Mushroom-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushroom Classification Using Different Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this project, we will examine the data and create a machine learning algorithm that will detect if the mushroom is edible or poisonous by its specifications like cap shape, cap color, gill color, etc. using different classifiers.\n",
    "#### The dataset used in this project is \"mushrooms.csv\" which contains 8124 instances of mushrooms with 23 features like cap-shape, cap-surface, cap-color, bruises, odor, etc. and is made available by UCI Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the files in the directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.ipynb_checkpoints', 'images', 'Mushroom-Classification', 'Mushroom-Classification.ipynb', 'mushrooms.csv']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"C:/Users/Kanchi/PycharmProjects/Mushroom-Classification\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the csv file of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas read_csv() function imports a CSV file (in our case, ‘mushrooms.csv’) to DataFrame format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"mushrooms.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examining the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After importing the data, to learn more about the dataset, we'll use .head() .info() and .describe() methods."
   ]
  },
  {
   "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",
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       "      <th>gill-spacing</th>\n",
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       "<p>5 rows × 23 columns</p>\n",
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      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
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       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   class                     8124 non-null   object\n",
      " 1   cap-shape                 8124 non-null   object\n",
      " 2   cap-surface               8124 non-null   object\n",
      " 3   cap-color                 8124 non-null   object\n",
      " 4   bruises                   8124 non-null   object\n",
      " 5   odor                      8124 non-null   object\n",
      " 6   gill-attachment           8124 non-null   object\n",
      " 7   gill-spacing              8124 non-null   object\n",
      " 8   gill-size                 8124 non-null   object\n",
      " 9   gill-color                8124 non-null   object\n",
      " 10  stalk-shape               8124 non-null   object\n",
      " 11  stalk-root                8124 non-null   object\n",
      " 12  stalk-surface-above-ring  8124 non-null   object\n",
      " 13  stalk-surface-below-ring  8124 non-null   object\n",
      " 14  stalk-color-above-ring    8124 non-null   object\n",
      " 15  stalk-color-below-ring    8124 non-null   object\n",
      " 16  veil-type                 8124 non-null   object\n",
      " 17  veil-color                8124 non-null   object\n",
      " 18  ring-number               8124 non-null   object\n",
      " 19  ring-type                 8124 non-null   object\n",
      " 20  spore-print-color         8124 non-null   object\n",
      " 21  population                8124 non-null   object\n",
      " 22  habitat                   8124 non-null   object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
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      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (8124, 23)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dataset shape:\", df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the count of edible and poisonous mushrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['p', 'e'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count = df['class'].value_counts()\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.barplot(count.index, count.values, alpha=0.8, palette=\"prism\")\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Class', fontsize=12)\n",
    "plt.title('Number of poisonous/edible mushrooms')\n",
    "#plt.savefig(\"mushrooms1.png\", format='png', dpi=900)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset is balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data is categorical so we’ll use LabelEncoder to convert it to ordinal. LabelEncoder converts each value in a column to a number.\n",
    "#### This approach requires the category column to be of ‘category’ datatype. By default, a non-numerical column is of ‘object’ datatype. From the df.describe() method, we saw that our columns are of ‘object’ datatype. So we will have to change the type to ‘category’ before using this approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       category\n",
       "cap-shape                   category\n",
       "cap-surface                 category\n",
       "cap-color                   category\n",
       "bruises                     category\n",
       "odor                        category\n",
       "gill-attachment             category\n",
       "gill-spacing                category\n",
       "gill-size                   category\n",
       "gill-color                  category\n",
       "stalk-shape                 category\n",
       "stalk-root                  category\n",
       "stalk-surface-above-ring    category\n",
       "stalk-surface-below-ring    category\n",
       "stalk-color-above-ring      category\n",
       "stalk-color-below-ring      category\n",
       "veil-type                   category\n",
       "veil-color                  category\n",
       "ring-number                 category\n",
       "ring-type                   category\n",
       "spore-print-color           category\n",
       "population                  category\n",
       "habitat                     category\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder=LabelEncoder()\n",
    "for column in df.columns:\n",
    "    df[column] = labelencoder.fit_transform(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0      1          5            2          4        1     6                1   \n",
       "1      0          5            2          9        1     0                1   \n",
       "2      0          0            2          8        1     3                1   \n",
       "3      1          5            3          8        1     6                1   \n",
       "4      0          5            2          3        0     5                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  ...  stalk-surface-below-ring  \\\n",
       "0             0          1           4  ...                         2   \n",
       "1             0          0           4  ...                         2   \n",
       "2             0          0           5  ...                         2   \n",
       "3             0          1           5  ...                         2   \n",
       "4             1          0           4  ...                         2   \n",
       "\n",
       "   stalk-color-above-ring  stalk-color-below-ring  veil-type  veil-color  \\\n",
       "0                       7                       7          0           2   \n",
       "1                       7                       7          0           2   \n",
       "2                       7                       7          0           2   \n",
       "3                       7                       7          0           2   \n",
       "4                       7                       7          0           2   \n",
       "\n",
       "   ring-number  ring-type  spore-print-color  population  habitat  \n",
       "0            1          4                  2           3        5  \n",
       "1            1          4                  3           2        1  \n",
       "2            1          4                  3           2        3  \n",
       "3            1          4                  2           3        5  \n",
       "4            1          0                  3           0        1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  The column \"veil-type\" is 0 and not contributing to the data so we remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "8119    0\n",
       "8120    0\n",
       "8121    0\n",
       "8122    0\n",
       "8123    0\n",
       "Name: veil-type, Length: 8124, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['veil-type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([\"veil-type\"],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick look at the characteristics of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The violin plot below represents the distribution of the classification characteristics. It is possible to see that \"gill-color\" property of the mushroom breaks to two parts, one below 3 and one above 3, that may contribute to the classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_div = pd.melt(df, \"class\", var_name=\"Characteristics\")\n",
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "p = sns.violinplot(ax = ax, x=\"Characteristics\", y=\"value\", hue=\"class\", split = True, data=df_div, inner = 'quartile', palette = 'Set1')\n",
    "df_no_class = df.drop([\"class\"],axis = 1)\n",
    "p.set_xticklabels(rotation = 90, labels = list(df_no_class.columns));\n",
    "#plt.savefig(\"violinplot.png\", format='png', dpi=900, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's look at the correlation between the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,12))\n",
    "sns.heatmap(df.corr(),linewidths=.1,cmap=\"Purples\", annot=True, annot_kws={\"size\": 7})\n",
    "plt.yticks(rotation=0);\n",
    "#plt.savefig(\"corr.png\", format='png', dpi=900, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Usually, the least correlating variable is the most important one for classification. In this case, \"gill-color\" has -0.53 so let's look at it closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>gill-color</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.721311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.670213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.428954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.255814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.204659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.156863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.106870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gill-color     class\n",
       "0            0  1.000000\n",
       "8            8  1.000000\n",
       "3            3  0.721311\n",
       "2            2  0.670213\n",
       "7            7  0.428954\n",
       "11          11  0.255814\n",
       "10          10  0.204659\n",
       "4            4  0.156863\n",
       "5            5  0.106870\n",
       "9            9  0.097561\n",
       "1            1  0.000000\n",
       "6            6  0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['class', 'gill-color']].groupby(['gill-color'], as_index=False).mean().sort_values(by='class', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's look closely at the feature \"gill-color\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kanchi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "c:\\users\\kanchi\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1036.8x324 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_var = df[['class', 'gill-color']]\n",
    "new_var = new_var[new_var['gill-color']<=3.5]\n",
    "sns.factorplot('class', col='gill-color', data=new_var, kind='count', size=4.5, aspect=.8, col_wrap=4);\n",
    "#plt.savefig(\"gillcolor1.png\", format='png', dpi=900, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1036.8x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_var=df[['class', 'gill-color']]\n",
    "new_var=new_var[new_var['gill-color']>3.5]\n",
    "\n",
    "sns.factorplot('class', col='gill-color', data=new_var, kind='count', size=4.5, aspect=.8, col_wrap=4);\n",
    "#plt.savefig(\"gillcolor2.png\", format='png', dpi=900, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Setting X and y axis and splitting the data into train and test respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['class'], axis=1)  \n",
    "y = df[\"class\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.1)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<text text-anchor=\"start\" x=\"147\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.223</text>\r\n",
       "<text text-anchor=\"start\" x=\"137\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 525</text>\r\n",
       "<text text-anchor=\"start\" x=\"131.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [458, 67]</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M371.223,-614.397C340.764,-602.261 304.641,-587.869 272.322,-574.991\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"273.456,-571.676 262.871,-571.226 270.865,-578.179 273.456,-571.676\"/>\r\n",
       "</g>\r\n",
       "<!-- 7 -->\r\n",
       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
       "<path fill=\"#3b9ee5\" stroke=\"black\" d=\"M493.5,-573C493.5,-573 387.5,-573 387.5,-573 381.5,-573 375.5,-567 375.5,-561 375.5,-561 375.5,-517 375.5,-517 375.5,-511 381.5,-505 387.5,-505 387.5,-505 493.5,-505 493.5,-505 499.5,-505 505.5,-511 505.5,-517 505.5,-517 505.5,-561 505.5,-561 505.5,-567 499.5,-573 493.5,-573\"/>\r\n",
       "<text text-anchor=\"start\" x=\"393.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;root ≤ 2.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"403\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.024</text>\r\n",
       "<text text-anchor=\"start\" x=\"389\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2446</text>\r\n",
       "<text text-anchor=\"start\" x=\"383.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [30, 2416]</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>1&#45;&gt;7</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M440.5,-608.884C440.5,-600.778 440.5,-591.982 440.5,-583.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"444,-583.299 440.5,-573.299 437,-583.299 444,-583.299\"/>\r\n",
       "</g>\r\n",
       "<!-- 3 -->\r\n",
       "<g id=\"node4\" class=\"node\"><title>3</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M93,-461.5C93,-461.5 12,-461.5 12,-461.5 6,-461.5 0,-455.5 0,-449.5 0,-449.5 0,-420.5 0,-420.5 0,-414.5 6,-408.5 12,-408.5 12,-408.5 93,-408.5 93,-408.5 99,-408.5 105,-414.5 105,-420.5 105,-420.5 105,-449.5 105,-449.5 105,-455.5 99,-461.5 93,-461.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"23.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"9\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 45</text>\r\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 45]</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
       "<g id=\"edge3\" class=\"edge\"><title>2&#45;&gt;3</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M141.643,-504.884C126.272,-493.006 108.983,-479.646 93.7513,-467.876\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"95.5787,-464.865 85.5258,-461.52 91.2986,-470.404 95.5787,-464.865\"/>\r\n",
       "</g>\r\n",
       "<!-- 4 -->\r\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
       "<path fill=\"#e68743\" stroke=\"black\" d=\"M233.5,-469C233.5,-469 135.5,-469 135.5,-469 129.5,-469 123.5,-463 123.5,-457 123.5,-457 123.5,-413 123.5,-413 123.5,-407 129.5,-401 135.5,-401 135.5,-401 233.5,-401 233.5,-401 239.5,-401 245.5,-407 245.5,-413 245.5,-413 245.5,-457 245.5,-457 245.5,-463 239.5,-469 233.5,-469\"/>\r\n",
       "<text text-anchor=\"start\" x=\"146\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">habitat ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"147\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.087</text>\r\n",
       "<text text-anchor=\"start\" x=\"137\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 480</text>\r\n",
       "<text text-anchor=\"start\" x=\"131.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [458, 22]</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>2&#45;&gt;4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M184.5,-504.884C184.5,-496.778 184.5,-487.982 184.5,-479.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"188,-479.299 184.5,-469.299 181,-479.299 188,-479.299\"/>\r\n",
       "</g>\r\n",
       "<!-- 5 -->\r\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M110,-357.5C110,-357.5 29,-357.5 29,-357.5 23,-357.5 17,-351.5 17,-345.5 17,-345.5 17,-316.5 17,-316.5 17,-310.5 23,-304.5 29,-304.5 29,-304.5 110,-304.5 110,-304.5 116,-304.5 122,-310.5 122,-316.5 122,-316.5 122,-345.5 122,-345.5 122,-351.5 116,-357.5 110,-357.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"40.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"26\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 22</text>\r\n",
       "<text text-anchor=\"start\" x=\"25\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 22]</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M147.163,-400.884C133.895,-389.116 118.988,-375.894 105.808,-364.203\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"108.076,-361.537 98.2725,-357.52 103.431,-366.774 108.076,-361.537\"/>\r\n",
       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M241,-357.5C241,-357.5 152,-357.5 152,-357.5 146,-357.5 140,-351.5 140,-345.5 140,-345.5 140,-316.5 140,-316.5 140,-310.5 146,-304.5 152,-304.5 152,-304.5 241,-304.5 241,-304.5 247,-304.5 253,-310.5 253,-316.5 253,-316.5 253,-345.5 253,-345.5 253,-351.5 247,-357.5 241,-357.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"167.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"149\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 458</text>\r\n",
       "<text text-anchor=\"start\" x=\"148\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [458, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M188.396,-400.884C189.651,-390.216 191.047,-378.352 192.321,-367.519\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"195.805,-367.86 193.498,-357.52 188.853,-367.042 195.805,-367.86\"/>\r\n",
       "</g>\r\n",
       "<!-- 8 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<path fill=\"#3a9de5\" stroke=\"black\" d=\"M453.5,-469C453.5,-469 275.5,-469 275.5,-469 269.5,-469 263.5,-463 263.5,-457 263.5,-457 263.5,-413 263.5,-413 263.5,-407 269.5,-401 275.5,-401 275.5,-401 453.5,-401 453.5,-401 459.5,-401 465.5,-407 465.5,-413 465.5,-413 465.5,-457 465.5,-457 465.5,-463 459.5,-469 453.5,-469\"/>\r\n",
       "<text text-anchor=\"start\" x=\"271.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;surface&#45;below&#45;ring ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"327\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.009</text>\r\n",
       "<text text-anchor=\"start\" x=\"313\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2427</text>\r\n",
       "<text text-anchor=\"start\" x=\"307.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 2416]</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M415.825,-504.884C409.321,-496.154 402.22,-486.625 395.432,-477.514\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"398.093,-475.227 389.311,-469.299 392.48,-479.409 398.093,-475.227\"/>\r\n",
       "</g>\r\n",
       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M577,-461.5C577,-461.5 496,-461.5 496,-461.5 490,-461.5 484,-455.5 484,-449.5 484,-449.5 484,-420.5 484,-420.5 484,-414.5 490,-408.5 496,-408.5 496,-408.5 577,-408.5 577,-408.5 583,-408.5 589,-414.5 589,-420.5 589,-420.5 589,-449.5 589,-449.5 589,-455.5 583,-461.5 577,-461.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"507.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"493\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 19</text>\r\n",
       "<text text-anchor=\"start\" x=\"492\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [19, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>7&#45;&gt;13</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M471.668,-504.884C482.537,-493.336 494.724,-480.387 505.572,-468.86\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"508.176,-471.201 512.481,-461.52 503.079,-466.403 508.176,-471.201\"/>\r\n",
       "</g>\r\n",
       "<!-- 9 -->\r\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
       "<path fill=\"#9ccef2\" stroke=\"black\" d=\"M396,-365C396,-365 307,-365 307,-365 301,-365 295,-359 295,-353 295,-353 295,-309 295,-309 295,-303 301,-297 307,-297 307,-297 396,-297 396,-297 402,-297 408,-303 408,-309 408,-309 408,-353 408,-353 408,-359 402,-365 396,-365\"/>\r\n",
       "<text text-anchor=\"start\" x=\"306\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ring&#45;type ≤ 2.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"314\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"start\" x=\"308\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 33</text>\r\n",
       "<text text-anchor=\"start\" x=\"303\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 22]</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>8&#45;&gt;9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M360.279,-400.884C359.246,-392.778 358.125,-383.982 357.041,-375.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"360.48,-374.777 355.744,-365.299 353.536,-375.662 360.48,-374.777\"/>\r\n",
       "</g>\r\n",
       "<!-- 12 -->\r\n",
       "<g id=\"node13\" class=\"node\"><title>12</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M536.5,-357.5C536.5,-357.5 438.5,-357.5 438.5,-357.5 432.5,-357.5 426.5,-351.5 426.5,-345.5 426.5,-345.5 426.5,-316.5 426.5,-316.5 426.5,-310.5 432.5,-304.5 438.5,-304.5 438.5,-304.5 536.5,-304.5 536.5,-304.5 542.5,-304.5 548.5,-310.5 548.5,-316.5 548.5,-316.5 548.5,-345.5 548.5,-345.5 548.5,-351.5 542.5,-357.5 536.5,-357.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"458.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"436\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2394</text>\r\n",
       "<text text-anchor=\"start\" x=\"434.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2394]</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>8&#45;&gt;12</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M404.435,-400.884C418.625,-389.116 434.569,-375.894 448.667,-364.203\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"451.263,-366.597 456.726,-357.52 446.794,-361.209 451.263,-366.597\"/>\r\n",
       "</g>\r\n",
       "<!-- 10 -->\r\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M330,-253.5C330,-253.5 249,-253.5 249,-253.5 243,-253.5 237,-247.5 237,-241.5 237,-241.5 237,-212.5 237,-212.5 237,-206.5 243,-200.5 249,-200.5 249,-200.5 330,-200.5 330,-200.5 336,-200.5 342,-206.5 342,-212.5 342,-212.5 342,-241.5 342,-241.5 342,-247.5 336,-253.5 330,-253.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"260.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"246\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\r\n",
       "<text text-anchor=\"start\" x=\"245\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M331.37,-296.884C324.619,-285.776 317.079,-273.372 310.278,-262.184\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"313.197,-260.247 305.012,-253.52 307.215,-263.883 313.197,-260.247\"/>\r\n",
       "</g>\r\n",
       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M453,-253.5C453,-253.5 372,-253.5 372,-253.5 366,-253.5 360,-247.5 360,-241.5 360,-241.5 360,-212.5 360,-212.5 360,-206.5 366,-200.5 372,-200.5 372,-200.5 453,-200.5 453,-200.5 459,-200.5 465,-206.5 465,-212.5 465,-212.5 465,-241.5 465,-241.5 465,-247.5 459,-253.5 453,-253.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"383.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"369\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 22</text>\r\n",
       "<text text-anchor=\"start\" x=\"368\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 22]</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M371.305,-296.884C377.948,-285.776 385.366,-273.372 392.057,-262.184\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"395.109,-263.899 397.238,-253.52 389.102,-260.306 395.109,-263.899\"/>\r\n",
       "</g>\r\n",
       "<!-- 15 -->\r\n",
       "<g id=\"node16\" class=\"node\"><title>15</title>\r\n",
       "<path fill=\"#53aae8\" stroke=\"black\" d=\"M729.5,-573C729.5,-573 631.5,-573 631.5,-573 625.5,-573 619.5,-567 619.5,-561 619.5,-561 619.5,-517 619.5,-517 619.5,-511 625.5,-505 631.5,-505 631.5,-505 729.5,-505 729.5,-505 735.5,-505 741.5,-511 741.5,-517 741.5,-517 741.5,-561 741.5,-561 741.5,-567 735.5,-573 729.5,-573\"/>\r\n",
       "<text text-anchor=\"start\" x=\"633.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;root ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"643\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.207</text>\r\n",
       "<text text-anchor=\"start\" x=\"633\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 648</text>\r\n",
       "<text text-anchor=\"start\" x=\"627.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [76, 572]</text>\r\n",
       "</g>\r\n",
       "<!-- 14&#45;&gt;15 -->\r\n",
       "<g id=\"edge15\" class=\"edge\"><title>14&#45;&gt;15</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M680.5,-608.884C680.5,-600.778 680.5,-591.982 680.5,-583.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"684,-583.299 680.5,-573.299 677,-583.299 684,-583.299\"/>\r\n",
       "</g>\r\n",
       "<!-- 18 -->\r\n",
       "<g id=\"node19\" class=\"node\"><title>18</title>\r\n",
       "<path fill=\"#e99457\" stroke=\"black\" d=\"M983.5,-573C983.5,-573 869.5,-573 869.5,-573 863.5,-573 857.5,-567 857.5,-561 857.5,-561 857.5,-517 857.5,-517 857.5,-511 863.5,-505 869.5,-505 869.5,-505 983.5,-505 983.5,-505 989.5,-505 995.5,-511 995.5,-517 995.5,-517 995.5,-561 995.5,-561 995.5,-567 989.5,-573 983.5,-573\"/>\r\n",
       "<text text-anchor=\"start\" x=\"885\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gill&#45;size ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"889\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.227</text>\r\n",
       "<text text-anchor=\"start\" x=\"875\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3692</text>\r\n",
       "<text text-anchor=\"start\" x=\"865.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3211, 481]</text>\r\n",
       "</g>\r\n",
       "<!-- 14&#45;&gt;18 -->\r\n",
       "<g id=\"edge18\" class=\"edge\"><title>14&#45;&gt;18</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M758.674,-609.586C787.114,-597.794 819.258,-584.466 847.748,-572.653\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"849.362,-575.773 857.259,-568.71 846.681,-569.307 849.362,-575.773\"/>\r\n",
       "</g>\r\n",
       "<!-- 16 -->\r\n",
       "<g id=\"node17\" class=\"node\"><title>16</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M700,-461.5C700,-461.5 619,-461.5 619,-461.5 613,-461.5 607,-455.5 607,-449.5 607,-449.5 607,-420.5 607,-420.5 607,-414.5 613,-408.5 619,-408.5 619,-408.5 700,-408.5 700,-408.5 706,-408.5 712,-414.5 712,-420.5 712,-420.5 712,-449.5 712,-449.5 712,-455.5 706,-461.5 700,-461.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"630.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"616\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 76</text>\r\n",
       "<text text-anchor=\"start\" x=\"615\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [76, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 15&#45;&gt;16 -->\r\n",
       "<g id=\"edge16\" class=\"edge\"><title>15&#45;&gt;16</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M673.682,-504.884C671.486,-494.216 669.043,-482.352 666.813,-471.519\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"670.199,-470.609 664.754,-461.52 663.343,-472.02 670.199,-470.609\"/>\r\n",
       "</g>\r\n",
       "<!-- 17 -->\r\n",
       "<g id=\"node18\" class=\"node\"><title>17</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M831,-461.5C831,-461.5 742,-461.5 742,-461.5 736,-461.5 730,-455.5 730,-449.5 730,-449.5 730,-420.5 730,-420.5 730,-414.5 736,-408.5 742,-408.5 742,-408.5 831,-408.5 831,-408.5 837,-408.5 843,-414.5 843,-420.5 843,-420.5 843,-449.5 843,-449.5 843,-455.5 837,-461.5 831,-461.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"757.5\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"739\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 572</text>\r\n",
       "<text text-anchor=\"start\" x=\"738\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 572]</text>\r\n",
       "</g>\r\n",
       "<!-- 15&#45;&gt;17 -->\r\n",
       "<g id=\"edge17\" class=\"edge\"><title>15&#45;&gt;17</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M714.915,-504.884C727.03,-493.226 740.628,-480.141 752.693,-468.532\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"755.2,-470.976 759.979,-461.52 750.347,-465.932 755.2,-470.976\"/>\r\n",
       "</g>\r\n",
       "<!-- 19 -->\r\n",
       "<g id=\"node20\" class=\"node\"><title>19</title>\r\n",
       "<path fill=\"#e6843e\" stroke=\"black\" d=\"M979.5,-469C979.5,-469 873.5,-469 873.5,-469 867.5,-469 861.5,-463 861.5,-457 861.5,-457 861.5,-413 861.5,-413 861.5,-407 867.5,-401 873.5,-401 873.5,-401 979.5,-401 979.5,-401 985.5,-401 991.5,-407 991.5,-413 991.5,-413 991.5,-457 991.5,-457 991.5,-463 985.5,-469 979.5,-469\"/>\r\n",
       "<text text-anchor=\"start\" x=\"870.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ring&#45;number ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"893\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.05</text>\r\n",
       "<text text-anchor=\"start\" x=\"875\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3101</text>\r\n",
       "<text text-anchor=\"start\" x=\"869.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3022, 79]</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;19 -->\r\n",
       "<g id=\"edge19\" class=\"edge\"><title>18&#45;&gt;19</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M926.5,-504.884C926.5,-496.778 926.5,-487.982 926.5,-479.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"930,-479.299 926.5,-469.299 923,-479.299 930,-479.299\"/>\r\n",
       "</g>\r\n",
       "<!-- 30 -->\r\n",
       "<g id=\"node31\" class=\"node\"><title>30</title>\r\n",
       "<path fill=\"#96cbf1\" stroke=\"black\" d=\"M1127.5,-469C1127.5,-469 1021.5,-469 1021.5,-469 1015.5,-469 1009.5,-463 1009.5,-457 1009.5,-457 1009.5,-413 1009.5,-413 1009.5,-407 1015.5,-401 1021.5,-401 1021.5,-401 1127.5,-401 1127.5,-401 1133.5,-401 1139.5,-407 1139.5,-413 1139.5,-413 1139.5,-457 1139.5,-457 1139.5,-463 1133.5,-469 1127.5,-469\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1020.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;shape ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"1037\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.435</text>\r\n",
       "<text text-anchor=\"start\" x=\"1027\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 591</text>\r\n",
       "<text text-anchor=\"start\" x=\"1017.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [189, 402]</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;30 -->\r\n",
       "<g id=\"edge30\" class=\"edge\"><title>18&#45;&gt;30</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M974.551,-504.884C988.451,-495.304 1003.75,-484.761 1018.11,-474.864\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1020.19,-477.681 1026.44,-469.125 1016.22,-471.918 1020.19,-477.681\"/>\r\n",
       "</g>\r\n",
       "<!-- 20 -->\r\n",
       "<g id=\"node21\" class=\"node\"><title>20</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M831,-357.5C831,-357.5 750,-357.5 750,-357.5 744,-357.5 738,-351.5 738,-345.5 738,-345.5 738,-316.5 738,-316.5 738,-310.5 744,-304.5 750,-304.5 750,-304.5 831,-304.5 831,-304.5 837,-304.5 843,-310.5 843,-316.5 843,-316.5 843,-345.5 843,-345.5 843,-351.5 837,-357.5 831,-357.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"761.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"747\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 34</text>\r\n",
       "<text text-anchor=\"start\" x=\"746\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 34]</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;20 -->\r\n",
       "<g id=\"edge20\" class=\"edge\"><title>19&#45;&gt;20</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M882.345,-400.884C866.361,-388.896 848.365,-375.399 832.566,-363.549\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"834.627,-360.72 824.527,-357.52 830.427,-366.32 834.627,-360.72\"/>\r\n",
       "</g>\r\n",
       "<!-- 21 -->\r\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\r\n",
       "<path fill=\"#e5833c\" stroke=\"black\" d=\"M979.5,-365C979.5,-365 873.5,-365 873.5,-365 867.5,-365 861.5,-359 861.5,-353 861.5,-353 861.5,-309 861.5,-309 861.5,-303 867.5,-297 873.5,-297 873.5,-297 979.5,-297 979.5,-297 985.5,-297 991.5,-303 991.5,-309 991.5,-309 991.5,-353 991.5,-353 991.5,-359 985.5,-365 979.5,-365\"/>\r\n",
       "<text text-anchor=\"start\" x=\"879\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">cap&#45;color ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"889\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.029</text>\r\n",
       "<text text-anchor=\"start\" x=\"875\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3067</text>\r\n",
       "<text text-anchor=\"start\" x=\"869.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3022, 45]</text>\r\n",
       "</g>\r\n",
       "<!-- 19&#45;&gt;21 -->\r\n",
       "<g id=\"edge21\" class=\"edge\"><title>19&#45;&gt;21</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M926.5,-400.884C926.5,-392.778 926.5,-383.982 926.5,-375.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"930,-375.299 926.5,-365.299 923,-375.299 930,-375.299\"/>\r\n",
       "</g>\r\n",
       "<!-- 22 -->\r\n",
       "<g id=\"node23\" class=\"node\"><title>22</title>\r\n",
       "<path fill=\"#f8dbc6\" stroke=\"black\" d=\"M833.5,-261C833.5,-261 701.5,-261 701.5,-261 695.5,-261 689.5,-255 689.5,-249 689.5,-249 689.5,-205 689.5,-205 689.5,-199 695.5,-193 701.5,-193 701.5,-193 833.5,-193 833.5,-193 839.5,-193 845.5,-199 845.5,-205 845.5,-205 845.5,-249 845.5,-249 845.5,-255 839.5,-261 833.5,-261\"/>\r\n",
       "<text text-anchor=\"start\" x=\"697.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">spore&#45;print&#45;color ≤ 6.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"730\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.486</text>\r\n",
       "<text text-anchor=\"start\" x=\"724\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 36</text>\r\n",
       "<text text-anchor=\"start\" x=\"719\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [21, 15]</text>\r\n",
       "</g>\r\n",
       "<!-- 21&#45;&gt;22 -->\r\n",
       "<g id=\"edge22\" class=\"edge\"><title>21&#45;&gt;22</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M874.877,-296.884C859.805,-287.214 843.202,-276.563 827.651,-266.587\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"829.442,-263.578 819.136,-261.125 825.663,-269.47 829.442,-263.578\"/>\r\n",
       "</g>\r\n",
       "<!-- 25 -->\r\n",
       "<g id=\"node26\" class=\"node\"><title>25</title>\r\n",
       "<path fill=\"#e5823b\" stroke=\"black\" d=\"M981.5,-261C981.5,-261 875.5,-261 875.5,-261 869.5,-261 863.5,-255 863.5,-249 863.5,-249 863.5,-205 863.5,-205 863.5,-199 869.5,-193 875.5,-193 875.5,-193 981.5,-193 981.5,-193 987.5,-193 993.5,-199 993.5,-205 993.5,-205 993.5,-249 993.5,-249 993.5,-255 987.5,-261 981.5,-261\"/>\r\n",
       "<text text-anchor=\"start\" x=\"872.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">ring&#45;number ≤ 1.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"895\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.02</text>\r\n",
       "<text text-anchor=\"start\" x=\"877\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3031</text>\r\n",
       "<text text-anchor=\"start\" x=\"871.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3001, 30]</text>\r\n",
       "</g>\r\n",
       "<!-- 21&#45;&gt;25 -->\r\n",
       "<g id=\"edge25\" class=\"edge\"><title>21&#45;&gt;25</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M927.149,-296.884C927.308,-288.778 927.481,-279.982 927.648,-271.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"931.15,-271.366 927.847,-261.299 924.152,-271.229 931.15,-271.366\"/>\r\n",
       "</g>\r\n",
       "<!-- 23 -->\r\n",
       "<g id=\"node24\" class=\"node\"><title>23</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M685,-149.5C685,-149.5 604,-149.5 604,-149.5 598,-149.5 592,-143.5 592,-137.5 592,-137.5 592,-108.5 592,-108.5 592,-102.5 598,-96.5 604,-96.5 604,-96.5 685,-96.5 685,-96.5 691,-96.5 697,-102.5 697,-108.5 697,-108.5 697,-137.5 697,-137.5 697,-143.5 691,-149.5 685,-149.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"615.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"601\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 15</text>\r\n",
       "<text text-anchor=\"start\" x=\"600\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 15]</text>\r\n",
       "</g>\r\n",
       "<!-- 22&#45;&gt;23 -->\r\n",
       "<g id=\"edge23\" class=\"edge\"><title>22&#45;&gt;23</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M727.565,-192.884C713.375,-181.116 697.431,-167.894 683.333,-156.203\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"685.206,-153.209 675.274,-149.52 680.737,-158.597 685.206,-153.209\"/>\r\n",
       "</g>\r\n",
       "<!-- 24 -->\r\n",
       "<g id=\"node25\" class=\"node\"><title>24</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M808,-149.5C808,-149.5 727,-149.5 727,-149.5 721,-149.5 715,-143.5 715,-137.5 715,-137.5 715,-108.5 715,-108.5 715,-102.5 721,-96.5 727,-96.5 727,-96.5 808,-96.5 808,-96.5 814,-96.5 820,-102.5 820,-108.5 820,-108.5 820,-137.5 820,-137.5 820,-143.5 814,-149.5 808,-149.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"738.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"724\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21</text>\r\n",
       "<text text-anchor=\"start\" x=\"723\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [21, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 22&#45;&gt;24 -->\r\n",
       "<g id=\"edge24\" class=\"edge\"><title>22&#45;&gt;24</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M767.5,-192.884C767.5,-182.326 767.5,-170.597 767.5,-159.854\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"771,-159.52 767.5,-149.52 764,-159.52 771,-159.52\"/>\r\n",
       "</g>\r\n",
       "<!-- 26 -->\r\n",
       "<g id=\"node27\" class=\"node\"><title>26</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M949.5,-149.5C949.5,-149.5 851.5,-149.5 851.5,-149.5 845.5,-149.5 839.5,-143.5 839.5,-137.5 839.5,-137.5 839.5,-108.5 839.5,-108.5 839.5,-102.5 845.5,-96.5 851.5,-96.5 851.5,-96.5 949.5,-96.5 949.5,-96.5 955.5,-96.5 961.5,-102.5 961.5,-108.5 961.5,-108.5 961.5,-137.5 961.5,-137.5 961.5,-143.5 955.5,-149.5 949.5,-149.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"871.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"849\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2716</text>\r\n",
       "<text text-anchor=\"start\" x=\"847.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2716, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;26 -->\r\n",
       "<g id=\"edge26\" class=\"edge\"><title>25&#45;&gt;26</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M919.409,-192.884C916.451,-182.106 913.157,-170.108 910.158,-159.184\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"913.528,-158.237 907.505,-149.52 906.778,-160.09 913.528,-158.237\"/>\r\n",
       "</g>\r\n",
       "<!-- 27 -->\r\n",
       "<g id=\"node28\" class=\"node\"><title>27</title>\r\n",
       "<path fill=\"#e88e4e\" stroke=\"black\" d=\"M1123.5,-157C1123.5,-157 991.5,-157 991.5,-157 985.5,-157 979.5,-151 979.5,-145 979.5,-145 979.5,-101 979.5,-101 979.5,-95 985.5,-89 991.5,-89 991.5,-89 1123.5,-89 1123.5,-89 1129.5,-89 1135.5,-95 1135.5,-101 1135.5,-101 1135.5,-145 1135.5,-145 1135.5,-151 1129.5,-157 1123.5,-157\"/>\r\n",
       "<text text-anchor=\"start\" x=\"987.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">spore&#45;print&#45;color ≤ 6.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1020\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.172</text>\r\n",
       "<text text-anchor=\"start\" x=\"1010\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 315</text>\r\n",
       "<text text-anchor=\"start\" x=\"1004.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [285, 30]</text>\r\n",
       "</g>\r\n",
       "<!-- 25&#45;&gt;27 -->\r\n",
       "<g id=\"edge27\" class=\"edge\"><title>25&#45;&gt;27</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M970.383,-192.884C982.211,-183.531 995.201,-173.259 1007.46,-163.568\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1009.71,-166.247 1015.39,-157.299 1005.37,-160.756 1009.71,-166.247\"/>\r\n",
       "</g>\r\n",
       "<!-- 28 -->\r\n",
       "<g id=\"node29\" class=\"node\"><title>28</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M977,-53C977,-53 896,-53 896,-53 890,-53 884,-47 884,-41 884,-41 884,-12 884,-12 884,-6 890,-0 896,-0 896,-0 977,-0 977,-0 983,-0 989,-6 989,-12 989,-12 989,-41 989,-41 989,-47 983,-53 977,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"907.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"893\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30</text>\r\n",
       "<text text-anchor=\"start\" x=\"892\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 30]</text>\r\n",
       "</g>\r\n",
       "<!-- 27&#45;&gt;28 -->\r\n",
       "<g id=\"edge28\" class=\"edge\"><title>27&#45;&gt;28</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1015.18,-88.9485C1003,-79.4346 989.732,-69.074 977.624,-59.6175\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"979.489,-56.6335 969.454,-53.2367 975.181,-62.1504 979.489,-56.6335\"/>\r\n",
       "</g>\r\n",
       "<!-- 29 -->\r\n",
       "<g id=\"node30\" class=\"node\"><title>29</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1108,-53C1108,-53 1019,-53 1019,-53 1013,-53 1007,-47 1007,-41 1007,-41 1007,-12 1007,-12 1007,-6 1013,-0 1019,-0 1019,-0 1108,-0 1108,-0 1114,-0 1120,-6 1120,-12 1120,-12 1120,-41 1120,-41 1120,-47 1114,-53 1108,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1034.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1016\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 285</text>\r\n",
       "<text text-anchor=\"start\" x=\"1015\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [285, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 27&#45;&gt;29 -->\r\n",
       "<g id=\"edge29\" class=\"edge\"><title>27&#45;&gt;29</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1059.6,-88.9485C1060.12,-80.7153 1060.68,-71.848 1061.22,-63.4814\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1064.73,-63.4384 1061.87,-53.2367 1057.74,-62.9948 1064.73,-63.4384\"/>\r\n",
       "</g>\r\n",
       "<!-- 31 -->\r\n",
       "<g id=\"node32\" class=\"node\"><title>31</title>\r\n",
       "<path fill=\"#6cb6ec\" stroke=\"black\" d=\"M1127.5,-365C1127.5,-365 1021.5,-365 1021.5,-365 1015.5,-365 1009.5,-359 1009.5,-353 1009.5,-353 1009.5,-309 1009.5,-309 1009.5,-303 1015.5,-297 1021.5,-297 1021.5,-297 1127.5,-297 1127.5,-297 1133.5,-297 1139.5,-303 1139.5,-309 1139.5,-309 1139.5,-353 1139.5,-353 1139.5,-359 1133.5,-365 1127.5,-365\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1036\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">habitat ≤ 1.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"1037\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.325</text>\r\n",
       "<text text-anchor=\"start\" x=\"1027\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 505</text>\r\n",
       "<text text-anchor=\"start\" x=\"1017.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [103, 402]</text>\r\n",
       "</g>\r\n",
       "<!-- 30&#45;&gt;31 -->\r\n",
       "<g id=\"edge31\" class=\"edge\"><title>30&#45;&gt;31</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1074.5,-400.884C1074.5,-392.778 1074.5,-383.982 1074.5,-375.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1078,-375.299 1074.5,-365.299 1071,-375.299 1078,-375.299\"/>\r\n",
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       "<!-- 38 -->\r\n",
       "<g id=\"node39\" class=\"node\"><title>38</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1251,-357.5C1251,-357.5 1170,-357.5 1170,-357.5 1164,-357.5 1158,-351.5 1158,-345.5 1158,-345.5 1158,-316.5 1158,-316.5 1158,-310.5 1164,-304.5 1170,-304.5 1170,-304.5 1251,-304.5 1251,-304.5 1257,-304.5 1263,-310.5 1263,-316.5 1263,-316.5 1263,-345.5 1263,-345.5 1263,-351.5 1257,-357.5 1251,-357.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1181.5\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1167\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 86</text>\r\n",
       "<text text-anchor=\"start\" x=\"1166\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [86, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 30&#45;&gt;38 -->\r\n",
       "<g id=\"edge38\" class=\"edge\"><title>30&#45;&gt;38</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1118.66,-400.884C1134.64,-388.896 1152.63,-375.399 1168.43,-363.549\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.57,-366.32 1176.47,-357.52 1166.37,-360.72 1170.57,-366.32\"/>\r\n",
       "</g>\r\n",
       "<!-- 32 -->\r\n",
       "<g id=\"node33\" class=\"node\"><title>32</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1116,-253.5C1116,-253.5 1027,-253.5 1027,-253.5 1021,-253.5 1015,-247.5 1015,-241.5 1015,-241.5 1015,-212.5 1015,-212.5 1015,-206.5 1021,-200.5 1027,-200.5 1027,-200.5 1116,-200.5 1116,-200.5 1122,-200.5 1128,-206.5 1128,-212.5 1128,-212.5 1128,-241.5 1128,-241.5 1128,-247.5 1122,-253.5 1116,-253.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1042.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1024\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 272</text>\r\n",
       "<text text-anchor=\"start\" x=\"1023\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 272]</text>\r\n",
       "</g>\r\n",
       "<!-- 31&#45;&gt;32 -->\r\n",
       "<g id=\"edge32\" class=\"edge\"><title>31&#45;&gt;32</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1073.53,-296.884C1073.21,-286.216 1072.86,-274.352 1072.54,-263.519\"/>\r\n",
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       "<!-- 33 -->\r\n",
       "<g id=\"node34\" class=\"node\"><title>33</title>\r\n",
       "<path fill=\"#d6ebfa\" stroke=\"black\" d=\"M1264.5,-261C1264.5,-261 1158.5,-261 1158.5,-261 1152.5,-261 1146.5,-255 1146.5,-249 1146.5,-249 1146.5,-205 1146.5,-205 1146.5,-199 1152.5,-193 1158.5,-193 1158.5,-193 1264.5,-193 1264.5,-193 1270.5,-193 1276.5,-199 1276.5,-205 1276.5,-205 1276.5,-249 1276.5,-249 1276.5,-255 1270.5,-261 1264.5,-261\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1170.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">bruises ≤ 0.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"1174\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.493</text>\r\n",
       "<text text-anchor=\"start\" x=\"1164\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 233</text>\r\n",
       "<text text-anchor=\"start\" x=\"1154.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [103, 130]</text>\r\n",
       "</g>\r\n",
       "<!-- 31&#45;&gt;33 -->\r\n",
       "<g id=\"edge33\" class=\"edge\"><title>31&#45;&gt;33</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1118.98,-296.884C1131.66,-287.442 1145.6,-277.064 1158.73,-267.292\"/>\r\n",
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       "<g id=\"node35\" class=\"node\"><title>34</title>\r\n",
       "<path fill=\"#e78b48\" stroke=\"black\" d=\"M1256,-157C1256,-157 1167,-157 1167,-157 1161,-157 1155,-151 1155,-145 1155,-145 1155,-101 1155,-101 1155,-95 1161,-89 1167,-89 1167,-89 1256,-89 1256,-89 1262,-89 1268,-95 1268,-101 1268,-101 1268,-145 1268,-145 1268,-151 1262,-157 1256,-157\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1164\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">cap&#45;color ≤ 6.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"1174\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.134</text>\r\n",
       "<text text-anchor=\"start\" x=\"1164\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 111</text>\r\n",
       "<text text-anchor=\"start\" x=\"1163\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [103, 8]</text>\r\n",
       "</g>\r\n",
       "<!-- 33&#45;&gt;34 -->\r\n",
       "<g id=\"edge34\" class=\"edge\"><title>33&#45;&gt;34</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1211.5,-192.884C1211.5,-184.778 1211.5,-175.982 1211.5,-167.472\"/>\r\n",
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       "<g id=\"node38\" class=\"node\"><title>37</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1387,-149.5C1387,-149.5 1298,-149.5 1298,-149.5 1292,-149.5 1286,-143.5 1286,-137.5 1286,-137.5 1286,-108.5 1286,-108.5 1286,-102.5 1292,-96.5 1298,-96.5 1298,-96.5 1387,-96.5 1387,-96.5 1393,-96.5 1399,-102.5 1399,-108.5 1399,-108.5 1399,-137.5 1399,-137.5 1399,-143.5 1393,-149.5 1387,-149.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1313.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1295\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 122</text>\r\n",
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       "</g>\r\n",
       "<!-- 33&#45;&gt;37 -->\r\n",
       "<g id=\"edge37\" class=\"edge\"><title>33&#45;&gt;37</title>\r\n",
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       "<g id=\"node36\" class=\"node\"><title>35</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1250,-53C1250,-53 1161,-53 1161,-53 1155,-53 1149,-47 1149,-41 1149,-41 1149,-12 1149,-12 1149,-6 1155,-0 1161,-0 1161,-0 1250,-0 1250,-0 1256,-0 1262,-6 1262,-12 1262,-12 1262,-41 1262,-41 1262,-47 1256,-53 1250,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1176.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1158\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 103</text>\r\n",
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       "<!-- 34&#45;&gt;35 -->\r\n",
       "<g id=\"edge35\" class=\"edge\"><title>34&#45;&gt;35</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1209.4,-88.9485C1208.88,-80.7153 1208.32,-71.848 1207.78,-63.4814\"/>\r\n",
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       "<!-- 36 -->\r\n",
       "<g id=\"node37\" class=\"node\"><title>36</title>\r\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1365,-53C1365,-53 1292,-53 1292,-53 1286,-53 1280,-47 1280,-41 1280,-41 1280,-12 1280,-12 1280,-6 1286,-0 1292,-0 1292,-0 1365,-0 1365,-0 1371,-0 1377,-6 1377,-12 1377,-12 1377,-41 1377,-41 1377,-47 1371,-53 1365,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"1299.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"1289\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\r\n",
       "<text text-anchor=\"start\" x=\"1288\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 8]</text>\r\n",
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       "<!-- 34&#45;&gt;36 -->\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1da09210340>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n",
    "\n",
    "dot_data = export_graphviz(dt, out_file=None, \n",
    "                         feature_names=X.columns,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)\n",
    "#graph.render(filename='DecisionTree')\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By all methods examined before the feature that is most important is \"gill-color\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_list = X.columns.values\n",
    "feature_importance = dt.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "\n",
    "plt.figure(figsize=(8,7))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center', color =\"red\")\n",
    "plt.yticks(range(len(sorted_idx)), features_list[sorted_idx])\n",
    "plt.xlabel('Importance')\n",
    "plt.title('Feature importance')\n",
    "plt.draw()\n",
    "#plt.savefig(\"featureimp.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting and estimating the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       433\n",
      "           1       1.00      1.00      1.00       380\n",
      "\n",
      "    accuracy                           1.00       813\n",
      "   macro avg       1.00      1.00      1.00       813\n",
      "weighted avg       1.00      1.00      1.00       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree Classifier report: \\n\\n\", classification_report(y_test, y_pred_dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Accuracy: {}%\".format(round(dt.score(X_test, y_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "#plt.savefig(\"dtcm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Logistic Regression Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 94.96%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression(solver=\"lbfgs\", max_iter=500)\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test Accuracy: {}%\".format(round(lr.score(X_test, y_test)*100,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification report of Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95       433\n",
      "           1       0.94      0.96      0.95       380\n",
      "\n",
      "    accuracy                           0.95       813\n",
      "   macro avg       0.95      0.95      0.95       813\n",
      "weighted avg       0.95      0.95      0.95       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_lr = lr.predict(X_test)\n",
    "print(\"Logistic Regression Classifier report: \\n\\n\", classification_report(y_test, y_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mBSepZlMbyuoj6QFJj0maLOknaf5pkmZImpSm3Sq2OVXSVEnPStq5tTKcMZmZWXvMB74UEfMk9QLulXRzWvariDi7cmVJGwMHAZsAawB3SBoWEYtaKsAZk5lZwalH7abWRGZeetorTVFlkxHAVRExPyKmAVOBraqV4cBkZlZ0Uu2mNhWnBkmTgNnA7RFxf1p0rKTHJV0oqX+atybwSsXm09O8FjkwmZnZEpJGSXqoYhrVdJ2IWBQRw4G1gK0kbQr8HlgfGA7MBM5p3GUzxVTLsHyNycys6GrZKS8ixgBj2rju25ImArtUXluSdAFwU3o6HRhcsdlawKvV9uuMycys4NRDNZtaLUtaVdJK6XFfYEfgGUmDKlbbB3gyPR4PHCRpGUnrAkOBB6qV4YzJzMzaYxAwVlIDWXIzLiJuknSppOFkzXQvAkcDRMRkSeOAp4CFwDHVeuSBA5OZWfF14g9sI+JxYPNm5h9eZZvRwOi2luHAZGZWcCUb+MHXmMzMrL44YzIzK7i2dFooEgcmM7OiK1lbnpvyzMysrjhjMjMruJIlTA5MZmZFV7ZrTG7KMzOzuuKMycys6MqVMDkwmZkVnW+tbmZmliNnTGZmBVe2zg8OTGZmBVeyljw35ZmZWX1xxmRmVnQlS5kcmMzMCq5s15jclGdmZnXFGZOZWcGVrCXPgcnMrPBKFpnclGdmZnXFGZOZWcGVbUgiByYzs4JTydq+SnY4ZmZWdM6YzMyKzk15ZmZWT0oWlxyYzMyKziM/mJmZ5cgZk5lZ0ZWsLc8ZUwH16CEueOR/+MWNhwKwQv++nH3bSC577jjOvm0ky6/UB4CevRo4+cK9ufDxY/jjpG8wfLshXVhrK7qZr83kyK8ewZ577cGIvffk0ssuXWr5RRdfyKaf3pg5c+Z0UQ27L6l2Uz1wYCqgfY/7T156+vUlzw85ZRsemfAChw37NY9MeIFDTtkGgD2+tgUAX93st3zny2P5n3N2Kd0P8azz9GzoyXe/cxI3jr+JKy6/iquuuoLnn58KZEHrvvvuY9CgQV1cSysDB6aCWXXNfnx+92H89Y8PL5m39YiNuGXsowDcMvZR/mvvTwGwzsar8siEFwB4+/X3mPf2B2y45RqdX2krhVVXXZWNN94YgOWWW4711l2PWbNmA3DWWWdywgkn+otPF1EP1WyqB7kHJkl9JW2YdzndxbHn7cofTrqVWBxL5g1YbTneem0eAG+9No/+A5cD4PnHXmPrERvR0NCD1YesxIZbDGLg4BW7pN5WLjNmzODpZ55ms8024847/87AgQPZaMONurpa3VfJ2vJyDUyS9gQmAbek58Mlja+y/ihJD0l6KM96FdV/7j6MObPf47lHZrZp/ZsvfJTXp8/lDw8dzbHn7cqT/3yFRQsX51xLK7v333+Pb3/7OE4++VQaGhoYc8EfOPaYb3Z1taxE8u6VdxqwFTARICImSRrS0soRMQYYAyApWlqvu9p067XZeq8N+fxuQ+ndpyfL9luG71+6L2/Neo8Bqy/PW6/NY8DqyzNn9nsALFq0mN+ecMuS7X/zj//H9ClvdlX1rQQWLFjA8d8+nt1334Mv7/hlnnvuOWbMmMG+++0DwKxZs9j/gH256sqrWWWVVbu4tt1HnSQ6NZN3YFoYEe+43bk2LvjeHVzwvTsAGL7dEA78ztaMPvzPfP2sndhl5OZcceY97DJyc/5xwzMALNO3FxJ88P4CtthxfRYtXLxUpwmz9ogIfvTjH7LeeusxcuQRAAwbNoy777p3yTo77bwjV191Df379++iWnZP9XJtqFbyDkxPSjoEaJA0FPgW8M+cy+x2rjjjHn487kB2O+o/mPXyO5y2/9UA9B+4HGfd+t/E4uCNGXP5+eF/7uKaWpE9+ugj3HjjeIYOHbYkQzruW8ez7bbbdXHNrGwUkV+LmaRlge8DOwECbgVOj4gP2rBtbMcPc6ubWaW7OJ0P5y/s6mpYN9F7mZ5ERM3SnG/uf0XNPsjPv+aQLk+/cs2YIuJ9ssD0/TzLMTPr1ro8lNRWLoFJ0o1AixE8IvbKo1wzMyu+vDKms3Par5mZNeHOD20QEXc1PpbUG9iILIN6NiI+zKNMM7PuqjN7PkvqA9wNLEMWQ66NiB9LGgBcDQwBXgQOiIg5aZtTgaOARcC3IuLWamXk/QPb3YHngf8FfgNMlbRrnmWamVmu5gNfiojPAMOBXSR9HjgFmBARQ4EJ6TmSNgYOAjYBdgF+J6mhWgF5dxc/B/hiRExNFVwf+Ctwc87lmpl1H53YlBdZV+556WmvNAUwAtg+zR9LNrDCyWn+VRExH5gmaSrZwAv3tVRG3mPlzW4MSskLwOycyzQz61ZqOVRe5dBwaRr18fLUIGkS2ef57RFxP7BaRMwESP8PTKuvCbxSsfn0NK9FefXK+0p6OFnS34BxZBF1f+DBPMo0M7NPrnJouCrrLAKGS1oJuF7SplVWby6dq/q7q7ya8vaseDwLaPxp+OuAxyoxM6uhrhr2LSLeljSR7NrRLEmDImKmpEF81Do2HRhcsdlawKvV9ptXr7wj89ivmZk1oxOvMUlaFViQglJfYEfgTGA8MBI4I/1/Q9pkPHCFpHOBNYChwAPVysirKe+kiDhL0vk0k7JFxLfyKNfMzHI3CBibetb1AMZFxE2S7gPGSToKeJns0g0RMVnSOOApYCFwTGoKbFFeTXlPp/99XyUzs5x1ZkteRDwObN7M/DeBHVrYZjQwuq1l5NWUd2P6f2we+zczs4945Ic28Fh5ZmbWUXmPlfcVYHXgsvT8YLKhKszMrFZKdjPWXMfKk3R6RGxbsehGSXfnUaaZWXdVtruE5z3yw6qS1mt8ImldYNWcyzQzswLLe6y8bwMTJb2Qng8Bjs65TDOzbkV5pxidLO872N4iaSjZbS8AnkkD+ZmZWY24Ka8NJJ1U8XSviHgsTfMl/TyPMs3MrBzySgAPqnh8apNlu+RUpplZ91TL4cXrQF5NeWrhcXPPzczsEyjbNaa8DidaeNzcczMzsyXyypg+I2kuWXbUNz0mPe+TU5lmZt1S2To/5PUD26r3czczsxoq2Vh5JWuZNDOzosv7B7ZmZpYzN+WZmVldKVlcclOemZnVF2dMZmZFV7LODw5MZmYFV7ZrTG7KMzOzuuKMycys4EqWMDkwmZkVXsmuMbkpz8zM6oozJjOzgitb5wcHJjOzgpOb8szMzPLjjMnMrOjKlTA5MJmZFV3ZrjG5Kc/MzOqKMyYzs4IrW+cHByYzs4IrW1OeA5OZWdGVKy75GpOZmdUXZ0xmZgXnpjwzM6srJYtLbsozM7P64ozJzKzgypYxOTCZmRVc2a4xuSnPzMzqijMmM7OCK1nC5IzJzKzoJNVsakNZgyXdKelpSZMlHZfmnyZphqRJadqtYptTJU2V9KyknVsrwxmTmZm1x0LgxIh4RNIKwMOSbk/LfhURZ1euLGlj4CBgE2AN4A5JwyJiUUsFODCZmRVcZzblRcRMYGZ6/K6kp4E1q2wyArgqIuYD0yRNBbYC7mtpAzflmZkVXC2b8iSNkvRQxTSqSrlDgM2B+9OsYyU9LulCSf3TvDWBVyo2m071QObAZGZmH4mIMRGxZcU0prn1JC0P/Bk4PiLmAr8H1geGk2VU5zSu2lwx1ergpjwzs4Lr7F55knqRBaXLI+I6gIiYVbH8AuCm9HQ6MLhi87WAV6vt3xmTmVnBqYb/Wi0r67r3J+DpiDi3Yv6gitX2AZ5Mj8cDB0laRtK6wFDggWplOGMyM7P22Bo4HHhC0qQ073vAwZKGkzXTvQgcDRARkyWNA54i69F3TLUeeeDAZGZWeJ3cK+9emr9u9Lcq24wGRre1DAcmM7OC88gPgKR/1LoiZmZm0PGMae2a1sLMzDqsbKOLdzQwVe2DbmZmnadkcanlwCTpKy0tAvrmUx0zM+vuqmVMe1ZZdlOVZWZm1plKljK1GJgi4sjOrIiZmXVMyeJSy73yJJ1X8fi4JssuzrFOZmbWjVXrLr5txeORTZZtlkNdzMysAzrzRoGdodo1JrXw2MzM6kidxJOaqRaYeqT7afSoeNx4+A2518zMzLqlaoFpReBhPgpGj1Qs8++YzMzqRL00wdVKtV55QzqxHmZm1kEli0vVx8qT1DPdewNJgyXtl4Y1NzMzy0W17uJfA2YDL6XHE4D9gKslndxJ9TMzs1aohlM9qHaN6Xiy+7evADwNrBMRb0haFngQOLMT6mdmZq3oNteYgA8jYg4wR9LUiHgDICLel/Rh51TPzMy6m2qBqa+kzcma+3qnx43ZXp/OqJyZmbWuZAlT1cA0Ezg3PX6t4nHjczMzqwPdpikvIr7Y0jJJvfKpjpmZdXdtvrW6Ml+S9Edgeo51MjOzdpBqN9WDVgOTpM9J+jXwEjAeuAfYKO+KmZlZ25RtENdqv2MaLWkK8HPgCWBz4PWIGJt665mZmdVctc4Po4Bngd8DN0XEB5I8Rp6ZWZ2pk0SnZqoFptWBnYCDgfMk3UnWhbxnRCzslNqZmVmruk1giohFwM3AzZL6AHsAywIzJE2IiEM6qY5mZtaNVMuYloiID4BrgWsl9QOOzbVWZmbWZvXSaaFW2txdvFFEzAW+nkNdzMysA7pdd/EW1En1zcysbNrUlNeMTumddxend0YxZgD0Xqajfw5mXatsTXkt/iVKOp/mA5CAlXKrUYWFCxZ1RjFm9OzVwL5Dz219RbMa+POUE2q7w3LFpaoZ00MdXGZmZtZh1bqLj+3MipiZWcd0m6Y8MzMrBgcmMzOrKyWLSx3uLm5mZpaLaqOLj6t4fGaTZbflWSkzM2u7bnPbC2BoxeMvN1m2ag51MTOzDuhOIz9U+xGtb39hZma5qBaYlpW0uaQtyG53sbmk/2h83kn1MzOzVnRmU56kwZLulPS0pMmSjkvzB0i6XdKU9H//im1OlTRV0rOSdm6tjGq98l4Dzm3mceNzMzOrA518bWghcGJEPCJpBeBhSbcDRwATIuIMSacApwAnS9oYOAjYBFgDuEPSsHRrpWZV+4Ht9rU7DjMzK4OImAnMTI/flfQ0sCYwAtg+rTYWmAicnOZfFRHzgWmSpgJbAfe1VEa1sfK+0rQ+wBvApIh4twPHY2ZmOahlwiRpFDCqYtaYiBjTwrpDgM2B+4HVUtAiImZKGphWWxP4V8Vm09O8FlVrytuzmXkDgM0kHRURf6+2YzMz6xy1bMpLQajZQNSkzOWBPwPHR8TcKnVobkHVDnTVmvKObKEy6wDjgM9V27GZmZWTpF5kQenyiLguzZ4laVDKlgYBs9P86cDgis3XAl6ttv+O3MH2JaBXe7czM7N8qIdqNrVaVpYa/Ql4OiIqO8WNB0amxyOBGyrmHyRpGUnrkv1G9oFqZbR7rDxJGwLz27udmZnlo5N/GLs1cDjwhKRJad73gDOAcZKOAl4G9geIiMlpJKGnyHr0HVOtRx5U7/xwIx9vBxwADAIOa/+xmJlZ0UXEvbR8a8IdWthmNDC6rWVUy5jObrpv4E1gSkR82NYCzMwsX/Uyxl2tVOv8cFdz8yU1SDo0Ii7Pr1pmZtZWJYtLVUcX75eGkfiNpJ2U+SbwAnBA51XRzMy6k2pNeZcCc8h+nfv/gO8CvYERETGpynZmZtaJuk1THrBeRHwaQNIfyUZ9WNujPpiZ1ZeyBaZqv2Na0Pggde2b5qBkZmZ5q5YxDZc0Nz0W2a0v5qbHERH9cq+dmZm1qmQJU9XA9FhEbN5pNTEzs44pWWTq6B1szczMclEtYxoo6YSWFjYZI8nMzLpI2To/VAtMDcDytDz0hJmZ1YGSxaWqgWlmRPy002piZmZG9cBUshhsZlZObbldRZFUC0zNjhJrZmb1pWxNeS32youItzqzImZmZtCBGwWamVl96U698szMrADKFpiq/cDWzMys0zljMjMruJIlTA5MZmZF56Y8MzOzHDljMjMruLJlTA5MZmYFV7K45KY8MzOrL86YzMwKzk15ZmZWV8oWmNyUZ2ZmdcUZk5lZwZUsYXJgMjMrurLdj8lNeWZmVlecMZmZFZyb8szMrK6IckUmN+WZmVldccZkZlZ05UqYHJjMzIrOP7A1MzPLkTMmM7OCKyMpODMAABLNSURBVFnC5MBkZlZ0bsozMzPLkTMmM7OCK1nC5IzJzKzoJNVsakNZF0qaLenJinmnSZohaVKadqtYdqqkqZKelbRzW47HGZOZWcF1csZ0MfAb4JIm838VEWdXzpC0MXAQsAmwBnCHpGERsahaAc6YzMyszSLibuCtNq4+ArgqIuZHxDRgKrBVaxs5MJmZFVwtm/IkjZL0UMU0qo3VOFbS46mpr3+atybwSsU609O8qhyYzMwKTqrdFBFjImLLimlMG6rwe2B9YDgwEzinsWrNrBut7cyByczMPpGImBURiyJiMXABHzXXTQcGV6y6FvBqa/tzYDIzK7haZkwdK1+DKp7uAzT22BsPHCRpGUnrAkOBB1rbn3vlmZkVXGfej0nSlcD2wCqSpgM/BraXNJysme5F4GiAiJgsaRzwFLAQOKa1HnngwGRmZu0QEQc3M/tPVdYfDYxuTxkOTGZmBVe2kR8cmMzMCs6DuJqZmeXIGZOZWcGVLGFyYDIzKzo35ZmZmeXIGZOZWcGVLGFyYDIzKzo35ZmZmeXIGZOZWdGVK2FyYDIzKzo35ZmZmeXIGZOZWcGVLGFyYDIzKzo35ZmZmeXIGZOZWcGVK19yYDIzKzw35ZmZmeXIGZOZWcGVLGFyYDIzKzo35ZmZmeXIGZOZWcGVLGFyYDIzK7qyBSY35ZmZWV1xxlRg3//B97nrrokMGDCA8TfcCMAJJ36badNeBODdd+eywgr9uP6667uwllZkvXo38NMrDqBX7wYaGnpw361TGPe/9wGw6+HD2eXQz7B4UfDwxGlc9st72GCz1Tj69B2B7IL8uPPv44Hbn+/KQ+gWytb5wYGpwPbZe28OPeQQTjn1lCXzzj3nV0sen3nWmayw/PJdUTUriQUfLuIn/30tH7y/gIaePfjZlQfw6F3T6N2nJ5/dYX1O3PMyFi5YRL8BfQF4+bk3OfkrV7B4UbDSqstxzvjDeOjvL7B4UXTxkZRbyeJS5zXlSeovabPOKq872HLLz7Liiis1uywiuPXWW9ht9907uVZWNh+8vwCAhp49aOjZAwJ2PvgzXD/mQRYuWATA3Lf+DcCHHyxcEoR6L9NAhAOStV+uGZOkicBeqZxJwOuS7oqIE/Is1+Dhhx9i5ZVXZsg6Q7q6KlZwPXqIM68/hNXXXolbL3+MKY+/xqB1V+JTW67JId/+Ah/OX8QlZ97N80/MAmDoZqvzjV/sxCprrMD5J93ibKkTlK0pL++MacWImAt8BbgoIrYAdmxpZUmjJD0k6aGc61V6f/3bX9ltN2dL9sktXhx8d8TlHL3tH9lgs9UZPHRlGhp6sHy/ZTh1/6u49Ky7OeG8j861KY+/xrd3v4RT9ruSfY7eil69G7qw9lZEeQemnpIGAQcAN7W2ckSMiYgtI2LLnOtVagsXLuSOO+5g11127eqqWIm8/+58Jj8wnc23GcKbr83j/tumAjD18VlEBP36911q/RnPv8X89xew9rBVuqK6VmB5B6afArcCUyPiQUnrAVNyLrPbu++++1h33XVZffXVu7oqVnD9+vdl2RWWAbJrRpt9YW1mvPAWD97xPJt+fjAAg4asRM9eDcyd828GrtWPHg1Zs9Iqa6zAGuv2Z/aMd7qs/t2FpJpN9SDXa0wRcQ1wTcXzF4B98yyzO/nOd07kgQcf4O233+aLX9qeY485ln333Y+bb/6bm/GsJvoPXI5jz9yZHj2Eeoh/3vwcD0+cRs9ePfjGz3fi3JsOZ+GCRfzm5FsB2GiLNdln1GdZuHARsTi44Cd/5905H3TxUZRfncSTmlGevWYkXQR8rICI+Gobto3GHj9meevZq4F9h57b1dWwbuLPU04gImoWTp5//s2afZCvv/7KXR7m8v4dU+V1pT7APsCrOZdpZmYFlndT3p8rn0u6ErgjzzLNzLqbsjXldfZYeUOBtTu5TDMzK5C8f2D7Ltk1JqX/XwNOzrNMM7PuRpQrZcq7KW+FPPdvZmZQsriU/yCukvYCtk1PJ0ZEqz+0NTOz7ivXa0ySzgCOA55K03GSfpFnmWZm3Y1Uu6n1snShpNmSnqyYN0DS7ZKmpP/7Vyw7VdJUSc9K2rktx5N354fdgC9HxIURcSGwC+BffpqZ1ZBq+K8NLib7LK90CjAhIoYCE9JzJG0MHARskrb5naRWB0/sjF55lfdlWLETyjMzs5xExN3AW01mjwDGpsdjgb0r5l8VEfMjYhowFdiqtTLyvsb0C+BRSXeSXZ7bFjg15zLNzLqXGnZ+kDQKGFUxa0xEjGlls9UiYiZARMyUNDDNXxP4V8V609O8qvLulXdluifTZ8leupMj4rU8yzQz625q2SkvBaHWAlFbNVe1VodP6oymvB7AG8AcYJikbVtZ38zMimVWusUR6f/Zaf50YHDFemvRhmHp8v6B7ZnAgcBkYHGaHcDdeZZrZtad1MHtKsYDI4Ez0v83VMy/QtK5wBpko/880NrO8r7GtDewYUTMz7kcM7PuqxPjUhrzdHtgFUnTgR+TBaRxko4CXgb2B4iIyZLGkf1caCFwTES0etuIvAPTC0AvwIHJzCwnnZkvRcTBLSzaoYX1RwOj21NG3oHpfWCSpAlUBKeI+FbO5ZqZWUHlHZjGp8nMzHJSB9eYairv7uJjJfUGhqVZz0bEgjzLNDOzYsu7V972ZL8CfpGsGXSwpJHpl8NmZmYfk3dT3jnAThHxLICkYcCVwBY5l2tm1m2UrCUv98DUqzEoAUTEc5J65VymmVm34mtM7fOQpD8Bl6bnhwEP51ymmZkVWN6B6X+AY4BvkV1juhv4Xc5lmplZgeXdK28+cC5wrqQBwFoeBcLMrLZK1pKX+x1sJ0rql4LSJOCiNGaSmZlZs/IeXXzFiJgLfAW4KCK2AHbMuUwzs26lk+9gm7u8A1PPNAT6AcBNOZdlZtY9qYZTHcg7MP0UuBWYGhEPSloPmJJzmWZmVmB5d364Brim4vkLwL55lmlm1t2UrfNDLoFJ0kkRcZak82nmNroeXdzMrHZKFpdyy5ieTv8/lNP+zcyspHIJTBFxY/p/LICkFbKnMS+P8szMurWSteXl/TumTSU9CjwJPCXpYUmb5FmmmVl3U7JOebn3yhsDnBAR60TE2sCJwAU5l2lmZgWW91h5y0XEnY1PImKipOVyLtPMrFspWUte7oHpBUk/ZOnRxaflXKaZWfdSssiUd1PeV4FVgevStApwZM5lmplZgeX1O6Y+wNeBDYAngBMjYkEeZZmZdXflypfya8obCywA7gF2BT4FHJ9TWWZm3VrJWvJyC0wbR8SnAdIdbB/IqRwzMyuZvALTkma7iFhYtvvRm5nVl3J9xuYVmD4jaW56LKBvei6yESD65VSumVm3U7bv/nkNSdSQx37NzKz88u4ubmZm1i55/8DWzMxyVramPGdMZmZWV5wxmZkVXrlSJgcmM7OCc1OemZlZjhyYzMysrrgpz8ys6NyUZ2Zmlh9nTGZmBaeSpUzOmMzMrK44MJmZWV1xU56ZWcF19u+YJL0IvAssAhZGxJaSBgBXA0OAF4EDImJOR/bvjMnMzDriixExPCK2TM9PASZExFBgQnreIQ5MZmZWCyOAsenxWGDvju7IgcnMrOikmk2SRkl6qGIa1UyJAdwm6eGK5atFxEyA9P/Ajh6OrzGZmRVcLS8xRcQYYEwrq20dEa9KGgjcLumZGlbBGZOZmbVPRLya/p8NXA9sBcySNAgg/T+7o/t3YDIzKzrVcGqtKGk5SSs0PgZ2Ap4ExgMj02ojgRs6ejhuyjMzK7hO7i2+GnC9sj7qPYErIuIWSQ8C4yQdBbwM7N/RAhyYzMyszSLiBeAzzcx/E9ihFmU4MJmZFV3J7hToa0xmZlZXHJjMzKyuuCnPzKzgytWQ58BkZlZ8JYtMDkxmZgXnGwWamZnlyBmTmVnRlSthcmAyMyu6ksUlN+WZmVl9ccZkZlZ0JUuZHJjMzAqvXJHJTXlmZlZXnDGZmRVcufIlByYzs+IrWWRyU56ZmdUVZ0xmZgVXsoTJgcnMrPB8o0AzM7P8ODCZmVldcVOemVnBlawlzxmTmZnVFwcmMzOrK27KMzMrOJWsLc8Zk5mZ1RVFRFfXoVmS6rNiZmY1EBE1S3MWLVxcs8/Lhp49ujz9qtvAZB0jaVREjOnqelj34XPOas1NeeUzqqsrYN2OzzmrKQcmMzOrKw5MZmZWVxyYysdt/dbZfM5ZTbnzg5mZ1RVnTGZmVlccmMzMrK44MNUpSYskTaqYTmlmne0l3ZQe79W4jqSLJe1XbX3rPirOpSclXSNp2Srr7tXcuWbWmTxWXv36d0QMb+vKETEeGJ9jfay4lpxLki4Hvg6c29yKPo+sHjhjKhhJu0h6RtK9wFcq5h8h6TcVq+4o6R5Jz0nao5n9LCfpQkkPSnpU0ojOqL91uXuADSQNkPQXSY9L+pekzWDp80jS/inLekzS3WleH0kXSXoinTdfrNjuOkm3SJoi6azGAiUdnNZ/UtKZFfPnVTzeT9LFLZVr3YszpvrVV9Kkiue/AG4ALgC+BEwFrq6y/RBgO2B94E5JGzRZ/n3g7xHxVUkrAQ9IuiMi3qvVAVh9kdQT2BW4BfgJ8GhE7C3pS8AlQNMM/UfAzhExI50jAMcARMSnJW0E3CZpWFo2HNgcmA88K+l8YBFwJrAFMCetv3dE/KVKVZsr17oRZ0z1698RMbxiuhrYCJgWEVMi6+d/WZXtx0XE4oiYAryQtq20E3BKCn4TgT7A2rU/DKsDjV9yHgJeBv4E/BdwKUBE/B1YWdKKTbb7B3CxpK8BDWle5XbPAC8BjYFpQkS8ExEfAE8B6wCfBSZGxOsRsRC4HNi2lfo2V651I86YiqetPzxrul7T5wL2jYhnP3mVrM597Hqlmr+Bz1LnSER8XdLngN2BSZKGk503LZlf8XgR2edLtfUry+tTrdyIeLPKfqxknDEVyzPAupLWT88PrrLu/pJ6pHXXA5oGoFuBbzZ+QEnavOa1tXp2N3AoZL01gTciYm7lCpLWj4j7I+JHwBvA4CbbDSPLsqt9ubkf2E7SKpIayM7Zu9KyWZI+JakHsE8r5Vo34oypfjW9xnRLRJwiaRTwV0lvAPcCm7aw/bNkHwCrAV+PiA+afEk+HTgPeDwFpxeBj3WSsNI6DbhI0uPA+8DIZtb5paShZFnPBOAxsi9H/yfpCWAhcEREzG/pDqoRMVPSqcCdaT9/i4gb0uJTgJuAV4AngeWrlGvdiIckMjOzuuKmPDMzqysOTGZmVlccmMzMrK44MJmZWV1xYDIzs7ri7uKWC0mLgCfIzrGngZER8X6T+dOAwyPibUlD0nqVv4k5NyIukfQi8G6a1wBcB5yeuikPAW6KiE1TuVsBZ5N1kw+yLvWPAl9L22+cylhENjTPM8AvgRkV5R5C1oX66bS8Tyr/txExtplj3R74TkQ0291e0mPAUxFxcMW8i8mGjHon7f/KiPhJWjYRGAT8O60+NSL2k3QaMC8izm6uHLOycGCyvLQ0onXl/LFkY6+NTts8X2VE9S9GxBuSlie7lfcYmvz2RtJqwDXAQRFxX/p91r7APRHx27TOi437Ss+PAK6OiGOb7GtIqs/m6fl6wHWSekTERW19ESR9iqxlYltJyzUZi/C7EXGtpD7AU5IuiYhpadmhEfFQW8sxKxM35VlnuAdoOogswH3Amu3ZUUTMIwtye0sa0GTxMcDYiLgvrRsRcW1EzOpAnZuW+wJwAvCtdm56CNnYcrcBe7WwTuNwPB5A1wwHJstZxYjWTzSZ3wDswNL3/llfS98ccZvm9pmGzpkGDG2yaFPg4Q5U88Am5fZtYb1H+PhguK3um2wU+Cv5+BBSv0yje0wHroqI2RXLLq+ozy/bWaZZobkpz/JSOaTSPWQjWlfOH0IWRG6v2KZaU15T1QYHba/mmvI+cZmSPgu8HhEvSZoOXCipf0TMSas0NuUtD0yQ9IWI+Gda5qY867acMVleKm/b8c2I+LByPtktEXqT7u/THpJWIAtszzVZNJnsvj952ZysQ0RbHQxslK5rPQ/0I7vmtZTUPDmR7JYSZt2eA5N1iYh4h+x6zXck9Wrrdim7+B3wl4rMo9FvgJHplgmN6x8mafVPWt/UGeJs4Pw2rt8D2B/YLCKGRMQQYATNjAifmjs/Rxa8zLo9N+VZl4mIR1NX6oPImvvWbzKi+oUR8b/p8Z2pl10P4Hqy0dGb7m+WpIOAsyUNBBaT3abhulaqcqCkymzlG8CrqT6P8lF38fOr9MjbITXXNfotMCMiKruh3w1sLGlQev5LST8gyxwnNKnn5ZIau4u/ERE7psc/kHR8xTGv1cqxmRWORxc3M7O64qY8MzOrKw5MZmZWVxyYzMysrjgwmZlZXXFgMjOzuuLAZGZmdcWByczM6sr/B7ZqJEfmjl5PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_lr)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for Logistic Regression Classifier')\n",
    "#plt.savefig(\"lrcm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. KNN Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best KNN Value: 1\n",
      "Test Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "best_Kvalue = 0\n",
    "best_score = 0\n",
    "\n",
    "for i in range(1,10):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    if knn.score(X_test, y_test) > best_score:\n",
    "        best_score = knn.score(X_train, y_train)\n",
    "        best_Kvalue = i\n",
    "        \n",
    "print(\"Best KNN Value: {}\".format(best_Kvalue))\n",
    "print(\"Test Accuracy: {}%\".format(round(best_score*100,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification report of KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00       433\n",
      "           1       0.99      1.00      1.00       380\n",
      "\n",
      "    accuracy                           1.00       813\n",
      "   macro avg       1.00      1.00      1.00       813\n",
      "weighted avg       1.00      1.00      1.00       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn = knn.predict(X_test)\n",
    "print(\"KNN Classifier report: \\n\\n\", classification_report(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for KNN Classifier')\n",
    "#plt.savefig(\"knncm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. SVM Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(random_state=42, gamma=\"auto\")\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test Accuracy: {}%\".format(round(svm.score(X_test, y_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification report of SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       433\n",
      "           1       1.00      1.00      1.00       380\n",
      "\n",
      "    accuracy                           1.00       813\n",
      "   macro avg       1.00      1.00      1.00       813\n",
      "weighted avg       1.00      1.00      1.00       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_svm = svm.predict(X_test)\n",
    "print(\"SVM Classifier report: \\n\\n\", classification_report(y_test, y_pred_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for SVM Classifier')\n",
    "#plt.savefig(\"svmcm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Naive Bayes Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 92.62%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test Accuracy: {}%\".format(round(nb.score(X_test, y_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification report of Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.92      0.93       433\n",
      "           1       0.91      0.94      0.92       380\n",
      "\n",
      "    accuracy                           0.93       813\n",
      "   macro avg       0.93      0.93      0.93       813\n",
      "weighted avg       0.93      0.93      0.93       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_nb = nb.predict(X_test)\n",
    "print(\"Naive Bayes Classifier report: \\n\\n\", classification_report(y_test, y_pred_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_nb)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for Naive Bayes Classifier')\n",
    "#plt.savefig(\"nbcm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test Accuracy: {}%\".format(round(rf.score(X_test, y_test)*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification report of Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier report: \n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       433\n",
      "           1       1.00      1.00      1.00       380\n",
      "\n",
      "    accuracy                           1.00       813\n",
      "   macro avg       1.00      1.00      1.00       813\n",
      "weighted avg       1.00      1.00      1.00       813\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf.predict(X_test)\n",
    "print(\"Random Forest Classifier report: \\n\\n\", classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix for Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "x_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "y_axis_labels = [\"Edible\", \"Poisonous\"]\n",
    "\n",
    "f, ax = plt.subplots(figsize =(7,7))\n",
    "sns.heatmap(cm, annot = True, linewidths=0.2, linecolor=\"black\", fmt = \".0f\", ax=ax, cmap=\"Purples\", xticklabels=x_axis_labels, yticklabels=y_axis_labels)\n",
    "plt.xlabel(\"PREDICTED LABEL\")\n",
    "plt.ylabel(\"TRUE LABEL\")\n",
    "plt.title('Confusion Matrix for Random Forest Classifier');\n",
    "#plt.savefig(\"rfcm.png\", format='png', dpi=900, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting some of the X_test results and matching it with true i.e. y_test values using Decision Tree Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1]\n",
      "[0 1 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "preds = dt.predict(X_test)\n",
    "\n",
    "print(preds[:36])\n",
    "print(y_test[:36].values)\n",
    "\n",
    "# 0 - Edible\n",
    "# 1 - Poisonous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see the predicted and the true values match 100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the confusion matrix, we saw that our train and test data is balanced.\n",
    "#### Most of the classification methods hit 100% accuracy with this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}