Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identifying Wild Mushrooms: what to forage, what to avoid?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through classification modeling, this project aims to classify mushrooms as edible or poisonous.\n",
    "\n",
    "In recent years, the popularity of mushrooms as a superfood and the understanding of their vast health benefits has surged. What used to be typically seen as just a traditional food, mushrooms are now being widely consumed and acknowledged for their healing and health abilities. With more and more people interested, foraging for mushrooms in the wild is likely to increase and I wanted to understand if edible/poisonous mushrooms could be easily indentified and what that looks like.\n",
    "\n",
    "The raw dataset utilized in this project was sourced from the UCI Machine Learning Repository. The dataset includes categorical characteristics on 8,124 mushroom samples from 23 species of gilled mushrooms.\n",
    "\n",
    "My original hypothesis was that a mushroom's cap color would greatly indicate its classification.\n",
    "\n",
    "## Data Cleaning\n",
    "\n",
    "- all binary features were re-assigned to 0 and 1 values (including the target feature)\n",
    "- stalk-root contained values labeled as '?', these were assigned to an 'other' value\n",
    "- veil-type was dropped since all values were the same and would carry no influence in the model \n",
    "- ring-number values were re-assigned to their true numerical representation -> 0,1,2\n",
    "- one-hot encoding was applied to all features that were not binary or ordinal/numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns',100)\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import pickle\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "- EDA was initally performed on the dataset before one-hot encoding was applied to understand categorical distributions of features\n",
    "\n",
    "Feature value key: \n",
    "1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s\n",
    "2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s\n",
    "3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y\n",
    "4. bruises?: bruises=1,no=0\n",
    "5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s\n",
    "6. gill-attachment: attached=1,free=0\n",
    "7. gill-spacing: close=0,crowded=1\n",
    "8. gill-size: broad=0,narrow=1\n",
    "9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y\n",
    "10. stalk-shape: enlarging=0,tapering=1\n",
    "11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?\n",
    "12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s\n",
    "14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y\n",
    "16. veil-color: brown=n,orange=o,white=w,yellow=y\n",
    "17. ring-number: none=n,one=o,two=t\n",
    "18. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z\n",
    "19. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y\n",
    "20. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y\n",
    "21. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in cleaned, complete dataset before one-hot encoding for visualizing\n",
    "data=pd.read_pickle('EDAdata.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <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>stalk-shape</th>\n",
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       "      <th>stalk-surface-above-ring</th>\n",
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      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       1    p               0   \n",
       "1     e         x           s         y       1    a               0   \n",
       "2     e         b           s         w       1    l               0   \n",
       "3     p         x           y         w       1    p               0   \n",
       "4     e         x           s         g       0    n               0   \n",
       "\n",
       "  gill-spacing gill-size gill-color stalk-shape stalk-root  \\\n",
       "0            0         1          k           0          e   \n",
       "1            0         0          k           0          c   \n",
       "2            0         0          n           0          c   \n",
       "3            0         1          n           0          e   \n",
       "4            1         0          k           1          e   \n",
       "\n",
       "  stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring  \\\n",
       "0                        s                        s                      w   \n",
       "1                        s                        s                      w   \n",
       "2                        s                        s                      w   \n",
       "3                        s                        s                      w   \n",
       "4                        s                        s                      w   \n",
       "\n",
       "  stalk-color-below-ring veil-color ring-number ring-type spore-print-color  \\\n",
       "0                      w          w           1         p                 k   \n",
       "1                      w          w           1         p                 n   \n",
       "2                      w          w           1         p                 n   \n",
       "3                      w          w           1         p                 k   \n",
       "4                      w          w           1         e                 n   \n",
       "\n",
       "  population habitat  \n",
       "0          s       u  \n",
       "1          n       g  \n",
       "2          n       m  \n",
       "3          s       u  \n",
       "4          a       g  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8124, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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-color', 'ring-number', 'ring-type',\n",
       "       'spore-print-color', 'population', 'habitat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What does the target / class balance look like? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qDnlVr2lauN2mV/uQM49pet+3vBUU7qRefVNOYJoWVQe865tRUaFNrmvRjyOzZ89mzZo1TJ8+naNHj3qWu1wuwsLCCAkJweVyNVgeGhraYHzheFsREfGdFgmHFStW8MwzzwDQvn17DMOgX79+lJaWAlBSUkJsbCwxMTGsW7cO0zQpLy/HNE0iIyPp06ePra2IiPhOi1xWGjZsGFOnTuWmm26ivr6erKwsevTowfTp0ykoKKB79+4kJibidDqJjY0lJSUF0zTJzs4GICMjw9ZWRER8x7Asy/J3Ed6qq3N7fV3Y7T5EUtLwU1SRnCmKilbjdPp3QDoo3MnqiiK/1iCtz/COSRw64PZqH34bcxARkdOTwkFERGwUDiIiYqNwEBERG4WDiIjY/Kxw2LdvX0vVISIirUiz9zm8/PLLtGvXjurqapYvX86QIUOYOnWqL2oTERE/afbMYdWqVYwaNYqSkhJWrVrFZ5995ou6RETEj5oNB8Mw+O677+jQoQOGYXDgwAFf1CUiIn7UbDgMGjSIP/zhD/zhD38gLy+PYcOG+aIuERHxo588fcaBAwdo3749bdq0aemafjZNnyEtRdNnSGvV0tNnNDsg/dFHHzFz5kzcbjdJSUlER0czZswYrwoSEZHWrdnLSvPmzeOVV16hQ4cO3HXXXfzlL3/xRV0iIuJHzYaDw+EgIiICwzBo27YtwcHBvqhLRET8qNlwOO+885g7dy5VVVU8++yzREdH+6IuERHxo2bDYebMmURHR/Pb3/6WoKAgcnNzfVGXiIj4UZMD0uvWrfP83LlzZzp37gzApk2biIuLa3KHdXV1ZGVlsXfvXmpra5kwYQLnnHMOd911F127dgUgNTWVq6++mgULFrB27VoCAgLIysqif//+7Nq1i8zMTAzDoGfPnuTk5DR4prSIiLS8JsNh1apVTW50snBYuXIlERERzJkzh/3793P99ddzzz33cNtttzFu3DhPu7KyMjZt2sTSpUvZt28fkyZNYtmyZeTn55Oens6gQYPIzs6muLiYhISEf/PwRETk39FkOOTn53t+/vTTT/nqq684//zzueCCC066w6SkpAbPfHY6nWzfvp2vvvqK4uJiunTpQlZWFlu2bCEuLg7DMIiOjsbtdlNZWUlZWRkDBw4EID4+nvXr1yscRER8rNn7HObNm8fGjRvp378/ixYtYujQodx+++1Ntj/+baaamhomT55Meno6tbW1jBkzhn79+vHUU0+xcOFCQkNDiYiIaLDdwYMHsSwLwzAaLGuO02kQEeHdjUr79x/G6dTlK2nI4fC+b3mr3qglQH1TTtDSfbPZcCgpKeGNN97A4XDgdrtJSUk5aTjAsam977nnHm688UZGjBhBdXU1YWFhACQkJJCbm8tVV12Fy+XybONyuQgNDW0wvuByuTzbnYzbbXl9h7RpWrjdplf7kDOPaXrft7wVFO6kXn1TTmCaFlUHvOubJ7tDutmPI+ecc47nTby+vp4OHTqctP3333/PuHHjeOCBB0hOTgZg/PjxbNu2DYANGzbQt29fYmJiWLduHaZpUl5ejmmaREZG0qdPH0pLS4FjwRQbG/vTjlJERE6ZZs8cKioqSExMpHfv3uzYsYPAwEDGjh0LwOLFi23tn376aaqrq3nyySd58sknAcjMzCQvL4/AwEA6dOhAbm4uISEhxMbGkpKSgmmaZGdnA5CRkcH06dMpKCige/fuDcYvRETEN5qdeG/v3r1Nrvv1r399ygv6d2jiPWkpmnhPWiu/T7z3ww8/sGrVKo4ePepZNmPGDK8KEhGR1q3ZcMjIyOCOO+74SQPDIiJyZmg2HLp06cLo0aN9UYuIiLQSzYZDYmIi9957Lz169PAsmzhxYosWJSIi/tVsOLz22mskJCTospKIyC9Is+EQHh7OnXfe6YtaRESklWg2HM466yyys7Pp06ePZ1qLlJSUFi9MRET85ycNSMOxO59FROSXodlwmDhxIhUVFdTX12NZFhUVFb6oS0RE/KjZcMjKymLr1q0cPnyYI0eO0LlzZ15//XVf1CYiIn7S7MR7//M//8OqVauIi4tj1apVtG3b1hd1iYiIHzUbDsHBwRiGwaFDh4iMjKSurs4XdYmIiB81Gw59+/bl+eefp2PHjtx777243d5N9CQiIq1fs2MOU6ZMoaamhnbt2lFSUkL//v19UZeIiPhRk2cOR48e5aWXXsKyLGpqapgyZQpvv/02zczwLSIiZ4Amw2HWrFmeJ7TNnDmT3r17k5iYqOm6RUR+AZoMh/LycqZOnUp9fT1btmzhjjvuICEhgcrKSl/WJyIiftDkmMPxqTL+8Y9/cNFFFxEYGAjQ4KE/jamrqyMrK4u9e/dSW1vLhAkTOP/888nMzMQwDHr27ElOTg4Oh4MFCxawdu1aAgICyMrKon///uzatavRtiIi4jtNhkNQUBBLlixhzZo1XHvttZimybJlyzj33HNPusOVK1cSERHBnDlz2L9/P9dffz29e/cmPT2dQYMGkZ2dTXFxMdHR0WzatImlS5eyb98+Jk2axLJly8jPz7e1TUhIOOUHLiIiTWsyHGbMmMHzzz/PVVddxfXXX8/GjRv5+9//zkMPPXTSHSYlJZGYmOh57XQ6KSsrY+DAgQDEx8ezfv16unXrRlxcHIZhEB0djdvtprKystG2zYWD02kQEeHdc3737z+M06kzFGnI4fC+b3mr3qglQH1TTtDSfbPJcIiMjOSBBx7wvB48eDCDBw9udofBwcEA1NTUMHnyZNLT05k9e7bnMlVwcDAHDx6kpqaGiIiIBtsdPHgQy7JsbZvjdltUVR1qtt3JmKaF2216tQ8585im933LW0HhTurVN+UEpmlRdcC7vhkVFdrkuhb5OLJv3z5uvvlmRo4cyYgRIxqMGbhcLsLCwggJCcHlcjVYHhoa2mhbERHxrSbD4ad8Ym/M999/z7hx43jggQdITk4GoE+fPpSWlgJQUlJCbGwsMTExrFu3DtM0PV+ZjYyMbLStiIj4VpPhcNdddwGQk5Pzs3b49NNPU11dzZNPPklaWhppaWmkp6dTWFhISkoKdXV1JCYm0q9fP2JjY0lJSWHSpElkZ2cDkJGRYWsrIiK+ZVhN3PI8fvx4qqqq2LVrF+effz6AZzxg8eLFPi2yOXV1bq+vC7vdh0hKGn6KKpIzRVHRapxO/w5IB4U7WV1R5NcapPUZ3jGJQwe8m+vuZGMOTQ5IP/fcc1RUVJCdnc2MGTM0bYaIyC9Ik+HgcDg455xzePLJJ1myZAk7duyga9eupKam+rI+ERHxg2a/rZSdnc3u3bv53e9+x969e5k2bZov6hIRET9qdsruXbt28eqrrwIwdOhQxo4d2+JFiYiIfzV75nD06FEOHz4MwJEjR/SwHxGRX4BmzxyO38zWs2dPduzYweTJk31Rl4iI+FGz4XDdddcRHx/Pnj176NSpE2eddZYv6hIRET9qNhwAIiIiGsyDJCIiZzZN9SgiIjbNhsM777zT4P8iInLma/Ky0m233UZwcDA7d+7kV7/6FS+//DLDhg3zZW0iIuInTZ45/PnPf2bOnDk4nU42btzIjh07GDdunGeCPBEROXM1eeaQlZXFRRddREhICHfddRcbN27khRdeYO/evb6sT0RE/KDJM4cpU6YQFRXF3r17ufvuu9mxYwePP/4427dv92V9IiLiB02GQ4cOHRg6dCgxMTE888wzXHzxxSQkJFBVVeXL+kRExA+avc9h/vz5ADzxxBMA9OvXr2UrEhERv2ux+xz++c9/kpaWBkBZWRlDhgzxPBnu7bffBmDBggUkJyczduxYtm3bBhyb6C81NZUbb7yRnJwcTFMPVhcR8bWfdIf0z/Xcc8+xcuVK2rdvD8Cnn37Kbbfdxrhx4zxtysrK2LRpE0uXLmXfvn1MmjSJZcuWkZ+fT3p6OoMGDSI7O5vi4mISEhJaokwREWlCi5w5nHfeeRQWFnpeb9++nbVr13LTTTeRlZVFTU0NW7ZsIS4uDsMwiI6Oxu12U1lZSVlZGQMHDgQgPj6eDz/8sCVKFBGRk2iRM4fExES++eYbz+v+/fszZswY+vXrx1NPPcXChQsJDQ1tMF9TcHAwBw8e9Dyn+sfLmuN0GkREePec3/37D+N0ajYRacjh8L5veaveqCVAfVNO0NJ9s0XC4UQJCQmEhYV5fs7NzeWqq67C5XJ52rhcLkJDQ3E4HA2WHd/uZNxui6qqQ17VaJoWbrfGN6Qh0/S+b3krKNxJvfqmnMA0LaoOeNc3o6JCm1znk48j48eP9ww4b9iwgb59+xITE8O6deswTZPy8nJM0yQyMpI+ffpQWloKQElJCbGxsb4oUUREfsQnZw4zZswgNzeXwMBAOnToQG5uLiEhIcTGxpKSkoJpmp5pOTIyMpg+fToFBQV0796dxMREX5QoIiI/YliWZfm7CG/V1bm9PvV3uw+RlDT8FFUkZ4qiotU4nf4dcwgKd7K6osivNUjrM7xjEocOePfYZr9fVhIRkdOLwkFERGwUDiIiYqNwEBERG4WDiIjYKBxERMRG4SAiIjYKBxERsVE4iIiIjcJBRERsFA4iImKjcBARERuFg4iI2CgcRETERuEgIiI2CgcREbFROIiIiE2LhcM///lP0tLSANi1axepqanceOON5OTkYJrHHpa+YMECkpOTGTt2rOcZ0021FRER32mRcHjuueeYNm0aR48eBSA/P5/09HRee+01LMuiuLiYsrIyNm3axNKlSykoKGDmzJlNthUREd9qkXA477zzKCws9LwuKytj4MCBAMTHx/Phhx+yZcsW4uLiMAyD6Oho3G43lZWVjbYVERHfCmiJnSYmJvLNN994XluWhWEYAAQHB3Pw4EFqamqIiIjwtDm+vLG2zXE6DSIivHsI/P79h3E6NQQjDTkc3vctb9UbtQSob8oJWrpvtkg4nMjh+P8d2+VyERYWRkhICC6Xq8Hy0NDQRts2x+22qKo65FWNpmnhdmt8QxoyTe/7lreCwp3Uq2/KCUzTouqAd30zKiq0yXU++TjSp08fSktLASgpKSE2NpaYmBjWrVuHaZqUl5djmiaRkZGNthUREd/yyZlDRkYG06dPp6CggO7du5OYmIjT6SQ2NpaUlBRM0yQ7O7vJtiIi4luGZVmWv4vwVl2d2+tTf7f7EElJw09RRXKmKCpajdPp3zGHoHAnqyuK/FqDtD7DOyZx6IDbq334/bKSiIicXhQOIiJio3AQEREbhYOIiNgoHERExEbhICIiNgoHERGxUTiIiIiNwkFERGwUDiIiYqNwEBERG4WDiIjYKBxERMRG4SAiIjYKBxERsVE4iIiIjcJBRERsfPKY0ONGjRpFaOixJw916tSJlJQUHn74YZxOJ3FxcUycOBHTNJkxYwZffPEFbdq0YdasWXTp0sWXZYqI/OL5LByOHj0KwKJFizzLRo4cSWFhIZ07d+bOO++krKyMvXv3Ultby5IlS9i6dSuPPPIITz31lK/KFBERfBgOn3/+OYcPH2bcuHHU19czadIkamtrOe+88wCIi4tjw4YNfPfddwwZMgSAAQMGsH379mb37XQaRER495zf/fsP43TqKps05HB437e8VW/UEqC+KSdo6b7ps3Bo164d48ePZ8yYMXz99dfccccdhIWFedYHBwezZ88eampqCAkJ8Sx3Op3U19cTENB0qW63RVXVIa/qM00Lt9v0ah9y5jFN7/uWt4LCndSrb8oJTNOi6oB3fTMqKrTJdT4Lh27dutGlSxcMw6Bbt26EhoZSVVXlWe9yuQgLC+PIkSO4XC7PctM0TxoMIiJy6vnsXPWNN97gkUceAeDbb7/l8OHDBAUFsXv3bizLYt26dcTGxhITE0NJSQkAW7dupVevXr4qUURE/o/PPpInJyczdepUUlNTMQyDvLw8HA4H999/P263m7i4OH7zm99w0UUXsX79esaOHYtlWeTl5fmqRBER+T+GZVmWv4vwVl2d2+vrwm73IZKShp+iiuRMUVS0GqfTvwPSQeFOVlcU+bUGaX2Gd0zi0AG3V/s42ZiDvgIhIiI2CgcREbFROIiIiI3CQUREbBQOIiJio3AQEREbhYOIiNgoHBCME9sAAAc9SURBVERExEbhICIiNgoHERGxUTiIiIiNwkFERGwUDiIiYqNwEBERG4WDiIjYKBxERMSmVT6c2TRNZsyYwRdffEGbNm2YNWsWXbp08XdZIiK/GK3yzOG9996jtraWJUuWcN9993mePS0iIr7RKsNhy5YtDBkyBIABAwawfft2P1ckIvLL0iovK9XU1BASEuJ57XQ6qa+vJyCg8XIDA50nfRbqTxPK1q3/8HIfIi0judNof5cgrVBwVMvtu1WeOYSEhOByuTyvTdNsMhhEROTUa5XhEBMTQ0lJCQBbt26lV69efq5IROSXxbAsy/J3ESc6/m2lL7/8EsuyyMvLo0ePHv4uS0TkF6NVhoOIiPhXq7ysJCIi/qVwEBERG4WDiIjYKBxOU6WlpQwePJi0tDTS0tL4/e9/z6JFixpt+9133zFjxgzfFijSjBP7cFpaGpMnT27Q5i9/+QuFhYUN+vCVV17J0aNHG7Rbvnw5jz32mK9K/0XQzQOnsUsuuYTHH38cgNraWpKSkhg5ciRhYWEN2kVFRSkcpFX6cR8+GfVh31M4nCFqampwOBx8+eWXzJ07F6fTSdu2bcnNzcU0TaZMmcLrr7/O448/zsaNGzFNk2uuuYZbb72VTz/9lNzcXNs29913H+eccw579uzhoosuYubMmVRXV/PAAw9QU1OD2+3mT3/6E4MHD+bKK69k9erVtG3blscee4zu3btz+eWXk56ejmVZ1NXVMXPmTC644AJ//6mkldu8eTN5eXmEh4fjcDgYMGAA33zzjacPA2RnZ7N3715+9atfMXv27AbbL1q0iL/97W8YhsHVV1/NzTff7I/DOO0pHE5jGzduJC0tDcMwCAwMZPr06eTl5fHwww9z4YUX8t577/HII4/wn//5n55tVqxYwSuvvMLZZ5/N8uXLAZg2bVqj23z99dc8//zztG/fnqFDh/Ldd9/xwgsvcOmll3LLLbfw7bffkpqaynvvvddofdu2bSM0NJS5c+eyY8cOampqfPJ3kdPH8T583GWXXcbq1auZO3cu3bp1Iycnp9HtUlNTGTBgAI8++iivv/66Z7qdHTt28Pbbb/Paa69hGAa33norcXFxdO/e3SfHcyZROJzGGjslf/DBB7nwwgsBuPjii5k7d26D9QUFBRQUFPD99997JjesqKhodJvzzjvP848uKiqKo0ePsnPnTkaMGAHA2WefTUhICJWVlQ1+x/FbZ+Lj4/n666+5++67CQgIYMKECafy8OUM0FgffvHFF+nWrRtwbLaE3bt3N1gfGBjIgAEDPOvXr1/PRRddBMCXX35JeXk5t956KwAHDhxg9+7dCod/gwakzzAdO3bk888/B+Cjjz6ia9eunnW1tbUUFRVRUFDASy+9xJtvvsnevXub3MYwDNv+e/TowebNmwH49ttvqa6uJiIigjZt2lBRUYFlWZ59lZaW0rFjR1544QUmTJhAQUFBCx65nCmioqLYuXMnAJ988oltfV1dHZ999hlw7BJUz549Peu6d+/O+eefz8svv8yiRYsYPXq0pt/5N+nM4Qwza9YscnNzsSwLp9NJXl6eZ12bNm0IDw9n5MiRhIeH87vf/Y7o6OiTbnOiP/7xj2RlZbFmzRqOHDnCQw89REBAALfffjt33nknv/71rz0D4r179+bee+/lpZdewuFwcM8997T48cvp5cTLSgCPPvooGRkZBAcHExwcTHh4eIP1gYGBLFq0iF27dhEdHc19993HW2+9BRzrc4MHDyY1NZXa2lr69+/P2Wef7bPjOZNo+gwREbHRZSUREbFROIiIiI3CQUREbBQOIiJio3AQEREbfZVV5N/wr3/9izlz5nD48GEOHTrEZZddxsCBA1myZMlPmitIpLVTOIj8TNXV1UyZMoXCwkK6du3qmWMqKirK36WJnDIKB5Gfqbi4mEGDBnnuJHc6ncyePZuPP/6YTZs2AfDKK6/wzjvvUF9fT2hoKIWFhezdu5epU6cSEBCA0+nk0UcfJTAwUJMTSqukcBD5mSoqKujcuXODZcHBwQQGBgJgmiZVVVW8+OKLOBwOxo8fzyeffMLnn39O3759yczMZPPmzRw4cIDy8nJNTiitksJB5GeKjo7m008/bbBsz549fPTRRwA4HA4CAwOZMmUKQUFB/O///i/19fUkJyfz3HPPcfvttxMaGsq9996ryQml1dK3lUR+piuuuIIPPvjAM1toXV0djzzyCGeddRYAn3/+Oe+99x7z5s1j+vTpmKaJZVkUFxfz29/+lpdeeomkpCT+67/+S5MTSquluZVE/g3bt2/n0UcfxbIsXC4XV1xxBRdffDFLliwhLy+PP/7xj9TU1NCmTRvatGlDcnIyAwYM4IEHHsDpdOJwOJg6dSrR0dHce++9HD582DM5YVxcnL8PT0ThICIidrqsJCIiNgoHERGxUTiIiIiNwkFERGwUDiIiYqNwEBERG4WDiIjY/D9rgeGW3kMnMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#target variable countplot\n",
    "#mostly balanced target\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure()\n",
    "fig1=sns.countplot(data['class'], alpha =.80, palette= ['black','lightgreen'])\n",
    "plt.title('Edible vs Poisonous')\n",
    "plt.ylabel('# of Samples')\n",
    "plt.xlabel('Class')\n",
    "fig1.set(xticklabels=['Poisonous','Edible'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing features\n",
    "- all features are categorical with an assortment of values\n",
    "- some features are mostly evenly spread per the samples like cap surface\n",
    "- some features like odor have clear majority values, possible correlation to edible or poisonous? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=data.drop(columns='class')\n",
    "\n",
    "#creating a function to countplot features to see breakout, input column name\n",
    "                       \n",
    "def featureplot(col):\n",
    "    sns.set_style('white')\n",
    "    plt.figure()\n",
    "    sns.countplot(features[col],alpha =.80)\n",
    "    plt.ylabel('# of samples')\n",
    "    plt.title('Mushroom' + ' ' + col.title())\n",
    "    plt.xlabel('Type of' + ' ' + col.title())\n",
    "    \n",
    "\n",
    "    plt.show() \n",
    "                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureplot('cap-shape')\n",
    "featureplot('cap-color')\n",
    "featureplot('odor')\n",
    "featureplot('gill-color')\n",
    "featureplot('habitat')\n",
    "featureplot('stalk-root')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureplot('spore-print-color')\n",
    "featureplot('cap-surface')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing feature/class separation - what do the relationships look like between a feature's values and whether the mushroom is edible or poisonous?\n",
    "- population, gill color, odor, surface above/below ring, spore print color all have dominant values which show seperation between classes\n",
    "- gill-attachment and veil-color hardly show any value seperation with class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4cCDR0dGkp6fTvn17atasybx585g7dy5+fn4EBARk2e7KlStZviXcXJYTs9lEQIC3sw9FpNAqKp8HX99ynDx5kpSUZAXEPTKZTBQrVozy5cvh7p77SQvz5FLW06dP8/rrr9OlSxdatmzJ5cuX8ff3ByA8PJyJEyfSpEkTLBaLYxuLxYKfn1+W8wsWi8Wx3Z1YrXZdrSSFyp2uEnGGovR5ePjhR1xdQpGQmpoBZL10N1+vVvrjjz/o1asXw4YNIyoqCoDevXtz8OBBAHbu3EmNGjWoU6cO27Ztw2azcerUKWw2G4GBgVSvXp1du3YBEB8fT2hoqLNLFBGRHDj9Poe4uDg2btxIxYoVHcuio6OZPn06Hh4eBAUFMXHiRHx9fZk9ezbx8fHYbDZGjRpFaGgoSUlJjB07loyMDCpWrEhcXBxms/mO+9R9DlLY6D4HKQju1HPQTXAiLqBwkIJAN8GJiMhdUTiIiIiBwkFERAwUDiIiYqBwEBERA4WDiIgYKBxERMRA4SAiIgYKBxERMVA4iIiIgcJBREQMFA4iImKgcBAREQOFg4iIGCgcRETEQOEgIiIGCgcRETFQOIiIiIHCQUREDBQOIiJioHAQEREDhYOIiBgoHERExEDhICIiBgoHERExUDiIiIiBwkFERAwUDiIiYqBwEBERA3dnN5iRkUFMTAwnT54kPT2dfv36UblyZUaOHInJZCIkJITx48fj5ubGnDlz2Lp1K+7u7sTExFCrVi2OHTuW7boiIpJ/nP5Xd+3atQQEBLBs2TIWLlzIxIkTmTJlCtHR0Sxbtgy73c6WLVtISEhg9+7drFy5kpkzZzJhwgSAbNcVEZH85fSeQ2RkJBEREY7XZrOZhIQE6tWrB8Dzzz/P9u3bqVChAg0aNMBkMlGmTBmsVivnz5/Pdt3w8PA77tNsNhEQ4O3sQxEptPR5kPvl9HDw8fEBIDU1lYEDBxIdHc20adMwmUyO969cuUJqaioBAQFZtrty5Qp2u92wbk6sVjsXL1519qGI5JmSJf3ytH19HiQ37vR7mCeD+adPn6ZHjx60bt2ali1bZjlnYLFY8Pf3x9fXF4vFkmW5n59ftuuKiEj+cno4/PHHH/Tq1Ythw4YRFRUFQPXq1dm1axcA8fHxhIaGUqdOHbZt24bNZuPUqVPYbDYCAwOzXVdERPKXyW63253ZYFxcHBs3bqRixYqOZaNHjyYuLo6MjAwqVqxIXFwcZrOZ2bNnEx8fj81mY9SoUYSGhpKUlMTYsWMN695JRoZV3WgpVEqW9HMMnzqb3W4nJSXn4ViROw0rOT0cXEHhIIWNwkEKgnw/5yAiIoWbwkFERAwUDiIiYnBX4XD69Om8qkNERAqQHG+CW7JkCV5eXly+fJk1a9YQFhbGqFGj8qM2ERFxkRx7DuvXr6dNmzbEx8ezfv16Dh8+nB91iYiIC+UYDiaTiZSUFIKCgjCZTFy6dCk/6hIRERfKMRzq169Pt27d6NatG5MnT6ZZs2b5UZeIiLhQrm+Cu3TpEg899BCenp55XdNd001wUtjoJjgpCO50E1yOJ6R/+OEHJkyYgNVqJTIykjJlytC+fXunFigiIgVLjsNK77zzDv/85z8JCgqib9++fPrpp/lRl4iIuFCO4eDm5kZAQAAmk4lixYo5ntcgIiJFV47hUK5cOd5++20uXrzIBx98QJkyZfKjLhERcaEcT0hnZmaycuVKfvnlFypVqkSHDh0K3ElpnZCWwkYnpKUguKcpu7dt23bbjRo0aHD/VTmRwkEKG4WDFAT3dLXS+vXrb7tRQQsHERFxrlzd5/Cf//yHpKQkKleuTNWqVfOjrruinoMUNuo5SEFwX/c5vPPOO3z//ffUqlWLpUuX0rRpU1555RWnFigiIgVLjuEQHx/PqlWrcHNzw2q10rFjR4WDiEgRl+OlrKVLl8ZisQA3rlwKCgrK86JERMS1cuw5nD17loiICKpVq0ZiYiIeHh506tQJgOXLl+d5gSIikv9yDId33303P+oQEZECJMdwOHfuHOvXr+f69euOZbGxsXlZk4iIuFiO4TBixAj69OmDv79/ftQjIiIFQI7hEBwcTLt27fKjFhERKSByDIeIiAgGDx5MpUqVHMsGDBiQp0WJiIhr5RgOy5YtIzw8XMNKIiIPkBzD4eGHH+bVV1/Nj1pERKSAyDEcihcvzrhx46hevbpjLpiOHTvm2PCBAweYMWMGS5cuJSEhgb59+1K+fHkAOnfuTPPmzZkzZw5bt27F3d2dmJgYatWqxbFjxxg5ciQmk4mQkBDGjx+Pm1uO9+qJiIgT5eqENMAff/yR60YXLlzI2rVreeihh4AbE/f17NmTXr16OdZJSEhg9+7drFy5ktOnT/PGG2+wevVqpkyZQnR0NPXr12fcuHFs2bKF8PDwuz0uERG5DzmGw4ABAzh79iyZmZnY7XbOnj2bY6PlypVj9uzZDB8+HIBDhw6RlJTEli1bCA4OJiYmhr1799KgQQNMJhNlypTBarVy/vx5EhISqFevHgDPP/8827dvVziIiOSzHMMhJiaG/fv3c+3aNdLS0ihbtiyfffbZHbeJiIggOTnZ8bpWrVq0b9+emjVrMm/ePObOnYufnx8BAQGOdXx8fLhy5Qp2u90xfHVzWU7MZhMBAd45rifyoNDnQe5XjuFw9OhR1q9fz7hx4xg8eDCDBg26653cerVTeHg4EydOpEmTJo4J/QAsFgt+fn5Zzi9YLJZcXSVltdr1PAcpVO40j74z6PMguXGn38Mcz/T6+PhgMpm4evUqgYGBZGRk3HUBvXv35uDBgwDs3LmTGjVqUKdOHbZt24bNZuPUqVPYbDYCAwOpXr06u3btAm5MFx4aGnrX+xMRkfuTY8+hRo0afPTRRzzyyCMMHjwYq9V61zuJjY1l4sSJeHh4EBQUxMSJE/H19SU0NJSOHTtis9kYN24ccGO6jrFjxzJz5kwqVqxIRETE3R+ViIjcl1w9JjQ1NRUvLy/i4+OpVatWgXumgx4TKoWNHhMqBcE9DStdv36dxYsXY7fbSU1NZciQIWzYsIFcZImIiBRytw2HuLg4x7mACRMmUK1aNSIiIjRdt4jIA+C24XDq1ClGjRpFZmYme/fupU+fPoSHh3P+/Pn8rE9ERFzgtuFwczx03759PPHEE3h4eABkeeiPiIgUTbe9Wsnb25sVK1awadMmWrRogc1mY/Xq1Tz66KP5WZ+IiLjAbXsOsbGxHD9+nCZNmtC2bVt27drFN998o3MOIiIPgFxdylrQ6VJWKWx0KasUBPd1h7SIiDx4bhsOuZnwTkREiqbbhkPfvn0BGD9+fL4VIyIiBcNtr1by8vLixRdf5NixY/z8888Ajum0ly9fnm8FiohI/rttOCxcuJCzZ88ybtw4YmNjNW2GiMgDJMerlTIzM1mxYgWJiYmUL1+ezp074+npmV/15YquVpLCRlcrSUFwX1crjRs3juPHj/Pcc89x8uRJxowZ49TiRESk4MnxeQ7Hjh3jk08+AaBp06Z06tQpz4sSERHXyrHncP36da5duwZAWlraPT3sR0RECpccew49evSgdevWhISEkJiYyMCBA/OjLhERcaFcTZ9x8eJFTpw4wV/+8heKFy+eH3XdFZ2QlsImL09IZ9gycDfl+L3vrqVbM7h0Ps3p7Yrr3OmEdK5+gwICAggICHBaQSKSd9xN7nx04iOnt9u7bG9A4fCg0NxKIiJikGM4fPXVV1n+LSIiRd9th5V69uyJj48PR44coUSJEixZsoRmzZrlZ20iIuIit+05fPzxx0yfPh2z2cz3339PYmIivXr1Yty4cflZn4iIuMBtew4xMTE88cQT+Pr60rdvX77//nsWLVrEyZMn87M+ERFxgdv2HIYMGULJkiU5efIk/fv3JzExkVmzZnHo0KH8rE9ERFzgtuEQFBRE06ZNqVOnDgsWLKBu3bqEh4dz8eLF/KxPRERcQM+QFnGBvJ6VNa/uc3D1bK8PB3rhafbIk7YfxJv87vsmOBGRgsDT7JEnwQe6ye/P8uwmuAMHDtC9e3fgxsyunTt3pkuXLowfPx6bzQbAnDlziIqKolOnThw8ePCO64qISP7Jk3BYuHAhY8aM4fr16wBMmTKF6Oholi1bht1uZ8uWLSQkJLB7925WrlzJzJkzmTBhwm3XFRGR/JUn4VCuXDlmz57teJ2QkEC9evUAeP7559mxYwd79+6lQYMGmEwmypQpg9Vq5fz589muKyIi+StPzjlERESQnJzseG232x0n33x8fLhy5QqpqalZJvO7uTy7dXNiNpsICPB28lGIyJ8V9c9ZUT++u5EvJ6Td3P5/B8ViseDv74+vry8WiyXLcj8/v2zXzYnVatfVSlKo3OkqkYLM1Z+zvP65ufr48tt9PUPaGapXr86uXbsAiI+PJzQ0lDp16rBt2zZsNhunTp3CZrMRGBiY7boiIpK/8qXnMGLECMaOHcvMmTOpWLEiERERmM1mQkND6dixIzabzTFnU3briohI/tJNcCIuoJvg7k3Jkn55ep+Dq48vv7l8WElERAoXhYOIiBgoHERExEDhICIiBpp47x5oZkgRKeoUDvdAM0OKSFGnYSURETFQOIiIiIHCQUREDBQOIiJioHAQEREDhYOIiBgoHERExEDhICIiBgoHERExUDiIiIiBwkFERAwUDiIiYqBwEBERA4WDiIgYKBxERMRAz3MQEafz8THj7e3t6jLkPigcRMTpvL29MZlMTm/Xbrc7vU3JnoaVRETEQOEgIiIGCgcRETFQOIiIiIHCQUREDBQOIiJikK+XsrZp0wY/Pz8A/vKXv9CxY0cmTZqE2WymQYMGDBgwAJvNRmxsLD///DOenp7ExcURHBycn2WKiDzw8i0crl+/DsDSpUsdy1q3bs3s2bMpW7Ysr776KgkJCZw8eZL09HRWrFjB/v37mTp1KvPmzcuvMkVEhHwMh//+979cu3aNXr16kZmZyRtvvEF6ejrlypUDoEGDBuzcuZOUlBTCwsIAePLJJzl06FCObZvNJgICis7dmEXpWKRoKeq/m0X9+O5GvoWDl5cXvXv3pn379vz222/06dMHf39/x/s+Pj6cOHGC1NRUfH19HcvNZjOZmZm4u9++VKvVzsWLV/O0/luVLOmXp+3n57GIa+T171Beye3vZlE/vqLiTv+f8i0cKlSoQHBwMCaTiQoVKuDn58fFixcd71ssFvz9/UlLS8NisTiW22y2OwaDiIg4X75drbRq1SqmTp0KwJkzZ7h27Rre3t4cP34cu93Otm3bCA0NpU6dOsTHxwOwf/9+qlSpkl8liojI/+TbV/KoqChGjRpF586dMZlMTJ48GTc3N4YOHYrVaqVBgwbUrl2bJ554gu3bt9OpUyfsdjuTJ0/OrxJFROR/8i0cPD09efvttw3LP/vssyyv3dzceOutt/KrLBERyYZughMREQOFg4iIGOgyIDF4ONALT7OH09tNt2Zw6Xya09sVEedTOIiBp9mDj0585PR2e5ftDbg+HBR+Ijkr0uGg59hKdop6+Ik4Q5EOBz3HVkTk3uiEtIiIGCgcRETEQOEgIiIGCgcRETEo0iekRUQKk7y6zBru/lJrhYOISAGRV5dZw91faq1hJRERMVA4iIiIgcJBREQMFA4iImKgcBAREQOFg4iIGCgcRETEQOEgIiIGCgcRETFQOIiIiIHCQUREDBQOIiJioHAQEREDhYOIiBgoHERExEDPcxARuUs+Pma8vb1dXUaeKpDhYLPZiI2N5eeff8bT05O4uDiCg4NdXZaICADe3t6YTCant2u3253e5r0qkMNKmzdvJj09nRUrVvDmm28ydepUV5ckIvJAKZDhsHfvXsLCwgB48sknOXTokIsrEhF5sJjsBakf8z+jR4+mWbNm/O1vfwOgYcOGbN68GXf3AjkKJiJS5BTInoOvry8Wi8Xx2mazKRhERPJRgQyHOnXqEB8fD8D+/fupUqWKiysSEXmwFMhhpZtXK/3yyy/Y7XYmT55MpUqVXF2WiMgDo0CGg4iIuFaBHFYSERHXUjiIiIiBwkFERAwUDiIiYqBwEBERA4XDXUhKSqJTp05069aNl156iTNnzri6JNTX6w8AAAdqSURBVKdas2YNgwYN4rXXXuOFF15gzZo1ri7JaVJTUxk0aBC9evWibdu2LFu2zNUlOdWAAQPYvXs3AAcPHqRfv34ursi53nzzTbZu3QrAkSNHePXVV11bkJOlpaUxePBgOnbsSLt27fjxxx9dXZLC4W7s2LGDGjVq8PHHH9O3b18uXbrk6pKcLjU1lQULFjBv3jw++OADV5fjNMeOHePvf/87ixYtYv78+fzjH/9wdUlO1b59ez7//HMAPv/8czp06ODiipzr1uNbtWoVUVFRLq7IuZYvX85jjz3GihUrmDp1KgcOHHB1SQqHuxEVFUXx4sV55ZVX+OSTTzCbza4uyemqVasGwKOPPkp6erqLq3GeoKAgNm/ezNChQ5k3bx6ZmZmuLsmpwsLC+Omnn7h48SJ79uzh+eefd3VJTlW/fn2OHj3KuXPn2L59O40aNXJ1SU519OhRnnzySQCqVKnCyy+/7NqCUDjclS1btvDXv/6VxYsXExkZyYcffujqkpwuL+aoLwgWLVrEk08+yYwZM4iMjCxQ8+Y7g5ubG5GRkcTGxtK0adMi98XFZDLRsmVLJk2axHPPPYeHh4erS3KqSpUq8dNPPwFw4sQJ3nzzTRdXVEAf9lNQ1axZk2HDhjF79mzc3NwYNWqUq0uSXGrUqBGxsbGsW7eOgIAAzGYz6enpeHp6uro0p3nxxRdp2rQpmzZtcnUpeaJdu3Y0bNiQf/3rX64uxek6depETEwM3bp1w2q1EhMT4+qSNH2GiBQOZ86cYfjw4SxevNjVpTwQNKwkIgXepk2beOWVVwrEcMuDQj0HERExUM9BREQMFA4iImKgcBAREQNdyiqF1tSpU0lISCAlJYW0tDTKli1L8eLFee+99/J835s3b+add96hQ4cO9OjRA4Dk5GRatWpFjRo1AEhPT6d+/foMGTLEKfts3LgxGzdupFixYtm+//XXX1OrVi3c3NyYO3cusbGxTtmvPJgUDlJojRw5ErgxJ9TRo0cZOnRovu3722+/ZciQITRu3DjL8sqVK7N06VLgxuNuO3fuzH//+1/Hned5acmSJcTGxlKpUiUFg9w3hYMUOTNnzqRUqVJ07dqVS5cu0bNnT0aMGMH8+fNxc3MjJSWFjh070rVrV37++Wfi4uIACAgIYPLkyfj5+Tnaunz5MsOGDSM1NRWr1cqgQYO4evUqW7du5eDBgxQvXpynnnoq2zrS0tJIT0/noYceIjk5mdGjR5OZmYnJZGLMmDFUq1aNJk2aULt2bY4fP05ISAiTJk1i7ty5BAUF0blzZ44cOUJsbKwjcAB++eUXpk6dis1m4/Lly4wZM4bLly9z+PBhRowYwfTp0xkxYgSfffYZ27dv55133qFYsWKO4zt8+DALFy7Ew8OD5ORkmjdvXuQm6pP7p3CQIqd9+/YMGTKErl278u9//5uWLVsCN26i+uKLL7DZbLRs2ZLIyEjGjh3L5MmTqVy5MitXruTDDz9k8ODBjrbmzZvHs88+65iFt3PnzmzevJmwsDCaN29uCIbExES6d+8OgNlspkePHgQHBzNw4EC6d+9O06ZNOXz4MDExMaxZs4YzZ84waNAggoODGTRoEJs3b87x+BITExkxYgRVq1Zl3bp1rFmzhri4OB5//HFiY2MdU0vY7XbGjh3Lp59+SqlSpVi8eDHz5s2jYcOGnDp1irVr15Kenk5YWJjCQQwUDlLklC1bFh8fHxITE1m3bh3vv/8+v/76K0899ZRjuoyQkBCOHz/OkSNHmDBhAgAZGRlUqFAhS1tHjhxxhEupUqXw9fXl/Pnzt933rcNKf26nbt26ADz++OP8/vvvwI0JDoODgwF46qmnSEpKyvH4HnnkEd5//328vLywWCz4+vpmu96FCxfw9fWlVKlSANStW5eZM2fSsGFDqlSpgru7O+7u7nh5eeW4T3nw6GolKZI6dOjAvHnzKFWqFIGBgQAcPnwYq9XKtWvXSExMJDg4mAoVKjBt2jSWLl3KsGHD+Nvf/palnUqVKrFnzx7gRs/j8uXLBAQE3HU9t7Zz+PBhgoKCHG2mpKQAsG/fPipXrkyxYsUcyxISEgxtTZo0iYEDBzJt2jSqVKnimETQZDJlmVCwePHipKamcvbsWQB2795N+fLlHeuK3Il6DlIkNW3alLfeeovp06c7lmVmZtKnTx8uXrxIv379CAwMJDY2lhEjRmC1WoEbf3hv9dprrxETE8OmTZtIS0vjrbfewt397j82w4cPZ+zYsSxatIjMzEzHfjw9PZk4cSKnT5+mdu3aNG7cmCpVqhAdHc0PP/xAzZo1DW21atWK/v37U6JECUqXLs2FCxeAGz2P4cOHM3HiROBGAMTFxfHGG29gMpl4+OGHmTJlCr/++utd1y8PHk2fIUXStWvX6NatGytXrsTNzY1du3axfPlyZs2a5erSsnjuuefYvn27q8sQMdCwkhQ5+/bto0OHDvTv3x83N/2Ki9wL9RxERMRAX6tERMRA4SAiIgYKBxERMVA4iIiIgcJBREQM/g9PAPmZY05VWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating function to create countplots for features stacked with class\n",
    "\n",
    "def featureclassplot(col):\n",
    "    fig,ax=plt.subplots()\n",
    "    sns.set_style('darkgrid')\n",
    "    sns.countplot(col,hue=data['class'],palette=['black','lightgreen'],data=data)\n",
    "    plt.ylabel('# of Samples')\n",
    "    plt.xlabel('Type of' + ' ' + col.title())\n",
    "    plt.title('Mushroom' + ' ' + col.title())\n",
    "    plt.legend(title=None,labels=['Poisonous','Edible'])\n",
    "    plt.show()\n",
    "    \n",
    "featureclassplot('cap-shape')\n",
    "featureclassplot('bruises')\n",
    "featureclassplot('odor')\n",
    "featureclassplot('gill-color')\n",
    "featureclassplot('spore-print-color')\n",
    "featureclassplot('population')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featureclassplot('gill-attachment')\n",
    "featureclassplot('veil-color')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nzf6oCwAAAAhaPkP62rVrNXDgQD388MOqqalRbm6uP+oCAAAAgpbPkH7DDTcoLCxMW7ZsUZ8+fRQREeGPugAAAICg5TOkZ2ZmqrKyUgcOHJDb7VZaWtplxzc2Nmrx4sWaPn26kpOTVVRUpIqKCqWkpGj69OnKyspqWdeen5+v5ORkTZs2TWVlZZLU5lgAAAAgWPgM6SdOnNAjjzyibt266a677lJtbe1lx7/xxhuKiorSSy+9pK1btyo7O1s5OTlKTU3VSy+9JK/Xq6KiIjmdTh08eFCFhYXasGGDVq5cKUmXHAsAAAAEE58hvbm5WTU1NZIkl8ulkJDLv+See+7RI4880vLYarXK6XRq5MiRkqSkpCS98847Ki0tVWJioiwWi/r27dsyz6XGAgAAAMHE5mtAamqqUlJSVFVVpalTpyo9Pf2y479Zs+5yubRw4UKlpqZq3bp1slgsLc/X1tbK5XIpKiqq1etqa2vl9Xq/NdYXu72bbDarz3HA1SwqqkegSwBa0HfRkehvwLf5DOkjR47U22+/rZqaGvXq1aslQF/OqVOnNG/ePE2fPl2TJk1qdUUYt9utyMhI2e12ud3uVtsdDkerM/XfjPXF5ar3OQboCDExjoDNfe7chYDNjeDR3mOcvtv10N+AwGjrs9dmSJ86dWqbgbygoKDNiaqrqzVr1ixlZmbqH//xHyVJgwcPVklJiUaNGqXi4mLdfvvtuvHGG5Wbm6vZs2friy++kMfjUe/evS85FgAAAAgmbYb0DRs2fKc33LJli86fP69NmzZp06ZNkqRly5Zp1apV2rBhg2JjYzVhwgRZrVYlJCRo6tSp8ng8yszMlCSlpaUpIyOj1VgAAAAgmFi8Xq/3cgPKy8v1+OOP6/jx44qLi1NaWpquv/56f9XXLlVVvtetAx0hJsah/Eff9Pu889dP4jiHX7R3yQPHY9cTE+PQvqQxfp93TPE+jicEtbb6rs+ru6SlpWnatGl65ZVXdN9992nJkiUdXhwAAACAv/AZ0sPDwzVmzBg5HA6NHTvW5yUYAQAAAPxtfF7d5brrrtOmTZt0++23y+l0KiwsTPv375ckJSYmdnqBAAAAQLDxGdItFotOnjypkydPSpKio6P11ltvSSKkAwAAAJ3BZ0jPycmRy+VSff1fronbp0+fTi0KAAAACGY+Q3paWppKS0vlcDha7gb62muv+aM2AAAAICj5DOnHjh3T7t27/VELAAAAALXj6i7Dhg3TsWPH/FELAAAAALXjTLrdbldycrJ69OjRsu2bq7sAAAAA6Hg+Q3pJSYkOHjwom83nUAAAAAAdwOdylwEDBujLL7/0Ry0AAAAA1I4z6f/7v/+ru+66S7169WrZxnIXAAAAoPP4DOm//e1v/VEHAAAAgD/zGdIPHTqkXbt2qbGxUZJ05swZ/frXv+70wgAAAIBg5XNN+qpVqzRy5Ei5XC717dtXUVFR/qgLAAAACFo+Q3pkZKT+6Z/+SXa7XQsWLNDp06f9URcAAAAQtHyGdIvFok8//VQXL17UsWPHVFVV5Y+6AAAAgKDlM6QvWbJEn376qWbMmKFFixYpJSXFH3UBAAAAQcvnH47GxcVp0KBB8nq9Wrp0qW699VZ/1AUAAAAELZ8hPTc3VzfccIMqKyvldDoVHR2tdevW+aM2AAAAICj5XO5SWlqqadOm6f3339evf/1rffHFF/6oCwAAAAhaPkO6x+NRWVmZ+vXrp4aGBtXU1PijLgAAACBo+Qzp9957r7KzszVr1izl5ubqwQcf9EddAAAAQNDyuSb9/vvv1/333y9JWrZsWacXBAAAAAQ7n2fSAQAAAPhXmyG9trbWn3UAAAAA+LM2Q/rcuXMlSVlZWX4rBgAAAMBl1qR3795d//Iv/6KKigp9/PHHkiSv1yuLxaKCggK/FQgAAAAEmzZD+tatW3XmzBllZmZqxYoV8nq9/qwLAAAACFpthvSQkBBde+212rRpk3bs2KEjR45owIABSklJ8Wd9AAAAQNDxeXWXzMxMnThxQqNHj9bnn3+u5cuX+6MuAAAAIGj5vE56RUWFXnzxRUnSuHHjNG3atE4vCgAAAAhmPs+k19fX6+LFi5Kkuro6NTc3d3pRAAAAQDDzeSb9wQcf1L333qu4uDgdOXJECxcu9EddAAAAQNDyGdJ//OMfKykpSSdPnlS/fv3Uq1evdr3xBx98oCeeeELPP/+8nE6n5s6dqwEDBkiSUlJSNHHiROXn52vv3r2y2WxKT0/XsGHDVFFRoSVLlshisSguLk5ZWVkKCeHGqAAAAAgePkO6JEVFRSkqKqrdb7p161a98cYbCg8PlyT98Y9/1MyZMzVr1qyWMU6nUwcPHlRhYaFOnTqlBQsWaOfOncrJyVFqaqpGjRqlzMxMFRUVafz48Ve4WwAAAMDVq1NOUd94443Ky8treXz48GHt3btX999/v9LT0+VyuVRaWqrExERZLBb17dtXzc3NqqmpkdPp1MiRIyVJSUlJeueddzqjRAAAAMBYPs+k//a3v9UPfvCDlv+2x4QJE/TZZ5+1PB42bJgmT56soUOHavPmzXryySflcDhanZ2PiIhQbW1ty11N//82X+z2brLZrO2qDbhaRUX1CHQJQAv6LjoS/Q34tjZD+syZMxUREaGjR4+qT58+eu6559od0v/a+PHjFRkZ2fL/2dnZuvvuu+V2u1vGuN1uORyOVuvP3W53y+sux+Wq/051AVcqJsYRsLnPnbsQsLkRPNp7jNN3ux76GxAYbX322lzusn37duXm5spqteq9997TkSNHNGvWLGVmZl7x5LNnz1ZZWZkk6d1339WQIUMUHx+v/fv3y+PxqLKyUh6PR71799bgwYNVUlIiSSouLlZCQsIVzwcAAABczdo8k56enq5bbrlFdrtdc+fO1Xvvvadt27bp888/v+JJVqxYoezsbIWGhio6OlrZ2dmy2+1KSEjQ1KlT5fF4WsJ/WlqaMjIytGHDBsXGxmrChAnffe8AAACAq5DF6/V6L/VEdXW1Dh06pJUrV2rw4MEqKyvTlClTNHjwYOOCc1WV73XrQEeIiXEo/9E3/T7v/PWTOM7hF+1d8sDx2PXExDi0L2mM3+cdU7yP4wlB7YqXu0RHR2vcuHGKj4/XU089pREjRmj8+PE6d+5cpxUJAAAAoB1Xd/nlL38pSdq4caMkaejQoZ1bEQAAABDkuJUnAAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYBhCOgAAAGAYQjoAAABgGEI6AAAAYJhOC+kffPCBZsyYIUmqqKhQSkqKpk+frqysLHk8HklSfn6+kpOTNW3aNJWVlV12LAAAABAsOiWkb926VcuXL1d9fb0kKScnR6mpqXrppZfk9XpVVFQkp9OpgwcPqrCwUBs2bNDKlSvbHAsAAAAEk04J6TfeeKPy8vJaHjudTo0cOVKSlJSUpHfeeUelpaVKTEyUxWJR37591dzcrJqamkuOBQAAAIKJrTPedMKECfrss89aHnu9XlksFklSRESEamtr5XK5FBUV1TLmm+2XGuuL3d5NNpu1g/cCMEtUVI9AlwC0oO+iI9HfgG/rlJD+10JC/nLC3u12KzIyUna7XW63u9V2h8NxybG+uFz1HVsw0IaYGEfA5j537kLA5kbwaO8xTt/teuhvQGC09dnzy9VdBg8erJKSEklScXGxEhISFB8fr/3798vj8aiyslIej0e9e/e+5FgAAAAgmPjlTHpaWpoyMjK0YcMGxcbGasKECbJarUpISNDUqVPl8XiUmZnZ5lgAAAAgmFi8Xq830EX8raqqfK9bBzpCTIxD+Y++6fd556+fxHEOv2jvkgeOx64nJsahfUlj/D7vmOJ9HE8IagFd7gIAAACg/QjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhCOkAAACAYQjpAAAAgGEI6QAAAIBhbIEuAEDX1KtnuGxh/m8xTQ1NOvvVxTaf79mzm8LCwvxY0dcaGhr01Vf1fp8XAHB1IqQD6BS2MJs+Wv17v89787K7Lvt8WFiYVqxY4Z9i/p+v5ySkAwDah+UuAAAAgGEI6QAAAIBh/Lrc5Z//+Z/lcDgkSf369dPUqVO1evVqWa1WJSYmav78+fJ4PFqxYoU+/vhjhYWFadWqVerfv78/ywQAAAACym8hvb7+67WYzz//fMu2e++9V3l5ebrhhhv08MMPy+l06vPPP1dDQ4N27NihQ4cOae3atdq8ebO/ygQAAAACzm8h/U9/+pMuXryoWbNmqampSQsWLFBDQ4NuvPFGSVJiYqLeffddVVVV6Y477pAkDR8+XIcPH/b53nZ7N9ls1k6tHwi0qKgegS7hqmHq18rUur4L+i46Ulf6bAAdxW8hvXv37po9e7YmT56s48ePa86cOYqMjGx5PiIiQidPnpTL5ZLdbm/ZbrVa1dTUJJut7VJdLq6YAP+IiXEEbO5z5y4EbO7vwtSvlal1maK9Xx/6btfDZwMIjLY+e34L6QMHDlT//v1lsVg0cOBAORwOnTt3ruV5t9utyMhI1dXVye12t2z3eDyXDegAAABAV+O3q7u8+uqrWrt2rSTp9OnTunjxonr06KETJ07I6/Vq//79SkhIUHx8vIqLiyVJhw4d0k033eSvEgEAAAAj+O0UdXJyspYuXaqUlBRZLBatWbNGISEhWrRokZqbm5WYmKhbb71Vt9xyiw4cOKBp06bJ6/VqzZo1/ioRAAAAMILfQnpYWJjWr1//re2vvPJKq8chISF67LHH/FUWAAAAYBxuZgQAAAAYhpAOAAAAGIaQDgAAABiGaxsCALqs3j1DZQ3r7vd5mxvqVPNV4yWfs/cMVXgAapKkiw11crVRFwCzENIBAF2WNay7Tjx2i9/nvTHzQ0mXDsPhYd01Om+0fwv6swMLDsjVRl0AzMJyFwAAAMAwhHQAAADAMIR0AAAAwDCEdAAAAMAwhHQAAADAMIR0AAAAwDCEdAAAAMAwhHQAAADAMIR0AAAAwDCEdAAAAMAwtkAX0Bnskd0V3i3U7/NerG+U63yd3+cFAABA19IlQ3p4t1Ddtvg5v89bmvugXCKkAwAA4G/DchcAAADAMIR0AAAAwDCEdAAAAMAwXXJNOhBsejrCFNa9m9/nbair11e1DX6fFwCAro6QDnQBYd27afUDyX6fd9kLr0qEdAAAOhzLXQAAAADDENIBAAAAwxDSAQAAAMMQ0gEAAADD8IejMFIve6hs4d39Pm/TxTqddTX6fV4AAID/j5AOI9nCu2tf0hi/zzumeJ9ESAcAAAHGchcAAADAMIR0AAAAwDCEdAAAAMAwhHQAAADAMIR0AAAAwDBGXt3F4/FoxYoV+vjjjxUWFqZVq1apf//+gS4LADpNVFSoQkP9f9nRxsY6nTvHFY0AwDRGhvTdu3eroaFBO3bs0KFDh7R27Vpt3rw50GUBQKcJDe2uVwpH+n3eKZMPSiKkA4BpjFzuUlpaqjvuuEOSNHz4cB0+fDjAFQEAAAD+Y/F6vd5AF/HXli1bph/84AcaM+brm9mMHTtWu3fvls1m5Il/AAAAoEMZeSbdbrfL7Xa3PPZ4PAR0AAAABA0jQ3p8fLyKi4slSYcOHdJNN90U4IoAAAAA/zFyucs3V3f55JNP5PV6tWbNGg0aNCjQZQEAAAB+YWRIBwAAAIKZkctdADGCZ0kAAAdZSURBVAAAgGBGSAcAAAAMQ0gHAAAADENIx3e2a9cuPfHEE4Eu46rQ3Nys2bNnKyUlRV999VWgywFwlaLvth99F1c7Lj4O+EFVVZXOnj2rXbt2BboUAAgK9F1c7Qjpf7Zr1y4VFRXJ5XLp7NmzmjdvniZMmBDQmlwul5YtW6ba2lqdPXtWkydP1vTp0wNak8l27dqlffv2qa6uTidOnNCcOXN03333BbosSVJGRoaOHz+uzMxMPfbYY4EuR5JUV1enpUuXqrKyUo2NjcrIyNA//MM/BLSmRx99VJMmTdLYsWN19OhRrVu3Tk8//XRAa5Kk8vJyLV26VDabTVarVY8//riuueaagNWza9cu7dmzR3V1daqqqtKDDz6ooqIiffrpp/rFL36hcePGBay2K0HfvfrRd68Mfbf96Lssd2nlwoUL2r59u7Zt26a1a9eqqakpoPVUVFToRz/6kbZt26YtW7bomWeeCWg9VwOXy6WnnnpKmzdvNqLJfCMrK0vf//73jflBIUkFBQW6/vrrtWPHDq1du1YffPBBoEvS5MmT9dprr0mSXn31VSUnJwe4oq+98847GjJkiLZv3665c+ca8atzt9utrVu3as6cOXr55ZeVn5+vxx577Ko7a0jfvfrRd9uPvtt+9F1CeisjRoxQSEiIoqOjFRkZqZqamoDWEx0drd27d2vRokXavHlzwH94XQ3+/u//XpJ03XXXqaGhIcDVmO3YsWMaPny4JOmmm27ST3/608AWJGnUqFE6duyYvvzySx04cEB33nlnoEuSJCUnJ6tXr1762c9+phdffFFWqzXQJenmm2+WJDkcDg0aNEgWi0U9e/ZUfX19gCu7MvTdqx99t/3ou+1H3yWkt+J0OiVJ1dXVcrlc6tOnT0Dr2bZtm4YPH64nnnhC99xzj7jvlG8WiyXQJVw1Bg0apA8//FCSdPLkST366KMBrujr79+kSZO0evVqjR49WqGhoYEuSZJUVFSk2267Tc8++6zuuece/epXvwp0SV3mWKfvXv26yrHoD/Td9qPvsia9lerqaj300EOqra1VVlZWwP/Vduedd2rFihV68803FRUVJavVqoaGBoWFhQW0LnQN06ZNU3p6uh544AE1NzcrPT090CVJku677z6NHTtW//Vf/xXoUloMHTpUixcvVl5enkJCQrR06dJAl9Rl0HcRTOi77UfflSxeThNI+voPAo4dO6ZFixYFuhQgqJ0+fVq/+MUv9Oyzzwa6FHQy+i5gBvqumVjuAsAYb7/9tn72s58Z8StgAAgG9F1zcSYdAAAAMAxn0gEAAADDENIBAAAAwxDSAQAAAMMQ0oHv6OWXX1ZeXt4Vv66+vl6FhYWdUBEAdG30XQQTQjrgZ1VVVfywAAA/ou/iasTNjIBLaGxsVHp6uk6ePKnm5mbNnDlTEydO1B/+8AetWbNGPXv2VEhISMvtnbdt26a33npLNptNCQkJLTdgeP/993XhwgWtXr1agwYNkiRt2bJFR44cUX5+vvbv36/s7GzFxcVp37592rt3r3r37t1yi+bz589r+fLlSkhI0G9+8xs988wzCgkJ0W233ca1pQF0KfRdoDXOpAOXsGPHDvXq1UsFBQXavn27/vM//1M1NTXKycnR+vXrtX37dvXr10+S9PHHH+s3v/mNCgoKVFBQoIqKCu3Zs0eSFBsbq4KCgpYfFJI0d+5cff/739f8+fM1efJkvfbaa5KknTt3Kjk5WZLUvXt3Pffcc8rNzdVjjz2mc+fOKS8vT88884xefvllnT59WgcOHPDzVwUAOg99F2iNkA5cwtGjRzVixAhJkt1u16BBg3Ty5EmdPn1aAwcOlCTFx8dLko4dO6Zbb71VoaGhslgsSkhI0KeffipJLWPbMnHiRP3+97/Xl19+qS+++EJDhgyRJN1+++2SpLi4OFVXV+vEiROqqanRww8/rBkzZujo0aM6efJkp+w7AAQCfRdojZAOXMKgQYP0hz/8QZLkcrn0ySefqF+/foqJidHRo0clSR9++KGkr8/alJWVqampSV6vV//zP//T8kMiJOTbH7GQkBB5PB5JUnh4uEaNGqXVq1fr3nvvbRnjdDolSZ988omuueYa9evXT9ddd522bdum559/Xg888IBuvfXWzvsCAICf0XeB1liTDlzClClTlJGRoZSUFNXX12v+/Pnq06ePcnNzlZaWpoiICEVERKhnz576u7/7O/3whz9USkqKPB6PbrvtNo0bN05/+tOfLvneffr0UWNjo3Jzc7V48WJNmTJFKSkpWrFiRcuYjz76SA899JAuXryo7Oxs9e7dWz/96U81Y8YMNTc36/rrr9cPf/hDP301AKDz0XeB1ixer9cb6CKAYFZWVqYXXnhBjz/+uCQpLy9P0dHRSklJCXBlANA10XdxNeBMOhBAL7zwgnbu3KmNGzcGuhQACAr0XVwtOJMOAAAAGIY/HAUAAAAMQ0gHAAAADENIBwAAAAxDSAcAAAAMQ0gHAAAADPN/1dopT0A0Bw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#more visualizations using catplot\n",
    "#side by side comparison of odor and class\n",
    "g=sns.catplot('odor',col='class',data=data,kind='count',col_wrap=4)\n",
    "g.set_axis_labels(\"odor type\", \"# of samples\")\n",
    "g.set_titles(\"{col_name} {col_var}\")\n",
    "g.despine(left=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a195e7a10>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('class',col='gill-color',data=data,kind='count',height=2.5,aspect=.8,col_wrap=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in final csv after one-hot encoding applied, original columns dropped\n",
    "#all values in dataset are now 0 and 1\n",
    "df=pd.read_csv('final_mushroom_data.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the correlation between features? between each feature and class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corr=df.corr()\n",
    "\n",
    "#plotting correlation matrix\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>bruises</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>cap-color_c</th>\n",
       "      <th>cap-color_e</th>\n",
       "      <th>cap-color_g</th>\n",
       "      <th>cap-color_n</th>\n",
       "      <th>cap-color_p</th>\n",
       "      <th>cap-color_r</th>\n",
       "      <th>cap-color_u</th>\n",
       "      <th>cap-color_w</th>\n",
       "      <th>cap-color_y</th>\n",
       "      <th>odor_c</th>\n",
       "      <th>odor_f</th>\n",
       "      <th>odor_l</th>\n",
       "      <th>odor_m</th>\n",
       "      <th>odor_n</th>\n",
       "      <th>odor_p</th>\n",
       "      <th>odor_s</th>\n",
       "      <th>odor_y</th>\n",
       "      <th>gill-color_e</th>\n",
       "      <th>gill-color_g</th>\n",
       "      <th>gill-color_h</th>\n",
       "      <th>gill-color_k</th>\n",
       "      <th>gill-color_n</th>\n",
       "      <th>gill-color_o</th>\n",
       "      <th>gill-color_p</th>\n",
       "      <th>gill-color_r</th>\n",
       "      <th>gill-color_u</th>\n",
       "      <th>gill-color_w</th>\n",
       "      <th>gill-color_y</th>\n",
       "      <th>stalk-root_c</th>\n",
       "      <th>stalk-root_e</th>\n",
       "      <th>stalk-root_other</th>\n",
       "      <th>stalk-root_r</th>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <th>stalk-surface-above-ring_s</th>\n",
       "      <th>stalk-surface-above-ring_y</th>\n",
       "      <th>stalk-surface-below-ring_k</th>\n",
       "      <th>stalk-surface-below-ring_s</th>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <th>stalk-color-above-ring_c</th>\n",
       "      <th>stalk-color-above-ring_e</th>\n",
       "      <th>stalk-color-above-ring_g</th>\n",
       "      <th>stalk-color-above-ring_n</th>\n",
       "      <th>stalk-color-above-ring_o</th>\n",
       "      <th>stalk-color-above-ring_p</th>\n",
       "      <th>stalk-color-above-ring_w</th>\n",
       "      <th>stalk-color-above-ring_y</th>\n",
       "      <th>stalk-color-below-ring_c</th>\n",
       "      <th>stalk-color-below-ring_e</th>\n",
       "      <th>stalk-color-below-ring_g</th>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <th>stalk-color-below-ring_o</th>\n",
       "      <th>stalk-color-below-ring_p</th>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <th>stalk-color-below-ring_y</th>\n",
       "      <th>veil-color_o</th>\n",
       "      <th>veil-color_w</th>\n",
       "      <th>veil-color_y</th>\n",
       "      <th>ring-type_f</th>\n",
       "      <th>ring-type_l</th>\n",
       "      <th>ring-type_n</th>\n",
       "      <th>ring-type_p</th>\n",
       "      <th>spore-print-color_h</th>\n",
       "      <th>spore-print-color_k</th>\n",
       "      <th>spore-print-color_n</th>\n",
       "      <th>spore-print-color_o</th>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <th>spore-print-color_u</th>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <th>spore-print-color_y</th>\n",
       "      <th>population_c</th>\n",
       "      <th>population_n</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>class</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bruises</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.767036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-attachment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.955097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.955097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-size</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>habitat_l</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>habitat_m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>habitat_p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>habitat_u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>habitat_w</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.702866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.702866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.702866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.744425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 class  bruises  gill-attachment  gill-spacing  gill-size  \\\n",
       "class              1.0      NaN              NaN           NaN        NaN   \n",
       "bruises            NaN      1.0              NaN           NaN        NaN   \n",
       "gill-attachment    NaN      NaN              1.0           NaN        NaN   \n",
       "gill-spacing       NaN      NaN              NaN           1.0        NaN   \n",
       "gill-size          NaN      NaN              NaN           NaN        1.0   \n",
       "...                ...      ...              ...           ...        ...   \n",
       "habitat_l          NaN      NaN              NaN           NaN        NaN   \n",
       "habitat_m          NaN      NaN              NaN           NaN        NaN   \n",
       "habitat_p          NaN      NaN              NaN           NaN        NaN   \n",
       "habitat_u          NaN      NaN              NaN           NaN        NaN   \n",
       "habitat_w          NaN      NaN              NaN           NaN        NaN   \n",
       "\n",
       "                 stalk-shape  ring-number  cap-shape_c  cap-shape_f  \\\n",
       "class                    NaN          NaN          NaN          NaN   \n",
       "bruises                  NaN          NaN          NaN          NaN   \n",
       "gill-attachment          NaN          NaN          NaN          NaN   \n",
       "gill-spacing             NaN          NaN          NaN          NaN   \n",
       "gill-size                NaN          NaN          NaN          NaN   \n",
       "...                      ...          ...          ...          ...   \n",
       "habitat_l                NaN          NaN          NaN          NaN   \n",
       "habitat_m                NaN          NaN          NaN          NaN   \n",
       "habitat_p                NaN          NaN          NaN          NaN   \n",
       "habitat_u                NaN          NaN          NaN          NaN   \n",
       "habitat_w                NaN          NaN          NaN          NaN   \n",
       "\n",
       "                 cap-shape_k  cap-shape_s  cap-shape_x  cap-surface_g  \\\n",
       "class                    NaN          NaN          NaN            NaN   \n",
       "bruises                  NaN          NaN          NaN            NaN   \n",
       "gill-attachment          NaN          NaN          NaN            NaN   \n",
       "gill-spacing             NaN          NaN          NaN            NaN   \n",
       "gill-size                NaN          NaN          NaN            NaN   \n",
       "...                      ...          ...          ...            ...   \n",
       "habitat_l                NaN          NaN          NaN            NaN   \n",
       "habitat_m                NaN          NaN          NaN            NaN   \n",
       "habitat_p                NaN          NaN          NaN            NaN   \n",
       "habitat_u                NaN          NaN          NaN            NaN   \n",
       "habitat_w                NaN          NaN          NaN            NaN   \n",
       "\n",
       "                 cap-surface_s  cap-surface_y  cap-color_c  cap-color_e  \\\n",
       "class                      NaN            NaN          NaN          NaN   \n",
       "bruises                    NaN            NaN          NaN          NaN   \n",
       "gill-attachment            NaN            NaN          NaN          NaN   \n",
       "gill-spacing               NaN            NaN          NaN          NaN   \n",
       "gill-size                  NaN            NaN          NaN          NaN   \n",
       "...                        ...            ...          ...          ...   \n",
       "habitat_l                  NaN            NaN          NaN          NaN   \n",
       "habitat_m                  NaN            NaN          NaN          NaN   \n",
       "habitat_p                  NaN            NaN          NaN          NaN   \n",
       "habitat_u                  NaN            NaN          NaN          NaN   \n",
       "habitat_w                  NaN            NaN          NaN          NaN   \n",
       "\n",
       "                 cap-color_g  cap-color_n  cap-color_p  cap-color_r  \\\n",
       "class                    NaN          NaN          NaN          NaN   \n",
       "bruises                  NaN          NaN          NaN          NaN   \n",
       "gill-attachment          NaN          NaN          NaN          NaN   \n",
       "gill-spacing             NaN          NaN          NaN          NaN   \n",
       "gill-size                NaN          NaN          NaN          NaN   \n",
       "...                      ...          ...          ...          ...   \n",
       "habitat_l                NaN          NaN          NaN          NaN   \n",
       "habitat_m                NaN          NaN          NaN          NaN   \n",
       "habitat_p                NaN          NaN          NaN          NaN   \n",
       "habitat_u                NaN          NaN          NaN          NaN   \n",
       "habitat_w                NaN          NaN          NaN          NaN   \n",
       "\n",
       "                 cap-color_u  cap-color_w  cap-color_y  odor_c  odor_f  \\\n",
       "class                    NaN          NaN          NaN     NaN     NaN   \n",
       "bruises                  NaN          NaN          NaN     NaN     NaN   \n",
       "gill-attachment          NaN          NaN          NaN     NaN     NaN   \n",
       "gill-spacing             NaN          NaN          NaN     NaN     NaN   \n",
       "gill-size                NaN          NaN          NaN     NaN     NaN   \n",
       "...                      ...          ...          ...     ...     ...   \n",
       "habitat_l                NaN          NaN          NaN     NaN     NaN   \n",
       "habitat_m                NaN          NaN          NaN     NaN     NaN   \n",
       "habitat_p                NaN          NaN          NaN     NaN     NaN   \n",
       "habitat_u                NaN          NaN          NaN     NaN     NaN   \n",
       "habitat_w                NaN          NaN          NaN     NaN     NaN   \n",
       "\n",
       "                 odor_l  odor_m  odor_n  odor_p  odor_s  odor_y  gill-color_e  \\\n",
       "class               NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "bruises             NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "gill-attachment     NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "gill-spacing        NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "gill-size           NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "...                 ...     ...     ...     ...     ...     ...           ...   \n",
       "habitat_l           NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "habitat_m           NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "habitat_p           NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "habitat_u           NaN     NaN     NaN     NaN     NaN     NaN           NaN   \n",
       "habitat_w           NaN     NaN     NaN     NaN     NaN     NaN      0.702866   \n",
       "\n",
       "                 gill-color_g  gill-color_h  gill-color_k  gill-color_n  \\\n",
       "class                     NaN           NaN           NaN           NaN   \n",
       "bruises                   NaN           NaN           NaN           NaN   \n",
       "gill-attachment           NaN           NaN           NaN           NaN   \n",
       "gill-spacing              NaN           NaN           NaN           NaN   \n",
       "gill-size                 NaN           NaN           NaN           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "habitat_l                 NaN           NaN           NaN           NaN   \n",
       "habitat_m                 NaN           NaN           NaN           NaN   \n",
       "habitat_p                 NaN           NaN           NaN           NaN   \n",
       "habitat_u                 NaN           NaN           NaN           NaN   \n",
       "habitat_w                 NaN           NaN           NaN           NaN   \n",
       "\n",
       "                 gill-color_o  gill-color_p  gill-color_r  gill-color_u  \\\n",
       "class                     NaN           NaN           NaN           NaN   \n",
       "bruises                   NaN           NaN           NaN           NaN   \n",
       "gill-attachment           NaN           NaN           NaN           NaN   \n",
       "gill-spacing              NaN           NaN           NaN           NaN   \n",
       "gill-size                 NaN           NaN           NaN           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "habitat_l                 NaN           NaN           NaN           NaN   \n",
       "habitat_m                 NaN           NaN           NaN           NaN   \n",
       "habitat_p                 NaN           NaN           NaN           NaN   \n",
       "habitat_u                 NaN           NaN           NaN           NaN   \n",
       "habitat_w                 NaN           NaN           NaN           NaN   \n",
       "\n",
       "                 gill-color_w  gill-color_y  stalk-root_c  stalk-root_e  \\\n",
       "class                     NaN           NaN           NaN           NaN   \n",
       "bruises                   NaN           NaN           NaN           NaN   \n",
       "gill-attachment           NaN           NaN           NaN           NaN   \n",
       "gill-spacing              NaN           NaN           NaN           NaN   \n",
       "gill-size                 NaN           NaN           NaN           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "habitat_l                 NaN           NaN           NaN           NaN   \n",
       "habitat_m                 NaN           NaN           NaN           NaN   \n",
       "habitat_p                 NaN           NaN           NaN           NaN   \n",
       "habitat_u                 NaN           NaN           NaN           NaN   \n",
       "habitat_w                 NaN           NaN           NaN           NaN   \n",
       "\n",
       "                 stalk-root_other  stalk-root_r  stalk-surface-above-ring_k  \\\n",
       "class                         NaN           NaN                         NaN   \n",
       "bruises                       NaN           NaN                         NaN   \n",
       "gill-attachment               NaN           NaN                         NaN   \n",
       "gill-spacing                  NaN           NaN                         NaN   \n",
       "gill-size                     NaN           NaN                         NaN   \n",
       "...                           ...           ...                         ...   \n",
       "habitat_l                     NaN           NaN                         NaN   \n",
       "habitat_m                     NaN           NaN                         NaN   \n",
       "habitat_p                     NaN           NaN                         NaN   \n",
       "habitat_u                     NaN           NaN                         NaN   \n",
       "habitat_w                     NaN           NaN                         NaN   \n",
       "\n",
       "                 stalk-surface-above-ring_s  stalk-surface-above-ring_y  \\\n",
       "class                                   NaN                         NaN   \n",
       "bruises                                 NaN                         NaN   \n",
       "gill-attachment                         NaN                         NaN   \n",
       "gill-spacing                            NaN                         NaN   \n",
       "gill-size                               NaN                         NaN   \n",
       "...                                     ...                         ...   \n",
       "habitat_l                               NaN                         NaN   \n",
       "habitat_m                               NaN                         NaN   \n",
       "habitat_p                               NaN                         NaN   \n",
       "habitat_u                               NaN                         NaN   \n",
       "habitat_w                               NaN                         NaN   \n",
       "\n",
       "                 stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "class                                   NaN                         NaN   \n",
       "bruises                                 NaN                         NaN   \n",
       "gill-attachment                         NaN                         NaN   \n",
       "gill-spacing                            NaN                         NaN   \n",
       "gill-size                               NaN                         NaN   \n",
       "...                                     ...                         ...   \n",
       "habitat_l                               NaN                         NaN   \n",
       "habitat_m                               NaN                         NaN   \n",
       "habitat_p                               NaN                         NaN   \n",
       "habitat_u                               NaN                         NaN   \n",
       "habitat_w                               NaN                         NaN   \n",
       "\n",
       "                 stalk-surface-below-ring_y  stalk-color-above-ring_c  \\\n",
       "class                                   NaN                       NaN   \n",
       "bruises                                 NaN                       NaN   \n",
       "gill-attachment                         NaN                       NaN   \n",
       "gill-spacing                            NaN                       NaN   \n",
       "gill-size                               NaN                       NaN   \n",
       "...                                     ...                       ...   \n",
       "habitat_l                               NaN                       NaN   \n",
       "habitat_m                               NaN                       NaN   \n",
       "habitat_p                               NaN                       NaN   \n",
       "habitat_u                               NaN                       NaN   \n",
       "habitat_w                               NaN                       NaN   \n",
       "\n",
       "                 stalk-color-above-ring_e  stalk-color-above-ring_g  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                       NaN   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                        0.702866                       NaN   \n",
       "\n",
       "                 stalk-color-above-ring_n  stalk-color-above-ring_o  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                  0.955097   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                             NaN                       NaN   \n",
       "\n",
       "                 stalk-color-above-ring_p  stalk-color-above-ring_w  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                       NaN   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                             NaN                       NaN   \n",
       "\n",
       "                 stalk-color-above-ring_y  stalk-color-below-ring_c  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                       NaN   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                             NaN                       NaN   \n",
       "\n",
       "                 stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                       NaN   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                        0.702866                       NaN   \n",
       "\n",
       "                 stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                  0.955097   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                             NaN                       NaN   \n",
       "\n",
       "                 stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "class                                 NaN                       NaN   \n",
       "bruises                               NaN                       NaN   \n",
       "gill-attachment                       NaN                       NaN   \n",
       "gill-spacing                          NaN                       NaN   \n",
       "gill-size                             NaN                       NaN   \n",
       "...                                   ...                       ...   \n",
       "habitat_l                             NaN                       NaN   \n",
       "habitat_m                             NaN                       NaN   \n",
       "habitat_p                             NaN                       NaN   \n",
       "habitat_u                             NaN                       NaN   \n",
       "habitat_w                             NaN                       NaN   \n",
       "\n",
       "                 stalk-color-below-ring_y  veil-color_o  veil-color_w  \\\n",
       "class                                 NaN           NaN           NaN   \n",
       "bruises                               NaN           NaN           NaN   \n",
       "gill-attachment                       NaN           NaN           NaN   \n",
       "gill-spacing                          NaN           NaN           NaN   \n",
       "gill-size                             NaN           NaN           NaN   \n",
       "...                                   ...           ...           ...   \n",
       "habitat_l                             NaN           NaN           NaN   \n",
       "habitat_m                             NaN           NaN           NaN   \n",
       "habitat_p                             NaN           NaN           NaN   \n",
       "habitat_u                             NaN           NaN           NaN   \n",
       "habitat_w                             NaN           NaN           NaN   \n",
       "\n",
       "                 veil-color_y  ring-type_f  ring-type_l  ring-type_n  \\\n",
       "class                     NaN          NaN          NaN          NaN   \n",
       "bruises                   NaN          NaN          NaN          NaN   \n",
       "gill-attachment           NaN          NaN          NaN          NaN   \n",
       "gill-spacing              NaN          NaN          NaN          NaN   \n",
       "gill-size                 NaN          NaN          NaN          NaN   \n",
       "...                       ...          ...          ...          ...   \n",
       "habitat_l                 NaN          NaN          NaN          NaN   \n",
       "habitat_m                 NaN          NaN          NaN          NaN   \n",
       "habitat_p                 NaN          NaN          NaN          NaN   \n",
       "habitat_u                 NaN          NaN          NaN          NaN   \n",
       "habitat_w                 NaN          NaN          NaN          NaN   \n",
       "\n",
       "                 ring-type_p  spore-print-color_h  spore-print-color_k  \\\n",
       "class                    NaN                  NaN                  NaN   \n",
       "bruises             0.767036                  NaN                  NaN   \n",
       "gill-attachment          NaN                  NaN                  NaN   \n",
       "gill-spacing             NaN                  NaN                  NaN   \n",
       "gill-size                NaN                  NaN                  NaN   \n",
       "...                      ...                  ...                  ...   \n",
       "habitat_l                NaN                  NaN                  NaN   \n",
       "habitat_m                NaN                  NaN                  NaN   \n",
       "habitat_p                NaN                  NaN                  NaN   \n",
       "habitat_u                NaN                  NaN                  NaN   \n",
       "habitat_w                NaN                  NaN                  NaN   \n",
       "\n",
       "                 spore-print-color_n  spore-print-color_o  \\\n",
       "class                            NaN                  NaN   \n",
       "bruises                          NaN                  NaN   \n",
       "gill-attachment                  NaN                  NaN   \n",
       "gill-spacing                     NaN                  NaN   \n",
       "gill-size                        NaN                  NaN   \n",
       "...                              ...                  ...   \n",
       "habitat_l                        NaN                  NaN   \n",
       "habitat_m                        NaN                  NaN   \n",
       "habitat_p                        NaN                  NaN   \n",
       "habitat_u                        NaN                  NaN   \n",
       "habitat_w                        NaN                  NaN   \n",
       "\n",
       "                 spore-print-color_r  spore-print-color_u  \\\n",
       "class                            NaN                  NaN   \n",
       "bruises                          NaN                  NaN   \n",
       "gill-attachment                  NaN                  NaN   \n",
       "gill-spacing                     NaN                  NaN   \n",
       "gill-size                        NaN                  NaN   \n",
       "...                              ...                  ...   \n",
       "habitat_l                        NaN                  NaN   \n",
       "habitat_m                        NaN                  NaN   \n",
       "habitat_p                        NaN                  NaN   \n",
       "habitat_u                        NaN                  NaN   \n",
       "habitat_w                        NaN                  NaN   \n",
       "\n",
       "                 spore-print-color_w  spore-print-color_y  population_c  \\\n",
       "class                            NaN                  NaN           NaN   \n",
       "bruises                          NaN                  NaN           NaN   \n",
       "gill-attachment                  NaN                  NaN           NaN   \n",
       "gill-spacing                     NaN                  NaN           NaN   \n",
       "gill-size                        NaN                  NaN           NaN   \n",
       "...                              ...                  ...           ...   \n",
       "habitat_l                        NaN                  NaN           NaN   \n",
       "habitat_m                        NaN                  NaN           NaN   \n",
       "habitat_p                        NaN                  NaN           NaN   \n",
       "habitat_u                        NaN                  NaN           NaN   \n",
       "habitat_w                        NaN                  NaN      0.744425   \n",
       "\n",
       "                 population_n  population_s  population_v  population_y  \\\n",
       "class                     NaN           NaN           NaN           NaN   \n",
       "bruises                   NaN           NaN           NaN           NaN   \n",
       "gill-attachment           NaN           NaN           NaN           NaN   \n",
       "gill-spacing              NaN           NaN           NaN           NaN   \n",
       "gill-size                 NaN           NaN           NaN           NaN   \n",
       "...                       ...           ...           ...           ...   \n",
       "habitat_l                 NaN           NaN           NaN           NaN   \n",
       "habitat_m                 NaN           NaN           NaN           NaN   \n",
       "habitat_p                 NaN           NaN           NaN           NaN   \n",
       "habitat_u                 NaN           NaN           NaN           NaN   \n",
       "habitat_w                 NaN           NaN           NaN           NaN   \n",
       "\n",
       "                 habitat_g  habitat_l  habitat_m  habitat_p  habitat_u  \\\n",
       "class                  NaN        NaN        NaN        NaN        NaN   \n",
       "bruises                NaN        NaN        NaN        NaN        NaN   \n",
       "gill-attachment        NaN        NaN        NaN        NaN        NaN   \n",
       "gill-spacing           NaN        NaN        NaN        NaN        NaN   \n",
       "gill-size              NaN        NaN        NaN        NaN        NaN   \n",
       "...                    ...        ...        ...        ...        ...   \n",
       "habitat_l              NaN        1.0        NaN        NaN        NaN   \n",
       "habitat_m              NaN        NaN        1.0        NaN        NaN   \n",
       "habitat_p              NaN        NaN        NaN        1.0        NaN   \n",
       "habitat_u              NaN        NaN        NaN        NaN        1.0   \n",
       "habitat_w              NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "                 habitat_w  \n",
       "class                  NaN  \n",
       "bruises                NaN  \n",
       "gill-attachment        NaN  \n",
       "gill-spacing           NaN  \n",
       "gill-size              NaN  \n",
       "...                    ...  \n",
       "habitat_l              NaN  \n",
       "habitat_m              NaN  \n",
       "habitat_p              NaN  \n",
       "habitat_u              NaN  \n",
       "habitat_w              1.0  \n",
       "\n",
       "[95 rows x 95 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr[abs(corr>.70)]\n",
    "#gill size, stalk surface above ring_k and odor_f all appear to be the highest correlated with class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                         1.000000\n",
       "odor_f                        0.623842\n",
       "stalk-surface-above-ring_k    0.587658\n",
       "stalk-surface-below-ring_k    0.573524\n",
       "gill-size                     0.540024\n",
       "spore-print-color_h           0.490229\n",
       "ring-type_l                   0.451619\n",
       "population_v                  0.443722\n",
       "spore-print-color_w           0.357384\n",
       "habitat_p                     0.323346\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr['class'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "Models that were run: Dummy Classifier (baseline), Logistic Regression, KNN, Decision Tree Classifier, Random Forest Classifier\n",
    "\n",
    "- Gridsearch CV was used to tune hyperparameters and find the best model for each classifier (except the baseline model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>bruises</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>cap-color_c</th>\n",
       "      <th>cap-color_e</th>\n",
       "      <th>cap-color_g</th>\n",
       "      <th>cap-color_n</th>\n",
       "      <th>cap-color_p</th>\n",
       "      <th>cap-color_r</th>\n",
       "      <th>cap-color_u</th>\n",
       "      <th>cap-color_w</th>\n",
       "      <th>cap-color_y</th>\n",
       "      <th>odor_c</th>\n",
       "      <th>odor_f</th>\n",
       "      <th>odor_l</th>\n",
       "      <th>odor_m</th>\n",
       "      <th>odor_n</th>\n",
       "      <th>odor_p</th>\n",
       "      <th>odor_s</th>\n",
       "      <th>odor_y</th>\n",
       "      <th>gill-color_e</th>\n",
       "      <th>gill-color_g</th>\n",
       "      <th>gill-color_h</th>\n",
       "      <th>gill-color_k</th>\n",
       "      <th>gill-color_n</th>\n",
       "      <th>gill-color_o</th>\n",
       "      <th>gill-color_p</th>\n",
       "      <th>gill-color_r</th>\n",
       "      <th>gill-color_u</th>\n",
       "      <th>gill-color_w</th>\n",
       "      <th>gill-color_y</th>\n",
       "      <th>stalk-root_c</th>\n",
       "      <th>stalk-root_e</th>\n",
       "      <th>stalk-root_other</th>\n",
       "      <th>stalk-root_r</th>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <th>stalk-surface-above-ring_s</th>\n",
       "      <th>stalk-surface-above-ring_y</th>\n",
       "      <th>stalk-surface-below-ring_k</th>\n",
       "      <th>stalk-surface-below-ring_s</th>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <th>stalk-color-above-ring_c</th>\n",
       "      <th>stalk-color-above-ring_e</th>\n",
       "      <th>stalk-color-above-ring_g</th>\n",
       "      <th>stalk-color-above-ring_n</th>\n",
       "      <th>stalk-color-above-ring_o</th>\n",
       "      <th>stalk-color-above-ring_p</th>\n",
       "      <th>stalk-color-above-ring_w</th>\n",
       "      <th>stalk-color-above-ring_y</th>\n",
       "      <th>stalk-color-below-ring_c</th>\n",
       "      <th>stalk-color-below-ring_e</th>\n",
       "      <th>stalk-color-below-ring_g</th>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <th>stalk-color-below-ring_o</th>\n",
       "      <th>stalk-color-below-ring_p</th>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <th>stalk-color-below-ring_y</th>\n",
       "      <th>veil-color_o</th>\n",
       "      <th>veil-color_w</th>\n",
       "      <th>veil-color_y</th>\n",
       "      <th>ring-type_f</th>\n",
       "      <th>ring-type_l</th>\n",
       "      <th>ring-type_n</th>\n",
       "      <th>ring-type_p</th>\n",
       "      <th>spore-print-color_h</th>\n",
       "      <th>spore-print-color_k</th>\n",
       "      <th>spore-print-color_n</th>\n",
       "      <th>spore-print-color_o</th>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <th>spore-print-color_u</th>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <th>spore-print-color_y</th>\n",
       "      <th>population_c</th>\n",
       "      <th>population_n</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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>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>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>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>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>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",
       "      <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>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",
       "      <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>0</td>\n",
       "    </tr>\n",
       "    <tr>\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>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>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>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>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",
       "      <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>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>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>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>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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>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",
       "      <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>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>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>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>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",
       "      <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>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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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",
       "      <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>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",
       "      <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>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>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>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>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",
       "      <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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <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",
       "      <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>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>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>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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  bruises  gill-attachment  gill-spacing  gill-size  stalk-shape  \\\n",
       "0      1        1                0             0          1            0   \n",
       "1      0        1                0             0          0            0   \n",
       "2      0        1                0             0          0            0   \n",
       "3      1        1                0             0          1            0   \n",
       "4      0        0                0             1          0            1   \n",
       "\n",
       "   ring-number  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            1            0            0            0            0   \n",
       "1            1            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            1            0            0            0            0   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_g  cap-surface_s  cap-surface_y  cap-color_c  \\\n",
       "0            1              0              1              0            0   \n",
       "1            1              0              1              0            0   \n",
       "2            0              0              1              0            0   \n",
       "3            1              0              0              1            0   \n",
       "4            1              0              1              0            0   \n",
       "\n",
       "   cap-color_e  cap-color_g  cap-color_n  cap-color_p  cap-color_r  \\\n",
       "0            0            0            1            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            1            0            0            0   \n",
       "\n",
       "   cap-color_u  cap-color_w  cap-color_y  odor_c  odor_f  odor_l  odor_m  \\\n",
       "0            0            0            0       0       0       0       0   \n",
       "1            0            0            1       0       0       0       0   \n",
       "2            0            1            0       0       0       1       0   \n",
       "3            0            1            0       0       0       0       0   \n",
       "4            0            0            0       0       0       0       0   \n",
       "\n",
       "   odor_n  odor_p  odor_s  odor_y  gill-color_e  gill-color_g  gill-color_h  \\\n",
       "0       0       1       0       0             0             0             0   \n",
       "1       0       0       0       0             0             0             0   \n",
       "2       0       0       0       0             0             0             0   \n",
       "3       0       1       0       0             0             0             0   \n",
       "4       1       0       0       0             0             0             0   \n",
       "\n",
       "   gill-color_k  gill-color_n  gill-color_o  gill-color_p  gill-color_r  \\\n",
       "0             1             0             0             0             0   \n",
       "1             1             0             0             0             0   \n",
       "2             0             1             0             0             0   \n",
       "3             0             1             0             0             0   \n",
       "4             1             0             0             0             0   \n",
       "\n",
       "   gill-color_u  gill-color_w  gill-color_y  stalk-root_c  stalk-root_e  \\\n",
       "0             0             0             0             0             1   \n",
       "1             0             0             0             1             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             0             1   \n",
       "4             0             0             0             0             1   \n",
       "\n",
       "   stalk-root_other  stalk-root_r  stalk-surface-above-ring_k  \\\n",
       "0                 0             0                           0   \n",
       "1                 0             0                           0   \n",
       "2                 0             0                           0   \n",
       "3                 0             0                           0   \n",
       "4                 0             0                           0   \n",
       "\n",
       "   stalk-surface-above-ring_s  stalk-surface-above-ring_y  \\\n",
       "0                           1                           0   \n",
       "1                           1                           0   \n",
       "2                           1                           0   \n",
       "3                           1                           0   \n",
       "4                           1                           0   \n",
       "\n",
       "   stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-below-ring_y  stalk-color-above-ring_c  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   stalk-color-above-ring_e  stalk-color-above-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_n  stalk-color-above-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_p  stalk-color-above-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-above-ring_y  stalk-color-below-ring_c  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-below-ring_y  veil-color_o  veil-color_w  veil-color_y  \\\n",
       "0                         0             0             1             0   \n",
       "1                         0             0             1             0   \n",
       "2                         0             0             1             0   \n",
       "3                         0             0             1             0   \n",
       "4                         0             0             1             0   \n",
       "\n",
       "   ring-type_f  ring-type_l  ring-type_n  ring-type_p  spore-print-color_h  \\\n",
       "0            0            0            0            1                    0   \n",
       "1            0            0            0            1                    0   \n",
       "2            0            0            0            1                    0   \n",
       "3            0            0            0            1                    0   \n",
       "4            0            0            0            0                    0   \n",
       "\n",
       "   spore-print-color_k  spore-print-color_n  spore-print-color_o  \\\n",
       "0                    1                    0                    0   \n",
       "1                    0                    1                    0   \n",
       "2                    0                    1                    0   \n",
       "3                    1                    0                    0   \n",
       "4                    0                    1                    0   \n",
       "\n",
       "   spore-print-color_r  spore-print-color_u  spore-print-color_w  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   spore-print-color_y  population_c  population_n  population_s  \\\n",
       "0                    0             0             0             1   \n",
       "1                    0             0             1             0   \n",
       "2                    0             0             1             0   \n",
       "3                    0             0             0             1   \n",
       "4                    0             0             0             0   \n",
       "\n",
       "   population_v  population_y  habitat_g  habitat_l  habitat_m  habitat_p  \\\n",
       "0             0             0          0          0          0          0   \n",
       "1             0             0          1          0          0          0   \n",
       "2             0             0          0          0          1          0   \n",
       "3             0             0          0          0          0          0   \n",
       "4             0             0          1          0          0          0   \n",
       "\n",
       "   habitat_u  habitat_w  \n",
       "0          1          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          1          0  \n",
       "4          0          0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining target and features for models\n",
    "\n",
    "y=df['class']\n",
    "X=df.drop(['class'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating test/train split\n",
    "#scaling not necessary\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting up results dictionary to save different model results here\n",
    "results_dict={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to get best model stats after running gridsearch\n",
    "def examinebestmodel(model_name):\n",
    "    print(model_name.best_score_)\n",
    "    print(model_name.best_params_)\n",
    "    print(model_name.best_estimator_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to print results with test set and prediction variable\n",
    "def getresults(test,pred_variable):\n",
    "    return \"F1:\", metrics.f1_score(test,pred_variable),\"Accuracy:\", metrics.accuracy_score(y_test,pred_variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummy Classifier\n",
    "- predicts the majority class of the training data (edible)\n",
    "- not applicable for real world usage as a false negative could be  deadly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1:', 0.0, 'Accuracy:', 0.5089230769230769)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dummyclassifier for baseline\n",
    "#just predicts majority class of training data\n",
    "#doesn't work great and not useful bc it would leave a person sick for about half\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf=DummyClassifier(strategy='most_frequent')\n",
    "dummy_clf.fit(X_train,y_train)\n",
    "dummy_pred=dummy_clf.predict(X_test)\n",
    "\n",
    "results_dict['Dummy Classifier']=getresults(y_test,dummy_pred)\n",
    "getresults(y_test,dummy_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot confusion matrix for dummy classifier\n",
    "plot_confusion_matrix(dummy_clf,X_test,y_test,cmap='inferno',values_format='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression with GridCV\n",
    "- predicted really well: ('F1:', 0.9981167608286252, 'Accuracy:', 0.9981538461538462)\n",
    "- after viewing the confusion matrix, the model had 3 false negatives which in terms of wild mushrooms could be deadly \n",
    "- best params from the grid search: {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logreg model\n",
    "logreg=LogisticRegression()\n",
    "penalty = ['l2']\n",
    "c_range = [0.001, 0.01, 0.1, 1]\n",
    "solver=['lbfgs']\n",
    "parameters_lr = dict(C=c_range, penalty=penalty,solver=solver)\n",
    "lr_1=GridSearchCV(logreg,parameters_lr,scoring='accuracy',cv=3,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training model and predicting on test set\n",
    "lr_1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9992307455220683\n",
      "{'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "examinebestmodel(lr_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1:', 0.9981167608286252, 'Accuracy:', 0.9981538461538462)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict on test set - logreg\n",
    "y_pred_lr_1=lr_1.best_estimator_.predict(X_test)\n",
    "results_dict['Log_Reg']=getresults(y_test,y_pred_lr_1)\n",
    "getresults(y_test,y_pred_lr_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting confusion matrix for LR model\n",
    "plot_confusion_matrix(lr_1.best_estimator_,X_test,y_test,cmap='inferno',values_format='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN with GridCV\n",
    "- this model performed perfectly on the test and .99 on the training set\n",
    "- best params from the grid search: {'algorithm': 'brute', 'metric': 'manhattan', 'n_neighbors': 5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN classifier with gridsearch\n",
    "knn = KNeighborsClassifier()\n",
    "neighbors=[5,9,13]\n",
    "metric=['manhattan','euclidean']\n",
    "algorithm=['brute','ball_tree']\n",
    "parameters_knn=dict(n_neighbors=neighbors,metric=metric,algorithm=algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:   27.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'algorithm': ['brute', 'ball_tree'],\n",
       "                         'metric': ['manhattan', 'euclidean'],\n",
       "                         'n_neighbors': [5, 9, 13]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training KNN model, finding best params\n",
    "gridknn=GridSearchCV(knn,parameters_knn,cv=3,verbose=1)\n",
    "gridknn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9995385325334564\n",
      "{'algorithm': 'brute', 'metric': 'manhattan', 'n_neighbors': 5}\n",
      "KNeighborsClassifier(algorithm='brute', leaf_size=30, metric='manhattan',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "#using my function to view the 'best' results\n",
    "examinebestmodel(gridknn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1:', 1.0, 'Accuracy:', 1.0)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predicting on test set\n",
    "y_pred_knn=gridknn.best_estimator_.predict(X_test)\n",
    "getresults(y_test,y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict['KNN']=getresults(y_test,y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7zLazWmFhIZYtW4a6ujpERUUhJSVF9flMekRktxtd0hNCoKSkBF988YUt6VVUVCAlJQXr16+Hh4cHpk2bhvHjx8Pf3x8LFixAVlYWBg4ciNmzZ2PHjh0ICwtr8x2tJr2lS5e2+cW0tDTNP4SIXJMiJCgqGwMp7SjqHT16FADwxBNP4Ny5c3j44Ydx0003YcKECfDx8QEAREREwGg0Yty4cQgICMDgwYMBAHFxcTAajfYnvaYXEBG1pj0lvfLy8hbXvL29m20DWVNTg5CQELz00ktoaGhAYmIioqKioNfrbff4+vpi//79qKysbHG+oqJCNeZWk15SUpLtzxaLBaWlpbjjjjtQV1eHXr16qT6YiFyfthkZjdeb2uGulJSUhOTkZNvnu+++G3fffbft89SpU7Fs2TLMmTPniucJSJIERVEgSVKL82pU2/T27duHuXPnQqfTYc2aNZg8eTJWr16NX/ziF6oPJyIXp6Ejo6knIzs7GwaDodmlqzf73rt3LxoaGhASEnL5qwJ+fn4wmUy2e0wmE3x9fWEwGK55Xo1KtMBrr72Gjz76CD4+PjAYDFi+fDnS09NVH0xErk8RkqYDAAwGA/z9/ZsdVye92tpaLF++HHV1dTCbzdiwYQNef/117N69G9XV1bh06RK2bduG0NBQjB49GseOHUNpaSlkWUZBQQFCQ0NVY1Yt6VksFgwbNsz2OSwsDJmZme39Z0NELuhG995OmjQJ+/btw4MPPghFUTBjxgyMGTMGKSkpSExMRENDA6ZOnYpRo0YBADIyMpCcnIy6ujqEhYUhMjJS9R2qSU+n0+H8+fO2unJT7woRkbbe2/ZNQ5s3bx7mzZvX7FxcXBzi4uJa3BsSEoL8/Px2PV816c2ZMwczZ86EyWTC888/j127dmHJkiXtegkRuab2dGQ4C9WkN2nSJAwZMgS7du2CoiiYO3cuhg4d6ojYiMjJCdEDiuqMDNWuA4fSNCPDarVCURTodDrbKGkioq4491Y1Ba9btw6JiYk4cOAA9u7di4SEBGzdutURsRGRk2uq3qodzkS12PbRRx9hw4YNtvEvp06dwuzZsxEREdHhwRGRc5NFD8gqHRlyV6veuru7NxvwN2jQILi7u3doUETUNQih3lHhZAsnt570fvjhBwDA8OHDsWTJEjzyyCNwc3PD+vXrORuDiAC4WO/tlfPhAODLL7+0/VmSJK6yQkQQV8y4aOseZ9Jq0tu+fbsj4yCiLsilSnpNqqurkZ+fjwsXLkAIAUVRUFpaijfeeMMR8RGRE3PJpDdv3jz07NkTR44cwcSJE1FYWIgxY8Y4IjYicnKyoqH3VuW6o6lGc+rUKfz5z39GaGgoZs6ciU8++YTzb4kIACCgYZwenKukp5r0brnlFgDAbbfdhn/9618YMGAArFZrhwdGRM5PCPXlpbrMkJUm/fv3x/vvv4+goCCsXLkSXl5esFgsjoiNiJxcV2zTUy3pLVmyBB4eHggODsbIkSPxzjvv4IUXXnBEbETk5NqziKiz0FTSS0xMBACkpqYiNTW1w4Mioq5BQL3Nztna9FpNenfffXebm2x89913HRIQEXUditIDsqSyiKiT9d62mvQKCgocGUcLQ4f8jh0mXUiNmQvLdhWnTpnxm9h1N+RZHdmm99prr+Hs2bPIyMjAoUOHsHDhQly4cAHBwcF4+eWXodPpcOrUKaSmpuLMmTO4/fbbsWLFCtx0001tPrfVFOzn59fmQUTUUW16u3fvxoYNG2yfU1NTsWjRImzduhVCCOTk5AAAXn75ZcyYMQNGoxEjR47EqlWrVJ/tXOVOIupSBDSsqdfOZ547dw6ZmZl4+umnAQAnT56ExWJBUFAQACA+Ph5GoxENDQ3Ys2ePbZm7pvNquAwyEdlNgQRFpaOi6Xp5eXmLa97e3i22gVy0aBFSUlJw+vRpAEBlZSX0er3tul6vR0VFBc6ePQsvLy/bau5N59VoSnoWiwWlpaW48847YbFY0KtXLy1fIyIXpyg9IKtUGJs6MhISElpcS0pKarai09q1azFw4ECEhIRg/fr1l7+vNOtUFUJAkiTb36/UVudrE9Wk989//hNJSUnQ6XRYs2YNJk+ejNWrV3NNPSJqV0dGdnY2DAZDs2tXl/I2b94Mk8mEyZMn4/z587h48SIkSYLJZLLdU1VVBV9fX/Tr1w+1tbWQZRlubm4wmUzNFjxujWqb3vLly/HRRx/Bx8cHBoMBy5cvR3p6uuqDicj1KRoPADAYDPD39292XJ30PvzwQxQUFCAvLw/PPvss7rvvPixbtgyenp4oKioCAOTl5SE0NBTu7u4IDg7G5s2bAQAbN25EaGioasyqSc9isWDYsGG2z2FhYZBlWfXBROT6HLUx0IoVK7Bs2TJERkbi4sWLtgkTixcvRk5ODqKjo7F3794Wm4Rfi2r1VqfT4fz587a6MldYIaImWoak2DsNLT4+HvHx8QCAwMBA5ObmtrjHz88PWVlZ7XquatKbM2cOZs6ciaqqKjz//PPYtWsXlizhQFQiAqBhGhq6yjS0JpMmTcKQIUOwa9cuKIqCuXPnYujQoY6IjYicnFUBrCpJzaq0ednhVJPeuXPn0LdvX0RHRzc75+Pj06GBEZHzc6kFB5pMmDChxdgXvV6Pr776qsOCIqKuQYGGNr2ulvQOHz5s+3N9fT0KCgpw7NixDg2KiLoIAfVpZk62cnK75t56eHggPj4eu3bt6qh4iKgLaZqGpnY4E01tek2EECguLkZNTU2HBkVEXYOWjX+cbbl4zW164vLuHv3798fChQs7PDAicn6ykCCrJD25qyW93NxcjBw50hGxEFEXI4R69dXZSnqqbXrcE4OIWtOeubfOQrWkN3z4cGzatAljxoxB7969bec5To+IXLJN7/PPP2+xGqkkSTh06FCHBUVEXYMi1MfhKU42ZKXVpFdfXw8PDw8cOHDAkfEQUReiQFLtqHC2ISuttuk98sgjjoyDiLogl2rTaxqiQkTUGk1tepCcaqGVVpNeXV0dDh482GryGzFiRIcFRURdg5aSXJcp6Z04cQLJycnXTHqSJOHzzz/v0MCIyPm5VElv2LBh2LhxoyNjIaIuRgj1kpyzNZRxs28ispssJE1He7z99tuIjo5GTEwMPvzwQwBAYWEh4uLiEB4ejszMTNu9hw4dQnx8PCIiIrBw4UJYrVbV57ea9IKDg9sVKBF1Pze69/bbb7/F119/jfz8fKxbtw5ZWVk4fPgwFixYgFWrVmHz5s0oLi7Gjh07ADTOGFu0aBG2bt0KIQRycnJU39Fq0ktLS2tHqETUHbVnN7Ty8nKUlZU1O65esWncuHH4+OOPodPpcObMGciyjJqaGgQEBGDw4MHQ6XSIi4uD0WjEyZMnYbFYEBQUBKBxI6GrJ1Jci+qMDCKi1ghob9NLSEhocS0pKQnJycnNzrm7u+Odd97BX/7yF0RGRqKyshJ6vd523dfXFxUVFS3O6/V6VFRUqMbMpEdEdhMaVk5uup6dnQ2DwdDs2tWbfTd59tln8dRTT+Hpp59GSUlJsy0rhBCQJAmKolzzvBomPSKym5aVkZuuGwwG+Pv7t3nvTz/9hPr6etx1113o1asXwsPDYTQa4ebmZrvHZDLB19cXBoMBJpPJdr6qqgq+vr6qMbP3lojsJgtAVlSOdoxZKSsrQ1paGurr61FfX4/PP/8c06ZNw7Fjx1BaWgpZllFQUIDQ0FD4+fnB09MTRUVFAIC8vDyEhoaqvoMlPSKym9BQ0mvPFpBhYWHYv38/HnzwQbi5uSE8PBwxMTHo168fkpOTUVdXh7CwMERGRgIAVqxYgbS0NJjNZowYMQKJiYmq72DSIyK7CdF4tHlPO5+ZnJzconMjJCQE+fn5Le4NDAxEbm5uu57PpEdEdnOpubdERGoUob5IKJMeEbkMRajvdsakR0QuoyPa9Doakx4R2Y1tekTUrbCkR0TdCkt6RNStCA29tyzpEZHLkIX6NDPZMaFoxqRHRHYT0NCm50T7YwBMekR0HdimR0TdiqY2PSdr1GPSIyK7CWhfRNRZMOkRkd2a1sxr8x7HhKIZk54DREePxtJlD8HTU4cD+0/gqf/6ALW1ls4Oq9v729/+iT+t3G37XFNjwcmTNTj84+/w+9TN2H+gHDf19kDCzCA8PWcCAODwoUo8++wmXDDXQ5KAPy55APffP6yzfkKn64pteh26crLZbEZsbCzKyso68jVO7ZZb+uD9D5/Ew1NWYkTgfBw7asKrGQ93dlgEYMaMIOzaPQe7ds/Bl1/Ngu8AL6x4IxovvbQNN3l5YM/eJHz+xZP47LMj2LLlRwDA8yl/x6OP3o1du+fgT6sfxG8Tc2C1OltZxnGa2vTaOpytTa/Dkt6+ffswffp0lJSUdNQruoQHwkdi756jOHKkcZem/169HTMSQjo5Krpa5ps7odffhCf+ayz++f1pTJs2Gm5uPeDhoUNExB3I23gQACDLCs6duwQAMNfWwdOze1eWhMbDmXRY0svJycHixYs1bdThygYP7ocTJ6ptn8vKqtG3b2/06dOzE6OiK52puoB3VxZiWUbjEuTBY/2wZs0+NDTIMJvrkJ93COXltQCANzJj8MaKnQi88w38Ju5jZL4VC53Ora3HuzS1Up6W9fau9u677yImJgYxMTFYvnw5AKCwsBBxcXEIDw9HZmam7d5Dhw4hPj4eERERWLhwIaxWq+rzOyzppaenIzg4uKMe32X06CFds3gvq7X+ksN8+GERomMCcfvt/QAA6a9GQJIk3DPxvzFj2hpMum8IPDzcYLE04LeJa7H6vQdx+F+/w5atj+O5ZzehrOx8J/+CztO04IDaoVVhYSF27tyJDRs2YOPGjfjhhx9QUFCABQsWYNWqVdi8eTOKi4uxY8cOAEBqaioWLVqErVu3QgiBnJwc1XdwN7QOdvx4NQYN8rF99vO7GdXVZly8WN+JUdGV1q8rxsyZd9s+19bWYckrD+CbPXORX/AYhACGDOmHgwcrcelSA6KihgMAxo0bjLvu8sXePd23zVoWgFWINo/27Iam1+sxf/58eHh4wN3dHUOHDkVJSQkCAgIwePBg6HQ6xMXFwWg04uTJk7BYLAgKCgIAxMfHw2g0qr6DSa+DfbbtAMZPGIphwwYAAGY/fR/y877v5Kioydmzl3D0aDXGTxhsO/eXD/YifekXAIDKCjM+/v/f4aGHR2HIkH6oqanDN18fBwAcPVqNw4dNGDV6YKfE7gza06ZXXl6OsrKyZkdNTU2z591xxx22JFZSUoItW7ZAkiTo9XrbPb6+vqioqEBlZWWz83q9HhUVFaoxd+9WWAcwmWrx5OPv49PcJHh46HD0p0r8NvHPnR0WXXb0aDUGGPrA3f3ndrnnf/crzHpqPcaP/ROEEFiQNgljxvgBALL/Ng2///0W1Fms0Ol64J2VcRgypF9nhd/pFGjfIyMhIaHFtaSkpBY7nwHAv//9b8yePRu///3v4ebm1qxDVAgBSZKgKAokSWpxXg2TngNs2bIfW7bs7+ww6BrGjPHDvv3PNTvXp48nPlkz/Zr3h4bdjh1fzXZEaF2CuPyX2j0AkJ2dDYPB0Oyat7d3i/uLiorw7LPPYsGCBYiJicG3334Lk8lku24ymeDr6wuDwdDsfFVVlaaO0w5Petu3b+/oVxBRJ9G0G9rl6waDAf7+/m3ee/r0acydOxeZmZkICWkc2jV69GgcO3YMpaWl8Pf3R0FBAaZMmQI/Pz94enqiqKgIY8aMQV5eHkJDQ1VjZkmPiOwmoD7joj0jVj744APU1dUhIyPDdm7atGnIyMhAcnIy6urqEBYWhsjIxuFFK1asQFpaGsxmM0aMGIHExETVdzDpEZHdZCHQQ2VMityOMStpaWlIS0u75rX8/PwW5wIDA5Gbm6v5+QCTHhFdByEEhEpSU7vuaEx6RGS3rrjgAJMeEdlNCAGFJT0i6i7aM2TFWTDpEZHdrBrWUbEy6RGRq1A0VG/Vrjsakx4RXRdnq76qYdIjIrspEJBUkp7iZEmRSY+I7MakR0TdCntviahbkSUZQmp7YyRF5bqjMekRkd0UDUNWWL0lIpfRWLlte6IZq7dE5DJY0iOibkVIClECHWgAAApBSURBVBRJpaSnct3RmPSIyG4KFEiq1VsmPSJyETJkSGh7g20B5+q95RaQRGS3puptW4c91Vuz2YzY2FiUlTXuKVxYWIi4uDiEh4cjMzPTdt+hQ4cQHx+PiIgILFy4EFZr2wkYYNIjouugaPyrPfbt24fp06fbtn20WCxYsGABVq1ahc2bN6O4uBg7duwAAKSmpmLRokXYunUrhBDIyclRfT6THhHZTUCBgKxytC/p5eTkYPHixbbtHPfv34+AgAAMHjwYOp0OcXFxMBqNOHnyJCwWi21z8Pj4eBiNRtXns02PiOymaFww3g1AeXl5iyve3t4t9r5NT09v9rmyshJ6vd722dfXFxUVFS3O6/V6VFRUqMbMpEdEdlM0LBHa1NGRkJDQ4lpSUhKSk5PbfoeiQJIk22chBCRJavW8GiY9IrKbAkW1+to0pCU7OxsGg6HZtatLeddiMBhgMplsn00mE3x9fVucr6qqslWJ28KkR0R2ExqSXlP112AwwN/fv93vGD16NI4dO4bS0lL4+/ujoKAAU6ZMgZ+fHzw9PVFUVIQxY8YgLy8PoaGhqs9j0iMiuymQIdB2lVK6znF6np6eyMjIQHJyMurq6hAWFobIyEgAwIoVK5CWlgaz2YwRI0YgMTFR9XlMekRkNy0LDqjNzW3N9u3bbX8OCQlBfn5+i3sCAwORm5vbrucy6RGR3YSQoajktB7CuWZkMOkRkd0UWDUMPubcWyJyEYpQVEt6EEx6ROQihIbV8rjKChG5DCEUCJXNvNWuOxqTHhHZTVxeUa8taltEOhqTHhHZrbGk13bSY0mPiFyGIqyqHRmSAJxpQScmPSKym7aODIBJj4hcgoAMtdpr42XnSTXOEwkRdTlCCA29t+rLPTkSkx4R2a2xequS9FR6dx2NSY+I7CaEFYpKSa8HS3pE5Coah6yo7HsrnKcTA2DSI6LromWPDOfCpEdEdtNW0nNQMBox6RGR3bQsIsqODCJyIVqqt86V9JyrhZGIuhQhrJqO9ti0aROio6MRHh6O7OzsGx4zS3pEdB0E1Et62stWFRUVyMzMxPr16+Hh4YFp06Zh/PjxGDZs2HVFeSWnS3qy3Lievk7n1smRUHucOmXu7BBIo4qKCwB+/nfteujcegCi7X9XdW6NSa+8vLzFNW9v72Z73xYWFmLChAnw8fEBAERERMBoNCIpKem6Y7XFc8OedIM0bd57663t3x+TOs9vYtd1dgjUTiaTCQEBAXZ918vLC3379gVu1Xa/p6cnEhISWpxPSkpCcnKy7XNlZSX0er3ts6+vL/bv329XjK1xuqQ3cuRIZGdnQ6/Xw82NpT2iG02WZZhMJowcOdLuZ/j4+GDbtm0wm7WV8IUQkKSWHRpXlvIAQFGUZve19r3r4XRJr2fPnggODu7sMIhcmr0lvCv5+PjYqqE3isFgwN69e22fTSYTfH19b+g72HtLRE5j4sSJ2L17N6qrq3Hp0iVs27YNoaGhN/QdTlfSI6Lua8CAAUhJSUFiYiIaGhowdepUjBo16oa+QxLOtoA9EVEHYvWWiLoVJj0i6laY9IioW2HSI6JuhUnPQTp6EjXdeGazGbGxsSgrK+vsUOgGYtJzgKZJ1H/729+wceNGfPrppzhy5Ehnh0Vt2LdvH6ZPn46SkpLODoVuMCY9B7hyEnXv3r1tk6jJeeXk5GDx4sU3fDYAdT4OTnYAR0yiphsrPT29s0OgDsKSngM4YhI1EWnDpOcABoPBtmQW0DGTqIlIGyY9B3DEJGoi0oZteg7giEnURKQNFxwgom6F1Vsi6laY9IioW2HSI6JuhUmPiLoVJj0i6laY9LqIsrIy3HXXXZg8ebLt+M1vfoPc3Nzrfvbs2bOxfv16AMDkyZNRU1PT6r21tbVITExs9zuMRiMeffTRFue/+eYbxMbGqn5/+PDhqK6ubtc758+fjw8++KBd3yHXx3F6XUjPnj2Rl5dn+1xRUYHY2FiMHDkSgYGBN+QdVz7/Ws6fP48DBw7ckHcRdQYmvS5swIABCAgIQElJCQ4ePIjc3FxcunQJXl5eyMrKwtq1a/HJJ59AURT4+PjgpZdewtChQ1FRUYH58+ejsrISgwYNwpkzZ2zPHD58OHbv3o1+/frhvffew4YNG6DT6RAQEICMjAy8+OKLsFgsmDx5MtavX4+SkhKkp6fj3LlzkGUZjz76KKZOnQoAePvtt7Fp0yb4+Pho2mf12LFjWLJkCS5cuACTyYTAwEC89dZb8PT0BAC89dZbOHDgABRFwbx58zBp0iQAaPV3El2ToC7hxIkTIigoqNm57777TowdO1acOnVKrFu3TowdO1bU1tYKIYT45ptvxIwZM8TFixeFEEL84x//EJGRkUIIIZ555hmRmZkphBCipKREBAUFiXXr1gkhhLjzzjvFmTNnxP/+7/+K8PBwce7cOSGEEK+++qpYtWpVszgaGhpEdHS0KC4uFkIIUVNTI6KiosT3338vPvvsMxEdHS1qa2tFQ0ODmDVrlpg5c2aL3/X111+LmJgYIYQQGRkZYuPGjUIIIerr60VsbKwwGo22uN577z0hhBA//vijGDdunDhz5kybv/MPf/iDeP/996/vHzy5HJb0upCmEhYAyLKMm2++Ga+//joGDhwIoLGU5uXlBQD48ssvUVpaimnTptm+X1NTg3PnzqGwsBB/+MMfADTudD9+/PgW79q9ezciIyPRt29fAMCLL74IAM1WES4pKcHx48exYMGCZjEePHgQP/30Ex544AFbPFOmTEFWVlabvy81NRW7du3C//zP/6CkpASVlZW4ePGi7fr06dMBAHfeeSeGDh2K77//HkVFRa3+TqJrYdLrQq5u07ta7969bX9WFAWTJ09Gamqq7XNlZSX69u0LSZIgrph9qNO1/L+Bm5tbs+WvampqWnRwyLKMPn36NIupqqoKffr0wfLly5u9w83NTfX3Pf/885BlGVFRUbj33ntx+vTpZs/o0ePnfjdFUaDT6dr8nUTXwt5bF3XPPffg73//OyorKwEAn3zyCR577DEAwK9+9St8+umnAIBTp07hm2++afH9iRMn4rPPPoPZbAYArFy5Eh999BF0Oh1kWYYQArfffnuzRHz69GnExsaiuLgYoaGhMBqNqKmpgaIoqh0kALBz507MnTsX0dHRABqXbJdl2XZ9w4YNAIAffvgBx48fx+jRo9v8nUTXwpKei7rnnnvw1FNP4YknnoAkSfDy8sK7774LSZKwePFivPjii4iKioLBYLhmz29YWBiOHDliq1IOGzYMr7zyCnr16oVRo0YhJiYG2dnZWLVqFdLT0/H+++/DarXiueeew5gxYwAAP/74I6ZMmQJvb28EBgbi7NmzbcackpKCuXPnonfv3vDy8sLYsWNx/Phx2/UTJ07gwQcfhCRJePPNN+Hj49Pm7yS6Fq6yQkTdCqu3RNStMOkRUbfCpEdE3QqTHhF1K0x6RNStMOkRUbfCpEdE3QqTHhF1K/8HlIoMU92TnikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion matrix for KNN \n",
    "plot_confusion_matrix(gridknn.best_estimator_,X_test,y_test,cmap='inferno',values_format='g')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree with GridCV\n",
    "- also performed perfectly \n",
    "- best params from grid search: {'criterion': 'gini', 'max_depth': 11, 'min_samples_leaf': 2, 'splitter': 'best'}\n",
    "- 5 largest feature importances within the DTC: ![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc= DecisionTreeClassifier(random_state=1)\n",
    "param_grid={'max_depth': [1,11,15],\n",
    "            'min_samples_leaf': [2,10,30],\n",
    "            'splitter': ['best','random'],\n",
    "            'criterion':['gini','entropy']}\n",
    "dtc_grid=GridSearchCV(dtc, param_grid, cv=3,verbose=1)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 108 out of 108 | elapsed:    1.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort='deprecated',\n",
       "                                              random_state=1, splitter='best'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [1, 11, 15],\n",
       "                         'min_samples_leaf': [2, 10, 30],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9995385325334564\n",
      "{'criterion': 'gini', 'max_depth': 11, 'min_samples_leaf': 2, 'splitter': 'best'}\n",
      "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=11, max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=2, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
      "                       random_state=5, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "examinebestmodel(dtc_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1:', 1.0, 'Accuracy:', 1.0)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_gridtree=dtc_grid.best_estimator_.predict(X_test)\n",
    "getresults(y_test,y_pred_gridtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict['DTC_Grid']=getresults(y_test,y_pred_gridtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion matrix for DTC \n",
    "plot_confusion_matrix(dtc_grid.best_estimator_,X_test,y_test,cmap='inferno',values_format='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1209pt\" height=\"870pt\"\n",
       " viewBox=\"0.00 0.00 1208.50 870.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 866)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-866 1204.5,-866 1204.5,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#fdf5f0\" stroke=\"#000000\" d=\"M811,-862C811,-862 691,-862 691,-862 685,-862 679,-856 679,-850 679,-850 679,-796 679,-796 679,-790 685,-784 691,-784 691,-784 811,-784 811,-784 817,-784 823,-790 823,-796 823,-796 823,-850 823,-850 823,-856 817,-862 811,-862\"/>\n",
       "<text text-anchor=\"start\" x=\"711.5\" y=\"-847.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">odor_n ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"714.5\" y=\"-833.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.499</text>\n",
       "<text text-anchor=\"start\" x=\"701\" y=\"-819.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6499</text>\n",
       "<text text-anchor=\"start\" x=\"687\" y=\"-805.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3381, 3118]</text>\n",
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       "<text text-anchor=\"start\" x=\"775\" y=\"-791.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#63b2eb\" stroke=\"#000000\" d=\"M694,-748C694,-748 582,-748 582,-748 576,-748 570,-742 570,-736 570,-736 570,-682 570,-682 570,-676 576,-670 582,-670 582,-670 694,-670 694,-670 700,-670 706,-676 706,-682 706,-682 706,-736 706,-736 706,-742 700,-748 694,-748\"/>\n",
       "<text text-anchor=\"start\" x=\"584\" y=\"-733.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">stalk&#45;root_c ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"601.5\" y=\"-719.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.289</text>\n",
       "<text text-anchor=\"start\" x=\"588\" y=\"-705.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3664</text>\n",
       "<text text-anchor=\"start\" x=\"578\" y=\"-691.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [643, 3021]</text>\n",
       "<text text-anchor=\"start\" x=\"607\" y=\"-677.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
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       "<!-- 0&#45;&gt;1 -->\n",
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       "<title>0&#45;&gt;1</title>\n",
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       "<text text-anchor=\"middle\" x=\"676.8497\" y=\"-769.6044\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n",
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       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#e68540\" stroke=\"#000000\" d=\"M935,-748C935,-748 793,-748 793,-748 787,-748 781,-742 781,-736 781,-736 781,-682 781,-682 781,-676 787,-670 793,-670 793,-670 935,-670 935,-670 941,-670 947,-676 947,-682 947,-682 947,-736 947,-736 947,-742 941,-748 935,-748\"/>\n",
       "<text text-anchor=\"start\" x=\"789\" y=\"-733.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">spore&#45;print&#45;color_r ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"827.5\" y=\"-719.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.066</text>\n",
       "<text text-anchor=\"start\" x=\"814\" y=\"-705.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2835</text>\n",
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       "<!-- 0&#45;&gt;16 -->\n",
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       "<title>0&#45;&gt;16</title>\n",
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       "<text text-anchor=\"middle\" x=\"825.1503\" y=\"-769.6044\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n",
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       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
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       "<text text-anchor=\"start\" x=\"312.5\" y=\"-619.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">stalk&#45;surface&#45;below&#45;ring_y ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"375.5\" y=\"-605.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.134</text>\n",
       "<text text-anchor=\"start\" x=\"362\" y=\"-591.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3232</text>\n",
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       "<!-- 1&#45;&gt;2 -->\n",
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       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M569.8861,-674.6416C547.4131,-663.3057 522.1119,-650.5432 498.4559,-638.6105\"/>\n",
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       "<!-- 13 -->\n",
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       "<title>13</title>\n",
       "<path fill=\"#e68844\" stroke=\"#000000\" d=\"M726.5,-634C726.5,-634 549.5,-634 549.5,-634 543.5,-634 537.5,-628 537.5,-622 537.5,-622 537.5,-568 537.5,-568 537.5,-562 543.5,-556 549.5,-556 549.5,-556 726.5,-556 726.5,-556 732.5,-556 738.5,-562 738.5,-568 738.5,-568 738.5,-622 738.5,-622 738.5,-628 732.5,-634 726.5,-634\"/>\n",
       "<text text-anchor=\"start\" x=\"545.5\" y=\"-619.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">stalk&#45;color&#45;above&#45;ring_c ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"601.5\" y=\"-605.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.101</text>\n",
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       "<!-- 1&#45;&gt;13 -->\n",
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       "<title>1&#45;&gt;13</title>\n",
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       "<!-- 3 -->\n",
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       "<title>3</title>\n",
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       "<text text-anchor=\"start\" x=\"184.5\" y=\"-505.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">spore&#45;print&#45;color_u ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"224.5\" y=\"-491.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.052</text>\n",
       "<text text-anchor=\"start\" x=\"211\" y=\"-477.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3081</text>\n",
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       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<!-- 12 -->\n",
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       "<title>12</title>\n",
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       "<text text-anchor=\"start\" x=\"391\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
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       "<text text-anchor=\"start\" x=\"444\" y=\"-456.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 2&#45;&gt;12 -->\n",
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       "<title>2&#45;&gt;12</title>\n",
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       "<!-- 4 -->\n",
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       "<title>4</title>\n",
       "<path fill=\"#3c9ee5\" stroke=\"#000000\" d=\"M246,-406C246,-406 142,-406 142,-406 136,-406 130,-400 130,-394 130,-394 130,-340 130,-340 130,-334 136,-328 142,-328 142,-328 246,-328 246,-328 252,-328 258,-334 258,-340 258,-340 258,-394 258,-394 258,-400 252,-406 246,-406\"/>\n",
       "<text text-anchor=\"start\" x=\"157\" y=\"-391.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">odor_l ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"157.5\" y=\"-377.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.027</text>\n",
       "<text text-anchor=\"start\" x=\"144\" y=\"-363.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3039</text>\n",
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       "<!-- 3&#45;&gt;4 -->\n",
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       "<title>3&#45;&gt;4</title>\n",
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       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
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       "<text text-anchor=\"start\" x=\"300\" y=\"-384.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"287\" y=\"-370.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 42</text>\n",
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       "<text text-anchor=\"start\" x=\"298\" y=\"-342.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
       "<text text-anchor=\"start\" x=\"353\" y=\"-342.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 3&#45;&gt;11 -->\n",
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       "<title>3&#45;&gt;11</title>\n",
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       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#3b9ee5\" stroke=\"#000000\" d=\"M179,-292C179,-292 75,-292 75,-292 69,-292 63,-286 63,-280 63,-280 63,-226 63,-226 63,-220 69,-214 75,-214 75,-214 179,-214 179,-214 185,-214 191,-220 191,-226 191,-226 191,-280 191,-280 191,-286 185,-292 179,-292\"/>\n",
       "<text text-anchor=\"start\" x=\"74.5\" y=\"-277.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gill&#45;spacing ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"90.5\" y=\"-263.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.015</text>\n",
       "<text text-anchor=\"start\" x=\"77\" y=\"-249.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3021</text>\n",
       "<text text-anchor=\"start\" x=\"71\" y=\"-235.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [23, 2998]</text>\n",
       "<text text-anchor=\"start\" x=\"96\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
       "<text text-anchor=\"start\" x=\"151\" y=\"-221.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">1</text>\n",
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       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M170.9424,-327.7677C165.889,-319.1694 160.4834,-309.9718 155.2528,-301.072\"/>\n",
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       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
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       "<text text-anchor=\"start\" x=\"233\" y=\"-270.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"220\" y=\"-256.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 18</text>\n",
       "<text text-anchor=\"start\" x=\"217.5\" y=\"-242.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [18, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"231\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
       "<text text-anchor=\"start\" x=\"286\" y=\"-228.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 4&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>4&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M217.4017,-327.7677C223.9496,-316.7904 231.0801,-304.8362 237.6798,-293.772\"/>\n",
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       "<!-- 6 -->\n",
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       "<title>6</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M108,-171C108,-171 12,-171 12,-171 6,-171 0,-165 0,-159 0,-159 0,-119 0,-119 0,-113 6,-107 12,-107 12,-107 108,-107 108,-107 114,-107 120,-113 120,-119 120,-119 120,-159 120,-159 120,-165 114,-171 108,-171\"/>\n",
       "<text text-anchor=\"start\" x=\"31\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"10\" y=\"-142.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2918</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-128.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 2918]</text>\n",
       "<text text-anchor=\"start\" x=\"29\" y=\"-114.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
       "<text text-anchor=\"start\" x=\"84\" y=\"-114.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">1</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M103.9424,-213.7677C97.4908,-202.7904 90.4652,-190.8362 83.9625,-179.772\"/>\n",
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       "<title>7</title>\n",
       "<path fill=\"#72b9ec\" stroke=\"#000000\" d=\"M239.5,-178C239.5,-178 150.5,-178 150.5,-178 144.5,-178 138.5,-172 138.5,-166 138.5,-166 138.5,-112 138.5,-112 138.5,-106 144.5,-100 150.5,-100 150.5,-100 239.5,-100 239.5,-100 245.5,-100 251.5,-106 251.5,-112 251.5,-112 251.5,-166 251.5,-166 251.5,-172 245.5,-178 239.5,-178\"/>\n",
       "<text text-anchor=\"start\" x=\"156\" y=\"-163.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">odor_c ≤ 0.5</text>\n",
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       "<!-- 5&#45;&gt;7 -->\n",
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       "<!-- 8 -->\n",
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       "<title>8</title>\n",
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       "<text text-anchor=\"start\" x=\"105\" y=\"-49.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
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       "<text text-anchor=\"start\" x=\"89.5\" y=\"-21.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [23, 0]</text>\n",
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       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>7&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M172.6322,-99.7647C167.6859,-91.0884 162.4317,-81.8721 157.4439,-73.123\"/>\n",
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       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M297.5,-64C297.5,-64 216.5,-64 216.5,-64 210.5,-64 204.5,-58 204.5,-52 204.5,-52 204.5,-12 204.5,-12 204.5,-6 210.5,0 216.5,0 216.5,0 297.5,0 297.5,0 303.5,0 309.5,-6 309.5,-12 309.5,-12 309.5,-52 309.5,-52 309.5,-58 303.5,-64 297.5,-64\"/>\n",
       "<text text-anchor=\"start\" x=\"228\" y=\"-49.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"215\" y=\"-35.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 80</text>\n",
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       "<!-- 7&#45;&gt;9 -->\n",
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       "<title>7&#45;&gt;9</title>\n",
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       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M623.5,-513C623.5,-513 534.5,-513 534.5,-513 528.5,-513 522.5,-507 522.5,-501 522.5,-501 522.5,-461 522.5,-461 522.5,-455 528.5,-449 534.5,-449 534.5,-449 623.5,-449 623.5,-449 629.5,-449 635.5,-455 635.5,-461 635.5,-461 635.5,-501 635.5,-501 635.5,-507 629.5,-513 623.5,-513\"/>\n",
       "<text text-anchor=\"start\" x=\"550\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"533\" y=\"-484.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 409</text>\n",
       "<text text-anchor=\"start\" x=\"530.5\" y=\"-470.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [409, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"548\" y=\"-456.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">class = y</text>\n",
       "<text text-anchor=\"start\" x=\"603\" y=\"-456.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 13&#45;&gt;14 -->\n",
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       "<title>13&#45;&gt;14</title>\n",
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       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M746.5,-513C746.5,-513 665.5,-513 665.5,-513 659.5,-513 653.5,-507 653.5,-501 653.5,-501 653.5,-461 653.5,-461 653.5,-455 659.5,-449 665.5,-449 665.5,-449 746.5,-449 746.5,-449 752.5,-449 758.5,-455 758.5,-461 758.5,-461 758.5,-501 758.5,-501 758.5,-507 752.5,-513 746.5,-513\"/>\n",
       "<text text-anchor=\"start\" x=\"677\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
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       "<text text-anchor=\"start\" x=\"661.5\" y=\"-470.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 23]</text>\n",
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       "<!-- 13&#45;&gt;15 -->\n",
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       "<title>13&#45;&gt;15</title>\n",
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       "<!-- 17 -->\n",
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       "<title>17</title>\n",
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       "<text text-anchor=\"start\" x=\"827.5\" y=\"-605.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.027</text>\n",
       "<text text-anchor=\"start\" x=\"814\" y=\"-591.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2776</text>\n",
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       "<text text-anchor=\"start\" x=\"888\" y=\"-563.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 16&#45;&gt;17 -->\n",
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       "<title>16&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M864,-669.7677C864,-661.6172 864,-652.9283 864,-644.4649\"/>\n",
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       "<!-- 28 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>28</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1082.5,-627C1082.5,-627 1001.5,-627 1001.5,-627 995.5,-627 989.5,-621 989.5,-615 989.5,-615 989.5,-575 989.5,-575 989.5,-569 995.5,-563 1001.5,-563 1001.5,-563 1082.5,-563 1082.5,-563 1088.5,-563 1094.5,-569 1094.5,-575 1094.5,-575 1094.5,-615 1094.5,-615 1094.5,-621 1088.5,-627 1082.5,-627\"/>\n",
       "<text text-anchor=\"start\" x=\"1013\" y=\"-612.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1000\" y=\"-598.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 59</text>\n",
       "<text text-anchor=\"start\" x=\"997.5\" y=\"-584.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 59]</text>\n",
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       "<text text-anchor=\"start\" x=\"1066\" y=\"-570.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">1</text>\n",
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       "<!-- 16&#45;&gt;28 -->\n",
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       "<title>16&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M925.2575,-669.7677C944.0096,-657.7579 964.5875,-644.5788 983.1739,-632.6751\"/>\n",
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       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M911,-520C911,-520 803,-520 803,-520 797,-520 791,-514 791,-508 791,-508 791,-454 791,-454 791,-448 797,-442 803,-442 803,-442 911,-442 911,-442 917,-442 923,-448 923,-454 923,-454 923,-508 923,-508 923,-514 917,-520 911,-520\"/>\n",
       "<text text-anchor=\"start\" x=\"799\" y=\"-505.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">cap&#45;shape_c ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"820.5\" y=\"-491.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.004</text>\n",
       "<text text-anchor=\"start\" x=\"807\" y=\"-477.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2730</text>\n",
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       "<text text-anchor=\"start\" x=\"881\" y=\"-449.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 17&#45;&gt;18 -->\n",
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       "<title>17&#45;&gt;18</title>\n",
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       "<!-- 25 -->\n",
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       "<title>25</title>\n",
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       "<text text-anchor=\"start\" x=\"949\" y=\"-505.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">stalk&#45;color&#45;above&#45;ring_n ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1005.5\" y=\"-491.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.405</text>\n",
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       "<text text-anchor=\"start\" x=\"993.5\" y=\"-463.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [13, 33]</text>\n",
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       "<text text-anchor=\"start\" x=\"1066\" y=\"-449.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">1</text>\n",
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       "<!-- 17&#45;&gt;25 -->\n",
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       "<title>17&#45;&gt;25</title>\n",
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       "<title>19</title>\n",
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       "<text text-anchor=\"start\" x=\"705\" y=\"-391.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gill&#45;size ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"709.5\" y=\"-377.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.002</text>\n",
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       "<text text-anchor=\"start\" x=\"694\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2725, 3]</text>\n",
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       "<text text-anchor=\"start\" x=\"770\" y=\"-335.8\" font-family=\"Helvetica,sans-Serif\" baseline-shift=\"sub\" font-size=\"14.00\" fill=\"#000000\">0</text>\n",
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       "<!-- 18&#45;&gt;19 -->\n",
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       "<title>18&#45;&gt;19</title>\n",
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       "<title>24</title>\n",
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       "<text text-anchor=\"start\" x=\"844\" y=\"-384.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
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       "<text text-anchor=\"start\" x=\"832.5\" y=\"-356.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 2]</text>\n",
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       "<!-- 18&#45;&gt;24 -->\n",
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       "<title>18&#45;&gt;24</title>\n",
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       "<title>20</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x1a1c6f9410>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#graphviz visualization of decision tree best estimator\n",
    "import graphviz \n",
    "import sklearn.tree\n",
    "dot_data=sklearn.tree.export_graphviz(dtc_grid.best_estimator_,out_file=None,feature_names = list(X.columns),class_names=True, filled=True,rounded=True,special_characters=True)\n",
    "graph=graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>odor_n</td>\n",
       "      <td>0.615409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-root_c</td>\n",
       "      <td>0.179599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-surface-below-ring_y</td>\n",
       "      <td>0.098303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spore-print-color_r</td>\n",
       "      <td>0.034647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spore-print-color_u</td>\n",
       "      <td>0.024855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-color-above-ring_c</td>\n",
       "      <td>0.013424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>odor_c</td>\n",
       "      <td>0.011013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>odor_l</td>\n",
       "      <td>0.010864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-color-above-ring_n</td>\n",
       "      <td>0.005749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>0.003058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "odor_n                      0.615409\n",
       "stalk-root_c                0.179599\n",
       "stalk-surface-below-ring_y  0.098303\n",
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       "stalk-color-above-ring_n    0.005749\n",
       "gill-spacing                0.003058"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature importance scores\n",
    "#Decision tree algorithms like classification offer importance scores based on the reduction in the criterion used to select split points, like Gini or entropy.\n",
    "feat_importanceDF = pd.DataFrame(dtc_grid.best_estimator_.feature_importances_,index=X.columns)\n",
    "feat_importanceDF.sort_values(by=0,ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting top 5 features\n",
    "feat_importances = pd.Series(dtc_grid.best_estimator_.feature_importances_,index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh',color=['blue','orange','green','red','pink','yellow','purple','brown','gray','black'])\n",
    "plt.title(\"Top 10 Feature Importance - DTC\")\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier with GridCV\n",
    "- Feature importances vary slightly with the DTC\n",
    "- best params: {'max_depth': 11, 'max_features': 50, 'min_samples_leaf': 2, 'n_estimators': 200}\n",
    "- model performance was perfectly accurate as well\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   34.3s\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  1.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'max_depth': [5, 11], 'max_features': [50, 75, 90],\n",
       "                         'min_samples_leaf': [2, 6],\n",
       "                         'n_estimators': [200, 500]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc=RandomForestClassifier()\n",
    "param_grid_rfc={\n",
    "    'max_depth': [5,11],\n",
    "    'n_estimators': [200,500],\n",
    "    'max_features': [50,75,90],\n",
    "    'min_samples_leaf': [2,6],\n",
    "}\n",
    "grid_rfc=GridSearchCV(rfc,param_grid_rfc,cv=3,scoring='accuracy',n_jobs=-1,verbose=1)\n",
    "grid_rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9995385325334564\n",
      "{'max_depth': 11, 'max_features': 50, 'min_samples_leaf': 2, 'n_estimators': 200}\n",
      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='gini', max_depth=11, max_features=50,\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=2, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "examinebestmodel(grid_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1:', 1.0, 'Accuracy:', 1.0)"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rfc2=grid_rfc.predict(X_test)\n",
    "getresults(y_test,y_pred_rfc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict['RFC']=getresults(y_test,y_pred_rfc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>odor_n</td>\n",
       "      <td>0.389131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>odor_f</td>\n",
       "      <td>0.122790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-size</td>\n",
       "      <td>0.111342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-root_c</td>\n",
       "      <td>0.068345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-surface-above-ring_k</td>\n",
       "      <td>0.046284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bruises</td>\n",
       "      <td>0.037396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spore-print-color_r</td>\n",
       "      <td>0.026983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-surface-below-ring_y</td>\n",
       "      <td>0.022388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>0.016606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>stalk-root_r</td>\n",
       "      <td>0.015241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "odor_n                      0.389131\n",
       "odor_f                      0.122790\n",
       "gill-size                   0.111342\n",
       "stalk-root_c                0.068345\n",
       "stalk-surface-above-ring_k  0.046284\n",
       "bruises                     0.037396\n",
       "spore-print-color_r         0.026983\n",
       "stalk-surface-below-ring_y  0.022388\n",
       "gill-spacing                0.016606\n",
       "stalk-root_r                0.015241"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature importance scores\n",
    "#Decision tree algorithms like classification offer importance scores based on the reduction in the criterion used to select split points, like Gini or entropy.\n",
    "feat_importanceDF_rfc_2 = pd.DataFrame(grid_rfc.best_estimator_.feature_importances_,index=X.columns)\n",
    "feat_importanceDF_rfc_2.sort_values(by=0,ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot top 5 features for the RFC\n",
    "feat_importances_2 = pd.Series(grid_rfc.best_estimator_.feature_importances_,index=X.columns)\n",
    "feat_importances_2.nlargest(10).plot(kind='barh',color=['blue','orange','green','red','pink','yellow','purple','brown','gray','black'])\n",
    "plt.title(\"Top 10 Feature Importance - RFC\")\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concluding Thoughts & Takeaways "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dummy Classifier': ('F1:', 0.0, 'Accuracy:', 0.5089230769230769),\n",
       " 'Log_Reg': ('F1:', 0.9981167608286252, 'Accuracy:', 0.9981538461538462),\n",
       " 'KNN': ('F1:', 1.0, 'Accuracy:', 1.0),\n",
       " 'DTC_Grid': ('F1:', 1.0, 'Accuracy:', 1.0),\n",
       " 'RFC': ('F1:', 1.0, 'Accuracy:', 1.0)}"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My classification models all performed really well with the dataset. Logistic Regression, which had a score of 99% would normally be a great choice but given that the model predicted false negatives which could be deadly, and that the other models performed perfectly, the other models are much better suited to classify mushrooms.\n",
    "\n",
    "To expand on this project and perhaps pick a 'final' model, I would like to see if there is similar data available for future analysis.  This way I can test my models further to find some insight into what will work best in a real world situation. \n",
    "\n",
    "Additionally, using my findings paired with image classification could also be a worthwhile experiment. \n",
    "\n",
    "So what were my findings? \n",
    "\n",
    "For one, mushrooms can definitely be classified as edible or poisonous from a set of descriptive characteristics.  \n",
    "\n",
    "And finally a few simple rules for identifying a poisonous mushroom (as learned from studying the feauture importance ranks of my DTC, RFC and EDA):\n",
    "\n",
    "1. *Odor: If the Mushroom has an odor, especially if the odor is not pleasant, it is likely to be poisonous.\n",
    "2. Gill Size: If the gills are narrow, it is likely to be poisonous.\n",
    "3. Spore Print Color: White, red, 'chocolate' are likely to be poisonous. \n",
    "4. Bruises: If the mushroom does not have bruises, it is likely to be poisonous. \n",
    "5. Stalk Surface Above Ring: If it appears silky, it is likely to be poisonous. "
   ]
  },
  {
   "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
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