Mushroom Dataset.ipynb

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      "text/plain": [
       "     class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0        p         x           s         n       t    p               f   \n",
       "1        e         x           s         y       t    a               f   \n",
       "2        e         b           s         w       t    l               f   \n",
       "3        p         x           y         w       t    p               f   \n",
       "4        e         x           s         g       f    n               f   \n",
       "...    ...       ...         ...       ...     ...  ...             ...   \n",
       "8119     e         k           s         n       f    n               a   \n",
       "8120     e         x           s         n       f    n               a   \n",
       "8121     e         f           s         n       f    n               a   \n",
       "8122     p         k           y         n       f    y               f   \n",
       "8123     e         x           s         n       f    n               a   \n",
       "\n",
       "     gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0               c         n          k  ...                        s   \n",
       "1               c         b          k  ...                        s   \n",
       "2               c         b          n  ...                        s   \n",
       "3               c         n          n  ...                        s   \n",
       "4               w         b          k  ...                        s   \n",
       "...           ...       ...        ...  ...                      ...   \n",
       "8119            c         b          y  ...                        s   \n",
       "8120            c         b          y  ...                        s   \n",
       "8121            c         b          n  ...                        s   \n",
       "8122            c         n          b  ...                        k   \n",
       "8123            c         b          y  ...                        s   \n",
       "\n",
       "     stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                         w                      w         p          w   \n",
       "1                         w                      w         p          w   \n",
       "2                         w                      w         p          w   \n",
       "3                         w                      w         p          w   \n",
       "4                         w                      w         p          w   \n",
       "...                     ...                    ...       ...        ...   \n",
       "8119                      o                      o         p          o   \n",
       "8120                      o                      o         p          n   \n",
       "8121                      o                      o         p          o   \n",
       "8122                      w                      w         p          w   \n",
       "8123                      o                      o         p          o   \n",
       "\n",
       "     ring-number ring-type spore-print-color population habitat  \n",
       "0              o         p                 k          s       u  \n",
       "1              o         p                 n          n       g  \n",
       "2              o         p                 n          n       m  \n",
       "3              o         p                 k          s       u  \n",
       "4              o         e                 n          a       g  \n",
       "...          ...       ...               ...        ...     ...  \n",
       "8119           o         p                 b          c       l  \n",
       "8120           o         p                 b          v       l  \n",
       "8121           o         p                 b          c       l  \n",
       "8122           o         e                 w          v       l  \n",
       "8123           o         p                 o          c       l  \n",
       "\n",
       "[8124 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df_mush=pd.read_csv('mushrooms.csv')\n",
    "df_mush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
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       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
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       "      <td>v</td>\n",
       "      <td>d</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
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       "      <td>1728</td>\n",
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       "      <td>4936</td>\n",
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       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
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       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       0\n",
       "cap-shape                   0\n",
       "cap-surface                 0\n",
       "cap-color                   0\n",
       "bruises                     0\n",
       "odor                        0\n",
       "gill-attachment             0\n",
       "gill-spacing                0\n",
       "gill-size                   0\n",
       "gill-color                  0\n",
       "stalk-shape                 0\n",
       "stalk-root                  0\n",
       "stalk-surface-above-ring    0\n",
       "stalk-surface-below-ring    0\n",
       "stalk-color-above-ring      0\n",
       "stalk-color-below-ring      0\n",
       "veil-type                   0\n",
       "veil-color                  0\n",
       "ring-number                 0\n",
       "ring-type                   0\n",
       "spore-print-color           0\n",
       "population                  0\n",
       "habitat                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush.isnull().sum()\n",
    "\n",
    "# we can see there is no null value present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       object\n",
       "cap-shape                   object\n",
       "cap-surface                 object\n",
       "cap-color                   object\n",
       "bruises                     object\n",
       "odor                        object\n",
       "gill-attachment             object\n",
       "gill-spacing                object\n",
       "gill-size                   object\n",
       "gill-color                  object\n",
       "stalk-shape                 object\n",
       "stalk-root                  object\n",
       "stalk-surface-above-ring    object\n",
       "stalk-surface-below-ring    object\n",
       "stalk-color-above-ring      object\n",
       "stalk-color-below-ring      object\n",
       "veil-type                   object\n",
       "veil-color                  object\n",
       "ring-number                 object\n",
       "ring-type                   object\n",
       "spore-print-color           object\n",
       "population                  object\n",
       "habitat                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>class</th>\n",
       "      <td>4208</td>\n",
       "      <td>1</td>\n",
       "      <td>e</td>\n",
       "      <td>4208</td>\n",
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       "    <tr>\n",
       "      <th>cap-shape</th>\n",
       "      <td>4208</td>\n",
       "      <td>5</td>\n",
       "      <td>x</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-surface</th>\n",
       "      <td>4208</td>\n",
       "      <td>3</td>\n",
       "      <td>f</td>\n",
       "      <td>1560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color</th>\n",
       "      <td>4208</td>\n",
       "      <td>10</td>\n",
       "      <td>n</td>\n",
       "      <td>1264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bruises</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>2752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor</th>\n",
       "      <td>4208</td>\n",
       "      <td>3</td>\n",
       "      <td>n</td>\n",
       "      <td>3408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-attachment</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>3008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>3920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-color</th>\n",
       "      <td>4208</td>\n",
       "      <td>10</td>\n",
       "      <td>w</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>2592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root</th>\n",
       "      <td>4208</td>\n",
       "      <td>5</td>\n",
       "      <td>b</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <td>4208</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>3640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <td>4208</td>\n",
       "      <td>4</td>\n",
       "      <td>s</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <td>4208</td>\n",
       "      <td>6</td>\n",
       "      <td>w</td>\n",
       "      <td>2752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <td>4208</td>\n",
       "      <td>6</td>\n",
       "      <td>w</td>\n",
       "      <td>2704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-type</th>\n",
       "      <td>4208</td>\n",
       "      <td>1</td>\n",
       "      <td>p</td>\n",
       "      <td>4208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-color</th>\n",
       "      <td>4208</td>\n",
       "      <td>3</td>\n",
       "      <td>w</td>\n",
       "      <td>4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number</th>\n",
       "      <td>4208</td>\n",
       "      <td>2</td>\n",
       "      <td>o</td>\n",
       "      <td>3680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-type</th>\n",
       "      <td>4208</td>\n",
       "      <td>3</td>\n",
       "      <td>p</td>\n",
       "      <td>3152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color</th>\n",
       "      <td>4208</td>\n",
       "      <td>8</td>\n",
       "      <td>n</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>4208</td>\n",
       "      <td>6</td>\n",
       "      <td>v</td>\n",
       "      <td>1192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>4208</td>\n",
       "      <td>7</td>\n",
       "      <td>d</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique top  freq\n",
       "class                     4208      1   e  4208\n",
       "cap-shape                 4208      5   x  1948\n",
       "cap-surface               4208      3   f  1560\n",
       "cap-color                 4208     10   n  1264\n",
       "bruises                   4208      2   t  2752\n",
       "odor                      4208      3   n  3408\n",
       "gill-attachment           4208      2   f  4016\n",
       "gill-spacing              4208      2   c  3008\n",
       "gill-size                 4208      2   b  3920\n",
       "gill-color                4208     10   w   956\n",
       "stalk-shape               4208      2   t  2592\n",
       "stalk-root                4208      5   b  1920\n",
       "stalk-surface-above-ring  4208      4   s  3640\n",
       "stalk-surface-below-ring  4208      4   s  3400\n",
       "stalk-color-above-ring    4208      6   w  2752\n",
       "stalk-color-below-ring    4208      6   w  2704\n",
       "veil-type                 4208      1   p  4208\n",
       "veil-color                4208      3   w  4016\n",
       "ring-number               4208      2   o  3680\n",
       "ring-type                 4208      3   p  3152\n",
       "spore-print-color         4208      8   n  1744\n",
       "population                4208      6   v  1192\n",
       "habitat                   4208      7   d  1880"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush[df_mush['class']=='e'].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <td>3916</td>\n",
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       "    <tr>\n",
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       "      <td>3916</td>\n",
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       "      <td>1708</td>\n",
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       "      <th>cap-color</th>\n",
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       "      <th>odor</th>\n",
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       "      <td>3916</td>\n",
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       "      <td>3898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing</th>\n",
       "      <td>3916</td>\n",
       "      <td>2</td>\n",
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       "      <td>3804</td>\n",
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       "    <tr>\n",
       "      <th>gill-size</th>\n",
       "      <td>3916</td>\n",
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       "      <td>n</td>\n",
       "      <td>2224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-color</th>\n",
       "      <td>3916</td>\n",
       "      <td>10</td>\n",
       "      <td>b</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape</th>\n",
       "      <td>3916</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root</th>\n",
       "      <td>3916</td>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <td>3916</td>\n",
       "      <td>4</td>\n",
       "      <td>k</td>\n",
       "      <td>2228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <td>3916</td>\n",
       "      <td>4</td>\n",
       "      <td>k</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <td>3916</td>\n",
       "      <td>6</td>\n",
       "      <td>w</td>\n",
       "      <td>1712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <td>3916</td>\n",
       "      <td>6</td>\n",
       "      <td>w</td>\n",
       "      <td>1680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-type</th>\n",
       "      <td>3916</td>\n",
       "      <td>1</td>\n",
       "      <td>p</td>\n",
       "      <td>3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-color</th>\n",
       "      <td>3916</td>\n",
       "      <td>2</td>\n",
       "      <td>w</td>\n",
       "      <td>3908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number</th>\n",
       "      <td>3916</td>\n",
       "      <td>3</td>\n",
       "      <td>o</td>\n",
       "      <td>3808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-type</th>\n",
       "      <td>3916</td>\n",
       "      <td>4</td>\n",
       "      <td>e</td>\n",
       "      <td>1768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color</th>\n",
       "      <td>3916</td>\n",
       "      <td>5</td>\n",
       "      <td>w</td>\n",
       "      <td>1812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>3916</td>\n",
       "      <td>4</td>\n",
       "      <td>v</td>\n",
       "      <td>2848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>3916</td>\n",
       "      <td>6</td>\n",
       "      <td>d</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique top  freq\n",
       "class                     3916      1   p  3916\n",
       "cap-shape                 3916      5   x  1708\n",
       "cap-surface               3916      4   y  1740\n",
       "cap-color                 3916      8   n  1020\n",
       "bruises                   3916      2   f  3292\n",
       "odor                      3916      7   f  2160\n",
       "gill-attachment           3916      2   f  3898\n",
       "gill-spacing              3916      2   c  3804\n",
       "gill-size                 3916      2   n  2224\n",
       "gill-color                3916     10   b  1728\n",
       "stalk-shape               3916      2   t  2016\n",
       "stalk-root                3916      4   b  1856\n",
       "stalk-surface-above-ring  3916      4   k  2228\n",
       "stalk-surface-below-ring  3916      4   k  2160\n",
       "stalk-color-above-ring    3916      6   w  1712\n",
       "stalk-color-below-ring    3916      6   w  1680\n",
       "veil-type                 3916      1   p  3916\n",
       "veil-color                3916      2   w  3908\n",
       "ring-number               3916      3   o  3808\n",
       "ring-type                 3916      4   e  1768\n",
       "spore-print-color         3916      5   w  1812\n",
       "population                3916      4   v  2848\n",
       "habitat                   3916      6   d  1268"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush[df_mush['class']=='p'].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see all the data is in categorical lets convert it into numeric data so that we can do classification\n",
    "# lets use sklearn to convert categorical data to numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>8119</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8120</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8121</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class  cap-shape  cap-surface  cap-color  bruises  odor  \\\n",
       "0         1          5            2          4        1     6   \n",
       "1         0          5            2          9        1     0   \n",
       "2         0          0            2          8        1     3   \n",
       "3         1          5            3          8        1     6   \n",
       "4         0          5            2          3        0     5   \n",
       "...     ...        ...          ...        ...      ...   ...   \n",
       "8119      0          3            2          4        0     5   \n",
       "8120      0          5            2          4        0     5   \n",
       "8121      0          2            2          4        0     5   \n",
       "8122      1          3            3          4        0     8   \n",
       "8123      0          5            2          4        0     5   \n",
       "\n",
       "      gill-attachment  gill-spacing  gill-size  gill-color  ...  \\\n",
       "0                   1             0          1           4  ...   \n",
       "1                   1             0          0           4  ...   \n",
       "2                   1             0          0           5  ...   \n",
       "3                   1             0          1           5  ...   \n",
       "4                   1             1          0           4  ...   \n",
       "...               ...           ...        ...         ...  ...   \n",
       "8119                0             0          0          11  ...   \n",
       "8120                0             0          0          11  ...   \n",
       "8121                0             0          0           5  ...   \n",
       "8122                1             0          1           0  ...   \n",
       "8123                0             0          0          11  ...   \n",
       "\n",
       "      stalk-surface-below-ring  stalk-color-above-ring  \\\n",
       "0                            2                       7   \n",
       "1                            2                       7   \n",
       "2                            2                       7   \n",
       "3                            2                       7   \n",
       "4                            2                       7   \n",
       "...                        ...                     ...   \n",
       "8119                         2                       5   \n",
       "8120                         2                       5   \n",
       "8121                         2                       5   \n",
       "8122                         1                       7   \n",
       "8123                         2                       5   \n",
       "\n",
       "      stalk-color-below-ring  veil-type  veil-color  ring-number  ring-type  \\\n",
       "0                          7          0           2            1          4   \n",
       "1                          7          0           2            1          4   \n",
       "2                          7          0           2            1          4   \n",
       "3                          7          0           2            1          4   \n",
       "4                          7          0           2            1          0   \n",
       "...                      ...        ...         ...          ...        ...   \n",
       "8119                       5          0           1            1          4   \n",
       "8120                       5          0           0            1          4   \n",
       "8121                       5          0           1            1          4   \n",
       "8122                       7          0           2            1          0   \n",
       "8123                       5          0           1            1          4   \n",
       "\n",
       "      spore-print-color  population  habitat  \n",
       "0                     2           3        5  \n",
       "1                     3           2        1  \n",
       "2                     3           2        3  \n",
       "3                     2           3        5  \n",
       "4                     3           0        1  \n",
       "...                 ...         ...      ...  \n",
       "8119                  0           1        2  \n",
       "8120                  0           4        2  \n",
       "8121                  0           1        2  \n",
       "8122                  7           4        2  \n",
       "8123                  4           1        2  \n",
       "\n",
       "[8124 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "for col in df_mush.columns:\n",
    "    df_mush[col]=le.fit_transform(df_mush[col].astype(str))\n",
    "df_mush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215b5496bb0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "import seaborn as sns\n",
    "sns.barplot(x='class', y='habitat', data=df_mush)\n",
    "# we can observe the habitat with espect to class in the below figure this gives us how many habitat are in class 0 or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x215b5527be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='population', hue='class',kind= 'count',data=df_mush)\n",
    "#we can observe abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y have distributed in different classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215b55a77c0>"
      ]
     },
     "execution_count": 12,
     "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.distplot(df_mush['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x215b5687a90>"
      ]
     },
     "execution_count": 13,
     "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.distplot(df_mush['habitat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.0</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.482029</td>\n",
       "      <td>3.348104</td>\n",
       "      <td>1.827671</td>\n",
       "      <td>4.504677</td>\n",
       "      <td>0.415559</td>\n",
       "      <td>4.144756</td>\n",
       "      <td>0.974151</td>\n",
       "      <td>0.161497</td>\n",
       "      <td>0.309207</td>\n",
       "      <td>4.810684</td>\n",
       "      <td>...</td>\n",
       "      <td>1.603644</td>\n",
       "      <td>5.816347</td>\n",
       "      <td>5.794682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.965534</td>\n",
       "      <td>1.069424</td>\n",
       "      <td>2.291974</td>\n",
       "      <td>3.596750</td>\n",
       "      <td>3.644018</td>\n",
       "      <td>1.508616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499708</td>\n",
       "      <td>1.604329</td>\n",
       "      <td>1.229873</td>\n",
       "      <td>2.545821</td>\n",
       "      <td>0.492848</td>\n",
       "      <td>2.103729</td>\n",
       "      <td>0.158695</td>\n",
       "      <td>0.368011</td>\n",
       "      <td>0.462195</td>\n",
       "      <td>3.540359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.675974</td>\n",
       "      <td>1.901747</td>\n",
       "      <td>1.907291</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.242669</td>\n",
       "      <td>0.271064</td>\n",
       "      <td>1.801672</td>\n",
       "      <td>2.382663</td>\n",
       "      <td>1.252082</td>\n",
       "      <td>1.719975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             class    cap-shape  cap-surface    cap-color      bruises  \\\n",
       "count  8124.000000  8124.000000  8124.000000  8124.000000  8124.000000   \n",
       "mean      0.482029     3.348104     1.827671     4.504677     0.415559   \n",
       "std       0.499708     1.604329     1.229873     2.545821     0.492848   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     2.000000     0.000000     3.000000     0.000000   \n",
       "50%       0.000000     3.000000     2.000000     4.000000     0.000000   \n",
       "75%       1.000000     5.000000     3.000000     8.000000     1.000000   \n",
       "max       1.000000     5.000000     3.000000     9.000000     1.000000   \n",
       "\n",
       "              odor  gill-attachment  gill-spacing    gill-size   gill-color  \\\n",
       "count  8124.000000      8124.000000   8124.000000  8124.000000  8124.000000   \n",
       "mean      4.144756         0.974151      0.161497     0.309207     4.810684   \n",
       "std       2.103729         0.158695      0.368011     0.462195     3.540359   \n",
       "min       0.000000         0.000000      0.000000     0.000000     0.000000   \n",
       "25%       2.000000         1.000000      0.000000     0.000000     2.000000   \n",
       "50%       5.000000         1.000000      0.000000     0.000000     5.000000   \n",
       "75%       5.000000         1.000000      0.000000     1.000000     7.000000   \n",
       "max       8.000000         1.000000      1.000000     1.000000    11.000000   \n",
       "\n",
       "       ...  stalk-surface-below-ring  stalk-color-above-ring  \\\n",
       "count  ...               8124.000000             8124.000000   \n",
       "mean   ...                  1.603644                5.816347   \n",
       "std    ...                  0.675974                1.901747   \n",
       "min    ...                  0.000000                0.000000   \n",
       "25%    ...                  1.000000                6.000000   \n",
       "50%    ...                  2.000000                7.000000   \n",
       "75%    ...                  2.000000                7.000000   \n",
       "max    ...                  3.000000                8.000000   \n",
       "\n",
       "       stalk-color-below-ring  veil-type   veil-color  ring-number  \\\n",
       "count             8124.000000     8124.0  8124.000000  8124.000000   \n",
       "mean                 5.794682        0.0     1.965534     1.069424   \n",
       "std                  1.907291        0.0     0.242669     0.271064   \n",
       "min                  0.000000        0.0     0.000000     0.000000   \n",
       "25%                  6.000000        0.0     2.000000     1.000000   \n",
       "50%                  7.000000        0.0     2.000000     1.000000   \n",
       "75%                  7.000000        0.0     2.000000     1.000000   \n",
       "max                  8.000000        0.0     3.000000     2.000000   \n",
       "\n",
       "         ring-type  spore-print-color   population      habitat  \n",
       "count  8124.000000        8124.000000  8124.000000  8124.000000  \n",
       "mean      2.291974           3.596750     3.644018     1.508616  \n",
       "std       1.801672           2.382663     1.252082     1.719975  \n",
       "min       0.000000           0.000000     0.000000     0.000000  \n",
       "25%       0.000000           2.000000     3.000000     0.000000  \n",
       "50%       2.000000           3.000000     4.000000     1.000000  \n",
       "75%       4.000000           7.000000     4.000000     2.000000  \n",
       "max       4.000000           8.000000     5.000000     6.000000  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052951</td>\n",
       "      <td>0.178446</td>\n",
       "      <td>-0.031384</td>\n",
       "      <td>-0.501530</td>\n",
       "      <td>-0.093552</td>\n",
       "      <td>0.129200</td>\n",
       "      <td>-0.348387</td>\n",
       "      <td>0.540024</td>\n",
       "      <td>-0.530566</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.298801</td>\n",
       "      <td>-0.154003</td>\n",
       "      <td>-0.146730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.145142</td>\n",
       "      <td>-0.214366</td>\n",
       "      <td>-0.411771</td>\n",
       "      <td>0.171961</td>\n",
       "      <td>0.298686</td>\n",
       "      <td>0.217179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-shape</th>\n",
       "      <td>0.052951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.050454</td>\n",
       "      <td>-0.048203</td>\n",
       "      <td>-0.035374</td>\n",
       "      <td>-0.021935</td>\n",
       "      <td>0.078865</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>0.054050</td>\n",
       "      <td>-0.006039</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032591</td>\n",
       "      <td>-0.031659</td>\n",
       "      <td>-0.030390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>-0.106534</td>\n",
       "      <td>-0.025457</td>\n",
       "      <td>-0.073416</td>\n",
       "      <td>0.063413</td>\n",
       "      <td>-0.042221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-surface</th>\n",
       "      <td>0.178446</td>\n",
       "      <td>-0.050454</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019402</td>\n",
       "      <td>0.070228</td>\n",
       "      <td>0.045233</td>\n",
       "      <td>-0.034180</td>\n",
       "      <td>-0.282306</td>\n",
       "      <td>0.208100</td>\n",
       "      <td>-0.161017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>0.066050</td>\n",
       "      <td>0.068885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>-0.106407</td>\n",
       "      <td>0.230364</td>\n",
       "      <td>0.021555</td>\n",
       "      <td>0.163887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap-color</th>\n",
       "      <td>-0.031384</td>\n",
       "      <td>-0.048203</td>\n",
       "      <td>-0.019402</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000764</td>\n",
       "      <td>-0.387121</td>\n",
       "      <td>0.041436</td>\n",
       "      <td>0.144259</td>\n",
       "      <td>-0.169464</td>\n",
       "      <td>0.084659</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047710</td>\n",
       "      <td>0.002364</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036130</td>\n",
       "      <td>-0.005822</td>\n",
       "      <td>0.162513</td>\n",
       "      <td>-0.293523</td>\n",
       "      <td>-0.144770</td>\n",
       "      <td>0.033925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bruises</th>\n",
       "      <td>-0.501530</td>\n",
       "      <td>-0.035374</td>\n",
       "      <td>0.070228</td>\n",
       "      <td>-0.000764</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061825</td>\n",
       "      <td>0.137359</td>\n",
       "      <td>-0.299473</td>\n",
       "      <td>-0.369596</td>\n",
       "      <td>0.527120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.458983</td>\n",
       "      <td>0.083538</td>\n",
       "      <td>0.092874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119770</td>\n",
       "      <td>0.056788</td>\n",
       "      <td>0.692973</td>\n",
       "      <td>-0.285008</td>\n",
       "      <td>0.088137</td>\n",
       "      <td>-0.075095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odor</th>\n",
       "      <td>-0.093552</td>\n",
       "      <td>-0.021935</td>\n",
       "      <td>0.045233</td>\n",
       "      <td>-0.387121</td>\n",
       "      <td>-0.061825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059590</td>\n",
       "      <td>0.063936</td>\n",
       "      <td>0.310495</td>\n",
       "      <td>-0.129213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061820</td>\n",
       "      <td>0.174532</td>\n",
       "      <td>0.169407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.057747</td>\n",
       "      <td>0.111905</td>\n",
       "      <td>-0.281387</td>\n",
       "      <td>0.469055</td>\n",
       "      <td>-0.043623</td>\n",
       "      <td>-0.026610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-attachment</th>\n",
       "      <td>0.129200</td>\n",
       "      <td>0.078865</td>\n",
       "      <td>-0.034180</td>\n",
       "      <td>0.041436</td>\n",
       "      <td>0.137359</td>\n",
       "      <td>-0.059590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071489</td>\n",
       "      <td>0.108984</td>\n",
       "      <td>-0.128567</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116177</td>\n",
       "      <td>0.099299</td>\n",
       "      <td>0.097160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.897518</td>\n",
       "      <td>0.093236</td>\n",
       "      <td>-0.146689</td>\n",
       "      <td>-0.029524</td>\n",
       "      <td>0.165575</td>\n",
       "      <td>-0.030304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-spacing</th>\n",
       "      <td>-0.348387</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>-0.282306</td>\n",
       "      <td>0.144259</td>\n",
       "      <td>-0.299473</td>\n",
       "      <td>0.063936</td>\n",
       "      <td>0.071489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.108333</td>\n",
       "      <td>0.100193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.213775</td>\n",
       "      <td>0.274574</td>\n",
       "      <td>0.253505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073363</td>\n",
       "      <td>0.243014</td>\n",
       "      <td>-0.195897</td>\n",
       "      <td>0.047323</td>\n",
       "      <td>-0.529253</td>\n",
       "      <td>-0.154680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-size</th>\n",
       "      <td>0.540024</td>\n",
       "      <td>0.054050</td>\n",
       "      <td>0.208100</td>\n",
       "      <td>-0.169464</td>\n",
       "      <td>-0.369596</td>\n",
       "      <td>0.310495</td>\n",
       "      <td>0.108984</td>\n",
       "      <td>-0.108333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.516736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.296548</td>\n",
       "      <td>0.278708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103809</td>\n",
       "      <td>-0.171362</td>\n",
       "      <td>-0.460872</td>\n",
       "      <td>0.622991</td>\n",
       "      <td>0.147682</td>\n",
       "      <td>0.161418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gill-color</th>\n",
       "      <td>-0.530566</td>\n",
       "      <td>-0.006039</td>\n",
       "      <td>-0.161017</td>\n",
       "      <td>0.084659</td>\n",
       "      <td>0.527120</td>\n",
       "      <td>-0.129213</td>\n",
       "      <td>-0.128567</td>\n",
       "      <td>0.100193</td>\n",
       "      <td>-0.516736</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.257224</td>\n",
       "      <td>-0.058299</td>\n",
       "      <td>-0.074781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.097583</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>-0.416135</td>\n",
       "      <td>-0.034090</td>\n",
       "      <td>-0.202972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-shape</th>\n",
       "      <td>-0.102019</td>\n",
       "      <td>0.063794</td>\n",
       "      <td>-0.014123</td>\n",
       "      <td>-0.456496</td>\n",
       "      <td>0.099364</td>\n",
       "      <td>0.459766</td>\n",
       "      <td>0.186485</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>0.214576</td>\n",
       "      <td>-0.175699</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034399</td>\n",
       "      <td>0.223439</td>\n",
       "      <td>0.235794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.162604</td>\n",
       "      <td>-0.293221</td>\n",
       "      <td>-0.291444</td>\n",
       "      <td>0.258831</td>\n",
       "      <td>0.087383</td>\n",
       "      <td>-0.269216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-root</th>\n",
       "      <td>-0.379361</td>\n",
       "      <td>0.030191</td>\n",
       "      <td>-0.126245</td>\n",
       "      <td>0.321274</td>\n",
       "      <td>0.244188</td>\n",
       "      <td>-0.205215</td>\n",
       "      <td>0.144063</td>\n",
       "      <td>0.350548</td>\n",
       "      <td>-0.344345</td>\n",
       "      <td>0.315080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087454</td>\n",
       "      <td>0.157140</td>\n",
       "      <td>0.159805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156213</td>\n",
       "      <td>-0.247357</td>\n",
       "      <td>0.210155</td>\n",
       "      <td>-0.536996</td>\n",
       "      <td>-0.306747</td>\n",
       "      <td>-0.007668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <td>-0.334593</td>\n",
       "      <td>-0.030417</td>\n",
       "      <td>0.089090</td>\n",
       "      <td>-0.060837</td>\n",
       "      <td>0.460824</td>\n",
       "      <td>0.118617</td>\n",
       "      <td>-0.088916</td>\n",
       "      <td>-0.212359</td>\n",
       "      <td>0.056310</td>\n",
       "      <td>0.224287</td>\n",
       "      <td>...</td>\n",
       "      <td>0.437164</td>\n",
       "      <td>0.132708</td>\n",
       "      <td>0.142835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.090591</td>\n",
       "      <td>0.107904</td>\n",
       "      <td>0.390091</td>\n",
       "      <td>0.100764</td>\n",
       "      <td>0.079604</td>\n",
       "      <td>-0.058076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <td>-0.298801</td>\n",
       "      <td>-0.032591</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>-0.047710</td>\n",
       "      <td>0.458983</td>\n",
       "      <td>0.061820</td>\n",
       "      <td>-0.116177</td>\n",
       "      <td>-0.213775</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.257224</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106933</td>\n",
       "      <td>0.110656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.077284</td>\n",
       "      <td>0.040006</td>\n",
       "      <td>0.394644</td>\n",
       "      <td>0.130974</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>-0.039628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <td>-0.154003</td>\n",
       "      <td>-0.031659</td>\n",
       "      <td>0.066050</td>\n",
       "      <td>0.002364</td>\n",
       "      <td>0.083538</td>\n",
       "      <td>0.174532</td>\n",
       "      <td>0.099299</td>\n",
       "      <td>0.274574</td>\n",
       "      <td>0.296548</td>\n",
       "      <td>-0.058299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067377</td>\n",
       "      <td>0.084917</td>\n",
       "      <td>-0.048878</td>\n",
       "      <td>0.271533</td>\n",
       "      <td>-0.240261</td>\n",
       "      <td>0.042561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <td>-0.146730</td>\n",
       "      <td>-0.030390</td>\n",
       "      <td>0.068885</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>0.092874</td>\n",
       "      <td>0.169407</td>\n",
       "      <td>0.097160</td>\n",
       "      <td>0.253505</td>\n",
       "      <td>0.278708</td>\n",
       "      <td>-0.074781</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110656</td>\n",
       "      <td>0.491510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065567</td>\n",
       "      <td>0.087580</td>\n",
       "      <td>-0.034284</td>\n",
       "      <td>0.254518</td>\n",
       "      <td>-0.242792</td>\n",
       "      <td>0.041594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>veil-color</th>\n",
       "      <td>0.145142</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>-0.016603</td>\n",
       "      <td>0.036130</td>\n",
       "      <td>0.119770</td>\n",
       "      <td>-0.057747</td>\n",
       "      <td>0.897518</td>\n",
       "      <td>0.073363</td>\n",
       "      <td>0.103809</td>\n",
       "      <td>-0.097583</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077284</td>\n",
       "      <td>0.067377</td>\n",
       "      <td>0.065567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>-0.143673</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>0.124924</td>\n",
       "      <td>-0.040581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-number</th>\n",
       "      <td>-0.214366</td>\n",
       "      <td>-0.106534</td>\n",
       "      <td>-0.026147</td>\n",
       "      <td>-0.005822</td>\n",
       "      <td>0.056788</td>\n",
       "      <td>0.111905</td>\n",
       "      <td>0.093236</td>\n",
       "      <td>0.243014</td>\n",
       "      <td>-0.171362</td>\n",
       "      <td>0.096054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040006</td>\n",
       "      <td>0.084917</td>\n",
       "      <td>0.087580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058312</td>\n",
       "      <td>0.338417</td>\n",
       "      <td>-0.242020</td>\n",
       "      <td>0.235835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ring-type</th>\n",
       "      <td>-0.411771</td>\n",
       "      <td>-0.025457</td>\n",
       "      <td>-0.106407</td>\n",
       "      <td>0.162513</td>\n",
       "      <td>0.692973</td>\n",
       "      <td>-0.281387</td>\n",
       "      <td>-0.146689</td>\n",
       "      <td>-0.195897</td>\n",
       "      <td>-0.460872</td>\n",
       "      <td>0.629398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.394644</td>\n",
       "      <td>-0.048878</td>\n",
       "      <td>-0.034284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.143673</td>\n",
       "      <td>0.058312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.487048</td>\n",
       "      <td>0.211763</td>\n",
       "      <td>-0.212080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spore-print-color</th>\n",
       "      <td>0.171961</td>\n",
       "      <td>-0.073416</td>\n",
       "      <td>0.230364</td>\n",
       "      <td>-0.293523</td>\n",
       "      <td>-0.285008</td>\n",
       "      <td>0.469055</td>\n",
       "      <td>-0.029524</td>\n",
       "      <td>0.047323</td>\n",
       "      <td>0.622991</td>\n",
       "      <td>-0.416135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130974</td>\n",
       "      <td>0.271533</td>\n",
       "      <td>0.254518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003600</td>\n",
       "      <td>0.338417</td>\n",
       "      <td>-0.487048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126859</td>\n",
       "      <td>0.185954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.298686</td>\n",
       "      <td>0.063413</td>\n",
       "      <td>0.021555</td>\n",
       "      <td>-0.144770</td>\n",
       "      <td>0.088137</td>\n",
       "      <td>-0.043623</td>\n",
       "      <td>0.165575</td>\n",
       "      <td>-0.529253</td>\n",
       "      <td>0.147682</td>\n",
       "      <td>-0.034090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046797</td>\n",
       "      <td>-0.240261</td>\n",
       "      <td>-0.242792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124924</td>\n",
       "      <td>-0.242020</td>\n",
       "      <td>0.211763</td>\n",
       "      <td>-0.126859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat</th>\n",
       "      <td>0.217179</td>\n",
       "      <td>-0.042221</td>\n",
       "      <td>0.163887</td>\n",
       "      <td>0.033925</td>\n",
       "      <td>-0.075095</td>\n",
       "      <td>-0.026610</td>\n",
       "      <td>-0.030304</td>\n",
       "      <td>-0.154680</td>\n",
       "      <td>0.161418</td>\n",
       "      <td>-0.202972</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039628</td>\n",
       "      <td>0.042561</td>\n",
       "      <td>0.041594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.040581</td>\n",
       "      <td>0.235835</td>\n",
       "      <td>-0.212080</td>\n",
       "      <td>0.185954</td>\n",
       "      <td>-0.174529</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             class  cap-shape  cap-surface  cap-color  \\\n",
       "class                     1.000000   0.052951     0.178446  -0.031384   \n",
       "cap-shape                 0.052951   1.000000    -0.050454  -0.048203   \n",
       "cap-surface               0.178446  -0.050454     1.000000  -0.019402   \n",
       "cap-color                -0.031384  -0.048203    -0.019402   1.000000   \n",
       "bruises                  -0.501530  -0.035374     0.070228  -0.000764   \n",
       "odor                     -0.093552  -0.021935     0.045233  -0.387121   \n",
       "gill-attachment           0.129200   0.078865    -0.034180   0.041436   \n",
       "gill-spacing             -0.348387   0.013196    -0.282306   0.144259   \n",
       "gill-size                 0.540024   0.054050     0.208100  -0.169464   \n",
       "gill-color               -0.530566  -0.006039    -0.161017   0.084659   \n",
       "stalk-shape              -0.102019   0.063794    -0.014123  -0.456496   \n",
       "stalk-root               -0.379361   0.030191    -0.126245   0.321274   \n",
       "stalk-surface-above-ring -0.334593  -0.030417     0.089090  -0.060837   \n",
       "stalk-surface-below-ring -0.298801  -0.032591     0.107965  -0.047710   \n",
       "stalk-color-above-ring   -0.154003  -0.031659     0.066050   0.002364   \n",
       "stalk-color-below-ring   -0.146730  -0.030390     0.068885   0.008057   \n",
       "veil-type                      NaN        NaN          NaN        NaN   \n",
       "veil-color                0.145142   0.072560    -0.016603   0.036130   \n",
       "ring-number              -0.214366  -0.106534    -0.026147  -0.005822   \n",
       "ring-type                -0.411771  -0.025457    -0.106407   0.162513   \n",
       "spore-print-color         0.171961  -0.073416     0.230364  -0.293523   \n",
       "population                0.298686   0.063413     0.021555  -0.144770   \n",
       "habitat                   0.217179  -0.042221     0.163887   0.033925   \n",
       "\n",
       "                           bruises      odor  gill-attachment  gill-spacing  \\\n",
       "class                    -0.501530 -0.093552         0.129200     -0.348387   \n",
       "cap-shape                -0.035374 -0.021935         0.078865      0.013196   \n",
       "cap-surface               0.070228  0.045233        -0.034180     -0.282306   \n",
       "cap-color                -0.000764 -0.387121         0.041436      0.144259   \n",
       "bruises                   1.000000 -0.061825         0.137359     -0.299473   \n",
       "odor                     -0.061825  1.000000        -0.059590      0.063936   \n",
       "gill-attachment           0.137359 -0.059590         1.000000      0.071489   \n",
       "gill-spacing             -0.299473  0.063936         0.071489      1.000000   \n",
       "gill-size                -0.369596  0.310495         0.108984     -0.108333   \n",
       "gill-color                0.527120 -0.129213        -0.128567      0.100193   \n",
       "stalk-shape               0.099364  0.459766         0.186485      0.080895   \n",
       "stalk-root                0.244188 -0.205215         0.144063      0.350548   \n",
       "stalk-surface-above-ring  0.460824  0.118617        -0.088916     -0.212359   \n",
       "stalk-surface-below-ring  0.458983  0.061820        -0.116177     -0.213775   \n",
       "stalk-color-above-ring    0.083538  0.174532         0.099299      0.274574   \n",
       "stalk-color-below-ring    0.092874  0.169407         0.097160      0.253505   \n",
       "veil-type                      NaN       NaN              NaN           NaN   \n",
       "veil-color                0.119770 -0.057747         0.897518      0.073363   \n",
       "ring-number               0.056788  0.111905         0.093236      0.243014   \n",
       "ring-type                 0.692973 -0.281387        -0.146689     -0.195897   \n",
       "spore-print-color        -0.285008  0.469055        -0.029524      0.047323   \n",
       "population                0.088137 -0.043623         0.165575     -0.529253   \n",
       "habitat                  -0.075095 -0.026610        -0.030304     -0.154680   \n",
       "\n",
       "                          gill-size  gill-color  ...  \\\n",
       "class                      0.540024   -0.530566  ...   \n",
       "cap-shape                  0.054050   -0.006039  ...   \n",
       "cap-surface                0.208100   -0.161017  ...   \n",
       "cap-color                 -0.169464    0.084659  ...   \n",
       "bruises                   -0.369596    0.527120  ...   \n",
       "odor                       0.310495   -0.129213  ...   \n",
       "gill-attachment            0.108984   -0.128567  ...   \n",
       "gill-spacing              -0.108333    0.100193  ...   \n",
       "gill-size                  1.000000   -0.516736  ...   \n",
       "gill-color                -0.516736    1.000000  ...   \n",
       "stalk-shape                0.214576   -0.175699  ...   \n",
       "stalk-root                -0.344345    0.315080  ...   \n",
       "stalk-surface-above-ring   0.056310    0.224287  ...   \n",
       "stalk-surface-below-ring   0.010894    0.257224  ...   \n",
       "stalk-color-above-ring     0.296548   -0.058299  ...   \n",
       "stalk-color-below-ring     0.278708   -0.074781  ...   \n",
       "veil-type                       NaN         NaN  ...   \n",
       "veil-color                 0.103809   -0.097583  ...   \n",
       "ring-number               -0.171362    0.096054  ...   \n",
       "ring-type                 -0.460872    0.629398  ...   \n",
       "spore-print-color          0.622991   -0.416135  ...   \n",
       "population                 0.147682   -0.034090  ...   \n",
       "habitat                    0.161418   -0.202972  ...   \n",
       "\n",
       "                          stalk-surface-below-ring  stalk-color-above-ring  \\\n",
       "class                                    -0.298801               -0.154003   \n",
       "cap-shape                                -0.032591               -0.031659   \n",
       "cap-surface                               0.107965                0.066050   \n",
       "cap-color                                -0.047710                0.002364   \n",
       "bruises                                   0.458983                0.083538   \n",
       "odor                                      0.061820                0.174532   \n",
       "gill-attachment                          -0.116177                0.099299   \n",
       "gill-spacing                             -0.213775                0.274574   \n",
       "gill-size                                 0.010894                0.296548   \n",
       "gill-color                                0.257224               -0.058299   \n",
       "stalk-shape                              -0.034399                0.223439   \n",
       "stalk-root                                0.087454                0.157140   \n",
       "stalk-surface-above-ring                  0.437164                0.132708   \n",
       "stalk-surface-below-ring                  1.000000                0.106933   \n",
       "stalk-color-above-ring                    0.106933                1.000000   \n",
       "stalk-color-below-ring                    0.110656                0.491510   \n",
       "veil-type                                      NaN                     NaN   \n",
       "veil-color                               -0.077284                0.067377   \n",
       "ring-number                               0.040006                0.084917   \n",
       "ring-type                                 0.394644               -0.048878   \n",
       "spore-print-color                         0.130974                0.271533   \n",
       "population                                0.046797               -0.240261   \n",
       "habitat                                  -0.039628                0.042561   \n",
       "\n",
       "                          stalk-color-below-ring  veil-type  veil-color  \\\n",
       "class                                  -0.146730        NaN    0.145142   \n",
       "cap-shape                              -0.030390        NaN    0.072560   \n",
       "cap-surface                             0.068885        NaN   -0.016603   \n",
       "cap-color                               0.008057        NaN    0.036130   \n",
       "bruises                                 0.092874        NaN    0.119770   \n",
       "odor                                    0.169407        NaN   -0.057747   \n",
       "gill-attachment                         0.097160        NaN    0.897518   \n",
       "gill-spacing                            0.253505        NaN    0.073363   \n",
       "gill-size                               0.278708        NaN    0.103809   \n",
       "gill-color                             -0.074781        NaN   -0.097583   \n",
       "stalk-shape                             0.235794        NaN    0.162604   \n",
       "stalk-root                              0.159805        NaN    0.156213   \n",
       "stalk-surface-above-ring                0.142835        NaN   -0.090591   \n",
       "stalk-surface-below-ring                0.110656        NaN   -0.077284   \n",
       "stalk-color-above-ring                  0.491510        NaN    0.067377   \n",
       "stalk-color-below-ring                  1.000000        NaN    0.065567   \n",
       "veil-type                                    NaN        NaN         NaN   \n",
       "veil-color                              0.065567        NaN    1.000000   \n",
       "ring-number                             0.087580        NaN    0.036380   \n",
       "ring-type                              -0.034284        NaN   -0.143673   \n",
       "spore-print-color                       0.254518        NaN   -0.003600   \n",
       "population                             -0.242792        NaN    0.124924   \n",
       "habitat                                 0.041594        NaN   -0.040581   \n",
       "\n",
       "                          ring-number  ring-type  spore-print-color  \\\n",
       "class                       -0.214366  -0.411771           0.171961   \n",
       "cap-shape                   -0.106534  -0.025457          -0.073416   \n",
       "cap-surface                 -0.026147  -0.106407           0.230364   \n",
       "cap-color                   -0.005822   0.162513          -0.293523   \n",
       "bruises                      0.056788   0.692973          -0.285008   \n",
       "odor                         0.111905  -0.281387           0.469055   \n",
       "gill-attachment              0.093236  -0.146689          -0.029524   \n",
       "gill-spacing                 0.243014  -0.195897           0.047323   \n",
       "gill-size                   -0.171362  -0.460872           0.622991   \n",
       "gill-color                   0.096054   0.629398          -0.416135   \n",
       "stalk-shape                 -0.293221  -0.291444           0.258831   \n",
       "stalk-root                  -0.247357   0.210155          -0.536996   \n",
       "stalk-surface-above-ring     0.107904   0.390091           0.100764   \n",
       "stalk-surface-below-ring     0.040006   0.394644           0.130974   \n",
       "stalk-color-above-ring       0.084917  -0.048878           0.271533   \n",
       "stalk-color-below-ring       0.087580  -0.034284           0.254518   \n",
       "veil-type                         NaN        NaN                NaN   \n",
       "veil-color                   0.036380  -0.143673          -0.003600   \n",
       "ring-number                  1.000000   0.058312           0.338417   \n",
       "ring-type                    0.058312   1.000000          -0.487048   \n",
       "spore-print-color            0.338417  -0.487048           1.000000   \n",
       "population                  -0.242020   0.211763          -0.126859   \n",
       "habitat                      0.235835  -0.212080           0.185954   \n",
       "\n",
       "                          population   habitat  \n",
       "class                       0.298686  0.217179  \n",
       "cap-shape                   0.063413 -0.042221  \n",
       "cap-surface                 0.021555  0.163887  \n",
       "cap-color                  -0.144770  0.033925  \n",
       "bruises                     0.088137 -0.075095  \n",
       "odor                       -0.043623 -0.026610  \n",
       "gill-attachment             0.165575 -0.030304  \n",
       "gill-spacing               -0.529253 -0.154680  \n",
       "gill-size                   0.147682  0.161418  \n",
       "gill-color                 -0.034090 -0.202972  \n",
       "stalk-shape                 0.087383 -0.269216  \n",
       "stalk-root                 -0.306747 -0.007668  \n",
       "stalk-surface-above-ring    0.079604 -0.058076  \n",
       "stalk-surface-below-ring    0.046797 -0.039628  \n",
       "stalk-color-above-ring     -0.240261  0.042561  \n",
       "stalk-color-below-ring     -0.242792  0.041594  \n",
       "veil-type                        NaN       NaN  \n",
       "veil-color                  0.124924 -0.040581  \n",
       "ring-number                -0.242020  0.235835  \n",
       "ring-type                   0.211763 -0.212080  \n",
       "spore-print-color          -0.126859  0.185954  \n",
       "population                  1.000000 -0.174529  \n",
       "habitat                    -0.174529  1.000000  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mush.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=df_mush.drop(columns=['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df_mush['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 22 columns</p>\n",
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       "            7         8         9   ...        12        13        14   15  \\\n",
       "0     1.494683 -0.228998 -1.144806  ...  0.586385  0.622441  0.631991  0.0   \n",
       "1    -0.669038 -0.228998 -1.144806  ...  0.586385  0.622441  0.631991  0.0   \n",
       "2    -0.669038  0.053477 -1.144806  ...  0.586385  0.622441  0.631991  0.0   \n",
       "3     1.494683  0.053477 -1.144806  ...  0.586385  0.622441  0.631991  0.0   \n",
       "4    -0.669038 -0.228998  0.873511  ...  0.586385  0.622441  0.631991  0.0   \n",
       "...        ...       ...       ...  ...       ...       ...       ...  ...   \n",
       "8119 -0.669038  1.748325 -1.144806  ...  0.586385 -0.429288 -0.416681  0.0   \n",
       "8120 -0.669038  1.748325 -1.144806  ...  0.586385 -0.429288 -0.416681  0.0   \n",
       "8121 -0.669038  0.053477 -1.144806  ...  0.586385 -0.429288 -0.416681  0.0   \n",
       "8122  1.494683 -1.358896  0.873511  ... -0.893053  0.622441  0.631991  0.0   \n",
       "8123 -0.669038  1.748325 -1.144806  ...  0.586385 -0.429288 -0.416681  0.0   \n",
       "\n",
       "            16        17        18        19        20        21  \n",
       "0     0.142037 -0.256132  0.948081 -0.670195 -0.514389  2.030028  \n",
       "1     0.142037 -0.256132  0.948081 -0.250471 -1.313108 -0.295730  \n",
       "2     0.142037 -0.256132  0.948081 -0.250471 -1.313108  0.867149  \n",
       "3     0.142037 -0.256132  0.948081 -0.670195 -0.514389  2.030028  \n",
       "4     0.142037 -0.256132 -1.272216 -0.250471 -2.910546 -0.295730  \n",
       "...        ...       ...       ...       ...       ...       ...  \n",
       "8119 -3.979055 -0.256132  0.948081 -1.509643 -2.111827  0.285710  \n",
       "8120 -8.100146 -0.256132  0.948081 -1.509643  0.284330  0.285710  \n",
       "8121 -3.979055 -0.256132  0.948081 -1.509643 -2.111827  0.285710  \n",
       "8122  0.142037 -0.256132 -1.272216  1.428426  0.284330  0.285710  \n",
       "8123 -3.979055 -0.256132  0.948081  0.169254 -2.111827  0.285710  \n",
       "\n",
       "[8124 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets use standard Scaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "x=sc.fit_transform(df_x)\n",
    "x=pd.DataFrame(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets use PCA to reduce down the column\n",
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=6)\n",
    "xpca=pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.574334</td>\n",
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       "      <td>-1.451817</td>\n",
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       "      <th>1</th>\n",
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       "      <td>-1.501314</td>\n",
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       "      <td>-1.858050</td>\n",
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       "      <td>1.055973</td>\n",
       "      <td>-1.810092</td>\n",
       "      <td>-0.872692</td>\n",
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       "      <th>3</th>\n",
       "      <td>-0.884788</td>\n",
       "      <td>-0.756421</td>\n",
       "      <td>-1.342071</td>\n",
       "      <td>0.370155</td>\n",
       "      <td>-2.111182</td>\n",
       "      <td>-2.109392</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.689628</td>\n",
       "      <td>1.239167</td>\n",
       "      <td>-3.113230</td>\n",
       "      <td>0.814729</td>\n",
       "      <td>1.541560</td>\n",
       "      <td>-0.557433</td>\n",
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       "      <th>...</th>\n",
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       "      <th>8119</th>\n",
       "      <td>-2.244864</td>\n",
       "      <td>-0.918437</td>\n",
       "      <td>2.528701</td>\n",
       "      <td>6.454484</td>\n",
       "      <td>2.787910</td>\n",
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       "    <tr>\n",
       "      <th>8120</th>\n",
       "      <td>-2.538740</td>\n",
       "      <td>-1.671864</td>\n",
       "      <td>4.324522</td>\n",
       "      <td>7.525904</td>\n",
       "      <td>3.974456</td>\n",
       "      <td>-2.182894</td>\n",
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       "    <tr>\n",
       "      <th>8121</th>\n",
       "      <td>-1.621517</td>\n",
       "      <td>-0.757534</td>\n",
       "      <td>2.733564</td>\n",
       "      <td>6.453654</td>\n",
       "      <td>2.478392</td>\n",
       "      <td>-1.573713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>3.670602</td>\n",
       "      <td>-1.032752</td>\n",
       "      <td>0.168453</td>\n",
       "      <td>-0.146528</td>\n",
       "      <td>-0.153892</td>\n",
       "      <td>-0.456577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>-1.575228</td>\n",
       "      <td>-1.228417</td>\n",
       "      <td>2.447193</td>\n",
       "      <td>6.539651</td>\n",
       "      <td>2.722816</td>\n",
       "      <td>-1.177039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5\n",
       "0    -0.574334 -0.975706 -1.221767  0.185497 -1.451817 -1.536997\n",
       "1    -2.282121  0.279187 -1.200512  0.272058 -1.501314 -1.485154\n",
       "2    -1.858050 -0.270880 -1.372388  1.055973 -1.810092 -0.872692\n",
       "3    -0.884788 -0.756421 -1.342071  0.370155 -2.111182 -2.109392\n",
       "4     0.689628  1.239167 -3.113230  0.814729  1.541560 -0.557433\n",
       "...        ...       ...       ...       ...       ...       ...\n",
       "8119 -2.244864 -0.918437  2.528701  6.454484  2.787910 -1.329297\n",
       "8120 -2.538740 -1.671864  4.324522  7.525904  3.974456 -2.182894\n",
       "8121 -1.621517 -0.757534  2.733564  6.453654  2.478392 -1.573713\n",
       "8122  3.670602 -1.032752  0.168453 -0.146528 -0.153892 -0.456577\n",
       "8123 -1.575228 -1.228417  2.447193  6.539651  2.722816 -1.177039\n",
       "\n",
       "[8124 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=pd.DataFrame(xpca)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>8121</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class\n",
       "0         1\n",
       "1         0\n",
       "2         0\n",
       "3         1\n",
       "4         0\n",
       "...     ...\n",
       "8119      0\n",
       "8120      0\n",
       "8121      0\n",
       "8122      1\n",
       "8123      0\n",
       "\n",
       "[8124 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 6)\n",
      "(7816, 6)\n"
     ]
    }
   ],
   "source": [
    "# lets check the outliers present in the data sets\n",
    "from scipy.stats import zscore\n",
    "z_score=abs(zscore(x))\n",
    "print(x.shape)\n",
    "df_x=x.loc[(z_score<3).all(axis=1)]\n",
    "print(df_x.shape)\n",
    "# we can see there were outliers present in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.734075\n",
       "1    0.666652\n",
       "2    0.044456\n",
       "3    1.323122\n",
       "4   -0.190884\n",
       "5    0.956977\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.skew()\n",
    "# we can observe the skewed data lets treat those data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\khurr\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "<ipython-input-24-1e04ea775e96>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_x[col]=np.sqrt(df_x[col])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#lets use square root tranformation\n",
    "import numpy as np\n",
    "for col in df_x.columns:\n",
    "    if df_x.skew().loc[col]>0.55:\n",
    "        df_x[col]=np.sqrt(df_x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.617276\n",
       "1   -0.684593\n",
       "2    0.044456\n",
       "3    0.510332\n",
       "4   -0.190884\n",
       "5    0.609351\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets start classification process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state=42,stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN=KNeighborsClassifier(n_neighbors=6)\n",
    "SVC=SVC()\n",
    "LR=LogisticRegression()\n",
    "DT=DecisionTreeClassifier(random_state=6)\n",
    "GNB=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "models= []\n",
    "models.append(('KNeighborsClassifier',KNN))\n",
    "models.append(('SVC',SVC))\n",
    "models.append(('LogisticRegression',LR))\n",
    "models.append(('DecisionTreeClassifier',DT))\n",
    "models.append(('GaussianNB',GNB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,roc_curve,auc,confusion_matrix,classification_report,mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************** KNeighborsClassifier ***************************\n",
      "\n",
      "\n",
      "KNeighborsClassifier(n_neighbors=6)\n",
      "\n",
      "\n",
      "accuracy_score= 0.9987692307692307\n",
      "\n",
      "\n",
      "cross_val_score 0.9539695162961481\n",
      "\n",
      "\n",
      "roc_auc_score =  0.9987228607918264\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       842\n",
      "           1       1.00      1.00      1.00       783\n",
      "\n",
      "    accuracy                           1.00      1625\n",
      "   macro avg       1.00      1.00      1.00      1625\n",
      "weighted avg       1.00      1.00      1.00      1625\n",
      "\n",
      "\n",
      "\n",
      "[[842   0]\n",
      " [  2 781]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** SVC ***************************\n",
      "\n",
      "\n",
      "SVC()\n",
      "\n",
      "\n",
      "accuracy_score= 0.9815384615384616\n",
      "\n",
      "\n",
      "cross_val_score 0.9275153145620125\n",
      "\n",
      "\n",
      "roc_auc_score =  0.9820510370309699\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98       842\n",
      "           1       0.97      1.00      0.98       783\n",
      "\n",
      "    accuracy                           0.98      1625\n",
      "   macro avg       0.98      0.98      0.98      1625\n",
      "weighted avg       0.98      0.98      0.98      1625\n",
      "\n",
      "\n",
      "\n",
      "[[815  27]\n",
      " [  3 780]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** LogisticRegression ***************************\n",
      "\n",
      "\n",
      "LogisticRegression()\n",
      "\n",
      "\n",
      "accuracy_score= 0.8947692307692308\n",
      "\n",
      "\n",
      "cross_val_score 0.8633825944170773\n",
      "\n",
      "\n",
      "roc_auc_score =  0.8930418665040665\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.94      0.90       842\n",
      "           1       0.93      0.85      0.89       783\n",
      "\n",
      "    accuracy                           0.89      1625\n",
      "   macro avg       0.90      0.89      0.89      1625\n",
      "weighted avg       0.90      0.89      0.89      1625\n",
      "\n",
      "\n",
      "\n",
      "[[792  50]\n",
      " [121 662]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** DecisionTreeClassifier ***************************\n",
      "\n",
      "\n",
      "DecisionTreeClassifier(random_state=6)\n",
      "\n",
      "\n",
      "accuracy_score= 0.9895384615384616\n",
      "\n",
      "\n",
      "cross_val_score 0.9414232393555462\n",
      "\n",
      "\n",
      "roc_auc_score =  0.9893232982347571\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       842\n",
      "           1       0.99      0.98      0.99       783\n",
      "\n",
      "    accuracy                           0.99      1625\n",
      "   macro avg       0.99      0.99      0.99      1625\n",
      "weighted avg       0.99      0.99      0.99      1625\n",
      "\n",
      "\n",
      "\n",
      "[[838   4]\n",
      " [ 13 770]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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cdU8lhE26bzLHf6X42J6b9TzX3XALbW1tZCbv2ecdHHfkYa/a7lnf/jJQjAk76eTTOf+Hp1bWD1tvHZ6Y8TSvXW9d2ts7eOHfL7L6aqsC8MK//80njv8yx4wby7bb2IAqdWeXXd7Efu9+J/vsvTsrrrgCq622KhPOP4uxH/5kq6umAS5tOa1F3SGsAawPPLzQ8uHluk5l5nhgPMC8px80ntdk+LB1+efd9/HSyy+z4gorcNOtdzBqq5GVMldedv6C5yd983TetuuO7PHWXXhg6sMcc8LX+dCYA1h7zTWY9fxs/v3ii6z/2mGL3O/b/+vNXPH7P7PdNq/nT9dcx05v3JaIYN68eRx74jd4z957sNfub+nrw5UGnZO+eAonfbH4kfS2t+7Mp4/7uAFMPWNLWC3qDmGfAq6OiMnAo+WyjYDNgaNrrosW4T9HbcWeb/8vDjrsGNra2thqi9dx4Oh9uPjy/wfA+w94V5fvfd2mG3PMRz/EuE+dRCMbvGa55Tjp05/oUQh777v34sRvnMY+Bx3O6qutymlfOwGAP/7lOm67425mzprNb37/ZwBOPunTbLXF6/rgaCVJCzjOqxbR2fibft1hxBBgR2AEEMA04JbM7NGlc7aESa2x0vq2Pkqt0j73sVh0qb7z768f0qvv2lW+/Ita6zdY1D5jfmY2gBvr3q8kSeohx4TVwkvLJElSVT/MExYRa0TEZRFxX0TcGxE7R8RaEXFVREwu/67ZVP7EiJgSEfdHxF79dqwtZAiTJElV/TNP2JnAHzNzK2Bb4F7gBODqzBwJXF2+JiK2BsYAo4C9gbMjoq3TrS7FDGGSJKmqj1vCImI14K3AzwAyc25mzgRGA/NvajoB2L98Phq4KDPnZOZUYArFePJBxRAmSZIqstHo1aMHNgOeAs6LiH9ExP9ExCrAsMycDlD+Xa8sP4JXZlGA4iK+EX13hAODIUySJFX1siUsIsZFxK1Nj3ELbXE54A3AjzNze+DflF2PXejsastBNztC7VdHSpKkAa6Xk7U2T6rehWnAtMy8qXx9GUUIezIihmfm9IgYDsxoKr9h0/s3AB7vVaWWAraESZKkqj4emJ+ZTwCPRsSW5aI9gHuAicDYctlY4Iry+URgTESsEBGbAiN55Z7Tg4YtYZIkqap/blt0DPCLiFgeeBA4jKIx6JKIOAJ4BDgQIDMnRcQlFEGtHTiqp5O6L00MYZIkqSL7IYRl5h3AmzpZtUcX5U8GTu7zigwghjBJklTlDbxrYQiTJElV3raoFoYwSZJUZUtYLQxhkiSpyhBWC0OYJEmqyDSE1cEQJkmSqmwJq4UhTJIkVRnCamEIkyRJFf0xT5hezRAmSZKqDGG1MIRJkqQqpwmrhSFMkiRV2B1ZD0OYJEmqMoTVwhAmSZKq7I6shSFMkiRV2B1ZD0OYJEmqsiWsFoYwSZJUYUtYPQxhkiSpypawWhjCJElSRRrCamEIkyRJVYawWhjCJElShS1h9TCESZKkKkNYLQxhkiSpwpawegxpdQUkSdLAko3ePXoiIh6KiLsi4o6IuLVctlZEXBURk8u/azaVPzEipkTE/RGxV/8caWsZwiRJUkV/hLDS2zNzu8x8U/n6BODqzBwJXF2+JiK2BsYAo4C9gbMjoq3PDnCAMIRJkqSqjN49Ft9oYEL5fAKwf9PyizJzTmZOBaYAOy7JjgYiQ5gkSarop5awBP4UEbdFxLhy2bDMnA5Q/l2vXD4CeLTpvdPKZYOKA/MlSVJFNnrXulWGqnFNi8Zn5viFiu2amY9HxHrAVRFxX3eb7KxavarUUsAQJkmSKnp7dWQZuBYOXQuXebz8OyMiLqfoXnwyIoZn5vSIGA7MKItPAzZsevsGwOO9q9XAZ3ekJEmqyIxePRYlIlaJiFXnPwfeCdwNTATGlsXGAleUzycCYyJihYjYFBgJ3NzHh9lytoRJkqSKfpgnbBhweURAkT1+mZl/jIhbgEsi4gjgEeBAgMycFBGXAPcA7cBRmdnR57VqMUOYJEmq6O2YsEVuL/NBYNtOlj8D7NHFe04GTu7TigwwhjBJklSRg24I/MBkCJMkSRV93RKmzhnCJElShSGsHoYwSZJUYXdkPQxhkiSpwpawehjCJElSRU/m/tKSM4RJkqSKfpgnTJ0whEmSpIqGLWG1MIRJkqQKuyPrYQiTJEkVDsyvhyFMkiRVOEVFPQxhkiSpwpawehjCJElShQPz62EIkyRJFQ7Mr4chTJIkVTgmrB6GMEmSVGF3ZD0MYZIkqcLuyHoYwiRJUoXdkfVY6kLYKiPe2uoqSMukF278caurIKkmdkfWY6kLYZIkqX/ZHVkPQ5gkSaqwJawehjBJklThkLB6DGl1BSRJ0sDSyOjVo6cioi0i/hERvytfrxURV0XE5PLvmk1lT4yIKRFxf0Ts1Q+H2XKGMEmSVJEZvXr0wrHAvU2vTwCuzsyRwNXlayJia2AMMArYGzg7Itr65OAGEEOYJEmqaPTy0RMRsQHwLuB/mhaPBiaUzycA+zctvygz52TmVGAKsOPiHs9AZQiTJEkVSfTqERHjIuLWpse4Tjb7feBzVHPbsMycDlD+Xa9cPgJ4tKnctHLZoOLAfEmSVNHo5cj8zBwPjO9qfUS8G5iRmbdFxG492GRnfZyD7noBQ5gkSapodJqBlsiuwHsiYl9gRWC1iLgAeDIihmfm9IgYDswoy08DNmx6/wbA431dqVazO1KSJFX0tjtykdvLPDEzN8jMTSgG3P8lMw8FJgJjy2JjgSvK5xOBMRGxQkRsCowEbu7r42w1W8IkSVJFTwfb94FTgEsi4gjgEeBAgMycFBGXAPcA7cBRmdlRX7XqYQiTJEkVPWndWuxtZ14DXFM+fwbYo4tyJwMn91tFBgBDmCRJqqixJWyZZgiTJEkVhrB6GMIkSVJFf3ZH6hWGMEmSVNEwg9XCECZJkir6YZ4wdcIQJkmSKgbd1PQDlCFMkiRVODC/HoYwSZJU0Qi7I+tgCJMkSRV2R9bDECZJkirsjqyHIUySJFU4RUU9DGGSJKnCKSrqYQiTJEkVjgmrhyFMkiRV2B1ZD0OYJEmqcGB+PQxhkiSpwu7IehjCJElShd2R9TCESZKkCrsj62EIkyRJFYawehjCJElSRdodWQtDmCRJqrAlrB6GMEmSVGEIq8eQVldAkiQNLNnLx6JExIoRcXNE3BkRkyLia+XytSLiqoiYXP5ds+k9J0bElIi4PyL26tsjHBgMYZIkqaIRvXv0wBxg98zcFtgO2Dsi3gycAFydmSOBq8vXRMTWwBhgFLA3cHZEtPX9kbaWIUySJFU0evlYlCy8UL58TflIYDQwoVw+Adi/fD4auCgz52TmVGAKsOMSHtaAYwiTJEkVvQ1hETEuIm5teoxbeJsR0RYRdwAzgKsy8yZgWGZOByj/rlcWHwE82vT2aeWyQcWB+ZIkqaK3ty3KzPHA+EWU6QC2i4g1gMsjYptuinfWyTno7qZkCJMkSRX9eduizJwZEddQjPV6MiKGZ+b0iBhO0UoGRcvXhk1v2wB4vP9q1Rp2R0qSpIq+HhMWEeuWLWBExErAO4D7gInA2LLYWOCK8vlEYExErBARmwIjgZuX/MgGFlvCJElSRT/0+w0HJpRXOA4BLsnM30XEDcAlEXEE8AhwIEBmToqIS4B7gHbgqLI7c1AxhEmSpIpGH8ewzPwnsH0ny58B9ujiPScDJ/dpRQYYQ5gkSapwxvx6GMIkSVLFoLsMcYAyhEmSpApbwuphCJMkSRX9OUWFXmEIkyRJFX09MF+dM4RJkqQKI1g9DGGSJKnCMWH1MIRJkqQKuyPrYQiTJEkVRrB6GMIkSVKF3ZH1MIRJkqQKuyPrYQiTJEkVRrB6GMIkSVKF3ZH1MIRJkqSKtC2sFoYwSZJUYUtYPQxhkiSpwoH59TCESZKkCiNYPQxh6jfjf/Jd9t33HTz11NNs/4Z3APDVr3yW/fbbi0ajwYynnuYjH/k006c/2eKaSgPLQ4/P4HNnXbDg9bQZz/KJ/96LQ/d9y4Jl5//2Gn7/99sBaO9oMPWxGVwz/qusPnTlxd7v3HntnHT2Rdw7dRqrD12ZU489lBHrrsV9Dz3Gyef+mhdenEPbkOAjB+zB3jtvt/gHqAHPlrB6RObSdaKXX2GDpavCy7D/+q+deOGFf3Peud9fEMJWXXUos2e/AMBRRx3O618/kqOPPrGV1VQPPX/D2a2uwjKpo9Fgz098gwu+8UnWX3fNTstcc9s9XPD7v/I/X/p4j7b52FPP8uUfX8zPvnxkZfnFf7qefz0ynS995H384fo7+Mstd3PasYfy0PSnCGDj4esy49lZHHzSmVz+3eNZbZWVlvTw1EMrvuE9Uef+PrrJgb36rv3pQ5fWWr/BwpYw9Zu//e0mNt54g8qy+QEMYJWVV2Jp+xEg1e2muyez4bC1uwxgAH+8/h/ss8v2C17/7rrb+OWVf6e9vZ1tNt+Ikw5/L21DhixyX/932ySOfN+eAOy5039wynmXk5lsMnzdBWXWW2t11lptKM89/4IhbBDz6sh6LPpfZU0i4rBW10H1+PrXPscDU27m4IMP4Gtf+26rqyMNaH+8/k72bgpYC3tpzlz+fuf9vGOn/wDgwcee5Mob72TCV4/iklM+TVsM4fd/u71H+5rx7Cxeu/YaACzX1sbQlVdk5uwXK2XumvII89o72HDY2ot5RFoaNHr50OIZSC1hXwPO62xFRIwDxgG0ta3BkLZV6qyX+tiXv3IqX/7KqXzu+KP4xJGH8fVvnN7qKkkD0rz2dq69bRLHjtmnyzLX3n4P2225yYKxYDfdPYV7H3yMQ754JgAvz21nrdWHAvCp08/n8aeeZV57B9OfnslBJ3wPgA/s/Rb2320HOmuYjqZOpqeee56Tzr6Ibx75fob0oGVNS6++bgmLiA2BnwOvpcht4zPzzIhYC7gY2AR4CDgoM58r33MicATQAXwyM6/s00oNALWGsIj4Z1ergGFdvS8zxwPjwTFhg8lFF/+GK34zwRAmdeFvd9zHVpuOYO01Vu2yzB+vv6PSFZmZ7PfWN3Lswfu+quz3P/NhoOsxYcPWXp0nnpnJsLXXoL2jgxdefHlBuHvhxZc5+tRzOfqgvfjPkRv3wdFpIOuH1q124DOZeXtErArcFhFXAR8Grs7MUyLiBOAE4PMRsTUwBhgFrA/8OSK2yMyOvq9a69T9U2YY8CFgv04ez9RcF7XA5ptvuuD5u9/9Tu6//4EW1kYa2P6wUMBa2OwXX+K2ex9ktzeOWrBsp21G8ueb7+KZWcX4y1kvvMjjTz3Xo/3t9satmfjX2wC46qa72HHU5kQE89rbOe57E9jvLW/knW/edgmOSEuLRmavHouSmdMz8/by+WzgXmAEMBqYUBabAOxfPh8NXJSZczJzKjAF2LGPD7Pl6u6O/B0wNDPvWHhFRFxTc13Uz/735z/krW/dmXXWWYsHH7iFr3/jdPbZe3e22GIzGo3kkUemcZRXRkqdemnOXG68azJf+sj7Fiy75KobADhoz50B+Mstd7Pzf27Byisuv6DM6zYYxlEH7cWR3x5Po5Est1wbXzjsgG4H9s93wG47ctLZF/HuT53CakNX5tRjDgHgyhvu5Pb7HmTWC/9m4l9vAeDrH38/W20yos+OV8uOiNgE2B64CRiWmdOhCGoRsV5ZbARwY9PbppXLBhWnqJDUI05RIbVO3VNUHLrxe3v1XfuLRy7/GOXY7dL4cihRRUQMBa4FTs7MX0fEzMxco2n9c5m5ZkT8CLghMy8ol/8M+H1m/mpxjmegGkgD8yVJ0gDQ28lam8dudyUiXgP8CvhFZv66XPxkRAwvW8GGAzPK5dOADZvevgHweK8qtRTw8hZJklSRvfxvUSIigJ8B92bm95pWTQTGls/HAlc0LR8TEStExKbASODmPjvAAcKWMEmSVNEPV0fuCnwQuCsi5o8L/wJwCnBJRBwBPAIcCJCZkyLiEuAeiisrjxpsV0aCIUySJC2kr+8dmZl/o5iOqjN7dPGek4GT+7QiA4whTJIkVXjbonoYwiRJUoW3IqqHIUySJFUsbdNXLa0MYZIkqaKvx4Spc4YwSZJUYXdkPQxhksFvrNgAACAASURBVCSpwoH59TCESZKkCrsj62EIkyRJFQ7Mr4chTJIkVTgmrB6GMEmSVOGYsHoYwiRJUoVjwuphCJMkSRWOCauHIUySJFXYElYPQ5gkSapwTFg9DGGSJKmiYXdkLQxhkiSpwghWD0OYJEmqcExYPQxhkiSpwhBWD0OYJEmqcIqKehjCJElShS1h9TCESZKkCqeoqIchTJIkVdgdWY8hra6AJEkaWBpkrx6LEhHnRsSMiLi7adlaEXFVREwu/67ZtO7EiJgSEfdHxF79dJgtZwiTJEkVmdmrRw+cD+y90LITgKszcyRwdfmaiNgaGAOMKt9zdkS09dWxDSSGMEmSVNHXLWGZ+Vfg2YUWjwYmlM8nAPs3Lb8oM+dk5lRgCrBj3xzZwGIIkyRJFdnL/xbTsMycDlD+Xa9cPgJ4tKnctHLZoOPAfEmSVNHbe0dGxDhgXNOi8Zk5fjF3H50sG5RXChjCJElSRW9bt8rA1dvQ9WREDM/M6RExHJhRLp8GbNhUbgPg8V5ue6lgd6QkSapoZPbqsZgmAmPL52OBK5qWj4mIFSJiU2AkcPMSHdAAZUuYJEmq6OvJWiPiQmA3YJ2ImAZ8BTgFuCQijgAeAQ4EyMxJEXEJcA/QDhyVmR19WqEBwhAmSZIqlqB1q1OZeXAXq/boovzJwMl9WokByBAmSZIqvG1RPQxhkiSpoq9bwtQ5Q5gkSaqwJawehjBJklSR2Wh1FZYJhjBJklTRk1sRackZwiRJUkUPb8qtJWQIkyRJFbaE1cMQJkmSKmwJq4chTJIkVThFRT0MYZIkqcIpKuphCJMkSRV2R9bDECZJkiocmF8PQ5gkSaqwJawehjBJklThwPx6GMIkSVKFLWH1MIRJkqQKx4TVwxAmSZIqbAmrhyFMkiRVOCasHoYwSZJU4WSt9TCESZKkClvC6mEIkyRJFY4Jq4chTJIkVdgdWQ9DmCRJqrAlrB6GMEmSVGEIq0d4olWniBiXmeNbXQ9pWeO/PWngGdLqCmiZM67VFZCWUf7bkwYYQ5gkSVILGMIkSZJawBCmujkmRWoN/+1JA4wD8yVJklrAljBJkqQWMIRJkiS1gCFMtYiIvSPi/oiYEhEntLo+0rIiIs6NiBkRcXer6yKpyhCmfhcRbcCPgH2ArYGDI2Lr1tZKWmacD+zd6kpIejVDmOqwIzAlMx/MzLnARcDoFtdJWiZk5l+BZ1tdD0mvZghTHUYAjza9nlYukyRpmWUIUx2ik2XOjSJJWqYZwlSHacCGTa83AB5vUV0kSRoQDGGqwy3AyIjYNCKWB8YAE1tcJ0mSWsoQpn6Xme3A0cCVwL3AJZk5qbW1kpYNEXEhcAOwZURMi4gjWl0nSQVvWyRJktQCtoRJkiS1gCFMkiSpBQxhkiRJLWAIkyRJagFDmCRJUgsYwiRJklrAECZJktQChjBJkqQWMIRJkiS1gCFMkiSpBQxhkiRJLWAIkyRJagFDmCRJUgsYwiRJklrAECZJktQChjBJkqQWMIRJkiS1gCFMkiSpBQxhkiRJLWAIk9SvIuIPETG2B+VeiIjN6qhTf4uI3SJiWj9u/5yI+FLT6yMj4snyHK49mM6lNJhFZra6DpIGgIh4CBgGtAMdwD3Az4HxmdloYdV6LCImARuXL1cC5lEcD8C3MvNbfbivHYGvArsADWAK8OPMPC8idgMuyMwN+mp/3dTjNcDzwJsz887+3p+kvmNLmKRm+2XmqhRB5hTg88DPWlulnsvMUZk5NDOHAtcBR89/3RzAImK5JdlPROwM/AW4FtgcWBs4EthnSba7mIYBKwKTlnRDS3peJPWOIUzSq2TmrMycCLwfGBsR20TEChHx3Yh4pOz6OiciVpr/nogYHRF3RMTzEfFAROxdLr8mIj5SPt88Iq6NiFkR8XREXNz0/oyIzcvnq0fEzyPiqYh4OCK+GBFDynUfjoi/lXV5LiKmRkS34SciNim3f0REPEIRoIiIwyPi3nI7V0bExk3v2SoiroqIZyPi/og4qGmTpwETMvM7mfl0Fm7LzIPoREScUJ6T2RFxT0Qc0LSu03MShTMiYka57p8RsU257vyI+GZEbAHcX25qZkTMP67mc9nl5za/2zQiPh8RTwDndXceJfUtQ5ikLmXmzcA04C3Ad4AtgO0oWn9GAF+GBV1zPweOB9YA3go81MkmvwH8CVgT2AD4QRe7/gGwOrAZ8DbgQ8BhTet3oggf6wCnAj+LiOjBIb0NeD2wV0TsD3wBeC+wLkXL2YXl8awCXAX8ElgPOBg4OyJGRcTKwM7AZT3Y33wPUJzD1YGvARdExPByXVfn5J0U53ELinP6fuCZ5o1m5r+AUeXLNTJz90723eXnVnotsBZF6+e4XhyTpCVkCJO0KI9TfEl/FDguM5/NzNnAt4AxZZkjgHMz86rMbGTmY5l5XyfbmkfxZb9+Zr6cmX9buEBEtFEEjhMzc3ZmPgScDnywqdjDmfnTzOwAJgDDKbrlFuWrmfnvzHwJ+Bjw7cy8NzPby+PZrmwNezfwUGael5ntmXk78CvgvynC0hBgeg/2B0BmXpqZj5fn5mJgMrDjIs7JPGBVYCuK8bv3ZmaP9wlFaxrdf25QjGf7SmbOKc+LpJoYwiQtyghgOWBl4LaImBkRM4E/UrQgAWxI0dqzKJ8DArg5IiZFxOGdlFkHWB54uGnZw2U95nti/pPMfLF8OrQH+3+06fnGwJlNx/NsWbcR5bqd5q8r1x9C0Wr0HEVwGU4PRcSHyq7a+dvapjxO6OKcZOZfgB8CPwKejIjxEbFaT/dZWpfuPzeApzLz5V5uV1IfMIRJ6lJE7EARSn4DvASMysw1ysfq5QB4KMLN6xa1vcx8IjM/mpnrU7REnT1/7FKTp3mldWi+jYDHlvBwAJovB38U+FjT8ayRmStl5vXlumsXWjc0M48sQ98NwPt6ssOyZe2nwNHA2pm5BnA3RfDq9pxk5lmZ+UaKLsctKLp7e+Npuv/cFj4nkmpkCJP0KhGxWkS8G7iIYqqFOymCxBkRsV5ZZkRE7FW+5WfAYRGxR0QMKddt1cl2D4yI+dM2PEcRADqay5RdjJcAJ0fEqmWI+TRwQR8f5jnAiRExqqzb6hFxYLnud8AWEfHBiHhN+dghIl5frv8c8OGIOD4i1i7fv21EXNTJflYpj/OpstxhFC1hlK87PSfl/naKYgqKfwMvs9C5WpRyapHuPjdJLWQIk9TstxExm6Il6CTge7wyIP7zFHNh3RgRzwN/BraEBQP4DwPOAGZRTN2wMa+2A3BTRLwATASOzcypnZQ7hiJ4PAj8jWKA/Ll9cYDzZeblFIPWLyqP527KKSbKsVPvpBg79ThF9+d3gBXK9dcDu5ePByPiWWA88PtO9nMPxZi2G4Angf8A/t5UpKtzshpFgHqOojv2GeC7i3GoXX5uklrLyVolSZJawJYwSZKkFjCESZIktYAhTJIkqQUMYZIkSS2w1N2sdZ111slNNtmk1dWQJElapNtuu+3pzFy3s3VLXQjbZJNNuPXWW1tdDUmSpEWKiIe7Wmd3pCRJUgsYwiRJklrAECZJktQChjBJkqQWMIRJkiS1gCFMkiSpBQxhkiRJLdBvISwizo2IGRFxdxfrIyLOiogpEfHPiHhDf9VFkiRpoOnPlrDzgb27Wb8PMLJ8jAN+3I91kSRJGlD6bcb8zPxrRGzSTZHRwM8zM4EbI2KNiBiemdP7q06SJA1UmUkjoZFJI5Nc8Lz4m42mdfDqMo3m18WyRW6z6b2dlp9frwa932Zz+Ubz+leXL5Z18/7s7P3N2+9NeVj/kcls+PD9rHDEh/nQzpu07DNv5W2LRgCPNr2eVi57VQiLiHEUrWVstNFGtVROkuqQnX4pdf5FuvDfHn0JNrrfZmfb6LxOXXwxvmr7Pf8i7dGXbaN323xVHRqQdHbM3R3DIsp39jk1yvNGF2GlsejPWYUIGBLBkIAo/xavo7KueN28viw/pPPyQfF6uWzwrTOOY4V5L3PxQQe19FhbGcKik2Wd/m+YmeOB8QBvetOb/F9Vg0JPfvXO//LotEwf/upt3k9//+qFxf1i7d2v3oVDRU9CTk++nDv7Il2sY2hap0L3X7YwZEj06Ms5evplvdD724YErxkSPS7f2d/u91cuG9LL8uX2e1JmSFMI6XH5hY5x4b+vOsdDer/NBdsYUj3u4nN/9XH2qfZ2+NWv4H//t/i7wgqwyxUwciTj1lqrb/fVS60MYdOADZtebwA83qK61KYnv3pZ3C/WHv+im//F0csv1k63v+gv0uxBmb741dv9eevbX70Lnz9/9S6+xf3V2/WXX9dfGl1+sQ4Z0u3+Ot1mZ1+eQxbzl3vTlzN0dTzzv7wX74u1Wr9XWgaq52Exv1ib3ttV+e4+p/llpD714otw3nlw+ukwdSqMHAkPPwxbbAE77dTq2gGtDWETgaMj4iJgJ2DWQBsPduafJ3PFHY8VX6C8EnL81ds3evKrN+h9k/Pi/uqNpjp198XVJ7966Ztfxj351RuwiGPou1+99HSb/fmrV5Ieegje9CZ45hl485uLIPae90BbW6trVtFvISwiLgR2A9aJiGnAV4DXAGTmOcDvgX2BKcCLwGH9VZfF9Zf7ZzB7Tjs7b7b2Ir6cu/6SYXG/WHv5qzd6tM2++9W7yDJDXv1l669eSVK/efBBuPNOOOAA2HhjOOQQOPBA2HXX4ktrAOrPqyMPXsT6BI7qr/33hUYj2Wb91Tjr4O1bXRVJktSZW2+F006Dyy6DNdeEffctxn2deWara7ZIzpjfjY5G0jZkYKZnSZKWaf/4B+y+O+ywA1x5JRx/PPzzn0UAW0q0ckzYgNfILLoUJUlS682bB88/D2uvXQx2nTwZvvtd+OhHYbXVWl27XjOEdcMQJknSADB7Nvz0p/D978Pb3lZMN7HttsVVj8stvVFm6a15DeyOlCSphZ54As46C84+G2bNKgLYBz7wyvqlOICBIaxbjXxl3h5JklSz004rWr/e+95izNeOO7a6Rn3KgfndaGTSZgaTJKke118P++8Pf/5z8fr44+H+++HSSwddAANDWLc6Go4JkySpXzUacMUVxXxeu+4K110H08u521/7Wth889bWrx/ZHdmNRiPtjpQkqT+94x3wf/9XTLB61llw+OGwyiqtrlUtbAnrRiOhzZYwSZL6zsyZxUSq8+YVrw8/HC68EKZMgWOOWWYCGNgS1q2OTIYYUyVJWnKPPloMsh8/Hl54AbbeGvbcEw49tNU1axkjRjcajgmTJGnJzJ4NY8fCZpsVLWCjRxez3e+5Z6tr1nKGsG400nnCJEnqtUx46KHi+SqrwH33wVFHwQMPwAUXwHbbtbR6A4Xdkd3w6khJknqhowN+/Ws49dTilkKPPgqrrgo33IDje17NM9KNRmIIkyRpUV58sZjVfost4KCDitntTz0Vll++WG8A65QtYd0oblvU6lpIkjTATZpUdDfutFNxQ+33vAfa2lpdqwHPENYNb+AtSVInHnwQvvc9iIAf/AB22AFuv70Y6+X3Zo/ZztONRjpZqyRJC9x2G4wZAyNHFlNNtLcXg/ABtt/eANZLtoR1o6ORTtYqSRIUs9kfeyysthp89rPF8/XXb3WtlmqGsG40ElvCJEnLpnnz4OKLYcsti+7G/faDuXNh3LgiiGmJ2R3ZhUajaF41g0mSlimzZ8MZZ8DrXgcf/CCcd16xfNNNixYwA1ifMYR1oaPs47Y7UpK0zDjtNNhoI/j0p4vQ9bvfwQ9/2OpaDVqGsC40yhBmd6QkaVCbPLmYZBWKLsg99oAbb4Rrr4V3vcs5vvqRZ7YLjUbx1ykqJEmD0g03wAEHFGO+fv3rYtmJJ8JllxXzfanfGcK6sKA70jMkSRosGg2YOBH+679gl12K1q4vfhHe9rZivQ0PtfLqyC4s6I70f0hJ0tIuswhYmcXg+rlz4cwz4fDDYejQVtdumWUI68IrV0cawiRJS6mZM+EnP4Ff/KLoflxlFfjDH2DjjWE5I0Cr2dnWhY7G/O5IQ5gkaSkzbVrR4rXRRnDCCTB8ODz9dLHuda8zgA0Qfgpd6PDqSEnS0mjKFHj964uux/e/H44/vrinowYcQ1gX5t8KywwmSRrQMuGaa+C+++DII2HzzeE734H3vhc22aTVtVM37I7swoLuSMeESZIGoo4OuPRS2HFH2H33InjNnVus+/SnDWBLAUNYF+aHMLsjJUkDzjXXFPN7HXRQMfj+nHPg3nth+eVbXTP1gt2RXZjfHWlLmCRpQHj6aXjhhaKFa/31Yd114dRTYfRoaGtrde20GGwJ68IrA/NbXBFJ0rJt6lQ45phX7ukIsMUWxZQT732vAWwpZktYFzqcJ0yS1Er/+EcxzuvSS4ugdeihxbQTGjQMYV3IdJ4wSVLN5o+FiSju5/iHPxTB65OfhBEjWls39Tk727rQ4W2LJEl1mTcPLrigmM9r4sRi2Wc/C488UrSGGcAGJUNYF+yOlCT1uxdegO9/v5jF/oMfhPb2V65wXH314qFBq19DWETsHRH3R8SUiDihk/WrR8RvI+LOiJgUEYf1Z316Y8HVkXZHSpL6y267wXHHwaabwu9+B3fdBfvs0+paqSb9FsIiog34EbAPsDVwcERsvVCxo4B7MnNbYDfg9IgYEJOcvNIS1uKKSJIGj3/9qwhdL79cvP7mN+HGG+Haa+Fd7/KS/GVMf37aOwJTMvPBzJwLXASMXqhMAqtGRABDgWeB9n6sU49570hJUp+ZP53EVlvBj38MN99cLN97b9hpp9bWTS3TnyFsBPBo0+tp5bJmPwReDzwO3AUcm5mNfqxTjy24OtIxYZKkxTVzJrzlLbDLLsUs9yedBA8/DG99a6trpgGgP0NYZ+klF3q9F3AHsD6wHfDDiFjtVRuKGBcRt0bErU899VTf17QTHWUUdGC+JKlX5swpuhihGFg/bBiceWZxpeM3vlG8lujfEDYN2LDp9QYULV7NDgN+nYUpwFRgq4U3lJnjM/NNmfmmddddt98q3OyVe0fWsjtJ0tJu5sxiOolNN4U99oBnny3m+7rssmKer6FDW11DDTD9GTFuAUZGxKblYPsxwMSFyjwC7AEQEcOALYEH+7FOPdawO1KS1BNPPAHHH1/cVuiEE2CbbeA3v4E112x1zTTA9duM+ZnZHhFHA1cCbcC5mTkpIj5erj8H+AZwfkTcRdF9+fnMfLq/6tQbDWfMlyR1p70dllsOnnmmmOvrwAOLMLb99q2umZYS/Xrbosz8PfD7hZad0/T8ceCd/VmHxTW/OzJsCZMkzZdZTCdx2mmw2mpw4YUwahQ89hist16ra6eljCOeumBLmCRpgY6OYmzXTjvB298Ot9wC2277yszeBjAtBkNYFxrl1ZGOCZMk8Z3vFN2NM2fCOecU00yccEIx8F5aTP3aHbk0mz9Zq/++JGkZ9Mwz8KMfwa67Flc6HnZYMdHq6NHQ1tbq2mmQsCWsC42G3ZGStMyZOhWOOaa40vErXykmWAUYPryY8d4Apj5kS1gXGt7AW5KWLZ/5THGVY1sbHHIIfPazxaB7qZ/YEtaFBfeONINJ0uCUCX/+M8ydW7zeYosiiE2dCuedZwBTv7MlrAvzuyO9bZEkDTLz5sEllxTTTNx5J/z85/DBD8LHPtbqmmkZY0tYF5yiQpIGmfb2ortx883h0EOLMHbuuXDQQa2umZZRtoR1ocOWMEkaHF5+GVZcsRjrdf75sPHGxZWP++7rDYLVUoawLsxvCRtiS5gkLZ3+9S84/XS4/HKYPBlWX72Y7X711VtdMwmwO7JLHU7WKklLpxtvLKaT2GormDABDjgA5swp1hnANIDYEtaFV1rCWlwRSVLP3X8/7LwzrLkmfOELxZxfw4a1ulZSpwxhXVgQwmwJk6SBa84c+OUvi2klvv512HJLuPRS2HtvGDq01bWTumU7TxfmD8y3O1KSBqBZs+DUU2HTTeHww+GPfyyufgT47/82gGmpYAjrwvwZ8x2YL0kDzG9/CxtuCJ//fDGh6p/+BDfdBMvZuaOli//HduGVyVpbXBFJEtx9NzQa8J//CdtuC/vtV9xWaPvtW10zabHZEtaFDidrlaTWyiymlHjXu+A//gO++MVi+UYbwS9+YQDTUs8Q1gUH5ktSC/3hD/DmN8Nuu8EttxSD7s87r9W1kvqU3ZFd8N6RklSzl16C17ymGNt1553w7LPw4x/D2LGw0kqtrp3U52wJ68KCyVrtjpSk/vXMM0VL18YbFzfWBjjuOLjvPvj4xw1gGrRsCevCK92RLa6IJA1WU6fCGWfAz34GL75Y3Mtx882LdSus0Nq6STUwhHWhkUkEhN2RktT3MmH//eHee+EDHyiudNxmm1bXSqpVj7ojI2KliNiyvyszkHQ00olaJamvZBbzeR1wAMyeDRHwP/8DDz4I559vANMyaZEhLCL2A+4A/li+3i4iJvZ3xVqtI9OJWiVpSc2bV9xWaPvtYa+9iklV77uvWLfDDrDBBq2tn9RCPWkJ+yqwIzATIDPvADbpvyoNDJneskiSlsizzxZjvA45BObOhXPPLcaB7bBDq2smDQg9GRPWnpmzlrWxUR2NdFC+JPXWjBnw978X3Y5rrQXvex/svnsx6H6IF+RLzXoSwu6OiA8AbRExEvgkcH3/Vqv1Ohp2R0pSj02eDKefXozvioDp02GNNeB732t1zaQBqyc/S44BRgFzgF8Cs4Bj+7NSA0FmOkeYJC3K5MlFa9eWWxYBbOzYYqLVNdZodc2kAa8nLWHvysyTgJPmL4iIA4FL+61WA0BHprPlS1JnGg2YObPobmxrg7/+Fb7wBTjmGBg2rNW1k5YaPWkJO7GHywaVjoa3LJKkijlzivs3brNN0eIFsNlm8Nhj8M1vGsCkXuqyJSwi9gH2BUZExFlNq1YD2vu7Yq1WdEe2uhaSNADMmgU/+Ql8//vFWK9tt4WDD35l/fLLt65u0lKsu+7Ix4FbgfcAtzUtnw0c15+VGgiKqyNtCZMkfvAD+NKXYI89inFfe+5ZDL6XtES6DGGZeSdwZ0T8MjPn1VinAcExYZKWWZMmwXe/C+95TzHVxCc+UUwx8YY3tLpm0qDSk4H5m0TEt4GtgRXnL8zMzfqtVgNAJl4dKWnZkQnXXQenngr/7//ByivDdtsV69Zaq3hI6lM9CWHnAV8BzgDeDhwGDPp04mStkpYphx5a3F5o3XXh618vWr/WXrvVtZIGtZ4MPV8pM68GIjMfzsyvArv3b7Vaz3tHShrUXnoJxo+HF18sXu+/P/z4x/Dww8X4LwOY1O960hL2ckQMASZHxNHAY8B6/Vut1ms00ntHShp8nnkGzj67GGz/1FMwdCh84ANw4IGtrpm0zOlJS9ingJUpblf0RuBQYGx/VmogaDhjvqTBZO5cOPZY2Ggj+PKXi5toX3NNdaoJSbXqNoRFRBtwUGa+kJnTMvP/t3fv4VbP6f/Hn3fn6IBKI502FeVQkiSMYyaGypRqnMOYmDQRo7ku4zsMI3ImUsTQKFOkjBJDDJl+FTonGp22MqXQSdRa9++P96q23WrvtWuv9Vl7r9fjuva11uew1rrtj92+9/t9f+53H3fv7u7TU3lzM+tsZovNbImZDdrDOaeb2WwzW2Bm7+3Ff0NaxOKQa4uWi0g5tGpVeKxSJSwndNFFMG9eKL4/7TS1mhCJUJHTke4eM7Pjzczc3UvyxokEbijQCcgHZprZRHdfWOCcA4AngM7uvsLMsmaaM65mrSJSVrnDv/4V7nT84INQ53XwwfD222GZIRHJCqnUhH0CTDCzscDmHTvd/ZViXtceWOLuXwCY2RigK7CwwDkXA6+4+4rEe64pQexpFXfVhIlIGbNtG4wdC0OGwOzZ8LOfwZ//DNWrh+NKwESySipJ2EHAOn56R6QDxSVhhwIrC2znAycWOqcFUNnM3gVqAo+4+/OF38jMrgWuBWjcuHEKIe+7WNw1HSkiZcvSpaHVxBFHwDPPwCWXQNWqUUclIntQbBLm7n328r2TZTCFpzQrEYr9zwKqA/8xs+nu/lmhGIYDwwHatWtXomnRvaXCfBHJemvWhLscv/oKRoyAFi1g+nRo1w4qqJ5CJNul86c0H2hUYLshYT3Kwue84e6b3f1r4N9A6zTGlLJ4HE1Hikh2+vxz6Ns33Ol4993wzTcQi4Vj7dsrARMpI9L5kzoTaG5meWZWBegNTCx0zgTgVDOrZGb7EaYrF6UxppTF3HXTkIhknxdfDNONzz0HV1wBixbBuHGq9xIpg1KpCdsr7r490dx1ClARGOnuC8ysb+L4MHdfZGZvAHOBOPC0u89PV0wlEY87VSrpr0kRiVg8DpMmQe3acOqpcNZZ8Mc/wg03hMJ7ESmzik3CzKw+8Feggbufa2atgJPc/ZniXuvuk4BJhfYNK7Q9BBhSoqgzQDVhIhKpH38Mo15DhsDChdCjR0jC6tcPU5AiUualMtTzHGE0q0Fi+zNCF/1yLeZq1ioiEXn2WcjLgz59oFIlGDUqJGQiUq6kkoTVdfd/EKYLcfftQCytUWWBsHZk1FGISM748kvYujU837QJWraEKVNCv69LLoHKlaONT0RKXSpJ2GYzq0OivYSZdQC+S2tUWSAW13SkiGTAwoVw1VVh5Ov5RJvEfv1Cx/tzztGyQiLlWCqF+QMJdzUebmbTgHpAj7RGlQXirmatIpIm7mE5ofvug3/+M3S0/+1voVOncFz/9ojkhFSatX5kZqcBRxAasC52921pjyxiWrZIRNLqpptg2TK44w64/nqoWzfqiEQkw1K5O3IO8BLwkrv/N/0hZQdNR4pIqfn+e/jb3+Cpp8I0Y506MHo0NGgA++0XdXQiEpFUasK6ANuBf5jZTDO72cwys4BjhNyhgpIwEdkX69fDXXdBkyZw3XXhTsevVh41RwAAIABJREFUvgrHmjVTAiaS44pNwtx9ubvf5+7HAxcDxwJL0x5ZxGLuKAcTkb22dm1YVuhPf4ITToCpU2HGDDjqqKgjE5EskVLHfDNrCvQEehHaU/whfSFlh1hcNWEiUkKzZ8O0afC730G9enDnnaHY/phjoo5MRLJQKjVh/w+oDIwFLnL3L9IeVRbQdKSIpMQd3n473On41ltwwAFw2WVQq1YovhcR2YNURsKucPdP0x5JlonFNR0pIsX45JPQ42v27LCO4+DBodVErVpRRyYiZcAekzAzu9TdRwHnmdl5hY+7+4NpjSxiMa0dKSLJbN4Ma9aE5qr164cFtp95JnS1r1o16uhEpAwpaiRs/8RjzSTHPA2xZBV3p4JqwkRkhzVr4PHHYehQaN0a3nkntJiYPVvNVUVkr+wxCXP3pxJP/+Xu0woeM7OT0xpVFgjTkfqHVSTnLVkCDzwAzz0HP/wAXbvCLbfsOq5/J0RkL6XSJ+yxFPeVK2rWKpLjPDHgP2ECjBwZiu0XLYLx46Fjx2hjE5FyoaiasJOAjkA9Myt4i08toGK6A4ta3NFImEiuicdh0iQYMiQU3F9xRSi0v+SSUHgvIlKKihoJqwLUICRqNQt8bSBHFvDWQJhIjvjxxzDdeMwxcMEFsHQpVK4cjtWooQRMRNKiqJqw94D3zOw5d1+ewZiygqYjRXJI167wxhshCXvhBejVa1cSJiKSJkVNRz7s7gOAx81st7sh3b1LWiOLWNxdzVpFyqtVq8KdjrfeCrVrw8CBMGAAnHOOCu1FJGOKalHxQuLx/kwEkm3ijpYtEilvFi6E+++HUaMgFoMTTwyjYGefHXVkIpKDipqO/Cjx+N6OfWZ2INDI3edmILZIqWO+SDnyww9w0UXw2mtQvXootr/pptBwVUQkIsW2qDCzd82slpkdBMwBnjWzct0tPx4Ps6+ajhQpw2IxmDUrPK9aNRTY33EHrFgBjz2mBExEIpfK2pG13X2DmV0DPOvu/2dm5XokLJ7oD6TpSJEy6Pvv4fnnQ4PVpUvhiy+gUSN48cWoIxMR+YlUmrVWMrNDgJ7AP9McT1aIuUbCRMqcb7+Fu++Gpk2hb99QcP/ii2FpIRGRLJRKEnYnMAX4r7vPNLPDgM/TG1a04vHwqGatImVALBYev/sO/vxnOP54mDoVZswIdWAVy31vaREpo4qdjnT3scDYAttfAN3TGVTUdk5HppKiikg0Zs8One2/+w7++U9o0gSWLYNDD406MhGRlKRSmN/QzMab2Roz+5+ZvWxmDTMRXFR2TkdqJEwku7jDv/4V+nkddxxMnAhHHLFrNEwJmIiUIamM9TwLTAQaAIcCryX2lVs7745UEiaSXUaMgE6dYN48uOceWLkyFOBrylFEyqBU7o6s5+4Fk67nzGxAugLKBrH4julIJWEikdq8GUaODO0kzj8fevYMCdell4a2EyIiZVgqI2Ffm9mlZlYx8XUpsC7dgUUpkYOpWatIVNasgdtvh8aNoX9/mDAh7D/gALj6aiVgIlIupJKEXUVoT/FV4qtHYl+5FVeLCpHoDB4ciuz/8hc49VT44IMwDSkiUs6kcnfkCqBcL9Zd2M7pSNWEiWTGjBnQsiXUrAkNG4bpxoED4cgjo45MRCRtUrk78jAze83M1ibukJyQ6BVWbmkkTCQD3GHSJDj99LCQ9siRYf+ll4aRLyVgIlLOpTId+SLwD+AQwh2SY4HR6QwqamrWKpJG7vC3v8Gxx8Ivfwn//S88+CBcVa6rHEREdpPK3ZHm7i8U2B5lZv3SFVA2iKlZq0jp27YNKlcGszDqZRbWeOzdO+wXEckxqSRhU81sEDAGcKAX8LqZHQTg7uvTGF8k4mrWKlJ6Vq2CRx6BZ5+FOXPgkEPglVfgoINCIiYikqNSScJ6JR5/W2j/VYSkrNzVh6lZq0gpWLgQ7r8fRo0KHe179IAffgjH6tSJNjYRkSyQyt2ReXv75mbWGXgEqAg87e6D93DeCcB0oJe7j9vbzystu6YjlYSJ7JX//S/UfFWpAtdeCzfdBIeVu7/XRET2SSojYXvFzCoCQ4FOQD4w08wmuvvCJOfdC0xJVywlpcJ8kRKKxUJD1Vmz4K9/hfr14cUX4cwzoW7dqKMTEclK6Sw9bw8scfcv3P1HQk1Z1yTn3QC8DKxJYywlsqsmLOJARLLd1q0wfHjo8dW9O7z0EmzaFI717KkETESkCOlMwg4FVhbYzk/s28nMDgUuBIalMY4S09qRIil4//3Q2f63v4VatUICtngx1KgRdWQiImVCKs1aLbF25O2J7cZm1j6F906WwXih7YeBW909VkwM15rZLDObtXbt2hQ+et+oWavIHqxYAZ98Ep63agUnnQTvvAMzZ4aRr0ppq3AQESl3UhkJewI4Cfh1YnsjodarOPlAowLbDYFVhc5pB4wxs2WENSmfMLNuhd/I3Ye7ezt3b1evXr0UPnrfqEWFSCFz5oRO9ocdBv0SbQLr1IFXX4UzzlCrCRGRvZBKEnaiu/8O2Arg7t8AVVJ43UyguZnlmVkVoDcwseAJ7p7n7k3dvSkwDrje3V8tyX9AOsQShflaO1Jy3ocfwi9+AW3ahML73/8exoyJOioRkXIhlbmDbYk7GB3AzOoB8eJe5O7bE531pxBaVIx09wVm1jdxPKvqwAraURNWQR3zJRdt3x5uEa5SBebODaNgf/0r9O0LBx4YdXQiIuVGKknYo8B44GAzu5swbXhbKm/u7pOASYX2JU2+3P3KVN4zE3xHnzCNhEku2bw5dLV/4AG45Ra4/nro0weuvBKqVYs6OhGRcieVZq1/N7OPgLMIxfbd3H1R2iOLUEyF+ZJL1q6Fxx8PX+vXQ8eO0KJFOFa1arSxiYiUY8UmYWbWGNgCvFZwn7uvSGdgUYpp2SLJJb16wdSp0LVrGAE7+eSoIxIRyQmpTEe+TqgHM6AakAcsBo5KY1yRSgyEqU+YlE8zZsBDD8Gjj0K9ejBkCOy/Pxx5ZNSRiYjklFSmI48puG1mbdl9Me9yZddIWMSBiJQWd5g8Ge67D957D2rXDgX3Z58Nxx8fdXQiIjmpxJ0V3f3jxILb5VZMfcKkPPn+e+jQIdzp2LBhKLz/zW+gZs2oIxMRyWmp1ITdVGCzAtAWSH/b+gjtvDtSQ2FSVm3YEDrZd+sG1avDOefAzTeH+q8qqbT5ExGRdEtlJKzgn8vbCTViL6cnnOywo1mrRsKkzFm1Ch55BIYNg40bYflyaNQo1H2JiEhWKTIJSzRpreHut2QonqwQ2zkSFnEgIqn68ku4/XZ44QWIxaBHj3CnY6NGxb9WREQiscckzMwqJbret81kQNnAVRMmZYF7mHasXTssnD1+PFx7Ldx0U1jjUUREslpRI2EzCPVfs81sIjAW2LzjoLu/kubYIqM+YZLV4vGwjuOQIVCxIrz/PtSvH0bDqlePOjoREUlRKjVhBwHrgDPZ1S/MgXKfhKkwX7LK1q1huvH+++GzzyAvDwYODElZhQpKwEREypiikrCDE3dGzmdX8rWDpzWqiMW1bJFko2efDes5Hn88vPQS/OpXYRpSRETKpKL+Ba8I1OCnydcO5TwJC49awFsitWIFPPwwHHccXHYZXH45HHEEnHEG6P9NEZEyr6gkbLW735mxSLKIOuZLpObODfVeo0eH7UGDwuP++8OZZ0YXl4iIlKqikrCcTUE0HSmRuemmsK7j/vtD//4wYAA0bhx1VCIikgZFJWFnZSyKLBPfUZivKR9Jt+3b4eWXoVMnOOigMNJVrx707QsHHhh1dCIikkZ7bEfq7uszGUg2iSVqwtSiQtJm82Z4/HFo0QJ694ZRo8L+88+HP/5RCZiISA5QT/gkdoyEVdB3R0qbO9xxBzRpAjfcAD/7WWiy2q9f1JGJiEiG6f72JOJawFtK29q1YZrRDGbOhJNPhj/8ITyKiEhOUhKWREzLFklpmTkz3Ok4YQJ8+mlosDp+PFSuHHVkIiISMU24JRHXskWyL9xh8uTQz6t9e3jzzXDXY40a4bgSMBERQSNhSe1s1qrpSNkbX30FXbuG9RwfeAB+8xuoWTPqqEREJMsoCUtCzVqlRDZsgBEjYPbssLbjIYfAO++EUbAqVaKOTkREspSmI5OIu2MGpulIKcrq1aGbfePGcPPNkJ8PW7aEY6ecogRMRESKpJGwJGJxV6NWKdqUKdClS2i22r073HILnHBC1FGJiEgZoiQsibhrySJJYto0+OGH0NX+pJPguutCr6/DD486MhERKYM0HZlE3F31YBLE4/Dqq9CxY5hivOOOsL9WLXj4YSVgIiKy15SEJaHpSAFC8tWyJVx4Ybjj8bHHYNKkqKMSEZFyQtORScTdNR2Zq775JvTxqlEDNm0Kj2PGhLqvSvpxERGR0qORsCTicVej1lyzYkVoqNq4MTz5ZNh38cUwaxb06qUETERESp1+syQRc1ej1lwxd25YVmjMmNDp/te/hs6dwzGt4C4iImmkJCyJuGvJopwxcCD85z/hLscBA8JImIiISAboT/0kwnRk1FFIqdu+Hf7xj9BeYuXKsO/JJ8PzBx9UAiYiIhmlJCyJWFzTkeXKli0wdCi0aBHqu9avD93tAZo1gwMPjDY+ERHJSZqOTELTkeXIli2hl9dXX0GHDmFB7S5doGLFqCMTEZEcpyQsidCiIuooZK/997+hn9cNN8B++8Ef/hCWFDr5ZFByLSIiWUJJWBJq1lpGzZoV7nQcNy60lOjeHRo0gBtvjDoyERGR3Wi8J4mYmrWWLUuWhPUcTzghLKx9yy2wdGlIwERERLJUWpMwM+tsZovNbImZDUpy/BIzm5v4+tDMWqcznlS5q1lr1tu2DZYtC8/r1IHVq+H++0PT1cGDlYCJiEjWS9t0pJlVBIYCnYB8YKaZTXT3hQVOWwqc5u7fmNm5wHDgxHTFlCpNR2axDRtgxIiweHa9evDRR+HuxoULVe8lIiJlSjprwtoDS9z9CwAzGwN0BXYmYe7+YYHzpwMN0xhPymJxNB2ZbVavhkcfDX29vvsOTj89TDvuoARMRETKmHRORx4KrCywnZ/YtydXA5PTGE/K3J2KqpbLDu7hcfJkuO8+OOccmDEDpk6F885T8iUiImVWOlONZL8dPemJZmcQkrBb93D8WjObZWaz1q5dW4ohJhdTTVj0pk2Dbt3gscfC9iWXwOLFoeP9CSdEG5uIiEgpSGcSlg80KrDdEFhV+CQzOxZ4Gujq7uuSvZG7D3f3du7erl69emkJtqBYXElYJOJxePXV0M/rlFPg/fd3jXRVrRq624uIiJQT6UzCZgLNzSzPzKoAvYGJBU8ws8bAK8Bl7v5ZGmMpEXe0bFEU+vSBCy+EVavCCNiKFaHhqoiISDmUtsJ8d99uZv2AKUBFYKS7LzCzvonjw4DbgTrAExZGPLa7e7t0xZSqmBbwzoxvv4Vhw+CKK+CQQ+Caa0KdV/fuodmqiIhIOZbW33TuPgmYVGjfsALPrwGuSWcMe0M1YWm2cmVoMTF8OGzaBHXrhgTs1FOjjkxERCRjNNyQhLtTSbdHlr54HK6+GkaNCnO+vXvDzTdDmzZRRyYiIpJxyjSSUGF+KXKHuXPD8woVQqF9v35hke1Ro5SAiYhIztJIWBIxV7PWfbZ9O7zySlhQe9as0NG+ZUsYOTLqyERERLKCRsKSiMedisrB9s7338MTT8ARR0CvXqG7/VNPQV5e1JGJiIhkFY2EJRFXYX7JuYepxs2bQ53XsceGBbW7dIGKFaOOTkREJOsoCUsiFndNR6bqiy/gwQfDdOPbb4c7HRcsgKZNtaSQiIhIETQdmUTcnYpKIIo2a1aYbmzePLSayMuDrVvDsbw8JWAiIiLF0EhYEnF1zC/a+PHwq19BrVpwyy3Qvz80aBB1VCIiImWKkrAk4nHXQE5B27bBmDFQvTr06AGdO8NDD8FVV4VETEREREpM05FJxNw1EgawcWOo9zr8cLj8cnj++bC/enUYMEAJmIiIyD5QEpaEasKAp5+GRo1g4EA47DB4/XWYMCHqqERERMoNTUcmEY+D5WIStnhxuLuxTp3w2KlTqPlq3z7qyERERModjYQlEYs7ObV05IcfQrduoaP9Y4+Ffd26wdixSsBERETSJJdSjZTFc6UmbOJEOPnk8PX++3DbbXD99VFHJSIikhM0HZlE3L38TkfGYrs62I8cCV9+CY8+Gu503H//aGMTERHJIUrCkojFy2Fh/rffwrBh8Pjj8O670KwZjBgBBx4IlfS/gYiISKbpt28SoSasnCRhK1fCww+HrvabNoVi+x2d7evVizY2ERGRHKYkLIkda1GXeZs2QatW8P33YYmhm2+G446LOioRERFBSVhSsbLaJ8wd3nsPJk+Ge++FGjVCv68OHaBJk6ijExERkQJ0d2QSZW46Mhbb1U7ijDPguefgf/8Lx3r1UgImIiKShZSEJeEOFcpKEjZvHrRoAT17wnffwVNPwfLlUL9+1JGJiIhIETQdmUTMnazOwb7+OiRaxx8PeXnQvDkMGQJdu+5qPyEiIiJZTUlYElnbomLp0rCg9jPPhCnGhQtD3dcbb0QdmYiIiJSQpiMLcXcgy6Yj58+H3r1Db6+nngrPX365nNzCKSIikps0ElZILJ5IwqJOcNxh2zaoUiUkYZMnhxYT/fvDoYdGG5uIiIjsM42EFRJLjIRFdnfktm3wwgvQujXcf3/Y16MHrFgR2k4oARMRESkXlIQVksjBMj8StnEjPPQQHH44XH55aDvRokU4VqkS1K6d2XhEREQkrTQdWciu6cgMf/BVV8G4cXDaafDkk3DuuVBBObKIiEh5pd/yhWRsOnLxYrj2Wli2LGzfdhtMnx4W1/7lL5WAiYiIlHMaCSsknu7C/A8/DD29JkwIRfdnnw1Nm4YaMBEREckZSsIKie+sCSvlN47FoFMnmDoVDjwwjHz16wcHH1zKHyQiIrlu27Zt5Ofns3Xr1qhDyRnVqlWjYcOGVK5cOeXXKAkrZEdNWKlMR/7wA7z1Fpx/fuhkf9JJ0K1bqP+qUWPf319ERCSJ/Px8atasSdOmTbGoWy7lAHdn3bp15Ofnk5eXl/LrlIQVEi+NZq3ffhuaqj7yCKxeDXPnwjHHwN13l1KUIiIie7Z161YlYBlkZtSpU4e1a9eW6HWq/i5kRxK2V8sWrV8fGqo2bgyDBsHRR8Obb4ZHERGRDFIClll78/1WElbIXnXM37IlPFaqBM8+CxdcAB9/HBKwTp20vJCIiOSk8ePHY2Z8+umnO/e9++67nH/++T8578orr2TcuHFAqGcbNGgQzZs35+ijj6Z9+/ZMnjx5n2O55557aNasGUcccQRTpkxJes6cOXM46aSTOOaYY7jgggvYsGEDAD/++CN9+vThmGOOoXXr1rz77rv7HA8oCdtNPB4ei52OdA/tJM47Dzp0CNu1asHy5fD3v8Nxx6U9VhERkWw2evRoTjnlFMaMGZPya/70pz+xevVq5s+fz/z583nttdfYuHHjPsWxcOFCxowZw4IFC3jjjTe4/vrricViu513zTXXMHjwYObNm8eFF17IkCFDABgxYgQA8+bN46233mLgwIHEdyQM+0BJWCE7pyP39J2JxWDsWDjxRDjjDPjoI+jVKyw3BCq4FxERATZt2sS0adN45plnUk7CtmzZwogRI3jssceoWrUqAPXr16dnz577FMuECRPo3bs3VatWJS8vj2bNmjFjxozdzlu8eDE///nPAejUqRMvv/wyEJK4s846C4CDDz6YAw44gFmzZu1TTKDC/N3saNa6x+nIceOgd29o1gyGDQtLDFWvnsEIRUREUnfHawtYuGpDqb5nqwa1+L8LjirynFdffZXOnTvTokULDjroID7++GPatm1b5GuWLFlC48aNqVWrVrEx3HjjjUydOnW3/b1792bQoEE/2ffll1/SoUOHndsNGzbkyy+/3O21Rx99NBMnTqRr166MHTuWlStXAtC6deudidzKlSv56KOPWLlyJe3bty82zqKkNQkzs87AI0BF4Gl3H1zouCWOnwdsAa5094/TGVNxdmvWum4dDB0KP/tZ6HD/q1/B+PGh7qtixQgjFRERyV6jR49mwIABQEiMRo8eTdu2bfdYwF7SwvaHHnoo5XN9x8LQxXzeyJEj6d+/P3feeSddunShSpUqAFx11VUsWrSIdu3a0aRJEzp27EilSvueQqUtCTOzisBQoBOQD8w0s4nuvrDAaecCzRNfJwJPJh4js6NZa41VK+Hpe2DkyFB4/5vfhCSscuXQ60tERKQMKG7EKh3WrVvHO++8w/z58zEzYrEYZsZ9991HnTp1+Oabb35y/vr166lbty7NmjVjxYoVbNy4kZo1axb5GSUZCWvYsOHOUS0IfdQaNGiw22uPPPJI3nzzTQA+++wzXn/9dQAqVar0k6SvY8eONG/evJjvQvHSWRPWHlji7l+4+4/AGKBroXO6As97MB04wMwOSWNMxYrFneumj+X08zuGXl+9esGCBTB8eJRhiYiIlBnjxo3j8ssvZ/ny5SxbtoyVK1eSl5fHBx98QPPmzVm1ahWLFi0CYPny5cyZM4c2bdqw3377cfXVV9O/f39+/PFHAFavXs2oUaN2+4yHHnqI2bNn7/ZVOAED6NKlC2PGjOGHH35g6dKlfP7550mnEtesWQNAPB7nrrvuom/fvkCoVdu8eTMAb731FpUqVaJVq1b7/H1KZxJ2KLCywHZ+Yl9Jz8HMrjWzWWY2q6SN0EqqYgVjxeGtWNnnOli6NIyElcI3WkREJFeMHj2aCy+88Cf7unfvzosvvkjVqlUZNWoUffr0oU2bNvTo0YOnn36a2rVrA3DXXXdRr149WrVqxdFHH023bt2oV6/ePsVz1FFH0bNnT1q1akXnzp0ZOnQoFRMlRddcc83OIvvRo0fTokULjjzySBo0aECfPn2AkJy1bduWli1bcu+99/LCCy/sUzw7WLJ50lJ5Y7OLgF+4+zWJ7cuA9u5+Q4FzXgfucfcPEttvA39w94/29L7t2rXz0rgjQUREpLxatGgRLVu2jDqMnJPs+25mH7l7u2Tnp3MkLB9oVGC7IbBqL84RERERKXfSmYTNBJqbWZ6ZVQF6AxMLnTMRuNyCDsB37r46jTGJiIiIZIW03R3p7tvNrB8whdCiYqS7LzCzvonjw4BJhPYUSwgtKvqkKx4RERGRbJLWPmHuPomQaBXcN6zAcwd+l84YREREcpG7axHvDNqbGnstWyQiIlLOVKtWjXXr1u1VYiAl5+6sW7eOatWqleh1WrZIRESknGnYsCH5+fmku62T7FKtWjUaNmxYotcoCRMRESlnKleuTF5eXtRhSDE0HSkiIiISASVhIiIiIhFQEiYiIiISgbQtW5QuZrYWWJ6Bj6oLfJ2Bz5HU6ZpkH12T7KTrkn10TbJTJq5LE3dPuvhlmUvCMsXMZu1prSeJhq5J9tE1yU66LtlH1yQ7RX1dNB0pIiIiEgElYSIiIiIRUBK2Z8OjDkB2o2uSfXRNspOuS/bRNclOkV4X1YSJiIiIREAjYSIiIiIRyOkkzMw6m9liM1tiZoOSHDczezRxfK6ZtY0izlyTwnW5JHE95prZh2bWOoo4c0lx16TAeSeYWczMemQyvlyVynUxs9PNbLaZLTCz9zIdY65J4d+v2mb2mpnNSVyTPlHEmUvMbKSZrTGz+Xs4Htnv+pxNwsysIjAUOBdoBfzazFoVOu1coHni61rgyYwGmYNSvC5LgdPc/VjgL6jWIq1SvCY7zrsXmJLZCHNTKtfFzA4AngC6uPtRwEUZDzSHpPiz8jtgobu3Bk4HHjCzKhkNNPc8B3Qu4nhkv+tzNgkD2gNL3P0Ld/8RGAN0LXROV+B5D6YDB5jZIZkONMcUe13c/UN3/yaxOR0o2bL1UlKp/KwA3AC8DKzJZHA5LJXrcjHwiruvAHB3XZv0SuWaOFDTzAyoAawHtmc2zNzi7v8mfJ/3JLLf9bmchB0KrCywnZ/YV9JzpHSV9Ht+NTA5rRFJsdfEzA4FLgSGZTCuXJfKz0oL4EAze9fMPjKzyzMWXW5K5Zo8DrQEVgHzgN+7ezwz4ckeRPa7vlImPiRLWZJ9hW8VTeUcKV0pf8/N7AxCEnZKWiOSVK7Jw8Ct7h4Lf+BLBqRyXSoBxwNnAdWB/5jZdHf/LN3B5ahUrskvgNnAmcDhwFtm9r67b0h3cLJHkf2uz+UkLB9oVGC7IeEvk5KeI6Urpe+5mR0LPA2c6+7rMhRbrkrlmrQDxiQSsLrAeWa23d1fzUyIOSnVf8O+dvfNwGYz+zfQGlASlh6pXJM+wGAP/aGWmNlS4EhgRmZClCQi+12fy9ORM4HmZpaXKIrsDUwsdM5E4PLEnRMdgO/cfXWmA80xxV4XM2sMvAJcpr/oM6LYa+Luee7e1N2bAuOA65WApV0q/4ZNAE41s0pmth9wIrAow3HmklSuyQrCyCRmVh84Avgio1FKYZH9rs/ZkTB3325m/Qh3clUERrr7AjPrmzg+DJgEnAcsAbYQ/oKRNErxutwO1AGeSIy8bNfCuOmT4jWRDEvlurj7IjN7A5gLxIGn3T3pbfqy71L8WfkL8JyZzSNMg93q7l9HFnQOMLPRhDtR65pZPvB/QGWI/ne9OuaLiIiIRCCXpyNFREREIqMkTERERCQCSsJEREREIqAkTERERCQCSsJEREREIqAkTETSwsxiZja7wFfTIs7dlLnI9szMGpjZuMTzNmZ2XoFjXcxsUAZjaWpmF2fq80Qk89SiQkTSwsw2uXuN0j43U8zsSqCdu/dL42dUcvekizeb2enAze5+fro+X0SipZEwEckIM6thZm+b2cdmNs/MuiY55xAz+3di5Gy+mZ2a2H+Omf0n8dqxZrZbwpZYpPphM/sw8dr2if0HmdmrZjYm80ZpAAADmElEQVTXzKYnlrzCzE4rMEr3iZnVTIw+zU90O78T6JU43svMrjSzx82stpktM7MKiffZz8xWmlllMzvczN5ILJb9vpkdmSTOP5vZcDN7E3g+8ZnvJ/7bPjazjolTBxO63c82sxvNrKKZDTGzmYn/lt+W0qURkYjkbMd8EUm76mY2O/F8KXARcKG7bzCzusB0M5voPx2OvxiY4u53m1lFYL/EubcBZ7v7ZjO7FbiJkCQVtr+7dzSznwMjgaOBO4BP3L2bmZ0JPA+0AW4Gfufu0xJJ3dYdb+LuP5rZ7RQYCUuMjOHu35nZHOA0YCpwQSLmbWY2HOjr7p+b2YnAE4SFmgs7HjjF3b9PLCfUyd23mllzYDRhLc5BFBgJM7NrCcupnGBmVYFpZvamuy9N4VqISBZSEiYi6fK9u7fZsWFmlYG/JhKkOHAoUB/4qsBrZgIjE+e+6u6zzew0oBUh6QCoAvxnD585GsDd/21mtczsAOAUoHti/ztmVsfMagPTgAfN7O/AK+6en3j/VLwE9CIkYb0JS2jVADoCYwu8T9U9vH6iu3+feF4ZeNzM2gAxoMUeXnMOcKyZ9Uhs1waaExJcESmDlISJSKZcAtQDjk+MGi0DqhU8IZE8/Rz4JfCCmQ0BvgHecvdfp/AZhYtcnbA+327nuftgM3udsGbcdDM7mwKjYcWYCNxjZgcRRrXeAfYHvi2YeBZhc4HnNwL/A1oTSkT2FIMBN7j7lBRjFJEsp5owEcmU2sCaRAJ2BtCk8Alm1iRxzgjgGaAtMB042cyaJc7Zz8z2NFrUK3HOKYSpu++AfxMSwB3F7l8npkQPd/d57n4vMAsoXL+1EaiZ7EPcfRMwA3gE+Ke7x9x9A7DUzC5KfJaZWesUvy+r3T0OXEZY+DnZ508BrkuMEmJmLcxs/xTeX0SylEbCRCRT/g68ZmazgNnAp0nOOR24xcy2AZuAy919baIea3SiFgpCjdhnSV7/jZl9CNQCrkrs+zPwrJnNBbYAVyT2D0gkgzFgITAZOKTAe00FBiXq2u5J8lkvAWMTMe9wCfCkmd1GmGYcA8xJ8tqCngBeTiRvU9k1SjYX2J6oP3uOkPA1BT62MN+5FuhWzHuLSBZTiwoRKRfM7F1CIfusqGMREUmFpiNFREREIqCRMBEREZEIaCRMREREJAJKwkREREQioCRMREREJAJKwkREREQioCRMREREJAJKwkREREQi8P8BioiAxAsjsBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** GaussianNB ***************************\n",
      "\n",
      "\n",
      "GaussianNB()\n",
      "\n",
      "\n",
      "accuracy_score= 0.9070769230769231\n",
      "\n",
      "\n",
      "cross_val_score 0.8888738419403899\n",
      "\n",
      "\n",
      "roc_auc_score =  0.9059027493379201\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.94      0.91       842\n",
      "           1       0.93      0.87      0.90       783\n",
      "\n",
      "    accuracy                           0.91      1625\n",
      "   macro avg       0.91      0.91      0.91      1625\n",
      "weighted avg       0.91      0.91      0.91      1625\n",
      "\n",
      "\n",
      "\n",
      "[[790  52]\n",
      " [ 99 684]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Model=[]\n",
    "score=[]\n",
    "cvs=[]\n",
    "rocscore=[]\n",
    "for name,model in models:\n",
    "    print('******************************',name,'***************************')\n",
    "    print('\\n')\n",
    "    Model.append(name)\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pre=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    AS=accuracy_score(y_test,pre)\n",
    "    print('accuracy_score=',AS)\n",
    "    score.append(AS*100)\n",
    "    print('\\n')\n",
    "    sc=cross_val_score(model,x,y,cv=10,scoring='accuracy').mean()\n",
    "    print('cross_val_score',sc)\n",
    "    cvs.append(sc*100)\n",
    "    print('\\n')\n",
    "    false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,pre)\n",
    "    roc_auc= auc(false_positive_rate,true_positive_rate)\n",
    "    print('roc_auc_score = ',roc_auc)\n",
    "    rocscore.append(roc_auc*100)\n",
    "    print('\\n')\n",
    "    print('classification_report',classification_report(y_test,pre))\n",
    "    print('\\n')\n",
    "    cm=confusion_matrix(y_test,pre)\n",
    "    print(cm)\n",
    "    print('\\n')\n",
    "    plt.figure(figsize=(10,50))\n",
    "    plt.subplot(911)\n",
    "    plt.title(name)\n",
    "    print(sns.heatmap(cm,annot=True))\n",
    "    plt.subplot(912)\n",
    "    plt.title(name)\n",
    "    plt.plot(false_positive_rate,true_positive_rate, label='AUC = %0.2f'% roc_auc)\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.show()\n",
    "    \n",
    "    print('\\n\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Model</th>\n",
       "      <th>Accuracy_score</th>\n",
       "      <th>Cross_val_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>99.876923</td>\n",
       "      <td>95.396952</td>\n",
       "      <td>99.872286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>98.153846</td>\n",
       "      <td>92.751531</td>\n",
       "      <td>98.205104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>89.476923</td>\n",
       "      <td>86.338259</td>\n",
       "      <td>89.304187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>98.953846</td>\n",
       "      <td>94.142324</td>\n",
       "      <td>98.932330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>90.707692</td>\n",
       "      <td>88.887384</td>\n",
       "      <td>90.590275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy_score  Cross_val_score  roc_auc_score\n",
       "0    KNeighborsClassifier       99.876923        95.396952      99.872286\n",
       "1                     SVC       98.153846        92.751531      98.205104\n",
       "2      LogisticRegression       89.476923        86.338259      89.304187\n",
       "3  DecisionTreeClassifier       98.953846        94.142324      98.932330\n",
       "4              GaussianNB       90.707692        88.887384      90.590275"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'Model':Model, 'Accuracy_score': score,'Cross_val_score':cvs,'roc_auc_score':rocscore})\n",
    "result"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#observation\n",
    "we can observe from the above table that kNeighbors and SVc is working well with accuracy score of 99 and 98 % and Cross validation score of 95 and 92 % respectively\n",
    "\n",
    "*** lets check other algorithm and grid search cv for better result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 1}\n",
      "1\n",
      "0.9979997629489155\n"
     ]
    }
   ],
   "source": [
    "# lets apply grid search cv to get the best parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbors={'n_neighbors':range(1,30)}\n",
    "knr=KNeighborsClassifier()\n",
    "gknr=GridSearchCV(knr,neighbors,cv=10)\n",
    "gknr.fit(x_train,y_train)\n",
    "print(gknr.best_params_)\n",
    "print(gknr.best_estimator_.n_neighbors)\n",
    "print(gknr.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Kneighbors classifier on CV data:  1.0\n"
     ]
    }
   ],
   "source": [
    "knr=KNeighborsClassifier(n_neighbors=1)\n",
    "knr.fit(x_train,y_train)\n",
    "pred=knr.predict(x_test)\n",
    "print(\"Accuracy for Kneighbors classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for KneighborsClassifier: 0.9760056713867631\n",
      "standard deviation in accuracy score for KneighborsClassifier: 0.04893801970999792\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# observation\n",
    "we can observe that with KNeighbors our accuracy score is 100% with cross validation score of 97% which is over fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9918448510688694\n",
      "rbf\n",
      "{'C': 10, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters={'kernel':('linear','rbf'),'C':[1,10]}\n",
    "svc=svm.SVC()\n",
    "clf=GridSearchCV(svc, parameters)\n",
    "clf.fit(x_train,y_train)\n",
    "print(clf.best_score_)\n",
    "print(clf.best_estimator_.kernel)\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for svc classifier on CV data:  0.9944615384615385\n"
     ]
    }
   ],
   "source": [
    "svc=svm.SVC(kernel='rbf',C=10)\n",
    "svc.fit(x_train,y_train)\n",
    "pred=svc.predict(x_test)\n",
    "print(\"Accuracy for svc classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for SVC Classifier: 0.9452393070728735\n",
      "standard deviation in accuracy score for SVC Classifier: 0.09330926815959602\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for SVC Classifier:',cross_val_score(svc,x,y,cv=10,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for SVC Classifier:',cross_val_score(svc,x,y,cv=10,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can observe with SVC we are getting accuracy score of 99% with cross validation score of 94%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'n_estimators': 500}\n",
      "0.9963071001361994\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "#randomforestclassifier(100)-----Default\n",
    "parameters={'n_estimators':[10,100,500,1000],'criterion' :['gini', 'entropy']}\n",
    "rfc=GridSearchCV(estimator=rfc,param_grid=parameters,cv=5)\n",
    "rfc.fit(x_train,y_train)\n",
    "print(rfc.best_params_)\n",
    "print(rfc.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Random Forest on CV data:  0.9987692307692307\n"
     ]
    }
   ],
   "source": [
    "rfc1=RandomForestClassifier(random_state=42,n_estimators=500,criterion='entropy')\n",
    "rfc1.fit(x_train,y_train)\n",
    "pred=rfc1.predict(x_test)\n",
    "print(\"Accuracy for Random Forest on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for random forest classifier: 0.8506475937855248\n",
      "standard deviation in accuracy score for random forest clasifier: 0.18299409362104418\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for random forest classifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for random forest clasifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we observe till now that kNeighbors is working quite well so lets save the model with KNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Kneighbors classifier on CV data:  1.0\n",
      "\n",
      "\n",
      "mean cross val score for KneighborsClassifier: 0.9760056713867631\n",
      "\n",
      "\n",
      "standard deviation in accuracy score for KneighborsClassifier: 0.04893801970999792\n",
      "\n",
      "\n",
      "RMSE is:  0.0\n"
     ]
    }
   ],
   "source": [
    "knr=KNeighborsClassifier(n_neighbors=1)\n",
    "knr.fit(x_train,y_train)\n",
    "pred=knr.predict(x_test)\n",
    "print(\"Accuracy for Kneighbors classifier on CV data: \",accuracy_score(y_test,pred))\n",
    "print('\\n')\n",
    "print('mean cross val score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').mean())\n",
    "print('\\n')\n",
    "print('standard deviation in accuracy score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').std())\n",
    "print('\\n')\n",
    "print('RMSE is: ',np.sqrt(mean_squared_error(y_test,pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mushroom_classification.pkl']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets save te model\n",
    "import joblib\n",
    "joblib.dump(knr,'mushroom_classification.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets test the model\n",
    "mush_test=knr.predict(x)\n",
    "mush_test"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "we can conclude that our model is working well with an accuracy score of 100% and it give the difference of output between different classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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