mushroom_classification.ipynb

{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushrooms Dataset Classification\n",
    "\n",
    "This is a fun project to apply the Exploratory Data Analysis (EDA) process and numerous classification algorithms on the [Mushrooms dataset from Kaggle](https://www.kaggle.com/datasets/uciml/mushroom-classification), which consists of 8143 data observations of mushrooms and 23 features that describe two classes of mushrooms - edible, and poisonous. I perform Exploratory Data Analysis on the dataset, and apply numerous classification algorithms to choose the best model for predicting whether a mushroom is edible or poisonous. Scroll to the end for a in-depth discussion and conclusion!\n",
    "\n",
    "You can also find this notebook on my [Github](https://github.com/awesomecosmos/Mushroom-Classification), and on [Kaggle](https://www.kaggle.com/code/theawesomecosmos/will-this-mushroom-be-poisonous-or-not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import graphviz \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import tree\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import plot_roc_curve, auc \n",
    "from xgboost import XGBRegressor\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by defining some functions which will come in handy later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_predictions(model, X_train, X_test, y_train, y_test, df, model_name):\n",
    "    \"\"\"Function to fit model to training data and generate predictions. \n",
    "    Another function, performance_metrics_recorder, is called to record the results\n",
    "    of the predictions to a dataframe for later analysis.\n",
    "\n",
    "    Args:\n",
    "        model (instance): An instance of a classifier model.\n",
    "        X_train (pd.DataFrame): Dataframe containing training data for all columns except target.\n",
    "        X_test (pd.DataFrame): Dataframe containing testing data for all columns except target.\n",
    "        y_train (pd.DataFrame): Dataframe containing training data for target column.\n",
    "        y_test (pd.DataFrame): Dataframe containing testing data for target column.\n",
    "        df (pd.DataFrame): Dataframe to commit model scores to.\n",
    "        model_name (str): Label for model name.\n",
    "\n",
    "    Returns:\n",
    "        df: Dataframe with committed model scores.\n",
    "        model_confusion: Confusion matrix for predictions.\n",
    "    \"\"\"\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    df, model_confusion = performance_metrics_recorder(predictions, y_test, df, model_name)\n",
    "\n",
    "    return (model, df, model_confusion)\n",
    "\n",
    "def performance_metrics_recorder(predictions, y_test, df, model_name):\n",
    "    \"\"\"Helper function to record scores for model predictions in a DataFrame.\n",
    "\n",
    "    Args:\n",
    "        predictions (pd.DataFrame): Dataframe of predictions generated by classifier.\n",
    "        y_test (pd.DataFrame): Dataframe containing testing data for target column.\n",
    "        df (pd.DataFrame): Dataframe to commit model scores to.\n",
    "        model_name (str): Label for model name.\n",
    "    \n",
    "    Returns:\n",
    "        df: Dataframe with committed model scores.\n",
    "        model_confusion: Confusion matrix for predictions.\n",
    "    \"\"\"\n",
    "    model_accuracy_score = accuracy_score(y_test, predictions)\n",
    "    model_precision_score = precision_score(y_test, predictions)\n",
    "    model_recall_score = recall_score(y_test, predictions)\n",
    "    model_f1_score = f1_score(y_test, predictions)\n",
    "    model_roc_auc = roc_auc_score(y_test, predictions)\n",
    "    model_confusion = confusion_matrix(y_test, predictions)\n",
    "\n",
    "    df.loc[len(df)] = [model_name,model_accuracy_score,model_precision_score,model_recall_score,model_f1_score,model_roc_auc]\n",
    "\n",
    "    return(df, model_confusion)\n",
    "\n",
    "def feature_importance_visualizer(X, model, label, color=None, grid=None):\n",
    "    \"\"\"Helper function to visualize feature importance.\n",
    "\n",
    "    Args:\n",
    "        X (pd.DataFrame): Dataframe containing all columns except target.\n",
    "        model (instance): An instance of a classifier model.\n",
    "        label (str): Label for model name.\n",
    "        color (str, optional): Color for bars in plot. Defaults to None.\n",
    "        grid (bool, optional): Whether gridlines should be included or not. Defaults to None.\n",
    "    \"\"\"\n",
    "    features_list = X.columns.values\n",
    "    feature_importance = model.feature_importances_\n",
    "    sorted_idx = np.argsort(feature_importance)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.barh(range(len(sorted_idx)),feature_importance[sorted_idx], align='center', color=color)\n",
    "    plt.yticks(range(len(sorted_idx)), features_list[sorted_idx])\n",
    "    plt.xlabel('Importance')\n",
    "    plt.title(\"Feature Importances According to \" + label + \" Classifier\")\n",
    "    if grid is True:\n",
    "        plt.grid(alpha=0.5)\n",
    "    plt.show()\n",
    "\n",
    "def feature_importance_visualizer_for_nonstandards(X, model, label, color, grid=None):\n",
    "    \"\"\"Helper function to visualize feature importance for algorithms that do \n",
    "    not support the model.feature_importances_ method.\n",
    "\n",
    "    Args:\n",
    "        X (pd.DataFrame): Dataframe containing all columns except target.\n",
    "        model (instance): An instance of a classifier model.\n",
    "        label (str): Label for model name.\n",
    "        color (str, optional): Color for bars in plot. Defaults to None.\n",
    "        grid (bool, optional): Whether gridlines should be included or not. Defaults to None.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        importance = model.coef_[0]\n",
    "    except:\n",
    "        importance = model.coef_\n",
    "\n",
    "    feature_importances = pd.DataFrame({\n",
    "        'feature':X.columns.to_list(), \n",
    "        'importance':importance.tolist()\n",
    "    }).sort_values('importance',ascending=True)\n",
    "\n",
    "    plt.barh(feature_importances['feature'],feature_importances['importance'], align='center', color=color)\n",
    "    plt.xlabel('Importance')\n",
    "    plt.title(\"Feature Importances According to \" + label + \" Classifier\")\n",
    "    if grid is True:\n",
    "        plt.grid(alpha=0.5)\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step One: Data Exploration, Analysis and Pre-Processing\n",
    "\n",
    "In this section, we explore the raw dataset and clean it for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
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       "      <td>e</td>\n",
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       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
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       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
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       "      <td>f</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
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       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the raw CSV data file\n",
    "df = pd.read_csv(\"data/mushrooms.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64), array([], dtype=int64))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding columns in the df which have any null/na values\n",
    "np.where(pd.isna(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       0\n",
       "cap-shape                   0\n",
       "cap-surface                 0\n",
       "cap-color                   0\n",
       "bruises                     0\n",
       "odor                        0\n",
       "gill-attachment             0\n",
       "gill-spacing                0\n",
       "gill-size                   0\n",
       "gill-color                  0\n",
       "stalk-shape                 0\n",
       "stalk-root                  0\n",
       "stalk-surface-above-ring    0\n",
       "stalk-surface-below-ring    0\n",
       "stalk-color-above-ring      0\n",
       "stalk-color-below-ring      0\n",
       "veil-type                   0\n",
       "veil-color                  0\n",
       "ring-number                 0\n",
       "ring-type                   0\n",
       "spore-print-color           0\n",
       "population                  0\n",
       "habitat                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that this is a rare perfect dataset with no NA or null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8124, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also note that there are 23 columns (i.e. features), and 8124 rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "# getting info on the columns of the df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <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",
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       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>b</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
       "count   8124      8124        8124      8124    8124  8124            8124   \n",
       "unique     2         6           4        10       2     9               2   \n",
       "top        e         x           y         n       f     n               f   \n",
       "freq    4208      3656        3244      2284    4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "count          8124      8124       8124  ...                     8124   \n",
       "unique            2         2         12  ...                        4   \n",
       "top               c         b          b  ...                        s   \n",
       "freq           6812      5612       1728  ...                     4936   \n",
       "\n",
       "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "count                    8124                   8124      8124       8124   \n",
       "unique                      9                      9         1          4   \n",
       "top                         w                      w         p          w   \n",
       "freq                     4464                   4384      8124       7924   \n",
       "\n",
       "       ring-number ring-type spore-print-color population habitat  \n",
       "count         8124      8124              8124       8124    8124  \n",
       "unique           3         5                 9          6       7  \n",
       "top              o         p                 w          v       d  \n",
       "freq          7488      3968              2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting statistical descriptions of the df\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class ['p' 'e']\n",
      "cap-shape ['x' 'b' 's' 'f' 'k' 'c']\n",
      "cap-surface ['s' 'y' 'f' 'g']\n",
      "cap-color ['n' 'y' 'w' 'g' 'e' 'p' 'b' 'u' 'c' 'r']\n",
      "bruises ['t' 'f']\n",
      "odor ['p' 'a' 'l' 'n' 'f' 'c' 'y' 's' 'm']\n",
      "gill-attachment ['f' 'a']\n",
      "gill-spacing ['c' 'w']\n",
      "gill-size ['n' 'b']\n",
      "gill-color ['k' 'n' 'g' 'p' 'w' 'h' 'u' 'e' 'b' 'r' 'y' 'o']\n",
      "stalk-shape ['e' 't']\n",
      "stalk-root ['e' 'c' 'b' 'r' '?']\n",
      "stalk-surface-above-ring ['s' 'f' 'k' 'y']\n",
      "stalk-surface-below-ring ['s' 'f' 'y' 'k']\n",
      "stalk-color-above-ring ['w' 'g' 'p' 'n' 'b' 'e' 'o' 'c' 'y']\n",
      "stalk-color-below-ring ['w' 'p' 'g' 'b' 'n' 'e' 'y' 'o' 'c']\n",
      "veil-type ['p']\n",
      "veil-color ['w' 'n' 'o' 'y']\n",
      "ring-number ['o' 't' 'n']\n",
      "ring-type ['p' 'e' 'l' 'f' 'n']\n",
      "spore-print-color ['k' 'n' 'u' 'h' 'w' 'r' 'o' 'y' 'b']\n",
      "population ['s' 'n' 'a' 'v' 'y' 'c']\n",
      "habitat ['u' 'g' 'm' 'd' 'p' 'w' 'l']\n"
     ]
    }
   ],
   "source": [
    "# getting unique values from each column, so we know which values to convert to numerical\n",
    "for col in df.columns:\n",
    "    print(col,df[col].unique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We noted the column names, their data types, and the number of unique values for each feature. We see that they are all object data types, i.e. strings, and have several distinct values. We will need to convert these strings to a numerical data type. Let's perform label encoding on these features for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a copy of the original dataframe\n",
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'e': 0, 'p': 1}\n"
     ]
    }
   ],
   "source": [
    "# performing Label Encoding on each of the 23 features\n",
    "for col in df2.columns:\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df2[col])\n",
    "    df2[col] = le.transform(df2[col])\n",
    "    if col == 'class':\n",
    "        le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
    "        print(le_name_mapping)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we learn that the LabelEncoding method has encoded class 'edible' to 0, and 'poisonous' to 1. Good to know for future reference!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    4208\n",
      "1    3916\n",
      "Name: class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check to see counts for each class\n",
    "counts = df2['class'].value_counts()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.7971442639094\n",
      "48.20285573609059\n"
     ]
    }
   ],
   "source": [
    "# checking ratio of class 'e' vs 'p'\n",
    "total_counts = 4208 + 3916\n",
    "print((4208/total_counts)*100) # class 0 = 'e'\n",
    "print((3916/total_counts)*100) # class 1 = 'p'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data seems balanced - there is very roughly a 50-50 ratio for both edible and poisonous mushrooms, which indicates our data is balanced. Therefore, we won't apply balancing methods like SMOTE here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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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       "      <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": [
    "# examining the label-encoded dataframe\n",
    "df2.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataframe is in a numerical format, we can perform some more Exploratory Data Analysis (EDA). Let's start by visualizing the correlation of the features to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pandas/io/formats/style.py:3554: RuntimeWarning: All-NaN slice encountered\n",
      "  smin = np.nanmin(gmap) if vmin is None else vmin\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pandas/io/formats/style.py:3555: RuntimeWarning: All-NaN slice encountered\n",
      "  smax = np.nanmax(gmap) if vmax is None else vmax\n"
     ]
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       "  background-color: #f7ad90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row0_col10, #T_037b9_row10_col11, #T_037b9_row14_col0, #T_037b9_row14_col22, #T_037b9_row15_col22, #T_037b9_row22_col7 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row0_col11, #T_037b9_row20_col6 {\n",
       "  background-color: #5a78e4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col14, #T_037b9_row12_col1, #T_037b9_row13_col1, #T_037b9_row14_col1, #T_037b9_row15_col1 {\n",
       "  background-color: #4f69d9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col15, #T_037b9_row5_col1, #T_037b9_row5_col6, #T_037b9_row5_col17, #T_037b9_row19_col0, #T_037b9_row19_col5, #T_037b9_row20_col9, #T_037b9_row21_col22 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col16, #T_037b9_row1_col16, #T_037b9_row2_col16, #T_037b9_row3_col16, #T_037b9_row4_col16, #T_037b9_row5_col16, #T_037b9_row6_col16, #T_037b9_row7_col16, #T_037b9_row8_col16, #T_037b9_row9_col16, #T_037b9_row10_col16, #T_037b9_row11_col16, #T_037b9_row12_col16, #T_037b9_row13_col16, #T_037b9_row14_col16, #T_037b9_row15_col16, #T_037b9_row16_col0, #T_037b9_row16_col1, #T_037b9_row16_col2, #T_037b9_row16_col3, #T_037b9_row16_col4, #T_037b9_row16_col5, #T_037b9_row16_col6, #T_037b9_row16_col7, #T_037b9_row16_col8, #T_037b9_row16_col9, #T_037b9_row16_col10, #T_037b9_row16_col11, #T_037b9_row16_col12, #T_037b9_row16_col13, #T_037b9_row16_col14, #T_037b9_row16_col15, #T_037b9_row16_col16, #T_037b9_row16_col17, #T_037b9_row16_col18, #T_037b9_row16_col19, #T_037b9_row16_col20, #T_037b9_row16_col21, #T_037b9_row16_col22, #T_037b9_row17_col16, #T_037b9_row18_col16, #T_037b9_row19_col16, #T_037b9_row20_col16, #T_037b9_row21_col16, #T_037b9_row22_col16 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col17, #T_037b9_row2_col15, #T_037b9_row3_col21, #T_037b9_row11_col6, #T_037b9_row15_col0 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row0_col18 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col19, #T_037b9_row2_col1, #T_037b9_row5_col3, #T_037b9_row12_col6 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row0_col20, #T_037b9_row2_col0 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row0_col21, #T_037b9_row21_col0 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row0_col22, #T_037b9_row10_col15 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col0, #T_037b9_row2_col4, #T_037b9_row4_col10, #T_037b9_row7_col20, #T_037b9_row8_col2 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col2, #T_037b9_row13_col22, #T_037b9_row15_col17, #T_037b9_row17_col12, #T_037b9_row17_col22 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col3, #T_037b9_row5_col13, #T_037b9_row10_col0, #T_037b9_row13_col3, #T_037b9_row13_col14 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col4, #T_037b9_row1_col19, #T_037b9_row2_col5, #T_037b9_row12_col15, #T_037b9_row12_col18, #T_037b9_row14_col13, #T_037b9_row18_col3, #T_037b9_row21_col12 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col5, #T_037b9_row5_col9, #T_037b9_row6_col9, #T_037b9_row10_col12, #T_037b9_row11_col17, #T_037b9_row18_col14, #T_037b9_row20_col21 {\n",
       "  background-color: #92b4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col6, #T_037b9_row3_col14, #T_037b9_row7_col19, #T_037b9_row8_col3 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col7 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col8, #T_037b9_row12_col8, #T_037b9_row13_col21, #T_037b9_row20_col7 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col9, #T_037b9_row2_col22, #T_037b9_row6_col3 {\n",
       "  background-color: #adc9fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col11, #T_037b9_row7_col10 {\n",
       "  background-color: #b6cefa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col12, #T_037b9_row3_col8, #T_037b9_row4_col17, #T_037b9_row6_col19, #T_037b9_row11_col12, #T_037b9_row18_col8, #T_037b9_row22_col14, #T_037b9_row22_col15 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col13, #T_037b9_row2_col18, #T_037b9_row3_col2, #T_037b9_row3_col12, #T_037b9_row10_col13, #T_037b9_row11_col22, #T_037b9_row13_col7, #T_037b9_row18_col0 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col14, #T_037b9_row1_col15, #T_037b9_row17_col13 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col17, #T_037b9_row5_col22, #T_037b9_row6_col22, #T_037b9_row7_col6, #T_037b9_row7_col17, #T_037b9_row14_col21, #T_037b9_row18_col11, #T_037b9_row18_col21 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row1_col20, #T_037b9_row2_col10, #T_037b9_row9_col18, #T_037b9_row13_col2, #T_037b9_row15_col19 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col21, #T_037b9_row5_col7, #T_037b9_row14_col4 {\n",
       "  background-color: #bcd2f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row1_col22 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row2_col3, #T_037b9_row6_col18, #T_037b9_row11_col13, #T_037b9_row12_col14, #T_037b9_row15_col9 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col6, #T_037b9_row11_col0, #T_037b9_row22_col6 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row2_col7, #T_037b9_row3_col6, #T_037b9_row4_col20, #T_037b9_row17_col1 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row2_col8 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col11, #T_037b9_row7_col8, #T_037b9_row10_col17, #T_037b9_row18_col15, #T_037b9_row21_col13, #T_037b9_row21_col20 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col12, #T_037b9_row5_col21, #T_037b9_row15_col3 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col13, #T_037b9_row4_col3, #T_037b9_row5_col18, #T_037b9_row14_col3, #T_037b9_row15_col13 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col14, #T_037b9_row4_col6, #T_037b9_row17_col14, #T_037b9_row17_col15, #T_037b9_row21_col5 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col17, #T_037b9_row18_col10, #T_037b9_row20_col3 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row2_col19, #T_037b9_row5_col2, #T_037b9_row13_col18, #T_037b9_row17_col18 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col20, #T_037b9_row11_col4 {\n",
       "  background-color: #dcdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row2_col21, #T_037b9_row18_col5 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col0, #T_037b9_row7_col5, #T_037b9_row20_col12, #T_037b9_row21_col9 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col1, #T_037b9_row9_col22 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row3_col4, #T_037b9_row5_col14 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col7, #T_037b9_row21_col8 {\n",
       "  background-color: #ccd9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col9, #T_037b9_row7_col15, #T_037b9_row10_col4, #T_037b9_row10_col7, #T_037b9_row15_col5, #T_037b9_row20_col2, #T_037b9_row20_col15 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col11 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row3_col13, #T_037b9_row6_col2, #T_037b9_row9_col10 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row3_col15, #T_037b9_row11_col10, #T_037b9_row18_col2, #T_037b9_row22_col13 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row3_col17, #T_037b9_row3_col20, #T_037b9_row18_col17 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row3_col18 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row3_col19, #T_037b9_row13_col20 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col0, #T_037b9_row8_col19, #T_037b9_row9_col6 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row4_col1, #T_037b9_row7_col13 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row4_col2, #T_037b9_row6_col14, #T_037b9_row6_col15, #T_037b9_row8_col7, #T_037b9_row15_col2 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col5, #T_037b9_row6_col5, #T_037b9_row17_col5, #T_037b9_row21_col2, #T_037b9_row21_col17 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col7, #T_037b9_row13_col0, #T_037b9_row21_col11 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row4_col8, #T_037b9_row8_col18, #T_037b9_row9_col2 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row4_col9 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col11 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col12 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col13 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col14, #T_037b9_row18_col13, #T_037b9_row22_col5 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col15, #T_037b9_row4_col18, #T_037b9_row12_col3, #T_037b9_row14_col2, #T_037b9_row19_col18, #T_037b9_row21_col6 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col19, #T_037b9_row19_col4 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row4_col21, #T_037b9_row10_col21, #T_037b9_row14_col5 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row4_col22, #T_037b9_row10_col1, #T_037b9_row19_col14, #T_037b9_row21_col1 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row5_col0 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row5_col8, #T_037b9_row19_col12 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row5_col10 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row5_col11, #T_037b9_row8_col17, #T_037b9_row19_col7 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row5_col12, #T_037b9_row8_col22, #T_037b9_row17_col3, #T_037b9_row22_col3 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row5_col15, #T_037b9_row6_col20, #T_037b9_row12_col11, #T_037b9_row18_col12, #T_037b9_row20_col13 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row5_col19, #T_037b9_row6_col13, #T_037b9_row19_col2 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row5_col20 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col0, #T_037b9_row8_col14 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col1, #T_037b9_row12_col22, #T_037b9_row19_col15 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row6_col4, #T_037b9_row17_col10, #T_037b9_row19_col3 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col7, #T_037b9_row17_col7, #T_037b9_row21_col4 {\n",
       "  background-color: #bed2f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col8, #T_037b9_row7_col3, #T_037b9_row7_col9, #T_037b9_row9_col7, #T_037b9_row17_col4, #T_037b9_row20_col14 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col10, #T_037b9_row6_col11, #T_037b9_row8_col21, #T_037b9_row17_col0 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row6_col12, #T_037b9_row9_col5, #T_037b9_row14_col17, #T_037b9_row15_col21, #T_037b9_row22_col19 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row6_col17, #T_037b9_row17_col6 {\n",
       "  background-color: #d24b40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row6_col21, #T_037b9_row15_col11 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row7_col1 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row7_col4, #T_037b9_row9_col15 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row7_col11, #T_037b9_row11_col7 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row7_col12, #T_037b9_row7_col22, #T_037b9_row9_col1, #T_037b9_row22_col17 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row7_col14, #T_037b9_row7_col18, #T_037b9_row12_col20 {\n",
       "  background-color: #c5d6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row8_col0 {\n",
       "  background-color: #f7ac8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row8_col1, #T_037b9_row9_col14, #T_037b9_row11_col21 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row8_col4 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row8_col5 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row8_col6 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row8_col9 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row8_col10, #T_037b9_row20_col0 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row8_col11, #T_037b9_row12_col0, #T_037b9_row22_col10 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row8_col15, #T_037b9_row9_col12 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row8_col20 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col3, #T_037b9_row10_col14, #T_037b9_row18_col4, #T_037b9_row21_col10 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col4 {\n",
       "  background-color: #f7b194;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col11, #T_037b9_row11_col9 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col13, #T_037b9_row17_col21 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col17, #T_037b9_row21_col18 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row9_col19, #T_037b9_row20_col8 {\n",
       "  background-color: #f4987a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row9_col20 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row9_col21, #T_037b9_row11_col14, #T_037b9_row11_col15, #T_037b9_row12_col10, #T_037b9_row13_col5 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row10_col2, #T_037b9_row12_col7, #T_037b9_row17_col2, #T_037b9_row18_col6, #T_037b9_row22_col12 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row10_col5 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row10_col8 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row10_col9, #T_037b9_row17_col19, #T_037b9_row22_col21 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row10_col19, #T_037b9_row11_col5 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row10_col20 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row11_col1, #T_037b9_row11_col2, #T_037b9_row20_col17 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row11_col3, #T_037b9_row19_col13 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row11_col8, #T_037b9_row19_col10 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row11_col18, #T_037b9_row19_col8 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row11_col19, #T_037b9_row21_col19, #T_037b9_row22_col20 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col4 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col5, #T_037b9_row18_col19 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col9 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col13, #T_037b9_row18_col20 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col17, #T_037b9_row19_col22 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row12_col19, #T_037b9_row13_col19, #T_037b9_row14_col15, #T_037b9_row15_col14 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row12_col21, #T_037b9_row15_col4, #T_037b9_row18_col22 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col4 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col6 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row13_col8, #T_037b9_row14_col12, #T_037b9_row22_col2 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col9, #T_037b9_row15_col7, #T_037b9_row15_col20 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col11, #T_037b9_row17_col8, #T_037b9_row18_col9, #T_037b9_row22_col18 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col12 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col15, #T_037b9_row17_col9, #T_037b9_row22_col4 {\n",
       "  background-color: #98b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row13_col17, #T_037b9_row22_col1 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row14_col7, #T_037b9_row14_col20, #T_037b9_row15_col8 {\n",
       "  background-color: #e3d9d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row14_col8 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row14_col9 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row14_col10 {\n",
       "  background-color: #d4dbe6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row14_col11, #T_037b9_row17_col11 {\n",
       "  background-color: #cfdaea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row14_col19, #T_037b9_row15_col18 {\n",
       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row15_col10 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row17_col20, #T_037b9_row22_col11 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row18_col7 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row19_col1 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row19_col9 {\n",
       "  background-color: #f39577;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row19_col11, #T_037b9_row19_col21 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row19_col20 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row20_col1 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_037b9_row20_col5 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row20_col10, #T_037b9_row20_col18, #T_037b9_row22_col0 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_037b9_row22_col8 {\n",
       "  background-color: #cedaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_037b9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_037b9_level0_col0\" class=\"col_heading level0 col0\" >class</th>\n",
       "      <th id=\"T_037b9_level0_col1\" class=\"col_heading level0 col1\" >cap-shape</th>\n",
       "      <th id=\"T_037b9_level0_col2\" class=\"col_heading level0 col2\" >cap-surface</th>\n",
       "      <th id=\"T_037b9_level0_col3\" class=\"col_heading level0 col3\" >cap-color</th>\n",
       "      <th id=\"T_037b9_level0_col4\" class=\"col_heading level0 col4\" >bruises</th>\n",
       "      <th id=\"T_037b9_level0_col5\" class=\"col_heading level0 col5\" >odor</th>\n",
       "      <th id=\"T_037b9_level0_col6\" class=\"col_heading level0 col6\" >gill-attachment</th>\n",
       "      <th id=\"T_037b9_level0_col7\" class=\"col_heading level0 col7\" >gill-spacing</th>\n",
       "      <th id=\"T_037b9_level0_col8\" class=\"col_heading level0 col8\" >gill-size</th>\n",
       "      <th id=\"T_037b9_level0_col9\" class=\"col_heading level0 col9\" >gill-color</th>\n",
       "      <th id=\"T_037b9_level0_col10\" class=\"col_heading level0 col10\" >stalk-shape</th>\n",
       "      <th id=\"T_037b9_level0_col11\" class=\"col_heading level0 col11\" >stalk-root</th>\n",
       "      <th id=\"T_037b9_level0_col12\" class=\"col_heading level0 col12\" >stalk-surface-above-ring</th>\n",
       "      <th id=\"T_037b9_level0_col13\" class=\"col_heading level0 col13\" >stalk-surface-below-ring</th>\n",
       "      <th id=\"T_037b9_level0_col14\" class=\"col_heading level0 col14\" >stalk-color-above-ring</th>\n",
       "      <th id=\"T_037b9_level0_col15\" class=\"col_heading level0 col15\" >stalk-color-below-ring</th>\n",
       "      <th id=\"T_037b9_level0_col16\" class=\"col_heading level0 col16\" >veil-type</th>\n",
       "      <th id=\"T_037b9_level0_col17\" class=\"col_heading level0 col17\" >veil-color</th>\n",
       "      <th id=\"T_037b9_level0_col18\" class=\"col_heading level0 col18\" >ring-number</th>\n",
       "      <th id=\"T_037b9_level0_col19\" class=\"col_heading level0 col19\" >ring-type</th>\n",
       "      <th id=\"T_037b9_level0_col20\" class=\"col_heading level0 col20\" >spore-print-color</th>\n",
       "      <th id=\"T_037b9_level0_col21\" class=\"col_heading level0 col21\" >population</th>\n",
       "      <th id=\"T_037b9_level0_col22\" class=\"col_heading level0 col22\" >habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row0\" class=\"row_heading level0 row0\" >class</th>\n",
       "      <td id=\"T_037b9_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row0_col1\" class=\"data row0 col1\" >0.052951</td>\n",
       "      <td id=\"T_037b9_row0_col2\" class=\"data row0 col2\" >0.178446</td>\n",
       "      <td id=\"T_037b9_row0_col3\" class=\"data row0 col3\" >-0.031384</td>\n",
       "      <td id=\"T_037b9_row0_col4\" class=\"data row0 col4\" >-0.501530</td>\n",
       "      <td id=\"T_037b9_row0_col5\" class=\"data row0 col5\" >-0.093552</td>\n",
       "      <td id=\"T_037b9_row0_col6\" class=\"data row0 col6\" >0.129200</td>\n",
       "      <td id=\"T_037b9_row0_col7\" class=\"data row0 col7\" >-0.348387</td>\n",
       "      <td id=\"T_037b9_row0_col8\" class=\"data row0 col8\" >0.540024</td>\n",
       "      <td id=\"T_037b9_row0_col9\" class=\"data row0 col9\" >-0.530566</td>\n",
       "      <td id=\"T_037b9_row0_col10\" class=\"data row0 col10\" >-0.102019</td>\n",
       "      <td id=\"T_037b9_row0_col11\" class=\"data row0 col11\" >-0.379361</td>\n",
       "      <td id=\"T_037b9_row0_col12\" class=\"data row0 col12\" >-0.334593</td>\n",
       "      <td id=\"T_037b9_row0_col13\" class=\"data row0 col13\" >-0.298801</td>\n",
       "      <td id=\"T_037b9_row0_col14\" class=\"data row0 col14\" >-0.154003</td>\n",
       "      <td id=\"T_037b9_row0_col15\" class=\"data row0 col15\" >-0.146730</td>\n",
       "      <td id=\"T_037b9_row0_col16\" class=\"data row0 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row0_col17\" class=\"data row0 col17\" >0.145142</td>\n",
       "      <td id=\"T_037b9_row0_col18\" class=\"data row0 col18\" >-0.214366</td>\n",
       "      <td id=\"T_037b9_row0_col19\" class=\"data row0 col19\" >-0.411771</td>\n",
       "      <td id=\"T_037b9_row0_col20\" class=\"data row0 col20\" >0.171961</td>\n",
       "      <td id=\"T_037b9_row0_col21\" class=\"data row0 col21\" >0.298686</td>\n",
       "      <td id=\"T_037b9_row0_col22\" class=\"data row0 col22\" >0.217179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row1\" class=\"row_heading level0 row1\" >cap-shape</th>\n",
       "      <td id=\"T_037b9_row1_col0\" class=\"data row1 col0\" >0.052951</td>\n",
       "      <td id=\"T_037b9_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row1_col2\" class=\"data row1 col2\" >-0.050454</td>\n",
       "      <td id=\"T_037b9_row1_col3\" class=\"data row1 col3\" >-0.048203</td>\n",
       "      <td id=\"T_037b9_row1_col4\" class=\"data row1 col4\" >-0.035374</td>\n",
       "      <td id=\"T_037b9_row1_col5\" class=\"data row1 col5\" >-0.021935</td>\n",
       "      <td id=\"T_037b9_row1_col6\" class=\"data row1 col6\" >0.078865</td>\n",
       "      <td id=\"T_037b9_row1_col7\" class=\"data row1 col7\" >0.013196</td>\n",
       "      <td id=\"T_037b9_row1_col8\" class=\"data row1 col8\" >0.054050</td>\n",
       "      <td id=\"T_037b9_row1_col9\" class=\"data row1 col9\" >-0.006039</td>\n",
       "      <td id=\"T_037b9_row1_col10\" class=\"data row1 col10\" >0.063794</td>\n",
       "      <td id=\"T_037b9_row1_col11\" class=\"data row1 col11\" >0.030191</td>\n",
       "      <td id=\"T_037b9_row1_col12\" class=\"data row1 col12\" >-0.030417</td>\n",
       "      <td id=\"T_037b9_row1_col13\" class=\"data row1 col13\" >-0.032591</td>\n",
       "      <td id=\"T_037b9_row1_col14\" class=\"data row1 col14\" >-0.031659</td>\n",
       "      <td id=\"T_037b9_row1_col15\" class=\"data row1 col15\" >-0.030390</td>\n",
       "      <td id=\"T_037b9_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row1_col17\" class=\"data row1 col17\" >0.072560</td>\n",
       "      <td id=\"T_037b9_row1_col18\" class=\"data row1 col18\" >-0.106534</td>\n",
       "      <td id=\"T_037b9_row1_col19\" class=\"data row1 col19\" >-0.025457</td>\n",
       "      <td id=\"T_037b9_row1_col20\" class=\"data row1 col20\" >-0.073416</td>\n",
       "      <td id=\"T_037b9_row1_col21\" class=\"data row1 col21\" >0.063413</td>\n",
       "      <td id=\"T_037b9_row1_col22\" class=\"data row1 col22\" >-0.042221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row2\" class=\"row_heading level0 row2\" >cap-surface</th>\n",
       "      <td id=\"T_037b9_row2_col0\" class=\"data row2 col0\" >0.178446</td>\n",
       "      <td id=\"T_037b9_row2_col1\" class=\"data row2 col1\" >-0.050454</td>\n",
       "      <td id=\"T_037b9_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row2_col3\" class=\"data row2 col3\" >-0.019402</td>\n",
       "      <td id=\"T_037b9_row2_col4\" class=\"data row2 col4\" >0.070228</td>\n",
       "      <td id=\"T_037b9_row2_col5\" class=\"data row2 col5\" >0.045233</td>\n",
       "      <td id=\"T_037b9_row2_col6\" class=\"data row2 col6\" >-0.034180</td>\n",
       "      <td id=\"T_037b9_row2_col7\" class=\"data row2 col7\" >-0.282306</td>\n",
       "      <td id=\"T_037b9_row2_col8\" class=\"data row2 col8\" >0.208100</td>\n",
       "      <td id=\"T_037b9_row2_col9\" class=\"data row2 col9\" >-0.161017</td>\n",
       "      <td id=\"T_037b9_row2_col10\" class=\"data row2 col10\" >-0.014123</td>\n",
       "      <td id=\"T_037b9_row2_col11\" class=\"data row2 col11\" >-0.126245</td>\n",
       "      <td id=\"T_037b9_row2_col12\" class=\"data row2 col12\" >0.089090</td>\n",
       "      <td id=\"T_037b9_row2_col13\" class=\"data row2 col13\" >0.107965</td>\n",
       "      <td id=\"T_037b9_row2_col14\" class=\"data row2 col14\" >0.066050</td>\n",
       "      <td id=\"T_037b9_row2_col15\" class=\"data row2 col15\" >0.068885</td>\n",
       "      <td id=\"T_037b9_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row2_col17\" class=\"data row2 col17\" >-0.016603</td>\n",
       "      <td id=\"T_037b9_row2_col18\" class=\"data row2 col18\" >-0.026147</td>\n",
       "      <td id=\"T_037b9_row2_col19\" class=\"data row2 col19\" >-0.106407</td>\n",
       "      <td id=\"T_037b9_row2_col20\" class=\"data row2 col20\" >0.230364</td>\n",
       "      <td id=\"T_037b9_row2_col21\" class=\"data row2 col21\" >0.021555</td>\n",
       "      <td id=\"T_037b9_row2_col22\" class=\"data row2 col22\" >0.163887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row3\" class=\"row_heading level0 row3\" >cap-color</th>\n",
       "      <td id=\"T_037b9_row3_col0\" class=\"data row3 col0\" >-0.031384</td>\n",
       "      <td id=\"T_037b9_row3_col1\" class=\"data row3 col1\" >-0.048203</td>\n",
       "      <td id=\"T_037b9_row3_col2\" class=\"data row3 col2\" >-0.019402</td>\n",
       "      <td id=\"T_037b9_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row3_col4\" class=\"data row3 col4\" >-0.000764</td>\n",
       "      <td id=\"T_037b9_row3_col5\" class=\"data row3 col5\" >-0.387121</td>\n",
       "      <td id=\"T_037b9_row3_col6\" class=\"data row3 col6\" >0.041436</td>\n",
       "      <td id=\"T_037b9_row3_col7\" class=\"data row3 col7\" >0.144259</td>\n",
       "      <td id=\"T_037b9_row3_col8\" class=\"data row3 col8\" >-0.169464</td>\n",
       "      <td id=\"T_037b9_row3_col9\" class=\"data row3 col9\" >0.084659</td>\n",
       "      <td id=\"T_037b9_row3_col10\" class=\"data row3 col10\" >-0.456496</td>\n",
       "      <td id=\"T_037b9_row3_col11\" class=\"data row3 col11\" >0.321274</td>\n",
       "      <td id=\"T_037b9_row3_col12\" class=\"data row3 col12\" >-0.060837</td>\n",
       "      <td id=\"T_037b9_row3_col13\" class=\"data row3 col13\" >-0.047710</td>\n",
       "      <td id=\"T_037b9_row3_col14\" class=\"data row3 col14\" >0.002364</td>\n",
       "      <td id=\"T_037b9_row3_col15\" class=\"data row3 col15\" >0.008057</td>\n",
       "      <td id=\"T_037b9_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row3_col17\" class=\"data row3 col17\" >0.036130</td>\n",
       "      <td id=\"T_037b9_row3_col18\" class=\"data row3 col18\" >-0.005822</td>\n",
       "      <td id=\"T_037b9_row3_col19\" class=\"data row3 col19\" >0.162513</td>\n",
       "      <td id=\"T_037b9_row3_col20\" class=\"data row3 col20\" >-0.293523</td>\n",
       "      <td id=\"T_037b9_row3_col21\" class=\"data row3 col21\" >-0.144770</td>\n",
       "      <td id=\"T_037b9_row3_col22\" class=\"data row3 col22\" >0.033925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row4\" class=\"row_heading level0 row4\" >bruises</th>\n",
       "      <td id=\"T_037b9_row4_col0\" class=\"data row4 col0\" >-0.501530</td>\n",
       "      <td id=\"T_037b9_row4_col1\" class=\"data row4 col1\" >-0.035374</td>\n",
       "      <td id=\"T_037b9_row4_col2\" class=\"data row4 col2\" >0.070228</td>\n",
       "      <td id=\"T_037b9_row4_col3\" class=\"data row4 col3\" >-0.000764</td>\n",
       "      <td id=\"T_037b9_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row4_col5\" class=\"data row4 col5\" >-0.061825</td>\n",
       "      <td id=\"T_037b9_row4_col6\" class=\"data row4 col6\" >0.137359</td>\n",
       "      <td id=\"T_037b9_row4_col7\" class=\"data row4 col7\" >-0.299473</td>\n",
       "      <td id=\"T_037b9_row4_col8\" class=\"data row4 col8\" >-0.369596</td>\n",
       "      <td id=\"T_037b9_row4_col9\" class=\"data row4 col9\" >0.527120</td>\n",
       "      <td id=\"T_037b9_row4_col10\" class=\"data row4 col10\" >0.099364</td>\n",
       "      <td id=\"T_037b9_row4_col11\" class=\"data row4 col11\" >0.244188</td>\n",
       "      <td id=\"T_037b9_row4_col12\" class=\"data row4 col12\" >0.460824</td>\n",
       "      <td id=\"T_037b9_row4_col13\" class=\"data row4 col13\" >0.458983</td>\n",
       "      <td id=\"T_037b9_row4_col14\" class=\"data row4 col14\" >0.083538</td>\n",
       "      <td id=\"T_037b9_row4_col15\" class=\"data row4 col15\" >0.092874</td>\n",
       "      <td id=\"T_037b9_row4_col16\" class=\"data row4 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row4_col17\" class=\"data row4 col17\" >0.119770</td>\n",
       "      <td id=\"T_037b9_row4_col18\" class=\"data row4 col18\" >0.056788</td>\n",
       "      <td id=\"T_037b9_row4_col19\" class=\"data row4 col19\" >0.692973</td>\n",
       "      <td id=\"T_037b9_row4_col20\" class=\"data row4 col20\" >-0.285008</td>\n",
       "      <td id=\"T_037b9_row4_col21\" class=\"data row4 col21\" >0.088137</td>\n",
       "      <td id=\"T_037b9_row4_col22\" class=\"data row4 col22\" >-0.075095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row5\" class=\"row_heading level0 row5\" >odor</th>\n",
       "      <td id=\"T_037b9_row5_col0\" class=\"data row5 col0\" >-0.093552</td>\n",
       "      <td id=\"T_037b9_row5_col1\" class=\"data row5 col1\" >-0.021935</td>\n",
       "      <td id=\"T_037b9_row5_col2\" class=\"data row5 col2\" >0.045233</td>\n",
       "      <td id=\"T_037b9_row5_col3\" class=\"data row5 col3\" >-0.387121</td>\n",
       "      <td id=\"T_037b9_row5_col4\" class=\"data row5 col4\" >-0.061825</td>\n",
       "      <td id=\"T_037b9_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row5_col6\" class=\"data row5 col6\" >-0.059590</td>\n",
       "      <td id=\"T_037b9_row5_col7\" class=\"data row5 col7\" >0.063936</td>\n",
       "      <td id=\"T_037b9_row5_col8\" class=\"data row5 col8\" >0.310495</td>\n",
       "      <td id=\"T_037b9_row5_col9\" class=\"data row5 col9\" >-0.129213</td>\n",
       "      <td id=\"T_037b9_row5_col10\" class=\"data row5 col10\" >0.459766</td>\n",
       "      <td id=\"T_037b9_row5_col11\" class=\"data row5 col11\" >-0.205215</td>\n",
       "      <td id=\"T_037b9_row5_col12\" class=\"data row5 col12\" >0.118617</td>\n",
       "      <td id=\"T_037b9_row5_col13\" class=\"data row5 col13\" >0.061820</td>\n",
       "      <td id=\"T_037b9_row5_col14\" class=\"data row5 col14\" >0.174532</td>\n",
       "      <td id=\"T_037b9_row5_col15\" class=\"data row5 col15\" >0.169407</td>\n",
       "      <td id=\"T_037b9_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row5_col17\" class=\"data row5 col17\" >-0.057747</td>\n",
       "      <td id=\"T_037b9_row5_col18\" class=\"data row5 col18\" >0.111905</td>\n",
       "      <td id=\"T_037b9_row5_col19\" class=\"data row5 col19\" >-0.281387</td>\n",
       "      <td id=\"T_037b9_row5_col20\" class=\"data row5 col20\" >0.469055</td>\n",
       "      <td id=\"T_037b9_row5_col21\" class=\"data row5 col21\" >-0.043623</td>\n",
       "      <td id=\"T_037b9_row5_col22\" class=\"data row5 col22\" >-0.026610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row6\" class=\"row_heading level0 row6\" >gill-attachment</th>\n",
       "      <td id=\"T_037b9_row6_col0\" class=\"data row6 col0\" >0.129200</td>\n",
       "      <td id=\"T_037b9_row6_col1\" class=\"data row6 col1\" >0.078865</td>\n",
       "      <td id=\"T_037b9_row6_col2\" class=\"data row6 col2\" >-0.034180</td>\n",
       "      <td id=\"T_037b9_row6_col3\" class=\"data row6 col3\" >0.041436</td>\n",
       "      <td id=\"T_037b9_row6_col4\" class=\"data row6 col4\" >0.137359</td>\n",
       "      <td id=\"T_037b9_row6_col5\" class=\"data row6 col5\" >-0.059590</td>\n",
       "      <td id=\"T_037b9_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row6_col7\" class=\"data row6 col7\" >0.071489</td>\n",
       "      <td id=\"T_037b9_row6_col8\" class=\"data row6 col8\" >0.108984</td>\n",
       "      <td id=\"T_037b9_row6_col9\" class=\"data row6 col9\" >-0.128567</td>\n",
       "      <td id=\"T_037b9_row6_col10\" class=\"data row6 col10\" >0.186485</td>\n",
       "      <td id=\"T_037b9_row6_col11\" class=\"data row6 col11\" >0.144063</td>\n",
       "      <td id=\"T_037b9_row6_col12\" class=\"data row6 col12\" >-0.088916</td>\n",
       "      <td id=\"T_037b9_row6_col13\" class=\"data row6 col13\" >-0.116177</td>\n",
       "      <td id=\"T_037b9_row6_col14\" class=\"data row6 col14\" >0.099299</td>\n",
       "      <td id=\"T_037b9_row6_col15\" class=\"data row6 col15\" >0.097160</td>\n",
       "      <td id=\"T_037b9_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row6_col17\" class=\"data row6 col17\" >0.897518</td>\n",
       "      <td id=\"T_037b9_row6_col18\" class=\"data row6 col18\" >0.093236</td>\n",
       "      <td id=\"T_037b9_row6_col19\" class=\"data row6 col19\" >-0.146689</td>\n",
       "      <td id=\"T_037b9_row6_col20\" class=\"data row6 col20\" >-0.029524</td>\n",
       "      <td id=\"T_037b9_row6_col21\" class=\"data row6 col21\" >0.165575</td>\n",
       "      <td id=\"T_037b9_row6_col22\" class=\"data row6 col22\" >-0.030304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row7\" class=\"row_heading level0 row7\" >gill-spacing</th>\n",
       "      <td id=\"T_037b9_row7_col0\" class=\"data row7 col0\" >-0.348387</td>\n",
       "      <td id=\"T_037b9_row7_col1\" class=\"data row7 col1\" >0.013196</td>\n",
       "      <td id=\"T_037b9_row7_col2\" class=\"data row7 col2\" >-0.282306</td>\n",
       "      <td id=\"T_037b9_row7_col3\" class=\"data row7 col3\" >0.144259</td>\n",
       "      <td id=\"T_037b9_row7_col4\" class=\"data row7 col4\" >-0.299473</td>\n",
       "      <td id=\"T_037b9_row7_col5\" class=\"data row7 col5\" >0.063936</td>\n",
       "      <td id=\"T_037b9_row7_col6\" class=\"data row7 col6\" >0.071489</td>\n",
       "      <td id=\"T_037b9_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row7_col8\" class=\"data row7 col8\" >-0.108333</td>\n",
       "      <td id=\"T_037b9_row7_col9\" class=\"data row7 col9\" >0.100193</td>\n",
       "      <td id=\"T_037b9_row7_col10\" class=\"data row7 col10\" >0.080895</td>\n",
       "      <td id=\"T_037b9_row7_col11\" class=\"data row7 col11\" >0.350548</td>\n",
       "      <td id=\"T_037b9_row7_col12\" class=\"data row7 col12\" >-0.212359</td>\n",
       "      <td id=\"T_037b9_row7_col13\" class=\"data row7 col13\" >-0.213775</td>\n",
       "      <td id=\"T_037b9_row7_col14\" class=\"data row7 col14\" >0.274574</td>\n",
       "      <td id=\"T_037b9_row7_col15\" class=\"data row7 col15\" >0.253505</td>\n",
       "      <td id=\"T_037b9_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row7_col17\" class=\"data row7 col17\" >0.073363</td>\n",
       "      <td id=\"T_037b9_row7_col18\" class=\"data row7 col18\" >0.243014</td>\n",
       "      <td id=\"T_037b9_row7_col19\" class=\"data row7 col19\" >-0.195897</td>\n",
       "      <td id=\"T_037b9_row7_col20\" class=\"data row7 col20\" >0.047323</td>\n",
       "      <td id=\"T_037b9_row7_col21\" class=\"data row7 col21\" >-0.529253</td>\n",
       "      <td id=\"T_037b9_row7_col22\" class=\"data row7 col22\" >-0.154680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row8\" class=\"row_heading level0 row8\" >gill-size</th>\n",
       "      <td id=\"T_037b9_row8_col0\" class=\"data row8 col0\" >0.540024</td>\n",
       "      <td id=\"T_037b9_row8_col1\" class=\"data row8 col1\" >0.054050</td>\n",
       "      <td id=\"T_037b9_row8_col2\" class=\"data row8 col2\" >0.208100</td>\n",
       "      <td id=\"T_037b9_row8_col3\" class=\"data row8 col3\" >-0.169464</td>\n",
       "      <td id=\"T_037b9_row8_col4\" class=\"data row8 col4\" >-0.369596</td>\n",
       "      <td id=\"T_037b9_row8_col5\" class=\"data row8 col5\" >0.310495</td>\n",
       "      <td id=\"T_037b9_row8_col6\" class=\"data row8 col6\" >0.108984</td>\n",
       "      <td id=\"T_037b9_row8_col7\" class=\"data row8 col7\" >-0.108333</td>\n",
       "      <td id=\"T_037b9_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row8_col9\" class=\"data row8 col9\" >-0.516736</td>\n",
       "      <td id=\"T_037b9_row8_col10\" class=\"data row8 col10\" >0.214576</td>\n",
       "      <td id=\"T_037b9_row8_col11\" class=\"data row8 col11\" >-0.344345</td>\n",
       "      <td id=\"T_037b9_row8_col12\" class=\"data row8 col12\" >0.056310</td>\n",
       "      <td id=\"T_037b9_row8_col13\" class=\"data row8 col13\" >0.010894</td>\n",
       "      <td id=\"T_037b9_row8_col14\" class=\"data row8 col14\" >0.296548</td>\n",
       "      <td id=\"T_037b9_row8_col15\" class=\"data row8 col15\" >0.278708</td>\n",
       "      <td id=\"T_037b9_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row8_col17\" class=\"data row8 col17\" >0.103809</td>\n",
       "      <td id=\"T_037b9_row8_col18\" class=\"data row8 col18\" >-0.171362</td>\n",
       "      <td id=\"T_037b9_row8_col19\" class=\"data row8 col19\" >-0.460872</td>\n",
       "      <td id=\"T_037b9_row8_col20\" class=\"data row8 col20\" >0.622991</td>\n",
       "      <td id=\"T_037b9_row8_col21\" class=\"data row8 col21\" >0.147682</td>\n",
       "      <td id=\"T_037b9_row8_col22\" class=\"data row8 col22\" >0.161418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row9\" class=\"row_heading level0 row9\" >gill-color</th>\n",
       "      <td id=\"T_037b9_row9_col0\" class=\"data row9 col0\" >-0.530566</td>\n",
       "      <td id=\"T_037b9_row9_col1\" class=\"data row9 col1\" >-0.006039</td>\n",
       "      <td id=\"T_037b9_row9_col2\" class=\"data row9 col2\" >-0.161017</td>\n",
       "      <td id=\"T_037b9_row9_col3\" class=\"data row9 col3\" >0.084659</td>\n",
       "      <td id=\"T_037b9_row9_col4\" class=\"data row9 col4\" >0.527120</td>\n",
       "      <td id=\"T_037b9_row9_col5\" class=\"data row9 col5\" >-0.129213</td>\n",
       "      <td id=\"T_037b9_row9_col6\" class=\"data row9 col6\" >-0.128567</td>\n",
       "      <td id=\"T_037b9_row9_col7\" class=\"data row9 col7\" >0.100193</td>\n",
       "      <td id=\"T_037b9_row9_col8\" class=\"data row9 col8\" >-0.516736</td>\n",
       "      <td id=\"T_037b9_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row9_col10\" class=\"data row9 col10\" >-0.175699</td>\n",
       "      <td id=\"T_037b9_row9_col11\" class=\"data row9 col11\" >0.315080</td>\n",
       "      <td id=\"T_037b9_row9_col12\" class=\"data row9 col12\" >0.224287</td>\n",
       "      <td id=\"T_037b9_row9_col13\" class=\"data row9 col13\" >0.257224</td>\n",
       "      <td id=\"T_037b9_row9_col14\" class=\"data row9 col14\" >-0.058299</td>\n",
       "      <td id=\"T_037b9_row9_col15\" class=\"data row9 col15\" >-0.074781</td>\n",
       "      <td id=\"T_037b9_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row9_col17\" class=\"data row9 col17\" >-0.097583</td>\n",
       "      <td id=\"T_037b9_row9_col18\" class=\"data row9 col18\" >0.096054</td>\n",
       "      <td id=\"T_037b9_row9_col19\" class=\"data row9 col19\" >0.629398</td>\n",
       "      <td id=\"T_037b9_row9_col20\" class=\"data row9 col20\" >-0.416135</td>\n",
       "      <td id=\"T_037b9_row9_col21\" class=\"data row9 col21\" >-0.034090</td>\n",
       "      <td id=\"T_037b9_row9_col22\" class=\"data row9 col22\" >-0.202972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row10\" class=\"row_heading level0 row10\" >stalk-shape</th>\n",
       "      <td id=\"T_037b9_row10_col0\" class=\"data row10 col0\" >-0.102019</td>\n",
       "      <td id=\"T_037b9_row10_col1\" class=\"data row10 col1\" >0.063794</td>\n",
       "      <td id=\"T_037b9_row10_col2\" class=\"data row10 col2\" >-0.014123</td>\n",
       "      <td id=\"T_037b9_row10_col3\" class=\"data row10 col3\" >-0.456496</td>\n",
       "      <td id=\"T_037b9_row10_col4\" class=\"data row10 col4\" >0.099364</td>\n",
       "      <td id=\"T_037b9_row10_col5\" class=\"data row10 col5\" >0.459766</td>\n",
       "      <td id=\"T_037b9_row10_col6\" class=\"data row10 col6\" >0.186485</td>\n",
       "      <td id=\"T_037b9_row10_col7\" class=\"data row10 col7\" >0.080895</td>\n",
       "      <td id=\"T_037b9_row10_col8\" class=\"data row10 col8\" >0.214576</td>\n",
       "      <td id=\"T_037b9_row10_col9\" class=\"data row10 col9\" >-0.175699</td>\n",
       "      <td id=\"T_037b9_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row10_col11\" class=\"data row10 col11\" >-0.163422</td>\n",
       "      <td id=\"T_037b9_row10_col12\" class=\"data row10 col12\" >0.015193</td>\n",
       "      <td id=\"T_037b9_row10_col13\" class=\"data row10 col13\" >-0.034399</td>\n",
       "      <td id=\"T_037b9_row10_col14\" class=\"data row10 col14\" >0.223439</td>\n",
       "      <td id=\"T_037b9_row10_col15\" class=\"data row10 col15\" >0.235794</td>\n",
       "      <td id=\"T_037b9_row10_col16\" class=\"data row10 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row10_col17\" class=\"data row10 col17\" >0.162604</td>\n",
       "      <td id=\"T_037b9_row10_col18\" class=\"data row10 col18\" >-0.293221</td>\n",
       "      <td id=\"T_037b9_row10_col19\" class=\"data row10 col19\" >-0.291444</td>\n",
       "      <td id=\"T_037b9_row10_col20\" class=\"data row10 col20\" >0.258831</td>\n",
       "      <td id=\"T_037b9_row10_col21\" class=\"data row10 col21\" >0.087383</td>\n",
       "      <td id=\"T_037b9_row10_col22\" class=\"data row10 col22\" >-0.269216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row11\" class=\"row_heading level0 row11\" >stalk-root</th>\n",
       "      <td id=\"T_037b9_row11_col0\" class=\"data row11 col0\" >-0.379361</td>\n",
       "      <td id=\"T_037b9_row11_col1\" class=\"data row11 col1\" >0.030191</td>\n",
       "      <td id=\"T_037b9_row11_col2\" class=\"data row11 col2\" >-0.126245</td>\n",
       "      <td id=\"T_037b9_row11_col3\" class=\"data row11 col3\" >0.321274</td>\n",
       "      <td id=\"T_037b9_row11_col4\" class=\"data row11 col4\" >0.244188</td>\n",
       "      <td id=\"T_037b9_row11_col5\" class=\"data row11 col5\" >-0.205215</td>\n",
       "      <td id=\"T_037b9_row11_col6\" class=\"data row11 col6\" >0.144063</td>\n",
       "      <td id=\"T_037b9_row11_col7\" class=\"data row11 col7\" >0.350548</td>\n",
       "      <td id=\"T_037b9_row11_col8\" class=\"data row11 col8\" >-0.344345</td>\n",
       "      <td id=\"T_037b9_row11_col9\" class=\"data row11 col9\" >0.315080</td>\n",
       "      <td id=\"T_037b9_row11_col10\" class=\"data row11 col10\" >-0.163422</td>\n",
       "      <td id=\"T_037b9_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row11_col12\" class=\"data row11 col12\" >-0.027065</td>\n",
       "      <td id=\"T_037b9_row11_col13\" class=\"data row11 col13\" >0.087454</td>\n",
       "      <td id=\"T_037b9_row11_col14\" class=\"data row11 col14\" >0.157140</td>\n",
       "      <td id=\"T_037b9_row11_col15\" class=\"data row11 col15\" >0.159805</td>\n",
       "      <td id=\"T_037b9_row11_col16\" class=\"data row11 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row11_col17\" class=\"data row11 col17\" >0.156213</td>\n",
       "      <td id=\"T_037b9_row11_col18\" class=\"data row11 col18\" >-0.247357</td>\n",
       "      <td id=\"T_037b9_row11_col19\" class=\"data row11 col19\" >0.210155</td>\n",
       "      <td id=\"T_037b9_row11_col20\" class=\"data row11 col20\" >-0.536996</td>\n",
       "      <td id=\"T_037b9_row11_col21\" class=\"data row11 col21\" >-0.306747</td>\n",
       "      <td id=\"T_037b9_row11_col22\" class=\"data row11 col22\" >-0.007668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row12\" class=\"row_heading level0 row12\" >stalk-surface-above-ring</th>\n",
       "      <td id=\"T_037b9_row12_col0\" class=\"data row12 col0\" >-0.334593</td>\n",
       "      <td id=\"T_037b9_row12_col1\" class=\"data row12 col1\" >-0.030417</td>\n",
       "      <td id=\"T_037b9_row12_col2\" class=\"data row12 col2\" >0.089090</td>\n",
       "      <td id=\"T_037b9_row12_col3\" class=\"data row12 col3\" >-0.060837</td>\n",
       "      <td id=\"T_037b9_row12_col4\" class=\"data row12 col4\" >0.460824</td>\n",
       "      <td id=\"T_037b9_row12_col5\" class=\"data row12 col5\" >0.118617</td>\n",
       "      <td id=\"T_037b9_row12_col6\" class=\"data row12 col6\" >-0.088916</td>\n",
       "      <td id=\"T_037b9_row12_col7\" class=\"data row12 col7\" >-0.212359</td>\n",
       "      <td id=\"T_037b9_row12_col8\" class=\"data row12 col8\" >0.056310</td>\n",
       "      <td id=\"T_037b9_row12_col9\" class=\"data row12 col9\" >0.224287</td>\n",
       "      <td id=\"T_037b9_row12_col10\" class=\"data row12 col10\" >0.015193</td>\n",
       "      <td id=\"T_037b9_row12_col11\" class=\"data row12 col11\" >-0.027065</td>\n",
       "      <td id=\"T_037b9_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row12_col13\" class=\"data row12 col13\" >0.437164</td>\n",
       "      <td id=\"T_037b9_row12_col14\" class=\"data row12 col14\" >0.132708</td>\n",
       "      <td id=\"T_037b9_row12_col15\" class=\"data row12 col15\" >0.142835</td>\n",
       "      <td id=\"T_037b9_row12_col16\" class=\"data row12 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row12_col17\" class=\"data row12 col17\" >-0.090591</td>\n",
       "      <td id=\"T_037b9_row12_col18\" class=\"data row12 col18\" >0.107904</td>\n",
       "      <td id=\"T_037b9_row12_col19\" class=\"data row12 col19\" >0.390091</td>\n",
       "      <td id=\"T_037b9_row12_col20\" class=\"data row12 col20\" >0.100764</td>\n",
       "      <td id=\"T_037b9_row12_col21\" class=\"data row12 col21\" >0.079604</td>\n",
       "      <td id=\"T_037b9_row12_col22\" class=\"data row12 col22\" >-0.058076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row13\" class=\"row_heading level0 row13\" >stalk-surface-below-ring</th>\n",
       "      <td id=\"T_037b9_row13_col0\" class=\"data row13 col0\" >-0.298801</td>\n",
       "      <td id=\"T_037b9_row13_col1\" class=\"data row13 col1\" >-0.032591</td>\n",
       "      <td id=\"T_037b9_row13_col2\" class=\"data row13 col2\" >0.107965</td>\n",
       "      <td id=\"T_037b9_row13_col3\" class=\"data row13 col3\" >-0.047710</td>\n",
       "      <td id=\"T_037b9_row13_col4\" class=\"data row13 col4\" >0.458983</td>\n",
       "      <td id=\"T_037b9_row13_col5\" class=\"data row13 col5\" >0.061820</td>\n",
       "      <td id=\"T_037b9_row13_col6\" class=\"data row13 col6\" >-0.116177</td>\n",
       "      <td id=\"T_037b9_row13_col7\" class=\"data row13 col7\" >-0.213775</td>\n",
       "      <td id=\"T_037b9_row13_col8\" class=\"data row13 col8\" >0.010894</td>\n",
       "      <td id=\"T_037b9_row13_col9\" class=\"data row13 col9\" >0.257224</td>\n",
       "      <td id=\"T_037b9_row13_col10\" class=\"data row13 col10\" >-0.034399</td>\n",
       "      <td id=\"T_037b9_row13_col11\" class=\"data row13 col11\" >0.087454</td>\n",
       "      <td id=\"T_037b9_row13_col12\" class=\"data row13 col12\" >0.437164</td>\n",
       "      <td id=\"T_037b9_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row13_col14\" class=\"data row13 col14\" >0.106933</td>\n",
       "      <td id=\"T_037b9_row13_col15\" class=\"data row13 col15\" >0.110656</td>\n",
       "      <td id=\"T_037b9_row13_col16\" class=\"data row13 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row13_col17\" class=\"data row13 col17\" >-0.077284</td>\n",
       "      <td id=\"T_037b9_row13_col18\" class=\"data row13 col18\" >0.040006</td>\n",
       "      <td id=\"T_037b9_row13_col19\" class=\"data row13 col19\" >0.394644</td>\n",
       "      <td id=\"T_037b9_row13_col20\" class=\"data row13 col20\" >0.130974</td>\n",
       "      <td id=\"T_037b9_row13_col21\" class=\"data row13 col21\" >0.046797</td>\n",
       "      <td id=\"T_037b9_row13_col22\" class=\"data row13 col22\" >-0.039628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row14\" class=\"row_heading level0 row14\" >stalk-color-above-ring</th>\n",
       "      <td id=\"T_037b9_row14_col0\" class=\"data row14 col0\" >-0.154003</td>\n",
       "      <td id=\"T_037b9_row14_col1\" class=\"data row14 col1\" >-0.031659</td>\n",
       "      <td id=\"T_037b9_row14_col2\" class=\"data row14 col2\" >0.066050</td>\n",
       "      <td id=\"T_037b9_row14_col3\" class=\"data row14 col3\" >0.002364</td>\n",
       "      <td id=\"T_037b9_row14_col4\" class=\"data row14 col4\" >0.083538</td>\n",
       "      <td id=\"T_037b9_row14_col5\" class=\"data row14 col5\" >0.174532</td>\n",
       "      <td id=\"T_037b9_row14_col6\" class=\"data row14 col6\" >0.099299</td>\n",
       "      <td id=\"T_037b9_row14_col7\" class=\"data row14 col7\" >0.274574</td>\n",
       "      <td id=\"T_037b9_row14_col8\" class=\"data row14 col8\" >0.296548</td>\n",
       "      <td id=\"T_037b9_row14_col9\" class=\"data row14 col9\" >-0.058299</td>\n",
       "      <td id=\"T_037b9_row14_col10\" class=\"data row14 col10\" >0.223439</td>\n",
       "      <td id=\"T_037b9_row14_col11\" class=\"data row14 col11\" >0.157140</td>\n",
       "      <td id=\"T_037b9_row14_col12\" class=\"data row14 col12\" >0.132708</td>\n",
       "      <td id=\"T_037b9_row14_col13\" class=\"data row14 col13\" >0.106933</td>\n",
       "      <td id=\"T_037b9_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row14_col15\" class=\"data row14 col15\" >0.491510</td>\n",
       "      <td id=\"T_037b9_row14_col16\" class=\"data row14 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row14_col17\" class=\"data row14 col17\" >0.067377</td>\n",
       "      <td id=\"T_037b9_row14_col18\" class=\"data row14 col18\" >0.084917</td>\n",
       "      <td id=\"T_037b9_row14_col19\" class=\"data row14 col19\" >-0.048878</td>\n",
       "      <td id=\"T_037b9_row14_col20\" class=\"data row14 col20\" >0.271533</td>\n",
       "      <td id=\"T_037b9_row14_col21\" class=\"data row14 col21\" >-0.240261</td>\n",
       "      <td id=\"T_037b9_row14_col22\" class=\"data row14 col22\" >0.042561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row15\" class=\"row_heading level0 row15\" >stalk-color-below-ring</th>\n",
       "      <td id=\"T_037b9_row15_col0\" class=\"data row15 col0\" >-0.146730</td>\n",
       "      <td id=\"T_037b9_row15_col1\" class=\"data row15 col1\" >-0.030390</td>\n",
       "      <td id=\"T_037b9_row15_col2\" class=\"data row15 col2\" >0.068885</td>\n",
       "      <td id=\"T_037b9_row15_col3\" class=\"data row15 col3\" >0.008057</td>\n",
       "      <td id=\"T_037b9_row15_col4\" class=\"data row15 col4\" >0.092874</td>\n",
       "      <td id=\"T_037b9_row15_col5\" class=\"data row15 col5\" >0.169407</td>\n",
       "      <td id=\"T_037b9_row15_col6\" class=\"data row15 col6\" >0.097160</td>\n",
       "      <td id=\"T_037b9_row15_col7\" class=\"data row15 col7\" >0.253505</td>\n",
       "      <td id=\"T_037b9_row15_col8\" class=\"data row15 col8\" >0.278708</td>\n",
       "      <td id=\"T_037b9_row15_col9\" class=\"data row15 col9\" >-0.074781</td>\n",
       "      <td id=\"T_037b9_row15_col10\" class=\"data row15 col10\" >0.235794</td>\n",
       "      <td id=\"T_037b9_row15_col11\" class=\"data row15 col11\" >0.159805</td>\n",
       "      <td id=\"T_037b9_row15_col12\" class=\"data row15 col12\" >0.142835</td>\n",
       "      <td id=\"T_037b9_row15_col13\" class=\"data row15 col13\" >0.110656</td>\n",
       "      <td id=\"T_037b9_row15_col14\" class=\"data row15 col14\" >0.491510</td>\n",
       "      <td id=\"T_037b9_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row15_col16\" class=\"data row15 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row15_col17\" class=\"data row15 col17\" >0.065567</td>\n",
       "      <td id=\"T_037b9_row15_col18\" class=\"data row15 col18\" >0.087580</td>\n",
       "      <td id=\"T_037b9_row15_col19\" class=\"data row15 col19\" >-0.034284</td>\n",
       "      <td id=\"T_037b9_row15_col20\" class=\"data row15 col20\" >0.254518</td>\n",
       "      <td id=\"T_037b9_row15_col21\" class=\"data row15 col21\" >-0.242792</td>\n",
       "      <td id=\"T_037b9_row15_col22\" class=\"data row15 col22\" >0.041594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row16\" class=\"row_heading level0 row16\" >veil-type</th>\n",
       "      <td id=\"T_037b9_row16_col0\" class=\"data row16 col0\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col1\" class=\"data row16 col1\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col2\" class=\"data row16 col2\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col4\" class=\"data row16 col4\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col5\" class=\"data row16 col5\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col6\" class=\"data row16 col6\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col7\" class=\"data row16 col7\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col8\" class=\"data row16 col8\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col9\" class=\"data row16 col9\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col10\" class=\"data row16 col10\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col11\" class=\"data row16 col11\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col12\" class=\"data row16 col12\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col13\" class=\"data row16 col13\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col14\" class=\"data row16 col14\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col15\" class=\"data row16 col15\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col16\" class=\"data row16 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col17\" class=\"data row16 col17\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col18\" class=\"data row16 col18\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col19\" class=\"data row16 col19\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col20\" class=\"data row16 col20\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col21\" class=\"data row16 col21\" >nan</td>\n",
       "      <td id=\"T_037b9_row16_col22\" class=\"data row16 col22\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row17\" class=\"row_heading level0 row17\" >veil-color</th>\n",
       "      <td id=\"T_037b9_row17_col0\" class=\"data row17 col0\" >0.145142</td>\n",
       "      <td id=\"T_037b9_row17_col1\" class=\"data row17 col1\" >0.072560</td>\n",
       "      <td id=\"T_037b9_row17_col2\" class=\"data row17 col2\" >-0.016603</td>\n",
       "      <td id=\"T_037b9_row17_col3\" class=\"data row17 col3\" >0.036130</td>\n",
       "      <td id=\"T_037b9_row17_col4\" class=\"data row17 col4\" >0.119770</td>\n",
       "      <td id=\"T_037b9_row17_col5\" class=\"data row17 col5\" >-0.057747</td>\n",
       "      <td id=\"T_037b9_row17_col6\" class=\"data row17 col6\" >0.897518</td>\n",
       "      <td id=\"T_037b9_row17_col7\" class=\"data row17 col7\" >0.073363</td>\n",
       "      <td id=\"T_037b9_row17_col8\" class=\"data row17 col8\" >0.103809</td>\n",
       "      <td id=\"T_037b9_row17_col9\" class=\"data row17 col9\" >-0.097583</td>\n",
       "      <td id=\"T_037b9_row17_col10\" class=\"data row17 col10\" >0.162604</td>\n",
       "      <td id=\"T_037b9_row17_col11\" class=\"data row17 col11\" >0.156213</td>\n",
       "      <td id=\"T_037b9_row17_col12\" class=\"data row17 col12\" >-0.090591</td>\n",
       "      <td id=\"T_037b9_row17_col13\" class=\"data row17 col13\" >-0.077284</td>\n",
       "      <td id=\"T_037b9_row17_col14\" class=\"data row17 col14\" >0.067377</td>\n",
       "      <td id=\"T_037b9_row17_col15\" class=\"data row17 col15\" >0.065567</td>\n",
       "      <td id=\"T_037b9_row17_col16\" class=\"data row17 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row17_col18\" class=\"data row17 col18\" >0.036380</td>\n",
       "      <td id=\"T_037b9_row17_col19\" class=\"data row17 col19\" >-0.143673</td>\n",
       "      <td id=\"T_037b9_row17_col20\" class=\"data row17 col20\" >-0.003600</td>\n",
       "      <td id=\"T_037b9_row17_col21\" class=\"data row17 col21\" >0.124924</td>\n",
       "      <td id=\"T_037b9_row17_col22\" class=\"data row17 col22\" >-0.040581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row18\" class=\"row_heading level0 row18\" >ring-number</th>\n",
       "      <td id=\"T_037b9_row18_col0\" class=\"data row18 col0\" >-0.214366</td>\n",
       "      <td id=\"T_037b9_row18_col1\" class=\"data row18 col1\" >-0.106534</td>\n",
       "      <td id=\"T_037b9_row18_col2\" class=\"data row18 col2\" >-0.026147</td>\n",
       "      <td id=\"T_037b9_row18_col3\" class=\"data row18 col3\" >-0.005822</td>\n",
       "      <td id=\"T_037b9_row18_col4\" class=\"data row18 col4\" >0.056788</td>\n",
       "      <td id=\"T_037b9_row18_col5\" class=\"data row18 col5\" >0.111905</td>\n",
       "      <td id=\"T_037b9_row18_col6\" class=\"data row18 col6\" >0.093236</td>\n",
       "      <td id=\"T_037b9_row18_col7\" class=\"data row18 col7\" >0.243014</td>\n",
       "      <td id=\"T_037b9_row18_col8\" class=\"data row18 col8\" >-0.171362</td>\n",
       "      <td id=\"T_037b9_row18_col9\" class=\"data row18 col9\" >0.096054</td>\n",
       "      <td id=\"T_037b9_row18_col10\" class=\"data row18 col10\" >-0.293221</td>\n",
       "      <td id=\"T_037b9_row18_col11\" class=\"data row18 col11\" >-0.247357</td>\n",
       "      <td id=\"T_037b9_row18_col12\" class=\"data row18 col12\" >0.107904</td>\n",
       "      <td id=\"T_037b9_row18_col13\" class=\"data row18 col13\" >0.040006</td>\n",
       "      <td id=\"T_037b9_row18_col14\" class=\"data row18 col14\" >0.084917</td>\n",
       "      <td id=\"T_037b9_row18_col15\" class=\"data row18 col15\" >0.087580</td>\n",
       "      <td id=\"T_037b9_row18_col16\" class=\"data row18 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row18_col17\" class=\"data row18 col17\" >0.036380</td>\n",
       "      <td id=\"T_037b9_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row18_col19\" class=\"data row18 col19\" >0.058312</td>\n",
       "      <td id=\"T_037b9_row18_col20\" class=\"data row18 col20\" >0.338417</td>\n",
       "      <td id=\"T_037b9_row18_col21\" class=\"data row18 col21\" >-0.242020</td>\n",
       "      <td id=\"T_037b9_row18_col22\" class=\"data row18 col22\" >0.235835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row19\" class=\"row_heading level0 row19\" >ring-type</th>\n",
       "      <td id=\"T_037b9_row19_col0\" class=\"data row19 col0\" >-0.411771</td>\n",
       "      <td id=\"T_037b9_row19_col1\" class=\"data row19 col1\" >-0.025457</td>\n",
       "      <td id=\"T_037b9_row19_col2\" class=\"data row19 col2\" >-0.106407</td>\n",
       "      <td id=\"T_037b9_row19_col3\" class=\"data row19 col3\" >0.162513</td>\n",
       "      <td id=\"T_037b9_row19_col4\" class=\"data row19 col4\" >0.692973</td>\n",
       "      <td id=\"T_037b9_row19_col5\" class=\"data row19 col5\" >-0.281387</td>\n",
       "      <td id=\"T_037b9_row19_col6\" class=\"data row19 col6\" >-0.146689</td>\n",
       "      <td id=\"T_037b9_row19_col7\" class=\"data row19 col7\" >-0.195897</td>\n",
       "      <td id=\"T_037b9_row19_col8\" class=\"data row19 col8\" >-0.460872</td>\n",
       "      <td id=\"T_037b9_row19_col9\" class=\"data row19 col9\" >0.629398</td>\n",
       "      <td id=\"T_037b9_row19_col10\" class=\"data row19 col10\" >-0.291444</td>\n",
       "      <td id=\"T_037b9_row19_col11\" class=\"data row19 col11\" >0.210155</td>\n",
       "      <td id=\"T_037b9_row19_col12\" class=\"data row19 col12\" >0.390091</td>\n",
       "      <td id=\"T_037b9_row19_col13\" class=\"data row19 col13\" >0.394644</td>\n",
       "      <td id=\"T_037b9_row19_col14\" class=\"data row19 col14\" >-0.048878</td>\n",
       "      <td id=\"T_037b9_row19_col15\" class=\"data row19 col15\" >-0.034284</td>\n",
       "      <td id=\"T_037b9_row19_col16\" class=\"data row19 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row19_col17\" class=\"data row19 col17\" >-0.143673</td>\n",
       "      <td id=\"T_037b9_row19_col18\" class=\"data row19 col18\" >0.058312</td>\n",
       "      <td id=\"T_037b9_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row19_col20\" class=\"data row19 col20\" >-0.487048</td>\n",
       "      <td id=\"T_037b9_row19_col21\" class=\"data row19 col21\" >0.211763</td>\n",
       "      <td id=\"T_037b9_row19_col22\" class=\"data row19 col22\" >-0.212080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row20\" class=\"row_heading level0 row20\" >spore-print-color</th>\n",
       "      <td id=\"T_037b9_row20_col0\" class=\"data row20 col0\" >0.171961</td>\n",
       "      <td id=\"T_037b9_row20_col1\" class=\"data row20 col1\" >-0.073416</td>\n",
       "      <td id=\"T_037b9_row20_col2\" class=\"data row20 col2\" >0.230364</td>\n",
       "      <td id=\"T_037b9_row20_col3\" class=\"data row20 col3\" >-0.293523</td>\n",
       "      <td id=\"T_037b9_row20_col4\" class=\"data row20 col4\" >-0.285008</td>\n",
       "      <td id=\"T_037b9_row20_col5\" class=\"data row20 col5\" >0.469055</td>\n",
       "      <td id=\"T_037b9_row20_col6\" class=\"data row20 col6\" >-0.029524</td>\n",
       "      <td id=\"T_037b9_row20_col7\" class=\"data row20 col7\" >0.047323</td>\n",
       "      <td id=\"T_037b9_row20_col8\" class=\"data row20 col8\" >0.622991</td>\n",
       "      <td id=\"T_037b9_row20_col9\" class=\"data row20 col9\" >-0.416135</td>\n",
       "      <td id=\"T_037b9_row20_col10\" class=\"data row20 col10\" >0.258831</td>\n",
       "      <td id=\"T_037b9_row20_col11\" class=\"data row20 col11\" >-0.536996</td>\n",
       "      <td id=\"T_037b9_row20_col12\" class=\"data row20 col12\" >0.100764</td>\n",
       "      <td id=\"T_037b9_row20_col13\" class=\"data row20 col13\" >0.130974</td>\n",
       "      <td id=\"T_037b9_row20_col14\" class=\"data row20 col14\" >0.271533</td>\n",
       "      <td id=\"T_037b9_row20_col15\" class=\"data row20 col15\" >0.254518</td>\n",
       "      <td id=\"T_037b9_row20_col16\" class=\"data row20 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row20_col17\" class=\"data row20 col17\" >-0.003600</td>\n",
       "      <td id=\"T_037b9_row20_col18\" class=\"data row20 col18\" >0.338417</td>\n",
       "      <td id=\"T_037b9_row20_col19\" class=\"data row20 col19\" >-0.487048</td>\n",
       "      <td id=\"T_037b9_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row20_col21\" class=\"data row20 col21\" >-0.126859</td>\n",
       "      <td id=\"T_037b9_row20_col22\" class=\"data row20 col22\" >0.185954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row21\" class=\"row_heading level0 row21\" >population</th>\n",
       "      <td id=\"T_037b9_row21_col0\" class=\"data row21 col0\" >0.298686</td>\n",
       "      <td id=\"T_037b9_row21_col1\" class=\"data row21 col1\" >0.063413</td>\n",
       "      <td id=\"T_037b9_row21_col2\" class=\"data row21 col2\" >0.021555</td>\n",
       "      <td id=\"T_037b9_row21_col3\" class=\"data row21 col3\" >-0.144770</td>\n",
       "      <td id=\"T_037b9_row21_col4\" class=\"data row21 col4\" >0.088137</td>\n",
       "      <td id=\"T_037b9_row21_col5\" class=\"data row21 col5\" >-0.043623</td>\n",
       "      <td id=\"T_037b9_row21_col6\" class=\"data row21 col6\" >0.165575</td>\n",
       "      <td id=\"T_037b9_row21_col7\" class=\"data row21 col7\" >-0.529253</td>\n",
       "      <td id=\"T_037b9_row21_col8\" class=\"data row21 col8\" >0.147682</td>\n",
       "      <td id=\"T_037b9_row21_col9\" class=\"data row21 col9\" >-0.034090</td>\n",
       "      <td id=\"T_037b9_row21_col10\" class=\"data row21 col10\" >0.087383</td>\n",
       "      <td id=\"T_037b9_row21_col11\" class=\"data row21 col11\" >-0.306747</td>\n",
       "      <td id=\"T_037b9_row21_col12\" class=\"data row21 col12\" >0.079604</td>\n",
       "      <td id=\"T_037b9_row21_col13\" class=\"data row21 col13\" >0.046797</td>\n",
       "      <td id=\"T_037b9_row21_col14\" class=\"data row21 col14\" >-0.240261</td>\n",
       "      <td id=\"T_037b9_row21_col15\" class=\"data row21 col15\" >-0.242792</td>\n",
       "      <td id=\"T_037b9_row21_col16\" class=\"data row21 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row21_col17\" class=\"data row21 col17\" >0.124924</td>\n",
       "      <td id=\"T_037b9_row21_col18\" class=\"data row21 col18\" >-0.242020</td>\n",
       "      <td id=\"T_037b9_row21_col19\" class=\"data row21 col19\" >0.211763</td>\n",
       "      <td id=\"T_037b9_row21_col20\" class=\"data row21 col20\" >-0.126859</td>\n",
       "      <td id=\"T_037b9_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
       "      <td id=\"T_037b9_row21_col22\" class=\"data row21 col22\" >-0.174529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_037b9_level0_row22\" class=\"row_heading level0 row22\" >habitat</th>\n",
       "      <td id=\"T_037b9_row22_col0\" class=\"data row22 col0\" >0.217179</td>\n",
       "      <td id=\"T_037b9_row22_col1\" class=\"data row22 col1\" >-0.042221</td>\n",
       "      <td id=\"T_037b9_row22_col2\" class=\"data row22 col2\" >0.163887</td>\n",
       "      <td id=\"T_037b9_row22_col3\" class=\"data row22 col3\" >0.033925</td>\n",
       "      <td id=\"T_037b9_row22_col4\" class=\"data row22 col4\" >-0.075095</td>\n",
       "      <td id=\"T_037b9_row22_col5\" class=\"data row22 col5\" >-0.026610</td>\n",
       "      <td id=\"T_037b9_row22_col6\" class=\"data row22 col6\" >-0.030304</td>\n",
       "      <td id=\"T_037b9_row22_col7\" class=\"data row22 col7\" >-0.154680</td>\n",
       "      <td id=\"T_037b9_row22_col8\" class=\"data row22 col8\" >0.161418</td>\n",
       "      <td id=\"T_037b9_row22_col9\" class=\"data row22 col9\" >-0.202972</td>\n",
       "      <td id=\"T_037b9_row22_col10\" class=\"data row22 col10\" >-0.269216</td>\n",
       "      <td id=\"T_037b9_row22_col11\" class=\"data row22 col11\" >-0.007668</td>\n",
       "      <td id=\"T_037b9_row22_col12\" class=\"data row22 col12\" >-0.058076</td>\n",
       "      <td id=\"T_037b9_row22_col13\" class=\"data row22 col13\" >-0.039628</td>\n",
       "      <td id=\"T_037b9_row22_col14\" class=\"data row22 col14\" >0.042561</td>\n",
       "      <td id=\"T_037b9_row22_col15\" class=\"data row22 col15\" >0.041594</td>\n",
       "      <td id=\"T_037b9_row22_col16\" class=\"data row22 col16\" >nan</td>\n",
       "      <td id=\"T_037b9_row22_col17\" class=\"data row22 col17\" >-0.040581</td>\n",
       "      <td id=\"T_037b9_row22_col18\" class=\"data row22 col18\" >0.235835</td>\n",
       "      <td id=\"T_037b9_row22_col19\" class=\"data row22 col19\" >-0.212080</td>\n",
       "      <td id=\"T_037b9_row22_col20\" class=\"data row22 col20\" >0.185954</td>\n",
       "      <td id=\"T_037b9_row22_col21\" class=\"data row22 col21\" >-0.174529</td>\n",
       "      <td id=\"T_037b9_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1127de1d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting correlation matrix of matrix\n",
    "corr = df2.corr()\n",
    "corr.style.background_gradient('coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the veil-type column, which has nan\n",
    "df2['veil-type'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the 'nan' in the correlation matrix for the veil-type column is because the label for that column has been encoded as 0. Looking back at the original data, it seems that there is only one value for veil-type, 'p', which has been encoded to 0. This may or may not be a problem later, only time will tell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardizing the data\n",
    "df3 = df2.copy()\n",
    "scaler = StandardScaler()\n",
    "df3 = scaler.fit_transform(df3)\n",
    "df3 = pd.DataFrame(df3, columns=df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pandas/io/formats/style.py:3554: RuntimeWarning: All-NaN slice encountered\n",
      "  smin = np.nanmin(gmap) if vmin is None else vmin\n",
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pandas/io/formats/style.py:3555: RuntimeWarning: All-NaN slice encountered\n",
      "  smax = np.nanmax(gmap) if vmax is None else vmax\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "}\n",
       "#T_40181_row1_col12, #T_40181_row3_col8, #T_40181_row4_col17, #T_40181_row6_col19, #T_40181_row11_col12, #T_40181_row18_col8, #T_40181_row22_col14, #T_40181_row22_col15 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row1_col13, #T_40181_row2_col18, #T_40181_row3_col2, #T_40181_row3_col12, #T_40181_row10_col13, #T_40181_row11_col22, #T_40181_row13_col7, #T_40181_row18_col0 {\n",
       "  background-color: #7da0f9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row1_col14, #T_40181_row1_col15, #T_40181_row17_col13 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row1_col17, #T_40181_row5_col22, #T_40181_row6_col22, #T_40181_row7_col6, #T_40181_row7_col17, #T_40181_row14_col21, #T_40181_row18_col11, #T_40181_row18_col21 {\n",
       "  background-color: #779af7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row1_col20, #T_40181_row2_col10, #T_40181_row9_col18, #T_40181_row13_col2, #T_40181_row15_col19 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row1_col21, #T_40181_row5_col7, #T_40181_row14_col4 {\n",
       "  background-color: #bcd2f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row1_col22 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row2_col3, #T_40181_row6_col18, #T_40181_row11_col13, #T_40181_row12_col14, #T_40181_row15_col9 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col6, #T_40181_row11_col0, #T_40181_row22_col6 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row2_col7, #T_40181_row3_col6, #T_40181_row4_col20, #T_40181_row17_col1 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row2_col8 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col11, #T_40181_row7_col8, #T_40181_row10_col17, #T_40181_row18_col15, #T_40181_row21_col13, #T_40181_row21_col20 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col12, #T_40181_row5_col21, #T_40181_row15_col3 {\n",
       "  background-color: #a5c3fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col13, #T_40181_row4_col3, #T_40181_row5_col18, #T_40181_row14_col3, #T_40181_row15_col13 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col14, #T_40181_row4_col6, #T_40181_row17_col14, #T_40181_row17_col15, #T_40181_row21_col5 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col17, #T_40181_row18_col10, #T_40181_row20_col3 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row2_col19, #T_40181_row5_col2, #T_40181_row13_col18, #T_40181_row17_col18 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col20, #T_40181_row11_col4 {\n",
       "  background-color: #dcdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row2_col21, #T_40181_row18_col5 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col0, #T_40181_row7_col5, #T_40181_row20_col12, #T_40181_row21_col9 {\n",
       "  background-color: #a7c5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col1, #T_40181_row9_col22 {\n",
       "  background-color: #4a63d3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row3_col4, #T_40181_row5_col14 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col7, #T_40181_row21_col8 {\n",
       "  background-color: #ccd9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col9, #T_40181_row7_col15, #T_40181_row10_col4, #T_40181_row10_col7, #T_40181_row15_col5, #T_40181_row20_col2, #T_40181_row20_col15 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col11 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row3_col13, #T_40181_row6_col2, #T_40181_row9_col10 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row3_col15, #T_40181_row11_col10, #T_40181_row18_col2, #T_40181_row22_col13 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row3_col17, #T_40181_row3_col20, #T_40181_row18_col17 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row3_col18 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row3_col19, #T_40181_row13_col20 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col0, #T_40181_row8_col19, #T_40181_row9_col6 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row4_col1, #T_40181_row7_col13 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row4_col2, #T_40181_row6_col14, #T_40181_row6_col15, #T_40181_row8_col7, #T_40181_row15_col2 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col5, #T_40181_row6_col5, #T_40181_row17_col5, #T_40181_row21_col2, #T_40181_row21_col17 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col7, #T_40181_row13_col0, #T_40181_row21_col11 {\n",
       "  background-color: #6a8bef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row4_col8, #T_40181_row8_col18, #T_40181_row9_col2 {\n",
       "  background-color: #5875e1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row4_col9 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col11 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col12 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col13 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col14, #T_40181_row18_col13, #T_40181_row22_col5 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col15, #T_40181_row4_col18, #T_40181_row12_col3, #T_40181_row14_col2, #T_40181_row19_col18, #T_40181_row21_col6 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col19, #T_40181_row19_col4 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row4_col21, #T_40181_row10_col21, #T_40181_row14_col5 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row4_col22, #T_40181_row10_col1, #T_40181_row19_col14, #T_40181_row21_col1 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row5_col0 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row5_col8, #T_40181_row19_col12 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row5_col10 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row5_col11, #T_40181_row8_col17, #T_40181_row19_col7 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row5_col12, #T_40181_row8_col22, #T_40181_row17_col3, #T_40181_row22_col3 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row5_col15, #T_40181_row6_col20, #T_40181_row12_col11, #T_40181_row18_col12, #T_40181_row20_col13 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row5_col19, #T_40181_row6_col13, #T_40181_row19_col2 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row5_col20 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col0, #T_40181_row8_col14 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col1, #T_40181_row12_col22, #T_40181_row19_col15 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row6_col4, #T_40181_row17_col10, #T_40181_row19_col3 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col7, #T_40181_row17_col7, #T_40181_row21_col4 {\n",
       "  background-color: #bed2f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col8, #T_40181_row7_col3, #T_40181_row7_col9, #T_40181_row9_col7, #T_40181_row17_col4, #T_40181_row20_col14 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col10, #T_40181_row6_col11, #T_40181_row8_col21, #T_40181_row17_col0 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row6_col12, #T_40181_row9_col5, #T_40181_row14_col17, #T_40181_row15_col21, #T_40181_row22_col19 {\n",
       "  background-color: #7699f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row6_col17, #T_40181_row17_col6 {\n",
       "  background-color: #d24b40;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row6_col21, #T_40181_row15_col11 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row7_col1 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row7_col4, #T_40181_row9_col15 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row7_col11, #T_40181_row11_col7 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row7_col12, #T_40181_row7_col22, #T_40181_row9_col1, #T_40181_row22_col17 {\n",
       "  background-color: #5673e0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row7_col14, #T_40181_row7_col18, #T_40181_row12_col20 {\n",
       "  background-color: #c5d6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row8_col0 {\n",
       "  background-color: #f7ac8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row8_col1, #T_40181_row9_col14, #T_40181_row11_col21 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row8_col4 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row8_col5 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row8_col6 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row8_col9 {\n",
       "  background-color: #3d50c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row8_col10, #T_40181_row20_col0 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row8_col11, #T_40181_row12_col0, #T_40181_row22_col10 {\n",
       "  background-color: #6282ea;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row8_col15, #T_40181_row9_col12 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row8_col20 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col3, #T_40181_row10_col14, #T_40181_row18_col4, #T_40181_row21_col10 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col4 {\n",
       "  background-color: #f7b194;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col11, #T_40181_row11_col9 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col13, #T_40181_row17_col21 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col17, #T_40181_row21_col18 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row9_col19, #T_40181_row20_col8 {\n",
       "  background-color: #f4987a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row9_col20 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row9_col21, #T_40181_row11_col14, #T_40181_row11_col15, #T_40181_row12_col10, #T_40181_row13_col5 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row10_col2, #T_40181_row12_col7, #T_40181_row17_col2, #T_40181_row18_col6, #T_40181_row22_col12 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row10_col5 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row10_col8 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row10_col9, #T_40181_row17_col19, #T_40181_row22_col21 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row10_col19, #T_40181_row11_col5 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row10_col20 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row11_col1, #T_40181_row11_col2, #T_40181_row20_col17 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row11_col3, #T_40181_row19_col13 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row11_col8, #T_40181_row19_col10 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row11_col18, #T_40181_row19_col8 {\n",
       "  background-color: #455cce;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row11_col19, #T_40181_row21_col19, #T_40181_row22_col20 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col4 {\n",
       "  background-color: #f6bfa6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col5, #T_40181_row18_col19 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col9 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col13, #T_40181_row18_col20 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col17, #T_40181_row19_col22 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row12_col19, #T_40181_row13_col19, #T_40181_row14_col15, #T_40181_row15_col14 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row12_col21, #T_40181_row15_col4, #T_40181_row18_col22 {\n",
       "  background-color: #bfd3f6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col4 {\n",
       "  background-color: #f5c0a7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col6 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row13_col8, #T_40181_row14_col12, #T_40181_row22_col2 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col9, #T_40181_row15_col7, #T_40181_row15_col20 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col11, #T_40181_row17_col8, #T_40181_row18_col9, #T_40181_row22_col18 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col12 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col15, #T_40181_row17_col9, #T_40181_row22_col4 {\n",
       "  background-color: #98b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row13_col17, #T_40181_row22_col1 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row14_col7, #T_40181_row14_col20, #T_40181_row15_col8 {\n",
       "  background-color: #e3d9d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row14_col8 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row14_col9 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row14_col10 {\n",
       "  background-color: #d4dbe6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row14_col11, #T_40181_row17_col11 {\n",
       "  background-color: #cfdaea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row14_col19, #T_40181_row15_col18 {\n",
       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row15_col10 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row17_col20, #T_40181_row22_col11 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row18_col7 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row19_col1 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row19_col9 {\n",
       "  background-color: #f39577;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row19_col11, #T_40181_row19_col21 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row19_col20 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row20_col1 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_40181_row20_col5 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row20_col10, #T_40181_row20_col18, #T_40181_row22_col0 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_40181_row22_col8 {\n",
       "  background-color: #cedaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_40181\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_40181_level0_col0\" class=\"col_heading level0 col0\" >class</th>\n",
       "      <th id=\"T_40181_level0_col1\" class=\"col_heading level0 col1\" >cap-shape</th>\n",
       "      <th id=\"T_40181_level0_col2\" class=\"col_heading level0 col2\" >cap-surface</th>\n",
       "      <th id=\"T_40181_level0_col3\" class=\"col_heading level0 col3\" >cap-color</th>\n",
       "      <th id=\"T_40181_level0_col4\" class=\"col_heading level0 col4\" >bruises</th>\n",
       "      <th id=\"T_40181_level0_col5\" class=\"col_heading level0 col5\" >odor</th>\n",
       "      <th id=\"T_40181_level0_col6\" class=\"col_heading level0 col6\" >gill-attachment</th>\n",
       "      <th id=\"T_40181_level0_col7\" class=\"col_heading level0 col7\" >gill-spacing</th>\n",
       "      <th id=\"T_40181_level0_col8\" class=\"col_heading level0 col8\" >gill-size</th>\n",
       "      <th id=\"T_40181_level0_col9\" class=\"col_heading level0 col9\" >gill-color</th>\n",
       "      <th id=\"T_40181_level0_col10\" class=\"col_heading level0 col10\" >stalk-shape</th>\n",
       "      <th id=\"T_40181_level0_col11\" class=\"col_heading level0 col11\" >stalk-root</th>\n",
       "      <th id=\"T_40181_level0_col12\" class=\"col_heading level0 col12\" >stalk-surface-above-ring</th>\n",
       "      <th id=\"T_40181_level0_col13\" class=\"col_heading level0 col13\" >stalk-surface-below-ring</th>\n",
       "      <th id=\"T_40181_level0_col14\" class=\"col_heading level0 col14\" >stalk-color-above-ring</th>\n",
       "      <th id=\"T_40181_level0_col15\" class=\"col_heading level0 col15\" >stalk-color-below-ring</th>\n",
       "      <th id=\"T_40181_level0_col16\" class=\"col_heading level0 col16\" >veil-type</th>\n",
       "      <th id=\"T_40181_level0_col17\" class=\"col_heading level0 col17\" >veil-color</th>\n",
       "      <th id=\"T_40181_level0_col18\" class=\"col_heading level0 col18\" >ring-number</th>\n",
       "      <th id=\"T_40181_level0_col19\" class=\"col_heading level0 col19\" >ring-type</th>\n",
       "      <th id=\"T_40181_level0_col20\" class=\"col_heading level0 col20\" >spore-print-color</th>\n",
       "      <th id=\"T_40181_level0_col21\" class=\"col_heading level0 col21\" >population</th>\n",
       "      <th id=\"T_40181_level0_col22\" class=\"col_heading level0 col22\" >habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row0\" class=\"row_heading level0 row0\" >class</th>\n",
       "      <td id=\"T_40181_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_40181_row0_col1\" class=\"data row0 col1\" >0.052951</td>\n",
       "      <td id=\"T_40181_row0_col2\" class=\"data row0 col2\" >0.178446</td>\n",
       "      <td id=\"T_40181_row0_col3\" class=\"data row0 col3\" >-0.031384</td>\n",
       "      <td id=\"T_40181_row0_col4\" class=\"data row0 col4\" >-0.501530</td>\n",
       "      <td id=\"T_40181_row0_col5\" class=\"data row0 col5\" >-0.093552</td>\n",
       "      <td id=\"T_40181_row0_col6\" class=\"data row0 col6\" >0.129200</td>\n",
       "      <td id=\"T_40181_row0_col7\" class=\"data row0 col7\" >-0.348387</td>\n",
       "      <td id=\"T_40181_row0_col8\" class=\"data row0 col8\" >0.540024</td>\n",
       "      <td id=\"T_40181_row0_col9\" class=\"data row0 col9\" >-0.530566</td>\n",
       "      <td id=\"T_40181_row0_col10\" class=\"data row0 col10\" >-0.102019</td>\n",
       "      <td id=\"T_40181_row0_col11\" class=\"data row0 col11\" >-0.379361</td>\n",
       "      <td id=\"T_40181_row0_col12\" class=\"data row0 col12\" >-0.334593</td>\n",
       "      <td id=\"T_40181_row0_col13\" class=\"data row0 col13\" >-0.298801</td>\n",
       "      <td id=\"T_40181_row0_col14\" class=\"data row0 col14\" >-0.154003</td>\n",
       "      <td id=\"T_40181_row0_col15\" class=\"data row0 col15\" >-0.146730</td>\n",
       "      <td id=\"T_40181_row0_col16\" class=\"data row0 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row0_col17\" class=\"data row0 col17\" >0.145142</td>\n",
       "      <td id=\"T_40181_row0_col18\" class=\"data row0 col18\" >-0.214366</td>\n",
       "      <td id=\"T_40181_row0_col19\" class=\"data row0 col19\" >-0.411771</td>\n",
       "      <td id=\"T_40181_row0_col20\" class=\"data row0 col20\" >0.171961</td>\n",
       "      <td id=\"T_40181_row0_col21\" class=\"data row0 col21\" >0.298686</td>\n",
       "      <td id=\"T_40181_row0_col22\" class=\"data row0 col22\" >0.217179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row1\" class=\"row_heading level0 row1\" >cap-shape</th>\n",
       "      <td id=\"T_40181_row1_col0\" class=\"data row1 col0\" >0.052951</td>\n",
       "      <td id=\"T_40181_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_40181_row1_col2\" class=\"data row1 col2\" >-0.050454</td>\n",
       "      <td id=\"T_40181_row1_col3\" class=\"data row1 col3\" >-0.048203</td>\n",
       "      <td id=\"T_40181_row1_col4\" class=\"data row1 col4\" >-0.035374</td>\n",
       "      <td id=\"T_40181_row1_col5\" class=\"data row1 col5\" >-0.021935</td>\n",
       "      <td id=\"T_40181_row1_col6\" class=\"data row1 col6\" >0.078865</td>\n",
       "      <td id=\"T_40181_row1_col7\" class=\"data row1 col7\" >0.013196</td>\n",
       "      <td id=\"T_40181_row1_col8\" class=\"data row1 col8\" >0.054050</td>\n",
       "      <td id=\"T_40181_row1_col9\" class=\"data row1 col9\" >-0.006039</td>\n",
       "      <td id=\"T_40181_row1_col10\" class=\"data row1 col10\" >0.063794</td>\n",
       "      <td id=\"T_40181_row1_col11\" class=\"data row1 col11\" >0.030191</td>\n",
       "      <td id=\"T_40181_row1_col12\" class=\"data row1 col12\" >-0.030417</td>\n",
       "      <td id=\"T_40181_row1_col13\" class=\"data row1 col13\" >-0.032591</td>\n",
       "      <td id=\"T_40181_row1_col14\" class=\"data row1 col14\" >-0.031659</td>\n",
       "      <td id=\"T_40181_row1_col15\" class=\"data row1 col15\" >-0.030390</td>\n",
       "      <td id=\"T_40181_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row1_col17\" class=\"data row1 col17\" >0.072560</td>\n",
       "      <td id=\"T_40181_row1_col18\" class=\"data row1 col18\" >-0.106534</td>\n",
       "      <td id=\"T_40181_row1_col19\" class=\"data row1 col19\" >-0.025457</td>\n",
       "      <td id=\"T_40181_row1_col20\" class=\"data row1 col20\" >-0.073416</td>\n",
       "      <td id=\"T_40181_row1_col21\" class=\"data row1 col21\" >0.063413</td>\n",
       "      <td id=\"T_40181_row1_col22\" class=\"data row1 col22\" >-0.042221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row2\" class=\"row_heading level0 row2\" >cap-surface</th>\n",
       "      <td id=\"T_40181_row2_col0\" class=\"data row2 col0\" >0.178446</td>\n",
       "      <td id=\"T_40181_row2_col1\" class=\"data row2 col1\" >-0.050454</td>\n",
       "      <td id=\"T_40181_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_40181_row2_col3\" class=\"data row2 col3\" >-0.019402</td>\n",
       "      <td id=\"T_40181_row2_col4\" class=\"data row2 col4\" >0.070228</td>\n",
       "      <td id=\"T_40181_row2_col5\" class=\"data row2 col5\" >0.045233</td>\n",
       "      <td id=\"T_40181_row2_col6\" class=\"data row2 col6\" >-0.034180</td>\n",
       "      <td id=\"T_40181_row2_col7\" class=\"data row2 col7\" >-0.282306</td>\n",
       "      <td id=\"T_40181_row2_col8\" class=\"data row2 col8\" >0.208100</td>\n",
       "      <td id=\"T_40181_row2_col9\" class=\"data row2 col9\" >-0.161017</td>\n",
       "      <td id=\"T_40181_row2_col10\" class=\"data row2 col10\" >-0.014123</td>\n",
       "      <td id=\"T_40181_row2_col11\" class=\"data row2 col11\" >-0.126245</td>\n",
       "      <td id=\"T_40181_row2_col12\" class=\"data row2 col12\" >0.089090</td>\n",
       "      <td id=\"T_40181_row2_col13\" class=\"data row2 col13\" >0.107965</td>\n",
       "      <td id=\"T_40181_row2_col14\" class=\"data row2 col14\" >0.066050</td>\n",
       "      <td id=\"T_40181_row2_col15\" class=\"data row2 col15\" >0.068885</td>\n",
       "      <td id=\"T_40181_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row2_col17\" class=\"data row2 col17\" >-0.016603</td>\n",
       "      <td id=\"T_40181_row2_col18\" class=\"data row2 col18\" >-0.026147</td>\n",
       "      <td id=\"T_40181_row2_col19\" class=\"data row2 col19\" >-0.106407</td>\n",
       "      <td id=\"T_40181_row2_col20\" class=\"data row2 col20\" >0.230364</td>\n",
       "      <td id=\"T_40181_row2_col21\" class=\"data row2 col21\" >0.021555</td>\n",
       "      <td id=\"T_40181_row2_col22\" class=\"data row2 col22\" >0.163887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row3\" class=\"row_heading level0 row3\" >cap-color</th>\n",
       "      <td id=\"T_40181_row3_col0\" class=\"data row3 col0\" >-0.031384</td>\n",
       "      <td id=\"T_40181_row3_col1\" class=\"data row3 col1\" >-0.048203</td>\n",
       "      <td id=\"T_40181_row3_col2\" class=\"data row3 col2\" >-0.019402</td>\n",
       "      <td id=\"T_40181_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_40181_row3_col4\" class=\"data row3 col4\" >-0.000764</td>\n",
       "      <td id=\"T_40181_row3_col5\" class=\"data row3 col5\" >-0.387121</td>\n",
       "      <td id=\"T_40181_row3_col6\" class=\"data row3 col6\" >0.041436</td>\n",
       "      <td id=\"T_40181_row3_col7\" class=\"data row3 col7\" >0.144259</td>\n",
       "      <td id=\"T_40181_row3_col8\" class=\"data row3 col8\" >-0.169464</td>\n",
       "      <td id=\"T_40181_row3_col9\" class=\"data row3 col9\" >0.084659</td>\n",
       "      <td id=\"T_40181_row3_col10\" class=\"data row3 col10\" >-0.456496</td>\n",
       "      <td id=\"T_40181_row3_col11\" class=\"data row3 col11\" >0.321274</td>\n",
       "      <td id=\"T_40181_row3_col12\" class=\"data row3 col12\" >-0.060837</td>\n",
       "      <td id=\"T_40181_row3_col13\" class=\"data row3 col13\" >-0.047710</td>\n",
       "      <td id=\"T_40181_row3_col14\" class=\"data row3 col14\" >0.002364</td>\n",
       "      <td id=\"T_40181_row3_col15\" class=\"data row3 col15\" >0.008057</td>\n",
       "      <td id=\"T_40181_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row3_col17\" class=\"data row3 col17\" >0.036130</td>\n",
       "      <td id=\"T_40181_row3_col18\" class=\"data row3 col18\" >-0.005822</td>\n",
       "      <td id=\"T_40181_row3_col19\" class=\"data row3 col19\" >0.162513</td>\n",
       "      <td id=\"T_40181_row3_col20\" class=\"data row3 col20\" >-0.293523</td>\n",
       "      <td id=\"T_40181_row3_col21\" class=\"data row3 col21\" >-0.144770</td>\n",
       "      <td id=\"T_40181_row3_col22\" class=\"data row3 col22\" >0.033925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row4\" class=\"row_heading level0 row4\" >bruises</th>\n",
       "      <td id=\"T_40181_row4_col0\" class=\"data row4 col0\" >-0.501530</td>\n",
       "      <td id=\"T_40181_row4_col1\" class=\"data row4 col1\" >-0.035374</td>\n",
       "      <td id=\"T_40181_row4_col2\" class=\"data row4 col2\" >0.070228</td>\n",
       "      <td id=\"T_40181_row4_col3\" class=\"data row4 col3\" >-0.000764</td>\n",
       "      <td id=\"T_40181_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_40181_row4_col5\" class=\"data row4 col5\" >-0.061825</td>\n",
       "      <td id=\"T_40181_row4_col6\" class=\"data row4 col6\" >0.137359</td>\n",
       "      <td id=\"T_40181_row4_col7\" class=\"data row4 col7\" >-0.299473</td>\n",
       "      <td id=\"T_40181_row4_col8\" class=\"data row4 col8\" >-0.369596</td>\n",
       "      <td id=\"T_40181_row4_col9\" class=\"data row4 col9\" >0.527120</td>\n",
       "      <td id=\"T_40181_row4_col10\" class=\"data row4 col10\" >0.099364</td>\n",
       "      <td id=\"T_40181_row4_col11\" class=\"data row4 col11\" >0.244188</td>\n",
       "      <td id=\"T_40181_row4_col12\" class=\"data row4 col12\" >0.460824</td>\n",
       "      <td id=\"T_40181_row4_col13\" class=\"data row4 col13\" >0.458983</td>\n",
       "      <td id=\"T_40181_row4_col14\" class=\"data row4 col14\" >0.083538</td>\n",
       "      <td id=\"T_40181_row4_col15\" class=\"data row4 col15\" >0.092874</td>\n",
       "      <td id=\"T_40181_row4_col16\" class=\"data row4 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row4_col17\" class=\"data row4 col17\" >0.119770</td>\n",
       "      <td id=\"T_40181_row4_col18\" class=\"data row4 col18\" >0.056788</td>\n",
       "      <td id=\"T_40181_row4_col19\" class=\"data row4 col19\" >0.692973</td>\n",
       "      <td id=\"T_40181_row4_col20\" class=\"data row4 col20\" >-0.285008</td>\n",
       "      <td id=\"T_40181_row4_col21\" class=\"data row4 col21\" >0.088137</td>\n",
       "      <td id=\"T_40181_row4_col22\" class=\"data row4 col22\" >-0.075095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row5\" class=\"row_heading level0 row5\" >odor</th>\n",
       "      <td id=\"T_40181_row5_col0\" class=\"data row5 col0\" >-0.093552</td>\n",
       "      <td id=\"T_40181_row5_col1\" class=\"data row5 col1\" >-0.021935</td>\n",
       "      <td id=\"T_40181_row5_col2\" class=\"data row5 col2\" >0.045233</td>\n",
       "      <td id=\"T_40181_row5_col3\" class=\"data row5 col3\" >-0.387121</td>\n",
       "      <td id=\"T_40181_row5_col4\" class=\"data row5 col4\" >-0.061825</td>\n",
       "      <td id=\"T_40181_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_40181_row5_col6\" class=\"data row5 col6\" >-0.059590</td>\n",
       "      <td id=\"T_40181_row5_col7\" class=\"data row5 col7\" >0.063936</td>\n",
       "      <td id=\"T_40181_row5_col8\" class=\"data row5 col8\" >0.310495</td>\n",
       "      <td id=\"T_40181_row5_col9\" class=\"data row5 col9\" >-0.129213</td>\n",
       "      <td id=\"T_40181_row5_col10\" class=\"data row5 col10\" >0.459766</td>\n",
       "      <td id=\"T_40181_row5_col11\" class=\"data row5 col11\" >-0.205215</td>\n",
       "      <td id=\"T_40181_row5_col12\" class=\"data row5 col12\" >0.118617</td>\n",
       "      <td id=\"T_40181_row5_col13\" class=\"data row5 col13\" >0.061820</td>\n",
       "      <td id=\"T_40181_row5_col14\" class=\"data row5 col14\" >0.174532</td>\n",
       "      <td id=\"T_40181_row5_col15\" class=\"data row5 col15\" >0.169407</td>\n",
       "      <td id=\"T_40181_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row5_col17\" class=\"data row5 col17\" >-0.057747</td>\n",
       "      <td id=\"T_40181_row5_col18\" class=\"data row5 col18\" >0.111905</td>\n",
       "      <td id=\"T_40181_row5_col19\" class=\"data row5 col19\" >-0.281387</td>\n",
       "      <td id=\"T_40181_row5_col20\" class=\"data row5 col20\" >0.469055</td>\n",
       "      <td id=\"T_40181_row5_col21\" class=\"data row5 col21\" >-0.043623</td>\n",
       "      <td id=\"T_40181_row5_col22\" class=\"data row5 col22\" >-0.026610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row6\" class=\"row_heading level0 row6\" >gill-attachment</th>\n",
       "      <td id=\"T_40181_row6_col0\" class=\"data row6 col0\" >0.129200</td>\n",
       "      <td id=\"T_40181_row6_col1\" class=\"data row6 col1\" >0.078865</td>\n",
       "      <td id=\"T_40181_row6_col2\" class=\"data row6 col2\" >-0.034180</td>\n",
       "      <td id=\"T_40181_row6_col3\" class=\"data row6 col3\" >0.041436</td>\n",
       "      <td id=\"T_40181_row6_col4\" class=\"data row6 col4\" >0.137359</td>\n",
       "      <td id=\"T_40181_row6_col5\" class=\"data row6 col5\" >-0.059590</td>\n",
       "      <td id=\"T_40181_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_40181_row6_col7\" class=\"data row6 col7\" >0.071489</td>\n",
       "      <td id=\"T_40181_row6_col8\" class=\"data row6 col8\" >0.108984</td>\n",
       "      <td id=\"T_40181_row6_col9\" class=\"data row6 col9\" >-0.128567</td>\n",
       "      <td id=\"T_40181_row6_col10\" class=\"data row6 col10\" >0.186485</td>\n",
       "      <td id=\"T_40181_row6_col11\" class=\"data row6 col11\" >0.144063</td>\n",
       "      <td id=\"T_40181_row6_col12\" class=\"data row6 col12\" >-0.088916</td>\n",
       "      <td id=\"T_40181_row6_col13\" class=\"data row6 col13\" >-0.116177</td>\n",
       "      <td id=\"T_40181_row6_col14\" class=\"data row6 col14\" >0.099299</td>\n",
       "      <td id=\"T_40181_row6_col15\" class=\"data row6 col15\" >0.097160</td>\n",
       "      <td id=\"T_40181_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row6_col17\" class=\"data row6 col17\" >0.897518</td>\n",
       "      <td id=\"T_40181_row6_col18\" class=\"data row6 col18\" >0.093236</td>\n",
       "      <td id=\"T_40181_row6_col19\" class=\"data row6 col19\" >-0.146689</td>\n",
       "      <td id=\"T_40181_row6_col20\" class=\"data row6 col20\" >-0.029524</td>\n",
       "      <td id=\"T_40181_row6_col21\" class=\"data row6 col21\" >0.165575</td>\n",
       "      <td id=\"T_40181_row6_col22\" class=\"data row6 col22\" >-0.030304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row7\" class=\"row_heading level0 row7\" >gill-spacing</th>\n",
       "      <td id=\"T_40181_row7_col0\" class=\"data row7 col0\" >-0.348387</td>\n",
       "      <td id=\"T_40181_row7_col1\" class=\"data row7 col1\" >0.013196</td>\n",
       "      <td id=\"T_40181_row7_col2\" class=\"data row7 col2\" >-0.282306</td>\n",
       "      <td id=\"T_40181_row7_col3\" class=\"data row7 col3\" >0.144259</td>\n",
       "      <td id=\"T_40181_row7_col4\" class=\"data row7 col4\" >-0.299473</td>\n",
       "      <td id=\"T_40181_row7_col5\" class=\"data row7 col5\" >0.063936</td>\n",
       "      <td id=\"T_40181_row7_col6\" class=\"data row7 col6\" >0.071489</td>\n",
       "      <td id=\"T_40181_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_40181_row7_col8\" class=\"data row7 col8\" >-0.108333</td>\n",
       "      <td id=\"T_40181_row7_col9\" class=\"data row7 col9\" >0.100193</td>\n",
       "      <td id=\"T_40181_row7_col10\" class=\"data row7 col10\" >0.080895</td>\n",
       "      <td id=\"T_40181_row7_col11\" class=\"data row7 col11\" >0.350548</td>\n",
       "      <td id=\"T_40181_row7_col12\" class=\"data row7 col12\" >-0.212359</td>\n",
       "      <td id=\"T_40181_row7_col13\" class=\"data row7 col13\" >-0.213775</td>\n",
       "      <td id=\"T_40181_row7_col14\" class=\"data row7 col14\" >0.274574</td>\n",
       "      <td id=\"T_40181_row7_col15\" class=\"data row7 col15\" >0.253505</td>\n",
       "      <td id=\"T_40181_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row7_col17\" class=\"data row7 col17\" >0.073363</td>\n",
       "      <td id=\"T_40181_row7_col18\" class=\"data row7 col18\" >0.243014</td>\n",
       "      <td id=\"T_40181_row7_col19\" class=\"data row7 col19\" >-0.195897</td>\n",
       "      <td id=\"T_40181_row7_col20\" class=\"data row7 col20\" >0.047323</td>\n",
       "      <td id=\"T_40181_row7_col21\" class=\"data row7 col21\" >-0.529253</td>\n",
       "      <td id=\"T_40181_row7_col22\" class=\"data row7 col22\" >-0.154680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row8\" class=\"row_heading level0 row8\" >gill-size</th>\n",
       "      <td id=\"T_40181_row8_col0\" class=\"data row8 col0\" >0.540024</td>\n",
       "      <td id=\"T_40181_row8_col1\" class=\"data row8 col1\" >0.054050</td>\n",
       "      <td id=\"T_40181_row8_col2\" class=\"data row8 col2\" >0.208100</td>\n",
       "      <td id=\"T_40181_row8_col3\" class=\"data row8 col3\" >-0.169464</td>\n",
       "      <td id=\"T_40181_row8_col4\" class=\"data row8 col4\" >-0.369596</td>\n",
       "      <td id=\"T_40181_row8_col5\" class=\"data row8 col5\" >0.310495</td>\n",
       "      <td id=\"T_40181_row8_col6\" class=\"data row8 col6\" >0.108984</td>\n",
       "      <td id=\"T_40181_row8_col7\" class=\"data row8 col7\" >-0.108333</td>\n",
       "      <td id=\"T_40181_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_40181_row8_col9\" class=\"data row8 col9\" >-0.516736</td>\n",
       "      <td id=\"T_40181_row8_col10\" class=\"data row8 col10\" >0.214576</td>\n",
       "      <td id=\"T_40181_row8_col11\" class=\"data row8 col11\" >-0.344345</td>\n",
       "      <td id=\"T_40181_row8_col12\" class=\"data row8 col12\" >0.056310</td>\n",
       "      <td id=\"T_40181_row8_col13\" class=\"data row8 col13\" >0.010894</td>\n",
       "      <td id=\"T_40181_row8_col14\" class=\"data row8 col14\" >0.296548</td>\n",
       "      <td id=\"T_40181_row8_col15\" class=\"data row8 col15\" >0.278708</td>\n",
       "      <td id=\"T_40181_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row8_col17\" class=\"data row8 col17\" >0.103809</td>\n",
       "      <td id=\"T_40181_row8_col18\" class=\"data row8 col18\" >-0.171362</td>\n",
       "      <td id=\"T_40181_row8_col19\" class=\"data row8 col19\" >-0.460872</td>\n",
       "      <td id=\"T_40181_row8_col20\" class=\"data row8 col20\" >0.622991</td>\n",
       "      <td id=\"T_40181_row8_col21\" class=\"data row8 col21\" >0.147682</td>\n",
       "      <td id=\"T_40181_row8_col22\" class=\"data row8 col22\" >0.161418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row9\" class=\"row_heading level0 row9\" >gill-color</th>\n",
       "      <td id=\"T_40181_row9_col0\" class=\"data row9 col0\" >-0.530566</td>\n",
       "      <td id=\"T_40181_row9_col1\" class=\"data row9 col1\" >-0.006039</td>\n",
       "      <td id=\"T_40181_row9_col2\" class=\"data row9 col2\" >-0.161017</td>\n",
       "      <td id=\"T_40181_row9_col3\" class=\"data row9 col3\" >0.084659</td>\n",
       "      <td id=\"T_40181_row9_col4\" class=\"data row9 col4\" >0.527120</td>\n",
       "      <td id=\"T_40181_row9_col5\" class=\"data row9 col5\" >-0.129213</td>\n",
       "      <td id=\"T_40181_row9_col6\" class=\"data row9 col6\" >-0.128567</td>\n",
       "      <td id=\"T_40181_row9_col7\" class=\"data row9 col7\" >0.100193</td>\n",
       "      <td id=\"T_40181_row9_col8\" class=\"data row9 col8\" >-0.516736</td>\n",
       "      <td id=\"T_40181_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_40181_row9_col10\" class=\"data row9 col10\" >-0.175699</td>\n",
       "      <td id=\"T_40181_row9_col11\" class=\"data row9 col11\" >0.315080</td>\n",
       "      <td id=\"T_40181_row9_col12\" class=\"data row9 col12\" >0.224287</td>\n",
       "      <td id=\"T_40181_row9_col13\" class=\"data row9 col13\" >0.257224</td>\n",
       "      <td id=\"T_40181_row9_col14\" class=\"data row9 col14\" >-0.058299</td>\n",
       "      <td id=\"T_40181_row9_col15\" class=\"data row9 col15\" >-0.074781</td>\n",
       "      <td id=\"T_40181_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row9_col17\" class=\"data row9 col17\" >-0.097583</td>\n",
       "      <td id=\"T_40181_row9_col18\" class=\"data row9 col18\" >0.096054</td>\n",
       "      <td id=\"T_40181_row9_col19\" class=\"data row9 col19\" >0.629398</td>\n",
       "      <td id=\"T_40181_row9_col20\" class=\"data row9 col20\" >-0.416135</td>\n",
       "      <td id=\"T_40181_row9_col21\" class=\"data row9 col21\" >-0.034090</td>\n",
       "      <td id=\"T_40181_row9_col22\" class=\"data row9 col22\" >-0.202972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row10\" class=\"row_heading level0 row10\" >stalk-shape</th>\n",
       "      <td id=\"T_40181_row10_col0\" class=\"data row10 col0\" >-0.102019</td>\n",
       "      <td id=\"T_40181_row10_col1\" class=\"data row10 col1\" >0.063794</td>\n",
       "      <td id=\"T_40181_row10_col2\" class=\"data row10 col2\" >-0.014123</td>\n",
       "      <td id=\"T_40181_row10_col3\" class=\"data row10 col3\" >-0.456496</td>\n",
       "      <td id=\"T_40181_row10_col4\" class=\"data row10 col4\" >0.099364</td>\n",
       "      <td id=\"T_40181_row10_col5\" class=\"data row10 col5\" >0.459766</td>\n",
       "      <td id=\"T_40181_row10_col6\" class=\"data row10 col6\" >0.186485</td>\n",
       "      <td id=\"T_40181_row10_col7\" class=\"data row10 col7\" >0.080895</td>\n",
       "      <td id=\"T_40181_row10_col8\" class=\"data row10 col8\" >0.214576</td>\n",
       "      <td id=\"T_40181_row10_col9\" class=\"data row10 col9\" >-0.175699</td>\n",
       "      <td id=\"T_40181_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_40181_row10_col11\" class=\"data row10 col11\" >-0.163422</td>\n",
       "      <td id=\"T_40181_row10_col12\" class=\"data row10 col12\" >0.015193</td>\n",
       "      <td id=\"T_40181_row10_col13\" class=\"data row10 col13\" >-0.034399</td>\n",
       "      <td id=\"T_40181_row10_col14\" class=\"data row10 col14\" >0.223439</td>\n",
       "      <td id=\"T_40181_row10_col15\" class=\"data row10 col15\" >0.235794</td>\n",
       "      <td id=\"T_40181_row10_col16\" class=\"data row10 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row10_col17\" class=\"data row10 col17\" >0.162604</td>\n",
       "      <td id=\"T_40181_row10_col18\" class=\"data row10 col18\" >-0.293221</td>\n",
       "      <td id=\"T_40181_row10_col19\" class=\"data row10 col19\" >-0.291444</td>\n",
       "      <td id=\"T_40181_row10_col20\" class=\"data row10 col20\" >0.258831</td>\n",
       "      <td id=\"T_40181_row10_col21\" class=\"data row10 col21\" >0.087383</td>\n",
       "      <td id=\"T_40181_row10_col22\" class=\"data row10 col22\" >-0.269216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row11\" class=\"row_heading level0 row11\" >stalk-root</th>\n",
       "      <td id=\"T_40181_row11_col0\" class=\"data row11 col0\" >-0.379361</td>\n",
       "      <td id=\"T_40181_row11_col1\" class=\"data row11 col1\" >0.030191</td>\n",
       "      <td id=\"T_40181_row11_col2\" class=\"data row11 col2\" >-0.126245</td>\n",
       "      <td id=\"T_40181_row11_col3\" class=\"data row11 col3\" >0.321274</td>\n",
       "      <td id=\"T_40181_row11_col4\" class=\"data row11 col4\" >0.244188</td>\n",
       "      <td id=\"T_40181_row11_col5\" class=\"data row11 col5\" >-0.205215</td>\n",
       "      <td id=\"T_40181_row11_col6\" class=\"data row11 col6\" >0.144063</td>\n",
       "      <td id=\"T_40181_row11_col7\" class=\"data row11 col7\" >0.350548</td>\n",
       "      <td id=\"T_40181_row11_col8\" class=\"data row11 col8\" >-0.344345</td>\n",
       "      <td id=\"T_40181_row11_col9\" class=\"data row11 col9\" >0.315080</td>\n",
       "      <td id=\"T_40181_row11_col10\" class=\"data row11 col10\" >-0.163422</td>\n",
       "      <td id=\"T_40181_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_40181_row11_col12\" class=\"data row11 col12\" >-0.027065</td>\n",
       "      <td id=\"T_40181_row11_col13\" class=\"data row11 col13\" >0.087454</td>\n",
       "      <td id=\"T_40181_row11_col14\" class=\"data row11 col14\" >0.157140</td>\n",
       "      <td id=\"T_40181_row11_col15\" class=\"data row11 col15\" >0.159805</td>\n",
       "      <td id=\"T_40181_row11_col16\" class=\"data row11 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row11_col17\" class=\"data row11 col17\" >0.156213</td>\n",
       "      <td id=\"T_40181_row11_col18\" class=\"data row11 col18\" >-0.247357</td>\n",
       "      <td id=\"T_40181_row11_col19\" class=\"data row11 col19\" >0.210155</td>\n",
       "      <td id=\"T_40181_row11_col20\" class=\"data row11 col20\" >-0.536996</td>\n",
       "      <td id=\"T_40181_row11_col21\" class=\"data row11 col21\" >-0.306747</td>\n",
       "      <td id=\"T_40181_row11_col22\" class=\"data row11 col22\" >-0.007668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row12\" class=\"row_heading level0 row12\" >stalk-surface-above-ring</th>\n",
       "      <td id=\"T_40181_row12_col0\" class=\"data row12 col0\" >-0.334593</td>\n",
       "      <td id=\"T_40181_row12_col1\" class=\"data row12 col1\" >-0.030417</td>\n",
       "      <td id=\"T_40181_row12_col2\" class=\"data row12 col2\" >0.089090</td>\n",
       "      <td id=\"T_40181_row12_col3\" class=\"data row12 col3\" >-0.060837</td>\n",
       "      <td id=\"T_40181_row12_col4\" class=\"data row12 col4\" >0.460824</td>\n",
       "      <td id=\"T_40181_row12_col5\" class=\"data row12 col5\" >0.118617</td>\n",
       "      <td id=\"T_40181_row12_col6\" class=\"data row12 col6\" >-0.088916</td>\n",
       "      <td id=\"T_40181_row12_col7\" class=\"data row12 col7\" >-0.212359</td>\n",
       "      <td id=\"T_40181_row12_col8\" class=\"data row12 col8\" >0.056310</td>\n",
       "      <td id=\"T_40181_row12_col9\" class=\"data row12 col9\" >0.224287</td>\n",
       "      <td id=\"T_40181_row12_col10\" class=\"data row12 col10\" >0.015193</td>\n",
       "      <td id=\"T_40181_row12_col11\" class=\"data row12 col11\" >-0.027065</td>\n",
       "      <td id=\"T_40181_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_40181_row12_col13\" class=\"data row12 col13\" >0.437164</td>\n",
       "      <td id=\"T_40181_row12_col14\" class=\"data row12 col14\" >0.132708</td>\n",
       "      <td id=\"T_40181_row12_col15\" class=\"data row12 col15\" >0.142835</td>\n",
       "      <td id=\"T_40181_row12_col16\" class=\"data row12 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row12_col17\" class=\"data row12 col17\" >-0.090591</td>\n",
       "      <td id=\"T_40181_row12_col18\" class=\"data row12 col18\" >0.107904</td>\n",
       "      <td id=\"T_40181_row12_col19\" class=\"data row12 col19\" >0.390091</td>\n",
       "      <td id=\"T_40181_row12_col20\" class=\"data row12 col20\" >0.100764</td>\n",
       "      <td id=\"T_40181_row12_col21\" class=\"data row12 col21\" >0.079604</td>\n",
       "      <td id=\"T_40181_row12_col22\" class=\"data row12 col22\" >-0.058076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row13\" class=\"row_heading level0 row13\" >stalk-surface-below-ring</th>\n",
       "      <td id=\"T_40181_row13_col0\" class=\"data row13 col0\" >-0.298801</td>\n",
       "      <td id=\"T_40181_row13_col1\" class=\"data row13 col1\" >-0.032591</td>\n",
       "      <td id=\"T_40181_row13_col2\" class=\"data row13 col2\" >0.107965</td>\n",
       "      <td id=\"T_40181_row13_col3\" class=\"data row13 col3\" >-0.047710</td>\n",
       "      <td id=\"T_40181_row13_col4\" class=\"data row13 col4\" >0.458983</td>\n",
       "      <td id=\"T_40181_row13_col5\" class=\"data row13 col5\" >0.061820</td>\n",
       "      <td id=\"T_40181_row13_col6\" class=\"data row13 col6\" >-0.116177</td>\n",
       "      <td id=\"T_40181_row13_col7\" class=\"data row13 col7\" >-0.213775</td>\n",
       "      <td id=\"T_40181_row13_col8\" class=\"data row13 col8\" >0.010894</td>\n",
       "      <td id=\"T_40181_row13_col9\" class=\"data row13 col9\" >0.257224</td>\n",
       "      <td id=\"T_40181_row13_col10\" class=\"data row13 col10\" >-0.034399</td>\n",
       "      <td id=\"T_40181_row13_col11\" class=\"data row13 col11\" >0.087454</td>\n",
       "      <td id=\"T_40181_row13_col12\" class=\"data row13 col12\" >0.437164</td>\n",
       "      <td id=\"T_40181_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_40181_row13_col14\" class=\"data row13 col14\" >0.106933</td>\n",
       "      <td id=\"T_40181_row13_col15\" class=\"data row13 col15\" >0.110656</td>\n",
       "      <td id=\"T_40181_row13_col16\" class=\"data row13 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row13_col17\" class=\"data row13 col17\" >-0.077284</td>\n",
       "      <td id=\"T_40181_row13_col18\" class=\"data row13 col18\" >0.040006</td>\n",
       "      <td id=\"T_40181_row13_col19\" class=\"data row13 col19\" >0.394644</td>\n",
       "      <td id=\"T_40181_row13_col20\" class=\"data row13 col20\" >0.130974</td>\n",
       "      <td id=\"T_40181_row13_col21\" class=\"data row13 col21\" >0.046797</td>\n",
       "      <td id=\"T_40181_row13_col22\" class=\"data row13 col22\" >-0.039628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row14\" class=\"row_heading level0 row14\" >stalk-color-above-ring</th>\n",
       "      <td id=\"T_40181_row14_col0\" class=\"data row14 col0\" >-0.154003</td>\n",
       "      <td id=\"T_40181_row14_col1\" class=\"data row14 col1\" >-0.031659</td>\n",
       "      <td id=\"T_40181_row14_col2\" class=\"data row14 col2\" >0.066050</td>\n",
       "      <td id=\"T_40181_row14_col3\" class=\"data row14 col3\" >0.002364</td>\n",
       "      <td id=\"T_40181_row14_col4\" class=\"data row14 col4\" >0.083538</td>\n",
       "      <td id=\"T_40181_row14_col5\" class=\"data row14 col5\" >0.174532</td>\n",
       "      <td id=\"T_40181_row14_col6\" class=\"data row14 col6\" >0.099299</td>\n",
       "      <td id=\"T_40181_row14_col7\" class=\"data row14 col7\" >0.274574</td>\n",
       "      <td id=\"T_40181_row14_col8\" class=\"data row14 col8\" >0.296548</td>\n",
       "      <td id=\"T_40181_row14_col9\" class=\"data row14 col9\" >-0.058299</td>\n",
       "      <td id=\"T_40181_row14_col10\" class=\"data row14 col10\" >0.223439</td>\n",
       "      <td id=\"T_40181_row14_col11\" class=\"data row14 col11\" >0.157140</td>\n",
       "      <td id=\"T_40181_row14_col12\" class=\"data row14 col12\" >0.132708</td>\n",
       "      <td id=\"T_40181_row14_col13\" class=\"data row14 col13\" >0.106933</td>\n",
       "      <td id=\"T_40181_row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "      <td id=\"T_40181_row14_col15\" class=\"data row14 col15\" >0.491510</td>\n",
       "      <td id=\"T_40181_row14_col16\" class=\"data row14 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row14_col17\" class=\"data row14 col17\" >0.067377</td>\n",
       "      <td id=\"T_40181_row14_col18\" class=\"data row14 col18\" >0.084917</td>\n",
       "      <td id=\"T_40181_row14_col19\" class=\"data row14 col19\" >-0.048878</td>\n",
       "      <td id=\"T_40181_row14_col20\" class=\"data row14 col20\" >0.271533</td>\n",
       "      <td id=\"T_40181_row14_col21\" class=\"data row14 col21\" >-0.240261</td>\n",
       "      <td id=\"T_40181_row14_col22\" class=\"data row14 col22\" >0.042561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row15\" class=\"row_heading level0 row15\" >stalk-color-below-ring</th>\n",
       "      <td id=\"T_40181_row15_col0\" class=\"data row15 col0\" >-0.146730</td>\n",
       "      <td id=\"T_40181_row15_col1\" class=\"data row15 col1\" >-0.030390</td>\n",
       "      <td id=\"T_40181_row15_col2\" class=\"data row15 col2\" >0.068885</td>\n",
       "      <td id=\"T_40181_row15_col3\" class=\"data row15 col3\" >0.008057</td>\n",
       "      <td id=\"T_40181_row15_col4\" class=\"data row15 col4\" >0.092874</td>\n",
       "      <td id=\"T_40181_row15_col5\" class=\"data row15 col5\" >0.169407</td>\n",
       "      <td id=\"T_40181_row15_col6\" class=\"data row15 col6\" >0.097160</td>\n",
       "      <td id=\"T_40181_row15_col7\" class=\"data row15 col7\" >0.253505</td>\n",
       "      <td id=\"T_40181_row15_col8\" class=\"data row15 col8\" >0.278708</td>\n",
       "      <td id=\"T_40181_row15_col9\" class=\"data row15 col9\" >-0.074781</td>\n",
       "      <td id=\"T_40181_row15_col10\" class=\"data row15 col10\" >0.235794</td>\n",
       "      <td id=\"T_40181_row15_col11\" class=\"data row15 col11\" >0.159805</td>\n",
       "      <td id=\"T_40181_row15_col12\" class=\"data row15 col12\" >0.142835</td>\n",
       "      <td id=\"T_40181_row15_col13\" class=\"data row15 col13\" >0.110656</td>\n",
       "      <td id=\"T_40181_row15_col14\" class=\"data row15 col14\" >0.491510</td>\n",
       "      <td id=\"T_40181_row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "      <td id=\"T_40181_row15_col16\" class=\"data row15 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row15_col17\" class=\"data row15 col17\" >0.065567</td>\n",
       "      <td id=\"T_40181_row15_col18\" class=\"data row15 col18\" >0.087580</td>\n",
       "      <td id=\"T_40181_row15_col19\" class=\"data row15 col19\" >-0.034284</td>\n",
       "      <td id=\"T_40181_row15_col20\" class=\"data row15 col20\" >0.254518</td>\n",
       "      <td id=\"T_40181_row15_col21\" class=\"data row15 col21\" >-0.242792</td>\n",
       "      <td id=\"T_40181_row15_col22\" class=\"data row15 col22\" >0.041594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row16\" class=\"row_heading level0 row16\" >veil-type</th>\n",
       "      <td id=\"T_40181_row16_col0\" class=\"data row16 col0\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col1\" class=\"data row16 col1\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col2\" class=\"data row16 col2\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col4\" class=\"data row16 col4\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col5\" class=\"data row16 col5\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col6\" class=\"data row16 col6\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col7\" class=\"data row16 col7\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col8\" class=\"data row16 col8\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col9\" class=\"data row16 col9\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col10\" class=\"data row16 col10\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col11\" class=\"data row16 col11\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col12\" class=\"data row16 col12\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col13\" class=\"data row16 col13\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col14\" class=\"data row16 col14\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col15\" class=\"data row16 col15\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col16\" class=\"data row16 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col17\" class=\"data row16 col17\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col18\" class=\"data row16 col18\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col19\" class=\"data row16 col19\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col20\" class=\"data row16 col20\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col21\" class=\"data row16 col21\" >nan</td>\n",
       "      <td id=\"T_40181_row16_col22\" class=\"data row16 col22\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row17\" class=\"row_heading level0 row17\" >veil-color</th>\n",
       "      <td id=\"T_40181_row17_col0\" class=\"data row17 col0\" >0.145142</td>\n",
       "      <td id=\"T_40181_row17_col1\" class=\"data row17 col1\" >0.072560</td>\n",
       "      <td id=\"T_40181_row17_col2\" class=\"data row17 col2\" >-0.016603</td>\n",
       "      <td id=\"T_40181_row17_col3\" class=\"data row17 col3\" >0.036130</td>\n",
       "      <td id=\"T_40181_row17_col4\" class=\"data row17 col4\" >0.119770</td>\n",
       "      <td id=\"T_40181_row17_col5\" class=\"data row17 col5\" >-0.057747</td>\n",
       "      <td id=\"T_40181_row17_col6\" class=\"data row17 col6\" >0.897518</td>\n",
       "      <td id=\"T_40181_row17_col7\" class=\"data row17 col7\" >0.073363</td>\n",
       "      <td id=\"T_40181_row17_col8\" class=\"data row17 col8\" >0.103809</td>\n",
       "      <td id=\"T_40181_row17_col9\" class=\"data row17 col9\" >-0.097583</td>\n",
       "      <td id=\"T_40181_row17_col10\" class=\"data row17 col10\" >0.162604</td>\n",
       "      <td id=\"T_40181_row17_col11\" class=\"data row17 col11\" >0.156213</td>\n",
       "      <td id=\"T_40181_row17_col12\" class=\"data row17 col12\" >-0.090591</td>\n",
       "      <td id=\"T_40181_row17_col13\" class=\"data row17 col13\" >-0.077284</td>\n",
       "      <td id=\"T_40181_row17_col14\" class=\"data row17 col14\" >0.067377</td>\n",
       "      <td id=\"T_40181_row17_col15\" class=\"data row17 col15\" >0.065567</td>\n",
       "      <td id=\"T_40181_row17_col16\" class=\"data row17 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "      <td id=\"T_40181_row17_col18\" class=\"data row17 col18\" >0.036380</td>\n",
       "      <td id=\"T_40181_row17_col19\" class=\"data row17 col19\" >-0.143673</td>\n",
       "      <td id=\"T_40181_row17_col20\" class=\"data row17 col20\" >-0.003600</td>\n",
       "      <td id=\"T_40181_row17_col21\" class=\"data row17 col21\" >0.124924</td>\n",
       "      <td id=\"T_40181_row17_col22\" class=\"data row17 col22\" >-0.040581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row18\" class=\"row_heading level0 row18\" >ring-number</th>\n",
       "      <td id=\"T_40181_row18_col0\" class=\"data row18 col0\" >-0.214366</td>\n",
       "      <td id=\"T_40181_row18_col1\" class=\"data row18 col1\" >-0.106534</td>\n",
       "      <td id=\"T_40181_row18_col2\" class=\"data row18 col2\" >-0.026147</td>\n",
       "      <td id=\"T_40181_row18_col3\" class=\"data row18 col3\" >-0.005822</td>\n",
       "      <td id=\"T_40181_row18_col4\" class=\"data row18 col4\" >0.056788</td>\n",
       "      <td id=\"T_40181_row18_col5\" class=\"data row18 col5\" >0.111905</td>\n",
       "      <td id=\"T_40181_row18_col6\" class=\"data row18 col6\" >0.093236</td>\n",
       "      <td id=\"T_40181_row18_col7\" class=\"data row18 col7\" >0.243014</td>\n",
       "      <td id=\"T_40181_row18_col8\" class=\"data row18 col8\" >-0.171362</td>\n",
       "      <td id=\"T_40181_row18_col9\" class=\"data row18 col9\" >0.096054</td>\n",
       "      <td id=\"T_40181_row18_col10\" class=\"data row18 col10\" >-0.293221</td>\n",
       "      <td id=\"T_40181_row18_col11\" class=\"data row18 col11\" >-0.247357</td>\n",
       "      <td id=\"T_40181_row18_col12\" class=\"data row18 col12\" >0.107904</td>\n",
       "      <td id=\"T_40181_row18_col13\" class=\"data row18 col13\" >0.040006</td>\n",
       "      <td id=\"T_40181_row18_col14\" class=\"data row18 col14\" >0.084917</td>\n",
       "      <td id=\"T_40181_row18_col15\" class=\"data row18 col15\" >0.087580</td>\n",
       "      <td id=\"T_40181_row18_col16\" class=\"data row18 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row18_col17\" class=\"data row18 col17\" >0.036380</td>\n",
       "      <td id=\"T_40181_row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "      <td id=\"T_40181_row18_col19\" class=\"data row18 col19\" >0.058312</td>\n",
       "      <td id=\"T_40181_row18_col20\" class=\"data row18 col20\" >0.338417</td>\n",
       "      <td id=\"T_40181_row18_col21\" class=\"data row18 col21\" >-0.242020</td>\n",
       "      <td id=\"T_40181_row18_col22\" class=\"data row18 col22\" >0.235835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row19\" class=\"row_heading level0 row19\" >ring-type</th>\n",
       "      <td id=\"T_40181_row19_col0\" class=\"data row19 col0\" >-0.411771</td>\n",
       "      <td id=\"T_40181_row19_col1\" class=\"data row19 col1\" >-0.025457</td>\n",
       "      <td id=\"T_40181_row19_col2\" class=\"data row19 col2\" >-0.106407</td>\n",
       "      <td id=\"T_40181_row19_col3\" class=\"data row19 col3\" >0.162513</td>\n",
       "      <td id=\"T_40181_row19_col4\" class=\"data row19 col4\" >0.692973</td>\n",
       "      <td id=\"T_40181_row19_col5\" class=\"data row19 col5\" >-0.281387</td>\n",
       "      <td id=\"T_40181_row19_col6\" class=\"data row19 col6\" >-0.146689</td>\n",
       "      <td id=\"T_40181_row19_col7\" class=\"data row19 col7\" >-0.195897</td>\n",
       "      <td id=\"T_40181_row19_col8\" class=\"data row19 col8\" >-0.460872</td>\n",
       "      <td id=\"T_40181_row19_col9\" class=\"data row19 col9\" >0.629398</td>\n",
       "      <td id=\"T_40181_row19_col10\" class=\"data row19 col10\" >-0.291444</td>\n",
       "      <td id=\"T_40181_row19_col11\" class=\"data row19 col11\" >0.210155</td>\n",
       "      <td id=\"T_40181_row19_col12\" class=\"data row19 col12\" >0.390091</td>\n",
       "      <td id=\"T_40181_row19_col13\" class=\"data row19 col13\" >0.394644</td>\n",
       "      <td id=\"T_40181_row19_col14\" class=\"data row19 col14\" >-0.048878</td>\n",
       "      <td id=\"T_40181_row19_col15\" class=\"data row19 col15\" >-0.034284</td>\n",
       "      <td id=\"T_40181_row19_col16\" class=\"data row19 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row19_col17\" class=\"data row19 col17\" >-0.143673</td>\n",
       "      <td id=\"T_40181_row19_col18\" class=\"data row19 col18\" >0.058312</td>\n",
       "      <td id=\"T_40181_row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
       "      <td id=\"T_40181_row19_col20\" class=\"data row19 col20\" >-0.487048</td>\n",
       "      <td id=\"T_40181_row19_col21\" class=\"data row19 col21\" >0.211763</td>\n",
       "      <td id=\"T_40181_row19_col22\" class=\"data row19 col22\" >-0.212080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row20\" class=\"row_heading level0 row20\" >spore-print-color</th>\n",
       "      <td id=\"T_40181_row20_col0\" class=\"data row20 col0\" >0.171961</td>\n",
       "      <td id=\"T_40181_row20_col1\" class=\"data row20 col1\" >-0.073416</td>\n",
       "      <td id=\"T_40181_row20_col2\" class=\"data row20 col2\" >0.230364</td>\n",
       "      <td id=\"T_40181_row20_col3\" class=\"data row20 col3\" >-0.293523</td>\n",
       "      <td id=\"T_40181_row20_col4\" class=\"data row20 col4\" >-0.285008</td>\n",
       "      <td id=\"T_40181_row20_col5\" class=\"data row20 col5\" >0.469055</td>\n",
       "      <td id=\"T_40181_row20_col6\" class=\"data row20 col6\" >-0.029524</td>\n",
       "      <td id=\"T_40181_row20_col7\" class=\"data row20 col7\" >0.047323</td>\n",
       "      <td id=\"T_40181_row20_col8\" class=\"data row20 col8\" >0.622991</td>\n",
       "      <td id=\"T_40181_row20_col9\" class=\"data row20 col9\" >-0.416135</td>\n",
       "      <td id=\"T_40181_row20_col10\" class=\"data row20 col10\" >0.258831</td>\n",
       "      <td id=\"T_40181_row20_col11\" class=\"data row20 col11\" >-0.536996</td>\n",
       "      <td id=\"T_40181_row20_col12\" class=\"data row20 col12\" >0.100764</td>\n",
       "      <td id=\"T_40181_row20_col13\" class=\"data row20 col13\" >0.130974</td>\n",
       "      <td id=\"T_40181_row20_col14\" class=\"data row20 col14\" >0.271533</td>\n",
       "      <td id=\"T_40181_row20_col15\" class=\"data row20 col15\" >0.254518</td>\n",
       "      <td id=\"T_40181_row20_col16\" class=\"data row20 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row20_col17\" class=\"data row20 col17\" >-0.003600</td>\n",
       "      <td id=\"T_40181_row20_col18\" class=\"data row20 col18\" >0.338417</td>\n",
       "      <td id=\"T_40181_row20_col19\" class=\"data row20 col19\" >-0.487048</td>\n",
       "      <td id=\"T_40181_row20_col20\" class=\"data row20 col20\" >1.000000</td>\n",
       "      <td id=\"T_40181_row20_col21\" class=\"data row20 col21\" >-0.126859</td>\n",
       "      <td id=\"T_40181_row20_col22\" class=\"data row20 col22\" >0.185954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row21\" class=\"row_heading level0 row21\" >population</th>\n",
       "      <td id=\"T_40181_row21_col0\" class=\"data row21 col0\" >0.298686</td>\n",
       "      <td id=\"T_40181_row21_col1\" class=\"data row21 col1\" >0.063413</td>\n",
       "      <td id=\"T_40181_row21_col2\" class=\"data row21 col2\" >0.021555</td>\n",
       "      <td id=\"T_40181_row21_col3\" class=\"data row21 col3\" >-0.144770</td>\n",
       "      <td id=\"T_40181_row21_col4\" class=\"data row21 col4\" >0.088137</td>\n",
       "      <td id=\"T_40181_row21_col5\" class=\"data row21 col5\" >-0.043623</td>\n",
       "      <td id=\"T_40181_row21_col6\" class=\"data row21 col6\" >0.165575</td>\n",
       "      <td id=\"T_40181_row21_col7\" class=\"data row21 col7\" >-0.529253</td>\n",
       "      <td id=\"T_40181_row21_col8\" class=\"data row21 col8\" >0.147682</td>\n",
       "      <td id=\"T_40181_row21_col9\" class=\"data row21 col9\" >-0.034090</td>\n",
       "      <td id=\"T_40181_row21_col10\" class=\"data row21 col10\" >0.087383</td>\n",
       "      <td id=\"T_40181_row21_col11\" class=\"data row21 col11\" >-0.306747</td>\n",
       "      <td id=\"T_40181_row21_col12\" class=\"data row21 col12\" >0.079604</td>\n",
       "      <td id=\"T_40181_row21_col13\" class=\"data row21 col13\" >0.046797</td>\n",
       "      <td id=\"T_40181_row21_col14\" class=\"data row21 col14\" >-0.240261</td>\n",
       "      <td id=\"T_40181_row21_col15\" class=\"data row21 col15\" >-0.242792</td>\n",
       "      <td id=\"T_40181_row21_col16\" class=\"data row21 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row21_col17\" class=\"data row21 col17\" >0.124924</td>\n",
       "      <td id=\"T_40181_row21_col18\" class=\"data row21 col18\" >-0.242020</td>\n",
       "      <td id=\"T_40181_row21_col19\" class=\"data row21 col19\" >0.211763</td>\n",
       "      <td id=\"T_40181_row21_col20\" class=\"data row21 col20\" >-0.126859</td>\n",
       "      <td id=\"T_40181_row21_col21\" class=\"data row21 col21\" >1.000000</td>\n",
       "      <td id=\"T_40181_row21_col22\" class=\"data row21 col22\" >-0.174529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40181_level0_row22\" class=\"row_heading level0 row22\" >habitat</th>\n",
       "      <td id=\"T_40181_row22_col0\" class=\"data row22 col0\" >0.217179</td>\n",
       "      <td id=\"T_40181_row22_col1\" class=\"data row22 col1\" >-0.042221</td>\n",
       "      <td id=\"T_40181_row22_col2\" class=\"data row22 col2\" >0.163887</td>\n",
       "      <td id=\"T_40181_row22_col3\" class=\"data row22 col3\" >0.033925</td>\n",
       "      <td id=\"T_40181_row22_col4\" class=\"data row22 col4\" >-0.075095</td>\n",
       "      <td id=\"T_40181_row22_col5\" class=\"data row22 col5\" >-0.026610</td>\n",
       "      <td id=\"T_40181_row22_col6\" class=\"data row22 col6\" >-0.030304</td>\n",
       "      <td id=\"T_40181_row22_col7\" class=\"data row22 col7\" >-0.154680</td>\n",
       "      <td id=\"T_40181_row22_col8\" class=\"data row22 col8\" >0.161418</td>\n",
       "      <td id=\"T_40181_row22_col9\" class=\"data row22 col9\" >-0.202972</td>\n",
       "      <td id=\"T_40181_row22_col10\" class=\"data row22 col10\" >-0.269216</td>\n",
       "      <td id=\"T_40181_row22_col11\" class=\"data row22 col11\" >-0.007668</td>\n",
       "      <td id=\"T_40181_row22_col12\" class=\"data row22 col12\" >-0.058076</td>\n",
       "      <td id=\"T_40181_row22_col13\" class=\"data row22 col13\" >-0.039628</td>\n",
       "      <td id=\"T_40181_row22_col14\" class=\"data row22 col14\" >0.042561</td>\n",
       "      <td id=\"T_40181_row22_col15\" class=\"data row22 col15\" >0.041594</td>\n",
       "      <td id=\"T_40181_row22_col16\" class=\"data row22 col16\" >nan</td>\n",
       "      <td id=\"T_40181_row22_col17\" class=\"data row22 col17\" >-0.040581</td>\n",
       "      <td id=\"T_40181_row22_col18\" class=\"data row22 col18\" >0.235835</td>\n",
       "      <td id=\"T_40181_row22_col19\" class=\"data row22 col19\" >-0.212080</td>\n",
       "      <td id=\"T_40181_row22_col20\" class=\"data row22 col20\" >0.185954</td>\n",
       "      <td id=\"T_40181_row22_col21\" class=\"data row22 col21\" >-0.174529</td>\n",
       "      <td id=\"T_40181_row22_col22\" class=\"data row22 col22\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14df56710>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting correlation matrix of standardized matrix\n",
    "corr = df3.corr()\n",
    "corr.style.background_gradient('coolwarm')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Two: Classification Models"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by splitting the dataset into two distinct sets, X - which contains all the features except for the target feature ('class'), and Y - which only contains the target feature ('class'), and creating train/test splits by an 80/20% split ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting dataset\n",
    "X = df2.drop(['class'],axis=1)\n",
    "Y = df2['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating train/test split using 80% data for training and 20% for testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining some empty dfs to record metrics from each model and stage of analysis\n",
    "column_names = [\"method\",\"accuracy\",\"precision\",\"recall\",\"f1\",\"roc_auc\"]\n",
    "performance_metrics = pd.DataFrame(columns=column_names)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will use some models and classify the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes\n",
    "nb = GaussianNB()\n",
    "nb, performance_metrics, nb_confusion = model_predictions(nb, X_train, X_test, y_train, y_test, performance_metrics, \"Naive Bayes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EmHSNIOkqSQf48TBJPfx4pqQOZZbRUdJDjWlnEARB0HREd3eNYGaXV6CM2cAxFTAnCIIgqAGiJV0FJP1a0gxJL0p6QFIfSQMlle1gJX1X0iR/TZTUTlInSdM8/o5M/IeSrvDwiyWNdYGTKxvrHIMgCIIVJ1rSTYyknsAPgZ2A1iwrPlIf+gDnmdkoSW2BRdlIMzvT69sCeIo0K/wgYBtgV9Ls8MGS9jGzEXk2hphJEARBDRAt6aZnT+BxM1tkZvOBJxpYzijg95IuIImWfJ2fQNIawIMk1bG3gYP8NZH0cNCZ5LSXIcRMgiAIaoNoSTcTJJ0H/Ng/HmZm10kaAhwGjJJ0MHmtaaA/8IiZPZcrBrjWzG5vEqODIAiCFSJa0k3PKOBISWt4N/UR5WQys1vMrJu/Zkvaysymmtn1pLXRnbPp3am3M7PrMsFPA6d7vUjaRNKGFTmrIAiCoOJES7qJMbOxkgYDU4D3ganAvAYUdZGk/YAlwHTg78DGmfg+wFeSJvnn/mbWX9J2wMuSABYAPyKJoBQkFMeCIAiqR4iZVAFJbc1sgSuBjQDOMrMJ1barECFmEqzqhIhJ0BBCzKSZIOkcSafkBQ/wFu4E4GFgiaTDKlTfvpKerERZQRAEQXWJ7u4MkloVmiW9guX1zw83sxPz0vUGegB/q1Td5VLpcw6CIAgqR7NuSUtqI2mIpMmSpknq5TKav5U0VdIYSVt72k6SnncRj6GSNvfwgZL6SxoN/FbSVpKekjRe0khJnYvUvUDSDZKme3kbePgwSTdKGgdcKKmvpD6ZuOvdrtcl7S1pNeAqoJcLj/QqUFdPSS/5eY5x4ZI1JN3l5znRx6fz860n6TE/51ckdfXwvpLulTQKuLciX0YQBEFQcZq1kwYOAWab2U5mtiNJtANgnpl1AW4GbvSwm4C7zawrcD/wx0w5mwJ7mNnPgAGkdcXdSZOvbi1SdxtgnJntQNoC8opM3Gq+zvh3BfK1MrNdgYuAK8zsS+ByYJDP3B6UTexOfBBwoZntBBwAfA6cB5if5wnA3b4uOsuVwEQ/5/8H3JOJ2x44wMxOyDdQ0lmSxkkat3DxJ0VOPwiCIGhsmruTngoc6K3Tvc0sN0v6gcz77n68O/BnP74X2CtTzoNmttiXJu0BPOhjxrez7IzpLEtIzhPgvrzyBi2f/Bse8ffxQKcS6XJ8G3jXzMYCmNmn3j29l9eLmb0GvA1sm5d3L7ylbGbPA+tLWtvjBpvZ54UqDDGTIAiC2qBZj0mb2euSdiEJelwtaWguKpusjKIW+nsLYK6ZdctGSmrJUunOwUU2w8jWs7BAfI4v/H0xRa6/pKeBjYBxwB9KWt5wStkYBEEQ1ADNuiUtqSPwmZndB/QDdvGoXpn3l/34JeB4Pz4JGJlfnpl9Crwl6VgvX5J2MrPFGSGRnINuwdIdp04EXlyBU5kPtMvYcbDXdSYwA9jYNb/x8ehWbv9JHrYtsLmnzZJNsy/wkZ9jEARB0Axo1i1poAvQT9IS4CvgXOAhYF1JU0it1tyY6/nAXZIuBj4ETitS5knAbZIuI22A8RdgcoF0C4FdPd0HLH0waAgvAL/0LvZrs+PSZvalTya7SdKapPHoA0hj5bdJmgp8DfQ2sy9cpCRHX+BOvxafAafW17AQMwmCIKgeK52YiaSZQA8z+6iR61lgZm0bs45aIMRMglWREDAJVpQQM2nGSLpK0gF+PExSDz+eKalDBcqvSDlBEARBdWnu3d3LYWadmqieBreii0w8qwpK/eMysyXVtiUIgiBYlmhJNzKSfi1phqQXJT0gqY8LqBxTd+5vymibES6ZIumHHn6Ch02TdH2RvD/z+GmSLvKwTm7TPcA0IPr2giAIapCVriVdS/iM7B8CO5EmoU1g6VKu+vBrlgq0IGldn9l+PdAdmAM8I+loM3ssU3930gS53Uh7SY+WNNzTbwOcamavFLD7LOAsgPatN2mAuUEQBEEliJZ047In8LiZLTKz+cATDSznAOCW3AczmwP0BIaZ2YcubnI/sE9evr2AR81soZktIAmp7O1xbxdy0F5+iJkEQRDUAOGkaxBJ57mO9yRvMTcGIWYSBEFQ44STblxGAUf6ZhhtgSPKyWRmt2TEU2YDz5K0uoHU3Q2MAb4rqYMrop1A0hDPMhI4WtJaktoA36eAiEsQBEFQm8SYdCNiZmMlDQamAO+TtMbnlc5VkKuBWyRNI8mJXmlmj0j6JUkIRcAQM3s8r/4JkgaSHDrAHWY2UVKncisOMZMgCILqsdKJmdQaktqa2QJJawEjgLPMbEK17SqXHj162Lhx46ptRhAEQbOiUmIm0ZJufAZIOgJ4D7hrRR20pN7AM94N3ui8N/1L+u04qymqCqpAKGsFQW0TTrrxOYnKioX0Jq1tbhInHQRBEFSPmDjWCBQQC1nsE7w6SXpV0p8kTZf0jG+agaSeLlQySVI/H3/OL/cYoAdwv6c7XNJjmfgDJT3qxwsk3eD1DJW0gYdvJekpSeMljZTUuQkuSRAEQdAAwkk3HtsAt5rZDsDbeeG3ePhcktgJwF3A2b6X9eJCBZrZQ6Q9pk/ydH8DOuccMEm45E4/bgOM83qGA1d4+ADgfDPrDvQh7aa1DJLOkjRO0riFiz+p73kHQRAEFSKcdONRTCzkLTOb5MfjgU6S2gPtzCy39/Wfy6nA0qy/e4EfeRm7A3/36CVAbsvL+4C9fBnYHsCDvi3m7cDGBcoNMZMgCIIaIMakG49iYiFfZI4XA2uWKkTSXcDOwGwzO6xAkrtISmaLgAddfawQRnoom+ut8CAIgqDGiZZ0DWBmc4H5knbzoOMzcae5qEnOQc8H2mXiZ5MmkV1Gctg5WgC5TTxOBF40s0+BtyQdC2kHLEk7NcIpBUEQBBUgWtK1wxnAnyQtIY0hFxM9GQj0l/Q5sLuZfU7S7d7AzF7NpFsI7CrpMuADoJeHnwTc5uGtgb8Ak4sZFWImQRAE1SPETGqEnOiJH/8S2NjMLiwz783ARDP730zYghXZ8zpHiJkEQRDUnxAzaSb4Hs4DzOyzOpKOltQK+AroTIEJXUXKH09qNf+8nnb1pgxRlBAzWfkIAZMgaD7EmHTjcxGwVhnpPiQtrdoRWGRmH5ZTuC+l2t/MvsgLr6sV3RtorB22giAIggoQTrqCSGojaYikyZKmSbqC5AhfkPSCp7nN1yBPl3RlHeV1kPSypMMLxA2U1F/SaOC3krpJesUFUR71nbIoFF5AFKXkDPMgCIKgOoSTriyHkJZK7eQt4htJM6/3M7P9PM2lPk7RlbTVZNdCBUnaCBgCXG5mQ4rUtymwh5n9DLgH+IWZdSXttpUTL1kuPF8UxSefZesOMZMgCIIaIJx0ZZkKHCjpekl7m1mhGdrHSZoATAR2ALYvkKY1MBS4xMyeLVHfg2a2WNI6QHszy+0nfTewT7Hwuk4ixEyCIAhqg3DSFcTMXgd2ITnrqyVdno2XtCVJivN73rIdAqxRoKivSWpkB2fyXuNd05My6YoJpgRBEAQrAeGkK4ikjsBnZnYf0I/ksLPiI2uTHOs8784+tEhRBpxO0uX+BYCZXepd092WS5xa7HMk7e1BJwPDi4X78TKiKEEQBEHtEUuwKksXoJ8LknwFnEvS035K0mwz20/SROA1YBYwqlhB3o19AjBY0nwzW24jjDxOJYmcrAW8Sdpso1T4QJYXRVmOEDMJgiCoHiFmEpQkxEyCIAjqT4iZrMRI6gQ86TPES6XZw8z+7J97AKeY2QWVtCXETJofIVYSBCsPMSbdfOlE2jgDADMbV2kHHQRBEFSXcNINQFInSa9Jul/Sq5IekrSWpO9JmihpqqQ7Ja3u6WdK+q2Hj5G0tYcPdGGRXLkLitQ1UtIEf+3hUdcBe/uM759K2lfSk55nPUmPuYDJK7m12JL6ul3DJL0pKZx6EARBDRNOuuF8G7jVzLYDPgV+RpqM1cvMupCGEs7NpJ/n4TeTRE7K5QPgQDPbhbST1R89/JfASJ/xfUNenitJG250Bf4fSdAkR2fS0q5dgSsktc6vMMRMgiAIaoNw0g1nlpnlZmffB3wPeMvXSsPywiEPZN53r0c9rUlbWE4FHqSw+Ek+ewH3ApjZ88D6ktb2uCFm9oWZfUR6ANgoP3OImQRBENQGMXGs4eRPi58LrF9m+tzx1/iDkqQWwGoF8v0UeB/YydMuaoCtWbIbcSwm7oEgCIKaJVrSDWdzSbkW8YkkLexOufFmlhUOgdRVnXt/2Y9nAt39+D9JreZ81gHeNbMlXmZLDy8lRjISOAlA0r7AR2b2aTknFQRBENQO0YpqODOA8yTdCfwDuAB4BXjQ94UeC/TPpF9X0hRSS/YED/sT8LikycBTFJb5vBV4WNIpeWmmAIs970CSFniOvsCdXt9nJEGTBhFiJkEQBNUjxEwaQDnrmPPSzwR6+DhwsyLETIIgCOpPVcVMJF0EDDCzz+pINwzoY2bjJC0ws7YNqa8edu1Nar1+RQmpy0aqexh+rk1VZ179VwEjzOy5SpYbYia1Q4iUBMGqR0PHpC8C1qqgHSuMpJakcdhrC+2RXEnMbGa5rWhP36kxW9GSWprZ5ZV20EEQBEF1qdNJS2ojaYikyZKmSboC6Ai8IOkFT3Obr6udLunKOsrrIOllSYcXiDvW65gsaYSH9ZZ0cybNkz4ZCkkLJP3Ox2V/BRwH/MZFRtpKGuoCIFMlHZUp4xQX+pgs6V4P20DSw5LG+mvPIvZf7vHTJA2QpEz0yS4uMk3Srp5+OWERSS1c4KR9ptw3JG1UDztmKu1bPQE4NiuM4nFXZs69c+Ycn/Xv6Q5Jb0vqUOr7CoIgCKpHOd3dhwCzzexwAEnrkHZS2i/TOrzUzD7x1uxQSV3NbEp+QUrbMw4GLjOzZwvUdTlwsJm9k3VgJWgDjDazn3v5W5PGih/yyVvfN7NP3RG9ImkwaZ3xZSTd648k5RYC/wG4wcxelLQ58DSwXYE6bzazq7y+e4EjgCc8bi0z6yZpH+BOYEeWCoscLWl/4B5P8zjwfeAuSbsBb5vZ+5L+XKYdAB+7yAmSDsmL+8jMdpH0E9Ie1mcCVwDPm9m1nv6MQoVKOgs4C6B9602KVB0EQRA0NuV0d08FDvRW296+R3E+x3mLbiKwA4UFN1oDQ4FLijhoSFs3DpT0Y5YuNSrFYuDhInEC/ttnOD8HbEIS7tgfeDD3gGFmOUmtA4CbJU0iPUisLanQGPp+kkYriYvsTzrfHA94mSM8f3uKC4sMYumyrOP9c33sIJOnEI/4+3iSzjduy1/clqeAOYUyhphJEARBbVBnS9rMXpe0C3AYcLWkodl4SVuSWmo9zWyOpIHAGgWK+prkMA7G1w9LugY43OvpZmbneKvycGC8pO5kBD+cbNmLzGxxEdNPAjYAupvZV0ozrAvZlaMF8B0zW0YsRNLTJOc+Dvgv0pKoHmY2S1LfvDLzp8qXmjr/MrC1pA2Ao4Gry7XDzM704EJLtnLkREtCsCQIgqCZUs6YdEfgMzO7D+gH7MKyQhprk5zFPO/OPrRIUQacDnSW9AsAM7vUnXM3r2srMxttZpcDHwKbkQQ/uvk47mYkzelyWAf4wB30fsAWHv48aQx3fa8z11R8Bjg/c97d3MaD3cYzWeqQP/LW7TebYzi9PO9eJK3ueRQRFrG09u1R4PfAq2b2cT3saCijSOP2SDoIWHcFygqCIAgamXJaWF2AfpKWkJY2nUvSnn5K0mwz20/SROA1YBbJERTEzBZLOgEYLGm+md2al6SfpG1IXdVDgcke/hZJMORVYEKZ53Y/8IR3S49z+zCz6d6CHy5pMamLvjdJjOQW7x5vBYwAzsmzf66kPwHTgPdIgiVZFvm1aE16IIHSwiKDvIzembA67VgBrgQekHQyqSX/HumBqyghZhIEQVA9QsxkFUJp68zFZva1kqTpbblejGKEmEkQBEH9UTXFTIIVR/VULSuQvwdwipnVZ0/ozYG/Km3m8SXw47oyhJhJ4xHiJEEQ1EU46RpGSaSk4MQ4VzarVxPXzN4Adq6EbUEQBEHjE7tgVZdWSsIrr0p6SNJaBURKhnmrOScEM9OP95X0pB9/V0lEZZKkiZLaefjFLogyRS4yo+XFaXoVNi0IgiCoNtGSri7fBs4ws1FKu2n9xMOzIiXlTBrrA5zn5bQlTWA7CNiGNBtepMl6+5CWpeWL0yxDiJkEQRDUBtGSri6zzCw3G/4+ktgIlBYpKcQo4PeSLgDam9nXwEH+mkiaEd+Z5LTrFKcJMZMgCILaIFrS1aWY+ElWpCQr5lJQjMXMrpM0hCQ4M0rSwaTW87Vmdnt++nxxmpzMaRAEQVBbREu6umzuS6EATgReLJBmJtDdj/PFU4BvRGCmmtn1pHXXnUma36fnJEUlbSJpwyLiNEEQBEENEi3p6jIDOM/Ho/8B3EZGbcz5H9KyqbOAIUXKuchV1ZYA04G/m9kXkrYDXlbaqGsB8CNga5YXpylKiJkEQRBUjxAzCUoSYiZBEAT1p+piJpIuAgaY2Wd1pBsG9DGzcZIWmFmxHZ0qgqS9gf6kVuLuZvZ5I9Qxk7TJxkd1pfX0vT39f1XalkwdVwEjzOy5Spa7qouZhOBIEATVZEXGpC8C1qqQHRVBaT/rk0gTpro1hoOuRVz05PJKO+ggCIKgupTlpAsIYFwBdARekPSCp7lN0jhJ03PCGSXK6yDpZUmHF4g71uuYLGmEh/WWdHMmzZO+oxSSFkj6naTJwK9Iuzz9xkVC2koaKmmCpKmSjsqUcYqLfEyWdK+HbSDpYRcAGStpzxKncYmXOUbS1uXml9RJ0vNe91BJm0tqKektJdpLWuxrmpE0QmnTkfxy8kVPBko6JhN3Zea8O2fse9a/ozskvS2pQ6nvKgiCIKge5XZ3H8LyAhinAftlunwvNbNPvDU7VFJXM5uSX5DSdpaDgcvM7NkCdV0OHGxm70hqX4ZtbYDRZvZzL39rkib2Q5JaAd83s0/dGb0iaTCwPXAZsIeZfaSl21X+AbjBzF6UtDlphvR2ReqdZ2ZdJJ0C3AgcUWb+m4C7zexuSacDfzSzoyXNcLu2JK1r3lvSaGAzl/MsRFb05JC8uI/MbBdJPyGJnZwJXAE8b2bXevozChWqEDMJgiCoCcp10lOB30m6nuQAR/qM4SzH+Z97K2BjksPJd9KtSVtQnmdmw4vUNQoYKOmvwCNl2LYYeLhInID/9lbpEmATYCNgf+DB3AOGmX3i6Q8Ats+c29qS2prZggJlP5B5v6FU/rx8uwM/8ON7gd/68UhgH5KTvpa0+cVwlt8OM0sp0ZPctRufqW8v4PsAZvaUpDmFMprZAGAAwKZrdo2ZhUEQBFWiLCdtZq8rTwAjGy9pS1JrraeZzZE0kMLCG1+TnMbBJAeE0t7Oh3s93czsHEm7edh4Sd1ZVtCDvLIXFduEgjQ+vQHQ3cy+8glfBQVBnBbAd8xsUd75PU1y7uPM7EwPzjovqyN/iSq/YQRpOVRHUm/CxcC+JOddzIaFyxfzDV/4+2JiqV0QBEGzpNwx6UICGPOBdp5kbZLDmOfd2YcWKcqA04HOkn4BYGaXunPu5nVtZWajzexy4ENgM5KgRzdJLSRtRtKjLod1gA/cQe8HbOHhz5PGcdf3OnPd3c+QWacsqZvbeLDbeCZL6ZV5f7lU/jxeAo7345NwJwyMAfYAlriTnwScTXLexWyoL6NIY/YoaXuvuwJlBUEQBI1MuS2sLiwvgLE78JSk2Wa2n6SJwGvALJIzKIiZLZZ0AmnDh/lmdmtekn4+UUqkrvHJHv4WSfDjVdKYbTncDzwhaSppW8fX3Ibp3oIfLmkxSd+6N3ABcIukKaRrMwIotsHFup7uC+AEDysn//nAXZIuJj2EnOY2fSFpFvCKpxvp5U4t81zL4UrgAUknkx4s3iM9bBUlxEyCIAiqR4iZrEJIWh1YbGZfK8mR3pbrwShGiJkEQRDUH1VbzCRYcSR1Ik3E27HM9AM9/UN54T2AU8zsAqWlaV+a2UsFiticJDHaAlidpZPWirIyipmEQEkQBM2FcNIrAWY2jtSdD2my2QLS2Hd+ujeAnQEk9QViH8ogCIIaJnbBqj4tJf3JBUaekbSmpB+7GMpkF0fJKrsdoCQa87qkIwAk7ask8NKJNAb+U0mTJO0t6UhJoyVNlPScpI0KpWvysw6CIAjqJJx09dkGuMXMdgDmAj8EHjGznma2E2miXFZ0pBNpdvvhQH9J3ywpM7OZJN3yG3wm+EjS9pffMbOdgb8AlxRJFwRBENQY0d1dfd4ys0l+PJ7khHeUdDXQHmhLUi7L8VczWwK8IelN0t7RpdgUGCRpY2A10iz5koTiWBAEQW0QLenq80XmOCc8MhD4LzPrQlo2lRVgyZ+OX9f0/JuAm72ssykt5pIKNBtgZj3MrEebljFsHQRBUC3CSdcm7YB3JbUmCZ5kOdZFXbYCvgXMyIvPisxAEnR5x49PLZEuCIIgqDGiu7s2+TUwmiR2Mpplnen/kdTJ1gbOMbNFebKjTwAPKe34dT7QF3jQdbqfJ2mDL5eu2Lh0iJkEQRBUjxAzCUoSYiZBEAT1J8RMqoykq4ARZvacpGFAHzMb55t49Mhs4dkUtnwjZlLpslcmMZMQMQmCoLkRTrqB+AYgNUGemEkQBEGwkhATx8pA0q8lzZD0oqQHJPWRNFDSMfUo47suHDLJhUXauQjJCElDvPz+LtmJpNtctGS6pCsz5fSU9JILnYzJlPOkx/eVdKekYZLelHRBJu9y51HJ6xQEQRBUlmhJ14GkniSBkZ2A1qQduMY3oKg+wHlmNkpSWyC35/SuwPbA28BTwA+Ah4BLzewTSS2BoZK6knbxGgT0MrOxktYGPi9QV2dgP9KEsxmSbgO6Veg8giAIgiYiWtJ1syfwuJktMrP5pFnRDWEU8Htv2bY3s689fIyZvWlmi4EHgL08/DhJE0jbaO5AcuTfBt41s7EAZvZpppwsQ8zsCx8X/wDYqD7nIeksb8WPW7j4kwaebhAEQbCihJNuJCSdl+ne7mhm1wFnAmsCoyTllMKWEyeRtCWp5f09M+sKDKEMEZIMhQRSyibETIIgCGqDcNJ1Mwo4UtIa3k19RDmZzOwW18XuZmazJW1lZlPN7HpgLEvlPHeVtKWPRfciaW2vDSwE5knaCDjU084ANvYueHw8ulwH3KDzCIIgCKpHjEnXgY/9DgamAO8DU4F5DSjqIkn7AUuA6cDfgd1JDvtmYGvgBeBRM1siaSJpDHoWycFiZl9K6gXcJGlN0nj0AY15HiFmEgRBUD1CzKQMJLU1swW+ZeQI4Cwzm1CBcvclra9uklZtQ84jxEyCIAjqT4iZNC0DJG1PGhe+uxIOukrU+zyak5hJiJUEQbCy0WROWtJFwAAz+6yOdMNYqt61wMzaVtiOepdpZic2UrnDgGH1yVOi/r8BJ5rZ3BL11XkeQRAEQe3QlBPHLgLWasL6Vph6TMqqGkq0MLPDSjnoIAiCoPnRKE5aUhtX0ZosaZqkK4COwAuSXvA0BRW1ipTXQdLLkg4vELeRpEe9rsmS9vDwn3nd07wVn59Pkvp5/FSfkIWrd430SVb/KJDvMUnj3e6z8uJu8PChkjbwsG6SXpE0xe1cV1JnSWMy+TpJmurH3SUN9zqelrRxARs6uXLYPcA0YDNJM/06dZL0qqQ/uS3P+CSznFrZFF8W1k/StFLXPQiCIKgujdWSPgSYbWY7mdmOwI3AbGA/M9vP01zqg+pdge8qKWothy9BGgJcbmZDCiT5IzDczHYCdgGmS+oOnAbsBnwH+LGknfPy/YCkwrUTaYZ0v4xD3AW40My2LVDf6WbWHegBXCBpfQ9vA4wzsx2A4cAVHn4P8Atf7zwVuMLMXgNW8/XQkJZeDVLaP/om4Biv407gmkLXBdgGuNXMdjCztwvE3eK2zCUpjQHcBZxtZt1I66cLohAzCYIgqAkay0lPBQ6UdL2kvc2s0FKfQopa+bQGhgKXmNmzReraH7gNwMwWe117kZYyLTSzBcAjwN55+fYCHvA875Mca0+PG2NmbxWp7wJJk4FXgM1IDhHS0qpBfnwfsJekdUjqYsM9/G5gHz/+K8k54++DSIpiOwLPSpoEXAZsWsSOt83slSJxb5nZJD8eD3SS1B5oZ2Yve/ifi+QNMZMgCIIaoVHGXM3sdUm7AIcBV0samo3XUkWtnmY2R9JACitqfU1yMgeTnCiSrgEO93q6NYb9JCERJG3GUvnM/qR1ywcAu5vZZz7JrZgSWF1r2wYBD0p6BDAze0NSF2C6me2eTVjAjqdyNhYhX3FszTpsCYIgCGqQRnHSkjoCn5jZfZLmkuQw55M2fPiIwopawwoUZcDpJGf2CzO73swuBS7NpBkKnAvcqLQZRVtgJDBQ0nWAgO8DJ+eVPRI4W9LdwHqkFu7FLFUCw8xmkbrEc+d1FDDHHXRnUld6jhbAMcBfgBOBF81snqQ53psw0m0Y7mX/S9Ji4NcsbYHPADaQtLuZvezd39ua2fQ8OzoVuFYlMbO5kuZL2s3MRgPHl5MvxEyCIAiqR2PNXu5CGuNdAnxFcqK7A09Jmm1m+6mAolYhzGyxpBOAwZLmm9mteUkuJK3/PYPUajzXHdxAIDc56w4zm5iX71G3aTLpYeASM3tPSzW1C/EUcI6kV0kONdvdvJAk8XkZaVOLXFf2qUB/JQGRN0lj5TkGAf2ALf1cv1Ta/vKP3lXeijSeP72ETfXhDOBP/r0Mp2HKaUEQBEETEYpjqxByxTE//iWwsZldWCrPpmt2tQu3KjRfr7qEcEkQBLWMKqQ4Fhts1IGkv/mkq5pFUm9JN5eR9HBffjWNNJHu6kY2LQiCIFgBal6so5pIEnCEmS2pti2VwMwGsXT8+xsktfT9rIMgCIIaIlrSeRQQCllcKZEQScN8WdoYSa9L2tvDl2kJS3pSafMNJC3wMqdLek7Srl7Om5L+M1P8Zh7+hpJ4TK6sH3l9kyTd7pPrcuX+zpeTLTObPAiCIKgNwkkX5huhEODtvPAGi4Q4rcxsV5JM6hV1pIUkkvK81zmf1EV9IGnG+lWZdLu6PV2BYyX1kLQdaQLbnhnbTsqUO9oFZ17MVhhiJkEQBLVBdHcXpphQSLkiIaW2nnwkm78MW74kzSqHJBLzhZl9pSQjms3/rJl9DOBrr/cirTPvDoxNPfesSZp5DslhP1yoQjMbAAyANHGsDBuDIAiCRiCcdGGKCYXUSyRE0l3AziSJ1MPyyljM0uv/Ncv2amQFUr6ypVPwl+Tym9kSLbsBSL4zNdIa8bvN7FcFzFsU49BBEAS1TTjpFaSUSIiZnVYia5aZwE8ktQA2IXVd15cDJa0HfA4cTRKB+Qx4XNINZvaBx7croPVdlBAzCYIgqB7hpCvDioqEjALeIu269SowoQE2jCF1X28K3Gdm4wBcXOUZfwD4CjiPZcfZgyAIgholxEwqQENEQpoLtSRmEgImQRA0F0LMpIpIushlPnMUFAnxJVE9/HhBA+sqV6gkCIIgWMmI7u6GcRFpO8rPoLhISBAEQRCsCNGSrgNJbSQNkTRZ0jQXCukIvCDpBU9zm68rni7pyjrK6yDpZUmHF4g71uuYLGlEJqqjpKdcqOS3mfQF65U0U9JvJU11IZOtPXwDSQ9LGuuvPVfw8gRBEASNSLSk6+YQ0hKqwwF8d6rTgP3M7CNPc6mZfeJqXkMldTWzKfkFKW3LORi4zMyeLVDX5cDBZvZOnl54N9JSri+AGZJu8m00S9U7z8y6SDqFtJPWEcAfgBvM7EVJmwNPA9sVsPMs4CyA9q03Kfc6BUEQBBUmWtJ1M5W0vOl6pX2hC83cPk7SBGAisAOwfYE0rUl7X19SxEFDmuU9UNKPgZaZ8KFmNs/MFpFmgG9RRr0PZN5zsp8HADdLmkR6WFhbUtt8I8xsgJn1MLMebVquV8TUIAiCoLGJlnQdmNnrknYBDgOuljQ0Gy9pS6AP0NPM5ijtY73G8iXxNUll7GDSMi0kXQMc7vV0M7NzJO3mYeMldfe8+SIqrcqo1woctwC+484+CIIgqHHCSdeBpI7AJ2Z2n6S5wJkkDe12wEfA2iSFsnnenX0oMKxAUUYSGHlQ0i/M7HozuxS4NFPXVi6IMlrSoUCpNUd11dsLuM7fc5KlzwDnA/28vm4ZmdOChJhJEARB9QgnXTddgH4uVPIVcC6p+/gpSbPNbD9JE4HXgFmkLuuCmNliSScAgyXNN7Nb85L0k7QNSc5zKDCZNB5dqKzJddS7rqQppFb4CR52AXCLh7cCRgDnlHMRgiAIgqYnxExWQiTNBHpkJrY1mGqKmYR4SRAEzZVmKWZSQASkWLoVFgGpo/wGl6m0r3TB/aJL5Bko6ZiG1llmHX/LmxEeBEEQNHOaenb3RUCdTrqWyNtpquZQooWZHWZmcwHMrFMlWtFBEARBdWk0J93EIiAbSXrU65osaQ8P/5nXPU3SRQXySVI/j58qqZeH7ytppKTBpCVP+bSSdL+kVyU9lOsdkNRd0nBJ4yU9LWnjAnV+T9JEr+9OSatL6qm0BzSSjpL0uaTVJK0h6c0CZXSSNEPSPcA0YDMlAZMOHveqpD/5dX1G0pqer6ekKUoSpv3q2yMQBEEQNC2N2ZLOiYDsZGY7kgQ1ZpNEQPbzNJd6n31X4LuSuhYqyGcvDwEuN7NCA6R/BIab2U7ALsB0X750GrAb8B3gx5J2zsv3A9LErJ1Ia4j7ZRzrLsCFZrZtgfq+DdxqZtsBn5K2mWwN3AQcY2bdgTuBa/LOYw1gINDLzLqQJm+dS1rn3M2T7U1yvD3d9tGFrgmwjduwQ4GtJ7cBbjGzHYC5wA89/C7gbDPrRlrKVRBJZ/nD07iFiz8pliwIgiBoZBrTSTelCMj+wG2QZlB7XXsBj5rZQt+h6hGSA8yyF/CA53mftH65p8eNMbO3itQ3y8xys6nv83K+DewIPKskFnIZadvILN8G3jKz1/3z3cA+ZvY18C9J25H2kv49sI/bO7KIDW+b2StF4t7KLK0aD3Ty8ep2ZpZbjvXnInlDzCQIgqBGaDQn7Y5oF5KzvlrS5dl4LRXj+J6ZdSW1lOsSAcnlvca7bCc1kvmQ1iAjabNcXZJyy5Xyp8QbadnUdBcl6WZmXczsoHrUN4K01vkr4DmS498LGFnEhoUlylpO/KQedgRBEAQ1QmOOSXcEPjOz+0jiGbuwVAQECotxFCInAtJZ0i8AzOzSnDP0NENJ3cZIaqmkrz0SOFrSWpLaAN9n+VbpSKCX59mA1Hods0zlZrMyjre/B28uKSe1eSLwIjAD2CAXLqm1pB3y6ptBatVu7Z9PxtXH3JaLgJfN7ENgfVLLe1oRG+qFTyqbr6RoBnB8Q8oJgiAImo7GbGE1pQjIhcAASWeQWo7nmtnLSlKZOad7h5lNzMv3qNs0mfQwcImZvSepcx3nNgM4T9KdpIllt5nZl0rLrP7oDwmtSOPw0zPnsUjSaSTVsVbAWCDndEcDG5Fa1ABTgP+wyi5kPwP4k38nw4FCQxDLEIpjQRAE1SPETFYhJLX18Xkk/RLY2MwuLJWnWmImIWQSBEFzRhUSM4mxypUQSQvMbLndrYDDJf2K9L2/DfRuUsOCIAiCehFOehXCzAYBg7Jhklr57PIgCIKgxoj9pBsBSae4aMhkSfdKOlLSaBcxec4nyiGpr8e/LOkNpX2kC5XX1GItQRAEQQ0QLekK4zO6LwP2MLOPJK1HmpT2HTMzSWcClwA/9yxdSWIrbYCJkoaY2ey8YnNiLd+X1BJomyfWItL2lsPzJsdlxVo6AGMl5Sam7QLsWGgtuKSzgLMA2rfeZEUuRxAEQbACREu68uwPPJjTzjazT0iiJk9LmgpcTBJuyfG4mX3u6V8giZkUKrPJxFpCzCQIgqA2CCfdNNwE3OxSoGezrGjLcsIoTSnWEgRBENQu4aQrz/PAsZLWB/Du7nWAdzz+1Lz0RyltpLE+sC8wtinFWoIgCILaJcakK4yZTZd0DTBc0mKSLnlfkoDJHJIT3zKTZQqpm7sD8JsC49HQ9GIt3xBiJkEQBNUjxEyqiKS+wAIz+59q21KMSoqZhEBJEASrCpUSM4nu7maGpL2V9omeJN8nOgiCIFg5ie7uKmJmfeuT3pdfnQRc6xuXBEEQBCsx0ZKug0YQJjnWxUUm59YsS+ot6eZMmicl7evHCyT9TtJk4FfAccBvJN0vqa2koZImuFjJUcXs9rANJD0saay/9mykyxYEQRBUgGhJl6CRhEkuBw42s3cktS/DjDbAaDP7udu0NfCkmT2ktJPW983sU0kdgFdcRWz7AnYD/AG4wcxelLQ58DSwXYHzDjGTIAiCGiCcdGmWEyaR1AUYJGljYDUgKwjyuJl9DnwuKSdM8lhemaOAgZL+ShIgqYvFwMNF4gT8t6R9gCXAJqTtLgsJqgAcAGwvKZd/bWV2xsphZgOAAZAmjpVhYxAEQdAIhJOuPzcBvzezwd4l3TcTV1CYBDgcwNc+nyNpNw8b7/KeX7Ps0ENW7GSRmS0uYstJwAZAdzP7StLMvLz5tCD1AiwqkSYIgiCoEWJMujQVFyaRtJWZjTazy4EPgc2AmUA3SS0kbUZhadBCrAN84A56P2CLEnYDPAOcn8ssqVuZ9QRBEARVIFrSJWgkYZJ+krYhdVUPJQmNQOo2/wfwKjChTBPvB55wTfBxwGsl7O4NXADcImkK6bsfAZxTqoIQMwmCIKgeIWZSIZqDMElDKCVmEuIkQRAEhQkxk1WE/OVZQRAEwapDdHdXiPoKkwRBEARBXURLegVoCqETp6OkpzzvbzPpb5M0zmVCr8yEz5T0Wxc4GeNrq0PMJAiCoJkRLekG0sRCJ92AnYEvgBmSbjKzWcClvna7JTBUUlczm+J55plZF0mnADcCRxBiJkEQBM2KaEk3nEKCIZsCT/ts64uBHTLpHzezzz19Tugkn5zQyY+BlpnwoWY2z9c3/4OlS62OkzSBNHt7B5LSWI4HMu+7+/EBwM2SJgGDcTGTfCPMbICZ9TCzHm1arpcfHQRBEDQR4aQry03AzWbWBTibZYVFCgqdKO1mNQnAzM4htc43IwmdrO9pv8jkWwy0krQl0Af4npl1BYaUqC93nBMz6eavTfLVxoIgCILaIZx0w2kqoZNirA0sBOb52PehefG9Mu8v+3GImQRBEDQjYky6gTSh0Em3IvVPljSRJGAyi9RVnmVdFy35AjjBw0LMJAiCoBkRYiZNQFMLnbiGd4/cePmK0KNHDxs3btyKGxUEQbAKUSkxk2hJVxlJL5nZHpI6kbag3HEFy+sEdKyEbQDvTf+SfjvOWi481MaCIAgan3DSTUApoRMz26MRqny93Fa0pFZm9nUj2BAEQRCsIDFxrMJIuk7SeZnPfSX1kXSxC4hMyRMeqXN2taStXRxlsqQJkrZSop+Ln0yV1KtAvjUk3eXxE32nrJzU6GBJz5PGvoMgCIIaJJx05RkEHJf5fBxppvY2pLXR3YDukvapR5n3A7eY2U7AHsC7wA+8rJ1I65/7Sdo4L995gPmSsBOAuyXllmntAhxjZt/Nr0zSWa5kNm7h4k/qYWYQBEFQScJJVxgzmwhsKKmjpJ2AOUAX4CDSDPAJQGeS064TSe2ATczsUS9/kZl9BuwFPGBmi83sfWA40DMv+17AfZ7vNeBtYFuPe9YFWAqdQ4iZBEEQ1AAxJt04PAgcA/wHqWW9BXCtmd1eTmZJd5FkQGezdL1zpVnYSOUGQRAEFSJa0o3DIOB4kqN+kKSRfXpOglPSJpI2LJbZzE5zkZPDzGw+8G9JR3ve1SWtBYwEeklqKWkDYB9gTF5RI4GTPN+2wObAjAqeZxAEQdCIREu6EXChk3bAO2b2LvCupO2AlyUBLAB+BHxQZpEnA7dLugr4CjgWeJSkyT2ZJPt5iZm950uwctwK3OZa4l8Dvc3sC7ehLELMJAiCoHqEmElQkhAzCYIgqD8hZtIAvCU6wsyekzQM6GNm4+qr0OWa1x3N7G/+eV/gSzN7qYF2LTCz5XajaizqY2+ImQRBEFSPVWpM2swuN7PnKlBUN+CwzOd9SUujmgv70rzsDYIgWCVZaVvSkn5NGvf9kLQBxXhgR5L05kNllrEr8AfSFpCfA6cBbwFXAWtK2ou0X/M5wGJJPyLtMtWetOXkasDHwElm9r5PHLsJ6EEaR77SzB72uq4BjvB6jvL0A/3zzsCGwOnAKaSx6NFm1tvzHgRcCawO/As4zcwWeA/B3cCRQGvSWPaifHvNbGS51zUIgiBoOlbKlrSknsAPSUIfh5KcYkN4DdjbzHYGLgf+28y+9ONBPgP7eqA/cIN/Hgm8SNq3eWfgL8AlXt6vgXlm1sX3gH7ew9sAr7hYyQjgxxkb1iU55Z8Cg4EbgB2ALpK6SepAeiA4wMx2AcYBP8vk/8jDbyN1788sYO8yhJhJEARBbbCytqT3BB43s0XAIklPNLCcdUgqXduQWr6ty8y3KTDIFcBWI7W+ISmDHZ9LZGZz/PBL4Ek/Hg8cmCnrCTMzn6H9vplNBZA0HejkdW0PjPJZ26uxdP9ogEcy5f6gHOPNbAAwAGDTNbvGzMIgCIIqsVK2pBuKpPMkTfJXR+A3wAu+M9WRpG7vcrgJuNnlOM8uI99XtnSa/WKWfXj6wt+XZI5zn1uR9p5+1lvF3cxsezM7o0D+/HKDIAiCGmdlddKjgCN9g4m2pLHeOjGzWzLObjapJf2OR/fOJJ0PtCvxOZvv1Ez4syQ9bQAkrVuOXXXwCrCnpK29zDYuXFKKfHuDIAiCGmSlbFmZ2VhJg4EpwPvAVGBeA4r6Lam7+zJgSCb8BeCXkiYB1wJPAA9JOoo0cawv8KCkOaRx5y0939XALZKmkVq2V7K0O7pBmNmHknoDD0ha3YMvA14vkW0Ze0tNHAsxkyAIguqx0oqZSGrrM5zXIk3GOsvMJlTbruZGiJkEQRDUn0qJmays3d0AA7ylOwF4uNYdtKSX/L2Tt7QLpentY+VBEATBKsBK2d0NYGYnVtuG+mBm5YiL9AamkXbHCoIgCFZyVuaWdFWRdJ2k7CSxvpL6SLpY0lhJUyRdmYlfUEd5x5DWe9/vs88Pl/RYJv5ASY/mypJ0g6Tpkob6LllI2krSU5LGSxopqXOFTzsIgiCoIOGkG49BwHGZz8eR1M+2AXYlSYt2l7RPOYW5Sto4knpZN+BvQOecAyapod3px22AcWa2AzAcuMLDB5AminUH+pB2yVqOrJjJhx9+WI55QRAEQSMQTrqRMLOJwIaSOkraCZgDdAEOAiaSxso7k5x2Q8o34F7gR5Lak1TJ/u7RS0gPCQD3AXv5UrQ9SLPOJwG3AxsXKXuAmfUwsx4bbLBBoSRBEARBE7DSjknXCA8CxwD/QXKaWwDXmtnt5WSWdBdJt3u2mR1WIMldpOVUi4AHzezrIkUZ6YFsrrfCgyAIgmZAtKQbl0EkGdBjSA77aeB0b9UiaRNJGxbLbGanubBKzkEvI0LigiuzSeui78pkbeF1ApwIvGhmnwJvSTrW65a38IMgCIIaJZx0I2Jm00lO9R0ze9fMngH+DLzsWtwPUT/lr4FAf584tqaH3Q/MMrNXM+kWArv6Uq79Sbt2AZwEnCFpMjAdOKqBpxYEQRA0ASutmMmqgqSbgYlm9r+ZsAVm1rYS5YeYSRAEQf2plJhJjEk3YySNJ7Waf15tW4IgCILKE066GeNLqQqFV6QVHQRBEFSXGJMOgiAIgholnHQQBEEQ1CjhpIMgCIKgRgknHQRBEAQ1SjjpIAiCIKhRwkkHQRAEQY0SYiZBSSTNB2ZU24560gH4qNpGNIDmaHfY3DSEzU1DJW3ewsxWeIeiWCcd1MWMSqjmNCWSxjU3m6F52h02Nw1hc9NQizZHd3cQBEEQ1CjhpIMgCIKgRgknHdTFgGob0ACao83QPO0Om5uGsLlpqDmbY+JYEARBENQo0ZIOgiAIgholnHQQBEEQ1CjhpFcxJB0iaYakf0r6ZYH41SUN8vjRkjpl4n7l4TMkHVxumdWyWdKBksZLmurv+2fyDPMyJ/lrwxqxuZOkzzN29c/k6e7n8k9Jf5SkGrH5pIy9kyQtkdTN46p9nfeRNEHS15KOyYs7VdIb/jo1E17t61zQZkndJL0sabqkKZJ6ZeIGSnorc527VdLmFbHb4xZnbBucCd/S76V/+r21Wi3YLGm/vHt6kaSjPa7Rr/UymFm8VpEX0BL4F/AtYDVgMrB9XpqfAP39+HhgkB9v7+lXB7b0clqWU2YVbd4Z6OjHOwLvZPIMA3rU4HXuBEwrUu4Y4DuAgL8Dh9aCzXlpugD/qqHr3AnoCtwDHJMJXw9409/X9eN1a+Q6F7N5W2AbP+4IvAu0988Ds2lr6Vp73IIi5f4VON6P+wPn1orNeffKJ8BaTXGt81/Rkl612BX4p5m9aWZfAn8BjspLcxRwtx8/BHzPWxJHAX8xsy/M7C3gn15eOWVWxWYzm2hmsz18OrCmpNUraFvFbS5WoKSNgbXN7BVL/xT3AEfXoM0neN6moE6bzWymmU0BluTlPRh41sw+MbM5wLPAIbVwnYvZbGavm9kbfjwb+ABYYUWrMlmRa10Qv3f2J91LkO6toytmceVsPgb4u5l9VkHbyiac9KrFJsCszOd/e1jBNGb2NTAPWL9E3nLKrJbNWX4ITDCzLzJhd3l31a8r3KW5ojZvKWmipOGS9s6k/3cdZVbT5hy9gAfywqp5neubtxauc51I2pXUOvxXJvga7wa/oREeRlfU7jUkjZP0Sq7bmHTvzPV7qSFl1kWl/puOZ/l7ujGv9TKEkw5WeiTtAFwPnJ0JPsnMugB7++vkathWgHeBzc1sZ+BnwJ8lrV1lm8pC0m7AZ2Y2LRNcq9e52eKt/XuB08ws1wL8FdAZ6Enqnv1FlcwrxhaW5DZPBG6UtFW1DSoHv9ZdgKczwU16rcNJr1q8A2yW+byphxVMI6kVsA7wcYm85ZRZLZuRtCnwKHCKmX3T6jCzd/x9PvBnUtdY1W324YSP3bbxpJbStp5+0zrKrIrNmfjlWhw1cJ3rm7cWrnNR/IFtCHCpmb2SCzezdy3xBXAXlb3OsIJ2Z+6DN0nzFHYm3Tvt/V6qd5llUIn/puOAR83sq1xAE1zrZQgnvWoxFtjGZ1SuRvpTHZyXZjCQm+l6DPC8j80NBo5XmuG7JbANaYJNOWVWxWZJ7Ul/aL80s1G5xJJaSergx62BI4BpVI4VsXkDSS3dtm+RrvObZvYu8Kmk73iX8SnA47Vgs9vagvSH9s14dI1c52I8DRwkaV1J6wIHAU/XyHUuiKd/FLjHzB7Ki9vY30Ua163kdYYVs3vdXJew3w97Av/we+cF0r0E6d6qiWud4QTyHjyb4FovS1PNUItXbbyAw4DXSS20Sz3sKuA//XgN4EHSxLAxwLcyeS/1fDPIzHgtVGYt2AxcBiwEJmVeGwJtgPHAFNKEsj8ALWvE5h+6TZOACcCRmTJ7kP4Q/gXcjCsGVttmj9sXeCWvvFq4zj1JY5ELSS236Zm8p/u5/JPUdVwr17mgzcCPgK/y7uduHvc8MNXtvg9oW4XfYTG793DbJvv7GZkyv+X30j/93lq9Fmz2uE6klneLvDIb/VpnXyELGgRBEAQ1SnR3B0EQBEGNEk46CIIgCGqUcNJBEARBUKOEkw6CIAiCGiWcdBAEQRDUKOGkgyCoGJIWNHF9nSSd2JR1BkFTEk46CIJmiStVdSJJTQbBSkk46SAIKo6kfX2DkMclvSnpOqV9p8co7dW8lacbKKm/b77wuqQjPHwNSXd52omS9vPw3pIGS3oeGApcB+ztG3j81FvWI5X2CJ4gaY+MPcMkPSTpNUn35zb7kNRT0kuSJrt97SS1lNRP0ljfSOHsgicaBI1Mq7qTBEEQNIidgO1Ie/G+CdxhZrtKuhA4H7jI03Ui6R9vBbwgaWvgPMDMrIukzsAzkrb19LsAXc3sE0n7An3MLOfc1wIONLNFkrYhSTr28Hw7AzsAs4FRwJ6SxgCDgF5mNta1sT8HzgDmmVlPl7QcJekZS9u0BkGTEU46CILGYqwlLWwk/Qt4xsOnAvtl0v3V0m5Ob0h6k7TD0F7ATQBm9pqkt0kbjYDvA12kztbAzZK6AYszeQDGmNm/3Z5JpIeDecC7ZjbW6/rU4w8CukrK6UqvQ9JRDycdNCnhpIMgaCyye3cvyXxewrL/PfnaxHVpFS8sEfdT4H1SK74FsKiIPYsp/f8n4Hwze7pEmiBodGJMOgiCanOspBY+Tv0t0gYuI4GTALybe3MPz2c+0C7zeR1Sy3gJae/qlnXUPQPYWFJPr6udT0h7GjjXd+9C0raS2jT0BIOgoURLOgiCavN/pJ2Q1gbO8fHkW4HbJE0FvgZ6m9kXPtcryxRgsaTJwEDgVuBhSacAT1G61Y2ZfSmpF3CTpDVJ49EHAHeQusMn+ASzD0nbEgZBkxK7YAVBUDUkDQSetLz9kYMgSER3dxAEQRDUKNGSDoIgCIIaJVrSQRAEQVCjhJMOgiAIgholnHQQBEEQ1CjhpIMgCIKgRgknHQRBEAQ1yv8HI3XPCrcnNIsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "label = \"Random Forest\"\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf, performance_metrics, rf_confusion = model_predictions(rf, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "feature_importance_visualizer(X, rf, label, color=\"blueviolet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1B+41s8Vmtgi4Bxjgy+YWapA9/2vNrK+Z9e3UbuMyqxxCCKG5olGuQ5JOkTTVX1tUqZjFVco3hBBCmeL0dXU9CfxR0q9J+/obwLVNrWRmV5LpGUt6GDgFGOrvNwLGA3/wUdbvAocDl+dlNRYYLuki0unrbwJHNWcDPttjbX4yMS45hxBCa4iechWZ2QRgJDAd+DswA1hQRlYXABv5YK1pwCAzexX4GfAoMA2YZGZ/ySt/MjCc1IA/A1xvZlPK3JwQQghVJjOrdR3WaJI6m9kiSesBjwMneGPZJmy1bi87fdtRta5GCHWtVoEmQv2SNMnM+jZ3vegpV98jkuaSRl7fXahBltRH0gGVKEzSQEn3VyKvEEIIrSuuKWdIau8jmSuZ3+4lJO0D9AX+VqmyS1XpbQ4hhFC+Nt1TltRJ0ihJ0/x662APMXmxB9UYL2k7T9sg6REPvjFaUnefP1zSNZKeAS6WtK2kByRNkjRW0o5Fyl4k6VJJszy/rj7/MUmXSZoInC5pmKQzMst+4/V6QdIASWsD5wODfbT14AJl9ZP0lG/neEldJHXMBBSZImlQgfU2lnSfb/PTknr5/GGSbpH0JHBLRf4ZIYQQWqxNN8rA14D5ZtbbzHYBHvD5uUAbVwCX+bzLgZvMrBfpnt4/ZPLZCtjTzH5MGh19qpntBpwBXFWk7E7ARDPrAYwBzs0sW9vv8/1tgfVyveehwLlm9hFwDjDCzPqY2YhsYm+0RwCnm1lv0j3LH5JGY5tv5+HATZI65pV1HjDFt/l/gJszy3YG9jWzw/MrKOkESRMlTVy8/J0imx9CCKHS2nqjPAPYz3ufA8wsN7L59szfPXx6D+BPPn0LKbBGzp1mtlxSZ2BP4E5JU4E/ApsXKXsFqbEEuDUvvxGrJ//EPf53EtDQSLqcHYBXfSQ3Zva+n27u7+ViZs8Dc4HP563bH+8Jm9kjwCaS1vdlI83sw0IFRvCQEEKojTZ9TdnMXpC0K3AAcIGk0blF2WQlZJULpLEW8J6Z9ckulNSOleExRxZ5oES2nMYCcyz1v8spsv8lPQhsBkwEft9ozcsXwUNCCKHOtOmeske7+sDMbgUuAXb1RYMzf8f59FPAd336SFJgjVWY2fvAy5IO8/wlqbeZLfdTy30yDfJaQO6hEkcAT7RgUxYCXTL12N/L+j4wG9jc42jj15Pbe/2P9HmfB7p72qxsmoHAW76NIYQQ6lCb7ikDPYFLJK0APgZOBu4iBdqYTuqV5q6ZngrcKOknwJukBzUUciRwtaSzSQ+R+DMpOEe+xcDunu4NVh4IlONR4Gd+yvzX2evKZvaRD/66XNK6pOvJ+5KudV8taQawDBhiZkslZfMdBtzg++ID4JjmViwieoUQQutZ44KHSJoD9DWzt6pcziIz61zNMupBBA8JbUUE8Aj1JIKHrEH89q2ZJaQ5IvO+r6Q/NLZOCCGE+rbGNcpm1lDtXrKXU+tecgPpWjYAZjbRzE6rXXVCCCG01BrXKLcG76U+L+k2Sc9JukvSepK+4oE8Zki6QdI6nr5YQJPhkg7N5LuoSFljJU32156+6CJggAcc+ZEy4TWbCBpygwcxeUlSNOIhhFBHolEu3w7AVWa2E/A+8GPSE5kGe0CP9qSBZzmFApqU4g1gPzPblTSYLHeK+mfAWB+lfWneOo0FDdkR2B/YHThXUof8AiN4SAgh1EY0yuWbZ2ZP+vStwFeAl83sBZ93E7B3Jn2hgCal6ABc56Os7yRF4mpKY0FDRpnZUj/F/wbpfuhVRPCQEEKojbZ+S1Qt5Q9bfw/YpMT0uell+IGRpLWAtQus9yPgdaC3p11SRl2zlmamiwYwCSGE0Pqip1y+7pJyPd4jSNG3GnLXi4GjSDGxcwoFNJkD7ObT/0XqFefbgBRmc4Xn2c7nrxJwJE8EDQkhhDYoeknlmw2cIukG4FngNOBpUtzs9sAE4JpM+kIBTa4D/iJpGulhGoVCX14F3C3p6Lw004Hlvu5wYEpmnWG0MGhITgQPCSGE1rPGBQ9pDZIagPv9yVSlpJ9DKwQ0qYYIHrLmi6AbIVReBA9p4ySdL2lfn35MUl+fniNp0xLz2ELSXdWsZwghhOqJ09dlMLM5QEm9ZE/fUEKaQk+eahYzm8/Kh2SEEEJoY6KnXAOSfiFptqQnJN0u6Yz8QCIl5LGPBw6Z6gFLumTDc0q6PrP8TUnn+vyfSJrggUXOq9Y2hhBCaL7oKbcyfwTjt0m3OHUAJrPyWc3NcQZwipk9KakzebdK+WMfkbQ1aYDYcElfBbYnBQ4RMFLS3mb2eF4dTwBOANiww5ZlVC2EEEI5oqfc+vYC/mJmS8xsIfDXMvN5Evidh8rc0MyW5SeQ1JEUcORUM5sLfNVfU0gHAzuSGulVRPCQEEKojegptxGSTgF+4G8PMLOLJI0CDgCelLQ/qwcWuQa4x8z+kcuG9LzmP7ZKpUMIITRL9JRb35PAQZI6+mnnb5Sykpld6XGu+5jZfEnbmtkMM/sN6Z7oHbPpvRHvYmYXZWY/CBzn5SJpS0mfqchWhRBCaLHoKbcyM5sgaSQp+MfrwAxgQRlZDZU0CFgBzAL+DmyeWX4G8LGkqf7+GjO7RtJOwDhJAIuA75FiYBcUwUNCCKH1RPCQGpDU2cwWSVoPeBw4wcwm17pehfTt29cmTpxY62qEEEKbUm7wkOgpV5mkocC1ZvZBZva1knYGOgI3mdlkSY8BZ5jZREmLzKxzles1BHjI720u6rVZH3HJLvOqWZWKiwhVIYS2Khrl6htKerTjJ42ymR1RyQIktS80+roJQ4CZQKONcgghhNYTA70qSFInSaMkTZM00wN2bAE8KulRT3O1pImSZjUVvEPSppLGSTqwwLLhkq6R9AxwsaQ+kp72oCD3StrI060234OU9AVu8+Ai61Z8Z4QQQmi2aJQr62vAfDPr7Q+ruIzUEx1kZoM8zVl+naEXsI+kXoUykrQZMAo4x8yKPRFiK2BPM/sxcDPwUzPrRRo8dq6nWW2+md1FetTkkT6a+8O8sk/wA4eJi5e/U85+CCGEUIZolCtrBrCfpN9IGmBmhUZVf0fSZFIAjx7AzgXSdABGA2ea2cONlHenmS2XtAEpgEju+c03AXsXm9/URkTwkBBCqI1olCvIzF4AdiU1zhdIWuUhE5K2Id2q9BXvuY4iDfbKt4wUenP/zLoX5mJZZ9IVev5yCCGENioa5QqStAXwgZndClxCaqAXAl08yfqkhnSBn57+epGsDDgO2FHSTwHM7Kxc8JDVEqce+buSBviso4Axxeb7dLZeIYQQ6kCMvq6snsAlklYAHwMnA3sAD0iab2aDJE0BngfmkaJ7FeSnpQ8nPTRioZld1UTZxwDX+L3PLwHHNjF/uM//ENgj/7pyTgQPCSGE1hPBQ0KjInhICCE0XwQPaWMkNQD3+yjtctbvCxxtZqdVtGJ5Wit4SAT8CCGEaJTrmqR2Zra80DIzm0i6rSmEEMIaIgZ61VZ7SbdJek7SXZLWkzTHb6maDBwm6THvFeeCiczx6YGS7vfpfXIjsyVNkdTF5/9E0gQPHHKez8sPcDK4NpseQgghX/SUa2sH4Hgze1LSDcAPff7bZrYrgKSTSsjnDOAUz6czsETSV4Htgd1Jz1EeKWlvoCspwMmBnv8G+ZlJOgE4AWDDDlu2aANDCCGULnrKtTXPzHIjsG8F+vv0iGbm8yTwO0mnkYKFLAO+6q8pwGTS85a3p4QAJxE8JIQQaiN6yrWVP/Q99z4bFGQZKw+eCgUawcwukjQKOAB4UtL+pN7xr83sj/npJe3qaS+QNNrMzm/BNoQQQqiQ6CnXVndJe/j0EcATBdLMAXbz6UMLZSJpWzObYWa/ASaQesUPAsf56WwkbSnpM0UCnIQQQqgD0VOurdnAKX49+VngauDUvDT/C9zh13mLPZhiqKRBwApgFvB3M1sqaSdgnCSARcD3gO1YPcBJURE8JIQQWk8EDwmNiuAhIYTQfBE8pBVJGgpca2YfNJHuMeAMM5soaZGZdS6jrCFAXzP7f+XUtaWqFTwkgoWEEMLq4ppyeYYC69W6EiGEENYs0Sg3oUCwjXOBLYBHJT3qaa6WNFHSrFyQjkby21TSOEkHFlh2mJcxTdLjmUVbSHpA0ouSLs6kL1iuByC5WNIMSeMlbefzu0q62wOKTJC0Vwt3TwghhAqK09dN+xqrB9s4FhhkZm95mrPM7B1J7YDRknqZ2fT8jPxxjSOBs83s4QJlnQPsb2avSNowM78P8AVgKTBb0uVmNq+JcheYWU9JRwOXAd8Afg9camZPSOpOGqG9U4F6RvCQEEKogegpN63JYBvAdzws5hSgB7BzgTQdgNHAmUUaZEhBQIZL+gHQLjN/tJktMLMlpFHaW5dQ7u2Zv7nbrvYFrpA0lXRwsH7ulqmsCB4SQgi1ET3lJpjZC/nBNrLLJW1DCnPZz8zelTScwkE+lgGTgP2BMb7uhcCBXk4fMztJ0hd93iRJufuTl2byWU6Kmd1UuVZgei3gS964hxBCqDPRU25CkWAbC4EunmR9UgSuBX56+utFsjLgOGBHST8FMLOzvDHu42Vta2bPmNk5wJtAY0OUmyp3cObvOJ9+iMx90JL6NJJ/CCGEVhY95ab1ZPVgG3sAD0iab2aDJE0BngfmkU5BF2RmyyUdTno4xEIzuyovySWStieFyBwNTCNdTy6U17Qmyt1I0nRSL/twn3cacKXPbw88DjT6wIsIHhJCCK0ngoesgZQe79g3MxCtbBE8JIQQmi+Ch1SJpL8BR5jZe7WuSzHVDDBS6eAhETQkhBCKi0a5EUpBo79hZitqXZfmMLOG5qSX1M7MllepOiGEEEoUA73ySGqQNFvSzcBMYLkH/GiQ9Jyk6zxYx0OS1vV1+kmaLmmqpEskzSyS92N+a9V4SS9IGuDzh0i6IpPufkkDfXqR5zlL0j8k7e75vCTpvzLZd/P5L3qAk1xe3/Pypkr6o9/TnMv3t5KmsfKWqRBCCDUUjXJh2wNXmVkPYG7e/Ct9/nvAt33+jcCJPoq6qR5nezPbnRSq89wm0gJ0Ah7xMhcCFwD7Ad8Ess9B3t3r0ws4TFJfpadEDQb2ytTtyEy+z5hZbzNb5ZGRkk7wSGETFy9/p4QqhhBCqIQ4fV3YXDN7usD8l81sqk9PAho88lYXM8vddvQnUvSsYu7Jrl9CXT4CHvDpGcBSM/tY0oy89R82s7cBJN0D9CfdG70bMCGdiWdd4A1Pvxy4u1CBZnYtcC3AVuv2ipGAIYTQSqJRLmxxkfn5QTzWbSwTSTeSwmPON7MD8vJYzsr9v4xVz1pkg4B8bCuHyK/IrW9mKyRl/3/5jaeRbq26ycx+XqB6S+I6cggh1Jc4fd1CPip7oUfiAvhuZtmxHhzkgIIrrzQH6CNpLUndSKeim2s/SRv7de5DSPctjwYOlfQZAF++dSN5hBBCqKHoKVfG8cB1HmBkDFAoPnZjngReJsW1fg6YXEYdxpNOR28F3GpmEwEknQ08JGktUvCTU1j1OnmjInhICCG0nggeUgGSOpvZIp/+GbC5mZ1e42pVRAQPCSGE5ovgIbV1oKSfk/bnXGBIKStJagDuN7NdSkw/3NPflTe/L3C0mZ3mt1J9ZGZPNZFXSemaGzwkgoOEEEL5olGuADMbAYyoYfkTgVx3diCwCGi0sW1GuhBCCK0kBnrVXrv8gCSSfiBpgqRpku6WtF4m/b5+D/ELkr4BqdfrAUcaSA+Y+JEHCxkg6SBJz0ia4sFHNiuUrtW3OoQQwmqiUa69QgFJ7jGzfmbWmzTw6/hM+gbS6OwDgWskfXL7lJnNAa4BLvVR32OBJ0jPUP4C8GfgzCLpPhHBQ0IIoTbi9HXtrRaQBNhF0gXAhkBn4MFM+js8FveLkl4Cdmwi/62AEZI2B9YmjfJuVAQPCSGE2oiecu3lByRpDwwH/p+Z9QTOY9VgIoWChDTmcuAKz+vEvLxCCCHUkWiU61MX4FVJHVgZqzrnMA8ysi3wOWB23vKFvn7OBsArPn1MI+lCCCHUWJy+rk+/AJ4B3vS/2cbz36RAIesDJ5nZEo9rnfNX4C5JBwOnAsOAOyW9CzwCbFMoXf515ZwIHhJCCK0ngoeERkXwkBBCaL5yg4e02ulrSUPzbu0plu4xD4aBpEVVqEfF86xmvs0o/2/+xKqKygUPyb1CCCFUT2teUx4KNNko15O8pzDVJSVrmdkB/nCMEEIIbVRVGmVJnSSN8uAXMyWdC2wBPCrpUU9ztd8LO0vSeU3kt6mkcZIOLLBsM0n3elnTJO3p83/sZc+UNLTAepJ0iS+fIWmwzx8oaaykkaQHROSvd5+kSV7vE/KWXerzR0vq6vP6SHpa0nSv50aSdpQ0PrNeg9LzkZG0m6QxXsaDfitTfh0aJM2WdDMwE+gmaY7vpwZJz+UHJPH1+nk9pua2vbH9HkIIoXVVq6f8NdIzhHt7XOfLgPnAIDMb5GnO8vPtvYB9JPUqlJGkzYBRwDlmNqpAkj8AYzzQxq7ALEm7AccCXwS+BPxA0hfy1vsW0AfoDewLXJJpAHcFTjezzxco7zgz2w3oC5wmaROf3wmY6EFAxgDn+vybgZ+aWS9gBnCumT0PrC0pN+hqMOle4g6kW5gO9TJuAC4stF9IQUeuMrMeZpb/1KdCAUkAbgRONLM+pNuvCorgISGEUBvVapRnkJ7v+xtJA8ys0KMMvyNpMjAF6AHsXCBNB9Izgc80s4eLlPVl4GoAM1vuZfUH7jWzxf70pnuA/FCS/YHbfZ3XSQ1pP1823syKBdk4TdI04GmgG6kBBFjByvjXtwL9JW0AbGhmY3z+TcDePn0HqTHG/44AdgB2AR6WNBU4mxT8o5C5ZvZ0kWWrBSTx681dzGycz/9TkXUxs2vNrK+Z9e3UbuNiyUIIIVRYVa6ZmtkLknYFDgAukDQ6u9x7iGcA/czsXaWnHxUKarGM1KjsT2o0kXQhKcQk3uOrhsVeVjfSrUOQwlI+T+pV72FmH0h6rEi9oemgHiNItyrdA5iZvSipJzDLzPbIJixQjwdydSwiPyDJuk3UJYQQQh2o1jXlLYAPzOxW4BLS6eBssIr1SY3KAj89/fUiWRlwHLCjpJ8CmNlZHq+5j6cZDZzs5bbz3ulY4BBJ60nqBHzT52WNBQb7Ol1JPdjx2QRmNi9XlpldQwrE8a43yDuSTo3nrAUc6tNHAE94r/1drXzgw1H4wYWZ/YvUYP6ClT3s2UBXSXv49nSQ1KNAPZrNB4EtlPRFn/XdcvIJIYRQPdUaXdyTdI12BfAxqdHcA3hA0nwzGyRpCqnnOQ94slhGZrZc0uHASEkLzeyqvCSnA9dKOp7UyJ1sZuO8951rZK83syl5693rdZpGavzPNLPXvLEt5gHgJEnPkRrQ7OnjxcDuks4G3mDlqeljSA+OWA94iXStO2cE6aBlG9/WjyQdCvzBDy7ak67Hz2qkTs1xPHCd/1/GAIUuK6wigoeEEELrieAhnyKSOvs1diT9DNjczE5vbJ0IHhJCCM2nMoOH1P19uKF5JA0DFpnZ/xZYfKCkn5P+73OBIU3llwsekvOTmdFrDiGEaolG+VPEzEaw8vo1kAKkmNmyGlUphBBCRjwlqo0pFBRF0lmSXpD0BOm2qlza1QKX+PzHJF0maSLpmnwIIYQ6ED3lNiQvKIqAZySNJY2k7kP6f04m3UYGKXDJqWY2RtL5pIAmQ33Z2uVc7wghhFA90VNuWwoFRTnQ531gZu8DIwGaCFwCeaexsyKiVwgh1EY0yp9eRYOPRESvEEKojWiU25ZCQVFG+bx1JXUBDgJoLHBJCCGE+hTXlNsQM5tcICjKJEkjSEFQ3gAmZFZpLHBJSSJ4SAghtJ4IHhIaFcFDQgih+coNHlL26WtJQ70H1lS6xyT19elF5ZbXjHoN8OcIT809R7gKZcyRtGkz0g+RdEU16pIp43xJ+1Y63/zgISGEEKqnJdeUhwJNNsqtSVI74Ejg1/7whg9rXafWIKmdmZ1jZv+odV1CCCGUr6RGWVInSaMkTfOgFecCWwCPSnrU01ztt9HMknReE/ltKmmcpAMLLDvMy5gm6XGft0pPU9L9kgb69CJJv/VnHP8c+A7wS0m3SeosabSkyZJmSDo4k8fRHlRjmqRbfF5XSXdLmuCvvRrZjDM9z/GStit1fUkNkh7xskdL6q70pKqXlWwoabmkvT3945K2L5DPHKXnVU8GDpM03B9mkVt2Xma7d8zU72H/H10vaW5zevwhhBCqq9SBXl8D5pvZgfDJPbDHAoPM7C1Pc5aZveO91dGSepnZ9PyMlB7VOBI428weLlDWOcD+ZvaKpA1LqFsn4Bkz+2/PfzvgfjO7S1J74Jtm9r43Pk9LGgnsDJwN7Glmb0nK3ffze+BSM3tCUnfgQWCnIuUuMLOeko4mPcnpGyWufzlwk5ndJOk44A9mdoik2V6vbUgBQAZIegboZmYvFqnD22a2q2/31/KWvWVmu0r6IenZ1d8nBQ95xMx+7emPL7ZTQwghtL5SG+UZwG8l/YbU4I2VlJ/mO5JO8Dw3JzUw+Y1yB9Lzj0/JBLXI9yQwXNIdpOAYTVkO3F1kmYBfea9zBbAlsBnwZeDO3AGFmeUiZOwL7JzZtvWVebJSntszfy9tbP289fYAvuXTtwAX+/RYUnCPbYBfAz8g3cI0geKKBgBh5b6blCmvP+k2KszsAUnvFlrR/48nAGzYYctGigghhFBJJZ2+NrMXgF1JjfMFks7JLpe0Dak39hUz60W6d7ZjgayWkRqJ/TPrXuiDsqZ6WSeRerHdgEmSNvH1snXN5r3EzJYXqfqRQFdgNzPrA7xepF45awFf8uvRfcxsSzNbJOlBr+P1mbRWYLrg+o2Ul/U4MADYHfgbsCEwkNRYU6QORQOAAEv973KaeetbBA8JIYTaKPWa8hbAB2Z2K3AJqYFeCHTxJOuTGogFfnr660WyMuA4YEdJPwUws7NyjZiXta2ZPWNm5wBvkhrnOUAfSWtJ6kZquEqxAfCGmX0saRCwtc9/hHQddhMvM9fyPAScmtnuPl7H/b2O38/kPTjzd1xj6+d5ihSrGtJBw1ifHg/sCawwsyXAVOBEUmNdrA7N9STpmjuSvgps1IK8QgghVFipPaiewCWSVgAfAyeTTsM+IGm+mQ2SNAV4HphH+vEvyMyWSzocGClpoZldlZfkEh/YJNKp7mk+/2XgWeA50jXXUtwG/FXSDGCi1w8zmyXpQmCMpOXAFNKzhU8DrpQ0nbRvHgdOKpL3Rp5uKXC4zytl/VOBGyX9hHTQcazXaamkecDTnm6s5zujxG0txXnA7ZKOIh1IvEY6uCoqgoeEEELrieAhnyKS1gGWm9kySXsAV+fOUBQTwUNCCKH5VGbwkAiz+enSHbhD0lrAR6TBZCGEEOpEWcFD9CmO5tVI2Z9say2ohIheZvaimX3BzHqbWT8za2xkdwghhFZWbkSvoUQ0r7qhiOgVQghrhCYbZUU0r/w6nuPLZ0q6VqvesH2U99JnStrd028s6T4v72lJvXwU+RxlgqNIelHSZs2oR0T0CiGENUwp15QjmteqrjCz8728W0iRvP7qy9Yzsz5KwUpuAHYhjXie4lG7vgzc7Gn+QgrkcaOkLwJzzex1SX8qsR5QpYheygQP6d69e5GiQwghVFopp69nAPt5r2yAmS0okOY73mObAvQgNXz5ctG8zizSIMPKaF4/ANqVULdSonlNB/5BadG8rlAKYjKSwtG4AAZJekbpNqsvk7Y353bP83Fff0NSFK1bfP4jwCaS1idF48rd6/xdVkbnKrUeUHpErwaf7g/82evyAFAwolc2eEjXrl0bKSKEEEIlNdlTNrMXJO0KHECK5jU6u1wro3n1M7N3JQ2n6WheY3zdC4EDvZw+ZnaS9xoPJEXz2o3KRPP6WNKcIvXKyUXjWpK3fQ+SGvOJwP8DrgL6mtk8ScPy8sy/v6yx+83GAdtJ6gocAlxQaj0yAUSqEtErhBBCbZRyTTmiea2MpJVrgN/y3uuheWUO9nX7kx5YsYAUBORInz+QdFr5fUs3iN8L/A54zszebkY9yhURvUIIoY6V0oOKaF4r6/+epOuAmaRoWPm3FC3xfdGBdAACMAy4wfP9ADgmk36E5zEkM685UcWaq9kRvUIIIbSeiOj1KaKI6BVCCK1CZUb0Kvc+5bKoToKOtCRPSQ2SZjZznU9uV6oWSX8rYcR6d2CC0i1kfyAieoUQQl1p7QFAQ4FbSadx2wRJ7c1sWa3rUYwkkc54HNBUWjN7EfhC9WsVQgihHFXrKat1g45sJuleL2uapD19/o+97JmShhZYT5Iu8eUzJOUGag2UNNbva362QHXaKwUoeU7SXbnev6TdJI2RNEnp+cebFyjzK5KmeHk3SFpHUj9J9/jygyV9KGltSR0lvVQgjwZJsyXdTLq+3U0pYMimvuw5Sdf5fn1IHnLUy5muFODkkub2+EMIIVRXNU9f54KO9DazXYDLgPmkoCODPM1Zfs69F7CPpF6FMvJR3aOAc8xsVIEkfwDGmFlv0ujwWUq3Ux0LfBH4EvADSfm9xG8BfYDepPuDL8k0pLsCp5vZ5wuUtwNwlZntBLwP/FBSB+By4FAz240UPOTCvO3oCAwHBptZT9KZipNJg836eLIBpIa2n9f9mUL7BNje69DDzOYWWHalmfUA3gO+7fNvBE7068jFbiVD0gl+sDTxzTffLJYshBBChVWzUW7NoCNfBq6GNMLby+oP3Gtmi81sESmYxoC89foDt/s6r5Pun+7ny8ab2ctFyptnZrlR5rd6PjuQIng9rBT442xgq7z1dgBeNrMX/P1NwN5+evxfknYi3fL1O2Bvr+/YInWYa2ZPF1n2splN9elJQINfb+5iZuN8/p+KrBvBQ0IIoUaq1ih7w7MrqXG+QNI52eVaGXTkK2bWi9QTbiroSG7dC/0U7NQqVR88MIekbrmyJOVuTSoUJETArNx912bW08y+2ozyHifd4/0xKQJZf3+NLVKHUgKHQAQPCSGENqOa15RbLegIqSd9spfbTik+91jgEEnrSepEijOd3+scCwz2dbqSeqfjVyncbF6mob3GZ3dXuqUI4AjgCWA20DU3X1IHSdkQnHiaBqUY3QBH4dHNvC5DgXFm9iawCalnPbNIHZrFzN4DFipFTIMU2jOEEEIdqWYPqjWDjpwOXCvpeFLP8GQzG6cU8jPXyF5vZlPy1rvX6zSN1PifaWavyZ+q1IjZwCmSbiANBLvazD5Suu3pD35Q0J50HX1WZjuWSDoWuFPpgRkTgFwj+wwpjObj/n468Fmr7I3kxwPX+f9kDFDokkIIIYQaieAhnyKSOvv1dST9DNjczE5vbJ0IHhJCCM2nthA8JBQn6Sn/WzQ4idKzpbdoQTEH+nXpmaRBZBc0tUIIIYTWEwOA6oSZ7VlCsiGk26Xml1nGCBp/3GMIIYQaip5ylUi6SNIpmffDJJ0h6SeSJngQj/MyyxsN/enXq/sCt3lv90BJ92WW7yfp3lxeki714CGjfRAbkraV9IAHNxlbwrXzEEIIrSga5eoZgT8m0X2H9DjK7Un3IvcBdpO0dymZmdldpKddHemjzv9GGpGeu5H4WFLAEoBOpOcu9yAN6DrX518LnOrBTc4gPRt6NRE8JIQQaiMa5Srxkd6fkbSFpN7Au6QR6V8lBUuZDOxIaqTLyd+AW4DveWCQPYC/++IVrDxNfSvQX+n5z3uSRn5PBf4IrBYG1POO4CEhhFADcU25uu4EDgU+S2oktwZ+bWZ/LGVlSTeSHiAxv8gDJ24E/gosAe5s5MEZRjoAe6+pRzWGEEKonegpV9cIUpCOQ0kN9IPAcd5rRdKWkj5TbGUzO9YDhuQa5GzwFcxsPmnQ19mkBjpnLS8TPLiJmb0PvCzpMC9b3oMPIYRQJ6JRriIzm0VqRF8xs1fN7CFSzOlxkmYAd5FpZEswHLjGB3qt6/NuI8Xifi6TbjGwu9/69GXgfJ9/JHC80vOUZwEHl7lpIYQQqiCCh7Rxkq4AppjZ/2XmLTKzzpXIP4KHhBBC80XwkDaqlKAhjaw7ifTYy1sz8xqAdYutE0IIoX7FQK8aKzFoSLF1dyuy6Lki81cjqX0jA8RCCCG0ougpV1ilg4Z4mu0k/UPSNEmTPQiIJF0iaaakGZIGF1ivo6QbffkUSYN8/hBJIyU9QnrCVgghhDoQjXLlVTRoiLsNuNLMepPuNX4V+Jbn1RvYl/RErvz7jk8h3dLcEzgcuElS7pnVuwKHmtk++YVF8JAQQqiNaJQrrNJBQyR1AbY0s3s9/yVm9gHQH7jdzJab2eukyF398lbvj19vNrPngbnA533Zw2b2TpFtiOAhIYRQA3FNuToqFjQEWO20dIUsrlK+IYQQyhQ95eqoWNAQM1sI/EfSIb7uOpLWA8YCgyW18/jXewPj87IaS7o3GUmfB7oDsyu4nSGEECooespVYGaz/LTzK2b2KvCqpJ1IQUMAFgHfA94oMcujgD9KOh/4GDgMuJcU73oaKYzmmWb2mt8SlXMVcLUHKlkGDDGzpV6HEEIIdSaCh4RGRfCQEEJovgge0soknS9pX59+TFJfn54jadNWrktfSX9ozTJDCCFUXpy+LpOZnVPrOuSY2UTSs5ZDCCG0YdFTLoGkX0iaLekJSbd7MJDhkg5teu1P8tjHHyQx1QN5dJE0UNLjkkZ5/tdIWsvTX+33Cs/KCzbST9JTHkhkfCaf+335MEk3eO/9JUmnNbYdldxPIYQQWiZ6yk2Q1A/4NilIRwfSfcaTysjqDOAUM3vSR2Ev8fm7AzuT7iF+gBQU5C7gLDN7R1I7YLSkXsDzpJHdg81sgqT1gQ8LlLUjMIj0BKrZkq4mBRopaTsknQCcANC9e/cyNjWEEEI5oqfctL2Av3jQjoXAX8vM50ngd95z3TATb3q8mb1kZsuB20kBPwC+I2kyKeBID1LDvQPwqplNADCz94vErR5lZkvN7C3SCO/NmrMdETwkhBBqIxrlKpF0SuZ09RZmdhHwfdITnJ6UtKMnzR/+bpK2IfWsv2JmvYBRQEdKtzQzvZw4IxJCCG1CNMpNexI4yB/u0Bn4RikrmdmVHgCkj5nNl7Stmc0ws98AE0inmAF2l7SNX0seDDwBrE+KuLVA0mbA1z3tbGBzP6WOX08utcEtaztCCCG0nuhBNcGv3Y4EpgOvAzOABWVkNdSf0rQCmAX8nRT8YwJwBbAd8Chwr5mtkDSFdA15HqlBxcw+8qdBXS5pXdL15H1beTtCCCFUSQQPKYGkzma2yMNbPg6cYGaTK5DvQOAMM2uVXms52xHBQ0IIofkieEgJWhDw41pJU0kjlu8GVkg6IJPvQEl7tqBeTT5TuUJy2/E8MKESBxYhhBAq51N1+rrcgB9mdkT2vaQhQF/gbz5rICme9VPNzPcx4LHWapRz2yFpGKm+IYQQ6sga2yhL+gXpoQ9vkq7LTgJ2Ae43s7tKzGN34Pekkc8fAscCLwPnA+tK6k+6jekkYLmk7wGnAhsCZwNrA28DR5rZ6z7A6nJSg27AeWZ2t5d1IWnw1YfAwZ5+uL//AvAZ4DjgaNK16GfMbIiv+1XgPGAd4F/AsX6aeg5wE3AQ6d7kw0j3R69SXzMbW+p+DSGEUD1r5OnrvIAfXyc1guV4HhhgZl8AzgF+ZWYf+fQIH1n9G+Aa4FJ/P5Y0gvpLvt6fgTM9v18AC8ysp9/q9IjP7wQ8bWa9Sdd6f5Cpw0akRvhHwEjgUtJ9yz0l9fHT7mcD+5rZrqRwmz/OrP+Wz7+adP16ToH6rkLSCR5NbOKbb75Z5q4LIYTQXGtqT/mTQBnAEknlBvzYALhJ0vaknm2HEtfbChghaXNSb/lln78v6TnLAJjZuz75EXC/T08C9svk9VczM3/84utmNgNA0iygwcvamXTvM17euMz692Ty/VYplTeza4FrIQ30KmWdEEIILbdG9pTLlR/wA/gl8KiZ7UI6BVxqAI/LgSvMrCdwYgnrfWwrh8HnB/vIBQJZwapBQVZ4OgEPZ+6J3tnMji+wfgQRCSGEOremNsoVCfhB6im/4ouHZJIuJMWVLvY+u94xmfkPA6fk3kjaqJR6NeFpYC9J23menSR9vol18usbQgihDqyRjbLHhs4Fyvg75QfKuBj4tQfyyPYyHwV29h71YFIc6W/6+wHAMOBOSZOAtzLrXQBsJGmmpGmkh0a0iJm9STpguF3SdNKp6x0bXWn1+oYQQqgDa2zwkGoF/Pi0ieAhIYTQfBE8ZHWrBPyoZYMs6W+SNqxQXkP8encIIYQ1zBo78Cc/4EetKA2J/oaZrahQlkOAmcD8CuUXQgihTqzJPeWakdQgabakm0kN6HJJm/r85yRdJ2mWpIf8wRJI6idpul/nvUTSzAL5Hkq65/o2T3egpPsyy/eTdK9PL5J0qZczWlJXn7+tpAckTZI0VisfIRlCCKHGolGunu2Bq8ysBzA3b/6VPv89UpATgBuBE82sD+n2pdV4JLKJpAhhfUhhPnfMNbikiGM3+HQnYKKXMwY41+dfS4ritRvpmc1X5ZcTwUNCCKE2olGunrlm9nSB+S+b2VSfngQ0+PXmLmaWC/rxp1IK8HubbwG+53nsQRptDuk+5hE+fSvQ328P25M0Mnwq8Edg8wL5Xmtmfc2sb9euXfMXhxBCqJI19ppyHVhcZH42AMhyYN3GMpF0Iyn29XwzO6BAkhtJtzgtAe40s2VFsjLSQdh73ssOIYRQZ6KnXAfM7D1goaQv+qxsKM5jPZhJrkFeJfCHBzmZT4p/fWMm27WAQ336COAJM3sfeFnSYZAGoUnqXYVNCiGEUIZolOvH8cB1flq5E8WDnQwHrvGBXrle9m3APDN7LpNuMbC7Dxj7MunJVgBHAsd78JJZwMEV3YoQQghlW2ODh7Q1uWAnPv0zYHMzO73Eda8AppjZ/2XmLTKzzi2tVwQPCSGE5is3eEhcU64fB0r6Oel/MpdVY20X5aE8FwP/nZm3qBoVDCGEUF3RKNcJMxvBytHSzVlvtyLzS+olS2rfyOCwEEIIrSiuKVeBpKM9EMg0SbdIOkjSM5KmSPqHpM083TBfPk7Si5J+UCS/zSTd6/lNk7Snz/+xP9xipqShBdZTLhCJpBn+8AwkDfTAISOBZ6u3J0IIITRH9JQrTFIP0kjoPc3sLUkbk25H+pKZmaTvA2ey8nRzL+BLpMFdUySN8hHVWX8AxpjZNyW1AzpL2o0ULOSLpGcqPyNpjJlNyaz3LaAP0BvYFJgg6XFftiuwi5m9XGAbTgBOAOjevXtLdkcIIYRmiJ5y5X2ZdL/wWwBm9g6wFfCgpBnAT4AemfR/MbMPPf2jwO5F8rza81tuZguA/sC9ZrbYB4jdA+Q/hrE/cLuv8zopslc/Xza+UIPsZUTwkBBCqIFolFvH5cAVZtYTOBHomFmWP/zdJF3otzxNrWKdigU3CSGEUCPRKFfeI8BhkjYB8NPXGwCv+PJj8tIfLKmjpx8ITDCzszxgSB9PMxo42fNrJ2kDYCxwiKT1JHUCvunzssYCg32drsDewPgKbmsIIYQKimvKFWZmsyRdCIyRtByYAgwjxZt+l9Rob5NZZTrptPWmwC8LXE8GOJ30fOjjSaE5TzazcZKGs7KRvT7vejLAvaR42NNIPfIzzey1eDJUCCHUpwgeUkOShgGLzOx/a12XYiJ4SAghNF+5wUPi9HUbI2mAPyM5G2YzhBDCGiBOX9eQmQ1rTnq/HepI4NdmdmtVKhVCCKFmoqfchCoEAjnMg3lMy90zLGmIx6/Opblf0kCfXiTpt/4AiZ8D3wF+Kek2SZ0ljZY02YODHJzJY5V6+7yuku6WNMFfe1Vpt4UQQihD9JQbUaVAIOcA+5vZK5I2LKEanYBnzOy/vU7bAfeb2V2S2gPfNLP3JW0KPO1RunYuUG+A3wOXmtkTkroDDwI7FdjuCB4SQgg1EI1y41YLBCKpJzBC0ubA2kA2AMdfzOxD4ENJuUAg9+Xl+SQwXNIdpIAfTVkO3F1kmYBfSdobWAFsCWxWqN6efl9gZ0m59ddX5ulUOWZ2LXAtpIFeJdQxhBBCBUSj3HyXA78zs5F+inlYZlnBQCDAgQB+7/FJkr7o8yZ5uMxlrHopIRtcZImZLS9SlyOBrsBuZvaxpDl56+Zbi9TLX9JImhBCCDUS15QbV/FAIJK2NbNnzOwc4E2gGzAH6CNpLUndKBxqs5ANgDe8QR4EbN1IvQEeAk7NrSypT4nlhBBCaAXRU25ElQKBXCJpe9Kp59GkwB6QToM/CzwHTC6xircBf/WY2hOB5xup9xDgNOBKSdNJ//vHgZNKLCuEEEKVRfCQCmkLgUDKEcFDQgih+SJ4yBoq/3apEEIIa644fV0hzQ0EEkIIIeSLnnILtEZgEbeFpAd83Ysz6a+WNNHDbp6XmT9H0sUeUGS839scwUNCCKHORU+5TK0cWKQP8AVgKTBb0uVmNg84y++dbgeMltTLzKb7OgvMrKeko4HLgG8QwUNCCKGuRU+5fIUCdGwFPOijoX8C9Mik/4uZfejpc4FF8uUCi/wAaJeZP9rMFvj9xc+y8tan70iaTBpd3YMUySvn9szfPXx6X+AKSVOBkXjwkPxKmNm1ZtbXzPp27dq1lH0RQgihAqJRrqzLgSvMrCdwIqsG8igYWETpaU9TAczsJFLvuxspsMgmnnZpZr3lQHtJ2wBnAF8xs17AqEbKy03ngof08deW+dG8Qggh1E40yuVrrcAixawPLAYW+LXrr+ctH5z5O86nI3hICCHUsbimXKZWDCzSp0j50yRNIQUMmUc69Z21kQcJWQoc7vMieEgIIdSxCB7SClo7sIjHwO6bu97dEhE8JIQQmi+Ch4QQQghtXJy+bgWtHVjEzBpas7wQQgiVET3lEEIIoU5EoxxCCCHUiWiUQwghhDoRjXIIIYRQJ6JRDiGEEOpENMohhBBCnYjgIaFRkhYCs2tdjxbYFGhxEJUaibrXRluuO7Tt+q9Jdd/azJr9RJ+4Tzk0ZXY5UWnqhaSJbbX+UffaaMt1h7Zd/6h7nL4OIYQQ6kY0yiGEEEKdiEY5NOXaWleghdpy/aPutdGW6w5tu/6f+rrHQK8QQgihTkRPOYQQQqgT0SiHEEIIdSIa5U8xSV+TNFvSPyX9rMDydSSN8OXPSGrILPu5z58taf9WrTjl111Sg6QPJU311zV1WPe9JU2WtEzSoXnLjpH0or+Oab1ar1KHltR/eWbfj2y9Wn9SflN1/7GkZyVNlzRa0taZZTXd9y2se033u9ehqfqfJGmG1/EJSTtnltX7703Bupf1e2Nm8foUvoB2wL+AzwFrA9OAnfPS/BC4xqe/C4zw6Z09/TrANp5PuzZS9wZgZp3v9wagF3AzcGhm/sbAS/53I5/eqK3U35ctqvN9PwhYz6dPznxuarrvW1L3Wu/3ZtR//cz0fwEP+HRb+L0pVvdm/95ET/nTa3fgn2b2kpl9BPwZODgvzcHATT59F/AVSfL5fzazpWb2MvBPz6+1tKTutdZk3c1sjplNB1bkrbs/8LCZvWNm7wIPA19rjUpntKT+tVZK3R81sw/87dPAVj5d633fkrrXg1Lq/37mbScgNwq57n9vGql7s0Wj/Om1JTAv8/4/Pq9gGjNbBiwANilx3WpqSd0BtpE0RdIYSQOqXdli9XLN2Xe13u+VqENHSRMlPS3pkIrWrGnNrfvxwN/LXLfSWlJ3qO1+hxLrL+kUSf8CLgZOa866VdSSukMzf28izGb4tHkV6G5mb0vaDbhPUo+8I91QPVub2SuSPgc8ImmGmf2r1pXKJ+l7QF9gn1rXpbmK1L1N7HczuxK4UtIRwNlATcZNlKNI3Zv9exM95U+vV4Bumfdb+byCaSS1BzYA3i5x3Woqu+5+CuxtADObRLpW9Pmq17hAvVxz9l2t93uL62Bmr/jfl4DHgC9UsnJNKKnukvYFzgL+y8yWNmfdKmpJ3Wu936H5++/PwCFlrltpZde9rN+b1rpYHq/6epHOkrxEGjiRG7zQIy/NKaw6WOoOn+7BqgMvXqJ1B160pO5dc3UlDdx4Bdi4nuqeSTuc1Qd6vUwaaLSRT7da3StQ/42AdXx6U+BF8gbM1LrupMbqX8D2efNruu9bWPea7vdm1H/7zPRBwESfbgu/N8Xq3uzfm1b7p8Sr/l7AAcAL/kU+y+edTzrKBugI3EkaWDEe+Fxm3bN8vdnA19tK3YFvA7OAqcBk4KA6rHs/0nWrxaQzE7My6x7n2/RP4Ng6/dwUrD+wJzDDf9RmAMfXYd3/Abzun4+pwMh62ffl1r0e9nuJ9f995rv5KJmGrw383hSsezm/NxFmM4QQQqgTcU05hBBCqBPRKIcQQgh1IhrlEEIIoU5EoxxCCCHUiWiUQwghhDoRjXIIoWIkLWrl8ho8glIIa4RolEMIbZJHamsAolEOa4xolEMIFSdpoAfg/4uklyRdJOlISeP9ubPberrhkq7xhyW8IOkbPr+jpBs97RRJg3z+EEkjJT0CjAYuAgb4s2p/5D3nsUrPc54sac9MfR6TdJek5yXdlntqmKR+kp6SNM3r10VSO0mXSJrgzyc+sSY7MnzqxAMpQgjV0hvYCXiHFKbwejPbXdLpwKnAUE/XQHo83rbAo5K2I4VJNTPrKWlH4CFJuZjBuwK9zOwdSQOBM8ws15ivB+xnZkskbQ/cTno4A6QwlD2A+cCTwF6SxgMjgMFmNkHS+sCHpKcsLTCzfpLWAZ6U9JClRweGUDXRKIcQqmWCmb0K4I+0e8jnzwAGZdLdYWYrgBclvQTsCPQHLgcws+clzWVlIP+HzeydImV2AK6Q1AdYzqrB/8eb2X+8PlNJBwMLgFfNbIKX9b4v/yrQS9Khvu4GwPakmNchVE00yiGEalmamV6Reb+CVX978mP9NhX7d3Ejy35Eiv/cm3R5bkmR+iyn8d8/Aaea2YNN1CWEiopryiGEWjtM0lp+nflzpIcOjAWOBPDT1t19fr6FQJfM+w1IPd8VwFFAuybKng1sLqmfl9XFB5A9CJwsqUOuDpI6lbuBIZQqesohhFr7N+lJXusDJ/n14KuAqyXNAJYBQ8xsqY/NypoOLJc0jfSoyKuAuyUdDTxA471qzOwjSYOByyWtS7qevC9wPen09mQfEPYmK5/vG0LVxFOiQgg1I2k4cL+Z3VXruoRQD+L0dQghhFAnoqccQggh1InoKYcQQgh1IhrlEEIIoU5EoxxCCCHUiWiUQwghhDoRjXIIIYRQJ/4/aFYH/I+HOGwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "label = \"Decision Tree\"\n",
    "dt = tree.DecisionTreeClassifier()\n",
    "dt, performance_metrics, dt_confusion = model_predictions(dt, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "feature_importance_visualizer(X, dt, label, color=\"blueviolet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# also plotting decision tree\n",
    "tree.plot_tree(dt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "label = \"Logistic Regression\"\n",
    "lr = LogisticRegression()\n",
    "lr, performance_metrics, lr_confusion = model_predictions(lr, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "feature_importance_visualizer_for_nonstandards(X, lr, label, color=\"blueviolet\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Support Vector Classifier\n",
    "label = \"SVC\"\n",
    "svc = SVC(kernel='linear')\n",
    "svc, performance_metrics, svc_confusion = model_predictions(svc, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "feature_importance_visualizer_for_nonstandards(X, svc, label, color=\"blueviolet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K Neighbours\n",
    "label = \"KNN\"\n",
    "knn = KNeighborsClassifier()\n",
    "knn, performance_metrics, knn_confusion = model_predictions(knn, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "# feature importance function not defined for KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WNNFfu/im3wC7+Qjtn0jqL+l+32cdSfd4oJFnJPX09UO8Xk9IekXSKdU+VyGEEMoXjXLrfQ24wsy2Bj4EfkoaVDXIg3y0B07MpM8F/7gM+GMLynkL2NPMegODgNwl6l8Ao82sl5ldkrfPecAkM+sJ/D/gpsy2rYC9gR2BcyV1yC9Q0nGSxksav2Dxey2oagghhOURjXLrzTazMb58C/At4FUze8nX3UjTYB63ZX7u3IJyOgDXSpoK3A5sU8Y+/YCbAczsMWBdSWv4tgfM7BMze4fU4K+Xv3MEDwkhhPqIe8qtlz9s/QNg3TLT55YX4V+MJK0ErFxgv58Ab5JCda4ELGxFXbM+ySwvJv4GQgihYURPufU2kZTr8R5KGmTVLXe/mBSkY1Qm/aDMz6d9eSYpdjXAf5F6xfnWBF73WZwOB9r5+nlA5yJ1Gw0cBiCpP/COmX1YzkGFEEKon+gltd4M4CRJ1wPPA6cAzwC3S2oPjAOuyqRfW9JzpJ7qIb7uWuBeDwjyIIVDX14B3CnpiLw0zwGLfd9hQDYoyBDgei/vI+DI1h5kRPQKIYTaieAhrSCpG3C/mW1bZvqZQB+/j9umfBGDh0RwihDC8orgIQ1K0glAx2bS9JK0T4XK+/zRqBBCCG1LNMoZftm5WWY2s5xesqT2ZnaVmXVtppfcC6hIo9xS5R5zCCGE6mvTjbKkjpIekDTFJ2sYVCJQRzdJj3lAjZGSNvH1wyRdJelZ4LeSNpf0oKQJHrRjqyJlz5d0iaTpnl9XX/+EpD9KGg+c6gE7Ts9su9jr9ZKk3SStDJwPDPJAIIMKlNVX0j/8OMdK6ixpVUk3+HFOkjSgwH6lgojcLGkM/uhUCCGE+mvTjTLwbWCOmW3nPdcHfX2hQB2XAjd6QI1bWRqEA2AjYBcz+ylpasWTzWwH4HTSQKtCOgLjzaw7aZT1uZltK/tzvr8vsF97M9uRNA3juWb2KXAOMNwDgQzPJvZGezhwqpltB+wBfEyaytH8OA8BbpS0al5ZpYKIbAPsYWaH5O0TwUNCCKFO2nqjPBXY03ufu5lZblrEQoE6dsbjRJN6h/0y+dxuZosldQJ2IY2gngxcDaxfpOwlpMYSUvCQbH7Dl03+ubv85wRS/OrmfI30SNQ4ADP70MwWeXm3+LoXgVnAV/P2LRVEZISZfVyowAgeEkII9dGm7yea2UuSepPux14gaWRuUzZZGVnlHjNaCfjAzHplN0pqR2pEITVm5xSqToH8CskF7ygauEPSQ6RIW+OBP5WseeuVqmMIIYQ6aNM9ZUkbAB+Z2S3AUKC3byoUqOMfwA98+TBSgI0mPMDGq5IO8vwlaTszW+yXlntlGuSVgNxkEocCTy3HoTQJBGJme3tZx5Keh15fUl+vU2cfnJUNEPJVYBNPmxVBREIIoQ1p0z1loAcwVNIS4DPSBBB3UDhQx8nADZLOAN4GjiqS52HAlZLOJkXY+iswpUC6BcCOnu4tln4RaI3HgV/4JfOLsveVzexTH/x1qaTVSPeT9yDd677SY2IvAgab2SeSsvkOYTmDiETwkBBCqJ0VLnhIrQJ1SJpvZp2qWUYj+KIFD4nAISGESojgIW2cpPMl7eHLT0jq48szJXUpM48NJN1RzXqGEEKonrZ++XoZZtatRuVUtJdcZPBYS/OYw9L73CGEENqY6CnXgaRfSZoh6SlJt0k63YOYlN2gSvqGBxuZ7MFDOnuAlGm+/brM9rclnevrz5A0zgOKnFetYwwhhNByK1xPudH5KOoDSPMjdwAmsvRxq5Y4HTjJzMb489VN5ln2kdtI2pQUVGWYpL2ALYEdAQEjJO1uZk/m1fE44DiAtTps2IqqhRBCaI3oKdfersC9ZrbQzOYB97UynzHAHySdAqzlAUWa8Ahft5MilM0C9vLXJNKXga1IjXQTETwkhBDqI3rKbYSkk4Af+dt9zOw3kh4gBU4ZI2lv8nrLpPmc7zKzR3PZkB65uromlQ4hhNAi0VOuvTHAvj6hRCfgu+XsZGaXZwKYzJG0uZlNNbOLgXGkXu/nvBHvbGa/yax+CDjay0XShpK+VJGjCiGEsNyip1xjZjZO0gjgOeBNUvzuuaX3Kug0nxlqCTAd+DtN43SfDnzmAUkArjKzqyRtDTztQUbmAz8kBT8pKIKHhBBC7axwwUPaAkmdzGy+pNWBJ4HjzGxivetVSJ8+fWz8+PH1rkYIIbQprQ0eEj3lVpB0GnCNmX3UTLongNPNbHxeBLBrJG0DrEqaTrJogyxpMClC2X9XpPIt9Mb0Txm67ex6FF1zEc0rhFBv0Si3zmmkaRNLNsrFmNmhFa1NCCGEFUIM9GqGpI6SHpA0RdI0D8KxAfC4pMc9zZWSxkua3lxADkldJD0taWCBbQd5GVMkZZ8d3kDSg5JelvTbTPqC5Xpozt9KmipprKQtfH1XSXd68JBxknZdztMTQgihgqKn3LxvA3PMbCCApDVJM0wNyEx6cZaZvefzLo+U1NPMnsvPSNJ6wAjgbDN7pEBZ5wB7m9lrktbKrO8FbE+a9WqGpEvNbHYz5c41sx6SjgD+SBrl/SfgEjN7StImpNHYWxeoZwQPCSGEOoiecvOmAntKuljSbmZWaKT0wZImkoJydAe2KZCmAzASOLNIgwzpcalhkn4EtMusH2lmc81sIfA8sGkZ5d6W+bmzL+8BXOYjskcAa+Qej8qK4CEhhFAf0VNuhpm9JKk3KUjHBZJGZrdL2oz0+FFfM3tf0jDSAK58i0jhNPcGRvm+FwIDvZxeZnaCpK/7ugmSdvB9P8nksxhoX0a5VmB5JWAnb9xDCCE0mOgpN0PSBsBHZnYLMBToDcwDOnuSNYAFwFy/PP2dIlkZcDSwlaSfA5jZWbmAIF7W5mb2rM8Y9TZQajhwc+UOyvx82pcfBk7OHFuvEvmHEEKosegpN68HMFTSEuAz4ETS5eAHJc0xswGSJgEvArNJl6ALMrPFkg4hTQQxz8yuyEsyVNKWpHCYI4EppPvJhfKa0ky5a0t6jtTLPsTXnQJc7uvbk56RPqHUwUfwkBBCqJ0IHrICkjST9GzzO82lbU4EDwkhhJZr+OAhFQi4Ual6VDzPaubbgvL/DzjUzD6oZL5tIXhIBP0IIawoanlP+TRg9RqWt9wkNfzlfSUrmdk+uQbZzLpVopccQgihtqrSKNc44MZ6ku72sqZI2sXX/9TLnua99Pz9JGmob58qaZCv7y9ptNKkEc8X2O8eSRO83sflbbvE14+U1NXX9ZL0jKTnvJ5rS9pK0tjMft0kTfXlHSSN8jIekrQ+eTz9DEk3AdOAjZUChnTxbS9Iutbr8rCk1Xy/vl6PybljL3XeQwgh1Fa1esq5gBvbmdm2pOAVc0gBNwZ4mrP8entP4BuSehbKyEcWPwCcY2YPFEjyZ2CUmW1HGhk93R8lOgr4OrAT8CNJ2+ft933SIKrtSM/vDs00gL2BU83sqwXKO9rMdgD6AKdIWtfXdwTGm1l30iNP5/r6m4Cfm1lP0jPP55rZi8DK/lgTpBHSwyV1AC4FDvQyrgcuLHRegC2BK8ysu5nNKrDtcq/LB8ABvv4G4Hgf7b24SL5IOs6/MI1fsPi9YslCCCFUWLUa5VoG3PgmcCWk0c1eVj/gbjNbYGbzgbuA3fL26wfc5vu8SWpI+/q2sWb2apHyTpE0BXiG9MjSlr5+CTDcl28B+ilF/1rLzEb5+huB3X35bzR9bGk48DVgW+ARpQAfZwMbFanHLDN7psi2V81ssi9PALopRQjrbGa5x6P+UmTfCB4SQgh1UpV7prUMuFGN+pOe/0XSxsB9vu4q0uNHewA7m9lHPiitUL2hafCOQoYDt0u6CzAze1lSD2C6me2cTVigHg/m6lhEfrCR1ZqpSwghhAZQrXvKNQu4QepJn+jltvPe6Whgf0mrS+oIfM/XZY0GBvk+XUk92LHZBGY2O1eWmV0FrAm87w3yVqRL4zkrAQf68qHAU95rf19Srpd+OP7lwsz+RWowf8XSHvYMoKuknf14OkjqXqAeLeaDwOYpRQwD+EFr8gkhhFA91RpdXMuAG6eS5ic+htTInWhmT3vvO9fIXmdmk/L2u9vrNIXU+J9pZm94Y1vMg8AJkl4gNaDZy8cLgB0lnQ28xdJL00cCV0laHXiFdK87ZzjpS8tmfqyfSjoQ+LN/uWhPuh8/vUSdWuIY4Fr/vYwCCt1WaCKCh4QQQu1E8JAvEEmd/B47kn4BrG9mp5baJ4KHhBBCy6nRg4eEpiR1A+730emt2b8PcISZndKC3QZK+iXp9z4LGNzcDo0cPCSChoQQVjTRKDcwSe3MrOCjS2Y2HmhRF9bMhrP0/nUIIYQGE7NE1Vd7Sbd6sI87fGDaTH+UbCJwkKQnvFecC6Iy05f7S7rfl7/hAUEmS5okqbOvP0PSOA8Ycp6vyw/sMqhw1UIIIdRa9JTr62vAMWY2RtL1wI99/btm1htAUslZnNzpwEmeTydgoaS9SM9Q70iadWqEpN2BrqTALgM9/zXzM1OKVHYcwFodNlyuAwwhhFC+6CnX12wzy408v4UU0ARafol5DPAHSaeQgpUsAvby1yRgIrAVqZFuNrBLBA8JIYT6iJ5yfeUPfc+9zwYGWcTSL08FA5WY2W8kPUAK1jJG0t6k3vFFZnZ1fvr8wC5mdv5yHEMIIYQKiZ5yfW2SCxSCBxwpkGYmsIMvH1hgO5I2N7OpZnYxMI7UK34IONovZyNpQ0lfKhLYJYQQQgOInnJ9zQBO8vvJz5NieJ+cl+Z3wN/8Pm+hCTkATpM0gBR/ezrwdzP7RNLWwNOSAOYDPwS2YNnALkVF8JAQQqidCB4SSorgISGE0HIRPCQAIGkIMN/MfleJ/CodPCQCfoQQQnFxT/kLTlJ8MQshhAYRjXIbI+mnHvRjmqTTfN1Zkl6S9BTp2edc2l6SnvHgIXdLWtvXPyHpj5LGkyb0CCGE0ACil9SGSNqBNMvU10mPPD0raTRpGsZepN/nRNIc1AA3ASeb2ShJ5wPnAqf5tpWL3e+I4CEhhFAf0VNuW/oBd5vZAp/t6S5goK/7yMw+BEbA55G61jKzUb7vjaQ5o3OKBiiJ4CEhhFAf0Sh/cS1oPkkIIYRaika5bRkN7O8TV3QEvkd6dnl/Sav5RBT7Anj4zPcl7eb7Hg6MKpRpCCGExhD3lNsQM5soaRgw1lddZ2YTJA0HpgBvkSJ65RwJXCVpdeAV0v3oFongISGEUDsRPCSUFMFDQgih5SJ4SIPyx5auMbOPmkn3BHC6mY2XNN/MOlW5XoOBh81sTql0rQ0eEkFCQgih5eKecvWdBqxezQJaGQBkMLBBhasSQghhOUSjXEGSOkp6QNIUD+5xLqnhe1zS457mSknjJU2XdF4z+XWR9LSkgQW2DZN0laRngd+WCBSyzHpJBwJ9gFslTZa0WsVPRgghhBaLRrmyvg3MMbPtzGxb4I/AHGCAmQ3wNGf5fYaewDck9SyUkaT1SCOrzzGzYrNDbQTsYmY/JQUK+bmZ9QSmkgKFUGi9md0BjAcOM7NeZvZxXtnH+ReH8QsWv9ea8xBCCKEVolGurKnAnpIulrSbP5aU72BJE4FJQHdgmwJpOgAjgTPN7JES5d1uZouLBQopI4BIQRE8JIQQ6iMa5Qoys5eA3qTG+QJJ52S3S9oMOB34lvdcHwBWLZDVIlKozL0z+17ol5onZ9JFAJAQQliBRKNcQZI2AD4ys1uAoaQGeh7Q2ZOsQWpI5/rl6e8UycqAo4GtJP0cwMzO8kvNvZZJXCRQSDMBRLL1CiGE0ADikajK6gEMlbQE+Aw4EdgZeFDSHDMbIGkS8CIwGxhTLCO/LH0IMELSPDO7opmyiwUKKbZ+mK//GNg5/75yTgQPCSGE2ongIaGkCB4SQggtF8FD2iBJ3YD7faR2OemHefo78tb3AY4ws1Mk9Qc+NbN/NJNXWekieEgIIdRONMorADMbT3rECaA/MB8o2di2IF0IIYQaiYFe9ddO0rUeTORhn+3pR5LGeRCSO/1+cM4e/gzxS5K+C6nXK+l+73mfAPzER2rvJmlfSc9KmiTpUUnrFUpX86MOIYSwjGiU629L4HIz6w58ABwA3GVmfc1sO+AF4JhM+m7AjsBA0kCtzx+pMrOZwFXAJT5SezTwFLCTmW0P/JX07HOhdJ+L4CEhhFAfcfm6/l41s8m+PIHU6G4r6QJgLaAT8FAm/d/MbAnwsqRXgK2ayX8jYLik9YGVgVebq5CZXQNcA7DRaj1jJGAIIdRI9JTr75PM8mLSF6VhwH+bWQ/gPJoGGMlvJJtrNC8FLvO8jqdwsJIQQggNIBrlxtQZeF1SB+CwvG0HSVpJ0ubAV4AZedvzg4KsCbzmy0eWSBdCCKHO4vJ1Y/oV8Czwtv/MNp7/BsaSooOdYGYLJWX3vQ+4Q9J+wMnAEOB2Se8DjwGbFUqXf185J4KHhBBC7UTwkFBSBA8JIYSWi+AhXxD++NJVpDCeRcNjVkpLgodEwJAQQlg+cU+5DZHUjnSP+aJC8yCHEEJo26JRboakIyQ954E8bi4UjMPTDfHtT0t6WdKPiuR3kKRpnt+Tvm6wpMsyae73MJhImi/p95KmAL8EDgZ+LelWSZ0kjZQ0UdJUvz9csN6+rqsHIxnnr12rdNpCCCG0Qly+LkFSd+BsYBcze0fSOqRHkHYyM5N0LHAm8DPfpSewE9ARmCTpATObk5ftOcDeZvaapLXKqEZH4Fkz+5nXaQs8/rWk9sD3zOxDSV2AZySNALYpUG+AP5EChjwlaRPS889bt+LUhBBCqIJolEv7JnC7mb0DYGbvSepB8WAc9/ol5Y8lPU6KvHVPXp5jgGGS/gbcVUYdFgN3Ftkm4H8k7Q4sATYE1itUb0+/B7BNZrT2GpI6mdn8JplKxwHHAazVYcMyqhhCCKES4vJ1y5UKxrFMYA9JF3p86ckAZnYCqRe7MTBB0rrAIpr+LrJ5LjSzxUXqchjQFdjBzHoBb1I6OMhKpF5+L39tmN8gex2vMbM+ZtanY7t1CmQTQgihGqJRLu0xUrCOdQH8MnCxYBwA+0la1dP3B8aZ2Vm5RtDz2NzMnjWzc0jPIW8MzAR6eVCQjUk97HKsCbxlZp9JGgBsWqLeAA+Tnl3G1/cqs5wQQgg1EJevSzCz6ZIuBEZJWgxMongwDoDngMeBLsCvC9xPBhgqaUvSpeeRwBRf/yrwPGkCiollVvFW4D5JU0lTN75Yot6DgVOAyyU9R/rdP0maLaqoCB4SQgi1E8FDKkTSEGC+mf2u3nWppAgeEkIILdcmgodIOg24xsw+aibdE8DpZjZe0nwz61TherQ6T5+L+H4z27YF+wzzfe5oTZlllvF/wKFm9kEl820ueEgEDAkhhMqp9eXr04BbgJKNciOR1N7MFjWXzsyG1KA6y1AaSi0z26ce5YcQQqicqg30ktRR0gMevGKapHOBDYDH/XEhJF0pabyk6ZLOaya/Lh6YY2CBbetJutvLmiJpF1//Uy97mvfS8/eTpKG+faqkQb6+v6TR/szv8wWq096Dd7wg6Q5Jq/t+O0gaJWmCpIf8san8Mr+lFHhkqqTrJa0iqa+ku3z7fpI+lrSyDxp7pUAe3STNkHQTMA3YWNJMP0fdvF7X+nl9WNJqvl9fpYAik3PHXeqchxBCqK1qjr7+NjDHzLbzS71/BOYAA8xsgKc5y6+59wS+IalnoYyUomY9AJxjZg8USPJnYJSZbQf0BqZL2gE4Cvg6KaDHjyRtn7ff94FewHakZ3iHZhrS3sCpZvbVAuV9DbjCzLYGPgR+rDTN4qXAgWa2A3A9cGHecaxKmit5kD9S1R44kTQQq5cn243U0Pb1uj9b6JwAW3odupvZrALbLjez7sAHwAG+/gbgeB8JXuwxqxBCCHVSzUZ5KrCnpIsl7WZmcwukOVjSRFKj1J0UiSpfB9Io5TPN7JEiZX0TuBLAzBZ7Wf2Au81sgT+LexepwcvqB9zm+7wJjCI1hgBjzexVCpttZmN8+RbP52vAtsAj/kzy2cBGeft9DXjVzF7y9zcCu/vl8X9J2pr0ONQfgN29vgWnVARmmdkzRba9amaTfXkC0E0pelhnM3va1/+lyL5IOs6vYIxfsPi9YslCCCFUWNUaZW94epMa5wsknZPdLmkz4HTgW2bWk9QTLhT4YhGpYdk7s2+TgBxVssDL2jhXlqTc40PLBAkhPeI0PROYo4eZ7dWC8p4EvkOa/elRUkPfDxhdpA4LSuT1SWZ5MS0cOxDBQ0IIoT6qeU95A+AjM7sFGEpqoOcBnT3JGqSGZa5fnv5OkawMOBrYStLPAfIDcpB60id6ue0krUnqYe4vaXVJHYHvsWyvczQwyPfpSuqdjm1SuNnsTEN7la/eRNLOvnwo8BQwA+iaWy+pg1Ls7KwZpF7rFv7+cFLvPFeX04CnzextYF1Sz3pakTq0iI/Knifp677qB63JJ4QQQvVUc/R1D9I92iWk3t+JwM7Ag5LmmNkASZNIAS9mk2JCF2RmiyUdAoyQNM/MrshLcipwjaRjSD3DE83saaVHkXKN7HVmNilvv7u9TlNIjf+ZZvaGpK2aObYZwEmSricNBLvSzD6VdCDwZ/9S0J50H3165jgWSjqKFHykPTCONDcypHvH65F6zJACkXzZKvsg+THAtf47GQUUuqXQRAQPCSGE2ongIV8gykw+IekXwPpmdmqpfSJ4SAghtJzaQvCQUHcDJf2S9HufRQq9WVKx4CERNCSEECqv1feUJZ2Wez63mXRPSOrjy8vMSFRpknbz53Mn557PrUIZM5XmLy43/WBJl1WjLpkyzpe0R6k0Zjbc70tva2YD/d51CCGEBrE8A71OA5ptlGtJUjvSdIYXeePzcb3rVAuS2pnZOWb2aL3rEkIIofXKapRV2+hcB3kZUyQ96eua9DQl3S+pvy/Pl/R7SVOAXwIHA79WirjVSdJISROVImjtl8njCI9uNUXSzb6uq6Q7JY3z164lDuNMz3NsbjR1OfsrRdx6zMseKWkTH/39qpK1JC2WtLunf1JpVqn8fGYqPQM+kTRN4zAfaJbbdl7muLfK1O8R/x1dJ2lWS3r8IYQQqqvce8q56FwDAXx08VGk6FzveJqzzOw9762OlNTTzJ7Lz0jp8acRwNlFgoGcA+xtZq8pBbxoTkfgWTP7mee/BT75g49w/p6ZfeiNzzNKoTO3IQX32MXM3tHS+Yb/BFxiZk9J2gR4CNi6SLlzzayHpCNIo6y/W+b+lwI3mtmNko4G/mxm+0ua4fXajDR1426SngU2NrOXi9ThXTPr7cf97bxt75hZb0k/Jj0PfixwLvCYmV3k6Y8plKmk44DjANbqsGGRokMIIVRauY3yVOD3ki4mNXijJeWnOdg/zNsD65MamPxGORed6yQzG0VhY4Bhkv5GisLVnMXAnUW2Cfgf73UuATYkPXb0TeD23BcKM8uFrdoD2CZzbGsoM2I5z22Zn5eU2j9vv51J4T0BbgZ+68ujSc9JbwZcBPyI9NjSuCLHBjC8xLbcuZuQKa8f6XltzOxBpTmhl2Fm1wDXAGy0Ws8Ynh9CCDVSVqNsZi9J6g3sQ4rONTK7XUujc/U1s/eVng9uLjrXKN/3QmCgl9PLzE5QCnAxEJigFMN6EU0vtWfzXmhmxeI4HwZ0BXYws88kzSxSr5yVgJ3MbGHe8T1EaszHm9mxvjrbWFkz+5co8nNPkp7l3oB0teAMoD8e8KRIHcqJ6tXiiF4hhBDqo9x7yjWLziVpczN71szOAd4GNgZmAr0krSRpY1J86HKsCbzlDfIAYFNf/xjpPuy6Xmbu8vXDwMmZ4+7lddzb63gsSw3K/Hy61P55/sHSaFqHsTTK2FhgF2CJN+qTgePxYCJF6tBSY0j33JG0F7B2czt8ufvKnDFt42VeIYQQKq/cHlQto3MN9YFNIl3qnuLrXyVFz3qBdM+1HLcC90maCoz3+mFm072HPkrSYtKEGIOBU4DLJT1HOjdPAicUyhhY29N9Ahzi68rZ/2TgBklnkL50HOV1+kTSbCA3ycRoz3dqmcdajvOA2yQdTvoi8Qbpy1UIIYQGEBG9vkAkrQIsNrNFSjG6r8zEDy9oo9V62qmbL50tM3rJIYTQPLUyolc1p25cIUj6vzJHgdeNyg9OsgkwTunxsT+TBpOFEEJoEDEAqASlEVrfNbMl9a5LJfijVdvnr1cKPlJssFwIIYQaiZ5yHg/uMUPSTcA0YLFSsJNukl6QdK0H33hYHsZTUl8PBjJZ0lBJ04rk/YQH/Bgr6SVJu/n65oKjDPUyH5W0o+fziqT/ymS/sa9/WSm4Sy6vH3p5kyVd7c+R5wdd2ZkQQgh1F41yYVsCV5hZd9LEDdn1l/v6D4ADfP0NwPF+f7a5Hmd7M9uRFKb03GbSQgqO8piXOQ+4ANiT9Lzx+Zl0O3p9epJGlveRtDVpdPiumbodlsn3WTPbzsyeyhYo6Til6GzjFyx+jxBCCLURl68Lm2VmzxRY/6qZTfblCUA3v9/c2cxyj0X9hRTdq5hsUI9uZdTlU+BBX54KfOKPeE3N2/8RM3sXQNJdpEAhi4AdSPeRAVYD3vL0RYOuRPCQEEKoj2iUCysWlOOTzPJiUiNXlKQbSPdw55jZPnl5ZIN6lAqO8pktHSK/JLe/mS1RCiOak994GumxshvN7JcFqlcq6EoIIYQ6iEZ5OZnZB5LmSfq6mT3L0sAgmNlRZWYzE/ixpJVIoUDLDY6StacHQfkY2J8UpOUj4F5Jl5jZW769s5nNKpFPE1/uvjJnjI/HoEIIoRaiUa6MY4BrPbjKKGBuC/cfQ+uCo2SNJV2O3gi4xczGA0g6G3jYG/zPgJNoep88hBBCg4jgIRWQnbRC0i+A9c3s1DpXqyKywUMicEgIIZQngofU10B/5GgasBtphPTnKhmAxB+f2qASeYUQQmgscfm6AsxsOEWmUaxCAJLBpOen51QovxBCCA0iespVUK0AJJIOBPoAt3q6gZLuyWzfU9Ldvjxf0iVezkhJXX395pIelDRB0mhJW9XglIQQQihDNMrVU/EAJGZ2B2m2q8M83f+RpsHs6kmOAq735Y6kuZe7kwaf5QKVXAOcbGY7kObAzp+lK4KHhBBCnUSjXD3LG4CkWf788s3ADz2PnYG/++YlLL2kfgvQT1In0pzNt0uaDFwNrF8g32vMrI+Z9enYbp38zSGEEKok7ilXTzUDkGTdANwHLARuN7NFRbIy0pewD5qbrjGEEEJ9RKPcAFoYgGQe0DmzfY6kOcDZwB6ZdCsBBwJ/BQ4FnjKzDyW9KukgM7vdB6H1NLMpxeoWwUNCCKF24vJ148gFIJlMuh9cLADJMOAqH+iV62XfCsw2sxcy6RYAO/qAsW+ydPKKw4BjfHao6cB+FT2KEEIIrRbBQxrE8gQg8WkfJ5nZ/2bWzTezTstbrwgeEkIILdfa4CFx+bpxDJT0S9LvZBbpeeRmSZpA6hX/LLNufjUqGEIIobqiUW4QpQKQNLPfDkXWl9VLltS+xOCwEEIINRT3lKtA0hEeCGSKpJsl7SvpWUmTJD0qaT1PN8S3Py3pZUk/KpLfepLu9vymSNrF1/9U0jR/nVZgP+UCkUiaKmmQr+/vgUNGkCbBCCGE0ACip1xhkrqTRkLvYmbv+HSJBuxkZibpWOBMll5u7gnsRBrcNUnSA2aWH0Lzz8AoM/uepHZAJ0k7kIKFfJ00b/KzkkaZ2aTMft8HegHbAV2AcZKe9G29gW3N7NUCx3AccBzAWh02XJ7TEUIIoQWip1x53yQ9L/wOgJm9R5pO8SFJU4EzgO6Z9Pea2cee/nEKz6X8TeBKz2+xmc0F+gF3m9kCHyB2F2kyjKx+wG2+z5ukyF59fdvYQg2ylxHBQ0IIoQ6iUa6NS4HLzKwHcDywamZb/vB3k3ShP/I0uYp1KhbcJIQQQp1Eo1x5jwEHSVoXwC9frwm85tuPzEu/n6RVPX1/YJyZnWVmvTKRt0YCJ3p+7SStCYwG9pe0uqSOwPd8XdZoYJDv0xXYHRhbwWMNIYRQQXFPucLMbLqkC4FRkhYDk4AhpHjT75Ma7c0yuzxHumzdBfh1gfvJAKcC10g6hhSa80Qze1rSMJY2stfl3U8GuJsUD3sKqUd+ppm90ZKZoSKiVwgh1E4ED6kjSUOA+Wb2u3rXpZiNVutp//n4uXpXI4QQ2pTWBg9p1eVrSadJWr2MdE9I6uPLVQ9oIWk3nz84G4KyJrLHWg+Szpe0R/MpQwghNKrWXr4+jTQd4EeVq8ry8UeFDgMuMrNb6l2fcpjZkErkI6mdmZ1TibxCCCHUT7M9ZUkdJT3gQSumSToX2AB4XNLjnuZKSeO9l3peM/l18WAZAwtsO8jLmJJ7nlbSYI/tnEtzv6T+vjxf0u99coVfAgcDv5Z0q6ROkkZKmuiBM/bL5NEkuIev6yrpTknj/LVrkfqf49unSbpGkjKbD/de+jRJO3r6dSTd4+U9I6mnpJUkzVSaAzmX78tKQULKrcdMSRdLmkgaWDZM0oGZbedljn2rzDE+4r+n6yTNktSl1O8rhBBC7ZTTU/42aS7fgQA+8vcoYEDuWVzgLDN7z3urIyX1NLNlbkQqRbIaAZxtZo8UKOscYG8zey3bYJXQEXjWzH7m+W8B3G9md0hqD3zPpyvsAjyjFMFqG5YN7gHwJ+ASM3tK0ibAQ8DWBcq8zMzO9/JuBr5Lms8YYHUz6yVpd+B6YFvgPNJkEftL+iZwk6e5lzRi+gZJXwdmmdmbkv5SZj0A3jWz3l6Xb+dte8fMekv6MXA6cCxwLvCYmV3k6Y8plKkieEgIIdRFOfeUpwJ7eq9sNw9cke9g77FNIgXG2KZAmg6kR3vOLNIgA4wBhimFm2xXRt0WA3cW2SbgfyQ9BzwKbAisR+HgHpDmIr5M6dngEcAakgrFjx6gFDJzqueVDQRym+f5pO+/FimAx82+/jFgXUlrkOJcD/L9fsDSuNfl1gNKx8q+y39OALr5cj/S/MqY2YPA+4V2jOAhIYRQH832lM3sJUm9gX2ACySNzG6XtBmpJ9bXzN5Xekxn1WVzYhGpgdibFFkKpUeHBno5vczsBO81DgQmKIWSXETTLw/ZvBea2eIiVT8M6ArsYGafSZpZpF45K5FCYS7MO76HSI35eOC/gSuAPmY2W2n0dMlAICXKexrYQun54f2BC8qth5kd66tLBQD5xH8uJh59CyGENqGce8obAB/54KmhpJjJ84DOnmQNUuMw1y9Pf6dIVgYcDWwl6ecA+UEyJG1uZs/6oKW3gY2BmUAvvw+7MYXDUBayJvCWN8gDgE19faHgHgAPAydnjruX13Fvr+OxLG2A3/He64F5ZeYmfOgHzPWrCqNJXxDwe+HvmNmHlp5Fuxv4A/CCmb3bgnq01hjSfXck7QWsvRx5hRBCqLByelA9gKGSlgCfkSJL7Qw8KGmOmQ2QNAl4EZhN+uAvyMwWSzoEGCFpnpldkZdkqKQtSZeeR5KCXgC8SprN6AVgYpnHditwn19mHu/1KxbcYzBwCnC5X+5uDzwJnJBX/w8kXQtMA94AxuWVudDPRQfSFxBIgUOu93w/omlEr+Gex+DMumbrsRzOA26TdDipp/4G6QtWUV/uvnKFig4hhNCcCB7yBSJpFWCxmS2StDNwZSaUZ0ERPCSEEFpOrQweEvcaG5ykwaR72P9dgew2Af4maSXgU6Dg/M0hhBDqIxrlLxAzexnYvt71CCGEUFjMErUclBeERNK+/rjUJEmP+sA3JA3x7U97kJCCPVQVCJ7iNpD0oO/720z6gkFbPHjIbz1wyFh/frvsACkhhBDqI3rKrSSpO8sGITHS40wm6VjgTOBnvktPYCdSwJNJkh4oMCNUseApvUg93E+AGZIuNbPZlA7aMtfMekg6AvgjKchJWQFSInhICCHUR/SUW69QEJKNgId8xPcZNA0scq+ZfezpH6fwo13FgqeMNLO5/uzy8yx9vKtU0JbbMj939uWyApNE8JAQQqiPaJQr61JSGM4ewPE0E1hE0oVKsbInA5jZCaTe98ak4CnretpPMvstBtpngrZ8y8x6Ag+UKC+3nAtM0stfG5pZ1WfvCiGEUJ5olFuvUBCSNYHXfPuReen3k7Sqp+8PjCszeEoxzQVtGZT5+bQvFwxMEkIIoTHEPeVWKhKEZAhwu6T3SY32ZpldniNdtu4C/LrA/WQoHDylV5HypzQTtGVtD0DyCXCIr2txYJIIHhJCCLUTwUNqwGNkzzez39WovJmkZ5vfaS5tc/r06WPjx49f/kqFEMIXSGuDh8Tl61aSdL6kPXz5CUl9fHmmajxHsaQ+kv5cyzJDCCFUXly+biW/71tu2iFVrApmNp4U3zv3vls1ywshhFAd0VMug6RfSZoh6SlJt0k6XdIwSfmzRJXK4xu5kdYeXKSzpP6SnpT0gOd/lYfALBUYpK+kf3iAkbGZfO737UMkXe+991cknVLqOCp5nkIIISyf6Ck3Q1Jf4ABgO9LsTxNJ80K31OnASWY2xp8Nzs2XvCPp+eJZwIPA94E7KBAYhDSoazgwyMzGSVoD+LhAWVsBA0jTa86QdCVpwFhZx5ENHrLJJpu04lBDCCG0RvSUm7crKfDHQjObB9zXynzGAH/wnutaZrbI1481s1fMbDEp0Ec/X18oMMjXgNfNbByAz8u8iGU9YGaf+ECvt4D1WnIc2eAhXbt2beXhhhBCaKlolKtE0kmZy9UbmNlvgGOB1YAxkrbypIWCijQXGKQ5ywQbad1RhBBCqKVolJs3BtjXA390IsWQbpaZXZ6JnDXHA4NMNbOLgXGkS8wAO0razO8lDwKeonhgkBnA+n5JHb+fXG6D26rjCCGEUDvRg2qG37sdQQr+8SYwFZjbiqxOkzQAWAJMB/5Oikk9DrgM2IIUXORuM1tSKDCImX0qaRBwqaTVSPeT96jxcYQQQqiSCB5SBkmdzGy+pNVJUbCOM7OJFci3P3C6mdWk19qa44jgISGE0HIRPKS6rvFJIyYCd1aiQc4n6R/+s5ukaUXSDJa0wXIUU/XjCCGE0Hpx+boMZnZolfJ9AnjCl3cpY5fBwDSgUNzscsqrynGEEEKojOgpV4mk30g6KfN+iAcdOUPSOEnP5QUFKTmFogcq6QPc6iO6B0q6J7N9T0l35/KSdIkHHhkpqauv31zSg5ImSBqdGQEeQgihAUSjXD3DgYMz7w8mTce4JSlgSC9gB0m7l5OZmd1BCqV5mE/1+H/AVrkGFzgKuN6XOwLjzaw7MAo419dfA5xsZjuQHrm6olBZko7zaGLj33777XKqF0IIoQKiUa4SM5sEfEnSBpK2A94HegB7kQKCTCQ9FrVlK/M34Gbgh5LWIo3k/rtvXkL6UgBwC9DPH4PahTS15GTgamD9InlH8JAQQqiDuKdcXbcDBwJfJjWSmwIXmdnV5ews6QZge2COme1TIMkNpMhcC4Hbi0T3ghSgZCXgA+9lhxBCaEDRU66u4cAPSA3z7cBDwNHea0XShpK+VGxnMzvKg4/kGuR5pHjWue1zSIO+ziY10DkreZkAhwJPmdmHwKuSDvKy5T34EEIIDSIa5Soys+mkRvQ1M3vdzB4G/gI8LWkqaeKJzqXyyDMMuMoHeq3m624FZpvZC5l0C0iRwqYB3wTO9/WHAcdImkIKYLJfKw8thBBCFUTwkDZO0mXAJDP738y6+WbWqRL5R/CQEEJouQge0kaVEzSkxL4TgJ6kwVy5dd1Ik16EEEJoY2KgV52VGTSk2L47FNn0QpH1y5DUvsQAsRBCCDUUPeUKq3TQEE+zhaRHJU2RNNGDgEjSUEnTJE31iSry91tV0g2+fZJPiJEL1zlC0mPAyAodegghhOUUjXLlVTRoiLsVuNzMtiM9a/w68H3PazvSTFFDJeU/d3wS6ZHmHsAhwI2ScvMy9wYONLNv5BcWwUNCCKE+olGusEoHDZHUGdjQzO72/Bea2UdAP+A2M1tsZm+SInf1zdu9H36/2cxeBGYBX/Vtj5jZe0WOIYKHhBBCHcQ95eqoWNAQYJnL0hWyoEr5hhBCaKXoKVdHxYKGmNk84D+S9vd9V/H5kEcDgyS18/jXuwNj87IaTXo2GUlfBTYBZlTwOEMIIVRQ9JSrwMym+2Xn18zsdeB1SVuTgoYAzAd+CLxVZpaHA1dLOh/4DDgIuJsU73oKKYzmmWb2hj8SlXMFcKUHKlkEDDazT7wOIYQQGkwEDwklRfCQEEJouQgeUgZJ50vaw5efkNTHl2dK6tKCfHpJ2ifzvr+kVj9vXM5jUZW0vPUNIYRQHV+oRtnMzjGzRyuQVS8gO2tTf9KjSm1Ff9pWfUMI4Qthhb2nLOlXpPu2bwOzgQnAtsD9ZnZHmXnsCPwJWBX4GDgKeJU0wcNqkvoBtwEnAIsl/RA4GViLNHPTysC7wGFm9qYP9LoU6EO6D3yemd3pZV0IfNfL2c/TD/P32wNfAo4GjiDdS37WzAb7vnsB5wGrAP8CjjKz+ZJmAjcC+wIdSPeiF+bX18xGl3teQwghVM8K2VOW1Bc4gBRY4zukRrA1XgR2M7PtgXOA/zGzT315uI+Qvhi4CrjE348GngJ28v3+Cpzp+f0KmGtmPcysJ/CYr+8IPOPBQZ4EfpSpw9qkRvgnwAjgEqA70MMvo3chfQHYw8x6A+OBn2b2f8fXXwmcbmYzC9S3iQgeEkII9bGi9pR3Be41s4XAQkn3tTKfNUlRsLYk9Ww7lLnfRsBwj7C1Mql3DSny1g9yiczsfV/8FLjflycAe2byus/MzEdQv2lmUwEkTQe6eVnbAGN8VPXKwNOZ/e/K5Pv9cipvZtcA10Aa6FXOPiGEEJbfCtlTbi1JJ/lcxZMlbQD8GnjczLYlXQJetXQOn7sUuMzDWx5fxn6f2dJh8Itp+mXpE/+5JLOce98eECk6Vy9/bWNmxxTYPz/fEEIIDWZFbZTHAPv6hAydSPdqm2Vml2catzmknvJrvnlwJuk8oHOJ99n9jsysf4QUjxoASWuXU69mPAPsKmkLz7OjBwopJb++IYQQGsAK2Sib2TjS/dfngL8DU4G5rcjqt8BFkibRtJf5OLCN96gHAfcB3/P3uwFDgNt9vuN3MvtdAKztMztNAQa0ok5NmNnbpC8Mt0l6jnTpeqtmdsuvbwghhAawwgYPkdTJRyCvTho8dZyZTax3vdqaCB4SQggt19rgISvyPcZrJG1Dup97YzTIIYQQGt0K2yib2aH1rkMIIYTQEivkPeUQQgihLYpGOYQQQmgQ0SiHEEIIDSIa5RBCCKFBRKMcQgghNIholEMIIYQGscIGDwmVIWkeMKPe9ShDF5pGT2tEUcfKaQv1jDpWTluoZ34dNzWzri3NZIV9TjlUzIzWRKWpNUnjG72eUcfKaQv1jDpWTluoZ6XqGJevQwghhAYRjXIIIYTQIKJRDs25pt4VKFNbqGfUsXLaQj2jjpXTFupZkTrGQK8QQgihQURPOYQQQmgQ0SiHEEIIDSIa5S8wSd+WNEPSPyX9osD2VSQN9+3PSuqW2fZLXz9D0t6NVkdJ3SR9LGmyv66qYx13lzRR0iJJB+ZtO1LSy/46slp1rEA9F2fO5Yg61vGnkp6X9JykkZI2zWyryblczjrW5DyWWc8TJE31ujzl88/ntjXK/3fBOtby/7ucembSHSDJJPXJrGvZuTSzeH0BX0A74F/AV4CVgSnANnlpfgxc5cs/AIb78jaefhVgM8+nXYPVsRswrUHOYzegJ3ATcGBm/TrAK/5zbV9eu9Hq6dvmN8i5HACs7ssnZn7fNTmXy1PHWp3HFtRzjczyfwEP+nIj/X8Xq2NN/r/Lraen6ww8CTwD9GntuYye8hfXjsA/zewVM/sU+CuwX16a/YAbffkO4FuS5Ov/amafmNmrwD89v0aqY600W0czm2lmzwFL8vbdG3jEzN4zs/eBR4BvN2A9a6WcOj5uZh/522eAjXy5VudyeepYS+XU88PM245AbtRvw/x/l6hjLZXzOQTwa+BiYGFmXYvPZTTKX1wbArMz7//j6wqmMbNFwFxg3TL3rXcdATaTNEnSKEm7VaF+5daxGvu21PKWtaqk8ZKekbR/RWu2VEvreAzw91bu21rLU0eozXmEMusp6SRJ/wJ+C5zSkn3rXEeozf93WfWU1BvY2MweaOm++SLMZlhRvQ5sYmbvStoBuEdS97xv3qF8m5rZa5K+AjwmaaqZ/atelZH0Q6AP8I161aE5RerYUOfRzC4HLpd0KHA2UNVxDa1RpI4N8/8taSXgD8DgSuQXPeUvrteAjTPvN/J1BdNIag+sCbxb5r51raNfLnoXwMwmkO7lfLVOdazGvi21XGWZ2Wv+8xXgCWD7SlbOlVVHSXsAZwH/ZWaftGTfOtexVuex7Hpm/BXYv5X7tlar61jD/29ovp6dgW2BJyTNBHYCRvhgr5afy1rcKI9X471IV0leIQ0+yA1e6J6X5iSaDqL6my93p+nghVeozkCQ5alj11ydSAM0XgPWqUcdM2mHsexAr1dJA5PW9uWK17EC9VwbWMWXuwAvU2CgS41+39uTPoC3zFtfk3O5nHWsyXlsQT23zCzvC4z35Ub6/y5Wx5r8f5dbz7z0T7B0oFeLz2XFDyBebecF7AO85B8gZ/m680nf7gFWBW4nDU4YC3wls+9Zvt8M4DuNVkfgAGA6MBmYCOxbxzr2Jd1LWkC60jA9s+/RXvd/AkfV+fddsJ7ALsBU/3CZChxTxzo+Crzpv9fJwIhan8vW1rGW57HMev4p8z/yOJmGpoH+vwvWsZb/3+XUMy/tE3ij3JpzGWE2QwghhAYR95RDCCGEBhGNcgghhNAgolEOIYQQGkQ0yiGEEEKDiEY5hBBCaBDRKIcQKkbS/BqX180jPYWwQohGOYTQJnkEt25ANMphhRGNcgih4iT194kC7pX0iqTfSDpM0lifH3dzTzdM0lU+ScNLkr7r61eVdIOnnSRpgK8fLGmEpMeAkcBvgN18Tt2feM95tNK80BMl7ZKpzxOS7pD0oqRbc7OJSeor6R+Spnj9OktqJ2mopHFK8yIfX5cTGb5wYkKKEEK1bAdsDbxHCi94nZntKOlU4GTgNE/XjTSd3ebA45K2IIVPNTPrIWkr4GFJudjGvYGeZvaepP7A6WaWa8xXB/Y0s4WStgRuI00KASn8ZXdgDjAG2FXSWGA4MMjMxklaA/iYNLvTXDPrK2kVYIykhy1NvxdC1USjHEKolnFm9jqAT733sK+fCgzIpPubmS0BXpb0CrAV0A+4FMDMXpQ0i6UTDjxiZu8VKbMDcJmkXsBimk5SMNbM/uP1mUz6MjAXeN3MxnlZH/r2vYCekg70fdcEtiTF1A6haqJRDiFUyyeZ5SWZ90to+tmTH+u3udi/C0ps+wkp7vR2pNtz2Qnns/VZTOnPPwEnm9lDzdQlhIqKe8ohhHo7SNJKfp/5K6TA/aOBwwD8svUmvj7fPNLUeTlrknq+S4DDgXbNlD0DWF9SXy+rsw8gewg4UVKHXB0kdWztAYZQrugphxDq7d+kGb7WAE7w+8FXAFdKmgosAgab2Sc+NivrOWCxpCmkKSevAO6UdATwIKV71ZjZp5IGAZdKWo10P3kP4DrS5e2JPiDsbZbONxxC1cQsUSGEupE0DLjfzO6od11CaARx+TqEEEJoENFTDiGEEBpE9JRDCCGEBhGNcgghhNAgolEOIYQQGkQ0yiGEEEKDiEY5hBBCaBD/HxnVlizWpxF+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost\n",
    "label = \"XGB\"\n",
    "xgboost = xgb.XGBClassifier()\n",
    "xgboost, performance_metrics, xgb_confusion = model_predictions(xgboost, X_train, X_test, y_train, y_test, performance_metrics, label)\n",
    "feature_importance_visualizer(X, xgboost, label, color=\"blueviolet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.921846</td>\n",
       "      <td>0.909887</td>\n",
       "      <td>0.929668</td>\n",
       "      <td>0.919671</td>\n",
       "      <td>0.922129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.947077</td>\n",
       "      <td>0.942748</td>\n",
       "      <td>0.947570</td>\n",
       "      <td>0.945153</td>\n",
       "      <td>0.947095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972923</td>\n",
       "      <td>0.981723</td>\n",
       "      <td>0.961637</td>\n",
       "      <td>0.971576</td>\n",
       "      <td>0.972515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.996308</td>\n",
       "      <td>0.992386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996178</td>\n",
       "      <td>0.996441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGB</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                method  accuracy  precision    recall        f1   roc_auc\n",
       "0          Naive Bayes  0.921846   0.909887  0.929668  0.919671  0.922129\n",
       "1        Random Forest  1.000000   1.000000  1.000000  1.000000  1.000000\n",
       "2        Decision Tree  1.000000   1.000000  1.000000  1.000000  1.000000\n",
       "3  Logistic Regression  0.947077   0.942748  0.947570  0.945153  0.947095\n",
       "4                  SVC  0.972923   0.981723  0.961637  0.971576  0.972515\n",
       "5                  KNN  0.996308   0.992386  1.000000  0.996178  0.996441\n",
       "6                  XGB  1.000000   1.000000  1.000000  1.000000  1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examining metrics dataframe\n",
    "performance_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mm/6lyr9rbs7vd53tnvwt0676zm0000gn/T/ipykernel_69318/4285751722.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 6000x3000 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing confusion matrices for this stage of analysis\n",
    "x_labels = performance_metrics['method']\n",
    "indices = np.arange(len(x_labels))\n",
    "\n",
    "fig, ax = plt.subplots(3,3,sharex=True, sharey=False, figsize=(20, 10), dpi=300)\n",
    "\n",
    "# first row\n",
    "ax[0, 0].set_title(x_labels[0])\n",
    "ax[0, 1].set_title(x_labels[1])\n",
    "ax[0, 2].set_title(x_labels[2])\n",
    "# second row\n",
    "ax[1, 0].set_title(x_labels[3])\n",
    "ax[1, 1].set_title(x_labels[4])\n",
    "ax[1, 2].set_title(x_labels[5])\n",
    "# third row\n",
    "ax[2, 1].set_title(x_labels[6])\n",
    "\n",
    "# first row\n",
    "ConfusionMatrixDisplay(nb_confusion,display_labels=[False,True]).plot(ax=ax[0, 0],cmap='plasma',colorbar=False)\n",
    "ConfusionMatrixDisplay(rf_confusion,display_labels=[False,True]).plot(ax=ax[0, 1],cmap='plasma',colorbar=False)\n",
    "ConfusionMatrixDisplay(dt_confusion,display_labels=[False,True]).plot(ax=ax[0, 2],cmap='plasma',colorbar=True)\n",
    "# second row\n",
    "ConfusionMatrixDisplay(lr_confusion,display_labels=[False,True]).plot(ax=ax[1, 0],cmap='plasma',colorbar=False)\n",
    "ConfusionMatrixDisplay(svc_confusion,display_labels=[False,True]).plot(ax=ax[1, 1],cmap='plasma',colorbar=False)\n",
    "ConfusionMatrixDisplay(knn_confusion,display_labels=[False,True]).plot(ax=ax[1, 2],cmap='plasma',colorbar=True)\n",
    "# third row\n",
    "ConfusionMatrixDisplay(xgb_confusion,display_labels=[False,True]).plot(ax=ax[2, 1],cmap='plasma',colorbar=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 6000x1500 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing all metrics\n",
    "fig, ax = plt.subplots(1, 5, sharex=True, sharey=False, figsize=(20, 5), dpi=300)\n",
    "x_labels = performance_metrics['method']\n",
    "indices = np.arange(len(x_labels))\n",
    "width = 0.3\n",
    "\n",
    "#row=0, col=0\n",
    "ax[0].bar(indices,performance_metrics['accuracy'],color=\"lightcoral\",alpha=1,label=\"accuracy\") \n",
    "ax[0].grid(True,alpha=0.5)\n",
    "ax[0].set_ylim(0,1)\n",
    "ax[0].set_xlabel(\"algorithm\")\n",
    "ax[0].set_ylabel(\"accuracy\")\n",
    "ax[0].set_title(\"Accuracy Comparison\")\n",
    "ax[0].legend(loc='lower right')\n",
    "ax[0].set_xticks(indices, x_labels, rotation=45)\n",
    "\n",
    "#row=0, col=1\n",
    "ax[1].bar(indices,performance_metrics['precision'],color=\"gold\",alpha=1,label=\"precision\") \n",
    "ax[1].grid(True,alpha=0.5)\n",
    "ax[1].set_ylim(0,1)\n",
    "ax[1].set_xlabel(\"algorithm\")\n",
    "ax[1].set_ylabel(\"precision\")\n",
    "ax[1].set_title(\"Precision Comparison\")\n",
    "ax[1].legend(loc='lower right')\n",
    "ax[1].set_xticks(indices, x_labels, rotation=45)\n",
    "\n",
    "#row=0, col=2\n",
    "ax[2].bar(indices,performance_metrics['recall'],color=\"lightgreen\",alpha=1,label=\"recall\") \n",
    "ax[2].grid(True,alpha=0.5)\n",
    "ax[2].set_ylim(0,1)\n",
    "ax[2].set_xlabel(\"algorithm\")\n",
    "ax[2].set_ylabel(\"recall\")\n",
    "ax[2].set_title(\"Recall Comparison\")\n",
    "ax[2].legend(loc='lower right')\n",
    "ax[2].set_xticks(indices, x_labels, rotation=45)\n",
    "\n",
    "#row=0, col=3\n",
    "ax[3].bar(indices,performance_metrics['f1'],color=\"lightskyblue\",alpha=1,label=\"f1\") \n",
    "ax[3].grid(True,alpha=0.5)\n",
    "ax[3].set_ylim(0,1)\n",
    "ax[3].set_xlabel(\"algorithm\")\n",
    "ax[3].set_ylabel(\"f1\")\n",
    "ax[3].set_title(\"F1 Score Comparison\")\n",
    "ax[3].legend(loc='lower right')\n",
    "ax[3].set_xticks(indices, x_labels, rotation=45)\n",
    "\n",
    "#row=0, col=4\n",
    "ax[4].bar(indices,performance_metrics['roc_auc'],color=\"darkviolet\",alpha=1,label=\"roc_auc\") \n",
    "ax[4].grid(True,alpha=0.5)\n",
    "ax[4].set_ylim(0,1)\n",
    "ax[4].set_xlabel(\"algorithm\")\n",
    "ax[4].set_ylabel(\"roc_auc\")\n",
    "ax[4].set_title(\"ROC/AUC Score Comparison\")\n",
    "ax[4].legend(loc='lower right')\n",
    "ax[4].set_xticks(indices, x_labels, rotation=45)\n",
    "\n",
    "fig.savefig(\"img/metrics.jpeg\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 3000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting ROC curves with AUC scores\n",
    "fig = plt.figure(figsize=(10, 5), dpi=300)\n",
    "fig = plot_roc_curve(nb, X_test, y_test)\n",
    "fig = plot_roc_curve(rf, X_test, y_test, ax = fig.ax_)\n",
    "fig = plot_roc_curve(dt, X_test, y_test, ax = fig.ax_)\n",
    "fig = plot_roc_curve(lr, X_test, y_test, ax = fig.ax_)\n",
    "fig = plot_roc_curve(svc, X_test, y_test, ax = fig.ax_)\n",
    "fig = plot_roc_curve(knn, X_test, y_test, ax = fig.ax_)\n",
    "fig = plot_roc_curve(xgboost, X_test, y_test, ax = fig.ax_)\n",
    "fig.figure_.suptitle(\"ROC Curve Comparison\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion and Conclusion\n",
    "\n",
    "For this project, our aim is to find the best possible algorithm which classifies a mushroom as either edible or poisonous. We used 7 algorithms and evailuated their performances. Now we will discuss a few evaluation metrics to judge the best algorithm that should be used to predict mushroom classifications. But first, some terminology! Most evaluation metrics are defined in terms of positives and negatives, as seen in the confusion matrices. In our confusion matrix for our binary classification problem, a positive is defined as 1, which corresponds to poisonous mushrooms. Therefore, the negative class corresponds to 0, which are edible mushrooms. This tells us that our models are finding which mushrooms are poisonous (which is the hypothesis), rather than the other way round. Therefore, here is some more terminology before we define the evaluation metrics:\n",
    "\n",
    "- TP (True Positives): how many data points were correctly classified as poisonous (actual = 'p', predicted = 'p').\n",
    "- FP (False Positives): how many data points were incorrectly classified as poisonous (actual = 'e', predicted = 'p').\n",
    "- FN (False Negatives): how many data points were incorrectly classified as edible (actual = 'p', predicted = 'e').\n",
    "- TN (True Negatives): how many data points were correctly classified as edible (actual = 'e', predicted = 'e').\n",
    "\n",
    "**Accuracy**\n",
    "Accuracy is defined as follows: $\\frac{TP+TN}{TP+TN+FP+FN}$\n",
    "\n",
    "- The accuracy metric is good for a balanced dataset (which we have), and for when every class is important.\n",
    "\n",
    "**Precision**\n",
    "Precision is defined as follows: $\\frac{TP}{TP+FP}$\n",
    "\n",
    "- The precision metric is good for measuring how often class 'p' is indeed classified as class 'p', i.e. maximizing on TPs. \n",
    "- In our case, this is a good metric to compare our models on, since we need to correctly predict poisonous mushrooms as poisonous, instead of edible, since it can lead to bad things for humans!\n",
    "\n",
    "**Recall**\n",
    "Recall is defined as follows: $\\frac{TP}{TP+FN}$\n",
    "\n",
    "- The recall metric is good for measuring how often class 'e' is indeed classified as class 'e', i.e. maximizing TNs.\n",
    "- This is also a good metric to compare our models, since we also want correctly-classified edible mushrooms!\n",
    "\n",
    "**F1 Score**\n",
    "F1 Score is defined as follows: $2\\times \\frac{precision\\times recall}{precision + recall}$\n",
    "\n",
    "- The F1 metric is referred to as a 'harmonic mean between precision and recall'. \n",
    "- This means that it is a good metric to measure how often edible mushrooms and poisonous mushrooms are each correctly classified.\n",
    "\n",
    "**ROC/AUC Score**\n",
    "- The ROC (Receiver Operating Characteristic) curve is a graph of the TP rate vs the FP rate.\n",
    "- The AUC (Area Under Curve) score is a measure of the area under the ROC curve.\n",
    "- The ROC/AUC score is good for measuring the probability of good predictions made for both edible and poisonous mushrooms.\n",
    "\n",
    "\n",
    "After looking at all these metrics, we will define our primary metric as precision, since we are interested in minimizing the chance of eating poisonous mushrooms, which will only happen if most of the poisonous samples in the dataset are correctly classified.\n",
    "\n",
    "One more thing before we start evaluating our models - we note that a few algorithms provided 100% on each metric. This shows that these models have likely overfitted, i.e. the model learned specific rules from the training data and was able to apply those to the testing dataset as-is. We will disregard these overfitted models (Random Forest, Decision Tree, K-Nearest Neighbours, and XGBoost) for final evaluation.\n",
    "\n",
    "Ok, now we have enough background to start evaluating our models. Once more, let's look at the performance metrics table, after filtering out the overfitted models, based on accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.996308</td>\n",
       "      <td>0.992386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996178</td>\n",
       "      <td>0.996441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.972923</td>\n",
       "      <td>0.981723</td>\n",
       "      <td>0.961637</td>\n",
       "      <td>0.971576</td>\n",
       "      <td>0.972515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.947077</td>\n",
       "      <td>0.942748</td>\n",
       "      <td>0.947570</td>\n",
       "      <td>0.945153</td>\n",
       "      <td>0.947095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.921846</td>\n",
       "      <td>0.909887</td>\n",
       "      <td>0.929668</td>\n",
       "      <td>0.919671</td>\n",
       "      <td>0.922129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                method  accuracy  precision    recall        f1   roc_auc\n",
       "5                  KNN  0.996308   0.992386  1.000000  0.996178  0.996441\n",
       "4                  SVC  0.972923   0.981723  0.961637  0.971576  0.972515\n",
       "3  Logistic Regression  0.947077   0.942748  0.947570  0.945153  0.947095\n",
       "0          Naive Bayes  0.921846   0.909887  0.929668  0.919671  0.922129"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_metrics[performance_metrics['accuracy']!=1].sort_values('precision', ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on our primary metric, precision, we find that the KNN model has performed the best, followed by SVC (linear kernel), followed by logistic regression. The least precise model was Naive Bayes. The KNN model provided 99% precision, SVC provided 98% precision, logistic regression provided 94% precision, and finally Naive Bayes provided 90% precision. This indicates that the KNN model classified 99% of all poisonous mushrooms as poisonous.\n",
    "\n",
    "We find that the other metrics also have similar performances, with KNN performing the best, followed by SVC, then logistic regression, then Naive Bayes. However, in terms of recall, we find that KNN has 100% recall, which shows that it correctly classified 100% of edible mushrooms as edible. Interesting!\n",
    "\n",
    "**Based on the extensive analysis performed and comparison of metrics, we will conclude that a K-Nearest Neighbours algorithm, or a Support Vector Classifier model should be used to classify this dataset.**\n",
    "\n",
    "So then what are the features that make a mushrooms poisonous anyway? According to the SVC model, the **top features that contribute to a mushroom being poisonous are its veil color, gill size, bruises, and the ring type**. The features that contributed least to the classification include gill spacing, stalk root, stalk shape, gill attachment, and ring number, just to name a few.\n",
    "\n",
    "Therefore, to conclude this analysis, if I had an algorithm with me while I was in the forest exploring mushrooms that told me the likelihood of a mushroom being poisonous or not, I would go with either the KNN algorithm, or the SVC algorithm. I would also pay close attention to the veil color, gill size, bruises, and ring type of the mushrooms to corroborate the algorithm's predictions. ¯/\\_(ツ)_/¯"
   ]
  }
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