokrOPbn4/YCSppjKQ7dMA5bBEo3DSKH3LLFC19DQWHnQlaIpPBMpdEotJA+9nmG5kPIlta5WiQLJtEUKVlLaYk+qIEe1XEhdp9vZ5lN56ECsuOnt5KH3dqPQZRtdTegaGmsBmtBT2HtMKHRqihVbLimF7gUYjvqNV90AByer+OYjhwAAg6XYQw+5UPA0us2PCouKqaZWtkK0vbk0oWdbLkDsr/dFsz8pxZFUPtBaoS9GlouGhsbKgSZ0BZMVF0dn67BNhnLDRxByaWdkeejUb7zuBfjgLU/gf//oeQCi8hOAzCWvuYEkWjeyXNLpfgnLJR976ICarmjinA09iaHUpNr7os6Kpeg8LRW68jiuFNX/G2horAXoNAUFDx+YAgC8aucIvrPneKLPeFaWy4a+WKGraYyU5UIkXXF9OKYB22RCoTeCRL44kCzu6ZWWSzR4IlLVhsFwx3uvTLyPtvVHCr0Y3ShaeuiW6qGL42uFrqGxNqClmYKHD0zDNBhevmMYADBZjYc1ZwVF+/I2bJOh6gaYifqk/MYrz8RQKfbQAUH4tmXAMgzZyyUdFLUTQdFsyyUL5In3FcR7ihHBq1kuqm+uKnQn1ctFQ0NjdUMTuoKHDkzh/E29WB8p76mKSujNCr3omCjYJupegKOzdbzugo344Bt3yc6LRMiVhg/HZFHlaGfLpTcVFG03dILIup1CNwwlp93SHrqGxlqFJvQIQcjx6MFpXLx1UBLqZBtCJ1+84JiYrLiYrnqyhzmBbJSqG8A2Ddimgbm6j5DH6YXpfYE4oOmkslyyQARNhJ7loQOxMs/00HWWi4bGmoAm9AiHpmqouAEu3NInCTVB6Ep5P+ccVU8o9KJjYf8JkRmzKUXojqLQidBnauKYaYVuZwRFKaja1nKxyXIRa940UMBwycG5G3qT+1nN7QJ0HrqGxtqCDopGOFFpAADW9+Yl2SY9dKUfSxAiCDmKjoWCbeK5cVHluam/kDhmwkM3DVgmk0Okm9IWo30Zi33wdB56FnKpLJfhkoMHP3h1037p1Efg5CpFNTQ0Vh70X3KEibIg76GSI+2KpIfe3P+8YJs4a30PKtHztEKnLBI/5HAs4WNPR5kz6cIi2rfHsWAYSQ++3SALUt6k0FWln9zPSPwL6KCohsZaw2lH6O/64kN4/9cfa3p9MlLoQyUnVuiVOG1RrRSlytCiY+L1F26Ur6c9dDtlb3Sj0FXvO27O1T7LxTQYhqLq1HQ6JCGfah8g1helLWoPXUNjTeC0s1yePV5GKWdhfK6BQ9M1XLR1AAAwEanx4Z64FzmV8tsmS1guROgFx8Srz12PQjTAOW2NqJkraQ89nbZIKllNN3QySv3TyNliNN3Vuzbgc79+ObYOFbP3y1Do6RF0GhoaqxunnUJv+CFmax7+8fvP4pf/6V75+mTZRcEWQU7bNJC3DRkU7c3bmZZL0bFQcExcd9FmXLilv+lcKlEWbBNW1E4XaA6KMibSGtXXyYZp56HvWNeDHSMlOJaBK85pPTQkS6HHaYun3f8GGhprEqedQq97ATgHTpQbmK37cP0QjmVgouLKgiBApA6SQu/NW4lKUWqXS8r5L9/yIrAMkataLhv789hzdFY+z7JGbNNAT95OPAfaE/pvXbEDv3XFjrbXDGgPXUPjdMBpJ80afojZuicnE81GTbgmKq7szQIIEp8sxymGCcvFiy0XQBTusAxGVy2XjX35RDZJ1ug22zQSQyO6qRTtFlkKPWcZ+I1XnolXn7v+pI+voaGx/DjtCL3uBai6gbRTiNgnKw0MJxS6hblomLMg9CzLpbVyBpIZJ5sG8gnFni4sAkRREPVSB5TmXG0UerfIWQYMllTjjDF88I278KLRZrtIQ0Nj9eG0sFzu3T+Bi7YNwDENOYzi0HQNgELoZRfnbuiT71GJtTdvZQZFOxN6TJ6b+guwZToiyxzwfON/vCSRKZMecHEyyNsm8raZ+UtCQ0NjbWDNK/Tjc3W89aaf4N8eOwIv4KDhPifKsULnnONEynKhUnpAKHTXD0FT9WhaUWEeCn1jXx6bBgRZbx3MzkS5eNtgojjJ7iJtsVucMVzCthYZMBoaGmsDa16hz9YE+c7UPNRTHRPFdg8VN4Drh4mgKE0dAuJUwv988yP46//wYtS8OMulHRzFYik4Jv7kTRfid646O3HsdugmKNotbrhyB97RRfBUQ0Nj9WLNEzp53zUvkHaLipmaJ4OfSUKPFfqV56zHwwem8S+PHsYv/NRonIfegWjT2SOmwbB5oNBi72ZcsXMEh6ZriUDpQsEYg64f0tBY21jzlgtVeNbdoGlIBQDMVD3Zs0UNig4oKvqy7YP48jtfDoMBD70whZobyArNdlCDoAvBi0b7o5RIzcQaGhqd0RXjMMauYYw9zRjbxxh7X5v9LmOMBYyxX1i8JZ4culHolFeuWig0FxSI8sNzFs7Z0IuHDkzJXuid4LToq6KhoaGxFOjIOIwxE8CNAF4PYBeAtzHGdrXY768A3L7YizwZkCoXhJ6h0Gse3CiDRfW8BwqxQicv+5IzBvHIwWlUGs1DnrNAhP6rr9i+4PVraGhodItuzNnLAezjnO8HAMbYzQCuA/Bkar/fBfA1AJct6gpPElKhu2FmUHSm5smURLWKUk1bpPTBi7cO4Iv3HsDjh2Y6ZrgAouDo6T+/BrYurdfQ0DgF6IZptgA4qDwfi16TYIxtAfAWAJ9qdyDG2DsYYw8wxh4YHx+f71oXBOmhZ1guG/vyCYWuErqaiUIe9q7NIk997/FyV5aLOKYp2+FqaGhoLCW6IfQsNuKp5x8D8F85580SWH0T5zdxzi/lnF86MtK6kdRiIumhJy2XbUPFlpZLVmrhZiVHfDGKfTQ0NDQWE90Q+hiArcrzUQCHU/tcCuBmxtjzAH4BwCcZY29ejAXOFzM1D/uOl+VzslNqbqzQSTCPDhYwm7BcYpJOz+QEhA1DKr5bha6hoaFxqtANod8PYCdj7EzGmAPgegC3qjtwzs/knG/nnG8H8FUAv8M5/+ZiL7Yb/OP3n8Uv/uM98jmReM0LJHGv68mhN29hoOhECl3soyr0LJuEMSanEnXjoWtoaGicSnQkdM65D+DdENkrTwH4Cud8N2PsBsbYDUu9wPlifK6ByYorG2g1Mjz0kd4c+gs2+gs2Km4gR8g5XeSNU6+Vgr3ma7I0NDRWGbpiJc75bQBuS72WGQDlnP/qyS9r4ahEOeUTlQZGnaLMbFHz0N9y8RbM1f2m2aG5LgidfHRtuWhoaKw0rLl8unJDkDa1x5V56G5sufynl5+B9159juyRMlv3wFh3gx5IoWtC19DQWGlY1b7BnqOzOHukB5ZSkVlpkEIXhJ5W6IzFBT8FR/w7U/PgmEZTif233/Mq2dyLoD10DQ2NlYpVq9CPz9Zx7cd/gFsfTSbcEKFTw620h5634p7g+SirZabmZdot523sw+VnDiVe26gtFw0NjRWKVUvoh6ZrCDlwcLKWeL3ciD10AKhHNosXcJQbQWKcG1kuMzUfTsbAiSzECn1V/7jR0NBYg1i1hD4+Jwh7vFxPvJ62XBpKdeh01U30FpceeguFnoWz1/fgynNGcOkZgwtfvIaGhsYSYNXKzONE6NG/hAoFRcvkocfVoVNNhC5IfLbmYaQ319V587aJz/765QtfuIaGhsYSYdUSOhE5Efux2TpCzuEGgsAnMxV6UolTYHOu4WPLYPeDJzQ0NDRWIlYvoZdjhT5T8/DSv7wLW5RpQCcq2Qp9o9KPJa/45t1aLhoaGhorFauWxcYVy+Uv/+0pACJQCgCMAZNRULThBbL97VTVQz5DoQPdVYlqaGhorGSsWhYjq6Xhh/jqQ2OJbRv78nHaoh/KcXKuHyY9dEsTuoaGxtrBqmWxE3MNScJByHHuhl65betQEZWou2LDCzBQiIdVqGmLOfVxl2mLGhoaGisVq5LQOecYn2vgvI0xib/ugg3yMXnps3UPdT9M9DZXFXrOMkDFoXr+p4aGxmrHqmSx2ZoPNwhxQTRBaFN/Hi8aHZDbN/SJ4p9y3UfDC9CvjJNT+5wzxqTtoqp1DQ0NjdWIVZfl8sShGfyvH+wHAOza3A/gIC7ZNpjII9/YJx6XG36k0GNCf9XO5KSkgmOi5gVaoWtoaKx6rDoWOzJTxw/3nsDoYAE/fdYwrjxnBG+6aHOS0KPy/OmqhyDkMigKAFeekyR0ynrRQVENDY3VjlWn0K/etQFX77paPqeqTZodCsSWy4koV32gqAZFk8FPeq6DohoaGqsdq47QWyFnmRgo2qg2AgyVhCKfiFIXS46Fv3jLhXjFWeua3keErhW6hobGaseaIXQAGOnJYYK56M0LRU4KPW8beOtl2zLfQ2mMmtA1NDRWO9YWoffmUPcDlHJCdY9LQm9tp1C1qC7919DQWO1YU4R+1bkjeGGiipxlwrEMnCh3nhUq0xY1oWtoaKxydEXojLFrAHwcgAng05zzj6S2XwfgzwCEAHwA7+Gc/3CR19oR77jiLPm4N2fhRNQeoF3AM68VuoaGxhpBR0JnjJkAbgRwNYAxAPczxm7lnD+p7HYXgFs555wx9mIAXwFw3lIsuFv05C0cmKwCgAySZoEUuvbQNTQ0Vju6YbHLAezjnO/nnLsAbgZwnboD57zMOefR0xIAjmVGT86S4+hobFwWdFBUQ0NjraAbFtsC4KDyfCx6LQHG2FsYY3sA/BuAX1+c5S0cVOJvGQzDPa2nERV0HrqGhsYaQTeEzjJea1LgnPNvcM7PA/BmCD+9+UCMvYMx9gBj7IHx8fF5LXS+6MmJ1MUNfXmYRtYlCMg8dF36r6GhscrRDYuNAdiqPB8FcLjVzpzzuwGcxRhrquLhnN/EOb+Uc37pyMhIxrsXD6TQ29ktQJy2qC0XDQ2N1Y5uWOx+ADsZY2cyxhwA1wO4Vd2BMXY2Y6IRLWPsEgAOgInFXux80JMThL6xA6FTdovOctHQ0Fjt6Jjlwjn3GWPvBnA7RNriZzjnuxljN0TbPwXgPwD4ZcaYB6AG4K1KkHRZ0KMVuoaGxmmGrvLQOee3Abgt9dqnlMd/BeCvFndpJ4fYcim03S8uLNJBUQ0NjdWNNStLe3PdKfTzNvVi+3BRTjnS0NDQWK1YU6X/Kshy6eShX7C5H9/7o1efiiVpaGhoLCnWrEK/8pz1eOeVO3Dhlv7lXoqGhobGKcGaVehDJQfvf/35y70MDQ0NjVOGNavQNTQ0NE43aELX0NDQWCPQhK6hoaGxRqAJXUNDQ2ONQBO6hoaGxhqBJnQNDQ2NNQJN6BoaGhprBJrQNTQ0NNYI2HI1RWSMjQN4YYFvXwfgxCIuZ6VhLV/fWr42QF/fasZqubYzOOeZAyWWjdBPBoyxBzjnly73OpYKa/n61vK1Afr6VjPWwrVpy0VDQ0NjjUATuoaGhsYawWol9JuWewFLjLV8fWv52gB9fasZq/7aVqWHrqGhoaHRjNWq0DU0NDQ0UtCErqGhobFGsOoInTF2DWPsacbYPsbY+5Z7PScLxtjzjLHHGWOPMMYeiF4bYoz9O2Nsb/Tv4HKvs1swxj7DGDvOGHtCea3l9TDG3h99l08zxl63PKvuHi2u78OMsUPRd/gIY+xaZduquT7G2FbG2HcZY08xxnYzxn4ven3Vf39trm1NfHcSnPNV8x8AE8CzAHYAcAA8CmDXcq/rJK/peQDrUq/9NYD3RY/fB+Cvlnud87ieKwBcAuCJTtcDYFf0HeYAnBl9t+ZyX8MCru/DAP4wY99VdX0ANgG4JHrcC+CZ6BpW/ffX5trWxHdH/602hX45gH2c8/2ccxfAzQCuW+Y1LQWuA/DZ6PFnAbx5+ZYyP3DO7wYwmXq51fVcB+BmznmDc/4cgH0Q3/GKRYvra4VVdX2c8yOc84eix3MAngKwBWvg+2tzba2waq5NxWoj9C0ADirPx9D+S1kN4ADuYIw9yBh7R/TaBs75EUD8jwhg/bKtbnHQ6nrW0vf5bsbYY5ElQ5bEqr0+xth2ABcDuBdr7PtLXRuwhr671UboLOO11Z53+dOc80sAvB7AuxhjVyz3gk4h1sr3+f8DOAvARQCOAPib6PVVeX2MsR4AXwPwHs75bLtdM15b0deXcW1r6rtbbYQ+BmCr8nwUwOFlWsuigHN+OPr3OIBvQPysO8YY2wQA0b/Hl2+Fi4JW17Mmvk/O+THOecA5DwH8L8Q/zVfd9THGbAjC+wLn/OvRy2vi+8u6trX03QGrj9DvB7CTMXYmY8wBcD2AW5d5TQsGY6zEGOulxwB+FsATENf0K9FuvwLgluVZ4aKh1fXcCuB6xliOMXYmgJ0A7luG9Z0UiOwivAXiOwRW2fUxxhiAfwLwFOf8b5VNq/77a3Vta+W7k1juqOwCotXXQkSonwXwgeVez0leyw6ISPqjAHbT9QAYBnAXgL3Rv0PLvdZ5XNOXIH66ehAq5zfaXQ+AD0Tf5dMAXr/c61/g9X0ewOMAHoMggk2r8foAvBLCVngMwCPRf9euhe+vzbWtie+O/tOl/xoaGhprBKvNctHQ0NDQaAFN6BoaGhprBJrQNTQ0NNYINKFraGhorBFoQtfQ0NBYI9CErqGhobFGoAldQ0NDY43g/wEE3DqX+MJR9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b5e8e39d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.reset_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c4236c80",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"model_92_percent_accuracy.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ba4b4aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 12.37,   1.63,  24.5 ,   2.22,   2.45,   1.9 ,   2.12,   0.89,\n",
       "         2.78, 342.  ])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.iloc[128].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "24e2a85d",
   "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>Classes</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2</td>\n",
       "      <td>12.37</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.30</td>\n",
       "      <td>24.5</td>\n",
       "      <td>88</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2</td>\n",
       "      <td>12.04</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.38</td>\n",
       "      <td>22.0</td>\n",
       "      <td>80</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.57</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>3</td>\n",
       "      <td>12.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>122</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.94</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.29</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3</td>\n",
       "      <td>12.88</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2.40</td>\n",
       "      <td>20.0</td>\n",
       "      <td>104</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.42</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>3</td>\n",
       "      <td>12.81</td>\n",
       "      <td>2.31</td>\n",
       "      <td>2.40</td>\n",
       "      <td>24.0</td>\n",
       "      <td>98</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.36</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>3</td>\n",
       "      <td>12.70</td>\n",
       "      <td>3.55</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>106</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.29</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>3</td>\n",
       "      <td>12.51</td>\n",
       "      <td>1.24</td>\n",
       "      <td>2.25</td>\n",
       "      <td>17.5</td>\n",
       "      <td>85</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.51</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3</td>\n",
       "      <td>12.60</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>94</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.94</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.58</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>3</td>\n",
       "      <td>12.25</td>\n",
       "      <td>4.72</td>\n",
       "      <td>2.54</td>\n",
       "      <td>21.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.27</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>3</td>\n",
       "      <td>12.53</td>\n",
       "      <td>5.51</td>\n",
       "      <td>2.64</td>\n",
       "      <td>25.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.10</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.69</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>3</td>\n",
       "      <td>13.49</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.19</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.82</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3</td>\n",
       "      <td>12.84</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2.61</td>\n",
       "      <td>24.0</td>\n",
       "      <td>101</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.920000</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.15</td>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>3</td>\n",
       "      <td>12.93</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.31</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>3</td>\n",
       "      <td>13.36</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.35</td>\n",
       "      <td>20.0</td>\n",
       "      <td>89</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.47</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>3</td>\n",
       "      <td>13.52</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.72</td>\n",
       "      <td>23.5</td>\n",
       "      <td>97</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.06</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>3</td>\n",
       "      <td>13.62</td>\n",
       "      <td>4.95</td>\n",
       "      <td>2.35</td>\n",
       "      <td>20.0</td>\n",
       "      <td>92</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.02</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2.05</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>3</td>\n",
       "      <td>12.25</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>112</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>8.210000</td>\n",
       "      <td>0.65</td>\n",
       "      <td>2.00</td>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>3</td>\n",
       "      <td>13.16</td>\n",
       "      <td>3.57</td>\n",
       "      <td>2.15</td>\n",
       "      <td>21.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.30</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.68</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>3</td>\n",
       "      <td>13.88</td>\n",
       "      <td>5.04</td>\n",
       "      <td>2.23</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.68</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.33</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>3</td>\n",
       "      <td>12.87</td>\n",
       "      <td>4.61</td>\n",
       "      <td>2.48</td>\n",
       "      <td>21.5</td>\n",
       "      <td>86</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.86</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.86</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>3</td>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>3</td>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>3</td>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>3</td>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>3</td>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3</td>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>3</td>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3</td>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>3</td>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>3</td>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>3</td>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3</td>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>3</td>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>3</td>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>3</td>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>3</td>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>3</td>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>3</td>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>3</td>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>3</td>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>3</td>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Classes  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "128        2    12.37        1.63  2.30               24.5         88   \n",
       "129        2    12.04        4.30  2.38               22.0         80   \n",
       "130        3    12.86        1.35  2.32               18.0        122   \n",
       "131        3    12.88        2.99  2.40               20.0        104   \n",
       "132        3    12.81        2.31  2.40               24.0         98   \n",
       "133        3    12.70        3.55  2.36               21.5        106   \n",
       "134        3    12.51        1.24  2.25               17.5         85   \n",
       "135        3    12.60        2.46  2.20               18.5         94   \n",
       "136        3    12.25        4.72  2.54               21.0         89   \n",
       "137        3    12.53        5.51  2.64               25.0         96   \n",
       "138        3    13.49        3.59  2.19               19.5         88   \n",
       "139        3    12.84        2.96  2.61               24.0        101   \n",
       "140        3    12.93        2.81  2.70               21.0         96   \n",
       "141        3    13.36        2.56  2.35               20.0         89   \n",
       "142        3    13.52        3.17  2.72               23.5         97   \n",
       "143        3    13.62        4.95  2.35               20.0         92   \n",
       "144        3    12.25        3.88  2.20               18.5        112   \n",
       "145        3    13.16        3.57  2.15               21.0        102   \n",
       "146        3    13.88        5.04  2.23               20.0         80   \n",
       "147        3    12.87        4.61  2.48               21.5         86   \n",
       "148        3    13.32        3.24  2.38               21.5         92   \n",
       "149        3    13.08        3.90  2.36               21.5        113   \n",
       "150        3    13.50        3.12  2.62               24.0        123   \n",
       "151        3    12.79        2.67  2.48               22.0        112   \n",
       "152        3    13.11        1.90  2.75               25.5        116   \n",
       "153        3    13.23        3.30  2.28               18.5         98   \n",
       "154        3    12.58        1.29  2.10               20.0        103   \n",
       "155        3    13.17        5.19  2.32               22.0         93   \n",
       "156        3    13.84        4.12  2.38               19.5         89   \n",
       "157        3    12.45        3.03  2.64               27.0         97   \n",
       "158        3    14.34        1.68  2.70               25.0         98   \n",
       "159        3    13.48        1.67  2.64               22.5         89   \n",
       "160        3    12.36        3.83  2.38               21.0         88   \n",
       "161        3    13.69        3.26  2.54               20.0        107   \n",
       "162        3    12.85        3.27  2.58               22.0        106   \n",
       "163        3    12.96        3.45  2.35               18.5        106   \n",
       "164        3    13.78        2.76  2.30               22.0         90   \n",
       "165        3    13.73        4.36  2.26               22.5         88   \n",
       "166        3    13.45        3.70  2.60               23.0        111   \n",
       "167        3    12.82        3.37  2.30               19.5         88   \n",
       "168        3    13.58        2.58  2.69               24.5        105   \n",
       "169        3    13.40        4.60  2.86               25.0        112   \n",
       "170        3    12.20        3.03  2.32               19.0         96   \n",
       "171        3    12.77        2.39  2.28               19.5         86   \n",
       "172        3    14.16        2.51  2.48               20.0         91   \n",
       "173        3    13.71        5.65  2.45               20.5         95   \n",
       "174        3    13.40        3.91  2.48               23.0        102   \n",
       "175        3    13.27        4.28  2.26               20.0        120   \n",
       "176        3    13.17        2.59  2.37               20.0        120   \n",
       "177        3    14.13        4.10  2.74               24.5         96   \n",
       "\n",
       "     Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "128           2.22        2.45                  0.40             1.90   \n",
       "129           2.10        1.75                  0.42             1.35   \n",
       "130           1.51        1.25                  0.21             0.94   \n",
       "131           1.30        1.22                  0.24             0.83   \n",
       "132           1.15        1.09                  0.27             0.83   \n",
       "133           1.70        1.20                  0.17             0.84   \n",
       "134           2.00        0.58                  0.60             1.25   \n",
       "135           1.62        0.66                  0.63             0.94   \n",
       "136           1.38        0.47                  0.53             0.80   \n",
       "137           1.79        0.60                  0.63             1.10   \n",
       "138           1.62        0.48                  0.58             0.88   \n",
       "139           2.32        0.60                  0.53             0.81   \n",
       "140           1.54        0.50                  0.53             0.75   \n",
       "141           1.40        0.50                  0.37             0.64   \n",
       "142           1.55        0.52                  0.50             0.55   \n",
       "143           2.00        0.80                  0.47             1.02   \n",
       "144           1.38        0.78                  0.29             1.14   \n",
       "145           1.50        0.55                  0.43             1.30   \n",
       "146           0.98        0.34                  0.40             0.68   \n",
       "147           1.70        0.65                  0.47             0.86   \n",
       "148           1.93        0.76                  0.45             1.25   \n",
       "149           1.41        1.39                  0.34             1.14   \n",
       "150           1.40        1.57                  0.22             1.25   \n",
       "151           1.48        1.36                  0.24             1.26   \n",
       "152           2.20        1.28                  0.26             1.56   \n",
       "153           1.80        0.83                  0.61             1.87   \n",
       "154           1.48        0.58                  0.53             1.40   \n",
       "155           1.74        0.63                  0.61             1.55   \n",
       "156           1.80        0.83                  0.48             1.56   \n",
       "157           1.90        0.58                  0.63             1.14   \n",
       "158           2.80        1.31                  0.53             2.70   \n",
       "159           2.60        1.10                  0.52             2.29   \n",
       "160           2.30        0.92                  0.50             1.04   \n",
       "161           1.83        0.56                  0.50             0.80   \n",
       "162           1.65        0.60                  0.60             0.96   \n",
       "163           1.39        0.70                  0.40             0.94   \n",
       "164           1.35        0.68                  0.41             1.03   \n",
       "165           1.28        0.47                  0.52             1.15   \n",
       "166           1.70        0.92                  0.43             1.46   \n",
       "167           1.48        0.66                  0.40             0.97   \n",
       "168           1.55        0.84                  0.39             1.54   \n",
       "169           1.98        0.96                  0.27             1.11   \n",
       "170           1.25        0.49                  0.40             0.73   \n",
       "171           1.39        0.51                  0.48             0.64   \n",
       "172           1.68        0.70                  0.44             1.24   \n",
       "173           1.68        0.61                  0.52             1.06   \n",
       "174           1.80        0.75                  0.43             1.41   \n",
       "175           1.59        0.69                  0.43             1.35   \n",
       "176           1.65        0.68                  0.53             1.46   \n",
       "177           2.05        0.76                  0.56             1.35   \n",
       "\n",
       "     Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "128         2.120000  0.89                          2.78      342  \n",
       "129         2.600000  0.79                          2.57      580  \n",
       "130         4.100000  0.76                          1.29      630  \n",
       "131         5.400000  0.74                          1.42      530  \n",
       "132         5.700000  0.66                          1.36      560  \n",
       "133         5.000000  0.78                          1.29      600  \n",
       "134         5.450000  0.75                          1.51      650  \n",
       "135         7.100000  0.73                          1.58      695  \n",
       "136         3.850000  0.75                          1.27      720  \n",
       "137         5.000000  0.82                          1.69      515  \n",
       "138         5.700000  0.81                          1.82      580  \n",
       "139         4.920000  0.89                          2.15      590  \n",
       "140         4.600000  0.77                          2.31      600  \n",
       "141         5.600000  0.70                          2.47      780  \n",
       "142         4.350000  0.89                          2.06      520  \n",
       "143         4.400000  0.91                          2.05      550  \n",
       "144         8.210000  0.65                          2.00      855  \n",
       "145         4.000000  0.60                          1.68      830  \n",
       "146         4.900000  0.58                          1.33      415  \n",
       "147         7.650000  0.54                          1.86      625  \n",
       "148         8.420000  0.55                          1.62      650  \n",
       "149         9.400000  0.57                          1.33      550  \n",
       "150         8.600000  0.59                          1.30      500  \n",
       "151        10.800000  0.48                          1.47      480  \n",
       "152         7.100000  0.61                          1.33      425  \n",
       "153        10.520000  0.56                          1.51      675  \n",
       "154         7.600000  0.58                          1.55      640  \n",
       "155         7.900000  0.60                          1.48      725  \n",
       "156         9.010000  0.57                          1.64      480  \n",
       "157         7.500000  0.67                          1.73      880  \n",
       "158        13.000000  0.57                          1.96      660  \n",
       "159        11.750000  0.57                          1.78      620  \n",
       "160         7.650000  0.56                          1.58      520  \n",
       "161         5.880000  0.96                          1.82      680  \n",
       "162         5.580000  0.87                          2.11      570  \n",
       "163         5.280000  0.68                          1.75      675  \n",
       "164         9.580000  0.70                          1.68      615  \n",
       "165         6.620000  0.78                          1.75      520  \n",
       "166        10.680000  0.85                          1.56      695  \n",
       "167        10.260000  0.72                          1.75      685  \n",
       "168         8.660000  0.74                          1.80      750  \n",
       "169         8.500000  0.67                          1.92      630  \n",
       "170         5.500000  0.66                          1.83      510  \n",
       "171         9.899999  0.57                          1.63      470  \n",
       "172         9.700000  0.62                          1.71      660  \n",
       "173         7.700000  0.64                          1.74      740  \n",
       "174         7.300000  0.70                          1.56      750  \n",
       "175        10.200000  0.59                          1.56      835  \n",
       "176         9.300000  0.60                          1.62      840  \n",
       "177         9.200000  0.61                          1.60      560  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8a2ecc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.7243458e-04, 9.9272233e-01, 6.5052737e-03]], dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[12.37,   1.63,  24.5 ,   2.22,   2.45,   1.9 ,   2.12,   0.89,\n",
    "         2.78, 342. ]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f5e626",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "546cdeed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58a6dd93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5f33c1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "290efce2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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