MushroomDataset_Project1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data file using Pandas  \n",
    "mu_data=pd.read_csv('mushrooms.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "      <td>o</td>\n",
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       "      <td>n</td>\n",
       "      <td>m</td>\n",
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       "      <td>p</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "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",
       "  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",
       "  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",
       "  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",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 23)\n"
     ]
    }
   ],
   "source": [
    "print(mu_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
      "       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
      "       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
      "       'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
      "       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
      "       'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(mu_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      "class                       8124 non-null object\n",
      "cap-shape                   8124 non-null object\n",
      "cap-surface                 8124 non-null object\n",
      "cap-color                   8124 non-null object\n",
      "bruises                     8124 non-null object\n",
      "odor                        8124 non-null object\n",
      "gill-attachment             8124 non-null object\n",
      "gill-spacing                8124 non-null object\n",
      "gill-size                   8124 non-null object\n",
      "gill-color                  8124 non-null object\n",
      "stalk-shape                 8124 non-null object\n",
      "stalk-root                  8124 non-null object\n",
      "stalk-surface-above-ring    8124 non-null object\n",
      "stalk-surface-below-ring    8124 non-null object\n",
      "stalk-color-above-ring      8124 non-null object\n",
      "stalk-color-below-ring      8124 non-null object\n",
      "veil-type                   8124 non-null object\n",
      "veil-color                  8124 non-null object\n",
      "ring-number                 8124 non-null object\n",
      "ring-type                   8124 non-null object\n",
      "spore-print-color           8124 non-null object\n",
      "population                  8124 non-null object\n",
      "habitat                     8124 non-null object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(mu_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "# using isnull() function for finding null values   \n",
    "print(mu_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kqcPOdqRnkeLUzwIOtN0IJHGOpGWBV3L5M704iSAIgp7Qf314bboJl4DtvSvKvwV8q6R8ixLxIAiCUUE/TrzWJTxogyAIGozhkX0dnf0ikm6UND0nCj8ml6+RM6vclzOtLJTLV5N0aU44foWklQt1rZqTk98t6a7mrCxBEAQjyVg2vaxjjfMvYFvb6wMbADtK2oyUUeV422sBz5DCIwB8D/hlTjh+LPCfhbp+SUpU/haSo9UTvTmNIAiCHjCerXGcmJlXF8yLgW1JmVUgZVrZJX9+K3Bp/nw5OTNLDqOwgO1Lcr0zbb/Ui5MIgiDoBZ5Vf+k36trZz58tcZ4gOUX9hRT4rHHKxQws0xlIUbgrsHi2wFkbeFbSuZJulfTdnKOx7HjhQRsEwbDjOfWXfqNWZ297do5ouTIDcW7mEsv/DwW2knQrsBUpjvMs0mTwFnn7O4A1gb0rjhc5aIMgGH7GsxqniO1ngSuAzYClsgctDPaSfdT2brY3BL6ay54jjf5vzcl3ZwG/AzbqyVkEQRD0gHE9spe0vKSl8ufXkLxm7ybp43fPYnsBv88yy0lq1HsEcHL+fBOwtKTl8/q2JMerIAiCUcFY7uzr2NmvAJya9evzAWfZ/oOku4AzJX0TuJUUUgFga+A/JRm4CjgQkipI0qHApTkEw1Tgpz09myAIgi7w7LGbJruOB+1tpLDGzeUPkPT3zeVnM2Cl07ztEmC99psZBEEw7+nHEXtdwoM2CIIg4zljd2TfsQdtYfuPJM0srK8q6fJsXnmbpPfn8k0kTcvLdEmRpSoIglHFeNfZNzxoZ+a49tdI+qPt6yVNICUbL/I1kl7/pOxINQVYnZSpakLOuL4CMF3S+QVb/SAIghHFHscj+yoP2jxh+13gK827AEvkz0syYJL5UqFjX4QBu/wgCIJRwXgf2ZM79qnAm4Af275B0sHAZNuPJeOaVzkauFjS54HFSKaajXo2JZlirgZ8Ikb1QRCMJuaMYWucjjxoJW0J7AH8qER8T+AXtlcmJRX/VcPu3vYNtt9G8qA9QtIiZceLcAlBEIwEnqPaS7/RqQftNqRR/v2SHgQWlXR/FtsXOCvLX0dS2SzXVM/dwIvAuhXHiXAJQRAMO+O6s6/woJ1q+w22V7e9OvCS7TflXR4CtsvybyF19n/P8e8XyOWrAesAD/b4fIIgCDrGrr/0Gx170LaQ/xLwU0mHkCZh97ZtSe8GDpf0CimM0AG2n+yy/UEQBD2jH0fsdenYg7ZJ5rWFz3eRkpQ3y/wK+FUHbQyCIBgWxrLpZXjQBkEQZGaPYWuc6OyDIAgyY3lk303C8e0k3ZLDH1wj6U25/PhCWIQ/S3q2UNdeOUH5fZL2mnenFQRB0D5j2Rqn43AJwEnAzrbvlnQAKUzC3rYPaeyYHas2zJ+XAY4CJpAmbqdKmmz7md6eUhAEQWf0o5VNXbpJOF4aFqGJPYEz8uf3ApfYfjp38JcAO3bR9iAIgp4y3kf2VeESPgVMkfQP4HlSqsLiPqsBawCX5aKVgIcLIsUk5c3H2w/YD+CIpdYnHKuCIBgOZs9py8+0r+g0XMK6wCHA+3NYhFOAHzTtNhE42/bsvF72KCx9aQoP2iAIRoKx7FTVabiE9wHr274hb/oN8M4m8YkMqHAgjeRXKay/mqQ8CIJgNDDHqr3UQdKOku6VdL+kw0u2LyzpN3n7DZJWL2w7IpffK+m93Z5bNwnHl5S0dhbbPpc19lkHWBq4rlDVRcAOkpaWtDSwQy4LgiAYFdiqvQxFVn//mDQ4fiuwZ87xUWRf4JkcbuZ44Li871tJA+a3keY2/zvX1zHdJBz/NHCOpDnAM8A+hX32BM60B152bD8t6RvATbnoWNtPd9P4IAiCXtJj9cwmwP05XzeSzgR2Bu4qyOxMCgsPKXf3iUox43cm9aH/Av6aA01uwuABdFt0k3D8POC8in2Orig/mRTPPgiCYNRRVz0Dgw1JMpNsTyqslxmlbNpUzasyOYvfc8Cyufz6pn1LDVrqEh60QRAEmXascXLHPqmFSB2jlCqZ2gYtdal9ZpLmz0nE/5DXT8sTB3dIOjk7XCFpa0nPFbxoj8zl6xTKpkl6XtIXuml8EARBL3EbSw3qGKW8KpNDwC8JPF1z37ZoxxrnYAqTsMBpwJuBtwOvAT5V2Ha17Q3yciyA7XsbZcDGwEtUqIGCIAhGgh5b49wErJVzeSxEmnCd3CQzGWiEjtkduCzPdU4GJmZrnTWAtYAbuzm3Wp29pJWBDwA/a5TZnpK9a50bsXIbx90O+Ivtv7XT2CAIgnlJL61xco7tz5GsDu8mGbfcKelYSTtlsZ8Dy+YJ2C8Ch+d97yRl/LsLuBA4sOCz1BF1dfYnAF8BFm/ekNU3nyCN/BtsLmk66bXj0NzwIs02+M11hgdtEATDzpwe12d7CjClqezIwud/kvJ5l+37LeBbvWpLHTv7DwJP2J5aIfLfwFW2r87rtwCr2V6flJD8d031LQTsBPy26pjhQRsEwUhgVHvpN+qocd4F7JQTi58JbCvp1wCSjgKWJ71+AGD7+UbgtPxUW1BSMeH4+4BbbD/em1MIgiDoDbOs2ku/USfq5RG2V86JxSeSJhA+ngOhvRfY0/arbz+S3pCdApC0ST7GU4Uqi5EwgyAIRg1jeWTfjZ39T4C/Adflvv3cbHmzO/BZSbOAfwATG560khYlhVb4TFetDoIgmAf0Wmc/mmirs7d9BSkQGrZL97V9InBixbaXSN5hQRAEo45+HLHXJTxogyAIMmN5ZN+NB60kfSvnmb1b0kFN8u+QNFvS7k3lS0h6RFLp6D8IgmCkmI1qL/1GOyP7hgdtIxXh3iR33jfbniPpdQ3BHCHzOMpDGH8DuLKj1gZBEMxD+jDbYG069qAFPksKUzwHwPYThW2fB84BimVI2hh4PXBxF20OgiCYJ8xBtZd+o64ap+FBW1RpvRH4qKSbJf1R0loAklYCdiVZ67yKpPmA7wNf7rrVQRAE84AeB0IbVXTjQbsw8E/bE4CfMhCn/gTgsJI4DgcAU2w/zBBI2i8/RG4+98UHhxIPgiDoCXPaWPqNOjr7hgft+4FFgCWyB+0MkqoGUvTKU/LnCcCZ2fZ+OeD92eZ+c2ALSQcArwUWkjTT9lx5GYtxom9eeZd+fIgGQdCHzFH/qWfqUidT1RHAEZBi1ZMCm31c0reBbUkj+q2AP2f5NRr7SvoF8Afbv6MQI0fS3sCEso4+CIJgpOgqrOQopxs7+28Dp0k6BJjJ4Hj2QRAEfcdYtsbpxoP2WZKFTiv5vSvKfwH8op1jB0EQzGv60cqmLuFBGwRBkBnLE4TR2QdBEGTGshqnrlPVg5Juz4nCb85ly0i6RNJ9+f/SuXxnSbc1ZCW9u1DPhZKebYRcCIIgGE2MZdPLdhKOb5MThk/I64cDl9peC7g0r5M/r58Ti+/DYK/b75JSGAZBEIw6Zqv+0m+009k3szNwav58KrALgO2Zjfj1wGIU1GC2LwVe6OKYQRAE84wY2acO+2JJU3MycIDX234MIP8vBkLbVdI9wAWk0X1bhAdtEAQjwVju7OtO0L7L9qM5suUluSOvxPZ5wHmStiRFuXxPO40KD9ogCEaCPkwtW5taI3vbj+b/T5BCI2wCPC5pBYD8/4mS/a4C3tiUcDwIgmBUMpZH9nUCoS0mafHGZ2AH4A5gMrBXFtsL+H2WeVMh4fhGwEIMTjgeBEEwKpndxtJv1FHjvJ6kkmnIn277Qkk3AWdJ2hd4CNgjy38Y+HdJr5ASjn+0kHD8auDNwGslzQD2tV2W4CQIgmDYGct29nUCoT0ArF9S/hSwXUn5caQsVWV1bdFBG4MgCIaFflTP1CU8aIMgCDJjubPv2IO2sO1QSW5Mwkp6s6TrJP1L0qFNsodIulPSHZLOkLRI704lCIKgO8Zypqp2Rvbb2H6yWCBpFWB7ks6+wdPAQWQnq4LsSrn8rbb/IeksYCIR/TIIglHCWNbZd+NBC3A8KTdt0Uv2Cds3Aa+UyC8AvEbSAsCiwKNdHj8IgqBnjGVrnI49aCXtBDxie3qtCuxHgO+R3gIeA56zfXGZbHjQBkEwEszBtZd+oxsP2q+SbO5rkaNi7gysATwL/FbSx23/ulk2PGiDIBgJxv0EbYkH7VakTnu6pAeBlYFbJL2hRTXvAf5q+++2XwHOBd7ZRduDIAh6ylieoO3Ug/Ym26+zvbrt1YEZwEa2/69FVQ8Bm0laNHvYbgfc3fUZBEEQ9IhxHS6B5EF7jaTpwI3ABbYvrBKW9IbsHftF4GuSZkhawvYNwNnALcDt+diTuj6DIAiCHjFLrr10Q1XypxK5vbLMfZL2Ktk+WdIddY7ZsQdtk8zqhc//R1LrlMkdBRxVp2FBEATDzTCqZxrJn74t6fC8flhRQNIypP5yQm7aVEmTbT+Tt+8GzKx7wG5NL4MgCMYMw6jGKU3+1MR7gUtsP507+EuAHQEkvZakPflm3QNGuIQgCILMMJpUDkr+lC0dm1kJeLiwPiOXQcoT8n3gpboHrNXZZ4ubF0i+BLNsT5C0AfATYBFgFnCA7Rvz5OsPgffnhuxt+5Zcz2ySvh7gIds71W1oEATBvKadrj77HO1XKJqUzcYb2/8ElFkofrXuIUrKnPveN9k+RNLqNevqKlzCd4BjbP9R0vvz+tbA+4C18rIpcFL+D/CPnIg8CIJg1NGOeqboD1SxvTJDn6THJa2QR/WlyZ9II/mtC+srA1cAmwMb50H4AsDrJF1he2ta0I3O3sAS+fOSDIQ+2Bn4pRPXA0s1MloFQRCMZmbj2kuXlCZ/auIiYAdJS2drnR2Ai2yfZHvFbBjzbuDPQ3X00F3C8S8A35X0MCkMwhG5vJWeaZEcBuF6SWUTEkCESwiCYGQYxgnabwPbS7qPFEzy2wCSJkj6GYDtp0m6+Zvycmwu64huwiXsDhxi+xxJHwF+TvKSLdUz5f+r5nrWBC6TdLvtv8wlHOESgiAYATxME7Qtkj/dDHyqsH4ycHKLeh4E1q1zzG4Sju9FCnkA8NtcBmkkv0ph95XJKp5CPQ+QdE8b1jl+EATBcDCuPWhbJBx/lBQjB2Bb4L78eTIpB60kbUaKbvlY1jstnOtZDngXcFdPzyYIgqALxnvUy6qE4zOBH+bY9P9kwARpCsns8n6S6eUnc/lbgP+RNIf0kPm27ejsgyAYNfRfF16fbhKOXwNsXFJu4MCS8muBt3fWzCAIgnnPrDHc3YcHbRAEQWa4JmhHgroJx5eSdLakeyTdLWlzSXvk5OFzJE0oyC4k6ZScoHy6pK1z+aKSLsh13Cnp2/PonIIgCDpiXE/QZn4IXGj7zSSVzt2kSdrdgKuaZD8NYPvtJPvR70tqHOd7uY4NgXdJel+X7Q+CIOgZbuOv3xhSjSNpCWBLYG8A2y8DL5NSC5Inbou8Fbg0yz4h6Vlggu0bgcsbdUi6hYpQyEEQBCNBP47Y61JnZL8m8HfgFEm3SvpZNsGsYjqws6QFJK1BmsQt2t0jaSngQ+SHQjPhQRsEwUgw26699Bt1OvsFgI2Ak2xvCLxICrRfxckkx6qbgROAa0lRMQHIpppnAP+VLX3mwvYk2xNsT9htsdXrnEcQBEHXjHc7+xnAjJxWEFJqwcrO3vYs4JDGuqRrGXC4ghQG4T7bJ7Tf3CAIgnlHP+ri6zLkyD6nGXxY0jq5aDtaeL5mq5vF8uftSfHv78rr3yRFyPxCtw0PgiDoNWPZGqeunf3ngdMkLQQ8AHxS0q7Aj4DlgQskTbP9XuB1wEXZU/YR4BMAklYmBe2/B7glT+yeaPtnvTyhIAiCTulH9UxdanX2tqeRkt4WOS8vzbIPAuuUlM+gPCJmEATBqGAsq3HCgzYIgiDTj1Y2denGg/ZoSY9ImpaX92fZjxXKpmUP2w3yto9Kui170H5nXp5YEARBu4xla5xuPGgBjre9QV6mANg+rVFG0tc/aHuapGWB7wLb2X4b8HpJcwXvD4IgGCnG8gRtnXj2DQ/an0PyfrX9bM369yTZ1ENyzvqz7b/n9T8BH26vuUEQBPOOsRwuoVsP2s9ltczJOSFuMx9loLO/H3izpNWzY9UuNHnWNggP2iAIRoLxrgHoCFwAACAASURBVMap8qA9CXgjsAHwGPD94k6SNgVesn0HgO1ngM8CvwGuBh6k4FlbJDxogyAYCWzXXvqNOp19mQftRrYftz3b9hzgpwzkoG0wkYFRPQC2z7e9qe3NgXsZ7FkbBEEwoszGtZd+o2MPWkkrFMR2JYU8BiCHNN4DOLNYl6TX5f9LAwcA4VAVBMGoYSyrcTr2oAX+K5tUmqSS+UxBfkvS20BzoLMfSmqkODzW9p87bnkQBEGP6Uf1TF268aD9RAv5K4DNSsr3bKdxQRAEw0k/jtjrEh60QRAEmX40qaxLdPZBEASZCJdQHi5hfUnX5cTi52fnq4b8ennbnXn7Irl8IUmTJP051xVOVUEQjBpignYgXMLueZJ2UeAS4FDbV0raB/gy8PXsMPVr4BO2p+cwCa/ker4KPGF77Wyxs0xPzyYIgqAL+rETr0vHCcezKeZVWewS4CLg68AOwG22p2f5pwrV7QO8OZfPAZ7syVkEQRD0gLFsjdNNuIQ7gJ2yzB4MhD5YG7CkiyTdIukr8GqScYBv5PLfSnp92QEjXEIQBCPBWFbjdBMuYR/gQElTgcWBlwvy7wY+lv/vmqNbLgCsDPyv7Y2A64DvlR0wwiUEQTASjPdAaFXhEu6xvYPtjUlhEf5SkL/S9pO2XwKmkB4WTwEvMZDd6re5PAiCYFQw23NqL/1GN+ESGqEP5gO+Bvwkb78IWC8nHl8A2Aq4y0kZdj6wdbGeXp1IEARBt4zlQGjdhEv4d0kH5u3nAqdAim4p6QfATaRQClNsX5DlDgN+JekE0jzAJ3tzGkEQBN3Tj7r4umi0P6FuXnmX0d3AIAhGDRNm/E7d7L/eGzav3d/c9n/XdXWs4aZuWsIgCIIxzxy79tINkpaRdImk+/L/suRPSNory9wnaa9C+Z7ZYfU2SRdKWm6oY9ZJS7hOUwLx5yV9QdI38oGmSbpY0opZfmtJzxXkj8zli0i6UdL07Fl7TP1LEwRBMO8ZRmucw4FLba8FXJrXByFpGeAoYFNSvpCjJC2d50J/CGxjez3gNuBzQx1wSJ297XtJ2aiQND/wCMmi5hnbX8/lBwFHAvvn3a62/cGmqv4FbGt7pqQFgWsk/dH29UO1IQiCYDgYRiubnRkwVjkVuII0p1nkvcAltp8GkHQJsCPJIlLAYpKeApYgpX1tSbuB0LYD/mL7b03li0HrR122xpmZVxfMS+jjgyAYNbSjnpG0H7BfoWiS7Uk1d3+97ccAbD/WsG5sYiXg4cL6DGAl269I+ixwO8nv6T7gwJL9B9Guzn5QqkFJ35L0MMmB6siC3OZZXfNHSW8ryM8vaRrwBOmJdQMlhAdtEAQjQTtqnKLzZ14GdfSS/iTpjpJl55rNKZsAdtaMfBbYEFiRpMY5YqjKanf22exyJ5IzVDqq/VXbqwCnMaAzugVYzfb6wI+A3xXkZ9vegORJu4mkdcuOFR60QRCMBL2coLX9Htvrliy/Bx5vpHbN/58oqWIGA2FoIPWbj5LV6rb/kjUmZwHvHKo97Yzs3wfcYvvxkm2nAx/ODXje9sz8eQqwYPNMse1nSTqqHds4fhAEwTxlGCdoJwMN65q9gN+XyFwE7JAnZZcmBZm8iDRv+lZJy2e57YG7hzpgO539ngxW4axV2LYTcE8uf4Mk5c+b5GM8JWn5RjA0Sa8B3tPYJwiCYDQw27NrL13ybWB7SfeROutvA0iaIOlnAHli9hskB9WbSHm7n7b9KHAMcJWk20gj/f8Y6oC1nKokLUqaKFjT9nO57BxgHWAO8Ddgf9uPSPocSZ80C/gH8EXb10pajzTrPD/pAXCW7WOHOnY4VQVBUJdunapWXebttfubh56+va+cquomHH8JWLaprDTLlO0TgRNLym8jTSgEQRCMSsZyuITIQRsEQZAZ7eFjuqEbD9rfFMoezCaVxf1WlTRT0qGFsh0l3SvpfklzeYwFQRCMJMMVLmEk6NiD1vYJDRlJ3weea9r1eOCPBZn5gR+TJiNmADdJmmw7whwHQTAq6MekJHXp2oM2W958BNi2ULYLKRTyi4V9NwHut/1AljmT5DIcnX0QBKOCfkxKUpeuPGgzWwCP274PQCk/7WEk06Aipa6/bR4/CIJgnjGWk5d05UGbGWR/T+rkj284VhWrKKm29IpFuIQgCEaCca2zLzCXB20OtbkbsHFBblNgd0nfAZYC5kj6JzCVctffucgxJiZB2NkHQTB89OOIvS7tdPbNI3jIXrC2ZzQKbG/R+CzpaGCm7RPzg2EtSWuQJnknAv/WacODIAh6zbi3s88etNsDn2naVKbDL8X2rOxdexHJi/Zk23e20dYgCIJ5yrgf2Zd50ObyvYfY7+im9SnAlPrNC4IgGD7GsjVOeNAGQRBk+nHitS7R2QdBEGTGvRonCIJgPBAetEEQBOOAGNkHQRCMA8ayzr4t9+DRtAD7zQvZ0VR3tCXaMlrq7ue2xJKv2Ug3oOOGw83zQnY01R1tibaMlrr7uS2xpKXdQGhBEARBHxKdfRAEwTignzv7SfNIdjTV3a58tGX4625Xvl/rbld+NLUlAJT1X0EQBMEYpp9H9kEQBEFNorMPgiAYB0RnHwRBMA4Y8519zonbc9l5gaT5JR3S5j7vqlM2r1FilaElS/cd8rqPlvPsZyTNJ+mdI92OTol7oDv6ZoJW0huBGbb/JWlrYD3gl7afrZB/J/Az4LW2V5W0PvAZ2wd0KTsfcJvtddto+x7AhbZfkPQ1YCPgm7ZvKZG9wvbWbdR9i+2NhiorbPtiSfFzwFTb00rk3w2sZfsUScuTrtFfK+qeanvjsm0V8u1c93bPc5mS4hdsv9KtfAfXsLa8pLWBk4DX215X0nrATra/WVLv/MBFtt9Tdk5lSLrO9uZtyNdqTye/i7zf/MDrKYRusf1QhWxb90AwmH6KjXMOMEHSm4CfA5OB04H3V8gfD7w3y2F7uqQtu5W1PUfSdEmrVt2UJXzd9m9zx/le4HukH9CmJbL/K+lE4DfAi4XjDnowSNoceCewfFNnsgQpE1gVE/Jyfl7/AHATsL+k39r+TuEYR2XZdYBTgAWBXwNVo6nrJb3D9k0tjl9kyOvexXneQsp5/Awp2f1SwGOSngA+bXtqF/K1r2EH8j8Fvgz8T74mt0k6HZirs7c9W9JLkpa0/VyLa1HkYkkfBs51vZFerfZ08ruQ9HngKOBxoJE1xKSBXFGu03sgKNBPnf0cp9SGuwIn2P6RpFtb7WD7YUnFotm9kAVWAO6UdCODO+SdKuQbdX0AOMn273N+3jIar9nHFpsHbNsktxDwWtJ3uHih/Hlg9xZtXxbYyPZMeLVDPxvYkpQUvtjx7ApsSOoIsf2opMWpZhvgM5L+RrouSrt5vaodalz3Ts/zQuA82xcBSNoB2BE4C/hv5n7QtiPfzjVsV35R2zc2XZNZLc7zn8Dtki5h8L14UIX8F4HFgNmS/sHAd7REhXw77Wn3d3EwsI7tpyq2N+j0HggK9FNn/4qkPYG9gA/lsgVbyD+c1QSWtBBwEHB3D2QBjmmv6Twi6X9ICdqPk7QwFfMltrepU6HtK4ErJf3C9t/aaMuqwMuF9VeA1Wz/Q9K/mmRftm1Jhlq69fe10Q6ocd2bzzM/bNzoOFswwfb+hXoulvQftr+Yr3838u1cw3bln8wqy8Y13x14rMV5XpCXWthu9bAuo532tPu7eJikzmpJF/d6UKCfOvtPAvsD37L9V0lrkFQKVewP/BBYCXiElOj8wBqyM4CLW8hi+0pJrwfekYtutP1Ei7Z8hDRK/J7tZyWtQHo1ngtJS5JebRvqjCuBY1u8pi8saRKwOoP1ns1vAg1OJ6lbfp/XPwSckTvyu5pkz8oPqaUkfRrYh6RjLyV3xusDW+Siq21Pr5Knve9o8fwmtwyApCeBvWzfUSH/tKTDgDPz+keBZ7KOuCzRaDvy7VzDduUPJHmHvlnSI8BfgY9VnCO2T80PyrVz0b1V8xKQJtJzfWvY/obSpPoKtm+s2KV2ezr4XTwAXCHpAuDVh57tH1TIvyTpu8DbgEUK8lX3elCgbyZoi0haGljF9m0jdPyPAN8FriC9Bm8BfNn22S32qTXRKekc4A7g1Fz0CWB927tV1Dsd+AlJHfCqCqREJ13cZwJJ7y7gGts3t5DdHtghy15k+5IWsgcDnwbOzUW7ApNs/6hqn7pIuhb4qu3L8/rWwH/YLrUukbQc6aH57tz2a0gjz+eAVW3f36V87WvYofxiwHy2XxhCbmvSvfJgrnsV0kPwqgr5k0gPr21tvyX/li62/Y4y+Xba0+7vIquz5sJ26RuCpItJc1mHkgYKewF/t31Yq7YHGY+C0Jt1FtINtARpZPcQqXP7QQv5NUkTYn8HngB+D6xZIXsqsFRhfWng5BZ1TwdeV1hfHpjeQv6o3JY/5/UVgf+tkJ1Wp6ywbWoH13L+3IZVG0uF3NdJD9ViWWUcceA2YLHC+mIkC41efEdzXd9W13wY7sda17CDa74s8F+keZKppDefZVt9/yS9d2N97Vb3BHBL/n9rnevYTnva/V0U5BYnDX6GkpvauM8KZVeO1D3Qb0s/2dkvaft5YDfgFCcTv1YmZ6eTJtdWIP3IfgucUSG7ngsmnLafIU1MVjGfB7+ePkVrn4VdgZ3Ik1a2H2XwRFORf+S3AOBVO+J/tKj7fEkHSFpB0jKNpUo4W0A8DlwC/IGk7/1DhfjngYskFecR9q+QhTSaK06wzs5lVbTzHT0g6euSVs/L10gqhfKGSGtLmiTpYkmXNZZeyLd5DduVP5P08PswafLx76TRbBUL2r63sWL7z7Sey3olq6YaOvjlKVdrddKetn4XktbNqrk7SBO7UyW9rVXb8//HJH1A0obAyi3kgyIj/bSpuwC3kzqFi4F3uOkJXyJ/Q0nZ9RWy04GlC+vLALe3qPu7JP3y3nn5I3BcC/kb8//GqKpyxAtskNvzIPA34FaSGqeq7r+WLA+0kL+fFiPFJtlbSaPQG0iv41AYEZbIfzG3/ei8TAO+0KPvaGkGRpi3kkaYS7eoezrwWWATYOPG0gv5dq5hB9d8rlE5LRJ1ACeTTJG3zstPSYOhKvmPkUxdHwG+BdwL7NGL9nTwu7gW2KawvjVwbQv5DwJLAusCl5PeND5U93sY70s/TdAeS7qRrrF9k6Q1gftayF8u6XDSyMSkCbcLGqNe208XZL8PXCupoVvcg/RDKMX2l5VslRs62Em2z2vRlrKJzp9W1D0NWF/SEnn9+Rb1YnuNVttLqGUBUaj/IUlbASdJ+i3wmhayP5B0BQN670/abmUeW/s7cnrbOihflzke2hpnlu2Thj7DjuTbuoZtyl8uaSLpjQfSaLqVtc1nSZOoB5Gu+VUkU9FSbJ8maSqwXS7axXYry7Pa7engd7GY8xxM3v+KISy+nnEyVHiOZOYbHrRt0JcTtHWQVPmKTzLdW7NJ/m2kG0jApbbLrCq6aU/LiU6Ve1kWG1xqoSBpUdKIelXb+0lai6TDLVUTSPo5yUlqSAsIST+1/enC+oHAl0quXaXaKNf9dFl5O9+RpLcDvyRb4wAtrXGU/BieAM5j8HlWtaW2fDvXsF15SS+Q7eBz0fwM2Kzb1fbwtZG0EemBbNLc0Vye3MPRHknnkd7UfpWLPk4ygd2lQj48aLugb0b2khYB9mVus6t9yuTbHfHavlPS3xt1q8QTMN/4ZU/Hlo4pebRyme1LJK0DrCNpQQ82kWvo8Nchma5NzusfIo3WqjiF9DrbsEqZQdJ9V+mEH8rLQnmppNjR5/UfAz8uEZ1Kui5l+nmTJmLL6m/nO/of4IsebI0ziYHzbmav/L9o4lrZljbla1/DduVd0w5e0u2U34uNekod2SQdSXpzPYf0fZ2i5MU7l4du3fZ0+rsgveEeQ7LearyVfLKk/vCg7QF9M7LPKoR7gH8jqXQ+Btxt++AW+6wLvJXBD4dflsjtRFLlrEga3a2W6241WdRO26eSzNCWBq4HbgZesj2XvXI2L/uws4mbkhPRb23vWFH3zbYnSLrV9oa5bLrt9bto71m2P1LVoVR1JB0eq+53NNc5dXueo5GsSjyZFEupcuJU0mqt6nGF85Gku4ENbf8zr7+GNJf0lm7aMy/JasStScYBPylsegE433YrdW6Q6ZuRPfAm23tI2tnJkeR0kg6/lGzDuzWpI5lC8u68hqQKaOYbwGbAn2xvmK1P9mzVGA12HrrKrW3+ZfslSfsCP7L9HVWHemj2tnyZ5DBVxcv5B9uwrngjBVVBob0n2P6CpPMp78CLLu2NB+gHWxy3lPzgbDiEXVGlTsqy7XxHD0j6OoNf+cv8FLa1fZmkUr8E2+d2Kt/mNWxbPvMT0uj2R3mA8wvb95Ts+2pnrvYcmR4kPVj/mdcXBv7SQr5WewptGfJ30e51cXjQ9oR+6uwbKo9n82jw/2jdCe4OrE+yHvlk/kFUeX++YvsppRCw89m+XNJxVRVrbueh0yS1ch5SfhX9GEkVBdXX/lfAjVmfaZKpaVnn1+AoUlyXVSSdRpoc27uiXkhB2Fpiu+EO/yTwD6cgV2sDbyZZWJQi6dukTue0XHSwpHfZPqJil3a+o+IrP1S88gNbAZcxEFJj0KkV9u9EvvY17FAe238C/qTkSb0ncImkh0kT+r9uUv2VOTL9SNJcjkySfpTP518kM8dL8vr2pAds1+1p43fR1nVpPByAE5VDdzS1sSr2TlDEo8AkqM4CfIqkBtmK5Gb9BLB/C/mGueNUkm5PwJ0Vsn8iBVr6EcnO+4e0NgFr13loK5IO/rC8vibwXy3kNyI5NH2N9Mo91LVZlhRk7YPAckPINjwhG+vzk4JdlclOBRYlhTN4mDR5edoQ16W57lbXpdZ3lOv5bhv3ynzAR+aFfG7Lr9uouy35wvd5MEndN5lkpfQj0ptSs2wtRybSnETl0ov2dPC7OLhm2cb5/1ZlSzvXdjwvI96AeXZiyfxsKZKe7z6SbfYpTTIL5/+L5R/lAvnmP4jWXou3A4sU1hehhV1+m+0+KNd/DGlu4jbg80Pssx7JaWu3xtJC9noK3oqkh1zpg40Bv4DPA1/Jn1vZ2d8GLFNYX2aIH/uQ31FB9rI2r+NV80qepD5caF7Ik0bFdwFHkGLWFLfNZd/efN+RHlw9uRfbbU+7v4vG/dVUVnl/5e0L5fv97e18B7H0gZ19pyaJHkiA8RNJFwJLeG794XWkUfRPbH8il53K0JwC3JBVLQC7kBxbmtveic72U8Bmtl/MdRyX21mqIpJ0Munmv5PBMcGb1RUNFnHBRt32zGy+WVF9bfUTwH8Ct0q6nDRK35LUSZRS8ztqcKukySRLo2L43KrzvETSocydF6DU9LJN+QdJeQcmN8lWBfBqR/5ntqcUCyQtbPtftieUyF8o6SIGPI8/SmtV2wdJc1Srkb7LoSxm2mlP3d/FniRDizXyNWmwOMnrtqrtHyDNIfwlt3sNSZ+xXXm+wQCjvrNnwCSxzLSvpSmRpJUYuKmRtKUHB4haSNJewDvLJuiqOhLXdx5qW2dL+yEHNrP91jbqf1HSRs621ZI2pjocw8Gkzvo8J9PUNUmei6XYPiNfl3fkNh9m+/9aNabGd9RgGVJHUIxw2Oqh1jDJLUbRbGV62Y78o3mZj+qwF53Kf5M0WV2kMSiZCydHpt0YuBeHcmQ6gfT2d7vzULlX7Wnjd3EtKUzyciQruAYvkN4Oq/g+yeP2fnjVGOECWjzcggH6yfTyVJI+79m8vjTwfVfY2ecR8UdJr6CNztPF0bRSDJqPkUIQT26qwi3q3oykWy6aR77V9g2dnl+h7i+SVEnF0dEvbJ9QIf9z0nWo5QQm6R0kj9VHc9EKwEfdIkpm3u8NNTruXUnqlufy+lLA1rZ/VyE/5Hc02qlzXerIS3oDaW7k16RRb+MBvwTpzfPNFfWtATzmwaaUr7f9YIX85cB2HsKMspP2zMvfRa7vKttbFtZFCoRWlYEuKNBPnf2rduStygrb7iUFOCtLJlGUmw84wnZleISytpAyDzXMHecj6S+r8qH+lXI1TukoUwMejiLpkitDDiil8TufZJ30LwZeyytt4SUtSHLeEnCPW8Q/L+wzpKeipGm2N2gq6/o76qQtTfKTbO83L+Q7aEupfH7D3JuUvvAmBjrX54FTq94yJd0MvNP2y3l9IZJXbGnI4vyw/wYpT0KlN28n7engd7EZST35FpIufn7gxWaVUuGte3vSW+BZpN/THqT4/V8qqz8YTD+ocRrMJ2lppxgpKLnot2r/A6Tofy07Eiezwg/SIhZOCSq+Auc6WrWlqNtchHSTVoYYyCqWShf2Jk4mxby/ndbRC4usw4Aj04aScIkjUxOtVEkNyiIcdv0dddiWImW67l7Jt9uWUnnbpwKnSvqw7XPaqG+BRkef63k5d/hVfAuYSfruK+U6bE+7v4sTgYmkeZgJwL8DbyqRK5rFPk6ywoEUgXPpmm0b9/RTZ18MVmaS6mWuDrpgT/wSME3SpQwewZTl5mw3CfMDkg4iJQ0HOIDUcZXiuXNsniDpGuDIGscaiodsN6ugKlF7jkxFSgO3NXGzpB+QQiqYZMUzl3qow++oSO00fJlWTkbdyte5LrXlix2rpD/YHsqx7e+SdmrcA5J2JvlHVLGM7R3qNrbN9rT1u8j13y9pftuzSaEbri2RKfOnCNqkb9Q4AJLeSpqgqwxWll8/K8kjluZ9GsGeZpE8C4eKdfM6UrjdbUmd1qWkUL6lnURWyzSYjzSK+ax74OovqWG+eD6DO8yq1/7bGXBkWl/Zkcn2hwoynQY2W4zkH9DIM3AxKY3ki01ynXxH72u2upC0v+2fNMs2t6n5+BVya9pu2TE1ydfKPJZljwWuJpm4DtmWwn6VKrCCzBtJTmwrku7bh4FP2C71ilVyfLvM9sV121G3PR38Lq4i3Ss/I6khHwP2rvpdqM34WMFg+qqz7xSNfBrDogXLLJKb//ddSDrRRd2nlBS3mly+0fYmSvF6tiFZQNzhQhygwhxDUeXQWHfVXEPTcVbwgCdunfNo+R3lEd/XbF+W1w8jTf6WJjlXSmT+M1InvKqSG/9nPGDu2Sx/FWlC8iaSd+7Vtm+vkD2K9MBex/baklYkxS8qDbcraR/SHMzmpOt9NWku5vdl8oX9Tq7bkUl6Len3PFQaw8bA5l8kr/ShTC87ak8dlOL7PEFS5R1CilX/325KAVmQbzs+VlDAo8DYf14stJHGkGQPPtdS8zhzOYY0bZ+PZO0y4tckt6e2I1OXx2l5XTr4jpYjOYRtQVLfnUPK0lRV9w2kfKzF9Ht3DNGehUjhJr6a2/N0hdw0UidZrLvSeawg8waS09xDwAvz4Jr/YaTvr3a+/w7qvLV4rUkPibac7cbz0k86+3ZZ0vbzkj5F6syOklQ1si+GtV2ElK1oKoNtuqtoOUHnNEl1IK1Ty3VMViF8mhQn6NXv09Whn4d0ZGpSO5XVUWfyuM7EZe3vyPaTSkHW/kT6bnZ3/sW3aOfDyTrvVWZXyWa1zBZ5WYoUIvrqCvGXbVs5TotaJ9xA0s9IcySP5zp3p2kCXtUhi4e0riqw0lAC2XprLlydoLysXc+RQid803PPR726a4s2dBSemfbjYwUFxnJnv4CkFUgTuV9tJeiCvhpA0irAd2oep85kYbvenO3we1IH8idadGZFNOCEY9LkbHMH+/25dhrA1HsI1pm4HPI70kCsdOX/C5EcnXaXZFerHx7Oqhxn65SDgFYZma4kdWD/CUxxwcKlhNqZxzLLkswKnwWeBp60PatJpu0IoyW0ygrWoN2BzR9J99XpeX1i/v888AvKA8hB699Fp+c6Kav7vkbyi3ktaY4oqMGY1dlL2oN0I1xj+wAl78/v2v5wjX1FelV8ewuZN5B+KAZucgvHGrVpZ98OKrFtH0L+v0nmbUX3+r/YPrB6ryHr7HRCt+PvqEabliMFtHsP6UFxMckpr3QkquQA9i6SCu8dJDPW62yXdiYaIvNYxT5vAd5L0k/Pb3vEk2U3Bja2S0N6S/pfN81FNMok3V78jUg6zvZhTbJzlXXR1oVJic9XZyCpum0f24v6xzpjtrNvh4IpICQd+wbAg7Y/XiH/KZLZ5GWkH/tWwLG2T66Qfw3JDK0xmr6a5IVYFaagnbZ/k2Tl0ezSXiV/J7BuQwWi5PhyuwdP0LYbE77rCd0W7e1IpSRpedt/b/NYbyF9l1uQMiM9ZHurErlDSBOyM2rW+8Fc55Yku/DrSBPAJxdkrrH97pI3mdIJ1B6pfYYc2EiaDuzn7AUraRPgp06WXIOsc1SeNvC2qrZocIarhUgd+FxOVQX5C0kqpKkU3mJtt3oTDTJjWY3zKmU3YRM3Fz7PAs6w/b8t5L9MCj38VK5/WVK8j9LOnhRc7XmSWRqkuOCnktQXHdHUKfw/SXWtK+4lJUhpJIFYhbnVOFsxEON9ro6Hpng0bj/pedn5VH1HnaqUrs0Pod8A5ziH2Whx/L+Qrs015IQdLVQ5SwAXSXqaFHribNuPt6j+fSQLnx/afrRMwPa78/9aaQnpUBVSMbCZ3mKXTwEnN6x9SPfxp/I8xX/mOj9LGsys2TTnsjhQ+TtqPldJu5DelqtY2RUZ24KhGRcj+zr2ygXZIc00lZyA3ufBLupTbL+nQn7UpNSTdCVJTXFjLnoHaaT5EgyOxCnpSwwesZs8srI9rSDX9YRuO99RXfIodCIpvtBdwJm2f10hO5/bTLsnaT2SGuzDwIyq7z/L1s4mpcE2/MsBi7vChr9dNNjHYRbpDbbVwKax35Kk/mKuh2betjSp8z+8sOmFduelJF1ve7OKbZNImd5KTWKD1oyLkT1DTKIqRerbiXQ9ppG8Eq+0XRVe+RFSKNffkzrAnUnZpb4IpaFrb5W0me3r8/E2pcWIpx3UZvAx2vPa3Zhkfs7ybQAAGIRJREFUTz6Z1OF/gGSHvr9SkurGJHYvJnSHnOhWzXy1hW03kr6X/wB+QHqbKu3sgRXzqPddDExcHzyEquYJkkXIU8DrWrR7D1Lk0yugOptUln3Vhp8UMnih3OZmvXlbap8GLnFYa0WTnnyBhnVTUU+e773ngD0lzQ+8nvRbeq2k19p+qKLuopqw4Ww41+izoLJaAPikpAeoGQcqGGBMj+zrTqI2RpVZF7+KswlgC13jUa2Oa/uYLNe4SRuBxx7K66sBd9let8NTK7al3eBji1GSatAlwdCU4qR/2Dn+fX6VPxvYlTS6bye0cllbanvFqiLMg+3dK+peIrdzIvBGUhTRs1wR3VMpTd/pDM5x+zHb25fIfpY0ol+edD1+4xZRR7Pee/vGaF7JXPZPZW92kqYBG5Ls1BsJ5CvvxXaR9C7gaOaOZ18VlK+2nlzS53Ldj1PIrdDid1R0CJxFivv/0+a3HnWYXD1owqPA2H9eLCRd40Mk87BTSTfSPhWyt5NC/V4MvCOXDekkU6MNq7VaenSec7WT1tmBaqcaJJkqLlRYX5jksQiDHYq2zf93K1tatOXaxr55/TDSg6fqO5qPnHKPNHo8v0XdfwWOBzaveR2n1SnL5d8GNmjjO6qdTYqBVI2NLGEtU/tlmXeT5hggOZ+t0UL2HtKD8nUkk9BlaZ2VraUjWpPs/a3qimVkl7GsxmlnEvVYUuq4a2zflE0A76uqOI/MvsLcMToGqSs8PCOOWsHHCsj2S5L2Jek/v5NHk2WcDlyf1VWQJmzPyG8HxZFsWxO6BXYC/iDpy8COpLeMqlj2jbeRWXnU/gTViUgA1rRtSYtnVcLMFrIAT0r6OAMmqXtSkTXJ9uGS1s8jWUiWNa0mOcuySVVZT7Vlw19X7VPgObeX2elaSW93PT35w6S3gFrk39kPgc1I98l1wCFuI0ZRUJ8xq8ZpdxK1zbovJll5HEoKO7AX8Hf3yJ64zbYUg4817Mm/6YqAW0oxxw8gjXr3dcpANcheukl+YwZi619j++YyuSxba0K3aZ/XMeAVu48rbkgl/4D/R1LLfIkUpneaKyIiZv3+r0ihGEQKh7uX7Tsq5FclhdzdPLf7WpLOfq4HtlJkx/0YeIjtSsoQVZo6Mu/zYVIH3MhRUJpNKs/7PEEKVgdwsVvY8Ler9lEKhDZ/bnsxcF6VCetdJL+MvzKEnlwpkc46pPmXylj5BfnrSYOUxkNwIinf8qZV5xt0zlju7H9JSko8aBIV+DO0vAHrJOmYanvj4o8qT+jOZZM92lBylz+UlODiuDy6+oKHDitcp+7TKZ/QfTPJLv07Wa7MK3ZW/mwPEZRL0uq0zleLUuC0r9q+PK9vDfyH7Xd2foav1n0bST3UyBO8GMkBq2u9eh6pf4TkaTukWacGAtvdYnujodqiwUH5Grj5rbQgX6ovr3gIls5lOc9hlcjf0Nyxt7LGCbpjLKtx/pKXBg1VxFB2zHViujQmMx9TSoL8KDAi3pB1VUqF8qtINt8opch7gBRKoBcsS8pU1JjQPYo0gbklaeT+ndyGurbkryLpUtvb5f0fbC4rYbFGR5/3uUIlMWw02O58LioegrXyBGuw01Dz/qUPtdwxHqMBs84rJbUy62xL7WN7m6ptTW1fwvbzpCidtajq1FtwuaTDSQ81k873AmWPbPcmnEiQGbOdfQc3XoM6sW6+qWRb/CVSWrUlgC90eLxuOY2kUvogBZVSzX2nUJHIukNWBYqOSK+QJqL/oeT0BbRnl68Uw3xRYDklH4hiLtQVW1TzgKSvM9i6psxWvVIt1YJTSKa35+X27Az8vFmok4dagVpmnSSrl6tJzk5rA0e2UvsUUetkJKdL+hApEcqDDH6YmcJ8iaQTbH9B0vmUhwWpmof5aP7/mabyfZqPEXTPmO3s2x3xFsw0b9DQSaT3IOmv7wC2ySOR75ESiAw3y9r+uaSDbV9JGgleWXPfdlPqDUXdCd127PI/Q3qQrkh6O2iofl4g6dir2Ac4hqSbFultZi79vpvszlUj2YntHyj5Zrw7F33SLfIE53prOUqVmHV+2q2TyS9OSujRUPu0k7OhMkpm4yGgZNo71ICg8UD9XhvHxj3wvA7aoJemPaNpIU1U7ksyH9yKZIVzXIVsbTPNLH9rnbJhOs/r8/+LSDryDUmBzerse8A8aM/GwMGkDnpCD+s9kqSnhzQhfR5JZTTUfkuSOtah5DYnPZAeyuvrkxJpVMlvlM/z80O1AziKNBD4c15fkTRnUibblllnYb/1SHH+7yHZ8NfZ5+QaMieSzZGHkJsf+HWbbV6QpEI8Oy+fo0WOgv/f3rkH21XVd/zzjTQ8ChEylCpqFDtMmcj7YRCi0wCZSnm0loBQKQy0lUxaQZ1BaiUgpLZYpAVRCQ8nVgvtYG0nNEWghkcELDERkwAOZSamDigFWlJCJYzAr3+stXP2OXfvc/Y69zz3+X1m9tx79l177bXPvfe791nr9/v+fJveNvQB9O3CQgQI5GKUgQdK2j5JLj6YMPf8ZJu+NwB75V7Ppk1se5+v8+QoaAcC9xGefk8paDe73TbE39OBhAXJc7KtpF1WsGI+4Sn9t4FH2vR7FCE2f0vcNgBHtGlfudhJvPFsIiQQXRH7vrRN310VO0l8H98SbzwP9bLveAN8jbD+tTFed2H/hAeOmQl930J4uDoubisIJTKH8rdY96220zikLaI+TfNC1DZCzHAZlYqfD4gXrZGuvgB2ZEm2sp42zpQMYX60LCuW4uLn2YLoSQTH0JWSPtum+68SPrl8N55rPkFMSiNmrHqxk7MIORzbY99XEYqR/HlJ+6RiJymkTvsovRhJYdnHErYAD0m6g+a6DYWRb4RPDPks4nsVso2dPlBnsU9ZRE3yujGzr0taR6P4+e+2+wfrM9czdZF1yj4bzfnRRTSKn5+nWPy8pO0zMerkBODzCp4tM9r0vS0TegAzezBGx5SRUuxkC2EdaHt8vTPNkV+tpBY7SeGdhNDZssS4VpKKkVhaYuBP4zaDzlFvAK9L+jWLxdFjGHClAjxOOnUW+5RF1OQwzSjuwxJ4JL2P4Ln+K9lNKTKLMH/a2r4XpQZ7TUpW7BmELNsvmNlWhQpXF7c2yl3n2iiwf08jrO/+NmNZTMjmfBvh5n830FTQJRem+SrwuIKfjgELCZ9ICjGzLygUO0mOmOmEmf1p51ZNHGvNxUg2qVGMpLB+Q8JYMk+oWeFl++LnhN/ffQrGZhDM1gqT5JzpU2exP9hydqxm9j+SCs3BrPswzWEyk1CWbSeab0ovEZ6YW+mFM2WvWafg0nkzYZrpZRrWy02Y2c/J2S6Y2c+AnxU0bb3OfKJPu3j6F4CPdBpv/LqesECccX+H4yDMde8axzBMi97dJc2z5mIku8eftZZKTELSkYSpsj3i6/8lBDqU2Xc8BNwIZLkSNxIsE5w+UOcM2g0Eq98X4+vZhAXaKbYAqWGao4SkT1nDajjbd7qZfXNYY+qGKlmxfT5/33xalFjZrJ9IOooQmdZUjAR4HDjJzG6fRt8bgT9uWSf5ipVn894ez39r3HUWIfDh9G7H4JRTZ7E/B/g0YdFqxyKqmX2joO3IeN2kouJScEX7kkoNDoKiDNgOWbGp/Z/E1Bt4Yb1SJfi0SNqfUKij1Vu/zCb4SeAYazHlM7Nf7+KyeoLaFCOZRp+l9WpL2o9MUZ9JoLbTOImLqNNJTBoKkk4Efgt4m6Qv5n40i+KP4906U/acaWTFppxjeTzHAsKi7yJKpoiyQ1oeBP5ODVfLVlYQpof+JvZ/Hu0T1FKjvfqGKhQjmQaF6yTZOkrBulDfivo4U6mt2EPSIurIeN0k8FPCHPKpNFsabwM+0drYzLK568cocKaUdGhCRMd06TYrNoVjzOxgBbO6KyRdQ8ENLU7vQYlPS0nfu5rZakmK0SqflfRdmtcHMgdLKIn2mu4FdslKGsVIXu3QNpWsiE6rIdoxFK8LzQPOkZRVspoD/CgLDy2b/nG6o9Zin8Aoed1UwoJ/+gZJt1lBlak2VC012DfM7DrgOkmXAdea2UsKPjaH07sFulfi159L2pfgMVMUftqaf5D3aTFgWcEx2yXNAJ6KT//PUOxfky2cl0V7DYO+Fe22iiZrObx4+CAZVjbXKG2ELL49c69nUyGVfBQ2YH/CusQTwOZsa9P+bmD33OvdgbsIkSJPDHjsSVmxiX0vBfYkTFk8S4jcubJHfR8V37e3E6Z0vgUcXfHYtwz57+Um4KABnGfVMK/Tt6lbu6SUSWJKmCbBY2YcWAHcQJinX0DIPp2yCJ2j1JmS3n+s78SUrFhCSOm0MbNlZrbVzL5FSDw6wMwqFVuXdFOHvr9vZi9bKEb+aTM7zeK8cwXKKlQNivnAeklPStooaVOMouk1pSZrznDwaZzADEl7WXOY5ri8N5Xmj3NUdaYcBKlZsd1yvZl9NKH9kQltU22ie+00mkqK/cF0aOsC6gyecRG0fjNKXjepVJ0/BsITr6Q7aZQaXGyNUoOdkop6TaWs2B6QIt4QMnmrkirevbJJSEJdFCPp8jy7AnPM7Px+nsdJp7Zx9qlImksjTHO1Dc/rJomYJPMjwvz0MsLi8tUJ0wq1R9JdVnFRUhX87FvaLzGzr7T5+QzC2sSBVfvsB5JWET7FvU5BMRIryRFIPMcpBEuSmWa2n6RDCeskZcVLnAHiYl8j1LnoysRRVbwVTNBuISxez5F0CHCBmS1paZcs3pJuJczt/6Rj4z5TlHDXw77XEx6Y7rcKxc+dweILtPVi2It/I4OkYyQ9QXSulHSIpNIncEKC1G8SQjSxENr6gdZGZvYGIeR1TsJw3kowTlst6Y5sSzi+lzwcPw32g9cs2G07I4jP2deLYS/+jRKZeN8BQbwlTRHvPFbdzz4T77U0+7aXTVeMktHecYSciv8kjD0rft6Lp+/HJP0e8KZoKXEh8HAP+nV6gIt9vRjK4t+okiDekOZnnyTeZvaAgld/9kS91sxSFoF7ST+jcT4GfIYQwnsbIaejrKCLM2B8zn5MyaX5FxJzBSaWGFn11wT7haMJ4n2kmZ1Z0n5vguvlCYSn3XuAi2xq5aasfWXxlnQGcDXBClnA+4GLzewf069sNJH0JuAqM+tHNJXTA1zsxxRJP6ZNmcFeRFeMM6nindh3kngr2G0vzG4ICpba37GauTtKutfGwBZ8UnGxdxxA0t8SbgZb4+u9gGuK4sVTxVvSJsvVUYgRPRusoLbCOBPN5vYHvknzWsbA7bOdqfic/Zii0SwzODKkiHek1TLjRZVUNgNmtEzb/DftI9vuknQ3Da/8D1PPyKnZhPci/3Q/UPtspxwX+/FlFMsMjhIp4g1plhlJ4m1mF0s6DTiWMO1zk5n9c1n7ccXMvH7sCOPTOE4tUUJZyvjzfGUzCAXrCyubxfZ58V5TR/FORdLbCRbhxxIeOB4kfLp6eqgDcwAX+7FFI1hmcJRIFe94zHsIzqE9scyQtI3iIufZIvqs6fQ/akj6N0LIZfYenw18xMwWDm9UToaL/Zgi6Qozu1zSCgrKDLoRVXfiLWkfmuvK/iT3s4kS71Qk/dDMDu20zxkOPmc/ptholRkcSczscUnPE8Vb0pwyfxpJpxLWQfYluF6+k5BU9Z5cf3sUHVuF6LXz/vhyjZn1w0N+2Lwg6WwaaxlnEe0nnOHj3jjjzxHAYkIK/77AR4HfAG6W9KkhjmuoSDpV0lPAj4EHCE6P325zyDJC8tV/mNl+wPG0KX4dvXb+JG5trQYkXQTcSrCe3ge4VdLHUq5nTDifYFv9bNwWxX3OCODTOGNOjAo5zcxejq93J8xTfwhYb2Zzhzm+YREXaI8jxL8fJmkBcFZZERNJ68zsyHjcYWb2hqS1ZvbegrYXAX9EI6TwQ4QIm+tL+t4IvC9z34yFYr7nbpDOIPEn+/FnlMoMjhK/iNmyMyTNMLP7gHZzx1vjjXIN4cn7OkKpxyL+AJhnZpdZKHV4NEH8yxDNvjyvU0PTOknvlvQvkp6X9JyklZImOpN7lPA5+/FnlMoMjhKt4v0cBeItaWcze5VQ7Hw78AlCxa43A1eW9J0q3iuARyRl4Zm/A3w14VrGhduALxM+6QCcSZi/nze0ETk78GmcGiDpCBplBh/MlRmcODLxjje77YT3JBPvW1u9cbJiHpK+YWa/X/EcnwTOBfLi/TUzu7bNMYfT+B2tMbPa1WiV9IiZzWvZ9+9mdvSwxuQ0cLF3akWqeEt6jGBqdhkF9W/L8hVSxFvS0cDjZrYtvt4DmGtmj1S5pnFB0lXAVuAfCFFhHwZ2JjztT7wT67BxsXdqRap4S5pPePI/g1jopLl5oRFaknhLehQ43OI/WzRCW9ev8oDDIjqxZmTCsiMkeNKdWIeNz9k7dWMxQbz3JKxf5JliymVmD0p6GHjazD5X8Rw3AHmh/r+CfXlkuaeqGOlTx/+9S4C7zOwlSUsJ78eySTflGxXq+AfnTDDdiHcU35OBqmKfKt6bJV1IuCEALAE2VzzXOHGpmd0ePy0tJCSp3YAv0I4EHnrp1A4LRcFPTjzsHkmnqaWOYQmbJV0o6ZfidhHtxXsxcAzwDPA0QfwK4/3HnCxC6SRguZmtBGYOcTxODp+zd2qJpCuAjcA/WYU/8uh788uE8MwsiqfQ7yb653yRkLRlwGrg4za8urIjgaRVhBvaCYTM7lcIJRtrVZFrXHGxd2pJingPeFw/qNvCbIak3YAPApvM7ClJbwUOMrN7hjw0Bxd7xwFA0geK9pvZmg7HJYm3pEfNrF0RFcfpC75A69SSLsQ7H6a5C/BeYD2dK36l2h78a2J7x+kJLvZOXUkSbzNrCtOU9A7gryqcp6N4SzrRzL4dz3Np3LfYzJZX6N9xeoJH4zi1xMxOyW0LgQOB/0ro4ul4zBQknZg7zw7xbtPXUkk7bjKSLiF48TjOwPAne2dSKBVvAEnX08j6nEFwyNxQ0nyppFfN7N547CWEGgJlT+qnAqskXUxYwDwg7nOcgeELtE4tKRHvLWZ2dkn7c3MvX4ttC4uXSNobWEWYKsrE+0wz+0Wb8ewDfIcwlXR+lXBQx+klLvZOLUkR74Jj9wLe0a50YBXxztWszeoDz4xjMUYgDNSZLFzsndpTUbzvJ0yt7AT8EHgeeMDMPplr4+LtjC0+Z+/UkiLxltQk3i28ORp4/SGwwswuj+UEd5BacDzaIJfiBmHOIHGxd+pKR/FuYaeY8XkG8JmiBl2I9zXtmtM5ht9xeoaLvVNXOop3C1cCdxMqfX0/1k59qqVNknib2YKE8TpOX/E5e6eWSDodWEoQ7yVRvK82s9OGNJ4DgbmEBC8AzOzrwxiLM5m42DtOC1X8blLEW9LlhDj8ucCdwImEm9CiXo3ZcTrhGbRO7ZGUuhDa1u8mivf1cVtAsFVolyS1CDgeeNbMzgMOIdRmdZyB4WLvTAK9NitLFe9XYkGV1yTNAp4DvB6rM1Bc7J1JoJJZWfZ9Bb+bVPFeJ2lP4GZCEtYPgLUVx+44PcHF3qklfTYrSxJvM1tiZlujy+VC4Nz4icBxBoYv0Dq1JBYdv7TVrMzMTixpn+x3E497FzCrQ3buajM7vtM+x+knHmfv1JUkp0kze0HSqTT8bhaVmZXlhdrMtrTuy7XbBdgN2DtaNmRrB7OAfadxbY6TjIu9U0uqineJ3827gUWSmvxuuhDvC4CPx5+tz51jG/ClXlyn41TFp3GcWtFPszJJF9EQ72doFu+bzOzLJcddBlwb7RuWAocDy9wbxxkkLvbORNONWVmqeEvaaGYHS5oP/AXBduHPzGze9K/AcarhYu/UilTxlnRf++Y2xawsVbwlPWpmh0n6S2CTmd2W7et4QY7TI1zsnVrRjXh3cY4k8Za0ijDtcwJwBPAKsNbMDpnuWBynKi72jhOp6neTKt6SdiNEBG0ys6eiG+dBZnZP76/CcYpxsXdqS7/Myly8nXHExd6pJalOk5I2ETxuHjWzQyT9KnCLmZ0yoCE7Tl9xuwSnrrhZmePk8KQqp668YmZvSOrWrOxl3KzMqREu9k5dSRJvM1sSv10u6S46+N04zrjhc/ZO7XGzMsfxOXunpkhanX1vZlvMbGN+X67dLpJmE/1uJM2O27twszKnRvg0jlMr3KzMcYrxJ3unblxAEO0D4tf1wDpgJQXibWbXmdl+wOeAQ+P3K4DNwPcGNWjH6Tcu9k6tmIZ4L4rGZvMJ1aS+BtzQ7/E6zqBwsXfqSqp4vx6/ngQsN7OVBHtkx6kFLvZOXUkV72ck3QicAdwpaWf8/8OpER566dQSNytznGZc7J1a4uLtOM242DuO40wAPifpOI4zAbjYO47jTAAu9o7jOBOAi73jOM4E8P8InHkvFmpD7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual Representation for finding null values using Heat Map\n",
    "sns.heatmap(mu_data.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>0</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "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",
       "  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",
       "  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",
       "  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",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class                        2\n",
      "cap-shape                    6\n",
      "cap-surface                  4\n",
      "cap-color                   10\n",
      "bruises                      2\n",
      "odor                         9\n",
      "gill-attachment              2\n",
      "gill-spacing                 2\n",
      "gill-size                    2\n",
      "gill-color                  12\n",
      "stalk-shape                  2\n",
      "stalk-root                   5\n",
      "stalk-surface-above-ring     4\n",
      "stalk-surface-below-ring     4\n",
      "stalk-color-above-ring       9\n",
      "stalk-color-below-ring       9\n",
      "veil-type                    1\n",
      "veil-color                   4\n",
      "ring-number                  3\n",
      "ring-type                    5\n",
      "spore-print-color            9\n",
      "population                   6\n",
      "habitat                      7\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(mu_data.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  class\n",
       "0     p\n",
       "1     e\n",
       "2     e\n",
       "3     p\n",
       "4     e"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mu_data1 = mu_data['class'].to_frame()\n",
    "mu_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e    4208\n",
      "p    3916\n",
      "Name: class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the edible=e, poisonous=p \n",
    "c_count=mu_data['class'].value_counts()\n",
    "print(c_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class Distribution')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual Representation of the dependent variable distribution in the dataset\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "class_c=mu_data['class']\n",
    "mu_data_count=sns.countplot(x=class_c, data=mu_data)\n",
    "plt.title(\"Class Distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualize/Analysis how each independent variables affects the dependent variable\n",
    "def plot_feature(class_target, feature_set):\n",
    "    for k, col in enumerate(mu_data.columns):\n",
    "        plt.figure(k)\n",
    "        sns.set(rc={'figure.figsize': (12,9)})\n",
    "        sns.set(style=\"whitegrid\")\n",
    "        c_count=sns.countplot(x=mu_data[col], hue=class_target, data=mu_data)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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m2muvzf33359KpZKVK1eWHBkAAKqqaHCvXLkyS5cuTUNDQ5Jk8ODBWbp0aQ455JDU1dXl6KOPzrZt25K8Gtxf//rX09jYmOuuuy5tbW3ZunVrdu/enQ9+8INJklmzZmX9+vUlRwYAgKoqGtzXX399TjjhhK7t0aNH50Mf+lCSZMeOHVmxYkX+5E/+JLt27coHPvCBLFy4MN///vfz0ksv5ZZbbsn27dszYsSIruePGDEiLS0tJUcGAICqqq/FO21packll1ySs88+OyeddFKS5Bvf+EbX4xdddFEWL16cSZMmpa6urmt/pVLZZ7s7mpubqzM0APRR48ePL7p+U1NT0fWhr+vx4P7FL36RSy65JHPnzs1FF12UJNm2bVs2bNiQ2bNnJ3k1rOvr6zNy5Mi0trZ2Pff555/vujylu8aOHZshQ4ZU7wAAgH2UDnro7dra2t70JG+P3hZw586dufjii/PpT3+6K7aTZOjQofnCF76Q5557LpVKJStWrMiUKVMyevToDBkypOtfzmvXrs2kSZN6cmQAAPid9OgZ7lWrVuX555/Pt771rXzrW99Kkpx66qn59Kc/neuuuy6XXXZZ2tvb84d/+Ie58MILkyQ33XRTrrnmmuzcuTPHHnts5s2b15MjAwDA76SuUqlUaj1ECa+d2ndJCQAk5y9aUWTdO5bPKbIu9CVv1Z1eaRIAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIKKBvfOnTszY8aMbNmyJUmyYcOGNDY2ZurUqfnSl77U9XZPPvlkZs2alWnTpmXJkiXp6OhIkmzbti1z5szJaaedlssuuyy7du0qOS4AAFRdseB+4oknct5552XTpk1Jkt27d2fx4sW55ZZbcu+996a5uTkPPvhgkmThwoW59tprc//996dSqWTlypVJks997nM5//zzs379+owdOza33HJLqXEBAKCIYsG9cuXKLF26NA0NDUmSjRs35vDDD8+YMWNSX1+fxsbGrF+/Plu3bs3u3bvzwQ9+MEkya9asrF+/Pu3t7Xnssccybdq0ffYDAEBfUl9q4euvv36f7e3bt2fEiBFd2w0NDWlpaXnd/hEjRqSlpSUvvvhiDjnkkNTX1++zHwAA+pJiwf2bOjs7U1dX17VdqVRSV1f3W/e/9v+/7je3u6O5uXn/hwaAfmD8+PFF129qaiq6PvR1PRbcI0eOTGtra9d2a2trGhoaXrf/+eefT0NDQ971rnfl5Zdfzt69ezNw4MCut3+7xo4dmyFDhlTlGACA1ysd9NDbtbW1velJ3h67LeC4cePy7LPPZvPmzdm7d2/WrVuXSZMmZfTo0RkyZEjXv47Xrl2bSZMmZdCgQTnhhBNy7733JknWrFmTSZMm9dS4AABQFT12hnvIkCFZtmxZrrjiirS1tWXy5Mk57bTTkiQ33XRTrrnmmuzcuTPHHnts5s2blyRZunRprrrqqnz1q1/NqFGj8sUvfrGnxgUAgKqoq1QqlVoPUcJrp/ZdUgIAyfmLVhRZ947lc4qsC33JW3WnV5oEAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAArqVnC3tLS8bt/Pf/7zqg8DsD86O9r75NoAHBje9JUmf/nLXyZJ/vzP/zy33357XnuNnI6Ojlx++eVZv359+QkB3sKA+kFpWn5JkbXHL/pmkXUBOHC8aXB/5jOfyf/6X/8rSXLSSSf9vyfV12fatGllJwMAgH7gTYP7tttuS5JcffXVufHGG3tkIAAA6E/eNLhfc+ONN2br1q351a9+1XVZSZIce+yxxQYDAID+oFvBffPNN+e2227Lu9/97q59dXV1+dGPflRsMHqfzo72DKgf1GfWBQDoDboV3GvWrMkDDzyQQw89tPQ89GKlfjHNL6UBAP1Zt24LOGrUKLENAAD7oVtnuCdOnJjly5fnT/7kTzJ06NCu/a7hBgCAN9et4F69enWS7HPfbddwAwDAW+tWcP/4xz8uPQcAAPRL3Qrub33rW2+4/8ILL6zqMAAA0N90K7iffvrprj/v2bMnjz32WCZOnFhsKAAA6C+6/cI3v66lpSVLliwpMhAAAPQn3bot4G869NBDs3Xr1mrPAgAA/c7bvoa7Uqmkubl5n1edBAAA3tjbvoY7efWFcBYtWlRkIAAA6E/e1jXcW7duTUdHRw4//PCiQwEAQH/RreDevHlzPvGJT2T79u3p7OzM7//+7+frX/96jjzyyNLzAQBAn9atX5q87rrrcskll+Sxxx5LU1NTLrvssnzuc58rPRsAAPR53QruF154IWeddVbX9tlnn50XX3yx2FAAANBfdCu49+7dm1/+8pdd2zt27Cg2EAAA9Cfduob7T//0T3POOedk+vTpqaury7333psLLrig9GwAANDndesM9+TJk5Mk7e3t+cUvfpGWlpZMmTKl6GAAANAfdOsM91VXXZU5c+Zk3rx5aWtry3e/+90sXrw43/jGN0rPBwAAfVq3znC/+OKLmTdvXpJkyJAh+bM/+7O0trYWHQwAAPqDbv/SZEtLS9f2888/n0qlUmwoAADoL7p1Scmf/dmf5cwzz8wpp5ySurq6bNiwwUu7AwBAN3QruGfPnp2xY8fmkUceycCBA3PxxRfn6KOPLj0bUCWdHe0ZUD+oz60NAP1Bt4I7SY455pgcc8wxJWcBChlQPyhNyy8psvb4Rd8ssi4A9BfduoYbAADYP4IbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUVN/T7/C//bf/lu985ztd21u2bMnMmTPzyiuvpKmpKQcddFCS5PLLL8+UKVOyYcOG3HjjjWlra8v06dOzYMGCnh4ZAAD2W48H98c+9rF87GMfS5L80z/9Uz75yU/m8ssvzwUXXJDvfOc7aWho6Hrb3bt3Z/Hixbn99tszatSozJ8/Pw8++GAmT57c02MDAMB+qeklJX/913+dBQsW5KCDDsq2bduyePHiNDY25uabb05nZ2c2btyYww8/PGPGjEl9fX0aGxuzfv36Wo4MAABvS4+f4X7Nhg0bsnv37kyfPj3PPfdcJkyYkKVLl2b48OGZP39+Vq1alYMPPjgjRozoek5DQ0NaWlpqNTIAALxtNQvu733ve7nwwguTJGPGjMlXvvKVrsfmzp2bNWvWZNq0aamrq+vaX6lU9tnujubm5uoMTMaPH19s7aampmJrU/Zzl9T+89ffjw9+V75HoLZqEtx79uzJY489lmXLliVJnnrqqWzatCnTpk1L8mpY19fXZ+TIkWltbe16Xmtr6z7XeHfH2LFjM2TIkOoNTxGl/2NAWf3989ffjw9+V75HONC1tbW96UnemlzD/dRTT+WII47IwQcfnOTVwL7hhhvyq1/9Ku3t7bnzzjszZcqUjBs3Ls8++2w2b96cvXv3Zt26dZk0aVItRgYAgP1SkzPczz33XEaOHNm1fcwxx+TSSy/Neeedl46OjkydOjUzZsxIkixbtixXXHFF2traMnny5Jx22mm1GBkAAPZLTYL79NNPz+mnn77Pvjlz5mTOnDmve9uJEyfm7rvv7qnRAACgqrzSJAAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4oRfZ07631iMAAFVWX+sBgP9n8KCBOX/Riqqve8fyOVVfEwDoHme4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCG6BK9rTv7VPrAtAz6ms9AEB/MXjQwJy/aEXV171j+ZyqrwlAz3GGGwAAChLcAABQUE0uKZk7d2527NiR+vpX3/11112X//N//k+++tWvpqOjIxdccEHmzHn1R6gbNmzIjTfemLa2tkyfPj0LFiyoxcgAALBfejy4K5VKNm3alP/xP/5HV3C3tLRkwYIFWb16dQYPHpxzzz03J510Ug477LAsXrw4t99+e0aNGpX58+fnwQcfzOTJk3t6bAAA2C89HtzPPPNMkuSiiy7KL3/5y3z84x/PsGHDMmHChLzzne9MkkybNi3r16/PiSeemMMPPzxjxoxJkjQ2Nmb9+vWC+wC2p31vBg8a2OfWBgAOXD0e3C+99FImTpyYz372s2lvb8+8efMyffr0jBgxouttGhoasnHjxmzfvv11+1taWt7W+2tubq7a7Ae68ePHF1u7qamp2zOUuAtE8uqdILo7RyklP8Yl9feP29v5+qz1DPBGesv3CByoejy4jz/++Bx//PFd27Nnz86NN96Yyy67rGtfpVJJXV1dOjs7U1dX97r9b8fYsWMzZMiQ331wiuotodlb5uhr+vvHrTccX2+YAX4bX58c6Nra2t70JG+P36Xk7/7u7/Lwww93bVcqlYwePTqtra1d+1pbW9PQ0JCRI0e+4X4AAOgrejy4X3755SxfvjxtbW3ZuXNnvv/97+cLX/hCHn744ezYsSOvvPJKHnjggUyaNCnjxo3Ls88+m82bN2fv3r1Zt25dJk2a1NMjAwDAfuvxS0o+8pGP5IknnsiZZ56Zzs7OnH/++Rk/fnwWLFiQefPmpb29PbNnz85xxx2XJFm2bFmuuOKKtLW1ZfLkyTnttNN6emQAANhvNbkP95VXXpkrr7xyn32NjY1pbGx83dtOnDgxd999d0+NBgAAVeWVJgEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgBqqrOjvU+tC/B21dd6AAAObAPqB6Vp+SVVX3f8om9WfU2A/eEMNwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4q6izo71PrQsAQHn1tR6gPxlQPyhNyy+p+rrjF32z6msCANAznOEGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgoPpavNMvf/nLue+++5IkkydPzqJFi3L11VenqakpBx10UJLk8ssvz5QpU7Jhw4bceOONaWtry/Tp07NgwYJajAwAAPulx4N7w4YN+du//dt8//vfT11dXS655JL88Ic/THNzc77zne+koaGh6213796dxYsX5/bbb8+oUaMyf/78PPjgg5k8eXJPjw0AAPulxy8pGTFiRK666qoMHp9wD6kAAA9HSURBVDw4gwYNypFHHplt27Zl27ZtWbx4cRobG3PzzTens7MzGzduzOGHH54xY8akvr4+jY2NWb9+fU+PDAAA+63Hz3AfddRRXX/etGlT7rvvvqxYsSI//elPs3Tp0gwfPjzz58/PqlWrcvDBB2fEiBFdb9/Q0JCWlpaeHhkAAPZbTa7hTpJ/+qd/yvz587No0aK8733vy1e+8pWux+bOnZs1a9Zk2rRpqaur69pfqVT22e6O5ubmqs38VsaPH19s7aampmJrd1dvOL6SM3R2tGdA/aCqr9v2yr+k+R+e7Nbbljy+kmr99Vn649Ybvj5r/TEuycetvN7yPQIHqpoEd1NTUz71qU9l8eLFOeOMM/LUU09l06ZNmTZtWpJXw7q+vj4jR45Ma2tr1/NaW1v3uca7O8aOHZshQ4ZUdf5a6Ksh1l294fgG1A9K0/JLqr7u+EXf7BXHV1J3j29P+94MHjSw8DTV1xs+f71hhr7Ix61n+DhzoGtra3vTk7w9Htz//M//nE9+8pP50pe+lIkTJyZ5NbBvuOGGTJgwIQcffHDuvPPOnHXWWRk3blyeffbZbN68OYcddljWrVuXs88+u6dHBqpk8KCBOX/Riqqve8fyOVVfEwCqpceD+7bbbktbW1uWLVvWte/cc8/NpZdemvPOOy8dHR2ZOnVqZsyYkSRZtmxZrrjiirS1tWXy5Mk57bTTenpkAADYbz0e3Ndcc02uueaaN3xszpzXn6WaOHFi7r777tJjAQBAEV5pEgAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHAD9HKdHe19al0A9tXjL3wDwNszoH5QmpZfUvV1xy/6ZtXXBOD1nOEGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLc/dCe9r21HgEAgP9ffa0HoPoGDxqY8xetqPq6dyyfU/U1AQD6O2e4AQCgIMENAAAFCW4AKKizo71PrQtUn2u4AaCgAfWD0rT8kqqvO37RN6u+JlCGM9wAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEEHZHDvad9b6xEAAGqqv78oU286vgPyhW8GDxqY8xetqPq6dyyfU/U1AQBK6O8vytSbju+APMMNAAA9RXADAEBBghuAbvH7LwD754C8hhuAt8/vvwDsH2e4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAECfVuqWlb3lVpi9ZQ72n9sCAgB9Wn+/ZWV/P74DQZ84w/2DH/wgp59+eqZOnZoVK6r/BQcAAKX0+jPcLS0t+dKXvpTVq1dn8ODBOffcc3PSSSfl/e9/f61HAwCAt9Trz3Bv2LAhEyZMyDvf+c4cfPDBmTZtWtavX1/rsQAAoFt6/Rnu7du3Z8SIEV3bDQ0N2bhx41s+r1KpJEn27Nnzho+/4+BB1Rnw17S1tSVDh5dZ923qz8dX4ti65uinx1fq2LrWfhsc337O0Au+NpP+f3zF9ILjK/p3Zy9Q7Guzl/C9t5966Phe683X+vM31VV+2yO9xFe/+tW0tbXlyiuvTJKsXLkyzc3Nue666970eS+//HKefvrpnhgRAABy9NFHZ/jw10d+rz/DPXLkyPzd3/1d13Zra2saGhre8nnDhg3L0UcfnUGDBqWurq7kiAAAHMAqlUra29szbNiwN3y81wf3ySefnP/8n/9zduzYkYMOOigPPPBA/uZv/uYtnzdgwIA3/BcGAABU29ChQ3/rY70+uA899NAsWLAg8+bNS3t7e2bPnp3jjjuu1mMBAEC39PpruAEAoC/r9bcFBACAvkxwAwBAQYIbAAAKEtwAAFCQ4AYAgIIEdxU8+uijmTt3bq3HAPoZf7fQW/nahLdHcAMAQEGCu8q+/e1vZ+7cuXnllVdqPUrVPProo7nooovyiU98ItOmTcunPvWp7Nmzp9ZjVc1/+A//IVOnTs0555yTyy+/PKtXr671SFXz6KOP5oILLsjFF1+cadOmZeHChf3ic9fY2Jhf/OIXSZLPfOYzWbp0aZLk8ccfz6WXXlrL0apux44d+fM///NMmzYtf/EXf9EvPn+v+c2zpFdddVW/+v679dZbc9ZZZ+WjH/1oli9fnv72shcvvvhiLr744jQ2NmbJkiX96mszefXrc/bs2Zk1a1b+/b//97Uep6oWLlyYlStXdm3PnTs3TzzxRA0nqq6vfe1rOf3009PY2Jhly5Zl7969tR5JcFfT6tWr88ADD+RrX/taDjrooFqPU1WPP/54rr322tx3333Ztm1b/vZv/7bWI1XFj3/84zQ1NWXdunW59dZb8w//8A+1HqnqHn/88SxZsiTr169PW1tbVqxYUeuRfmeTJ0/Oww8/nCR5+umn87Of/SxJ8pOf/CQf/vCHazhZ9W3btq3re+/555/Phg0baj0S3fDQQw+lubk5q1atypo1a9LS0pK777671mNV1ZYtW/LZz342d999d3bt2pXvfve7tR6p6jZt2pRvf/vb+fznP1/rUarq7LPPztq1a5MkW7duzY4dOzJu3LgaT1UdDz74YH784x/nrrvuyve///1s3rw53/ve92o9luCulqeffjqf/exnM2/evAwbNqzW41TdUUcdlZEjR2bAgAE58sgj86tf/arWI1XFhg0bMn369AwePDi/93u/l3/37/5drUequj/6oz/K+973vtTV1WXmzJl55JFHaj3S7+y14P75z3+e97///RkwYEBeeOGFPPTQQ/0uuI855piMGTOm63vvxRdfrPVIdMPDDz+cjRs3ZtasWTnrrLPS3Nycn//857Ueq6pOOOGEHHHEEamrq0tjY2N++tOf1nqkqnvve9+b4cOH13qMqjvppJOyffv2bNmyJWvWrMnMmTNrPVLVPPLIIznjjDNy0EEHpb6+PmeffXbXCZpaqq/1AP3FsGHDcuONN+b666/PKaeckoMPPrjWI1XVkCFDuv5cV1fXb340OmDAgHR2dtZ6jKIGDhzY9edKpbLPdl91/PHH56qrrsqGDRty4okn5t3vfnfWr1+fjo6O/MEf/EGtx6uq+vr/99d0f/reS15/PO3t7TWcprr27t2bCy64IBdeeGGS5KWXXuoX33u/7te/NiuVyj7b/cXQoUNrPUIRdXV1OfPMM3PPPffkvvvuy2233Vbrkarmjf6b3tHRUYNJ9uUMd5WMHj06p556ak488cTcfPPNtR6Hbjr55JPzwAMPZM+ePdm5c2f+5//8n6mrq6v1WFXV1NSUlpaWdHZ2Zs2aNZk0aVKtR/qd1dfX57jjjsvtt9+eE088MRMmTMjXvva1TJ48udaj8Tb8/u//fp577rm0tbXll7/8ZZqammo9UtVMmDAha9euza5du9LR0ZFPfvKTuf/++2s9VlU1NTVl27ZtXX+3nHzyybUeibdh1qxZ+d73vpdRo0bl0EMPrfU4VTNhwoTcc8892b17dzo6OnLXXXdlwoQJtR7LGe5qW7RoUWbMmJHGxsYce+yxtR6Ht/DhD384jz/+eM4666z83u/9XhoaGvY5m98fNDQ0ZNGiRWlpacmHPvShfOxjH6v1SFUxefLkPPbYYznyyCMzYsSIvPDCC/3ucpL+7qijjsrkyZNzxhlnZPTo0Rk/fnytR6qaU089Nf/4j/+Yj3/849m7d29OOeWUnHXWWbUeq6re//73Z/HixWltbc2ECRMye/bsWo/E2zBq1KiMGjWq331dfuQjH8mTTz6Zs88+Ox0dHfnjP/7j/Omf/mmtx0pdpT/9fBLepscffzybNm3KWWedlfb29pxzzjm54YYbcswxx9R6tKp49NFH8+Uvfzm33357rUcBoJeoVCrZvn175s6dm3Xr1mXw4MG1Hqnfc0kJB7T3vve9WbduXT760Y9m1qxZOeOMM/pNbAPAG7n//vszc+bM/OVf/qXY7iHOcAMAQEHOcAMAQEGCGwAAChLcAABQkOAG6Gf+03/6T1mzZk2S5F//63+dHTt2ZPXq1Zk/f35V1t+yZUuOP/74qqwFcCBwH26AfubTn/50rUcA4NcIboA+7NZbb82qVasybNiwnHDCCfnRj36UE088MUcddVQuvvjibq3R2tqapUuX5plnnsmAAQNy7rnnZt68efm///f/5q//+q+zdevWVCqVnHnmmbnkkkv2eW57e3uWLVuWhx9+OAMHDsxxxx2Xq6++OoccckhOPfXUHHfccXnqqafyl3/5l5kyZUqJDwFAr+eSEoA+6ic/+UlWr16dVatWZfXq1dm1a9d+rfO5z30uRxxxRNavX58777wzK1euzObNm/NXf/VXOemkk/KDH/wg3/3ud3P33Xfnnnvu2ee5X/3qV7N9+/asXbs2a9euTWdnZ5YvX971+FFHHZX77rtPbAMHNMEN0Ec9+OCDOe200/KOd7wjdXV1mTNnzn6ts2HDhpxzzjlJkuHDh2fdunUZMWJEfvazn3WtOXz48MyaNSsPPfTQPs996KGHcu6552bQoEEZMGBA5s6dm5/85Cddj59wwgn7eXQA/YdLSgD6qPr6+vz6a5cNHDjwLZ/T0tKSSy+9tGv71ltvTX19ferq6rr2Pffcc3nnO9+Z33xdtM7OznR0dLxu368/t7OzM+3t7V3bBx98cPcPCKCfcoYboI+aPHlyHnjggbz88stJklWrVr3lcw499NCuyz/Wrl2bQw89NBMnTsxdd92VJHn55ZdzwQUXZPPmzRk3blxWrFjRtX/NmjU5+eST91nvlFNOyXe/+920t7ens7MzK1asyIc+9KEqHylA3ya4AfqoiRMn5uMf/3jOOeeczJo1Ky+//HIOOuigt73Otddem2eeeSaNjY0577zzMn/+/IwdOzY33XRTHn744TQ2Nmb27NmZOnVqZs2atc9zL7vssrznPe/JmWeemenTp6ejoyNLliyp1iEC9At1ld/8mSEAfcLf//3f5/HHH8+8efOSJN/61rfyxBNP5D/+x/9Y48kA+HWCG6CP2rlzZxYvXpxnnnkmdXV1GTVqVP7mb/4mhx56aK1HA+DXCG4AACjINdwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AACjo/wMXccEdDC/oRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XZuHChamvr0+SbNu2LRs2bMj8+fPT2NiY66+/Pl1dXVmzZk3233//jB49OrW1tWlsbMyqVavywgsvpK2tLUcccUSSZNq0aVm1alXJkQEAoFfVltz4VVddtdPypk2bcvTRR2fhwoUZNmxYZs2alWXLlmXvvffO8OHDu+9XX1+flpaWbNy4caf1w4cPT0tLy27N0Nzc/M6eBMBvMX78+GqPwG5qamqq9gi8Sw3k9wvfV2+taHD/e6NHj86NN97YvTxjxowsX748U6ZMSU1NTff6SqWSmpqadHV1veH63TF27NgMGTLknQ8PwB5vIEcPVIvvq6S9vf1ND/L26aeUPPXUU7n//vu7lyuVSmprazNixIi0trZ2r29tbU19ff0u6zdt2tR9egoAAOwJ+jS4K5VKrr766rzyyivp6OjIHXfckcmTJ2fcuHF57rnnsm7dunR2duaee+5JQ0NDRo0alSFDhnT/r4oVK1akoaGhL0cGAIB3pE9PKRkzZkw+97nP5ayzzsqOHTtywgkn5JRTTkmSXHPNNbnooovS3t6eSZMm5cQTT0ySLF68OAsWLMiWLVty2GGHZebMmX05MgAAvCN9EtwPPvhg99+nT5+e6dOn73KfiRMnZuXKlbusHzNmTJYtW1Z0PgAAKMWVJgEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoqHhwb9myJaecckrWr1+fJLnjjjtyyimnpLGxMfPmzcv27duTJDfccEM+9rGP5dRTT82pp56aJUuWJEmeeOKJTJs2LVOmTMlll12WHTt2lB4ZAAB6TdHgfvzxx3PWWWdl7dq1SZLnnnsut9xyS37wgx9k5cqV6erqyu23354kaW5uznXXXZcVK1ZkxYoVmT59epJkzpw5ufzyy3P//fenUqlk6dKlJUcGAIBeVTS4ly5dmoULF6a+vj5JUldXl4ULF+a9731vampqcsghh2TDhg1JfhPcN998cxobG3PllVemvb09L7zwQtra2nLEEUckSaZNm5ZVq1aVHBkAAHpV0eC+6qqrMmHChO7lUaNG5Y//+I+TJC+++GKWLFmSP/3TP83WrVtz6KGHZs6cOfnRj36UV199NTfddFM2btyY4cOHdz9++PDhaWlpKTkyAAD0qtpq7LSlpSXnn39+PvnJT+aoo45KknzrW9/qvv28887L/Pnz09DQkJqamu71lUplp+WeaG5u7p2hAf6d8ePHV3sEdlNTU1O1R+BdaiC/X/i+emt9HtzPPvtszj///MyYMSPnnXdekmTDhg1ZvXp1Tj/99CS/Ceva2tqMGDEira2t3Y/dtGlT9+kpPTV27NgMGTKk954AAHusgRw9UC2+r5L29vY3Pcjbpx8LuGXLlnz2s5/NxRdf3B3bSTJ06NB89atfzfPPP59KpZIlS5Zk8uTJGTVqVIYMGdL9k9OKFSvS0NDQlyMDAMA70qdHuJctW5ZNmzbl29/+dr797W8nSY4//vhcfPHFufLKK/OFL3whHR0d+chHPpJzzz03SbJ48eIsWLAgW7ZsyWGHHZaZM2f25cgAAPCO9ElwP/jgg0mSz3zmM/nMZz7zhveZMmVKpkyZssv6MWPGZNmyZSXHAwCAYlxpEgAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAK6lFwt7S07LLuf/2v/9XrwwAAwEDzpsH98ssv5+WXX86f//mf55VXXule3rRpUy688MK+mhEAAPZYtW9241/8xV/kZz/7WZLkqKOO+n8Pqq3NlClTyk4GAAADwJsG9y233JIkmTdvXhYtWtQnAwEAwEDypsH9ukWLFuWFF17IK6+8kkql0r3+sMMOKzYYAAAMBD0K7uuvvz633HJLfvd3f7d7XU1NTf7hH/6h2GAAADAQ9Ci4ly9fnp/85Cf5wAc+UHoeAAAYUHr0sYAjR44U2wAA8Db06Aj3xIkTc+211+ZP//RPM3To0O71zuEGAIA316Pgvuuuu5Ikq1at6l7nHG4AAHhrPQruBx98sPQcAAAwIPUouL/97W+/4fpzzz33TR+3ZcuWnHnmmfnGN76R/fbbL6tXr86iRYvS3t6eqVOnZvbs2UmSJ554Ipdddlm2bt2aCRMm5IorrkhtbW02bNiQOXPmZPPmzTnwwAOzePHi7LPPPrv5FAEAoHp69EuTTz/9dPef5ubmfPvb386TTz75po95/PHHc9ZZZ2Xt2rVJkra2tsyfPz833XRT7r333jQ3N+ehhx5KksyZMyeXX3557r///lQqlSxdujRJcsUVV+Tss8/OqlWrMnbs2Nx0003v4KkCAEDf61FwL1q0qPvPf/2v/zV33nlnNm/e/KaPWbp0aRYuXJj6+vokyZo1a7L//vtn9OjRqa2tTWNjY1atWpUXXnghbW1tOeKII5Ik06ZNy6pVq9LR0ZHHHnus+xLyr68HAIA9SY9OKfn3PvCBD+SFF1540/tcddVVOy1v3Lgxw4cP716ur69PS0vLLuuHDx+elpaWvPTSS3nve9+b2trandYDAMCeZLfP4a5UKmlubt7pqpM90dXVlZqamp22U1NT81vXv/7f/9+/X+6J5ubm3X4MQE+MHz++2iOwm5qamqo9Au9SA/n9wvfVW+tRcD/99NM7LY8cOTJz587drR2NGDEira2t3cutra2pr6/fZf2mTZtSX1+ffffdN6+99lo6OzszaNCg7vvvrrFjx2bIkCG7/TgABp6BHD1QLb6vkvb29jc9yNuj4F60aFGS5IUXXsiOHTuy//777/Yg48aNy3PPPZd169Zlv/32yz333JNPfvKTGTVqVIYMGZKmpqaMHz8+K1asSENDQwYPHpwJEybk3nvvTWNjY5YvX56Ghobd3i8AAFRTj4J73bp1+eIXv5iNGzemq6sr73//+3PzzTfnQx/6UI93NGTIkFxzzTW56KKL0t7enkmTJuXEE09MkixevDgLFizIli1bcthhh2XmzJlJkoULF+bSSy/N17/+9YwcOTLXXXfd23iKAABQPT0K7iuvvDLnn39+PvGJTyRJfvjDH+aKK67I9773vbd87P9/0ZyJEydm5cqVu9xnzJgxWbZs2S7rR40alVtvvbUnIwIAQL/Uo48F3Lx5c3dsJ8knP/nJvPTSS8WGAgCAgaJHwd3Z2ZmXX365e/nFF18sNhAAAAwkPTql5NOf/nT+7M/+LFOnTk1NTU3uvffenHPOOaVnAwCAPV6PjnBPmjQpSdLR0ZFnn302LS0tmTx5ctHBAABgIOjREe5LL70006dPz8yZM9Pe3p7vf//7mT9/fr71rW+Vng8AAPZoPTrC/dJLL3V/VN+QIUPymc98ZqeL1QAAAG+sx7802dLS0r28adOmVCqVYkMBAMBA0aNTSj7zmc/ktNNOy3HHHZeampqsXr16ty/tDgAA70Y9Cu7TTz89Y8eOzT//8z9n0KBB+exnP5tDDjmk9GwAALDH61FwJ7+5GuSYMWNKzgIAAANOj87hBgAA3h7BDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCe53ia4dHdUeoYiB+rwAgIGjttoD0Df2qh2cpmvPr/YYvW783L+t9ggAAG/KEW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFFTb1zu88847c9ttt3Uvr1+/Pqeeemq2bduWpqamvOc970mSXHjhhZk8eXJWr16dRYsWpb29PVOnTs3s2bP7emQAAHjb+jy4zzjjjJxxxhlJkmeeeSYXXHBBLrzwwpxzzjm57bbbUl9f333ftra2zJ8/P7feemtGjhyZWbNm5aGHHsqkSZP6emwAAHhbqnpKyV/91V9l9uzZec973pMNGzZk/vz5aWxszPXXX5+urq6sWbMm+++/f0aPHp3a2to0NjZm1apV1RwZAAB2S58f4X7d6tWr09bWlqlTp+b555/P0UcfnYULF2bYsGGZNWtWli1blr333jvDhw/vfkx9fX1aWlp2az/Nzc29Pfoeafz48dUeoZimpqZqj8C71ED+vhqovF9QLQP5/cL31VurWnD/4Ac/yLnnnpskGT16dG688cbu22bMmJHly5dnypQpqamp6V5fqVR2Wu6JsWPHZsiQIb0zNP3SQH4TA3qX9wvofb6vkvb29jc9yFuVU0q2b9+exx57LMcff3yS5Kmnnsr999/ffXulUkltbW1GjBiR1tbW7vWtra07neMNAAD9XVWC+6mnnsoBBxyQvffeO8lvAvvqq6/OK6+8ko6Ojtxxxx2ZPHlyxo0bl+eeey7r1q1LZ2dn7rnnnjQ0NFRjZAAAeFuqckrJ888/nxEjRnQvjxkzJp/73Ody1llnZceOHTnhhBNyyimnJEmuueaaXHTRRWlvb8+kSZNy4oknVmNkAAB4W6oS3CeddFJOOumkndZNnz4906dP3+W+EydOzMqVK/tqNAAA6FWuNAkAAAUJbgAAKEhwAwDwtnTt6Kj2CL2uxHOq2udwAwCwZ9urdnCarj2/2mP0qvFz/7bXt+kINwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFBQbTV2OmPGjLz44ouprf3N7q+88sr87//9v/P1r389O3bsyDnnnJPp06cnSVavXp1Fixalvb09U6dOzezZs6sxMgAAvC19HtyVSiVr167NP/7jP3YHd0tLS2bPnp277rordXV1OfPMM3PUUUdlv/32y/z583Prrbdm5MiRmTVrVh566KFMmjSpr8eGAalrR0f2qh1c7TF63UB9XgDsmfo8uH/5y18mSc4777y8/PLL+dSnPpV99tknRx99dN73vvclSaZMmZJVq1blj/7oj7L//vtn9OjRSZLGxsasWrVKcEMv2at2cJquPb/aY/S68XP/ttojAEC3Pj+H+9VXX83EiRNz44035jvf+U5+8IMfZMOGDRk+fHj3ferr69PS0pKNGze+4XoAANhT9PkR7iOPPDJHHnlk9/Lpp5+eRYsW5Qtf+EL3ukqlkpqamnR1daWmpmaX9bujubn5nQ89AIwfP77aIxTT1NRU7RH2WF4X78xA/voNVN4vqMdo5cwAAA7eSURBVBbvF3uW3n6v6PPg/vnPf56Ojo5MnDgxyW8ietSoUWltbe2+T2tra+rr6zNixIg3XL87xo4dmyFDhvTO8PRL3sR4I14XvBGvC6Andve9or29/U0P8vb5KSWvvfZarr322rS3t2fLli350Y9+lK9+9at5+OGH8+KLL2bbtm35yU9+koaGhowbNy7PPfdc1q1bl87Oztxzzz1paGjo65EBAOBt6/Mj3B/72Mfy+OOP57TTTktXV1fOPvvsjB8/PrNnz87MmTPT0dGR008/PYcffniS5JprrslFF12U9vb2TJo0KSeeeGJfjwwAAG9bVT6H+5JLLskll1yy07rGxsY0Njbuct+JEydm5cqVfTUaAAD0KleaBACAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAXVVmOnN9xwQ+67774kyaRJkzJ37tzMmzcvTU1Nec973pMkufDCCzN58uSsXr06ixYtSnt7e6ZOnZrZs2dXY2QAAHhb+jy4V69enf/+3/97fvSjH6Wmpibnn39+HnjggTQ3N+e2225LfX19933b2toyf/783HrrrRk5cmRmzZqVhx56KJMmTerrsQEA4G3p81NKhg8fnksvvTR1dXUZPHhwPvShD2XDhg3ZsGFD5s+fn8bGxlx//fXp6urKmjVrsv/++2f06NGpra1NY2NjVq1aVXzG7R2dxfcBAMC7Q58f4T744IO7/7527drcd999WbJkSR599NEsXLgww4YNy6xZs7Js2bLsvffeGT58ePf96+vr09LSUnzGusGDcvbcJcX305duv3Z6tUcAAHhXqso53EnyzDPPZNasWZk7d24++MEP5sYbb+y+bcaMGVm+fHmmTJmSmpqa7vWVSmWn5Z5obm7e7dnGjx+/24+hepqamqo9wh5rIL/W++J1MZC/fgOV9wuqxfvFnqW33yuqEtxNTU350pe+lPnz5+fkk0/OU089lbVr12bKlClJfhPWtbW1GTFiRFpbW7sf19rautM53j0xduzYDBkypFfnp3/xJsYb8brgjXhdAD2xu+8V7e3tb3qQt8/P4f7Vr36VCy64IIsXL87JJ5+c5DeBffXVV+eVV15JR0dH7rjjjkyePDnjxo3Lc889l3Xr1qWzszP33HNPGhoa+npkAAB42/r8CPctt9yS9vb2XHPNNd3rzjzzzHzuc5/LWWedlR07duSEE07IKaeckiS55pprctFFF6W9vT2TJk3KiSee2NcjAwDA29bnwb1gwYIsWLDgDW+bPn3XX+ybOHFiVq5cWXosAAAowpUmAQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQC8pa4dHdUeodcNxOdE/9Tnl3YHAPY8e9UOTtO151d7jF41fu7fVnsE3iUc4QYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuKGHtnd0VnsEAGAPVFvtAWBPUTd4UM6eu6TaY/Sq26+dXu0RAGDAc4QbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgoD0iuO++++6cdNJJOeGEE7JkycD6lAgAAAa2fv+xgC0tLfna176Wu+66K3V1dTnzzDNz1FFH5aCDDqr2aAAA8Jb6fXCvXr06Rx99dN73vvclSaZMmZJVq1blwgsvfNPHVSqVJMn27dvf1n5/Z+/Bb+tx/VV7e3sydFi1x+h17e3tfbo/r4s9Q1++LgbaayLxuuBNDLDXhX9D3rmB+H7xdl4Xr/fm6/3579VUftst/cTNN9+cX//615k9e3aS5M4778yaNWvy13/912/6uNdeey1PP/10X4wIAAA55JBDMmzYrj+A9Psj3F1dXampqelerlQqOy3/Nvvss08OOeSQDB48uEf3BwCAt6NSqaSjoyP77LPPG97e74N7xIgR+fnPf9693Nramvr6+rd83F577fWGP2EAAEBvGzp06G+9rd9/SskxxxyThx9+OC+++GK2bduWn/zkJ2loaKj2WAAA0CP9/gj3Bz7wgcyePTszZ85MR0dHTj/99Bx++OHVHgsAAHqk3//SJAAA7Mn6/SklAACwJxPcAABQkOAGAICCBDcAABQkuAFIkjzyyCOZMWNGtccA9gB/93d/l5NOOiknn3xyLr/88nR0dFR7pH5NcAMA0GO/+MUvcuedd+bOO+/MihUrsmnTptx+++3VHqtf6/efw807881vfjP33XdfOjs7c+yxx2bOnDkudU8qlUoWL16cv//7v8+gQYPyZ3/2ZznnnHOqPRb9wEsvvZTPfvaz2bhxYw4//PAsXLgwdXV11R6LKnvkkUfy1a9+NV1dXTn44IPzla98pdojUUXDhg3LX/3VX3VfxnzChAn5P//n/1R5qv7NEe4B7Kc//Wmam5uzbNmyLF++PC0tLVm5cmW1x6IfWLVqVX7xi1/k7rvvzp133pm77rorra2t1R6LfmD9+vX5L//lv2TlypXZunVrvv/971d7JPqJtWvX5rvf/a7YJgcffHCOOuqoJMmzzz6b2267LY2NjVWeqn9zhHsAe/jhh7NmzZpMmzYtSdLW1pbf//3fr/JU9AePPfZYpk6dmrq6utTV1WXFihXVHol+YsKECTnggAOSJI2Njbnrrrv83w+SJAceeGCGDRtW7THoR15++eX8+Z//ea644op8+MMfrvY4/ZrgHsA6Oztzzjnn5Nxzz02SvPrqqxk0aFCVp6I/qK2t3enUovXr12fffffN3nvvXcWp6A9qa//fPwuVSmWnZd7dhg4dWu0R6GceeuihHHPMMTnuuOOqPUq/55SSAezoo4/OihUrsnXr1uzYsSMXXHBB7r///mqPRT/w0Y9+ND/5yU/S0dGRbdu25fzzz09LS0u1x6IfaGpqyoYNG9LV1ZXly5fnmGOOqfZIQD915JFHdh/U4805dDGAHX/88XnyySfzqU99Kp2dnTnuuOPyiU98otpj0Q9Mnjw5zc3NmTZtWrq6ujJz5swceOCB1R6LfuCggw7K/Pnz09ramqOPPjqnn356tUcC+qmf/exn2bhxYy6++OJqj9Lv1VQqlUq1hwAAgIHKKSUAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW6AAeK8887Liy+++Kb3eeSRR3LKKackSS699NLccsstfTFaXnvttcycObNP9gXQ3whugAHiZz/7WbVH+K1eeeWV/Mu//Eu1xwCoChe+AdjDbN26NfPmzcu6deuy11575bDDDktnZ2eS5Jxzzsk3v/nNPPnkk7n55puzffv2vPjiiznttNNyySWX/NZtXn311Xnqqady0003ZZ999tnptksvvTQvv/xynn/++fzJn/xJPv/5z+eKK67Ik08+mZqamhx33HH5T//pP6W2tjY///nPc+2112bbtm0ZPHhwLrnkkjQ0NGTevHlpa2vLqaeemrvuuiuDBg0q+jUC6E8EN8Ae5oEHHsjWrVuzYsWKdHZ2ZuHChfn85z+fu+66K9/97nfz/ve/P3Pnzs0111yTAw44IC0tLfnYxz72hqd0VCqVXHnlldm4cWO+9a1vpa6u7g332dbWlh//+MdJkr/8y7/M+973vtx9993p6OjIF77whfzd3/1dzjjjjHzpS1/K17/+9YwbNy7PPPNMPv3pT2fZsmVZtGhRGhsbs2LFiqJfG4D+SHAD7GHGjx+fr33ta5kxY0aOOeaYnHPOOdl///27b6+pqck3vvGN/NM//VPuueeePPvss6lUKtm2bdsu2/rOd76TzZs3Z/ny5b81tl/f5+t++tOf5vvf/35qampSV1eXM888M9/97nfzH//jf8wf/MEfZNy4cUmSgw8+OB/5yEfy6KOP5qijjurFrwDAnsU53AB7mNGjR+eBBx7I5z73uWzZsiXnnntuHnzwwe7bf/3rX+cTn/hE/vVf/zUf/vCHM3fu3NTW1qZSqeyyrY9+9KOZP39+5s2bl46OjiTJqaee2v3n9fOu99577+7HdHV1paamZqflHTt2pLOzc6f1yW+OoO/YsaNXnz/AnkZwA+xhbr/99sybNy/HHnts5syZk2OPPTb/83/+zwwaNCg7duzIunXrsmXLllxyySU5/vjj88gjj2T79u3p6uraZVtjx47Npz/96QwbNiw33HBDkmTFihXdf/7wD/9wl8cce+yxue2221KpVLJ9+/YsXbo0xxxzTI444oj88pe/zJo1a5IkzzzzTB577LH80R/9UWpra9PZ2fmG0Q8w0DmlBGAPc9ppp+XRRx/NSSedlPe85z0ZOXJkZsyYkV/+8peZMWNG/uZv/iZ/8id/kqlTp6auri6HHHJIDjrooKxbt+4NTxupqanJ1VdfndNOOy2TJk3KRz7ykTfd/4IFC/LlL385jY2N6ejoyHHHHZfPf/7zqaury9/8zd/kr//6r9PW1paamposWrQoBx54YDo7O3P44Yfn5JNPzpIlS/L+97+/1JcHoN+pqTjcAAAAxTilBAAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBB/xdgYXV9YagSHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUVDe6rrroqJ554YqZNm5brr78+SbJ69eo0NzensbExS5cuHTh37dq1aWlpSVNTU+bOnZve3t4kyaZNmzJz5sxMnTo15557bp599tmSIwMAwKAqFtwPPPBA7r///tx222351re+lRtuuCE///nPM2fOnCxbtiwrV65MR0dHVq1alSS5+OKLM3/+/Nx5552pVCpZvnx5kuSyyy7LjBkz0tbWlvHjx2fZsmWlRgYAgEFXLLjf+c535mtf+1qqq6vz+OOPp6+vL7/97W9z2GGH5dBDD011dXWam5vT1taWjRs3Ztu2bZk4cWKSpKWlJW1tbenp6cmDDz6YpqamHdYBAGC4KHpLSU1NTa6++upMmzYtkydPzubNm1NXVzfweH19fTo7O3dar6urS2dnZ5588skccMABqa6u3mEdAACGi+rSL3D++efn4x//eM4555ysW7cuVVVVA49VKpVUVVWlv7//Bdef//eP/enxS+no6Hh1b2AvMWnSpKEeAQDYC7W3tw/1CHu8YsH9yCOPZPv27TnqqKOy7777prGxMW1tbRkxYsTAOV1dXamvr8+YMWPS1dU1sL5ly5bU19dn9OjReeaZZ9LX15cRI0YMnP9yjB8/PrW1tYP2vgAA+AObekl3d/eLbvIWu6Vkw4YNmTdvXrZv357t27fnu9/9bs4444w8+uijWb9+ffr6+nL77benoaEhY8eOTW1t7cBvSCtWrEhDQ0NqampyzDHHZOXKlUmS1tbWNDQ0lBoZAAAGXbEd7ilTpmTNmjU55ZRTMmLEiDQ2NmbatGkZPXp0zjvvvHR3d2fKlCmZOnVqkmTJkiWZN29etm7dmqOPPjqzZs1KkixYsCCXXHJJrr322hxyyCG58sorS40MAACDrqpSqVSGeogSnt/ad0vJH8yYfeNQjwAMAzctnpn2xR8b6jGAYWDS7H8b6hH2CC/Vnb5pEgAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AACioaHB/8YtfzLRp0zJt2rQsXrw4SXLppZemsbEx06dPz/Tp03P33XcnSVavXp3m5uY0NjZm6dKlA9dYu3ZtWlpa0tTUlLlz56a3t7fkyAAAMKiKBffq1avzgx/8ILfeemtaW1vzs5/9LHfffXc6Ojry9a9/PStWrMiKFStywgknZNu2bZkzZ06WLVuWlStXpqOjI6tWrUqSXHzxxZk/f37uvPPOVCqVLF++vNTIAAAw6IoFd11dXS655JKMHDkyNTU1OeKII7Jp06Zs2rQpc+bMSXNzc66++ur09/dnzZo1Oeyww3LooYemuro6zc3NaWtry8aNG7Nt27ZMnDgxSdLS0pK2trZSIwMAwKCrLnXht7zlLQM/r1u3LnfccUduvPHGPPDAA1mwYEEOPPDAnH322bn55puz3377pa6ubuD8+vr6dHZ2ZvPmzTus19XVpbOz82XN0dHR8erfzF5g0qRJQz0CALAXam9vH+oR9njFgvt5v/zlL3P22Wdn9uzZOfzww3PNNdcMPHbWWWeltbU1TU1NqaqqGlivVCqpqqpKf3//C66/HOPHj09tbe2rfyMAAOzEpl7S3d39opu8Rf9osr29PR/+8Ifzmc98Jqeeemp+8Ytf5M477xx4vFKppLq6OmPGjElXV9fAeldXV+rr63da37JlS+rr60uODAAAg6pYcD/22GP51Kc+lSVLlmTatGlJfh/YV1xxRZ5++un09PTkm9/8Zk444YRMmDAhjz76aNavX5++vr7cfvvtaWhoyNixY1NbWzvwf1WsWLEiDQ0NpUYGAIBBV+yWkuuuuy7d3d1ZtGjRwNoZZ5yRT3ziEznzzDPT29ubxsbGnHTSSUmSRYsW5bzzzkt3d3emTJmSqVOnJkmWLFmSefPmZevWrTn66KMza9asUiMDAMCgq6pUKpWhHqKE5++lcQ/3H8yYfeNQjwAMAzctnpn2xR8b6jGAYWDS7H8b6hH2CC/Vnb5pEgAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAXtUnB3dnbutParX/1q0IcBAIC9zYsG91NPPZWnnnoqH//4x/P0008PHG/ZsiWf/vSnd9eMAAAwbFW/2IOf+cxncu+99yZJjjvuuD88qbo6TU1NZScDAIC9wIsG93XXXZckufTSS7Nw4cLdMhAAAOxNXjS4n7dw4cJs3LgxTz/9dCqVysD60UcfXWwwAADYG+xScF999dW57rrr8vrXv35graqqKt/97neLDQYAAHuDXQru1tbW3HXXXTn44INLzwMAAHuVXfpYwEMOOURsAwDAK7BLO9yTJ0/O4sWL8773vS+jRo0aWHcPNwAAvLhdCu5bbrklSdLW1jaw5h5uAAB4absU3Pfcc0/pOQAAYK+0S8F9/fXXv+D6Rz7ykUEdBgAA9ja7FNwPP/zwwM/bt2/Pgw8+mMmTJxcbCgAA9ha7/MU3f6yzszNz584tMhAAAOxNduljAf/UwQcfnI0bNw72LAAAsNd52fdwVyqVdHR07PCtkwAAwAt72fdwJ7//IpzZs2cXGQgAAPYmL+se7o0bN6a3tzeHHXZY0aEAAGBvsUvBvX79+nzyk5/M5s2b09/fn9e97nX513/91xxxxBGl5wMAgGFtl/5o8rOf/Ww+9rGP5cEHH0x7e3vOPffcXHbZZaVnAwCAYW+Xgvvxxx/PqaeeOnD813/913nyySeLDQUAAHuLXQruvr6+PPXUUwPHTzzxRLGBAABgb7JL93B/8IMfzN/8zd/k/e9/f6qqqrJy5cp86EMfKj0bAAAMe7u0wz1lypQkSU9PTx555JF0dnbmhBNOKDoYAADsDXZph/uSSy7JzJkzM2vWrHR3d+cb3/hG5syZky9/+cul5wMAgGFtl3a4n3zyycyaNStJUltbmw9/+MPp6uoqOhgAAOwNdvmPJjs7OweOt2zZkkqlUmwoAADYW+zSLSUf/vCHc8opp+T4449PVVVVVq9e7avdAQBgF+xScJ922mkZP3587r///owYMSJ/+7d/myOPPLL0bAAAMOztUnAnybhx4zJu3LiSswAAwF5nl+7hBgAAXhnBDQAABRUN7i9+8YuZNm1apk2blsWLFydJVq9enebm5jQ2Nmbp0qUD565duzYtLS1pamrK3Llz09vbmyTZtGlTZs6cmalTp+bcc8/Ns88+W3JkAAAYVMWCe/Xq1fnBD36QW2+9Na2trfnZz36W22+/PXPmzMmyZcuycuXKdHR0ZNWqVUmSiy++OPPnz8+dd96ZSqWS5cuXJ0kuu+yyzJgxI21tbRk/fnyWLVtWamQAABh0xYK7rq4ul1xySUaOHJmampocccQRWbduXQ477LAceuihqa6uTnNzc9ra2rJx48Zs27YtEydOTJK0tLSkra0tPT09efDBB9PU1LTDOgAADBfFgvstb3nLQECvW7cud9xxR6qqqlJXVzdwTn19fTo7O7N58+Yd1uvq6tLZ2Zknn3wyBxxwQKqrq3dYBwCA4WKXPxbwlfrlL3+Zs88+O7Nnz86IESOybt26gccqlUqqqqrS39+fqqqqndaf//eP/enxS+no6HhV8+8tJk2aNNQjAAB7ofb29qEeYY9XNLjb29tz/vnnZ86cOZk2bVoeeOCBdHV1DTze1dWV+vr6jBkzZof1LVu2pL6+PqNHj84zzzyTvr6+jBgxYuD8l2P8+PGpra0dtPcEAMAf2NRLuru7X3STt9gtJY899lg+9alPZcmSJZk2bVqSZMKECXn00Uezfv369PX15fbbb09DQ0PGjh2b2tragd+QVqxYkYaGhtTU1OSYY47JypUrkyStra1paGgoNTIAAAy6Yjvc1113Xbq7u7No0aKBtTPOOCOLFi3Keeedl+7u7kyZMiVTp05NkixZsiTz5s3L1q1bc/TRR2fWrFlJkgULFuSSSy7Jtddem0MOOSRXXnllqZEBAGDQFQvuefPmZd68eS/42G233bbT2rhx43LzzTfvtD527NjccMMNgz4fAADsDr5pEgAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFFQ/urVu35qSTTsqGDRuSJJdeemkaGxszffr0TJ8+PXfffXeSZPXq1Wlubk5jY2OWLl068Py1a9empaUlTU1NmTt3bnp7e0uPDAAAg6ZocP/kJz/JmWeemXXr1g2sdXR05Otf/3pWrFiRFStW5IQTTsi2bdsyZ86cLFu2LCtXrkxHR0dWrVqVJLn44oszf/783HnnnalUKlm+fHnJkQEAYFAVDe7ly5dnwYIFqa+vT5I899xz2bRpU+bMmZPm5uZcffXV6e/vz5o1a3LYYYfl0EMPTXV1dZqbm9PW1paNGzdm27ZtmThxYpKkpaUlbW1tJUcGAIBBVV3y4pdffvkOx1u2bMm73vWuLFiwIAceeGDOPvvs3Hzzzdlvv/1SV1c3cF59fX06OzuzefPmHdbr6urS2dlZcmQAABhURYP7Tx166KG55pprBo7POuustLa2pqmpKVVVVQPrlUolVVVV6e/vf8H1l6Ojo+PVD74XmDRp0lCPAADshdrb24d6hD3ebg3uX/ziF1m3bl2ampqS/D6gq6urM2bMmHR1dQ2c19XVlfr6+p3Wt2zZMnB7yq4aP358amtrB+cNAACwA5t6SXd394tu8u7WjwWsVCq54oor8vTTT6enpyff/OY3c8IJJ2TChAl59NFHs379+vT19eX2229PQ0NDxo4dm9ra2oHfnFasWJGGhobdOTIAALwqu3WHe9y4cfnEJz6RM888M729vWlsbMxJJ52UJFm0aFHOO++8dHd3Z8qUKZk6dWqSZMmSJZk3b162bt2ao48+OrNmzdqdIwMAwKuyW4L7nnvuGfh55syZmTlz5k7nTJ48ObfddttO6+PGjcvNN99cdD4AACjFN00CAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABRUP7q1bt+akk07Khg0bkiSrV69Oc3NzGhsbs3Tp0oHz1q5dm5aWljQ1NWXu3Lnp7d2HahEAAAxNSURBVO1NkmzatCkzZ87M1KlTc+655+bZZ58tPTIAAAyaosH9k5/8JGeeeWbWrVuXJNm2bVvmzJmTZcuWZeXKleno6MiqVauSJBdffHHmz5+fO++8M5VKJcuXL0+SXHbZZZkxY0ba2toyfvz4LFu2rOTIAAAwqIoG9/Lly7NgwYLU19cnSdasWZPDDjsshx56aKqrq9Pc3Jy2trZs3Lgx27Zty8SJE5MkLS0taWtrS09PTx588ME0NTXtsA4AAMNFdcmLX3755Tscb968OXV1dQPH9fX16ezs3Gm9rq4unZ2defLJJ3PAAQekurp6h3UAABguigb3n+rv709VVdXAcaVSSVVV1Z9df/7fP/anxy+lo6Pj1Q29l5g0adJQjwAA7IXa29uHeoQ93m4N7jFjxqSrq2vguKurK/X19Tutb9myJfX19Rk9enSeeeaZ9PX1ZcSIEQPnvxzjx49PbW3toL0HAAD+wKZe0t3d/aKbvLv1YwEnTJiQRx99NOvXr09fX19uv/32NDQ0ZOzYsamtrR34DWnFihVpaGhITU1NjjnmmKxcuTJJ0tramoaGht05MgAAvCq7dYe7trY2ixYtynnnnZfu7u5MmTIlU6dOTZIsWbIk8+bNy9atW3P00Udn1qxZSZIFCxbkkksuybXXXptDDjkkV1555e4cGQAAXpXdEtz33HPPwM+TJ0/ObbfdttM548aNy80337zT+tixY3PDDTcUnQ8AAErxTZMAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKKh6KF70rLPOyhNPPJHq6t+//Gc/+9n85je/ybXXXpve3t586EMfysyZM5Mkq1evzsKFC9Pd3Z33v//9ueiii4ZiZAAAeEV2e3BXKpWsW7cu//3f/z0Q3J2dnbnoootyyy23ZOTIkTnjjDNy3HHH5Y1vfGPmzJmTG264IYccckjOPvvsrFq1KlOmTNndYwMAwCuy24P717/+dZLkox/9aJ566ql84AMfyP777593vetdOeigg5IkTU1NaWtryzvf+c4cdthhOfTQQ5Mkzc3NaWtrE9wAAAwbu/0e7t/+9reZPHlyrrnmmnzlK1/Jf/zHf2TTpk2pq6sbOKe+vj6dnZ3ZvHnzC64DAMBwsdt3uN/xjnfkHe94x8DxaaedloULF+bcc88dWKtUKqmqqkp/f3+qqqp2Wn85Ojo6Xv3Qe4FJkyYN9QgAwF6ovb19qEfY4+324H7ooYfS09OTyZMnJ/l9RI8dOzZdXV0D53R1daW+vj5jxox5wfWXY/z48amtrR2c4QEA2IFNvaS7u/tFN3l3+y0lzzzzTBYvXpzu7u5s3bo1t956a/7pn/4p9913X5544ok899xzueuuu9LQ0JAJEybk0Ucfzfr169PX15fbb789DQ0Nu3tkAAB4xXb7Dvdf/dVf5Sc/+UlOOeWU9Pf3Z8aMGZk0aVIuuuiizJo1Kz09PTnttNPy9re/PUmyaNGinHfeeenu7s6UKVMyderU3T0yAAC8YkPyOdwXXnhhLrzwwh3Wmpub09zcvNO5kydPzm233ba7RgMAgEHlmyYBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUJDgBgCAggQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAKEtwAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AAChIcAMAQEGCGwAAChLcAABQkOAGAICCBDcAABQkuAEAoCDBDQAABQluAAAoSHADAEBBghsAAAoS3AAAUNCwCO5vf/vbOfHEE9PY2Jgbb7xxqMcBAIBdVj3UA7yUzs7OLF26NLfccktGjhyZM844I8cdd1ze/OY3D/VoAADwkvb4He7Vq1fnXe96Vw466KDst99+aWpqSltb21CPBQAAu2SP3+HevHlz6urqBo7r6+uzZs2al3xepVJJkmzfvr3YbMPN/7dfzVCPAAwD3d3dyagDh3oMYBjo7u4e6hH2CM/35vP9+af2+ODu7+9PVVXVwHGlUtnh+M/p6elJkjz88MPFZhtuPt58xFCPAAwDHR0dyXs+ONRjAMNAR0fHUI+wR+np6cmoUaN2Wt/jg3vMmDF56KGHBo67urpSX1//ks/bf//9c+SRR6ampmaXAh0AAF6JSqWSnp6e7L///i/4+B4f3O9+97vzhS98IU888UT23Xff3HXXXfmHf/iHl3zePvvskwMP9H+JAgBQ3gvtbD9vjw/ugw8+OBdddFFmzZqVnp6enHbaaXn7298+1GMBAMAuqar8ubu7AQCAV22P/1hAAAAYzgQ3AAAUJLgBAKAgwQ0AAAUJbgAAKEhwAwBAQYIbAAAK2uO/+AaA3eOHP/xhli1blurq6mzYsCFvf/vbc/nll2fkyJFDPRrAsGaHG4ABP/7xjzN37ty0tbWlu7s7N95441CPBDDsCW4ABhx77LE5/PDDU1VVlenTp+f+++8f6pEAhj3BDcCAESNGDPxcqVR2OAbglRHcAAxob29PZ2dn+vv709ramoaGhqEeCWDY80eTAAyor6/P7Nmz09nZmfe85z05/fTTh3okgGFPcAMw4A1veEO++tWvDvUYAHsVt5QAAEBBVZVKpTLUQwAAwN7KDjcAABQkuAEAoCDBDQAABQlugNeIb3zjG/nSl76UJHnve9+bn/70py943kc/+tE88cQTu3M0gL2ajwUEeI0488wzd+m8e++9t/AkAK8tdrgBhqnPfOYz+fd///eB45tuuikXXnhh7rnnnpx++uk55ZRTcsYZZ+THP/5xkuQLX/hCPvvZz77oNS+99NIkyYc+9KE88MAD+cu//Mv09/cnSZ577rlMnjw5TzzxRN773vfmn//5n9PS0pITTjghN91008A1/tzrA7xW2eEGGKZOP/30XH755fnoRz+aJLn11ltz+umnZ+nSpfna176W173udfnlL3+Zj3zkI7nrrrt26ZoLFy7MLbfckq9+9asZPXp0/uIv/iLf//73M2XKlHznO9/J5MmTM3r06CTJ008/nW9961vp7OzMKaeckkmTJqW2tvbPvv5+++1X7D8LgD2Z4AYYpo477rh0d3fnpz/9afbdd9888cQT6enpyebNm/PhD3944Lyqqqr85je/eUWvMXPmzCxfvjxTpkzJN7/5zcyePXvgsRkzZqSqqipjxozJ8ccfn3vvvTe1tbV/9vXHjRv3St8qwLAmuAGGqaqqqpx22mlZsWJFampqctppp6W/vz+TJ0/O5z//+YHzHnvssdTX1+fuu+/e6RpXXXVV7rnnniS//0PKCy64YIfHm5ubc+WVV+b+++/P7373uxx77LEDj1VX/+F/Qvr7+7PPPvu86OsDvFa5hxtgGDv11FNzzz335M4770xLS0smT56ce++9N4888kiSZNWqVTn55JOzbdu2F3z+BRdckBUrVmTFihUDsT1ixIj09vYmSfbdd9+cfPLJmTNnTs4444wdntva2pok2bRpU+699940NDS87NcHeC2www0wjNXV1eWtb31rent7c/DBB+fggw/OZz/72fzd3/1dKpVKqqurc+2112b//fff5WtOnTo1Z511Vr7whS/kyCOPTEtLS5YvX55TTjllh/M2bNiQlpaWbNu2LfPmzcvhhx+eJK/69QH2NlWVSqUy1EMAsGeqVCr58pe/nI0bN+ayyy4bWH/ve9+bq666Km9729uGcDqA4cEONwB/1vve977U19dn2bJlQz0KwLBlhxsAAAryR5MAAFCQ4AYAgIIENwAAFCS4AQCgIMENAAAFCW4AACjo/wcbee2qGQgfrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot all graphs\n",
    "class_c=mu_data['class']\n",
    "feature_to_plot=mu_data.drop('class', axis=1)\n",
    "plot_feature(class_c, feature_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 22)\n",
      "(8124,)\n"
     ]
    }
   ],
   "source": [
    "# Seprate Data into features and target set\n",
    "feature_data=mu_data.drop('class', axis=1)\n",
    "target_class=mu_data['class']\n",
    "print(feature_data.shape)\n",
    "print(target_class.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Label Encoders for Target and Feaature variables Encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>p</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>k</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>s</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>d</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>8094</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>g</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8095</th>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>m</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8096</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8098</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8099</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8100</th>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>o</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8101</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8102</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8103</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8104</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8105</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>y</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8106</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>o</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8107</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8108</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8109</th>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>w</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8110</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>o</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8111</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>p</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8112</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>o</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8113</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8114</th>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>m</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>y</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8115</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8116</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8117</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8118</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>0</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8120</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8121</th>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>b</td>\n",
       "      <td>c</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>1</td>\n",
       "      <td>k</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>k</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>o</td>\n",
       "      <td>c</td>\n",
       "      <td>l</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 gill-attachment  \\\n",
       "0         1         x           s         n       t    p               f   \n",
       "1         0         x           s         y       t    a               f   \n",
       "2         0         b           s         w       t    l               f   \n",
       "3         1         x           y         w       t    p               f   \n",
       "4         0         x           s         g       f    n               f   \n",
       "5         0         x           y         y       t    a               f   \n",
       "6         0         b           s         w       t    a               f   \n",
       "7         0         b           y         w       t    l               f   \n",
       "8         1         x           y         w       t    p               f   \n",
       "9         0         b           s         y       t    a               f   \n",
       "10        0         x           y         y       t    l               f   \n",
       "11        0         x           y         y       t    a               f   \n",
       "12        0         b           s         y       t    a               f   \n",
       "13        1         x           y         w       t    p               f   \n",
       "14        0         x           f         n       f    n               f   \n",
       "15        0         s           f         g       f    n               f   \n",
       "16        0         f           f         w       f    n               f   \n",
       "17        1         x           s         n       t    p               f   \n",
       "18        1         x           y         w       t    p               f   \n",
       "19        1         x           s         n       t    p               f   \n",
       "20        0         b           s         y       t    a               f   \n",
       "21        1         x           y         n       t    p               f   \n",
       "22        0         b           y         y       t    l               f   \n",
       "23        0         b           y         w       t    a               f   \n",
       "24        0         b           s         w       t    l               f   \n",
       "25        1         f           s         w       t    p               f   \n",
       "26        0         x           y         y       t    a               f   \n",
       "27        0         x           y         w       t    l               f   \n",
       "28        0         f           f         n       f    n               f   \n",
       "29        0         x           s         y       t    a               f   \n",
       "...     ...       ...         ...       ...     ...  ...             ...   \n",
       "8094      0         b           s         g       f    n               f   \n",
       "8095      1         x           y         c       f    m               f   \n",
       "8096      0         k           f         w       f    n               f   \n",
       "8097      1         k           y         n       f    s               f   \n",
       "8098      1         k           s         e       f    y               f   \n",
       "8099      0         k           f         w       f    n               f   \n",
       "8100      0         f           s         n       f    n               a   \n",
       "8101      1         k           s         e       f    s               f   \n",
       "8102      0         x           s         n       f    n               a   \n",
       "8103      0         k           s         n       f    n               a   \n",
       "8104      0         k           s         n       f    n               a   \n",
       "8105      0         k           s         n       f    n               a   \n",
       "8106      0         k           s         n       f    n               a   \n",
       "8107      0         x           s         n       f    n               a   \n",
       "8108      1         k           y         e       f    y               f   \n",
       "8109      0         b           s         w       f    n               f   \n",
       "8110      0         x           s         n       f    n               a   \n",
       "8111      0         k           s         w       f    n               f   \n",
       "8112      0         k           s         n       f    n               a   \n",
       "8113      1         k           y         e       f    y               f   \n",
       "8114      1         f           y         c       f    m               a   \n",
       "8115      0         x           s         n       f    n               a   \n",
       "8116      1         k           y         n       f    s               f   \n",
       "8117      1         k           s         e       f    y               f   \n",
       "8118      1         k           y         n       f    f               f   \n",
       "8119      0         k           s         n       f    n               a   \n",
       "8120      0         x           s         n       f    n               a   \n",
       "8121      0         f           s         n       f    n               a   \n",
       "8122      1         k           y         n       f    y               f   \n",
       "8123      0         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",
       "5               c         b          n  ...                        s   \n",
       "6               c         b          g  ...                        s   \n",
       "7               c         b          n  ...                        s   \n",
       "8               c         n          p  ...                        s   \n",
       "9               c         b          g  ...                        s   \n",
       "10              c         b          g  ...                        s   \n",
       "11              c         b          n  ...                        s   \n",
       "12              c         b          w  ...                        s   \n",
       "13              c         n          k  ...                        s   \n",
       "14              w         b          n  ...                        f   \n",
       "15              c         n          k  ...                        s   \n",
       "16              w         b          k  ...                        s   \n",
       "17              c         n          n  ...                        s   \n",
       "18              c         n          n  ...                        s   \n",
       "19              c         n          k  ...                        s   \n",
       "20              c         b          k  ...                        s   \n",
       "21              c         n          n  ...                        s   \n",
       "22              c         b          k  ...                        s   \n",
       "23              c         b          w  ...                        s   \n",
       "24              c         b          g  ...                        s   \n",
       "25              c         n          n  ...                        s   \n",
       "26              c         b          n  ...                        s   \n",
       "27              c         b          w  ...                        s   \n",
       "28              c         n          k  ...                        s   \n",
       "29              w         n          n  ...                        s   \n",
       "...           ...       ...        ...  ...                      ...   \n",
       "8094            w         b          g  ...                        s   \n",
       "8095            c         b          y  ...                        y   \n",
       "8096            w         b          w  ...                        s   \n",
       "8097            c         n          b  ...                        k   \n",
       "8098            c         n          b  ...                        k   \n",
       "8099            w         b          w  ...                        k   \n",
       "8100            c         b          o  ...                        s   \n",
       "8101            c         n          b  ...                        s   \n",
       "8102            c         b          y  ...                        s   \n",
       "8103            c         b          y  ...                        s   \n",
       "8104            c         b          y  ...                        s   \n",
       "8105            c         b          y  ...                        s   \n",
       "8106            c         b          o  ...                        s   \n",
       "8107            c         b          y  ...                        s   \n",
       "8108            c         n          b  ...                        s   \n",
       "8109            w         b          w  ...                        s   \n",
       "8110            c         b          o  ...                        s   \n",
       "8111            w         b          p  ...                        s   \n",
       "8112            c         b          o  ...                        s   \n",
       "8113            c         n          b  ...                        k   \n",
       "8114            c         b          y  ...                        y   \n",
       "8115            c         b          y  ...                        s   \n",
       "8116            c         n          b  ...                        k   \n",
       "8117            c         n          b  ...                        s   \n",
       "8118            c         n          b  ...                        s   \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",
       "5                         w                      w         p          w   \n",
       "6                         w                      w         p          w   \n",
       "7                         w                      w         p          w   \n",
       "8                         w                      w         p          w   \n",
       "9                         w                      w         p          w   \n",
       "10                        w                      w         p          w   \n",
       "11                        w                      w         p          w   \n",
       "12                        w                      w         p          w   \n",
       "13                        w                      w         p          w   \n",
       "14                        w                      w         p          w   \n",
       "15                        w                      w         p          w   \n",
       "16                        w                      w         p          w   \n",
       "17                        w                      w         p          w   \n",
       "18                        w                      w         p          w   \n",
       "19                        w                      w         p          w   \n",
       "20                        w                      w         p          w   \n",
       "21                        w                      w         p          w   \n",
       "22                        w                      w         p          w   \n",
       "23                        w                      w         p          w   \n",
       "24                        w                      w         p          w   \n",
       "25                        w                      w         p          w   \n",
       "26                        w                      w         p          w   \n",
       "27                        w                      w         p          w   \n",
       "28                        w                      w         p          w   \n",
       "29                        w                      w         p          w   \n",
       "...                     ...                    ...       ...        ...   \n",
       "8094                      w                      w         p          w   \n",
       "8095                      c                      c         p          w   \n",
       "8096                      w                      w         p          w   \n",
       "8097                      p                      p         p          w   \n",
       "8098                      w                      p         p          w   \n",
       "8099                      w                      w         p          w   \n",
       "8100                      o                      o         p          n   \n",
       "8101                      p                      w         p          w   \n",
       "8102                      o                      o         p          n   \n",
       "8103                      o                      o         p          n   \n",
       "8104                      o                      o         p          o   \n",
       "8105                      o                      o         p          n   \n",
       "8106                      o                      o         p          o   \n",
       "8107                      o                      o         p          o   \n",
       "8108                      p                      w         p          w   \n",
       "8109                      w                      w         p          w   \n",
       "8110                      o                      o         p          o   \n",
       "8111                      w                      w         p          w   \n",
       "8112                      o                      o         p          n   \n",
       "8113                      p                      p         p          w   \n",
       "8114                      c                      c         p          w   \n",
       "8115                      o                      o         p          o   \n",
       "8116                      p                      w         p          w   \n",
       "8117                      p                      w         p          w   \n",
       "8118                      p                      w         p          w   \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",
       "5              o         p                 k          n       g  \n",
       "6              o         p                 k          n       m  \n",
       "7              o         p                 n          s       m  \n",
       "8              o         p                 k          v       g  \n",
       "9              o         p                 k          s       m  \n",
       "10             o         p                 n          n       g  \n",
       "11             o         p                 k          s       m  \n",
       "12             o         p                 n          s       g  \n",
       "13             o         p                 n          v       u  \n",
       "14             o         e                 k          a       g  \n",
       "15             o         p                 n          y       u  \n",
       "16             o         e                 n          a       g  \n",
       "17             o         p                 k          s       g  \n",
       "18             o         p                 n          s       u  \n",
       "19             o         p                 n          s       u  \n",
       "20             o         p                 n          s       m  \n",
       "21             o         p                 n          v       g  \n",
       "22             o         p                 n          s       m  \n",
       "23             o         p                 n          n       m  \n",
       "24             o         p                 k          s       m  \n",
       "25             o         p                 n          v       g  \n",
       "26             o         p                 n          n       m  \n",
       "27             o         p                 n          n       m  \n",
       "28             o         p                 k          y       u  \n",
       "29             o         p                 n          v       d  \n",
       "...          ...       ...               ...        ...     ...  \n",
       "8094           t         p                 w          n       g  \n",
       "8095           n         n                 w          c       d  \n",
       "8096           t         p                 w          n       g  \n",
       "8097           o         e                 w          v       l  \n",
       "8098           o         e                 w          v       d  \n",
       "8099           t         p                 w          s       g  \n",
       "8100           o         p                 b          v       l  \n",
       "8101           o         e                 w          v       p  \n",
       "8102           o         p                 n          c       l  \n",
       "8103           o         p                 o          c       l  \n",
       "8104           o         p                 n          v       l  \n",
       "8105           o         p                 y          v       l  \n",
       "8106           o         p                 n          v       l  \n",
       "8107           o         p                 n          c       l  \n",
       "8108           o         e                 w          v       l  \n",
       "8109           t         p                 w          n       g  \n",
       "8110           o         p                 n          v       l  \n",
       "8111           t         p                 w          n       g  \n",
       "8112           o         p                 b          v       l  \n",
       "8113           o         e                 w          v       d  \n",
       "8114           n         n                 w          c       d  \n",
       "8115           o         p                 o          v       l  \n",
       "8116           o         e                 w          v       l  \n",
       "8117           o         e                 w          v       d  \n",
       "8118           o         e                 w          v       d  \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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label Encoder used for the Target class\n",
    "le=LabelEncoder()\n",
    "mu_data['class']= le.fit_transform(mu_data['class'])\n",
    "mu_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>8094</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8095</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8096</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8097</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8098</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8099</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8100</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8101</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8102</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8103</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8104</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8105</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8106</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8107</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8108</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8109</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8110</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8111</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8112</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8113</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8114</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8115</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8116</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8117</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8118</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8119</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8120</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8121</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8122</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8123</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8124 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0               0            0            0            0            0   \n",
       "1               0            0            0            0            0   \n",
       "2               1            0            0            0            0   \n",
       "3               0            0            0            0            0   \n",
       "4               0            0            0            0            0   \n",
       "5               0            0            0            0            0   \n",
       "6               1            0            0            0            0   \n",
       "7               1            0            0            0            0   \n",
       "8               0            0            0            0            0   \n",
       "9               1            0            0            0            0   \n",
       "10              0            0            0            0            0   \n",
       "11              0            0            0            0            0   \n",
       "12              1            0            0            0            0   \n",
       "13              0            0            0            0            0   \n",
       "14              0            0            0            0            0   \n",
       "15              0            0            0            0            1   \n",
       "16              0            0            1            0            0   \n",
       "17              0            0            0            0            0   \n",
       "18              0            0            0            0            0   \n",
       "19              0            0            0            0            0   \n",
       "20              1            0            0            0            0   \n",
       "21              0            0            0            0            0   \n",
       "22              1            0            0            0            0   \n",
       "23              1            0            0            0            0   \n",
       "24              1            0            0            0            0   \n",
       "25              0            0            1            0            0   \n",
       "26              0            0            0            0            0   \n",
       "27              0            0            0            0            0   \n",
       "28              0            0            1            0            0   \n",
       "29              0            0            0            0            0   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8094            1            0            0            0            0   \n",
       "8095            0            0            0            0            0   \n",
       "8096            0            0            0            1            0   \n",
       "8097            0            0            0            1            0   \n",
       "8098            0            0            0            1            0   \n",
       "8099            0            0            0            1            0   \n",
       "8100            0            0            1            0            0   \n",
       "8101            0            0            0            1            0   \n",
       "8102            0            0            0            0            0   \n",
       "8103            0            0            0            1            0   \n",
       "8104            0            0            0            1            0   \n",
       "8105            0            0            0            1            0   \n",
       "8106            0            0            0            1            0   \n",
       "8107            0            0            0            0            0   \n",
       "8108            0            0            0            1            0   \n",
       "8109            1            0            0            0            0   \n",
       "8110            0            0            0            0            0   \n",
       "8111            0            0            0            1            0   \n",
       "8112            0            0            0            1            0   \n",
       "8113            0            0            0            1            0   \n",
       "8114            0            0            1            0            0   \n",
       "8115            0            0            0            0            0   \n",
       "8116            0            0            0            1            0   \n",
       "8117            0            0            0            1            0   \n",
       "8118            0            0            0            1            0   \n",
       "8119            0            0            0            1            0   \n",
       "8120            0            0            0            0            0   \n",
       "8121            0            0            1            0            0   \n",
       "8122            0            0            0            1            0   \n",
       "8123            0            0            0            0            0   \n",
       "\n",
       "      cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0               1              0              0              1              0   \n",
       "1               1              0              0              1              0   \n",
       "2               0              0              0              1              0   \n",
       "3               1              0              0              0              1   \n",
       "4               1              0              0              1              0   \n",
       "5               1              0              0              0              1   \n",
       "6               0              0              0              1              0   \n",
       "7               0              0              0              0              1   \n",
       "8               1              0              0              0              1   \n",
       "9               0              0              0              1              0   \n",
       "10              1              0              0              0              1   \n",
       "11              1              0              0              0              1   \n",
       "12              0              0              0              1              0   \n",
       "13              1              0              0              0              1   \n",
       "14              1              1              0              0              0   \n",
       "15              0              1              0              0              0   \n",
       "16              0              1              0              0              0   \n",
       "17              1              0              0              1              0   \n",
       "18              1              0              0              0              1   \n",
       "19              1              0              0              1              0   \n",
       "20              0              0              0              1              0   \n",
       "21              1              0              0              0              1   \n",
       "22              0              0              0              0              1   \n",
       "23              0              0              0              0              1   \n",
       "24              0              0              0              1              0   \n",
       "25              0              0              0              1              0   \n",
       "26              1              0              0              0              1   \n",
       "27              1              0              0              0              1   \n",
       "28              0              1              0              0              0   \n",
       "29              1              0              0              1              0   \n",
       "...           ...            ...            ...            ...            ...   \n",
       "8094            0              0              0              1              0   \n",
       "8095            1              0              0              0              1   \n",
       "8096            0              1              0              0              0   \n",
       "8097            0              0              0              0              1   \n",
       "8098            0              0              0              1              0   \n",
       "8099            0              1              0              0              0   \n",
       "8100            0              0              0              1              0   \n",
       "8101            0              0              0              1              0   \n",
       "8102            1              0              0              1              0   \n",
       "8103            0              0              0              1              0   \n",
       "8104            0              0              0              1              0   \n",
       "8105            0              0              0              1              0   \n",
       "8106            0              0              0              1              0   \n",
       "8107            1              0              0              1              0   \n",
       "8108            0              0              0              0              1   \n",
       "8109            0              0              0              1              0   \n",
       "8110            1              0              0              1              0   \n",
       "8111            0              0              0              1              0   \n",
       "8112            0              0              0              1              0   \n",
       "8113            0              0              0              0              1   \n",
       "8114            0              0              0              0              1   \n",
       "8115            1              0              0              1              0   \n",
       "8116            0              0              0              0              1   \n",
       "8117            0              0              0              1              0   \n",
       "8118            0              0              0              0              1   \n",
       "8119            0              0              0              1              0   \n",
       "8120            1              0              0              1              0   \n",
       "8121            0              0              0              1              0   \n",
       "8122            0              0              0              0              1   \n",
       "8123            1              0              0              1              0   \n",
       "\n",
       "      ...  population_s  population_v  population_y  habitat_d  habitat_g  \\\n",
       "0     ...             1             0             0          0          0   \n",
       "1     ...             0             0             0          0          1   \n",
       "2     ...             0             0             0          0          0   \n",
       "3     ...             1             0             0          0          0   \n",
       "4     ...             0             0             0          0          1   \n",
       "5     ...             0             0             0          0          1   \n",
       "6     ...             0             0             0          0          0   \n",
       "7     ...             1             0             0          0          0   \n",
       "8     ...             0             1             0          0          1   \n",
       "9     ...             1             0             0          0          0   \n",
       "10    ...             0             0             0          0          1   \n",
       "11    ...             1             0             0          0          0   \n",
       "12    ...             1             0             0          0          1   \n",
       "13    ...             0             1             0          0          0   \n",
       "14    ...             0             0             0          0          1   \n",
       "15    ...             0             0             1          0          0   \n",
       "16    ...             0             0             0          0          1   \n",
       "17    ...             1             0             0          0          1   \n",
       "18    ...             1             0             0          0          0   \n",
       "19    ...             1             0             0          0          0   \n",
       "20    ...             1             0             0          0          0   \n",
       "21    ...             0             1             0          0          1   \n",
       "22    ...             1             0             0          0          0   \n",
       "23    ...             0             0             0          0          0   \n",
       "24    ...             1             0             0          0          0   \n",
       "25    ...             0             1             0          0          1   \n",
       "26    ...             0             0             0          0          0   \n",
       "27    ...             0             0             0          0          0   \n",
       "28    ...             0             0             1          0          0   \n",
       "29    ...             0             1             0          1          0   \n",
       "...   ...           ...           ...           ...        ...        ...   \n",
       "8094  ...             0             0             0          0          1   \n",
       "8095  ...             0             0             0          1          0   \n",
       "8096  ...             0             0             0          0          1   \n",
       "8097  ...             0             1             0          0          0   \n",
       "8098  ...             0             1             0          1          0   \n",
       "8099  ...             1             0             0          0          1   \n",
       "8100  ...             0             1             0          0          0   \n",
       "8101  ...             0             1             0          0          0   \n",
       "8102  ...             0             0             0          0          0   \n",
       "8103  ...             0             0             0          0          0   \n",
       "8104  ...             0             1             0          0          0   \n",
       "8105  ...             0             1             0          0          0   \n",
       "8106  ...             0             1             0          0          0   \n",
       "8107  ...             0             0             0          0          0   \n",
       "8108  ...             0             1             0          0          0   \n",
       "8109  ...             0             0             0          0          1   \n",
       "8110  ...             0             1             0          0          0   \n",
       "8111  ...             0             0             0          0          1   \n",
       "8112  ...             0             1             0          0          0   \n",
       "8113  ...             0             1             0          1          0   \n",
       "8114  ...             0             0             0          1          0   \n",
       "8115  ...             0             1             0          0          0   \n",
       "8116  ...             0             1             0          0          0   \n",
       "8117  ...             0             1             0          1          0   \n",
       "8118  ...             0             1             0          1          0   \n",
       "8119  ...             0             0             0          0          0   \n",
       "8120  ...             0             1             0          0          0   \n",
       "8121  ...             0             0             0          0          0   \n",
       "8122  ...             0             1             0          0          0   \n",
       "8123  ...             0             0             0          0          0   \n",
       "\n",
       "      habitat_l  habitat_m  habitat_p  habitat_u  habitat_w  \n",
       "0             0          0          0          1          0  \n",
       "1             0          0          0          0          0  \n",
       "2             0          1          0          0          0  \n",
       "3             0          0          0          1          0  \n",
       "4             0          0          0          0          0  \n",
       "5             0          0          0          0          0  \n",
       "6             0          1          0          0          0  \n",
       "7             0          1          0          0          0  \n",
       "8             0          0          0          0          0  \n",
       "9             0          1          0          0          0  \n",
       "10            0          0          0          0          0  \n",
       "11            0          1          0          0          0  \n",
       "12            0          0          0          0          0  \n",
       "13            0          0          0          1          0  \n",
       "14            0          0          0          0          0  \n",
       "15            0          0          0          1          0  \n",
       "16            0          0          0          0          0  \n",
       "17            0          0          0          0          0  \n",
       "18            0          0          0          1          0  \n",
       "19            0          0          0          1          0  \n",
       "20            0          1          0          0          0  \n",
       "21            0          0          0          0          0  \n",
       "22            0          1          0          0          0  \n",
       "23            0          1          0          0          0  \n",
       "24            0          1          0          0          0  \n",
       "25            0          0          0          0          0  \n",
       "26            0          1          0          0          0  \n",
       "27            0          1          0          0          0  \n",
       "28            0          0          0          1          0  \n",
       "29            0          0          0          0          0  \n",
       "...         ...        ...        ...        ...        ...  \n",
       "8094          0          0          0          0          0  \n",
       "8095          0          0          0          0          0  \n",
       "8096          0          0          0          0          0  \n",
       "8097          1          0          0          0          0  \n",
       "8098          0          0          0          0          0  \n",
       "8099          0          0          0          0          0  \n",
       "8100          1          0          0          0          0  \n",
       "8101          0          0          1          0          0  \n",
       "8102          1          0          0          0          0  \n",
       "8103          1          0          0          0          0  \n",
       "8104          1          0          0          0          0  \n",
       "8105          1          0          0          0          0  \n",
       "8106          1          0          0          0          0  \n",
       "8107          1          0          0          0          0  \n",
       "8108          1          0          0          0          0  \n",
       "8109          0          0          0          0          0  \n",
       "8110          1          0          0          0          0  \n",
       "8111          0          0          0          0          0  \n",
       "8112          1          0          0          0          0  \n",
       "8113          0          0          0          0          0  \n",
       "8114          0          0          0          0          0  \n",
       "8115          1          0          0          0          0  \n",
       "8116          1          0          0          0          0  \n",
       "8117          0          0          0          0          0  \n",
       "8118          0          0          0          0          0  \n",
       "8119          1          0          0          0          0  \n",
       "8120          1          0          0          0          0  \n",
       "8121          1          0          0          0          0  \n",
       "8122          1          0          0          0          0  \n",
       "8123          1          0          0          0          0  \n",
       "\n",
       "[8124 rows x 117 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding applied to feature variables\n",
    "feature_data= pd.get_dummies(feature_data)\n",
    "feature_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "feature_data_std = sc.fit_transform(feature_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8124, 1)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the target variable \n",
    "target_class=mu_data['class'].values.reshape(-1,1)\n",
    "print(target_class.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max r2 score corresponding to  42  is  1.0\n"
     ]
    }
   ],
   "source": [
    "#Here we will find out random state value with which our linear model learns maximum\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics \n",
    "max_r_score=0\n",
    "for r_state in range(42,200):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(feature_data_std, target_class ,random_state = r_state,test_size=0.30)\n",
    "    lda = LinearDiscriminantAnalysis(solver='lsqr')\n",
    "    lda.fit(x_train,y_train)\n",
    "    y_pred = lda.predict(x_test)\n",
    "    r2_scr=r2_score(y_test,y_pred)\n",
    "    if r2_scr>max_r_score:\n",
    "        max_r_score=r2_scr\n",
    "        final_r_state=r_state\n",
    "print(\"max r2 score corresponding to \",final_r_state,\" is \",max_r_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation score for 5 iternations: -1.180492478179479%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate cross validation score to find out the overfitting or underfitting issues while training \n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score=cross_val_score(LinearDiscriminantAnalysis(solver='lsqr'),feature_data_std, target_class,cv=5,scoring=\"r2\")\n",
    "print(\"Cross Validation score for 5 iternations: {}%\".format (cross_val_score.mean())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finalise the model \n",
    "x_train, x_test, y_train, y_test = train_test_split(feature_data_std, target_class, random_state = 44,test_size=0.30)\n",
    "lda = LinearDiscriminantAnalysis(solver='lsqr')\n",
    "lda.fit(x_train,y_train)\n",
    "y_pred = lda.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE is:  0.0\n",
      "r2_score is:  1.0\n"
     ]
    }
   ],
   "source": [
    "#Findout the rmse and r2 score\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(\"RMSE is: \",np.sqrt(mean_squared_error(y_test,y_pred)))\n",
    "print(\"r2_score is: \",r2_score(y_test,y_pred) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*Test Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "print(\"*Test Accuracy: {}%\".format(round(lda.score(x_test,y_test)*100,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*Confusion Matrix: \n",
      " [[1228    0]\n",
      " [   0 1210]]\n"
     ]
    }
   ],
   "source": [
    "#false positive and false negative rates are 0, meaning that all mushrooms were correctly classified as poisonous or not\n",
    "print(\"*Confusion Matrix: \\n {}\".format (confusion_matrix(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1228\n",
      "           1       1.00      1.00      1.00      1210\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report: \\n {}\".format (classification_report(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2851191e7f0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The classification report visualizer displays the precision, recall, F1, and support scores for the model.\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "mu_viz = ClassificationReport(LinearDiscriminantAnalysis(solver='lsqr'), cmap='RdYlGn')\n",
    "mu_viz.fit(x_train, y_train)\n",
    "mu_viz.score(x_test, y_test)\n",
    "mu_viz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC Score:  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "#Area under the ROC curve for LogisticRegression\n",
    "lda_Reg_ROC_Curve=metrics.roc_auc_score(y_test,y_pred)\n",
    "print(\"ROC Score: \", lda_Reg_ROC_Curve)\n",
    "\n",
    "# Define Function to plot the ROC curve \n",
    "def plot_roc_curve(roc_auc):\n",
    "    plt.plot(fp_rate, tp_rate, color='blue', label='ROC = %0.2f' % lda_Reg_ROC_Curve)\n",
    "    plt.plot([0, 1], [0, 1], color='red', linestyle='-.')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Plot AUC_ROC curve \n",
    "fp_rate, tp_rate, thresholds= roc_curve(y_test, y_pred)\n",
    "roc_auc=auc(fp_rate, tp_rate)\n",
    "plot_roc_curve(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mushrooms_poisonous_nonpoisonous.pkl']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lastly Save the model for futher use\n",
    "from sklearn.externals import joblib  \n",
    "joblib.dump(lda, 'Mushrooms_poisonous_nonpoisonous.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}